From b24c64c62cf1634e49cc35d2762c873e61817a97 Mon Sep 17 00:00:00 2001 From: u2370093 Date: Thu, 2 Apr 2026 12:44:32 +0100 Subject: [PATCH 01/20] add plotting with matplotlib and allow user to choose backend --- .../echem_identification_pitfalls.ipynb | 5 +- .../ecm_monte_carlo_sampling.ipynb | 3 +- .../ecm_multipulse_identification.ipynb | 5 +- .../ecm_scipy_constraints.ipynb | 3 +- .../electrode_balancing.ipynb | 3 +- .../lgm50_pulse_validation.ipynb | 5 +- .../pouch_cell_identification.ipynb | 5 +- .../sensitivity_analysis_hessian.ipynb | 1471 +++++++++-------- .../sensitivity_analysis_salib.ipynb | 3 +- .../comparing_cost_functions.ipynb | 4 +- .../optimiser_calibration.ipynb | 3 +- .../energy_based_electrode_design.ipynb | 3 +- .../cost_compute_methods.ipynb | 2 +- .../maximum_a_posteriori.ipynb | 3 +- .../optimising_with_adamw.ipynb | 3 +- .../setting_optimiser_options.ipynb | 2 +- .../using_transformations.ipynb | 1 + .../comparison_examples/grouped_SPMe.py | 17 +- pybop/plot/__init__.py | 37 +- pybop/plot/matplotlib/__init__.py | 9 + pybop/plot/matplotlib/contour.py | 237 +++ pybop/plot/matplotlib/convergence.py | 50 + pybop/plot/matplotlib/dataset.py | 54 + pybop/plot/matplotlib/nyquist.py | 86 + pybop/plot/matplotlib/parameters.py | 71 + pybop/plot/matplotlib/problem.py | 114 ++ pybop/plot/matplotlib/samples.py | 138 ++ pybop/plot/matplotlib/standard_plots.py | 386 +++++ pybop/plot/matplotlib/voronoi.py | 142 ++ pybop/plot/plotly/__init__.py | 10 + pybop/plot/{ => plotly}/contour.py | 2 +- pybop/plot/{ => plotly}/convergence.py | 2 +- pybop/plot/{ => plotly}/dataset.py | 2 +- pybop/plot/{ => plotly}/nyquist.py | 2 +- pybop/plot/{ => plotly}/parameters.py | 2 +- pybop/plot/{ => plotly}/plotly_manager.py | 0 pybop/plot/{ => plotly}/problem.py | 2 +- pybop/plot/{ => plotly}/samples.py | 2 +- pybop/plot/{ => plotly}/standard_plots.py | 12 +- pybop/plot/plotly/voronoi.py | 216 +++ pybop/plot/plots.py | 287 ++++ pybop/plot/util.py | 50 + pybop/plot/voronoi.py | 208 +-- tests/plotting/test_plotly_manager.py | 2 +- tests/unit/test_plots.py | 29 +- 45 files changed, 2701 insertions(+), 992 deletions(-) create mode 100644 pybop/plot/matplotlib/__init__.py create mode 100644 pybop/plot/matplotlib/contour.py create mode 100644 pybop/plot/matplotlib/convergence.py create mode 100644 pybop/plot/matplotlib/dataset.py create mode 100644 pybop/plot/matplotlib/nyquist.py create mode 100644 pybop/plot/matplotlib/parameters.py create mode 100644 pybop/plot/matplotlib/problem.py create mode 100644 pybop/plot/matplotlib/samples.py create mode 100644 pybop/plot/matplotlib/standard_plots.py create mode 100644 pybop/plot/matplotlib/voronoi.py create mode 100644 pybop/plot/plotly/__init__.py rename pybop/plot/{ => plotly}/contour.py (99%) rename pybop/plot/{ => plotly}/convergence.py (96%) rename pybop/plot/{ => plotly}/dataset.py (95%) rename pybop/plot/{ => plotly}/nyquist.py (98%) rename pybop/plot/{ => plotly}/parameters.py (97%) rename pybop/plot/{ => plotly}/plotly_manager.py (100%) rename pybop/plot/{ => plotly}/problem.py (98%) rename pybop/plot/{ => plotly}/samples.py (98%) rename pybop/plot/{ => plotly}/standard_plots.py (96%) create mode 100644 pybop/plot/plotly/voronoi.py create mode 100644 pybop/plot/plots.py create mode 100644 pybop/plot/util.py diff --git a/examples/notebooks/battery_parameterisation/echem_identification_pitfalls.ipynb b/examples/notebooks/battery_parameterisation/echem_identification_pitfalls.ipynb index 1eb2a59c5..069d68660 100644 --- a/examples/notebooks/battery_parameterisation/echem_identification_pitfalls.ipynb +++ b/examples/notebooks/battery_parameterisation/echem_identification_pitfalls.ipynb @@ -32,8 +32,9 @@ "\n", "import pybop\n", "\n", - "go = pybop.plot.PlotlyManager().go\n", - "pybop.plot.PlotlyManager().pio.renderers.default = \"notebook_connected\"\n", + "pybop.plot.set_backend(\"plotly\")\n", + "go = pybop.plot.plotly.PlotlyManager().go\n", + "pybop.plot.plotly.PlotlyManager().pio.renderers.default = \"notebook_connected\"\n", "\n", "np.random.seed(8) # users can remove this line" ] diff --git a/examples/notebooks/battery_parameterisation/ecm_monte_carlo_sampling.ipynb b/examples/notebooks/battery_parameterisation/ecm_monte_carlo_sampling.ipynb index dce5640a7..addca5b86 100644 --- a/examples/notebooks/battery_parameterisation/ecm_monte_carlo_sampling.ipynb +++ b/examples/notebooks/battery_parameterisation/ecm_monte_carlo_sampling.ipynb @@ -46,7 +46,8 @@ "\n", "import pybop\n", "\n", - "pybop.plot.PlotlyManager().pio.renderers.default = \"notebook_connected\"\n", + "pybop.plot.set_backend(\"plotly\")\n", + "pybop.plot.plotly.PlotlyManager().pio.renderers.default = \"notebook_connected\"\n", "\n", "np.random.seed(8) # users can remove this line" ] diff --git a/examples/notebooks/battery_parameterisation/ecm_multipulse_identification.ipynb b/examples/notebooks/battery_parameterisation/ecm_multipulse_identification.ipynb index 51c852358..e17669e50 100644 --- a/examples/notebooks/battery_parameterisation/ecm_multipulse_identification.ipynb +++ b/examples/notebooks/battery_parameterisation/ecm_multipulse_identification.ipynb @@ -48,7 +48,8 @@ "\n", "import pybop\n", "\n", - "pybop.plot.PlotlyManager().pio.renderers.default = \"notebook_connected\"\n", + "pybop.plot.set_backend(\"plotly\")\n", + "pybop.plot.plotly.PlotlyManager().pio.renderers.default = \"notebook_connected\"\n", "\n", "np.random.seed(8) # users can remove this line" ] @@ -505,7 +506,7 @@ " [3600 3]], duration=3600)]" ] }, - "execution_count": null, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } diff --git a/examples/notebooks/battery_parameterisation/ecm_scipy_constraints.ipynb b/examples/notebooks/battery_parameterisation/ecm_scipy_constraints.ipynb index ce327fe6c..70915b6e9 100644 --- a/examples/notebooks/battery_parameterisation/ecm_scipy_constraints.ipynb +++ b/examples/notebooks/battery_parameterisation/ecm_scipy_constraints.ipynb @@ -31,7 +31,8 @@ "\n", "import pybop\n", "\n", - "pybop.plot.PlotlyManager().pio.renderers.default = \"notebook_connected\"\n", + "pybop.plot.set_backend(\"plotly\")\n", + "pybop.plot.plotly.PlotlyManager().pio.renderers.default = \"notebook_connected\"\n", "\n", "np.random.seed(8) # users can remove this line" ] diff --git a/examples/notebooks/battery_parameterisation/electrode_balancing.ipynb b/examples/notebooks/battery_parameterisation/electrode_balancing.ipynb index adc764f25..94b981c61 100644 --- a/examples/notebooks/battery_parameterisation/electrode_balancing.ipynb +++ b/examples/notebooks/battery_parameterisation/electrode_balancing.ipynb @@ -29,7 +29,8 @@ "\n", "import pybop\n", "\n", - "pybop.plot.PlotlyManager().pio.renderers.default = \"notebook_connected\"\n", + "pybop.plot.set_backend(\"plotly\")\n", + "pybop.plot.plotly.PlotlyManager().pio.renderers.default = \"notebook_connected\"\n", "\n", "np.random.seed(8) # users can remove this line" ] diff --git a/examples/notebooks/battery_parameterisation/lgm50_pulse_validation.ipynb b/examples/notebooks/battery_parameterisation/lgm50_pulse_validation.ipynb index ae8a0d3ba..3d63a9ef3 100644 --- a/examples/notebooks/battery_parameterisation/lgm50_pulse_validation.ipynb +++ b/examples/notebooks/battery_parameterisation/lgm50_pulse_validation.ipynb @@ -32,8 +32,9 @@ "\n", "import pybop\n", "\n", - "go = pybop.plot.PlotlyManager().go\n", - "pybop.plot.PlotlyManager().pio.renderers.default = \"notebook_connected\"\n", + "pybop.plot.set_backend(\"plotly\")\n", + "go = pybop.plot.plotly.PlotlyManager().go\n", + "pybop.plot.plotly.PlotlyManager().pio.renderers.default = \"notebook_connected\"\n", "\n", "np.random.seed(8) # users can remove this line" ] diff --git a/examples/notebooks/battery_parameterisation/pouch_cell_identification.ipynb b/examples/notebooks/battery_parameterisation/pouch_cell_identification.ipynb index ce32e337b..1a73079e4 100644 --- a/examples/notebooks/battery_parameterisation/pouch_cell_identification.ipynb +++ b/examples/notebooks/battery_parameterisation/pouch_cell_identification.ipynb @@ -30,8 +30,9 @@ "\n", "import pybop\n", "\n", - "go = pybop.plot.PlotlyManager().go\n", - "pybop.plot.PlotlyManager().pio.renderers.default = \"notebook_connected\"\n", + "pybop.plot.set_backend(\"plotly\")\n", + "go = pybop.plot.plotly.PlotlyManager().go\n", + "pybop.plot.plotly.PlotlyManager().pio.renderers.default = \"notebook_connected\"\n", "\n", "np.random.seed(8) # users can remove this line" ] diff --git a/examples/notebooks/battery_parameterisation/sensitivity_analysis_hessian.ipynb b/examples/notebooks/battery_parameterisation/sensitivity_analysis_hessian.ipynb index 3cab87a0d..d5a7ecd4f 100644 --- a/examples/notebooks/battery_parameterisation/sensitivity_analysis_hessian.ipynb +++ b/examples/notebooks/battery_parameterisation/sensitivity_analysis_hessian.ipynb @@ -29,6 +29,7 @@ "\n", 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570, + 560, + 550, + 540, + 530, + 520, + 510, + 500, + 490, + 480, + 470, + 460, + 450, + 440, + 430, + 420, + 410, + 400, + 390, + 380, + 370, + 360, + 350, + 340, + 330, + 320, + 310, + 300, + 290, + 280, + 270, + 260, + 250, + 240, + 230, + 220, + 210, + 200, + 190, + 180, + 170, + 160, + 150, + 140, + 130, + 120, + 110, + 100, + 90, + 80, + 70, + 60, + 50, + 40, + 30, + 20, + 10, + 0 ], "y": [ 4.0531258862154065, @@ -1744,7 +1745,7 @@ }, "colorscale": [ [ - 0.0, + 0, "#0d0887" ], [ @@ -1780,7 +1781,7 @@ "#fdca26" ], [ - 1.0, + 1, "#f0f921" ] ], @@ -1804,7 +1805,7 @@ }, "colorscale": [ [ - 0.0, + 0, "#0d0887" ], [ @@ -1840,7 +1841,7 @@ "#fdca26" ], [ - 1.0, + 1, "#f0f921" ] ], @@ -1867,7 +1868,7 @@ }, "colorscale": [ [ - 0.0, + 0, "#0d0887" ], [ @@ -1903,7 +1904,7 @@ "#fdca26" ], [ - 1.0, + 1, "#f0f921" ] ], @@ -1918,7 +1919,7 @@ }, "colorscale": [ [ - 0.0, + 0, "#0d0887" ], [ @@ -1954,7 +1955,7 @@ "#fdca26" ], [ - 1.0, + 1, "#f0f921" ] ], @@ -2110,7 +2111,7 @@ }, "colorscale": [ [ - 0.0, + 0, "#0d0887" ], [ @@ -2146,7 +2147,7 @@ "#fdca26" ], [ - 1.0, + 1, "#f0f921" ] ], @@ -2237,7 +2238,7 @@ ], "sequential": [ [ - 0.0, + 0, "#0d0887" ], [ @@ -2273,13 +2274,13 @@ "#fdca26" ], [ - 1.0, + 1, "#f0f921" ] ], "sequentialminus": [ [ - 0.0, + 0, "#0d0887" ], [ @@ -2315,7 +2316,7 @@ "#fdca26" ], [ - 1.0, + 1, "#f0f921" ] ] @@ -2503,7 +2504,7 @@ { "colorscale": [ [ - 0.0, + 0, "#440154" ], [ @@ -2539,7 +2540,7 @@ "#b5de2b" ], [ - 1.0, + 1, "#fde725" ] ], @@ -15963,7 +15964,7 @@ "hoverinfo": "text", "marker": { "color": [ - 0.0, + 0, 0.001968503937007874, 0.003937007874015748, 0.005905511811023622, @@ -16474,7 +16475,7 @@ ], "colorscale": [ [ - 0.0, + 0, "rgb(255,255,255)" ], [ @@ -16506,7 +16507,7 @@ "rgb(37,37,37)" ], [ - 1.0, + 1, "rgb(0,0,0)" ] ], @@ -18165,7 +18166,7 @@ }, "colorscale": [ [ - 0.0, + 0, "#0d0887" ], [ @@ -18201,7 +18202,7 @@ "#fdca26" ], [ - 1.0, + 1, "#f0f921" ] ], @@ -18225,7 +18226,7 @@ }, "colorscale": [ [ - 0.0, + 0, "#0d0887" ], [ @@ -18261,7 +18262,7 @@ "#fdca26" ], [ - 1.0, + 1, "#f0f921" ] ], @@ -18288,7 +18289,7 @@ }, "colorscale": [ [ - 0.0, + 0, "#0d0887" ], [ @@ -18324,7 +18325,7 @@ "#fdca26" ], [ - 1.0, + 1, "#f0f921" ] ], @@ -18339,7 +18340,7 @@ }, "colorscale": [ [ - 0.0, + 0, "#0d0887" ], [ @@ -18375,7 +18376,7 @@ "#fdca26" ], [ - 1.0, + 1, "#f0f921" ] ], @@ -18531,7 +18532,7 @@ }, "colorscale": [ [ - 0.0, + 0, "#0d0887" ], [ @@ -18567,7 +18568,7 @@ "#fdca26" ], [ - 1.0, + 1, "#f0f921" ] ], @@ -18658,7 +18659,7 @@ ], "sequential": [ [ - 0.0, + 0, "#0d0887" ], [ @@ -18694,13 +18695,13 @@ "#fdca26" ], [ - 1.0, + 1, "#f0f921" ] ], "sequentialminus": [ [ - 0.0, + 0, "#0d0887" ], [ @@ -18736,7 +18737,7 @@ "#fdca26" ], [ - 1.0, + 1, "#f0f921" ] ] diff --git a/examples/notebooks/battery_parameterisation/sensitivity_analysis_salib.ipynb b/examples/notebooks/battery_parameterisation/sensitivity_analysis_salib.ipynb index e7a1a308c..4324ce855 100644 --- a/examples/notebooks/battery_parameterisation/sensitivity_analysis_salib.ipynb +++ b/examples/notebooks/battery_parameterisation/sensitivity_analysis_salib.ipynb @@ -58,7 +58,8 @@ "\n", "import pybop\n", "\n", - "pybop.plot.PlotlyManager().pio.renderers.default = \"notebook_connected\"\n", + "pybop.plot.set_backend(\"matplotlib\")\n", + "pybop.plot.plotly.PlotlyManager().pio.renderers.default = \"notebook_connected\"\n", "\n", "np.random.seed(8) # users can remove this line" ] diff --git a/examples/notebooks/comparison_examples/comparing_cost_functions.ipynb b/examples/notebooks/comparison_examples/comparing_cost_functions.ipynb index 71dc80155..b6a4251bd 100644 --- a/examples/notebooks/comparison_examples/comparing_cost_functions.ipynb +++ b/examples/notebooks/comparison_examples/comparing_cost_functions.ipynb @@ -26,8 +26,8 @@ "\n", "import pybop\n", "\n", - "go = pybop.plot.PlotlyManager().go\n", - "pybop.plot.PlotlyManager().pio.renderers.default = \"notebook_connected\"\n", + "go = pybop.plot.plotly.PlotlyManager().go\n", + "pybop.plot.plotly.PlotlyManager().pio.renderers.default = \"notebook_connected\"\n", "\n", "np.random.seed(8) # users can remove this line" ] diff --git a/examples/notebooks/comparison_examples/optimiser_calibration.ipynb b/examples/notebooks/comparison_examples/optimiser_calibration.ipynb index 9ee9e69d5..c23fdf369 100644 --- a/examples/notebooks/comparison_examples/optimiser_calibration.ipynb +++ b/examples/notebooks/comparison_examples/optimiser_calibration.ipynb @@ -32,7 +32,8 @@ "\n", "import pybop\n", "\n", - "pybop.plot.PlotlyManager().pio.renderers.default = \"notebook_connected\"\n", + "pybop.plot.set_backend(\"plotly\")\n", + "pybop.plot.plotly.PlotlyManager().pio.renderers.default = \"notebook_connected\"\n", "\n", "np.random.seed(8) # users can remove this line" ] diff --git a/examples/notebooks/design_optimisation/energy_based_electrode_design.ipynb b/examples/notebooks/design_optimisation/energy_based_electrode_design.ipynb index 6250b85e9..e4541c8d8 100644 --- a/examples/notebooks/design_optimisation/energy_based_electrode_design.ipynb +++ b/examples/notebooks/design_optimisation/energy_based_electrode_design.ipynb @@ -35,7 +35,8 @@ "\n", "import pybop\n", "\n", - "pybop.plot.PlotlyManager().pio.renderers.default = \"notebook_connected\"\n", + "pybop.plot.set_backend(\"plotly\")\n", + "pybop.plot.plotly.PlotlyManager().pio.renderers.default = \"notebook_connected\"\n", "\n", "np.random.seed(8) # users can remove this line" ] diff --git a/examples/notebooks/getting_started/cost_compute_methods.ipynb b/examples/notebooks/getting_started/cost_compute_methods.ipynb index 3a424f38c..909bcddd5 100644 --- a/examples/notebooks/getting_started/cost_compute_methods.ipynb +++ b/examples/notebooks/getting_started/cost_compute_methods.ipynb @@ -28,7 +28,7 @@ "\n", "import pybop\n", "\n", - "pybop.plot.PlotlyManager().pio.renderers.default = \"notebook_connected\"\n", + "pybop.plot.plotly.PlotlyManager().pio.renderers.default = \"notebook_connected\"\n", "\n", "np.random.seed(8) # users can remove this line" ] diff --git a/examples/notebooks/getting_started/maximum_a_posteriori.ipynb b/examples/notebooks/getting_started/maximum_a_posteriori.ipynb index ccd95b2ea..7da706f7f 100644 --- a/examples/notebooks/getting_started/maximum_a_posteriori.ipynb +++ b/examples/notebooks/getting_started/maximum_a_posteriori.ipynb @@ -51,7 +51,8 @@ "\n", "import pybop\n", "\n", - "pybop.plot.PlotlyManager().pio.renderers.default = \"notebook_connected\"\n", + "pybop.plot.set_backend(\"plotly\")\n", + "pybop.plot.plotly.PlotlyManager().pio.renderers.default = \"notebook_connected\"\n", "\n", "np.random.seed(8) # users can remove this line" ] diff --git a/examples/notebooks/getting_started/optimising_with_adamw.ipynb b/examples/notebooks/getting_started/optimising_with_adamw.ipynb index d6fc110af..ab708410e 100644 --- a/examples/notebooks/getting_started/optimising_with_adamw.ipynb +++ b/examples/notebooks/getting_started/optimising_with_adamw.ipynb @@ -34,7 +34,8 @@ "\n", "import pybop\n", "\n", - "pybop.plot.PlotlyManager().pio.renderers.default = \"notebook_connected\"\n", + "pybop.plot.set_backend(\"plotly\")\n", + "pybop.plot.plotly.PlotlyManager().pio.renderers.default = \"notebook_connected\"\n", "\n", "np.random.seed(8) # users can remove this line" ] diff --git a/examples/notebooks/getting_started/setting_optimiser_options.ipynb b/examples/notebooks/getting_started/setting_optimiser_options.ipynb index a790b26b2..65760ef97 100644 --- a/examples/notebooks/getting_started/setting_optimiser_options.ipynb +++ b/examples/notebooks/getting_started/setting_optimiser_options.ipynb @@ -32,7 +32,7 @@ "\n", "import pybop\n", "\n", - "pybop.plot.PlotlyManager().pio.renderers.default = \"notebook_connected\"\n", + "pybop.plot.plotly.PlotlyManager().pio.renderers.default = \"notebook_connected\"\n", "\n", "np.random.seed(8) # users can remove this line" ] diff --git a/examples/notebooks/getting_started/using_transformations.ipynb b/examples/notebooks/getting_started/using_transformations.ipynb index 236dc8bba..2de75b771 100644 --- a/examples/notebooks/getting_started/using_transformations.ipynb +++ b/examples/notebooks/getting_started/using_transformations.ipynb @@ -29,6 +29,7 @@ "\n", "import pybop\n", "\n", + "pybop.plot.set_backend(\"plotly\")\n", "pybop.plot.PlotlyManager().pio.renderers.default = \"notebook_connected\"\n", "\n", "np.random.seed(8) # users can remove this line" diff --git a/examples/scripts/comparison_examples/grouped_SPMe.py b/examples/scripts/comparison_examples/grouped_SPMe.py index 012104f32..0a21a1a42 100644 --- a/examples/scripts/comparison_examples/grouped_SPMe.py +++ b/examples/scripts/comparison_examples/grouped_SPMe.py @@ -11,11 +11,10 @@ """ # Prepare figure -layout_options = dict( - xaxis_title="Time / s", - yaxis_title="Voltage / V", -) -plot_dict = pybop.plot.StandardPlot(layout_options=layout_options) +pybop.plot.set_backend('matplotlib') +plot_dict = pybop.plot.StandardPlot() +plt.xlabel("Time / s") +plt.ylabel("Voltage / V") # Use the Chen2020 parameters parameter_values = pybamm.ParameterValues("Chen2020") @@ -46,18 +45,18 @@ ) SPMe_model = pybamm.lithium_ion.SPMe(options=model_options) grouped_SPMe_model = pybop.lithium_ion.GroupedSPMe(options=model_options) -for model, param, line_style in zip( +for model, param, linestyle in zip( [SPMe_model, grouped_SPMe_model], [parameter_values, grouped_parameter_values], - ["solid", "dash"], + ["-", "--"], strict=False, ): solution = pybamm.Simulation( model, parameter_values=param, experiment=experiment ).solve(initial_soc=init_soc) dataset = pybop.import_pybamm_solution(solution) - plot_dict.add_traces( - dataset["Time [s]"], dataset["Voltage [V]"], line_dash=line_style + plot_dict.create_trace( + dataset["Time [s]"], dataset["Voltage [V]"], label=None, linestyle=linestyle ) plot_dict() diff --git a/pybop/plot/__init__.py b/pybop/plot/__init__.py index f58db2016..744e749ab 100644 --- a/pybop/plot/__init__.py +++ b/pybop/plot/__init__.py @@ -1,13 +1,30 @@ +# Plotting backend default +DEFAULT_BACKEND = 'matplotlib' +backend=DEFAULT_BACKEND + +from .util import set_backend, call_plotting_function, get_class + # # Import plots # -from .plotly_manager import PlotlyManager -from .standard_plots import StandardPlot, StandardSubplot, trajectories -from .contour import contour -from .dataset import dataset -from .convergence import convergence -from .parameters import parameters -from .problem import problem -from .nyquist import nyquist -from .voronoi import surface -from .samples import trace, chains, posterior, summary_table +from .plots import ( + chains, + contour, + convergence, + dataset, + nyquist, + parameters, + posterior, + problem, + summary_table, + surface, + trace, + trajectories + ) + +from .voronoi import voronoi_data, _voronoi_regions +from . import matplotlib +from . import plotly + +StandardPlot = matplotlib.StandardPlot +StandardSubplot = matplotlib.StandardSubplot diff --git a/pybop/plot/matplotlib/__init__.py b/pybop/plot/matplotlib/__init__.py new file mode 100644 index 000000000..863ad89ec --- /dev/null +++ b/pybop/plot/matplotlib/__init__.py @@ -0,0 +1,9 @@ +from .standard_plots import StandardPlot, StandardSubplot, trajectories +from .dataset import dataset +from .convergence import convergence +from .parameters import parameters +from .problem import problem +from .contour import contour +from .voronoi import surface +from .nyquist import nyquist +from .samples import chains, posterior, summary_table, trace \ No newline at end of file diff --git a/pybop/plot/matplotlib/contour.py b/pybop/plot/matplotlib/contour.py new file mode 100644 index 000000000..eb7ee77f3 --- /dev/null +++ b/pybop/plot/matplotlib/contour.py @@ -0,0 +1,237 @@ +import warnings +from collections.abc import Callable +from typing import TYPE_CHECKING + +import numpy as np +from matplotlib import pyplot as plt +from scipy.interpolate import griddata + +from pybop.problems.problem import Problem + +if TYPE_CHECKING: + from pybop._result import Result + + +def contour( + call_object: "Problem | Result", + gradient: bool = False, + bounds: np.ndarray | None = None, + transformed: bool = False, + steps: int = 10, + show: bool = True, + title: str = 'Cost Landscape', +): + """ + Plot a 2D visualisation of a cost landscape using Plotly. + + This function generates a contour plot representing the cost landscape for a provided + callable cost function over a grid of parameter values within the specified bounds. + + Parameters + ---------- + call_object : pybop.Problem | pybop.Result + Either: + - the cost function to be evaluated. Must accept a list of parameter values and return a cost value. + - an optimiser result which provides a specific optimisation trace overlaid on the cost landscape. + gradient : bool, optional + If True, the gradient is shown (default: False). + bounds : numpy.ndarray | list[list[float]], optional + A 2x2 array specifying the [min, max] bounds for each parameter. If None, uses + `parameters.get_bounds_for_plotly`. + transformed : bool, optional + Uses the transformed parameter values (as seen by the optimiser) for plotting. + steps : int, optional + The number of grid points to divide the parameter space into along each dimension (default: 10). + show : bool, optional + If True, the figure is shown upon creation (default: True). + **layout_kwargs : optional + Valid Plotly layout keys and their values, + e.g. `xaxis_title="Time [s]"` or + `xaxis={"title": "Time [s]", font={"size":14}}` + + Returns + ------- + plotly.graph_objs.Figure + The Plotly figure object containing the cost landscape plot. + + Raises + ------ + ValueError + If the cost function does not return a valid cost when called with a parameter list. + """ + plot_optim = False + problem = call_object + + # Assign input as a cost or optimisation result + if not isinstance(call_object, Callable): + plot_optim = True + result = call_object + problem = result.problem + + parameters = problem.parameters + names = parameters.names + additional_values = [] + + if len(parameters) < 2: + raise ValueError("This cost function takes fewer than 2 parameters.") + + if len(parameters) > 2: + warnings.warn( + "This cost function requires more than 2 parameters. " + "Plotting in 2d with fixed values for the additional parameters.", + UserWarning, + stacklevel=2, + ) + for ( + i, + (name, param), + ) in enumerate(parameters.items()): + if i > 1: + # TODO: Update from the initial to the intended value + additional_values.append(param.get_initial_value()) + print(f"Fixed {name}:", param.get_initial_value()) + + # Set up parameter bounds + if bounds is None: + bounds = parameters.get_bounds_for_plotly() + else: + bounds = np.asarray(bounds) + + # Generate grid + x = np.linspace(bounds[0, 0], bounds[0, 1], steps) + y = np.linspace(bounds[1, 0], bounds[1, 1], steps) + + # Initialize cost matrix + costs = np.zeros((len(y), len(x))) + + if gradient: + grad_parameter_costs = [] + + # Create an array to hold the gradient with respect to each parameter + grads = [np.zeros((len(y), len(x))) for _ in range(len(parameters))] + + # Populate cost matrix + for i, xi in enumerate(x): + for j, yj in enumerate(y): + if gradient: + out = problem.evaluate( + np.asarray([xi, yj] + additional_values), + calculate_sensitivities=True, + ).get_values() + costs[j, i], sensitivities = out[0][0], out[1] + for k, key in enumerate(problem.parameters.names): + grads[k][j, i] = sensitivities[key].item() + else: + costs[j, i] = problem.evaluate( + np.asarray([xi, yj] + additional_values), + ).get_values()[0] + + # Append the arrays to the grad_parameter_costs list + if gradient: + grad_parameter_costs.extend(grads) + + # Apply any transformation if requested + def transform_array_of_values(list_of_values, parameter): + """Apply transformation if requested.""" + if transformed: + return np.asarray( + [parameter.transformation.to_search(value) for value in list_of_values] + ).flatten() + return list_of_values + + x = transform_array_of_values(x, parameters[names[0]]) + y = transform_array_of_values(y, parameters[names[1]]) + bounds[0] = transform_array_of_values(bounds[0], parameters[names[0]]) + bounds[1] = transform_array_of_values(bounds[1], parameters[names[1]]) + + # define levels + exponent = np.floor(np.log10(np.abs(np.max(costs)))) + levels = np.linspace(np.floor(np.min(costs)/(10**exponent))*(10**exponent), np.ceil(np.max(costs)/(10**exponent))*(10**exponent), 2 * steps - 1) + + # Create contour plot and update the layout + fig = plt.figure(figsize=(6, 6), dpi=100) + plt.contourf(x, y, costs, levels=levels, extend='both', cmap='viridis') + plt.colorbar() + plt.contour(x, y, costs, levels=levels, colors=('k',), linestyles='solid', linewidths=0.1) + + # Layout + plt.xlabel("Transformed " + names[0] if transformed else names[0], labelpad=15) + plt.ticklabel_format(axis='both', **dict(style='sci',scilimits=(-4,4))) + plt.ylabel("Transformed " + names[1] if transformed else names[1], labelpad=15) + plt.title(title, pad=40) + plt.xlim(bounds[0]) + plt.ylim(bounds[1]) + + if plot_optim: + # Plot the optimisation trace + optim_trace = np.asarray([item[:2] for item in result.x_model]) + optim_trace = optim_trace.reshape(-1, 2) + + plt.scatter( + transform_array_of_values(optim_trace[:, 0], parameters[names[0]]), + transform_array_of_values(optim_trace[:, 1], parameters[names[1]]), + c=[i / len(optim_trace) for i in range(len(optim_trace))], + cmap='Grays', + zorder=1, + ) + + # Plot the initial guess + if len(result.x_model) > 0: + x0 = result.x_model[0] + plt.plot( + transform_array_of_values([x0[0]], parameters[names[0]]), + transform_array_of_values([x0[1]], parameters[names[1]]), + 'X', + markersize=14, + markerfacecolor='w', + markeredgecolor='k', + label="Initial values", + linestyle='None', + ) + + # Plot optimised value + if result.x is not None: + x_best = result.x + plt.plot( + transform_array_of_values([x_best[0]], parameters[names[0]]), + transform_array_of_values([x_best[1]], parameters[names[1]]), + "P", + markersize=14, + markerfacecolor='k', + markeredgecolor='w', + label="Final values", + linestyle='None', + ) + + plt.legend(ncols=2, loc='lower center', bbox_to_anchor=(0.5, 1.0)) + + plt.tight_layout() + + if show: + plt.show() + + # if gradient: + # grad_figs = [] + # for i, grad_costs in enumerate(grad_parameter_costs): + # # Update title for gradient plots + # updated_layout_options = layout_options.copy() + # updated_layout_options["title"] = f"Gradient for Parameter: {i + 1}" + + # # Create contour plot with updated layout options + # grad_layout = go.Layout(updated_layout_options) + + # # Create fig + # grad_fig = go.Figure( + # data=[go.Contour(x=x, y=y, z=grad_costs)], layout=grad_layout + # ) + # grad_fig.update_layout(**layout_kwargs) + + # if show: + # grad_fig.show() + + # # append grad_fig to list + # grad_figs.append(grad_fig) + + # return fig, grad_figs + + return fig diff --git a/pybop/plot/matplotlib/convergence.py b/pybop/plot/matplotlib/convergence.py new file mode 100644 index 000000000..4ad2f31ad --- /dev/null +++ b/pybop/plot/matplotlib/convergence.py @@ -0,0 +1,50 @@ +from typing import TYPE_CHECKING + +from pybop.plot.matplotlib.standard_plots import StandardPlot +import matplotlib.pyplot as plt + +if TYPE_CHECKING: + from pybop._result import Result + + +def convergence(result: "Result", show=True): + """ + Plot the convergence of the optimisation algorithm. + + Parameters + ----------- + result : pybop.Result + Optimisation result containing the history of parameter values and associated cost. + show : bool, optional + If True, the figure is shown upon creation (default: True). + + Returns + --------- + fig : plotly.graph_objs.Figure + The Plotly figure object for the convergence plot. + """ + + # Extract log from the optimisation object + cost_log = result.cost_convergence + + # Generate a list of iteration numbers + iteration_numbers = list(range(1, len(cost_log) + 1)) + + # Create a plot dictionary + plot_dict = StandardPlot( + x=iteration_numbers, + y=cost_log, + trace_names=result.method_name, + ) + + # Generate and display the figure + fig = plot_dict(show=False) + plt.xlabel("Evaluation") + plt.ylabel("Cost") + plt.title("Convergence") + plt.tight_layout() + + if show: + plt.show() + + return fig \ No newline at end of file diff --git a/pybop/plot/matplotlib/dataset.py b/pybop/plot/matplotlib/dataset.py new file mode 100644 index 000000000..44cd3ce25 --- /dev/null +++ b/pybop/plot/matplotlib/dataset.py @@ -0,0 +1,54 @@ +import matplotlib.pyplot as plt +from pybop.plot.matplotlib.standard_plots import StandardPlot, trajectories + + +def dataset(dataset, signal=None, trace_names=None, show=True): + """ + Quickly plot a PyBOP Dataset using Plotly. + + Parameters + ---------- + dataset : object + A PyBOP dataset. + signal : list or str, optional + The name of the time series to plot (default: "Voltage [V]"). + trace_names : list or str, optional + Name(s) for the trace(s) (default: "Data"). + show : bool, optional + If True, the figure is shown upon creation (default: True). + + Returns + ------- + plotly.graph_objs.Figure + The Plotly figure object for the scatter plot. + """ + + # Get data dictionary + if signal is None: + signal = ["Voltage [V]"] + dataset.check(signal=signal) + + # Compile ydata and labels or legend + y = [dataset[s] for s in signal] + if len(signal) == 1: + yaxis_title = signal[0] + if trace_names is None: + trace_names = ["Data"] + else: + yaxis_title = "Output" + if trace_names is None: + trace_names = StandardPlot.remove_brackets(signal) + + # Create the figure + fig = trajectories( + x=dataset[dataset.domain], + y=y, + trace_names=trace_names, + show=False, + xaxis_title=StandardPlot.remove_brackets(dataset.domain), + yaxis_title=yaxis_title, + ) + if show: + plt.show() + + return fig diff --git a/pybop/plot/matplotlib/nyquist.py b/pybop/plot/matplotlib/nyquist.py new file mode 100644 index 000000000..c45b29beb --- /dev/null +++ b/pybop/plot/matplotlib/nyquist.py @@ -0,0 +1,86 @@ +from pybop.parameters.parameter import Inputs +from pybop.plot.matplotlib.standard_plots import StandardPlot +from matplotlib import pyplot as plt + + +def nyquist(problem, inputs: Inputs = None, show=True, **layout_kwargs): + """ + Generates Nyquist plots for the given problem by evaluating the model's output and target values. + + Parameters + ---------- + problem : pybop.Problem + An instance of a problem class that contains the parameters and methods + for evaluation and target retrieval. + inputs : Inputs, optional + Input parameters for the problem. If not provided, the default parameters from the problem + instance will be used. These parameters are verified before use (default is None). + show : bool, optional + If True, the plots will be displayed. + **layout_kwargs : dict, optional + Additional keyword arguments for customising the plot layout. These arguments are passed to + `fig.update_layout()`. + + Returns + ------- + list + A list of plotly `Figure` objects, each representing a Nyquist plot for the model's output and target values. + + Notes + ----- + - The function extracts the real part of the impedance from the model's output and the real and imaginary parts + of the impedance from the target output. + - For each signal in the problem, a Nyquist plot is created with the model's impedance plotted as a scatter plot. + - An additional trace for the reference (target output) is added to the plot. + - The plot layout can be customised using `layout_kwargs`. + + Example + ------- + >>> problem = pybop.EISProblem() + >>> nyquist_figures = nyquist(problem, show=True, title="Nyquist Plot", xaxis_title="Real(Z)", yaxis_title="Imag(Z)") + >>> # The plots will be displayed and nyquist_figures will contain the list of figure objects. + """ + if not isinstance(inputs, dict): + inputs = problem.parameters.to_dict(inputs) + + model_output = problem.simulate(inputs) + domain_data = model_output["Impedance"].data.real + target_output = problem.target_data + + figure_list = [] + for var in problem.target: + plot_dict = StandardPlot( + x=domain_data, + y=-model_output[var].data.imag, + trace_names="Model", + ) + + fig = plot_dict(show=False) + plot_dict.traces[0].set_color("#00CC96") + plot_dict.traces[0].set_linewidth(2) + plot_dict.traces[0].set_marker('.') + plot_dict.traces[0].set_markersize(8) + + target_trace = plot_dict.create_trace( + x=target_output[var].real, + y=-target_output[var].imag, + label="Reference", + ) + target_trace.set_linestyle('None') + target_trace.set_marker('o') + target_trace.set_fillstyle('none') + target_trace.set_markersize(8) + target_trace.set_markeredgecolor("#636EFA") + + # Layout + plt.title('Nyquist Plot', fontsize=14, x=0.2) + plt.xlabel(r"$Z_{re} / \Omega$", fontsize=16) + plt.ylabel(r"$-Z_{im} / \Omega$", fontsize=16) + plt.legend(loc='upper right', bbox_to_anchor=(1, 1.08), ncols=2) + + if show: + plt.show() + + figure_list.append(fig) + + return figure_list diff --git a/pybop/plot/matplotlib/parameters.py b/pybop/plot/matplotlib/parameters.py new file mode 100644 index 000000000..671f00cb2 --- /dev/null +++ b/pybop/plot/matplotlib/parameters.py @@ -0,0 +1,71 @@ +from typing import TYPE_CHECKING + +import warnings + +from pybop.costs.log_likelihoods import GaussianLogLikelihood +from pybop.plot.matplotlib.standard_plots import StandardSubplot +import matplotlib.pyplot as plt + +if TYPE_CHECKING: + from pybop._result import Result + + +def parameters(result: "Result", show=True, **layout_kwargs): + """ + Plot the evolution of parameters during the optimisation process using Plotly. + + Parameters + ---------- + result : pybop.Result + Optimisation result containing the history of parameter values and associated cost. + show : bool, optional + If True, the figure is shown upon creation (default: True). + + Returns + ------- + plotly.graph_objs.Figure + A Plotly figure object showing the parameter evolution over iterations. + """ + + if len(layout_kwargs) > 0: + warnings.warn( + "The following layout argument keys are ignored for the current plotting backend (matplotlib): \n" + f"{list(layout_kwargs.keys())}", + UserWarning, + stacklevel=2, + ) + + # Extract parameters and log from the optimisation object + parameters = result.problem.parameters + x = list(range(len(result.x_model))) + y = [list(item) for item in zip(*result.x_model, strict=False)] + + # Create lists of axis titles and trace names + axis_titles = [] + trace_names = parameters.names + for name in trace_names: + axis_titles.append(("Evaluation", name)) + + if isinstance(result.problem, GaussianLogLikelihood): + axis_titles.append(("Evaluation", "Sigma")) + trace_names.append("Sigma") + + + # Create a plot dictionary + plot_dict = StandardSubplot( + x=x, + y=y, + axis_titles=axis_titles, + trace_names=trace_names, + trace_name_width=50, + figsize= (18, 8), + ) + + plt.suptitle("Parameter Convergence") + + # Generate the figure and update the layout + fig = plot_dict(show=False) + if show: + plt.show() + + return fig diff --git a/pybop/plot/matplotlib/problem.py b/pybop/plot/matplotlib/problem.py new file mode 100644 index 000000000..451ed76a1 --- /dev/null +++ b/pybop/plot/matplotlib/problem.py @@ -0,0 +1,114 @@ +import numpy as np + +from pybop.costs.design_cost import DesignCost +from pybop.costs.error_measures import ErrorMeasure +from pybop.parameters.parameter import Inputs +from pybop.plot.matplotlib.standard_plots import StandardPlot +from pybop.problems.meta_problem import MetaProblem +from pybop.problems.problem import Problem +from pybop.simulators.solution import Solution + +import matplotlib.pyplot as plt + + +def problem( + problem: Problem, + inputs: Inputs = None, + show: bool = True, + title = 'Scatter Plot', +): + """ + Produce a quick plot of the target dataset against optimised model output. + + Generates an interactive plot comparing the simulated model output with + an optional target dataset and visualises uncertainty. + + Parameters + ---------- + problem : pybop.Problem + Problem object with dataset and targets attributes. + inputs : Inputs + Optimised (or example) parameter values. + show : bool, optional + If True, the figure is shown upon creation (default: True). + + Returns + ------- + plotly.graph_objs.Figure + The Plotly figure object for the scatter plot. + """ + if inputs is None: + inputs = problem.parameters.to_dict() + elif not isinstance(inputs, dict): + raise TypeError(f"Expecting a dictionary, received {type(inputs)}") + + domain = problem.domain + if problem.domain_data is None: + # Simulate the model for the both the initial and the given inputs + target = problem.target + problem.set_target(target + [domain]) + initial_inputs = problem.simulator.parameters.to_dict("initial") + target_output = problem.simulate(initial_inputs) + target_domain = target_output[domain].data + model_output = problem.simulate(inputs) + model_domain = model_output[domain].data + problem.set_target(target) + else: + # Extract the time data and simulate the model for the given inputs + target_output = Solution() + for target in problem.target: + target_output.set_solution_variable( + target, data=problem.target_data[target] + ) + target_domain = problem.domain_data + model_output = problem.simulate(inputs) + model_domain = target_domain[: len(model_output[target].data)] + + # Create a plot for each output + figure_list = [] + for var in problem.target: + # Create a plot dictionary + plot_dict = StandardPlot() + + plot_dict.create_trace( + x=target_domain, + y=target_output[var].data, + label="Reference", + marker=".", + linestyle="None" + ) + + plot_dict.create_trace( + x=model_domain, + y=model_output[var].data, + label="Optimised" if isinstance(problem.cost, DesignCost) else "Model", + marker="." if isinstance(problem, MetaProblem) else None, + linestyle='None' if isinstance(problem, MetaProblem) else "-", + ) + + if isinstance(problem.cost, ErrorMeasure) and len( + model_output[var].data + ) == len(target_output[var].data): + # Compute the standard deviation as proxy for uncertainty + plot_dict.sigma = np.std(model_output[var].data - target_output[var].data) + + # Convert x and upper and lower limits into lists to create a filled trace + x = target_domain.tolist() + y_upper = (model_output[var].data + plot_dict.sigma).tolist() + y_lower = (model_output[var].data - plot_dict.sigma).tolist() + + plt.fill_between(x, y_upper, y_lower, color=[(1.0, 0.898, 0.800, 0.8)]) + + # Generate the figure and update the layout + fig = plot_dict(show=False) + plt.xlabel("Time / s") + plt.ylabel(StandardPlot.remove_brackets(var)) + plt.title(title) + plt.legend() + plt.tight_layout() + if show: + plt.show() + + figure_list.append(fig) + + return figure_list diff --git a/pybop/plot/matplotlib/samples.py b/pybop/plot/matplotlib/samples.py new file mode 100644 index 000000000..dd14cf908 --- /dev/null +++ b/pybop/plot/matplotlib/samples.py @@ -0,0 +1,138 @@ +from typing import TYPE_CHECKING +import warnings + +from matplotlib import pyplot as plt + +if TYPE_CHECKING: + from pybop.samplers.base_pints_sampler import SamplingResult + + +def trace(result: "SamplingResult", show=True, **kwargs): + """ + Plot trace plots for the posterior samples. + """ + # Warning if layout arguments ignored + if len(kwargs) > 0: + warnings.warn( + "The following layout argument keys are ignored for the current plotting backend (matplotlib): \n" + f"{list(kwargs.keys())}", + UserWarning, + stacklevel=2, + ) + figlist = [] + for i in range(result.n_parameters): + fig = plt.figure() + + for j, chain in enumerate(result.chains): + plt.plot(chain[:, i], label=f"Chain {j}") + + plt.title(f"Parameter {i} Trace Plot") + plt.xlabel("Sample Index") + plt.ylabel("Value") + plt.legend(fontsize=12) + figlist.append(fig) + + + if show: + plt.show() + else: + return figlist + + + +def chains(result: "SamplingResult", show=True, **kwargs): + """ + Plot posterior distributions for each chain. + """ + # Warning if layout arguments ignored + if len(kwargs) > 0: + warnings.warn( + "The following layout argument keys are ignored for the current plotting backend (matplotlib): \n" + f"{list(kwargs.keys())}", + UserWarning, + stacklevel=2, + ) + fig = plt.figure(figsize=(15, 8), dpi=100) + + for i, chain in enumerate(result.chains): + for j in range(chain.shape[1]): + plt.hist( + x=chain[:, j], + label=f"Chain {i} - Parameter {j}", + alpha=0.5, + rwidth=2.0 + ) + + for j in range(chain.shape[1]): + plt.plot([result.mean[j], result.mean[j]], [0, result.max[j]],"--", lw=3, label=f"Mean - Parameter {j}") + + plt.legend(loc="upper left", bbox_to_anchor=(1.01, 1.0)) + plt.grid(axis='y', zorder=-1) + plt.title("Posterior Distribution") + plt.xlabel("Value") + plt.ylabel("Density") + plt.tight_layout() + if show: + plt.show() + else: + return fig + +def posterior(result: "SamplingResult", show=True, **kwargs): + """ + Plot the summed posterior distribution across chains. + """ + # Warning if layout arguments ignored + if len(kwargs) > 0: + warnings.warn( + "The following layout argument keys are ignored for the current plotting backend (matplotlib): \n" + f"{list(kwargs.keys())}", + UserWarning, + stacklevel=2, + ) + + fig = plt.figure(figsize=(15, 8), dpi=100) + + for j in range(result.all_samples.shape[1]): + plt.hist( + x=result.all_samples[:, j], + label=f"Parameter {j}", + alpha=0.75, + ) + plt.axvline(result.mean[j], ls='--', c='k', lw=3) + + plt.legend(loc="upper left", bbox_to_anchor=(1.01, 1.0)) + plt.grid(axis='y', zorder=-1) + plt.title("Posterior Distribution") + plt.xlabel("Value") + plt.ylabel("Density") + plt.tight_layout() + if show: + plt.show() + else: + return fig + + +def summary_table(result: "SamplingResult"): + """ + Display summary statistics in a table. + """ + + summary_stats = result.get_summary_statistics() + + header = ["Statistic", "Value"] + values = [ + ["Mean", ', '.join(summary_stats["mean"].astype(str))], + ["Median", ', '.join(summary_stats["median"].astype(str))], + ["Standard Deviation", ', '.join(summary_stats["std"].astype(str))], + ["95% CI Lower", ', '.join(summary_stats["ci_lower"].astype(str))], + ["95% CI Upper", ', '.join(summary_stats["ci_upper"].astype(str))], + ] + fig, ax = plt.subplots(figsize=(6, 2), dpi=100) + + # hide axes + ax.axis('off') + ax.axis('tight') + ax.table(cellText=values, colLabels=header, loc='center', cellLoc='center', colColours=['lightsteelblue', 'lightsteelblue']) + ax.set_title("Summary Statistics") + fig.tight_layout() + plt.show() diff --git a/pybop/plot/matplotlib/standard_plots.py b/pybop/plot/matplotlib/standard_plots.py new file mode 100644 index 000000000..578ef365b --- /dev/null +++ b/pybop/plot/matplotlib/standard_plots.py @@ -0,0 +1,386 @@ +import math +import textwrap +import warnings + +import numpy as np + +from matplotlib import pyplot as plt + +DEFAULT_TRACE_OPTIONS = dict(linewidth=2.0) + +class StandardPlot: + """ + A class for creating and displaying interactive Plotly figures. + + Parameters + ---------- + x : list or np.ndarray, optional + X-axis data points. + y : list or np.ndarray, optional + Primary Y-axis data points for simulated model output. + trace_options : dict, optional + Settings to modify the default trace type (default: DEFAULT_TRACE_OPTIONS). + trace_names : str, optional + Name(s) for the primary trace(s) (default: None). + trace_name_width : int, optional + Maximum length of the trace names before text wrapping is used (default: 40). + + Returns + ------- + plotly.graph_objs.Figure + The generated Plotly figure. + """ + + def __init__( + self, + x=None, + y=None, + trace_options=None, + trace_names=None, + trace_name_width=20, + figsize=(8, 6), + **kwargs, + ): + # Warning if layout arguments ignored + if len(kwargs) > 0: + warnings.warn( + "The following layout argument keys are ignored for the current plotting backend (matplotlib): \n" + f"{list(kwargs.keys())}", + UserWarning, + stacklevel=2, + ) + self.traces = [] + self.trace_name_width = trace_name_width + + # Set default trace options and update if provided + self.trace_options = DEFAULT_TRACE_OPTIONS.copy() + if trace_options: + self.trace_options.update(trace_options) + + + # Parse the data + x, y = self.parse_data(x, y) + self.x = x + self.y = y + # Check and wrap trace names + if trace_names is not None: + if isinstance(trace_names, str): + trace_names = [trace_names] + for i, name in enumerate(trace_names): + trace_names[i] = self.wrap_text(name, width=self.trace_name_width) + self.trace_names = trace_names + + self.fig = plt.figure(figsize=figsize, dpi=100) + + + + def __call__(self, show=True): + """ + Generate and show the figure. + + Parameters + ---------- + show : bool, optional + If True, the figure is shown upon creation (default: True). + """ + # Add traces + if self.x is not None and self.y is not None: + self.add_traces(self.x, self.y, self.trace_names) + self.default_layout() + if show: + plt.show() + + return self.fig + + def default_layout(self): + + plt.tick_params(axis='both', labelsize=12) + plt.ticklabel_format(axis='both', style='sci', scilimits=(-4, 4)) + + def add_traces(self, x, y, trace_names=None, **trace_options): + """ + Add a set of traces to the plot dictionary. + + Parameters + ---------- + x : list or np.ndarray + X-axis data points. + y : list or np.ndarray + Primary Y-axis data points for simulated model output. + trace_names : str or list[str], optional + Name(s) for the primary trace(s) (default: None). + """ + + options = self.trace_options.copy() + options.update(trace_options) + + # Create a trace for each trajectory + xi = x[0] + for i in range(0, len(y)): + trace_options = options.copy() + if len(x) > 1: + xi = x[i] + + label = None + if trace_names is not None: + label = trace_names[i] + + self.traces.append(self.create_trace(xi, y[i], label, **trace_options)) + + if self.trace_names is not None: + plt.legend(**dict(loc="best", fontsize=12),) + + + def parse_data(self, x, y): + """ + Check the type and dimensions of the data and convert if necessary to a list + of 'things plotly can take', e.g. numpy arrays or lists of numbers. + + Parameters + ---------- + x : list or np.ndarray, optional + X-axis data points. + y : list or np.ndarray, optional + Primary Y-axis data points for simulated model output. + """ + if x is None or y is None: + return None, None + if isinstance(x, list): + # If it's a list of numpy arrays, it's fine + # If it's a list of lists, it's fine + # If it's neither, it's a list of numbers that we need to wrap + if not isinstance(x[0], np.ndarray) and not isinstance(x[0], list): + x = [x] + elif isinstance(x, np.ndarray): + x = np.squeeze(x) + if x.ndim == 1: + x = [x] + else: + x = x.tolist() + if isinstance(y, list): + if not isinstance(y[0], np.ndarray) and not isinstance(y[0], list): + y = [y] + if isinstance(y, np.ndarray): + y = np.squeeze(y) + if y.ndim == 1: + y = [y] + else: + y = y.tolist() + if len(x) > 1 and len(x) != len(y): + raise ValueError( + "Input x should have either one data series or the same number as y." + ) + return x, y + + def create_trace(self, x, y, label, ax=None, **trace_options): + """ + Create a trace for the Plotly figure. + + Returns + ------- + plotly.graph_objs.Scatter + A trace for a Plotly figure. + """ + + if ax is None: + ax = plt.gca() + + line = ax.plot( + x, + y, + label=label, + **trace_options, + ) + if len(line) > 1: + return line + else: + return line[0] + + + @staticmethod + def wrap_text(text, width): + """ + Wrap text to a specified width with HTML line breaks. + + Parameters + ---------- + text : str + The text to wrap. + width : int + The width to wrap the text to. + + Returns + ------- + str + The wrapped text. + """ + wrapped_text = textwrap.fill(text, width=width, break_long_words=False) + return wrapped_text + + @staticmethod + def remove_brackets(s): + """ + Remove square brackets from a string and replace with forward slashes + as per section 7.1 of the SI Handbook + """ + # If s is an iterable (but not a string), apply the function recursively to each element + if hasattr(s, "__iter__") and not isinstance(s, str): + return type(s)(StandardPlot.remove_brackets(i) for i in s) + elif isinstance(s, str): + start = s.find("[") + end = s.find("]") + if start != -1 and end != -1: + char_in_brackets = s[start + 1 : end] + return s[:start] + " / " + char_in_brackets + s[end + 1 :] + return s + + + +class StandardSubplot(StandardPlot): + """ + A class for creating and displaying a set of interactive Plotly figures in a grid layout. + + Parameters + ---------- + x : list or np.ndarray + X-axis data points. + y : list or np.ndarray + Primary Y-axis data points for simulated model output. + num_rows : int, optional + Number of rows of subplots, can be set automatically (default: None). + num_cols : int, optional + Number of columns of subplots, can be set automatically (default: None). + layout : Plotly layout, optional + A layout for the figure, overrides the layout options (default: None). + trace_options : dict, optional + Settings to modify the default trace type (default: DEFAULT_TRACE_OPTIONS). + trace_names : str, optional + Name(s) for the primary trace(s) (default: None). + trace_name_width : int, optional + Maximum length of the trace names before text wrapping is used (default: 40). + + Returns + ------- + plotly.graph_objs.Figure + The generated Plotly figure. + """ + + def __init__( + self, + x, + y, + num_rows=None, + num_cols=None, + axis_titles=None, + trace_options=DEFAULT_TRACE_OPTIONS, + trace_names=None, + trace_name_width=40, + figsize=(8, 6) + ): + super().__init__( + x, y, trace_options, trace_names, trace_name_width, figsize + ) + self.num_traces = len(self.y) + self.num_rows = num_rows + self.num_cols = num_cols + if self.num_rows is None and self.num_cols is None: + # Work out the number of subplots + self.num_cols = int(math.ceil(math.sqrt(self.num_traces))) + self.num_rows = int(math.ceil(self.num_traces / self.num_cols)) + elif self.num_rows is None: + self.num_rows = int(math.ceil(self.num_traces / self.num_cols)) + elif self.num_cols is None: + self.num_cols = int(math.ceil(self.num_traces / self.num_rows)) + self.axis_titles = axis_titles + + + + def __call__(self, show): + """ + Generate and show the set of figures. + + Parameters + ---------- + show : bool, optional + If True, the figure is shown upon creation (default: True). + """ + + color_cycle = plt.rcParams['axes.prop_cycle']() + + xi = self.x[0] + lines = [] + for idx, yi in enumerate(self.y): + ax = self.fig.add_subplot(self.num_rows, self.num_cols, idx+1) + if self.axis_titles and idx < len(self.axis_titles): + x_title, y_title = self.axis_titles[idx] + ax.set_xlabel(x_title) + ax.set_ylabel(y_title) + if len(self.x)>1: + xi = self.x[idx] + + label = None + if self.trace_names is not None: + label = self.trace_names[idx] + + lines.append(self.create_trace(xi, yi, label, ax = ax, **next(color_cycle))) + + + lines_labels = [ax.get_legend_handles_labels() for ax in self.fig.axes] + lines, labels = [sum(lol, []) for lol in zip(*lines_labels)] + if self.trace_names is not None: + self.fig.legend(lines, labels, loc='upper right', ncol=len(lines), bbox_to_anchor=(0.99, 0.95)) + plt.tight_layout(rect=[0, 0, 1, 0.95]) + if show: + plt.show() + + return self.fig + + +def trajectories(x, y, trace_names=None, show=True, xaxis_title='', yaxis_title='', title='', **layout_kwargs): + """ + Quickly plot one or more trajectories using Plotly. + + Parameters + ---------- + x : list or np.ndarray + X-axis data points. + y : list or np.ndarray + Y-axis data points for each trajectory. + trace_names : list or str, optional + Name(s) for the trace(s) (default: None). + **layout_kwargs : optional + This argument is ignored for the matplotlib backend. + Valid Plotly layout keys and their values, + e.g. `xaxis_title="Time / s"` or + `xaxis={"title": "Time [s]", font={"size":14}}` + + Returns + ------- + plotly.graph_objs.Figure + The Plotly figure object for the scatter plot. + """ + + if len(layout_kwargs) > 0: + warnings.warn( + "The following layout argument keys are ignored for the current plotting backend (matplotlib): \n" + f"{list(layout_kwargs.keys())}", + UserWarning, + stacklevel=2, + ) + # Create a plot dictionary + plot_dict = StandardPlot( + x=x, + y=y, + trace_names=trace_names, + ) + + # Generate the figure and update the layout + fig = plot_dict(show=False) + plt.title(title) + plt.xlabel(xaxis_title, fontsize=12) + plt.ylabel(yaxis_title, fontsize=12) + plt.tight_layout() + if show: + plt.show() + + return plot_dict \ No newline at end of file diff --git a/pybop/plot/matplotlib/voronoi.py b/pybop/plot/matplotlib/voronoi.py new file mode 100644 index 000000000..1a44f0f77 --- /dev/null +++ b/pybop/plot/matplotlib/voronoi.py @@ -0,0 +1,142 @@ +from typing import TYPE_CHECKING + +import numpy as np +from scipy.spatial import cKDTree +from matplotlib import pyplot as plt +import matplotlib as mpl +import warnings + +if TYPE_CHECKING: + from pybop._result import Result + +from pybop.plot.voronoi import voronoi_data + +def surface( + result: "Result", + bounds=None, + normalise=True, + title='Voronoi Cost Landscape', + show=True, + **layout_kwargs +): + """ + Plot a 2D representation of the Voronoi diagram with color-coded regions. + + Parameters: + ----------- + result : pybop.Result + Optimisation result containing the history of parameter values and associated cost. + bounds : numpy.ndarray, optional + A 2x2 array specifying the [min, max] bounds for each parameter. If None, uses + `cost.parameters.get_bounds_for_plotly`. + normalise : bool, optional + If True, the voronoi regions are computed using the Euclidean distance between + points normalised with respect to the bounds (default: True). + resolution : int, optional + Resolution of the plot. Default is 500. + show : bool, optional + If True, the figure is shown upon creation (default: True). + """ + + if len(layout_kwargs) > 0: + warnings.warn( + "The following layout argument keys are ignored for the current plotting backend (matplotlib): \n" + f"{list(layout_kwargs.keys())}", + UserWarning, + stacklevel=2, + ) + + points = result.x_model + parameters = result.problem.parameters + + if points[0].shape[0] != 2: + raise ValueError("This plot method requires two parameters.") + + x_optim, y_optim = map(list, zip(*points, strict=False)) + f = result.cost + + # Translate bounds, taking only the first two elements + xlim, ylim = ( + bounds if bounds is not None else [param.bounds for param in parameters] + )[:2] + + _, _, f, regions, relative_sizes = voronoi_data(xlim, ylim, x_optim, y_optim, f, normalise) + + + # Construct figure + plt.figure(figsize=(7, 6), dpi=100) + + # normalise cost + f_min = np.nanmin(f[np.isfinite(f)]) + f_max = np.nanmax(f[np.isfinite(f)]) + norm = mpl.colors.Normalize(vmin=f_min, vmax=f_max, clip=True) + norm_f = norm(f, clip=True) + + # get colours + cmap = mpl.colormaps['viridis'] + colors = cmap(norm_f) + + # Add Voronoi edges and fill Voronoi regions + for j, (region, size) in enumerate(zip(regions, relative_sizes, strict=False)): + x_region = region[:, 0].tolist() + [region[0, 0]] + y_region = region[:, 1].tolist() + [region[0, 1]] + + plt.fill(x_region, y_region, color=colors[j]) + plt.plot(x_region, y_region, color='w', linewidth=0.5 + size*0.1) + + plt.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap), ax = plt.gca()) + + + # Add original points + plt.scatter( + x_optim, + y_optim, + c=[i / len(x_optim) for i in range(len(x_optim))], + cmap='Grays', + zorder=2.5, + ) + + # Plot the initial guess + if len(result.x_model) > 0: + x0 = result.x_model[0] + plt.plot( + [x0[0]], + [x0[1]], + 'X', + markersize=14, + markerfacecolor='w', + markeredgecolor='k', + label="Initial values", + linestyle='None', + zorder=2.6, + ) + + # Plot optimised value + if result.x is not None: + x_best = result.x + plt.plot( + [x_best[0]], + [x_best[1]], + "P", + markersize=14, + markerfacecolor='k', + markeredgecolor='w', + label="Final values", + linestyle='None', + zorder=2.6, + ) + + + # Layout + names = result.problem.parameters.names + plt.xlabel(names[0], labelpad=15) + plt.ticklabel_format(axis='both', **dict(style='sci',scilimits=(-4,4))) + plt.ylabel(names[1], labelpad=15) + plt.title(title, pad=40) + plt.legend(ncols=2, loc='lower center', bbox_to_anchor=(0.5, 1.0)) + plt.xlim(xlim[0], xlim[1]) + plt.ylim(ylim[0], ylim[1]) + plt.tight_layout() + + if show: + plt.show() diff --git a/pybop/plot/plotly/__init__.py b/pybop/plot/plotly/__init__.py new file mode 100644 index 000000000..0a3a1f91a --- /dev/null +++ b/pybop/plot/plotly/__init__.py @@ -0,0 +1,10 @@ +from .plotly_manager import PlotlyManager +from .standard_plots import StandardPlot, StandardSubplot, trajectories +from .contour import contour +from .dataset import dataset +from .convergence import convergence +from .parameters import parameters +from .problem import problem +from .nyquist import nyquist +from .voronoi import surface +from .samples import chains, posterior, summary_table, trace \ No newline at end of file diff --git a/pybop/plot/contour.py b/pybop/plot/plotly/contour.py similarity index 99% rename from pybop/plot/contour.py rename to pybop/plot/plotly/contour.py index da2d6a1a6..4fb1a1156 100644 --- a/pybop/plot/contour.py +++ b/pybop/plot/plotly/contour.py @@ -4,7 +4,7 @@ import numpy as np -from pybop.plot.plotly_manager import PlotlyManager +from pybop.plot.plotly.plotly_manager import PlotlyManager from pybop.problems.problem import Problem if TYPE_CHECKING: diff --git a/pybop/plot/convergence.py b/pybop/plot/plotly/convergence.py similarity index 96% rename from pybop/plot/convergence.py rename to pybop/plot/plotly/convergence.py index deabf66d5..6b190a93c 100644 --- a/pybop/plot/convergence.py +++ b/pybop/plot/plotly/convergence.py @@ -1,6 +1,6 @@ from typing import TYPE_CHECKING -from pybop.plot.standard_plots import StandardPlot +from pybop.plot.plotly.standard_plots import StandardPlot if TYPE_CHECKING: from pybop._result import Result diff --git a/pybop/plot/dataset.py b/pybop/plot/plotly/dataset.py similarity index 95% rename from pybop/plot/dataset.py rename to pybop/plot/plotly/dataset.py index b6b20a7d7..dc02d01dc 100644 --- a/pybop/plot/dataset.py +++ b/pybop/plot/plotly/dataset.py @@ -1,4 +1,4 @@ -from pybop.plot.standard_plots import StandardPlot, trajectories +from pybop.plot.plotly.standard_plots import StandardPlot, trajectories def dataset(dataset, signal=None, trace_names=None, show=True, **layout_kwargs): diff --git a/pybop/plot/nyquist.py b/pybop/plot/plotly/nyquist.py similarity index 98% rename from pybop/plot/nyquist.py rename to pybop/plot/plotly/nyquist.py index 80f7eb77a..bbc5fa49a 100644 --- a/pybop/plot/nyquist.py +++ b/pybop/plot/plotly/nyquist.py @@ -1,5 +1,5 @@ from pybop.parameters.parameter import Inputs -from pybop.plot.standard_plots import StandardPlot +from pybop.plot.plotly.standard_plots import StandardPlot def nyquist(problem, inputs: Inputs = None, show=True, **layout_kwargs): diff --git a/pybop/plot/parameters.py b/pybop/plot/plotly/parameters.py similarity index 97% rename from pybop/plot/parameters.py rename to pybop/plot/plotly/parameters.py index a13d9e3fb..02cc281f0 100644 --- a/pybop/plot/parameters.py +++ b/pybop/plot/plotly/parameters.py @@ -1,7 +1,7 @@ from typing import TYPE_CHECKING from pybop.costs.log_likelihoods import GaussianLogLikelihood -from pybop.plot.standard_plots import StandardSubplot +from pybop.plot.plotly.standard_plots import StandardSubplot if TYPE_CHECKING: from pybop._result import Result diff --git a/pybop/plot/plotly_manager.py b/pybop/plot/plotly/plotly_manager.py similarity index 100% rename from pybop/plot/plotly_manager.py rename to pybop/plot/plotly/plotly_manager.py diff --git a/pybop/plot/problem.py b/pybop/plot/plotly/problem.py similarity index 98% rename from pybop/plot/problem.py rename to pybop/plot/plotly/problem.py index f87e6f88a..820284da2 100644 --- a/pybop/plot/problem.py +++ b/pybop/plot/plotly/problem.py @@ -3,7 +3,7 @@ from pybop.costs.design_cost import DesignCost from pybop.costs.error_measures import ErrorMeasure from pybop.parameters.parameter import Inputs -from pybop.plot.standard_plots import StandardPlot +from pybop.plot.plotly.standard_plots import StandardPlot from pybop.problems.meta_problem import MetaProblem from pybop.problems.problem import Problem from pybop.simulators.solution import Solution diff --git a/pybop/plot/samples.py b/pybop/plot/plotly/samples.py similarity index 98% rename from pybop/plot/samples.py rename to pybop/plot/plotly/samples.py index 55ee77cd0..f5016c325 100644 --- a/pybop/plot/samples.py +++ b/pybop/plot/plotly/samples.py @@ -1,6 +1,6 @@ from typing import TYPE_CHECKING -from pybop.plot import PlotlyManager +from pybop.plot.plotly import PlotlyManager if TYPE_CHECKING: from pybop.samplers.base_pints_sampler import SamplingResult diff --git a/pybop/plot/standard_plots.py b/pybop/plot/plotly/standard_plots.py similarity index 96% rename from pybop/plot/standard_plots.py rename to pybop/plot/plotly/standard_plots.py index 4422516b8..8e3961d71 100644 --- a/pybop/plot/standard_plots.py +++ b/pybop/plot/plotly/standard_plots.py @@ -2,8 +2,9 @@ import textwrap import numpy as np +import warnings -from pybop.plot.plotly_manager import PlotlyManager +from pybop.plot.plotly.plotly_manager import PlotlyManager DEFAULT_LAYOUT_OPTIONS = dict( title=None, @@ -71,7 +72,16 @@ def __init__( trace_options=None, trace_names=None, trace_name_width=40, + **kwargs, ): + # Warning if layout arguments ignored + if len(kwargs) > 0: + warnings.warn( + "The following layout argument keys are ignored for the current plotting backend (plotly): \n" + f"{list(kwargs.keys())}", + UserWarning, + stacklevel=2, + ) self.traces = [] self.layout = layout self.trace_name_width = trace_name_width diff --git a/pybop/plot/plotly/voronoi.py b/pybop/plot/plotly/voronoi.py new file mode 100644 index 000000000..dc1e5ea1e --- /dev/null +++ b/pybop/plot/plotly/voronoi.py @@ -0,0 +1,216 @@ +from typing import TYPE_CHECKING + +import numpy as np +from scipy.spatial import Voronoi, cKDTree + +if TYPE_CHECKING: + from pybop._result import Result +from pybop.plot.plotly.plotly_manager import PlotlyManager +from pybop.plot import voronoi_data + + + +def assign_nearest_value(x, y, f, xi, yi): + """ + Computes an array of values given by the score of the nearest point. + + Parameters + ---------- + x : array-like + The x coordinates of points with known scores. + y : array-like + The y coordinates of points with known scores. + f : array-like + The score function at the given x and y coordinates. + xi : array-like + The x coordinates of grid points. + yi : array-like + The y coordinates of grid points. + + Returns + ------- + A numpy array containing the scores corresponding to the grid points. + """ + # Create a KD-tree for efficient nearest neighbor search + tree = cKDTree(np.column_stack((x, y))) + + # Find the nearest point for each grid point + _, indices = tree.query(np.column_stack((xi.ravel(), yi.ravel()))) + zi = f[indices].reshape(xi.shape) + + return zi + + +def surface( + result: "Result", + bounds=None, + normalise=True, + resolution=250, + show=True, + **layout_kwargs, +): + """ + Plot a 2D representation of the Voronoi diagram with color-coded regions. + + Parameters: + ----------- + result : pybop.Result + Optimisation result containing the history of parameter values and associated cost. + bounds : numpy.ndarray, optional + A 2x2 array specifying the [min, max] bounds for each parameter. If None, uses + `cost.parameters.get_bounds_for_plotly`. + normalise : bool, optional + If True, the voronoi regions are computed using the Euclidean distance between + points normalised with respect to the bounds (default: True). + resolution : int, optional + Resolution of the plot. Default is 500. + show : bool, optional + If True, the figure is shown upon creation (default: True). + **layout_kwargs : optional + Valid Plotly layout keys and their values, + e.g. `xaxis_title="Time [s]"` or + `xaxis={"title": "Time [s]", font={"size":14}}` + """ + points = result.x_model + parameters = result.problem.parameters + + if points[0].shape[0] != 2: + raise ValueError("This plot method requires two parameters.") + + x_optim, y_optim = map(list, zip(*points, strict=False)) + f = result.cost + + # Translate bounds, taking only the first two elements + xlim, ylim = ( + bounds if bounds is not None else [param.bounds for param in parameters] + )[:2] + + x, y, f, regions, relative_sizes = voronoi_data(xlim, ylim, x_optim, y_optim, f, normalise) + + # Create a grid for plot + xi = np.linspace(xlim[0], xlim[1], resolution) + yi = np.linspace(ylim[0], ylim[1], resolution) + xi, yi = np.meshgrid(xi, yi) + + + if normalise: + # Create a normalised grid + norm_xi = np.linspace(0, 1, resolution) + norm_xi, norm_yi = np.meshgrid(norm_xi, norm_xi) + + # Assign a value to each point in the grid + zi = assign_nearest_value(x, y, f, norm_xi, norm_yi) + else: + # Assign a value to each point in the grid + zi = assign_nearest_value(x, y, f, xi, yi) + + # Calculate the size of each Voronoi region + region_sizes = np.array([len(region) for region in regions]) + relative_sizes = (region_sizes - region_sizes.min()) / ( + region_sizes.max() - region_sizes.min() + ) + + # Construct figure + go = PlotlyManager().go + fig = go.Figure() + + # Heatmap + fig.add_trace( + go.Heatmap( + x=xi[0], + y=yi[:, 0], + z=zi, + colorscale="Viridis", + zsmooth="best", + ) + ) + + # Add Voronoi edges + for region, size in zip(regions, relative_sizes, strict=False): + x_region = region[:, 0].tolist() + [region[0, 0]] + y_region = region[:, 1].tolist() + [region[0, 1]] + + fig.add_trace( + go.Scatter( + x=x_region, + y=y_region, + mode="lines", + line=dict(color="white", width=0.5 + size * 0.1), + showlegend=False, + ) + ) + + # Add original points + fig.add_trace( + go.Scatter( + x=x_optim, + y=y_optim, + mode="markers", + marker=dict( + color=[i / len(x_optim) for i in range(len(x_optim))], + colorscale="Greys", + size=8, + showscale=False, + ), + text=[f"f={val:.2f}" for val in f], + hoverinfo="text", + showlegend=False, + ) + ) + + # Plot the initial guess + if len(result.x_model) > 0: + x0 = result.x_model[0] + fig.add_trace( + go.Scatter( + x=[x0[0]], + y=[x0[1]], + mode="markers", + marker_symbol="x", + marker=dict( + color="white", + line_color="black", + line_width=1, + size=14, + showscale=False, + ), + name="Initial values", + ) + ) + + # Plot optimised value + if result.x is not None: + x_best = result.x + fig.add_trace( + go.Scatter( + x=[x_best[0]], + y=[x_best[1]], + mode="markers", + marker_symbol="cross", + marker=dict( + color="black", + line_color="white", + line_width=1, + size=14, + showscale=False, + ), + name="Final values", + ) + ) + + names = parameters.names + fig.update_layout( + title="Voronoi Cost Landscape", + title_x=0.5, + title_y=0.905, + xaxis_title=names[0], + yaxis_title=names[1], + width=600, + height=600, + xaxis=dict(range=xlim, showexponent="last", exponentformat="e"), + yaxis=dict(range=ylim, showexponent="last", exponentformat="e"), + legend=dict(orientation="h", yanchor="bottom", y=1, xanchor="right", x=1), + ) + fig.update_layout(**layout_kwargs) + if show: + fig.show() \ No newline at end of file diff --git a/pybop/plot/plots.py b/pybop/plot/plots.py new file mode 100644 index 000000000..daadc5d11 --- /dev/null +++ b/pybop/plot/plots.py @@ -0,0 +1,287 @@ +from typing import TYPE_CHECKING +import numpy as np + +if TYPE_CHECKING: + from pybop._result import Result + from pybop.samplers.base_pints_sampler import SamplingResult + +from pybop.parameters.parameter import Inputs +from pybop.problems.problem import Problem +from pybop.plot.util import call_plotting_function, get_class + + + +def chains(result: "SamplingResult", show=True, backend=None, **kwargs): + """ + Plot posterior distributions for each chain. + """ + return call_plotting_function('chains', backend, result=result, **kwargs) + +def contour( + call_object: "Problem | Result", + gradient: bool = False, + bounds: np.ndarray | None = None, + transformed: bool = False, + steps: int = 10, + show: bool = True, + backend = None, + **layout_kwargs, +): + """ + Plot a 2D visualisation of a cost landscape using Plotly. + + This function generates a contour plot representing the cost landscape for a provided + callable cost function over a grid of parameter values within the specified bounds. + + Parameters + ---------- + call_object : pybop.Problem | pybop.Result + Either: + - the cost function to be evaluated. Must accept a list of parameter values and return a cost value. + - an optimiser result which provides a specific optimisation trace overlaid on the cost landscape. + gradient : bool, optional + If True, the gradient is shown (default: False). + bounds : numpy.ndarray | list[list[float]], optional + A 2x2 array specifying the [min, max] bounds for each parameter. If None, uses + `parameters.get_bounds_for_plotly`. + transformed : bool, optional + Uses the transformed parameter values (as seen by the optimiser) for plotting. + steps : int, optional + The number of grid points to divide the parameter space into along each dimension (default: 10). + show : bool, optional + If True, the figure is shown upon creation (default: True). + **layout_kwargs : optional + Valid Plotly layout keys and their values, + e.g. `xaxis_title="Time [s]"` or + `xaxis={"title": "Time [s]", font={"size":14}}` + + Returns + ------- + plotly.graph_objs.Figure + The Plotly figure object containing the cost landscape plot. + + Raises + ------ + ValueError + If the cost function does not return a valid cost when called with a parameter list. + """ + return call_plotting_function('contour', backend, call_object=call_object, gradient=gradient, bounds=bounds, transformed=transformed, steps=steps, show=show, **layout_kwargs) + +def convergence(result: "Result", show=True, backend=None, **layout_kwargs): + """ + Plot the convergence of the optimisation algorithm. + + Parameters + ----------- + result : pybop.Result + Optimisation result containing the history of parameter values and associated cost. + show : bool, optional + If True, the figure is shown upon creation (default: True). + **layout_kwargs : optional + Valid Plotly layout keys and their values, + e.g. `xaxis_title="Time [s]"` or + `xaxis={"title": "Time [s]", font={"size":14}}` + + Returns + --------- + fig : plotly.graph_objs.Figure + The Plotly figure object for the convergence plot. + """ + return call_plotting_function('convergence', backend, result = result, show=show, **layout_kwargs) + +def dataset(dataset, signal=None, trace_names=None, show=True, backend=None, **layout_kwargs): + """ + Quickly plot a PyBOP Dataset using Plotly. + + Parameters + ---------- + dataset : object + A PyBOP dataset. + signal : list or str, optional + The name of the time series to plot (default: "Voltage [V]"). + trace_names : list or str, optional + Name(s) for the trace(s) (default: "Data"). + show : bool, optional + If True, the figure is shown upon creation (default: True). + **layout_kwargs : optional + Valid Plotly layout keys and their values, + e.g. `xaxis_title="Time / s"` or + `xaxis={"title": "Time [s]", font={"size":14}}` + + Returns + ------- + plotly.graph_objs.Figure + The Plotly figure object for the scatter plot. + """ + call_plotting_function('dataset', backend, dataset=dataset, signal=signal, trace_names=trace_names, show=show, **layout_kwargs) + +def nyquist(problem, inputs: Inputs = None, show=True, backend=None, **layout_kwargs): + """ + Generates Nyquist plots for the given problem by evaluating the model's output and target values. + + Parameters + ---------- + problem : pybop.Problem + An instance of a problem class that contains the parameters and methods + for evaluation and target retrieval. + inputs : Inputs, optional + Input parameters for the problem. If not provided, the default parameters from the problem + instance will be used. These parameters are verified before use (default is None). + show : bool, optional + If True, the plots will be displayed. + **layout_kwargs : dict, optional + Additional keyword arguments for customising the plot layout. These arguments are passed to + `fig.update_layout()`. + + Returns + ------- + list + A list of plotly `Figure` objects, each representing a Nyquist plot for the model's output and target values. + + Notes + ----- + - The function extracts the real part of the impedance from the model's output and the real and imaginary parts + of the impedance from the target output. + - For each signal in the problem, a Nyquist plot is created with the model's impedance plotted as a scatter plot. + - An additional trace for the reference (target output) is added to the plot. + - The plot layout can be customised using `layout_kwargs`. + + Example + ------- + >>> problem = pybop.EISProblem() + >>> nyquist_figures = nyquist(problem, show=True, title="Nyquist Plot", xaxis_title="Real(Z)", yaxis_title="Imag(Z)") + >>> # The plots will be displayed and nyquist_figures will contain the list of figure objects. + """ + return call_plotting_function('nyquist', backend, problem=problem, inputs=inputs, show=show, **layout_kwargs) + +def parameters(result: "Result", show=True, backend=None, **layout_kwargs): + """ + Plot the evolution of parameters during the optimisation process using Plotly. + + Parameters + ---------- + result : pybop.Result + Optimisation result containing the history of parameter values and associated cost. + show : bool, optional + If True, the figure is shown upon creation (default: True). + **layout_kwargs : optional + Valid Plotly layout keys and their values, + e.g. `xaxis_title="Time [s]"` or + `xaxis={"title": "Time [s]", font={"size":14}}` + + Returns + ------- + plotly.graph_objs.Figure + A Plotly figure object showing the parameter evolution over iterations. + """ + return call_plotting_function('parameters', backend, result = result, show=show, **layout_kwargs) + +def posterior(result: "SamplingResult", show=True, backend=None, **kwargs): + """ + Plot the summed posterior distribution across chains. + """ + return call_plotting_function('posterior', backend, result=result, **kwargs) + +def problem( + problem: Problem, + inputs: Inputs = None, + show: bool = True, + backend=None, + **layout_kwargs, +): + """ + Produce a quick plot of the target dataset against optimised model output. + + Generates an interactive plot comparing the simulated model output with + an optional target dataset and visualises uncertainty. + + Parameters + ---------- + problem : pybop.Problem + Problem object with dataset and targets attributes. + inputs : Inputs + Optimised (or example) parameter values. + show : bool, optional + If True, the figure is shown upon creation (default: True). + **layout_kwargs : optional + Valid Plotly layout keys and their values, + e.g. `xaxis_title="Time / s"` or + `xaxis={"title": "Time [s]", font={"size":14}}` + + Returns + ------- + plotly.graph_objs.Figure + The Plotly figure object for the scatter plot. + """ + return call_plotting_function('problem', backend, problem=problem, inputs=inputs, show=show, **layout_kwargs) + +def summary_table(result: "SamplingResult", backend=None): + """ + Display summary statistics in a table. + """ + + return call_plotting_function('summary_table', backend, result=result) + +def surface( + result: "Result", + bounds=None, + normalise=True, + resolution=250, + show=True, + backend=None, + **layout_kwargs, +): + """ + Plot a 2D representation of the Voronoi diagram with color-coded regions. + + Parameters: + ----------- + result : pybop.Result + Optimisation result containing the history of parameter values and associated cost. + bounds : numpy.ndarray, optional + A 2x2 array specifying the [min, max] bounds for each parameter. If None, uses + `cost.parameters.get_bounds_for_plotly`. + normalise : bool, optional + If True, the voronoi regions are computed using the Euclidean distance between + points normalised with respect to the bounds (default: True). + resolution : int, optional + Resolution of the plot. Default is 500. + show : bool, optional + If True, the figure is shown upon creation (default: True). + **layout_kwargs : optional + Valid Plotly layout keys and their values, + e.g. `xaxis_title="Time [s]"` or + `xaxis={"title": "Time [s]", font={"size":14}}` + """ + return call_plotting_function('surface', backend, result = result, bounds = bounds, normalise=normalise, resolution=resolution, show=show, **layout_kwargs) + +def trace(result: "SamplingResult", backend=None, **kwargs): + """ + Plot trace plots for the posterior samples. + """ + return call_plotting_function('trace', backend, result=result, **kwargs) + +def trajectories(x, y, trace_names=None, show=True, backend=None, **layout_kwargs): + """ + Quickly plot one or more trajectories using Plotly. + + Parameters + ---------- + x : list or np.ndarray + X-axis data points. + y : list or np.ndarray + Y-axis data points for each trajectory. + trace_names : list or str, optional + Name(s) for the trace(s) (default: None). + **layout_kwargs : optional + Valid Plotly layout keys and their values, + e.g. `xaxis_title="Time / s"` or + `xaxis={"title": "Time [s]", font={"size":14}}` + + Returns + ------- + plotly.graph_objs.Figure + The Plotly figure object for the scatter plot. + """ + + return call_plotting_function('trajectories', backend, x=x, y=y, trace_names=trace_names, show=show, **layout_kwargs) \ No newline at end of file diff --git a/pybop/plot/util.py b/pybop/plot/util.py new file mode 100644 index 000000000..3ec299ce3 --- /dev/null +++ b/pybop/plot/util.py @@ -0,0 +1,50 @@ +import importlib.util +import pybop.plot + +def get_class(class_name): + err_msg = f"Plotting backend {pybop.plot.backend} is not available." + try: + module = importlib.import_module('pybop.plot.' + pybop.plot.backend) + if hasattr(module, class_name): + return getattr(module, class_name) + + else: + err_msg = f"Plotting backend {pybop.plot.backend} has no attribute {class_name}." + raise ModuleNotFoundError(err_msg) + + except ModuleNotFoundError as error: + # Raise an ModuleNotFoundError if the module or attribute is not available + raise ModuleNotFoundError(err_msg) from error + +def set_backend(backend): + err_msg = f"Plotting backend {backend} is not available. The default backend has not been updated. \n"\ + f"The default backend is set to {pybop.plot.backend}" + try: + importlib.import_module('pybop.plot.' + backend) + pybop.plot.backend = backend + for attr in ['StandardPlot', 'StandardSubplot']: + setattr(pybop.plot, attr, get_class(attr)) + + except ModuleNotFoundError as error: + # Raise an ModuleNotFoundError if the module or attribute is not available + raise ModuleNotFoundError(err_msg) from error + + +def call_plotting_function(function_name, backend, **kwargs): + if backend is None: + backend = pybop.plot.backend + err_msg = f"Plotting backend {backend} is not available." + try: + module = importlib.import_module('pybop.plot.' + backend) + if hasattr(module, function_name): + plotting_function = getattr(module, function_name) + # Return the imported attribute + return plotting_function(**kwargs) + else: + err_msg = f"Plotting backend {backend} has no attribute {function_name}." + raise ModuleNotFoundError(err_msg) + + except ModuleNotFoundError as error: + # Raise an ModuleNotFoundError if the module or attribute is not available + raise ModuleNotFoundError(err_msg) from error + diff --git a/pybop/plot/voronoi.py b/pybop/plot/voronoi.py index 29be3c096..2ac57fada 100644 --- a/pybop/plot/voronoi.py +++ b/pybop/plot/voronoi.py @@ -1,11 +1,10 @@ from typing import TYPE_CHECKING import numpy as np -from scipy.spatial import Voronoi, cKDTree +from scipy.spatial import Voronoi if TYPE_CHECKING: - from pybop._result import Result -from pybop.plot.plotly_manager import PlotlyManager + pass def _voronoi_regions(x, y, f, xlim, ylim): @@ -195,85 +194,7 @@ def interpolate_point(p, q, axis, boundary_val): return np.array([boundary_val, s]) if axis == 0 else np.array([s, boundary_val]) -def assign_nearest_value(x, y, f, xi, yi): - """ - Computes an array of values given by the score of the nearest point. - - Parameters - ---------- - x : array-like - The x coordinates of points with known scores. - y : array-like - The y coordinates of points with known scores. - f : array-like - The score function at the given x and y coordinates. - xi : array-like - The x coordinates of grid points. - yi : array-like - The y coordinates of grid points. - - Returns - ------- - A numpy array containing the scores corresponding to the grid points. - """ - # Create a KD-tree for efficient nearest neighbor search - tree = cKDTree(np.column_stack((x, y))) - - # Find the nearest point for each grid point - _, indices = tree.query(np.column_stack((xi.ravel(), yi.ravel()))) - zi = f[indices].reshape(xi.shape) - - return zi - - -def surface( - result: "Result", - bounds=None, - normalise=True, - resolution=250, - show=True, - **layout_kwargs, -): - """ - Plot a 2D representation of the Voronoi diagram with color-coded regions. - - Parameters: - ----------- - result : pybop.Result - Optimisation result containing the history of parameter values and associated cost. - bounds : numpy.ndarray, optional - A 2x2 array specifying the [min, max] bounds for each parameter. If None, uses - `cost.parameters.get_bounds_for_plotly`. - normalise : bool, optional - If True, the voronoi regions are computed using the Euclidean distance between - points normalised with respect to the bounds (default: True). - resolution : int, optional - Resolution of the plot. Default is 500. - show : bool, optional - If True, the figure is shown upon creation (default: True). - **layout_kwargs : optional - Valid Plotly layout keys and their values, - e.g. `xaxis_title="Time [s]"` or - `xaxis={"title": "Time [s]", font={"size":14}}` - """ - points = result.x_model - parameters = result.problem.parameters - - if points[0].shape[0] != 2: - raise ValueError("This plot method requires two parameters.") - - x_optim, y_optim = map(list, zip(*points, strict=False)) - f = result.cost - - # Translate bounds, taking only the first two elements - xlim, ylim = ( - bounds if bounds is not None else [param.bounds for param in parameters] - )[:2] - - # Create a grid for plot - xi = np.linspace(xlim[0], xlim[1], resolution) - yi = np.linspace(ylim[0], ylim[1], resolution) - xi, yi = np.meshgrid(xi, yi) +def voronoi_data(xlim, ylim, pts_x, pts_y, f, normalise=True): if normalise: if xlim[1] <= xlim[0] or ylim[1] <= ylim[0]: @@ -282,21 +203,14 @@ def surface( # Normalise the region x_range = xlim[1] - xlim[0] y_range = ylim[1] - ylim[0] - norm_x_optim = (np.asarray(x_optim) - xlim[0]) / x_range - norm_y_optim = (np.asarray(y_optim) - ylim[0]) / y_range + norm_x_optim = (np.asarray(pts_x) - xlim[0]) / x_range + norm_y_optim = (np.asarray(pts_y) - ylim[0]) / y_range # Compute regions - norm_x, norm_y, f, norm_regions = _voronoi_regions( + x, y, f, norm_regions = _voronoi_regions( norm_x_optim, norm_y_optim, f, (0, 1), (0, 1) ) - # Create a normalised grid - norm_xi = np.linspace(0, 1, resolution) - norm_xi, norm_yi = np.meshgrid(norm_xi, norm_xi) - - # Assign a value to each point in the grid - zi = assign_nearest_value(norm_x, norm_y, f, norm_xi, norm_yi) - # Rescale for plotting regions = [] for norm_region in norm_regions: @@ -307,10 +221,7 @@ def surface( else: # Compute regions - x, y, f, regions = _voronoi_regions(x_optim, y_optim, f, xlim, ylim) - - # Assign a value to each point in the grid - zi = assign_nearest_value(x, y, f, xi, yi) + x, y, f, regions = _voronoi_regions(pts_x, pts_y, f, xlim, ylim) # Calculate the size of each Voronoi region region_sizes = np.array([len(region) for region in regions]) @@ -318,107 +229,4 @@ def surface( region_sizes.max() - region_sizes.min() ) - # Construct figure - go = PlotlyManager().go - fig = go.Figure() - - # Heatmap - fig.add_trace( - go.Heatmap( - x=xi[0], - y=yi[:, 0], - z=zi, - colorscale="Viridis", - zsmooth="best", - ) - ) - - # Add Voronoi edges - for region, size in zip(regions, relative_sizes, strict=False): - x_region = region[:, 0].tolist() + [region[0, 0]] - y_region = region[:, 1].tolist() + [region[0, 1]] - - fig.add_trace( - go.Scatter( - x=x_region, - y=y_region, - mode="lines", - line=dict(color="white", width=0.5 + size * 0.1), - showlegend=False, - ) - ) - - # Add original points - fig.add_trace( - go.Scatter( - x=x_optim, - y=y_optim, - mode="markers", - marker=dict( - color=[i / len(x_optim) for i in range(len(x_optim))], - colorscale="Greys", - size=8, - showscale=False, - ), - text=[f"f={val:.2f}" for val in f], - hoverinfo="text", - showlegend=False, - ) - ) - - # Plot the initial guess - if len(result.x_model) > 0: - x0 = result.x_model[0] - fig.add_trace( - go.Scatter( - x=[x0[0]], - y=[x0[1]], - mode="markers", - marker_symbol="x", - marker=dict( - color="white", - line_color="black", - line_width=1, - size=14, - showscale=False, - ), - name="Initial values", - ) - ) - - # Plot optimised value - if result.x is not None: - x_best = result.x - fig.add_trace( - go.Scatter( - x=[x_best[0]], - y=[x_best[1]], - mode="markers", - marker_symbol="cross", - marker=dict( - color="black", - line_color="white", - line_width=1, - size=14, - showscale=False, - ), - name="Final values", - ) - ) - - names = parameters.names - fig.update_layout( - title="Voronoi Cost Landscape", - title_x=0.5, - title_y=0.905, - xaxis_title=names[0], - yaxis_title=names[1], - width=600, - height=600, - xaxis=dict(range=xlim, showexponent="last", exponentformat="e"), - yaxis=dict(range=ylim, showexponent="last", exponentformat="e"), - legend=dict(orientation="h", yanchor="bottom", y=1, xanchor="right", x=1), - ) - fig.update_layout(**layout_kwargs) - if show: - fig.show() + return x, y, f, regions, relative_sizes diff --git a/tests/plotting/test_plotly_manager.py b/tests/plotting/test_plotly_manager.py index f64e608f1..66e9ba098 100644 --- a/tests/plotting/test_plotly_manager.py +++ b/tests/plotting/test_plotly_manager.py @@ -8,7 +8,7 @@ import pytest import pybop -from pybop.plot import PlotlyManager +from pybop.plot.plotly import PlotlyManager # Find the Python executable python_executable = which("python") diff --git a/tests/unit/test_plots.py b/tests/unit/test_plots.py index 58b707900..db9e0c6c1 100644 --- a/tests/unit/test_plots.py +++ b/tests/unit/test_plots.py @@ -6,6 +6,7 @@ import pybop +@pytest.mark.parametrize("backend", ["plotly", "matplotlib"]) class TestPlots: """ A class to test the plot classes. @@ -13,7 +14,8 @@ class TestPlots: pytestmark = pytest.mark.unit - def test_standard_plot(self): + def test_standard_plot(self, backend): + pybop.plot.set_backend(backend) # Test standard plot trace_names = pybop.plot.StandardPlot.remove_brackets( ["Trace [1]", "Trace [2]"] @@ -69,7 +71,8 @@ def dataset(self, model): solution = pybamm.Simulation(model).solve(t_eval=t_eval, t_interp=t_eval) return pybop.import_pybamm_solution(solution) - def test_dataset_plots(self, dataset): + def test_dataset_plots(self, dataset, backend): + pybop.plot.set_backend(backend) # Test plot of Dataset objects pybop.plot.trajectories( dataset["Time [s]"], @@ -112,7 +115,8 @@ def design_problem(self, model, parameters, experiment): ) return pybop.Problem(simulator) - def test_problem_plots(self, fitting_problem, design_problem): + def test_problem_plots(self, fitting_problem, design_problem, backend): + pybop.plot.set_backend(backend) # Test plot of Problem objects pybop.plot.problem(fitting_problem, title="Optimised Comparison") pybop.plot.problem(design_problem) @@ -122,7 +126,8 @@ def test_problem_plots(self, fitting_problem, design_problem): fitting_problem, inputs=fitting_problem.parameters.to_dict([0.6, 0.6]) ) - def test_cost_plots(self, fitting_problem, fitting_problem_no_bounds): + def test_cost_plots(self, fitting_problem, fitting_problem_no_bounds, backend): + pybop.plot.set_backend(backend) # Test plot of Cost objects pybop.plot.contour(fitting_problem, gradient=True, steps=5) @@ -143,7 +148,8 @@ def result(self, fitting_problem): optim = pybop.XNES(fitting_problem) return optim.run() - def test_optim_plots(self, result): + def test_optim_plots(self, result, backend): + pybop.plot.set_backend(backend) bounds = np.asarray([[0.5, 0.8], [0.4, 0.7]]) # Plot convergence @@ -185,7 +191,8 @@ def sampling_result(self, model, parameters, dataset): sampler = pybop.SliceStepoutMCMC(log_pdf, options=options) return sampler.run() - def test_posterior_plots(self, sampling_result): + def test_posterior_plots(self, sampling_result, backend): + pybop.plot.set_backend(backend) sampling_result.get_summary_statistics() # Plot trace @@ -200,9 +207,11 @@ def test_posterior_plots(self, sampling_result): # Plot summary table sampling_result.summary_table() - def test_with_ipykernel(self, dataset, fitting_problem, result): + def test_with_ipykernel(self, dataset, fitting_problem, result, backend): import ipykernel + pybop.plot.set_backend(backend) + assert version.parse(ipykernel.__version__) >= version.parse("0.6") pybop.plot.dataset(dataset, signal=["Voltage [V]"]) pybop.plot.contour(fitting_problem, gradient=True, steps=5) @@ -210,7 +219,8 @@ def test_with_ipykernel(self, dataset, fitting_problem, result): result.plot_parameters() result.plot_contour(steps=5) - def test_contour_incorrect_number_of_parameters(self, model, dataset): + def test_contour_incorrect_number_of_parameters(self, model, dataset, backend): + pybop.plot.set_backend(backend) parameter_values = model.default_parameter_values # Test with less than two paramters @@ -251,7 +261,8 @@ def test_contour_incorrect_number_of_parameters(self, model, dataset): fitting_problem = pybop.Problem(simulator, cost) pybop.plot.contour(fitting_problem) - def test_nyquist(self): + def test_nyquist(self, backend): + pybop.plot.set_backend(backend) # Define model model = pybamm.lithium_ion.SPM(options={"surface form": "differential"}) parameter_values = model.default_parameter_values From 8ca515855765e4b772939179b0f819a19b4f6ef5 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Thu, 2 Apr 2026 17:33:15 +0000 Subject: [PATCH 02/20] style: pre-commit fixes --- .../ecm_multipulse_identification.ipynb | 2 +- .../comparison_examples/grouped_SPMe.py | 2 +- pybop/plot/matplotlib/__init__.py | 2 +- pybop/plot/matplotlib/contour.py | 41 +++++---- pybop/plot/matplotlib/convergence.py | 5 +- pybop/plot/matplotlib/dataset.py | 1 + pybop/plot/matplotlib/nyquist.py | 17 ++-- pybop/plot/matplotlib/parameters.py | 7 +- pybop/plot/matplotlib/problem.py | 11 ++- pybop/plot/matplotlib/samples.py | 46 ++++++---- pybop/plot/matplotlib/standard_plots.py | 64 +++++++------ pybop/plot/matplotlib/voronoi.py | 49 +++++----- pybop/plot/plotly/__init__.py | 2 +- pybop/plot/plotly/standard_plots.py | 2 +- pybop/plot/plotly/voronoi.py | 12 +-- pybop/plot/plots.py | 90 +++++++++++++++---- pybop/plot/util.py | 32 ++++--- 17 files changed, 233 insertions(+), 152 deletions(-) diff --git a/examples/notebooks/battery_parameterisation/ecm_multipulse_identification.ipynb b/examples/notebooks/battery_parameterisation/ecm_multipulse_identification.ipynb index f03a01249..8140c73c1 100644 --- a/examples/notebooks/battery_parameterisation/ecm_multipulse_identification.ipynb +++ b/examples/notebooks/battery_parameterisation/ecm_multipulse_identification.ipynb @@ -507,7 +507,7 @@ " [3600 3]], duration=3600)]" ] }, - "execution_count": 11, + "execution_count": null, "metadata": {}, "output_type": "execute_result" } diff --git a/examples/scripts/comparison_examples/grouped_SPMe.py b/examples/scripts/comparison_examples/grouped_SPMe.py index 85119a9da..cb1d060e2 100644 --- a/examples/scripts/comparison_examples/grouped_SPMe.py +++ b/examples/scripts/comparison_examples/grouped_SPMe.py @@ -11,7 +11,7 @@ """ # Prepare figure -pybop.plot.set_backend('matplotlib') +pybop.plot.set_backend("matplotlib") plot_dict = pybop.plot.StandardPlot() plt.xlabel("Time / s") plt.ylabel("Voltage / V") diff --git a/pybop/plot/matplotlib/__init__.py b/pybop/plot/matplotlib/__init__.py index 863ad89ec..be1f45a58 100644 --- a/pybop/plot/matplotlib/__init__.py +++ b/pybop/plot/matplotlib/__init__.py @@ -6,4 +6,4 @@ from .contour import contour from .voronoi import surface from .nyquist import nyquist -from .samples import chains, posterior, summary_table, trace \ No newline at end of file +from .samples import chains, posterior, summary_table, trace diff --git a/pybop/plot/matplotlib/contour.py b/pybop/plot/matplotlib/contour.py index eb7ee77f3..d0127375a 100644 --- a/pybop/plot/matplotlib/contour.py +++ b/pybop/plot/matplotlib/contour.py @@ -4,7 +4,6 @@ import numpy as np from matplotlib import pyplot as plt -from scipy.interpolate import griddata from pybop.problems.problem import Problem @@ -19,7 +18,7 @@ def contour( transformed: bool = False, steps: int = 10, show: bool = True, - title: str = 'Cost Landscape', + title: str = "Cost Landscape", ): """ Plot a 2D visualisation of a cost landscape using Plotly. @@ -146,17 +145,23 @@ def transform_array_of_values(list_of_values, parameter): # define levels exponent = np.floor(np.log10(np.abs(np.max(costs)))) - levels = np.linspace(np.floor(np.min(costs)/(10**exponent))*(10**exponent), np.ceil(np.max(costs)/(10**exponent))*(10**exponent), 2 * steps - 1) + levels = np.linspace( + np.floor(np.min(costs) / (10**exponent)) * (10**exponent), + np.ceil(np.max(costs) / (10**exponent)) * (10**exponent), + 2 * steps - 1, + ) # Create contour plot and update the layout fig = plt.figure(figsize=(6, 6), dpi=100) - plt.contourf(x, y, costs, levels=levels, extend='both', cmap='viridis') + plt.contourf(x, y, costs, levels=levels, extend="both", cmap="viridis") plt.colorbar() - plt.contour(x, y, costs, levels=levels, colors=('k',), linestyles='solid', linewidths=0.1) + plt.contour( + x, y, costs, levels=levels, colors=("k",), linestyles="solid", linewidths=0.1 + ) - # Layout + # Layout plt.xlabel("Transformed " + names[0] if transformed else names[0], labelpad=15) - plt.ticklabel_format(axis='both', **dict(style='sci',scilimits=(-4,4))) + plt.ticklabel_format(axis="both", **dict(style="sci", scilimits=(-4, 4))) plt.ylabel("Transformed " + names[1] if transformed else names[1], labelpad=15) plt.title(title, pad=40) plt.xlim(bounds[0]) @@ -171,9 +176,9 @@ def transform_array_of_values(list_of_values, parameter): transform_array_of_values(optim_trace[:, 0], parameters[names[0]]), transform_array_of_values(optim_trace[:, 1], parameters[names[1]]), c=[i / len(optim_trace) for i in range(len(optim_trace))], - cmap='Grays', + cmap="Grays", zorder=1, - ) + ) # Plot the initial guess if len(result.x_model) > 0: @@ -181,12 +186,12 @@ def transform_array_of_values(list_of_values, parameter): plt.plot( transform_array_of_values([x0[0]], parameters[names[0]]), transform_array_of_values([x0[1]], parameters[names[1]]), - 'X', + "X", markersize=14, - markerfacecolor='w', - markeredgecolor='k', + markerfacecolor="w", + markeredgecolor="k", label="Initial values", - linestyle='None', + linestyle="None", ) # Plot optimised value @@ -197,14 +202,14 @@ def transform_array_of_values(list_of_values, parameter): transform_array_of_values([x_best[1]], parameters[names[1]]), "P", markersize=14, - markerfacecolor='k', - markeredgecolor='w', + markerfacecolor="k", + markeredgecolor="w", label="Final values", - linestyle='None', + linestyle="None", ) - plt.legend(ncols=2, loc='lower center', bbox_to_anchor=(0.5, 1.0)) - + plt.legend(ncols=2, loc="lower center", bbox_to_anchor=(0.5, 1.0)) + plt.tight_layout() if show: diff --git a/pybop/plot/matplotlib/convergence.py b/pybop/plot/matplotlib/convergence.py index 4ad2f31ad..2e53e4738 100644 --- a/pybop/plot/matplotlib/convergence.py +++ b/pybop/plot/matplotlib/convergence.py @@ -1,8 +1,9 @@ from typing import TYPE_CHECKING -from pybop.plot.matplotlib.standard_plots import StandardPlot import matplotlib.pyplot as plt +from pybop.plot.matplotlib.standard_plots import StandardPlot + if TYPE_CHECKING: from pybop._result import Result @@ -47,4 +48,4 @@ def convergence(result: "Result", show=True): if show: plt.show() - return fig \ No newline at end of file + return fig diff --git a/pybop/plot/matplotlib/dataset.py b/pybop/plot/matplotlib/dataset.py index 44cd3ce25..7ade00091 100644 --- a/pybop/plot/matplotlib/dataset.py +++ b/pybop/plot/matplotlib/dataset.py @@ -1,4 +1,5 @@ import matplotlib.pyplot as plt + from pybop.plot.matplotlib.standard_plots import StandardPlot, trajectories diff --git a/pybop/plot/matplotlib/nyquist.py b/pybop/plot/matplotlib/nyquist.py index c45b29beb..eba00efec 100644 --- a/pybop/plot/matplotlib/nyquist.py +++ b/pybop/plot/matplotlib/nyquist.py @@ -1,6 +1,7 @@ +from matplotlib import pyplot as plt + from pybop.parameters.parameter import Inputs from pybop.plot.matplotlib.standard_plots import StandardPlot -from matplotlib import pyplot as plt def nyquist(problem, inputs: Inputs = None, show=True, **layout_kwargs): @@ -58,7 +59,7 @@ def nyquist(problem, inputs: Inputs = None, show=True, **layout_kwargs): fig = plot_dict(show=False) plot_dict.traces[0].set_color("#00CC96") plot_dict.traces[0].set_linewidth(2) - plot_dict.traces[0].set_marker('.') + plot_dict.traces[0].set_marker(".") plot_dict.traces[0].set_markersize(8) target_trace = plot_dict.create_trace( @@ -66,17 +67,17 @@ def nyquist(problem, inputs: Inputs = None, show=True, **layout_kwargs): y=-target_output[var].imag, label="Reference", ) - target_trace.set_linestyle('None') - target_trace.set_marker('o') - target_trace.set_fillstyle('none') + target_trace.set_linestyle("None") + target_trace.set_marker("o") + target_trace.set_fillstyle("none") target_trace.set_markersize(8) target_trace.set_markeredgecolor("#636EFA") - # Layout - plt.title('Nyquist Plot', fontsize=14, x=0.2) + # Layout + plt.title("Nyquist Plot", fontsize=14, x=0.2) plt.xlabel(r"$Z_{re} / \Omega$", fontsize=16) plt.ylabel(r"$-Z_{im} / \Omega$", fontsize=16) - plt.legend(loc='upper right', bbox_to_anchor=(1, 1.08), ncols=2) + plt.legend(loc="upper right", bbox_to_anchor=(1, 1.08), ncols=2) if show: plt.show() diff --git a/pybop/plot/matplotlib/parameters.py b/pybop/plot/matplotlib/parameters.py index 671f00cb2..1c6e24e26 100644 --- a/pybop/plot/matplotlib/parameters.py +++ b/pybop/plot/matplotlib/parameters.py @@ -1,10 +1,10 @@ +import warnings from typing import TYPE_CHECKING -import warnings +import matplotlib.pyplot as plt from pybop.costs.log_likelihoods import GaussianLogLikelihood from pybop.plot.matplotlib.standard_plots import StandardSubplot -import matplotlib.pyplot as plt if TYPE_CHECKING: from pybop._result import Result @@ -50,7 +50,6 @@ def parameters(result: "Result", show=True, **layout_kwargs): axis_titles.append(("Evaluation", "Sigma")) trace_names.append("Sigma") - # Create a plot dictionary plot_dict = StandardSubplot( x=x, @@ -58,7 +57,7 @@ def parameters(result: "Result", show=True, **layout_kwargs): axis_titles=axis_titles, trace_names=trace_names, trace_name_width=50, - figsize= (18, 8), + figsize=(18, 8), ) plt.suptitle("Parameter Convergence") diff --git a/pybop/plot/matplotlib/problem.py b/pybop/plot/matplotlib/problem.py index 451ed76a1..da080207c 100644 --- a/pybop/plot/matplotlib/problem.py +++ b/pybop/plot/matplotlib/problem.py @@ -1,3 +1,4 @@ +import matplotlib.pyplot as plt import numpy as np from pybop.costs.design_cost import DesignCost @@ -8,14 +9,12 @@ from pybop.problems.problem import Problem from pybop.simulators.solution import Solution -import matplotlib.pyplot as plt - def problem( problem: Problem, inputs: Inputs = None, show: bool = True, - title = 'Scatter Plot', + title="Scatter Plot", ): """ Produce a quick plot of the target dataset against optimised model output. @@ -75,15 +74,15 @@ def problem( y=target_output[var].data, label="Reference", marker=".", - linestyle="None" + linestyle="None", ) plot_dict.create_trace( x=model_domain, y=model_output[var].data, label="Optimised" if isinstance(problem.cost, DesignCost) else "Model", - marker="." if isinstance(problem, MetaProblem) else None, - linestyle='None' if isinstance(problem, MetaProblem) else "-", + marker="." if isinstance(problem, MetaProblem) else None, + linestyle="None" if isinstance(problem, MetaProblem) else "-", ) if isinstance(problem.cost, ErrorMeasure) and len( diff --git a/pybop/plot/matplotlib/samples.py b/pybop/plot/matplotlib/samples.py index dd14cf908..0d2ca526a 100644 --- a/pybop/plot/matplotlib/samples.py +++ b/pybop/plot/matplotlib/samples.py @@ -1,5 +1,5 @@ -from typing import TYPE_CHECKING import warnings +from typing import TYPE_CHECKING from matplotlib import pyplot as plt @@ -32,14 +32,12 @@ def trace(result: "SamplingResult", show=True, **kwargs): plt.legend(fontsize=12) figlist.append(fig) - if show: plt.show() else: return figlist - def chains(result: "SamplingResult", show=True, **kwargs): """ Plot posterior distributions for each chain. @@ -57,17 +55,20 @@ def chains(result: "SamplingResult", show=True, **kwargs): for i, chain in enumerate(result.chains): for j in range(chain.shape[1]): plt.hist( - x=chain[:, j], - label=f"Chain {i} - Parameter {j}", - alpha=0.5, - rwidth=2.0 + x=chain[:, j], label=f"Chain {i} - Parameter {j}", alpha=0.5, rwidth=2.0 ) for j in range(chain.shape[1]): - plt.plot([result.mean[j], result.mean[j]], [0, result.max[j]],"--", lw=3, label=f"Mean - Parameter {j}") + plt.plot( + [result.mean[j], result.mean[j]], + [0, result.max[j]], + "--", + lw=3, + label=f"Mean - Parameter {j}", + ) plt.legend(loc="upper left", bbox_to_anchor=(1.01, 1.0)) - plt.grid(axis='y', zorder=-1) + plt.grid(axis="y", zorder=-1) plt.title("Posterior Distribution") plt.xlabel("Value") plt.ylabel("Density") @@ -77,6 +78,7 @@ def chains(result: "SamplingResult", show=True, **kwargs): else: return fig + def posterior(result: "SamplingResult", show=True, **kwargs): """ Plot the summed posterior distribution across chains. @@ -98,10 +100,10 @@ def posterior(result: "SamplingResult", show=True, **kwargs): label=f"Parameter {j}", alpha=0.75, ) - plt.axvline(result.mean[j], ls='--', c='k', lw=3) + plt.axvline(result.mean[j], ls="--", c="k", lw=3) plt.legend(loc="upper left", bbox_to_anchor=(1.01, 1.0)) - plt.grid(axis='y', zorder=-1) + plt.grid(axis="y", zorder=-1) plt.title("Posterior Distribution") plt.xlabel("Value") plt.ylabel("Density") @@ -121,18 +123,24 @@ def summary_table(result: "SamplingResult"): header = ["Statistic", "Value"] values = [ - ["Mean", ', '.join(summary_stats["mean"].astype(str))], - ["Median", ', '.join(summary_stats["median"].astype(str))], - ["Standard Deviation", ', '.join(summary_stats["std"].astype(str))], - ["95% CI Lower", ', '.join(summary_stats["ci_lower"].astype(str))], - ["95% CI Upper", ', '.join(summary_stats["ci_upper"].astype(str))], + ["Mean", ", ".join(summary_stats["mean"].astype(str))], + ["Median", ", ".join(summary_stats["median"].astype(str))], + ["Standard Deviation", ", ".join(summary_stats["std"].astype(str))], + ["95% CI Lower", ", ".join(summary_stats["ci_lower"].astype(str))], + ["95% CI Upper", ", ".join(summary_stats["ci_upper"].astype(str))], ] fig, ax = plt.subplots(figsize=(6, 2), dpi=100) # hide axes - ax.axis('off') - ax.axis('tight') - ax.table(cellText=values, colLabels=header, loc='center', cellLoc='center', colColours=['lightsteelblue', 'lightsteelblue']) + ax.axis("off") + ax.axis("tight") + ax.table( + cellText=values, + colLabels=header, + loc="center", + cellLoc="center", + colColours=["lightsteelblue", "lightsteelblue"], + ) ax.set_title("Summary Statistics") fig.tight_layout() plt.show() diff --git a/pybop/plot/matplotlib/standard_plots.py b/pybop/plot/matplotlib/standard_plots.py index 578ef365b..31e8898cc 100644 --- a/pybop/plot/matplotlib/standard_plots.py +++ b/pybop/plot/matplotlib/standard_plots.py @@ -3,11 +3,11 @@ import warnings import numpy as np - from matplotlib import pyplot as plt DEFAULT_TRACE_OPTIONS = dict(linewidth=2.0) + class StandardPlot: """ A class for creating and displaying interactive Plotly figures. @@ -57,9 +57,8 @@ def __init__( if trace_options: self.trace_options.update(trace_options) - # Parse the data - x, y = self.parse_data(x, y) + x, y = self.parse_data(x, y) self.x = x self.y = y # Check and wrap trace names @@ -68,12 +67,10 @@ def __init__( trace_names = [trace_names] for i, name in enumerate(trace_names): trace_names[i] = self.wrap_text(name, width=self.trace_name_width) - self.trace_names = trace_names + self.trace_names = trace_names self.fig = plt.figure(figsize=figsize, dpi=100) - - def __call__(self, show=True): """ Generate and show the figure. @@ -94,8 +91,8 @@ def __call__(self, show=True): def default_layout(self): - plt.tick_params(axis='both', labelsize=12) - plt.ticklabel_format(axis='both', style='sci', scilimits=(-4, 4)) + plt.tick_params(axis="both", labelsize=12) + plt.ticklabel_format(axis="both", style="sci", scilimits=(-4, 4)) def add_traces(self, x, y, trace_names=None, **trace_options): """ @@ -123,13 +120,14 @@ def add_traces(self, x, y, trace_names=None, **trace_options): label = None if trace_names is not None: - label = trace_names[i] + label = trace_names[i] - self.traces.append(self.create_trace(xi, y[i], label, **trace_options)) + self.traces.append(self.create_trace(xi, y[i], label, **trace_options)) if self.trace_names is not None: - plt.legend(**dict(loc="best", fontsize=12),) - + plt.legend( + **dict(loc="best", fontsize=12), + ) def parse_data(self, x, y): """ @@ -186,7 +184,7 @@ def create_trace(self, x, y, label, ax=None, **trace_options): ax = plt.gca() line = ax.plot( - x, + x, y, label=label, **trace_options, @@ -196,7 +194,6 @@ def create_trace(self, x, y, label, ax=None, **trace_options): else: return line[0] - @staticmethod def wrap_text(text, width): """ @@ -235,7 +232,6 @@ def remove_brackets(s): return s - class StandardSubplot(StandardPlot): """ A class for creating and displaying a set of interactive Plotly figures in a grid layout. @@ -275,11 +271,9 @@ def __init__( trace_options=DEFAULT_TRACE_OPTIONS, trace_names=None, trace_name_width=40, - figsize=(8, 6) + figsize=(8, 6), ): - super().__init__( - x, y, trace_options, trace_names, trace_name_width, figsize - ) + super().__init__(x, y, trace_options, trace_names, trace_name_width, figsize) self.num_traces = len(self.y) self.num_rows = num_rows self.num_cols = num_cols @@ -293,8 +287,6 @@ def __init__( self.num_cols = int(math.ceil(self.num_traces / self.num_rows)) self.axis_titles = axis_titles - - def __call__(self, show): """ Generate and show the set of figures. @@ -305,30 +297,35 @@ def __call__(self, show): If True, the figure is shown upon creation (default: True). """ - color_cycle = plt.rcParams['axes.prop_cycle']() + color_cycle = plt.rcParams["axes.prop_cycle"]() xi = self.x[0] lines = [] for idx, yi in enumerate(self.y): - ax = self.fig.add_subplot(self.num_rows, self.num_cols, idx+1) + ax = self.fig.add_subplot(self.num_rows, self.num_cols, idx + 1) if self.axis_titles and idx < len(self.axis_titles): x_title, y_title = self.axis_titles[idx] ax.set_xlabel(x_title) ax.set_ylabel(y_title) - if len(self.x)>1: + if len(self.x) > 1: xi = self.x[idx] label = None if self.trace_names is not None: label = self.trace_names[idx] - lines.append(self.create_trace(xi, yi, label, ax = ax, **next(color_cycle))) - + lines.append(self.create_trace(xi, yi, label, ax=ax, **next(color_cycle))) lines_labels = [ax.get_legend_handles_labels() for ax in self.fig.axes] lines, labels = [sum(lol, []) for lol in zip(*lines_labels)] if self.trace_names is not None: - self.fig.legend(lines, labels, loc='upper right', ncol=len(lines), bbox_to_anchor=(0.99, 0.95)) + self.fig.legend( + lines, + labels, + loc="upper right", + ncol=len(lines), + bbox_to_anchor=(0.99, 0.95), + ) plt.tight_layout(rect=[0, 0, 1, 0.95]) if show: plt.show() @@ -336,7 +333,16 @@ def __call__(self, show): return self.fig -def trajectories(x, y, trace_names=None, show=True, xaxis_title='', yaxis_title='', title='', **layout_kwargs): +def trajectories( + x, + y, + trace_names=None, + show=True, + xaxis_title="", + yaxis_title="", + title="", + **layout_kwargs, +): """ Quickly plot one or more trajectories using Plotly. @@ -383,4 +389,4 @@ def trajectories(x, y, trace_names=None, show=True, xaxis_title='', yaxis_title= if show: plt.show() - return plot_dict \ No newline at end of file + return plot_dict diff --git a/pybop/plot/matplotlib/voronoi.py b/pybop/plot/matplotlib/voronoi.py index 1a44f0f77..e7502b941 100644 --- a/pybop/plot/matplotlib/voronoi.py +++ b/pybop/plot/matplotlib/voronoi.py @@ -1,23 +1,23 @@ +import warnings from typing import TYPE_CHECKING +import matplotlib as mpl import numpy as np -from scipy.spatial import cKDTree from matplotlib import pyplot as plt -import matplotlib as mpl -import warnings if TYPE_CHECKING: from pybop._result import Result from pybop.plot.voronoi import voronoi_data + def surface( result: "Result", bounds=None, normalise=True, - title='Voronoi Cost Landscape', + title="Voronoi Cost Landscape", show=True, - **layout_kwargs + **layout_kwargs, ): """ Plot a 2D representation of the Voronoi diagram with color-coded regions. @@ -60,8 +60,9 @@ def surface( bounds if bounds is not None else [param.bounds for param in parameters] )[:2] - _, _, f, regions, relative_sizes = voronoi_data(xlim, ylim, x_optim, y_optim, f, normalise) - + _, _, f, regions, relative_sizes = voronoi_data( + xlim, ylim, x_optim, y_optim, f, normalise + ) # Construct figure plt.figure(figsize=(7, 6), dpi=100) @@ -73,26 +74,25 @@ def surface( norm_f = norm(f, clip=True) # get colours - cmap = mpl.colormaps['viridis'] + cmap = mpl.colormaps["viridis"] colors = cmap(norm_f) - # Add Voronoi edges and fill Voronoi regions + # Add Voronoi edges and fill Voronoi regions for j, (region, size) in enumerate(zip(regions, relative_sizes, strict=False)): x_region = region[:, 0].tolist() + [region[0, 0]] y_region = region[:, 1].tolist() + [region[0, 1]] plt.fill(x_region, y_region, color=colors[j]) - plt.plot(x_region, y_region, color='w', linewidth=0.5 + size*0.1) + plt.plot(x_region, y_region, color="w", linewidth=0.5 + size * 0.1) + + plt.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap), ax=plt.gca()) - plt.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap), ax = plt.gca()) - - # Add original points plt.scatter( x_optim, y_optim, c=[i / len(x_optim) for i in range(len(x_optim))], - cmap='Grays', + cmap="Grays", zorder=2.5, ) @@ -102,12 +102,12 @@ def surface( plt.plot( [x0[0]], [x0[1]], - 'X', + "X", markersize=14, - markerfacecolor='w', - markeredgecolor='k', + markerfacecolor="w", + markeredgecolor="k", label="Initial values", - linestyle='None', + linestyle="None", zorder=2.6, ) @@ -119,21 +119,20 @@ def surface( [x_best[1]], "P", markersize=14, - markerfacecolor='k', - markeredgecolor='w', + markerfacecolor="k", + markeredgecolor="w", label="Final values", - linestyle='None', + linestyle="None", zorder=2.6, ) - - # Layout + # Layout names = result.problem.parameters.names plt.xlabel(names[0], labelpad=15) - plt.ticklabel_format(axis='both', **dict(style='sci',scilimits=(-4,4))) + plt.ticklabel_format(axis="both", **dict(style="sci", scilimits=(-4, 4))) plt.ylabel(names[1], labelpad=15) plt.title(title, pad=40) - plt.legend(ncols=2, loc='lower center', bbox_to_anchor=(0.5, 1.0)) + plt.legend(ncols=2, loc="lower center", bbox_to_anchor=(0.5, 1.0)) plt.xlim(xlim[0], xlim[1]) plt.ylim(ylim[0], ylim[1]) plt.tight_layout() diff --git a/pybop/plot/plotly/__init__.py b/pybop/plot/plotly/__init__.py index 0a3a1f91a..d3560892a 100644 --- a/pybop/plot/plotly/__init__.py +++ b/pybop/plot/plotly/__init__.py @@ -7,4 +7,4 @@ from .problem import problem from .nyquist import nyquist from .voronoi import surface -from .samples import chains, posterior, summary_table, trace \ No newline at end of file +from .samples import chains, posterior, summary_table, trace diff --git a/pybop/plot/plotly/standard_plots.py b/pybop/plot/plotly/standard_plots.py index 8e3961d71..cdd0d8341 100644 --- a/pybop/plot/plotly/standard_plots.py +++ b/pybop/plot/plotly/standard_plots.py @@ -1,8 +1,8 @@ import math import textwrap +import warnings import numpy as np -import warnings from pybop.plot.plotly.plotly_manager import PlotlyManager diff --git a/pybop/plot/plotly/voronoi.py b/pybop/plot/plotly/voronoi.py index dc1e5ea1e..d60b9597e 100644 --- a/pybop/plot/plotly/voronoi.py +++ b/pybop/plot/plotly/voronoi.py @@ -1,13 +1,12 @@ from typing import TYPE_CHECKING import numpy as np -from scipy.spatial import Voronoi, cKDTree +from scipy.spatial import cKDTree if TYPE_CHECKING: from pybop._result import Result -from pybop.plot.plotly.plotly_manager import PlotlyManager from pybop.plot import voronoi_data - +from pybop.plot.plotly.plotly_manager import PlotlyManager def assign_nearest_value(x, y, f, xi, yi): @@ -85,14 +84,15 @@ def surface( bounds if bounds is not None else [param.bounds for param in parameters] )[:2] - x, y, f, regions, relative_sizes = voronoi_data(xlim, ylim, x_optim, y_optim, f, normalise) + x, y, f, regions, relative_sizes = voronoi_data( + xlim, ylim, x_optim, y_optim, f, normalise + ) # Create a grid for plot xi = np.linspace(xlim[0], xlim[1], resolution) yi = np.linspace(ylim[0], ylim[1], resolution) xi, yi = np.meshgrid(xi, yi) - if normalise: # Create a normalised grid norm_xi = np.linspace(0, 1, resolution) @@ -213,4 +213,4 @@ def surface( ) fig.update_layout(**layout_kwargs) if show: - fig.show() \ No newline at end of file + fig.show() diff --git a/pybop/plot/plots.py b/pybop/plot/plots.py index daadc5d11..83598f4de 100644 --- a/pybop/plot/plots.py +++ b/pybop/plot/plots.py @@ -1,21 +1,22 @@ from typing import TYPE_CHECKING + import numpy as np if TYPE_CHECKING: from pybop._result import Result from pybop.samplers.base_pints_sampler import SamplingResult - + from pybop.parameters.parameter import Inputs +from pybop.plot.util import call_plotting_function from pybop.problems.problem import Problem -from pybop.plot.util import call_plotting_function, get_class - def chains(result: "SamplingResult", show=True, backend=None, **kwargs): """ Plot posterior distributions for each chain. """ - return call_plotting_function('chains', backend, result=result, **kwargs) + return call_plotting_function("chains", backend, result=result, **kwargs) + def contour( call_object: "Problem | Result", @@ -24,7 +25,7 @@ def contour( transformed: bool = False, steps: int = 10, show: bool = True, - backend = None, + backend=None, **layout_kwargs, ): """ @@ -65,7 +66,18 @@ def contour( ValueError If the cost function does not return a valid cost when called with a parameter list. """ - return call_plotting_function('contour', backend, call_object=call_object, gradient=gradient, bounds=bounds, transformed=transformed, steps=steps, show=show, **layout_kwargs) + return call_plotting_function( + "contour", + backend, + call_object=call_object, + gradient=gradient, + bounds=bounds, + transformed=transformed, + steps=steps, + show=show, + **layout_kwargs, + ) + def convergence(result: "Result", show=True, backend=None, **layout_kwargs): """ @@ -87,9 +99,14 @@ def convergence(result: "Result", show=True, backend=None, **layout_kwargs): fig : plotly.graph_objs.Figure The Plotly figure object for the convergence plot. """ - return call_plotting_function('convergence', backend, result = result, show=show, **layout_kwargs) + return call_plotting_function( + "convergence", backend, result=result, show=show, **layout_kwargs + ) + -def dataset(dataset, signal=None, trace_names=None, show=True, backend=None, **layout_kwargs): +def dataset( + dataset, signal=None, trace_names=None, show=True, backend=None, **layout_kwargs +): """ Quickly plot a PyBOP Dataset using Plotly. @@ -113,7 +130,16 @@ def dataset(dataset, signal=None, trace_names=None, show=True, backend=None, **l plotly.graph_objs.Figure The Plotly figure object for the scatter plot. """ - call_plotting_function('dataset', backend, dataset=dataset, signal=signal, trace_names=trace_names, show=show, **layout_kwargs) + call_plotting_function( + "dataset", + backend, + dataset=dataset, + signal=signal, + trace_names=trace_names, + show=show, + **layout_kwargs, + ) + def nyquist(problem, inputs: Inputs = None, show=True, backend=None, **layout_kwargs): """ @@ -152,7 +178,10 @@ def nyquist(problem, inputs: Inputs = None, show=True, backend=None, **layout_kw >>> nyquist_figures = nyquist(problem, show=True, title="Nyquist Plot", xaxis_title="Real(Z)", yaxis_title="Imag(Z)") >>> # The plots will be displayed and nyquist_figures will contain the list of figure objects. """ - return call_plotting_function('nyquist', backend, problem=problem, inputs=inputs, show=show, **layout_kwargs) + return call_plotting_function( + "nyquist", backend, problem=problem, inputs=inputs, show=show, **layout_kwargs + ) + def parameters(result: "Result", show=True, backend=None, **layout_kwargs): """ @@ -174,13 +203,17 @@ def parameters(result: "Result", show=True, backend=None, **layout_kwargs): plotly.graph_objs.Figure A Plotly figure object showing the parameter evolution over iterations. """ - return call_plotting_function('parameters', backend, result = result, show=show, **layout_kwargs) + return call_plotting_function( + "parameters", backend, result=result, show=show, **layout_kwargs + ) + def posterior(result: "SamplingResult", show=True, backend=None, **kwargs): """ Plot the summed posterior distribution across chains. """ - return call_plotting_function('posterior', backend, result=result, **kwargs) + return call_plotting_function("posterior", backend, result=result, **kwargs) + def problem( problem: Problem, @@ -213,14 +246,18 @@ def problem( plotly.graph_objs.Figure The Plotly figure object for the scatter plot. """ - return call_plotting_function('problem', backend, problem=problem, inputs=inputs, show=show, **layout_kwargs) + return call_plotting_function( + "problem", backend, problem=problem, inputs=inputs, show=show, **layout_kwargs + ) + def summary_table(result: "SamplingResult", backend=None): """ Display summary statistics in a table. """ - return call_plotting_function('summary_table', backend, result=result) + return call_plotting_function("summary_table", backend, result=result) + def surface( result: "Result", @@ -253,13 +290,24 @@ def surface( e.g. `xaxis_title="Time [s]"` or `xaxis={"title": "Time [s]", font={"size":14}}` """ - return call_plotting_function('surface', backend, result = result, bounds = bounds, normalise=normalise, resolution=resolution, show=show, **layout_kwargs) + return call_plotting_function( + "surface", + backend, + result=result, + bounds=bounds, + normalise=normalise, + resolution=resolution, + show=show, + **layout_kwargs, + ) + def trace(result: "SamplingResult", backend=None, **kwargs): """ Plot trace plots for the posterior samples. """ - return call_plotting_function('trace', backend, result=result, **kwargs) + return call_plotting_function("trace", backend, result=result, **kwargs) + def trajectories(x, y, trace_names=None, show=True, backend=None, **layout_kwargs): """ @@ -284,4 +332,12 @@ def trajectories(x, y, trace_names=None, show=True, backend=None, **layout_kwarg The Plotly figure object for the scatter plot. """ - return call_plotting_function('trajectories', backend, x=x, y=y, trace_names=trace_names, show=show, **layout_kwargs) \ No newline at end of file + return call_plotting_function( + "trajectories", + backend, + x=x, + y=y, + trace_names=trace_names, + show=show, + **layout_kwargs, + ) diff --git a/pybop/plot/util.py b/pybop/plot/util.py index 3ec299ce3..12b66fc26 100644 --- a/pybop/plot/util.py +++ b/pybop/plot/util.py @@ -1,41 +1,48 @@ import importlib.util + import pybop.plot + def get_class(class_name): err_msg = f"Plotting backend {pybop.plot.backend} is not available." try: - module = importlib.import_module('pybop.plot.' + pybop.plot.backend) + module = importlib.import_module("pybop.plot." + pybop.plot.backend) if hasattr(module, class_name): return getattr(module, class_name) else: - err_msg = f"Plotting backend {pybop.plot.backend} has no attribute {class_name}." + err_msg = ( + f"Plotting backend {pybop.plot.backend} has no attribute {class_name}." + ) raise ModuleNotFoundError(err_msg) - + except ModuleNotFoundError as error: # Raise an ModuleNotFoundError if the module or attribute is not available raise ModuleNotFoundError(err_msg) from error + def set_backend(backend): - err_msg = f"Plotting backend {backend} is not available. The default backend has not been updated. \n"\ + err_msg = ( + f"Plotting backend {backend} is not available. The default backend has not been updated. \n" f"The default backend is set to {pybop.plot.backend}" + ) try: - importlib.import_module('pybop.plot.' + backend) + importlib.import_module("pybop.plot." + backend) pybop.plot.backend = backend - for attr in ['StandardPlot', 'StandardSubplot']: + for attr in ["StandardPlot", "StandardSubplot"]: setattr(pybop.plot, attr, get_class(attr)) except ModuleNotFoundError as error: - # Raise an ModuleNotFoundError if the module or attribute is not available - raise ModuleNotFoundError(err_msg) from error - + # Raise an ModuleNotFoundError if the module or attribute is not available + raise ModuleNotFoundError(err_msg) from error + def call_plotting_function(function_name, backend, **kwargs): if backend is None: - backend = pybop.plot.backend + backend = pybop.plot.backend err_msg = f"Plotting backend {backend} is not available." try: - module = importlib.import_module('pybop.plot.' + backend) + module = importlib.import_module("pybop.plot." + backend) if hasattr(module, function_name): plotting_function = getattr(module, function_name) # Return the imported attribute @@ -43,8 +50,7 @@ def call_plotting_function(function_name, backend, **kwargs): else: err_msg = f"Plotting backend {backend} has no attribute {function_name}." raise ModuleNotFoundError(err_msg) - + except ModuleNotFoundError as error: # Raise an ModuleNotFoundError if the module or attribute is not available raise ModuleNotFoundError(err_msg) from error - From f0e5566fccc4adfd18843e2d7d952b7275a8188d Mon Sep 17 00:00:00 2001 From: u2370093 Date: Wed, 8 Apr 2026 14:45:31 +0100 Subject: [PATCH 03/20] start cleaning up StandardPlot --- .../using_transformations.ipynb | 2 +- pybop/plot/__init__.py | 9 +- pybop/plot/matplotlib/__init__.py | 2 +- pybop/plot/matplotlib/nyquist.py | 26 +- pybop/plot/matplotlib/parameters.py | 3 +- pybop/plot/matplotlib/standard_plots.py | 200 ++++----------- pybop/plot/plotly/__init__.py | 2 +- pybop/plot/plotly/parameters.py | 3 +- pybop/plot/plotly/standard_plots.py | 148 ++---------- pybop/plot/plots.py | 34 --- pybop/plot/standard_plots.py | 228 ++++++++++++++++++ pybop/plot/util.py | 20 -- tests/plotting/test_plotly_manager.py | 6 + 13 files changed, 321 insertions(+), 362 deletions(-) create mode 100644 pybop/plot/standard_plots.py diff --git a/examples/notebooks/getting_started/using_transformations.ipynb b/examples/notebooks/getting_started/using_transformations.ipynb index 2de75b771..5d5d9a47f 100644 --- a/examples/notebooks/getting_started/using_transformations.ipynb +++ b/examples/notebooks/getting_started/using_transformations.ipynb @@ -30,7 +30,7 @@ "import pybop\n", "\n", "pybop.plot.set_backend(\"plotly\")\n", - "pybop.plot.PlotlyManager().pio.renderers.default = \"notebook_connected\"\n", + "pybop.plot.plotly.PlotlyManager().pio.renderers.default = \"notebook_connected\"\n", "\n", "np.random.seed(8) # users can remove this line" ] diff --git a/pybop/plot/__init__.py b/pybop/plot/__init__.py index 744e749ab..29116410e 100644 --- a/pybop/plot/__init__.py +++ b/pybop/plot/__init__.py @@ -2,7 +2,7 @@ DEFAULT_BACKEND = 'matplotlib' backend=DEFAULT_BACKEND -from .util import set_backend, call_plotting_function, get_class +from .util import set_backend, call_plotting_function # # Import plots @@ -18,13 +18,10 @@ problem, summary_table, surface, - trace, - trajectories + trace ) +from .standard_plots import StandardPlot, StandardSubplot, trajectories from .voronoi import voronoi_data, _voronoi_regions from . import matplotlib from . import plotly - -StandardPlot = matplotlib.StandardPlot -StandardSubplot = matplotlib.StandardSubplot diff --git a/pybop/plot/matplotlib/__init__.py b/pybop/plot/matplotlib/__init__.py index be1f45a58..21da2259a 100644 --- a/pybop/plot/matplotlib/__init__.py +++ b/pybop/plot/matplotlib/__init__.py @@ -1,4 +1,4 @@ -from .standard_plots import StandardPlot, StandardSubplot, trajectories +from .standard_plots import Plotter, SubplotPlotter, trajectories from .dataset import dataset from .convergence import convergence from .parameters import parameters diff --git a/pybop/plot/matplotlib/nyquist.py b/pybop/plot/matplotlib/nyquist.py index eba00efec..c702e9049 100644 --- a/pybop/plot/matplotlib/nyquist.py +++ b/pybop/plot/matplotlib/nyquist.py @@ -56,23 +56,27 @@ def nyquist(problem, inputs: Inputs = None, show=True, **layout_kwargs): trace_names="Model", ) - fig = plot_dict(show=False) - plot_dict.traces[0].set_color("#00CC96") - plot_dict.traces[0].set_linewidth(2) - plot_dict.traces[0].set_marker(".") - plot_dict.traces[0].set_markersize(8) + plotting_options = dict(color="#00CC96", linewidth=2, marker=".", markersize=8) + plot_dict.traces[0].update(plotting_options) target_trace = plot_dict.create_trace( x=target_output[var].real, y=-target_output[var].imag, label="Reference", ) - target_trace.set_linestyle("None") - target_trace.set_marker("o") - target_trace.set_fillstyle("none") - target_trace.set_markersize(8) - target_trace.set_markeredgecolor("#636EFA") + plotting_options = dict( + linestyle="none", + marker="o", + fillstyle="none", + markersize=8, + markeredgecolor="#636EFA", + ) + target_trace.update(plotting_options) + + plot_dict.traces.append(target_trace) + + fig = plot_dict(show=False) # Layout plt.title("Nyquist Plot", fontsize=14, x=0.2) plt.xlabel(r"$Z_{re} / \Omega$", fontsize=16) @@ -80,7 +84,7 @@ def nyquist(problem, inputs: Inputs = None, show=True, **layout_kwargs): plt.legend(loc="upper right", bbox_to_anchor=(1, 1.08), ncols=2) if show: - plt.show() + fig.show() figure_list.append(fig) diff --git a/pybop/plot/matplotlib/parameters.py b/pybop/plot/matplotlib/parameters.py index 1c6e24e26..eef582f0e 100644 --- a/pybop/plot/matplotlib/parameters.py +++ b/pybop/plot/matplotlib/parameters.py @@ -4,7 +4,7 @@ import matplotlib.pyplot as plt from pybop.costs.log_likelihoods import GaussianLogLikelihood -from pybop.plot.matplotlib.standard_plots import StandardSubplot +from pybop.plot.standard_plots import StandardSubplot if TYPE_CHECKING: from pybop._result import Result @@ -58,6 +58,7 @@ def parameters(result: "Result", show=True, **layout_kwargs): trace_names=trace_names, trace_name_width=50, figsize=(18, 8), + backend="matplotlib", ) plt.suptitle("Parameter Convergence") diff --git a/pybop/plot/matplotlib/standard_plots.py b/pybop/plot/matplotlib/standard_plots.py index 31e8898cc..e96cf19af 100644 --- a/pybop/plot/matplotlib/standard_plots.py +++ b/pybop/plot/matplotlib/standard_plots.py @@ -1,14 +1,13 @@ -import math -import textwrap import warnings -import numpy as np from matplotlib import pyplot as plt +from pybop.plot import StandardPlot + DEFAULT_TRACE_OPTIONS = dict(linewidth=2.0) -class StandardPlot: +class Plotter: """ A class for creating and displaying interactive Plotly figures. @@ -33,14 +32,11 @@ class StandardPlot: def __init__( self, - x=None, - y=None, trace_options=None, - trace_names=None, - trace_name_width=20, figsize=(8, 6), **kwargs, ): + self.backend = "matplotlib" # Warning if layout arguments ignored if len(kwargs) > 0: warnings.warn( @@ -49,27 +45,14 @@ def __init__( UserWarning, stacklevel=2, ) - self.traces = [] - self.trace_name_width = trace_name_width # Set default trace options and update if provided self.trace_options = DEFAULT_TRACE_OPTIONS.copy() if trace_options: self.trace_options.update(trace_options) - # Parse the data - x, y = self.parse_data(x, y) - self.x = x - self.y = y - # Check and wrap trace names - if trace_names is not None: - if isinstance(trace_names, str): - trace_names = [trace_names] - for i, name in enumerate(trace_names): - trace_names[i] = self.wrap_text(name, width=self.trace_name_width) - self.trace_names = trace_names - self.fig = plt.figure(figsize=figsize, dpi=100) + self.traces = [] def __call__(self, show=True): """ @@ -81,19 +64,28 @@ def __call__(self, show=True): If True, the figure is shown upon creation (default: True). """ # Add traces - if self.x is not None and self.y is not None: - self.add_traces(self.x, self.y, self.trace_names) - self.default_layout() - if show: - plt.show() - - return self.fig - - def default_layout(self): + for trace in self.traces: + self._plot_trace(**trace) plt.tick_params(axis="both", labelsize=12) plt.ticklabel_format(axis="both", style="sci", scilimits=(-4, 4)) + labels_in_fig = True + for ax in self.fig.axes: + if not ax.get_legend_handles_labels() == ([], []): + break + else: + labels_in_fig = False + if labels_in_fig: + plt.legend( + **dict(loc="best", fontsize=12), + ) + + if show: + plt.show() + else: + return self.fig + def add_traces(self, x, y, trace_names=None, **trace_options): """ Add a set of traces to the plot dictionary. @@ -124,62 +116,21 @@ def add_traces(self, x, y, trace_names=None, **trace_options): self.traces.append(self.create_trace(xi, y[i], label, **trace_options)) - if self.trace_names is not None: - plt.legend( - **dict(loc="best", fontsize=12), - ) - - def parse_data(self, x, y): - """ - Check the type and dimensions of the data and convert if necessary to a list - of 'things plotly can take', e.g. numpy arrays or lists of numbers. - - Parameters - ---------- - x : list or np.ndarray, optional - X-axis data points. - y : list or np.ndarray, optional - Primary Y-axis data points for simulated model output. - """ - if x is None or y is None: - return None, None - if isinstance(x, list): - # If it's a list of numpy arrays, it's fine - # If it's a list of lists, it's fine - # If it's neither, it's a list of numbers that we need to wrap - if not isinstance(x[0], np.ndarray) and not isinstance(x[0], list): - x = [x] - elif isinstance(x, np.ndarray): - x = np.squeeze(x) - if x.ndim == 1: - x = [x] - else: - x = x.tolist() - if isinstance(y, list): - if not isinstance(y[0], np.ndarray) and not isinstance(y[0], list): - y = [y] - if isinstance(y, np.ndarray): - y = np.squeeze(y) - if y.ndim == 1: - y = [y] - else: - y = y.tolist() - if len(x) > 1 and len(x) != len(y): - raise ValueError( - "Input x should have either one data series or the same number as y." - ) - return x, y - def create_trace(self, x, y, label, ax=None, **trace_options): """ - Create a trace for the Plotly figure. + Add line to plot. Returns ------- plotly.graph_objs.Scatter A trace for a Plotly figure. """ + size = min(len(x), len(y)) + trace = dict(x=x[:size], y=y[:size], label=label, ax=ax) + trace.update(trace_options) + return trace + def _plot_trace(self, x, y, label, ax=None, **trace_options): if ax is None: ax = plt.gca() @@ -189,50 +140,14 @@ def create_trace(self, x, y, label, ax=None, **trace_options): label=label, **trace_options, ) + if len(line) > 1: return line else: return line[0] - @staticmethod - def wrap_text(text, width): - """ - Wrap text to a specified width with HTML line breaks. - - Parameters - ---------- - text : str - The text to wrap. - width : int - The width to wrap the text to. - - Returns - ------- - str - The wrapped text. - """ - wrapped_text = textwrap.fill(text, width=width, break_long_words=False) - return wrapped_text - @staticmethod - def remove_brackets(s): - """ - Remove square brackets from a string and replace with forward slashes - as per section 7.1 of the SI Handbook - """ - # If s is an iterable (but not a string), apply the function recursively to each element - if hasattr(s, "__iter__") and not isinstance(s, str): - return type(s)(StandardPlot.remove_brackets(i) for i in s) - elif isinstance(s, str): - start = s.find("[") - end = s.find("]") - if start != -1 and end != -1: - char_in_brackets = s[start + 1 : end] - return s[:start] + " / " + char_in_brackets + s[end + 1 :] - return s - - -class StandardSubplot(StandardPlot): +class SubplotPlotter(Plotter): """ A class for creating and displaying a set of interactive Plotly figures in a grid layout. @@ -263,31 +178,15 @@ class StandardSubplot(StandardPlot): def __init__( self, - x, - y, - num_rows=None, - num_cols=None, axis_titles=None, trace_options=DEFAULT_TRACE_OPTIONS, - trace_names=None, - trace_name_width=40, figsize=(8, 6), + **kwargs, ): - super().__init__(x, y, trace_options, trace_names, trace_name_width, figsize) - self.num_traces = len(self.y) - self.num_rows = num_rows - self.num_cols = num_cols - if self.num_rows is None and self.num_cols is None: - # Work out the number of subplots - self.num_cols = int(math.ceil(math.sqrt(self.num_traces))) - self.num_rows = int(math.ceil(self.num_traces / self.num_cols)) - elif self.num_rows is None: - self.num_rows = int(math.ceil(self.num_traces / self.num_cols)) - elif self.num_cols is None: - self.num_cols = int(math.ceil(self.num_traces / self.num_rows)) + super().__init__(trace_options, figsize, **kwargs) self.axis_titles = axis_titles - def __call__(self, show): + def __call__(self, show=True, num_rows=1, num_cols=1): """ Generate and show the set of figures. @@ -299,26 +198,23 @@ def __call__(self, show): color_cycle = plt.rcParams["axes.prop_cycle"]() - xi = self.x[0] lines = [] - for idx, yi in enumerate(self.y): - ax = self.fig.add_subplot(self.num_rows, self.num_cols, idx + 1) + show_legend = False + for idx, trace in enumerate(self.traces): + ax = self.fig.add_subplot(num_rows, num_cols, idx + 1) + trace["ax"] = ax if self.axis_titles and idx < len(self.axis_titles): x_title, y_title = self.axis_titles[idx] ax.set_xlabel(x_title) ax.set_ylabel(y_title) - if len(self.x) > 1: - xi = self.x[idx] - - label = None - if self.trace_names is not None: - label = self.trace_names[idx] + if "label" in trace.keys() and trace["label"] is not None: + show_legend = True - lines.append(self.create_trace(xi, yi, label, ax=ax, **next(color_cycle))) + lines.append(self._plot_trace(**trace, **next(color_cycle))) lines_labels = [ax.get_legend_handles_labels() for ax in self.fig.axes] - lines, labels = [sum(lol, []) for lol in zip(*lines_labels)] - if self.trace_names is not None: + lines, labels = [sum(lol, []) for lol in zip(*lines_labels, strict=False)] + if show_legend: self.fig.legend( lines, labels, @@ -374,11 +270,7 @@ def trajectories( stacklevel=2, ) # Create a plot dictionary - plot_dict = StandardPlot( - x=x, - y=y, - trace_names=trace_names, - ) + plot_dict = StandardPlot(x=x, y=y, trace_names=trace_names, backend="matplotlib") # Generate the figure and update the layout fig = plot_dict(show=False) @@ -387,6 +279,6 @@ def trajectories( plt.ylabel(yaxis_title, fontsize=12) plt.tight_layout() if show: - plt.show() + fig.show() - return plot_dict + return fig diff --git a/pybop/plot/plotly/__init__.py b/pybop/plot/plotly/__init__.py index d3560892a..b3b907abd 100644 --- a/pybop/plot/plotly/__init__.py +++ b/pybop/plot/plotly/__init__.py @@ -1,5 +1,5 @@ from .plotly_manager import PlotlyManager -from .standard_plots import StandardPlot, StandardSubplot, trajectories +from .standard_plots import Plotter, SubplotPlotter, trajectories from .contour import contour from .dataset import dataset from .convergence import convergence diff --git a/pybop/plot/plotly/parameters.py b/pybop/plot/plotly/parameters.py index 02cc281f0..e93907248 100644 --- a/pybop/plot/plotly/parameters.py +++ b/pybop/plot/plotly/parameters.py @@ -1,7 +1,7 @@ from typing import TYPE_CHECKING from pybop.costs.log_likelihoods import GaussianLogLikelihood -from pybop.plot.plotly.standard_plots import StandardSubplot +from pybop.plot.standard_plots import StandardSubplot if TYPE_CHECKING: from pybop._result import Result @@ -59,6 +59,7 @@ def parameters(result: "Result", show=True, **layout_kwargs): layout_options=layout_options, trace_names=trace_names, trace_name_width=50, + backend="plotly", ) # Generate the figure and update the layout diff --git a/pybop/plot/plotly/standard_plots.py b/pybop/plot/plotly/standard_plots.py index cdd0d8341..62cd708a2 100644 --- a/pybop/plot/plotly/standard_plots.py +++ b/pybop/plot/plotly/standard_plots.py @@ -1,9 +1,6 @@ -import math -import textwrap import warnings -import numpy as np - +from pybop.plot import StandardPlot from pybop.plot.plotly.plotly_manager import PlotlyManager DEFAULT_LAYOUT_OPTIONS = dict( @@ -36,7 +33,7 @@ DEFAULT_SUBPLOT_TRACE_OPTIONS = dict(line=dict(width=2), mode="lines") -class StandardPlot: +class Plotter: """ A class for creating and displaying interactive Plotly figures. @@ -65,13 +62,9 @@ class StandardPlot: def __init__( self, - x=None, - y=None, layout=None, layout_options=None, trace_options=None, - trace_names=None, - trace_name_width=40, **kwargs, ): # Warning if layout arguments ignored @@ -82,9 +75,11 @@ def __init__( UserWarning, stacklevel=2, ) + + self.backend = "plotly" + self.traces = [] self.layout = layout - self.trace_name_width = trace_name_width # Set default layout options and update if provided if self.layout is None: @@ -104,10 +99,6 @@ def __init__( if self.layout is None: self.layout = self.go.Layout(**self.layout_options) - # Add traces - if x is not None and y is not None: - self.add_traces(x, y, trace_names) - def __call__(self, show=True): """ Generate and show the figure. @@ -139,16 +130,6 @@ def add_traces(self, x, y, trace_names=None, **trace_options): options = self.trace_options.copy() options.update(trace_options) - # Check and wrap trace names - if trace_names is not None: - if isinstance(trace_names, str): - trace_names = [trace_names] - for i, name in enumerate(trace_names): - trace_names[i] = self.wrap_text(name, width=self.trace_name_width) - - # Parse the data - x, y = self.parse_data(x, y) - # Create a trace for each trajectory xi = x[0] for i in range(0, len(y)): @@ -162,45 +143,6 @@ def add_traces(self, x, y, trace_names=None, **trace_options): trace = self.create_trace(xi, y[i], **trace_options) self.traces.append(trace) - def parse_data(self, x, y): - """ - Check the type and dimensions of the data and convert if necessary to a list - of 'things plotly can take', e.g. numpy arrays or lists of numbers. - - Parameters - ---------- - x : list or np.ndarray, optional - X-axis data points. - y : list or np.ndarray, optional - Primary Y-axis data points for simulated model output. - """ - if isinstance(x, list): - # If it's a list of numpy arrays, it's fine - # If it's a list of lists, it's fine - # If it's neither, it's a list of numbers that we need to wrap - if not isinstance(x[0], np.ndarray) and not isinstance(x[0], list): - x = [x] - elif isinstance(x, np.ndarray): - x = np.squeeze(x) - if x.ndim == 1: - x = [x] - else: - x = x.tolist() - if isinstance(y, list): - if not isinstance(y[0], np.ndarray) and not isinstance(y[0], list): - y = [y] - if isinstance(y, np.ndarray): - y = np.squeeze(y) - if y.ndim == 1: - y = [y] - else: - y = y.tolist() - if len(x) > 1 and len(x) != len(y): - raise ValueError( - "Input x should have either one data series or the same number as y." - ) - return x, y - def create_trace(self, x, y, **trace_options): """ Create a trace for the Plotly figure. @@ -217,45 +159,8 @@ def create_trace(self, x, y, **trace_options): **trace_options, ) - @staticmethod - def wrap_text(text, width): - """ - Wrap text to a specified width with HTML line breaks. - - Parameters - ---------- - text : str - The text to wrap. - width : int - The width to wrap the text to. - - Returns - ------- - str - The wrapped text. - """ - wrapped_text = textwrap.fill(text, width=width, break_long_words=False) - return wrapped_text.replace("\n", "
") - @staticmethod - def remove_brackets(s): - """ - Remove square brackets from a string and replace with forward slashes - as per section 7.1 of the SI Handbook - """ - # If s is an iterable (but not a string), apply the function recursively to each element - if hasattr(s, "__iter__") and not isinstance(s, str): - return type(s)(StandardPlot.remove_brackets(i) for i in s) - elif isinstance(s, str): - start = s.find("[") - end = s.find("]") - if start != -1 and end != -1: - char_in_brackets = s[start + 1 : end] - return s[:start] + " / " + char_in_brackets + s[end + 1 :] - return s - - -class StandardSubplot(StandardPlot): +class SubplotPlotter(Plotter): """ A class for creating and displaying a set of interactive Plotly figures in a grid layout. @@ -288,33 +193,14 @@ class StandardSubplot(StandardPlot): def __init__( self, - x, - y, - num_rows=None, - num_cols=None, axis_titles=None, layout=None, layout_options=DEFAULT_LAYOUT_OPTIONS, subplot_options=DEFAULT_SUBPLOT_OPTIONS, trace_options=DEFAULT_SUBPLOT_TRACE_OPTIONS, - trace_names=None, - trace_name_width=40, + **kwargs, ): - super().__init__( - x, y, layout, layout_options, trace_options, trace_names, trace_name_width - ) - self.num_traces = len(self.traces) - self.num_rows = num_rows - self.num_cols = num_cols - if self.num_rows is None and self.num_cols is None: - # Work out the number of subplots - self.num_cols = int(math.ceil(math.sqrt(self.num_traces))) - self.num_rows = int(math.ceil(self.num_traces / self.num_cols)) - elif self.num_rows is None: - self.num_rows = int(math.ceil(self.num_traces / self.num_cols)) - elif self.num_cols is None: - self.num_cols = int(math.ceil(self.num_traces / self.num_rows)) - self.axis_titles = axis_titles + super().__init__(layout, layout_options, trace_options, **kwargs) self.subplot_options = subplot_options.copy() if subplot_options is not None: for arg, value in subplot_options.items(): @@ -323,7 +209,9 @@ def __init__( # Attempt to import plotly when an instance is created self.make_subplots = PlotlyManager().make_subplots - def __call__(self, show): + self.axis_titles = axis_titles + + def __call__(self, show=True, num_rows=1, num_cols=1): """ Generate and show the set of figures. @@ -333,8 +221,8 @@ def __call__(self, show): If True, the figure is shown upon creation (default: True). """ fig = self.make_subplots( - rows=self.num_rows, - cols=self.num_cols, + rows=num_rows, + cols=num_cols, horizontal_spacing=0.1, vertical_spacing=0.15, **self.subplot_options, @@ -342,8 +230,8 @@ def __call__(self, show): fig.update_layout(self.layout_options) for idx, trace in enumerate(self.traces): - row = (idx // self.num_cols) + 1 - col = (idx % self.num_cols) + 1 + row = (idx // num_cols) + 1 + col = (idx % num_cols) + 1 fig.add_trace(trace, row=row, col=col) if self.axis_titles and idx < len(self.axis_titles): @@ -386,11 +274,7 @@ def trajectories(x, y, trace_names=None, show=True, **layout_kwargs): The Plotly figure object for the scatter plot. """ # Create a plot dictionary - plot_dict = StandardPlot( - x=x, - y=y, - trace_names=trace_names, - ) + plot_dict = StandardPlot(x=x, y=y, trace_names=trace_names, backend="plotly") # Generate the figure and update the layout fig = plot_dict(show=False) diff --git a/pybop/plot/plots.py b/pybop/plot/plots.py index 83598f4de..95fb1b701 100644 --- a/pybop/plot/plots.py +++ b/pybop/plot/plots.py @@ -307,37 +307,3 @@ def trace(result: "SamplingResult", backend=None, **kwargs): Plot trace plots for the posterior samples. """ return call_plotting_function("trace", backend, result=result, **kwargs) - - -def trajectories(x, y, trace_names=None, show=True, backend=None, **layout_kwargs): - """ - Quickly plot one or more trajectories using Plotly. - - Parameters - ---------- - x : list or np.ndarray - X-axis data points. - y : list or np.ndarray - Y-axis data points for each trajectory. - trace_names : list or str, optional - Name(s) for the trace(s) (default: None). - **layout_kwargs : optional - Valid Plotly layout keys and their values, - e.g. `xaxis_title="Time / s"` or - `xaxis={"title": "Time [s]", font={"size":14}}` - - Returns - ------- - plotly.graph_objs.Figure - The Plotly figure object for the scatter plot. - """ - - return call_plotting_function( - "trajectories", - backend, - x=x, - y=y, - trace_names=trace_names, - show=show, - **layout_kwargs, - ) diff --git a/pybop/plot/standard_plots.py b/pybop/plot/standard_plots.py new file mode 100644 index 000000000..86111bbae --- /dev/null +++ b/pybop/plot/standard_plots.py @@ -0,0 +1,228 @@ +import math +import textwrap + +import numpy as np +import warnings + +from pybop.plot.util import call_plotting_function + +class StandardPlot: + def __init__( + self, + x=None, + y=None, + trace_options=None, + trace_names=None, + trace_name_width=20, + backend = None, + **kwargs): + + self.plotter = call_plotting_function('Plotter', backend, trace_options=trace_options, **kwargs) + + self.trace_name_width = trace_name_width + + # Add traces + if x is not None and y is not None: + self.add_traces(x, y, trace_names) + + def __call__(self, show=True): + return self.plotter(show=show) + + @property + def traces(self): + return self.plotter.traces + + @traces.setter + def traces(self, value): + self.plotter.traces = value + + def add_traces(self, x, y, trace_names): + # Check and wrap trace names + if trace_names is not None: + if isinstance(trace_names, str): + trace_names = [trace_names] + for i, name in enumerate(trace_names): + trace_names[i] = self.wrap_text(name, width=self.trace_name_width, backend=self.plotter.backend) + + # Parse the data + x, y = self.parse_data(x, y) + + # Add traces + self.plotter.add_traces(x, y, trace_names) + + + def parse_data(self, x, y): + """ + Check the type and dimensions of the data and convert if necessary to a list + of 'things plotly can take', e.g. numpy arrays or lists of numbers. + + Parameters + ---------- + x : list or np.ndarray, optional + X-axis data points. + y : list or np.ndarray, optional + Primary Y-axis data points for simulated model output. + """ + if isinstance(x, list): + # If it's a list of numpy arrays, it's fine + # If it's a list of lists, it's fine + # If it's neither, it's a list of numbers that we need to wrap + if not isinstance(x[0], np.ndarray) and not isinstance(x[0], list): + x = [x] + elif isinstance(x, np.ndarray): + x = np.squeeze(x) + if x.ndim == 1: + x = [x] + else: + x = x.tolist() + if isinstance(y, list): + if not isinstance(y[0], np.ndarray) and not isinstance(y[0], list): + y = [y] + if isinstance(y, np.ndarray): + y = np.squeeze(y) + if y.ndim == 1: + y = [y] + else: + y = y.tolist() + if len(x) > 1 and len(x) != len(y): + raise ValueError( + "Input x should have either one data series or the same number as y." + ) + return x, y + + def create_trace(self, x, y, **trace_options): + return self.plotter.create_trace(x, y, **trace_options) + + @staticmethod + def wrap_text(text, width, backend='matplotlib'): + """ + Wrap text to a specified width with HTML line breaks. + + Parameters + ---------- + text : str + The text to wrap. + width : int + The width to wrap the text to. + + Returns + ------- + str + The wrapped text. + """ + wrapped_text = textwrap.fill(text, width=width, break_long_words=False) + if backend == 'plotly': + return wrapped_text.replace("\n", "
") + else: + return wrapped_text + + @staticmethod + def remove_brackets(s): + """ + Remove square brackets from a string and replace with forward slashes + as per section 7.1 of the SI Handbook + """ + # If s is an iterable (but not a string), apply the function recursively to each element + if hasattr(s, "__iter__") and not isinstance(s, str): + return type(s)(StandardPlot.remove_brackets(i) for i in s) + elif isinstance(s, str): + start = s.find("[") + end = s.find("]") + if start != -1 and end != -1: + char_in_brackets = s[start + 1 : end] + return s[:start] + " / " + char_in_brackets + s[end + 1 :] + return s + + +class StandardSubplot(StandardPlot): + """ + A class for creating and displaying a set of interactive Plotly figures in a grid layout. + + Parameters + ---------- + x : list or np.ndarray + X-axis data points. + y : list or np.ndarray + Primary Y-axis data points for simulated model output. + num_rows : int, optional + Number of rows of subplots, can be set automatically (default: None). + num_cols : int, optional + Number of columns of subplots, can be set automatically (default: None). + layout : Plotly layout, optional + A layout for the figure, overrides the layout options (default: None). + layout_options : dict, optional + Settings to modify the default layout (default: DEFAULT_LAYOUT_OPTIONS). + trace_options : dict, optional + Settings to modify the default trace type (default: DEFAULT_TRACE_OPTIONS). + trace_names : str, optional + Name(s) for the primary trace(s) (default: None). + trace_name_width : int, optional + Maximum length of the trace names before text wrapping is used (default: 40). + + Returns + ------- + plotly.graph_objs.Figure + The generated Plotly figure. + """ + + def __init__( + self, + x, + y, + backend=None, + num_rows=None, + num_cols=None, + axis_titles=None, + trace_options=None, + trace_names=None, + trace_name_width=40, + **kwargs, + ): + self.plotter = call_plotting_function('SubplotPlotter', backend, axis_titles=axis_titles, trace_options=trace_options, **kwargs) + + self.trace_name_width = trace_name_width + + # Add traces + if x is not None and y is not None: + self.add_traces(x, y, trace_names) + + self.num_traces = len(self.plotter.traces) + self.num_rows = num_rows + self.num_cols = num_cols + if self.num_rows is None and self.num_cols is None: + # Work out the number of subplots + self.num_cols = int(math.ceil(math.sqrt(self.num_traces))) + self.num_rows = int(math.ceil(self.num_traces / self.num_cols)) + elif self.num_rows is None: + self.num_rows = int(math.ceil(self.num_traces / self.num_cols)) + elif self.num_cols is None: + self.num_cols = int(math.ceil(self.num_traces / self.num_rows)) + + def __call__(self, show=True): + return self.plotter(show=show, num_rows=self.num_rows, num_cols=self.num_cols) + + +def trajectories(x, y, trace_names=None, show=True, backend=None, **layout_kwargs): + """ + Quickly plot one or more trajectories using Plotly. + + Parameters + ---------- + x : list or np.ndarray + X-axis data points. + y : list or np.ndarray + Y-axis data points for each trajectory. + trace_names : list or str, optional + Name(s) for the trace(s) (default: None). + **layout_kwargs : optional + Valid Plotly layout keys and their values, + e.g. `xaxis_title="Time / s"` or + `xaxis={"title": "Time [s]", font={"size":14}}` + + Returns + ------- + plotly.graph_objs.Figure + The Plotly figure object for the scatter plot. + """ + + return call_plotting_function('trajectories', backend, x=x, y=y, trace_names=trace_names, show=show, **layout_kwargs) \ No newline at end of file diff --git a/pybop/plot/util.py b/pybop/plot/util.py index 12b66fc26..39a1ecfd2 100644 --- a/pybop/plot/util.py +++ b/pybop/plot/util.py @@ -3,24 +3,6 @@ import pybop.plot -def get_class(class_name): - err_msg = f"Plotting backend {pybop.plot.backend} is not available." - try: - module = importlib.import_module("pybop.plot." + pybop.plot.backend) - if hasattr(module, class_name): - return getattr(module, class_name) - - else: - err_msg = ( - f"Plotting backend {pybop.plot.backend} has no attribute {class_name}." - ) - raise ModuleNotFoundError(err_msg) - - except ModuleNotFoundError as error: - # Raise an ModuleNotFoundError if the module or attribute is not available - raise ModuleNotFoundError(err_msg) from error - - def set_backend(backend): err_msg = ( f"Plotting backend {backend} is not available. The default backend has not been updated. \n" @@ -29,8 +11,6 @@ def set_backend(backend): try: importlib.import_module("pybop.plot." + backend) pybop.plot.backend = backend - for attr in ["StandardPlot", "StandardSubplot"]: - setattr(pybop.plot, attr, get_class(attr)) except ModuleNotFoundError as error: # Raise an ModuleNotFoundError if the module or attribute is not available diff --git a/tests/plotting/test_plotly_manager.py b/tests/plotting/test_plotly_manager.py index 66e9ba098..b39df37cd 100644 --- a/tests/plotting/test_plotly_manager.py +++ b/tests/plotting/test_plotly_manager.py @@ -128,6 +128,9 @@ def dataset(plotly_installed): @pytest.mark.unit def test_standard_plot(dataset, plotly_installed): + # Set plotting backend + pybop.plot.set_backend("plotly") + # Check the StandardPlot class pybop.plot.StandardPlot(dataset["Time [s]"], dataset["Voltage [V]"]) @@ -167,6 +170,9 @@ def test_standard_plot(dataset, plotly_installed): @pytest.mark.unit def test_plot_dataset(dataset, plotly_installed): + # Set plotting backend + pybop.plot.set_backend("plotly") + # Test plot of a dataset pybop.plot.dataset(dataset, signal=["Voltage [V]"]) pybop.plot.dataset(dataset, signal=["Voltage [V]", "Current [A]"]) From 58f2bcfa64c46158d0b7aaccfac271e7d5a50088 Mon Sep 17 00:00:00 2001 From: u2370093 Date: Wed, 8 Apr 2026 17:08:27 +0100 Subject: [PATCH 04/20] update nyquist --- pybop/plot/__init__.py | 2 +- pybop/plot/matplotlib/nyquist.py | 65 +++++++--------- pybop/plot/nyquist.py | 74 +++++++++++++++++++ pybop/plot/plotly/nyquist.py | 122 +++++++++++++------------------ pybop/plot/plots.py | 42 ----------- pybop/plot/standard_plots.py | 48 ++++++++---- 6 files changed, 187 insertions(+), 166 deletions(-) create mode 100644 pybop/plot/nyquist.py diff --git a/pybop/plot/__init__.py b/pybop/plot/__init__.py index 29116410e..656dbee6c 100644 --- a/pybop/plot/__init__.py +++ b/pybop/plot/__init__.py @@ -12,7 +12,6 @@ contour, convergence, dataset, - nyquist, parameters, posterior, problem, @@ -22,6 +21,7 @@ ) from .standard_plots import StandardPlot, StandardSubplot, trajectories +from .nyquist import nyquist from .voronoi import voronoi_data, _voronoi_regions from . import matplotlib from . import plotly diff --git a/pybop/plot/matplotlib/nyquist.py b/pybop/plot/matplotlib/nyquist.py index c702e9049..5c6a64144 100644 --- a/pybop/plot/matplotlib/nyquist.py +++ b/pybop/plot/matplotlib/nyquist.py @@ -1,7 +1,9 @@ +import warnings + from matplotlib import pyplot as plt from pybop.parameters.parameter import Inputs -from pybop.plot.matplotlib.standard_plots import StandardPlot +from pybop.plot.nyquist import _nyquist def nyquist(problem, inputs: Inputs = None, show=True, **layout_kwargs): @@ -41,42 +43,31 @@ def nyquist(problem, inputs: Inputs = None, show=True, **layout_kwargs): >>> nyquist_figures = nyquist(problem, show=True, title="Nyquist Plot", xaxis_title="Real(Z)", yaxis_title="Imag(Z)") >>> # The plots will be displayed and nyquist_figures will contain the list of figure objects. """ - if not isinstance(inputs, dict): - inputs = problem.parameters.to_dict(inputs) - - model_output = problem.simulate(inputs) - domain_data = model_output["Impedance"].data.real - target_output = problem.target_data - - figure_list = [] - for var in problem.target: - plot_dict = StandardPlot( - x=domain_data, - y=-model_output[var].data.imag, - trace_names="Model", - ) - - plotting_options = dict(color="#00CC96", linewidth=2, marker=".", markersize=8) - plot_dict.traces[0].update(plotting_options) - target_trace = plot_dict.create_trace( - x=target_output[var].real, - y=-target_output[var].imag, - label="Reference", + if len(layout_kwargs) > 0: + warnings.warn( + "The following layout argument keys are ignored for the current plotting backend (matplotlib): \n" + f"{list(layout_kwargs.keys())}", + UserWarning, + stacklevel=2, ) - - plotting_options = dict( - linestyle="none", - marker="o", - fillstyle="none", - markersize=8, - markeredgecolor="#636EFA", - ) - target_trace.update(plotting_options) - - plot_dict.traces.append(target_trace) - - fig = plot_dict(show=False) + trace_options_model = dict( + label="Model", color="#00CC96", linewidth=2, marker=".", markersize=8 + ) + trace_options_reference = dict( + label="Reference", + linestyle="none", + marker="o", + fillstyle="none", + markersize=8, + markeredgecolor="#636EFA", + ) + figure_list = _nyquist( + problem, trace_options_model, trace_options_reference, inputs=inputs + ) + + for fig in figure_list: + plt.sca(fig.gca()) # Layout plt.title("Nyquist Plot", fontsize=14, x=0.2) plt.xlabel(r"$Z_{re} / \Omega$", fontsize=16) @@ -84,8 +75,6 @@ def nyquist(problem, inputs: Inputs = None, show=True, **layout_kwargs): plt.legend(loc="upper right", bbox_to_anchor=(1, 1.08), ncols=2) if show: - fig.show() - - figure_list.append(fig) + plt.show() return figure_list diff --git a/pybop/plot/nyquist.py b/pybop/plot/nyquist.py new file mode 100644 index 000000000..792991110 --- /dev/null +++ b/pybop/plot/nyquist.py @@ -0,0 +1,74 @@ +from pybop.parameters.parameter import Inputs +from pybop.plot.util import call_plotting_function +from pybop.plot.standard_plots import StandardPlot + +def nyquist(problem, inputs: Inputs = None, show=True, backend=None, **layout_kwargs): + """ + Generates Nyquist plots for the given problem by evaluating the model's output and target values. + + Parameters + ---------- + problem : pybop.Problem + An instance of a problem class that contains the parameters and methods + for evaluation and target retrieval. + inputs : Inputs, optional + Input parameters for the problem. If not provided, the default parameters from the problem + instance will be used. These parameters are verified before use (default is None). + show : bool, optional + If True, the plots will be displayed. + **layout_kwargs : dict, optional + Additional keyword arguments for customising the plot layout. These arguments are passed to + `fig.update_layout()`. + + Returns + ------- + list + A list of plotly `Figure` objects, each representing a Nyquist plot for the model's output and target values. + + Notes + ----- + - The function extracts the real part of the impedance from the model's output and the real and imaginary parts + of the impedance from the target output. + - For each signal in the problem, a Nyquist plot is created with the model's impedance plotted as a scatter plot. + - An additional trace for the reference (target output) is added to the plot. + - The plot layout can be customised using `layout_kwargs`. + + Example + ------- + >>> problem = pybop.EISProblem() + >>> nyquist_figures = nyquist(problem, show=True, title="Nyquist Plot", xaxis_title="Real(Z)", yaxis_title="Imag(Z)") + >>> # The plots will be displayed and nyquist_figures will contain the list of figure objects. + """ + return call_plotting_function( + "nyquist", backend, problem=problem, inputs=inputs, show=show, **layout_kwargs + ) + +def _nyquist(problem, trace_options_model: dict, trace_options_reference, inputs: Inputs = None): + + if not isinstance(inputs, dict): + inputs = problem.parameters.to_dict(inputs) + + model_output = problem.simulate(inputs) + domain_data = model_output["Impedance"].data.real + target_output = problem.target_data + figure_list = [] + for var in problem.target: + plot_dict = StandardPlot( + x=domain_data, + y=-model_output[var].data.imag, + trace_names="Model", + ) + + plot_dict.traces[0].update(trace_options_model) + + target_trace = plot_dict.create_trace( + x=target_output[var].real, + y=-target_output[var].imag, + **trace_options_reference, + ) + plot_dict.traces.append(target_trace) + + fig = plot_dict(show=False) + figure_list.append(fig) + + return figure_list diff --git a/pybop/plot/plotly/nyquist.py b/pybop/plot/plotly/nyquist.py index bbc5fa49a..1578d2462 100644 --- a/pybop/plot/plotly/nyquist.py +++ b/pybop/plot/plotly/nyquist.py @@ -1,5 +1,5 @@ from pybop.parameters.parameter import Inputs -from pybop.plot.plotly.standard_plots import StandardPlot +from pybop.plot.nyquist import _nyquist def nyquist(problem, inputs: Inputs = None, show=True, **layout_kwargs): @@ -39,81 +39,63 @@ def nyquist(problem, inputs: Inputs = None, show=True, **layout_kwargs): >>> nyquist_figures = nyquist(problem, show=True, title="Nyquist Plot", xaxis_title="Real(Z)", yaxis_title="Imag(Z)") >>> # The plots will be displayed and nyquist_figures will contain the list of figure objects. """ - if not isinstance(inputs, dict): - inputs = problem.parameters.to_dict(inputs) + default_layout_options = dict( + title="Nyquist Plot", + font=dict(family="Arial", size=14), + plot_bgcolor="white", + paper_bgcolor="white", + xaxis=dict( + title=dict(text="Zre / Ω", font=dict(size=16), standoff=15), + showline=True, + linewidth=2, + linecolor="black", + mirror=True, + ticks="outside", + tickwidth=2, + tickcolor="black", + ticklen=5, + ), + yaxis=dict( + title=dict(text="-Zim / Ω", font=dict(size=16), standoff=15), + showline=True, + linewidth=2, + linecolor="black", + mirror=True, + ticks="outside", + tickwidth=2, + tickcolor="black", + ticklen=5, + scaleanchor="x", + scaleratio=1, + ), + legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1), + width=600, + height=600, + ) - model_output = problem.simulate(inputs) - domain_data = model_output["Impedance"].data.real - target_output = problem.target_data + # Overwrite with user-kwargs + default_layout_options.update(layout_kwargs) - figure_list = [] - for var in problem.target: - default_layout_options = dict( - title="Nyquist Plot", - font=dict(family="Arial", size=14), - plot_bgcolor="white", - paper_bgcolor="white", - xaxis=dict( - title=dict(text="Zre / Ω", font=dict(size=16), standoff=15), - showline=True, - linewidth=2, - linecolor="black", - mirror=True, - ticks="outside", - tickwidth=2, - tickcolor="black", - ticklen=5, - ), - yaxis=dict( - title=dict(text="-Zim / Ω", font=dict(size=16), standoff=15), - showline=True, - linewidth=2, - linecolor="black", - mirror=True, - ticks="outside", - tickwidth=2, - tickcolor="black", - ticklen=5, - scaleanchor="x", - scaleratio=1, - ), - legend=dict( - orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1 - ), - width=600, - height=600, - ) + trace_options_model = dict( + mode="lines+markers", + line=dict(color="#00CC96", width=2), + marker=dict(size=8, color="#00CC96", symbol="circle"), + ) - plot_dict = StandardPlot( - x=domain_data, - y=-model_output[var].data.imag, - layout_options=default_layout_options, - trace_names="Model", - ) + trace_options_reference = dict( + name="Reference", + mode="markers", + marker=dict(size=8, color="#636EFA", symbol="circle-open"), + showlegend=True, + ) - plot_dict.traces[0].update( - mode="lines+markers", - line=dict(color="#00CC96", width=2), - marker=dict(size=8, color="#00CC96", symbol="circle"), - ) + figure_list = _nyquist( + problem, trace_options_model, trace_options_reference, inputs=inputs + ) - target_trace = plot_dict.create_trace( - x=target_output[var].real, - y=-target_output[var].imag, - name="Reference", - mode="markers", - marker=dict(size=8, color="#636EFA", symbol="circle-open"), - showlegend=True, - ) - plot_dict.traces.append(target_trace) - - fig = plot_dict(show=False) - - # Overwrite with user-kwargs - fig.update_layout(**layout_kwargs) + for fig in figure_list: + fig.update_layout(**default_layout_options) if show: fig.show() - figure_list.append(fig) - return figure_list diff --git a/pybop/plot/plots.py b/pybop/plot/plots.py index 95fb1b701..7e6425aee 100644 --- a/pybop/plot/plots.py +++ b/pybop/plot/plots.py @@ -141,48 +141,6 @@ def dataset( ) -def nyquist(problem, inputs: Inputs = None, show=True, backend=None, **layout_kwargs): - """ - Generates Nyquist plots for the given problem by evaluating the model's output and target values. - - Parameters - ---------- - problem : pybop.Problem - An instance of a problem class that contains the parameters and methods - for evaluation and target retrieval. - inputs : Inputs, optional - Input parameters for the problem. If not provided, the default parameters from the problem - instance will be used. These parameters are verified before use (default is None). - show : bool, optional - If True, the plots will be displayed. - **layout_kwargs : dict, optional - Additional keyword arguments for customising the plot layout. These arguments are passed to - `fig.update_layout()`. - - Returns - ------- - list - A list of plotly `Figure` objects, each representing a Nyquist plot for the model's output and target values. - - Notes - ----- - - The function extracts the real part of the impedance from the model's output and the real and imaginary parts - of the impedance from the target output. - - For each signal in the problem, a Nyquist plot is created with the model's impedance plotted as a scatter plot. - - An additional trace for the reference (target output) is added to the plot. - - The plot layout can be customised using `layout_kwargs`. - - Example - ------- - >>> problem = pybop.EISProblem() - >>> nyquist_figures = nyquist(problem, show=True, title="Nyquist Plot", xaxis_title="Real(Z)", yaxis_title="Imag(Z)") - >>> # The plots will be displayed and nyquist_figures will contain the list of figure objects. - """ - return call_plotting_function( - "nyquist", backend, problem=problem, inputs=inputs, show=show, **layout_kwargs - ) - - def parameters(result: "Result", show=True, backend=None, **layout_kwargs): """ Plot the evolution of parameters during the optimisation process using Plotly. diff --git a/pybop/plot/standard_plots.py b/pybop/plot/standard_plots.py index 86111bbae..0dc0bfddf 100644 --- a/pybop/plot/standard_plots.py +++ b/pybop/plot/standard_plots.py @@ -2,10 +2,10 @@ import textwrap import numpy as np -import warnings from pybop.plot.util import call_plotting_function + class StandardPlot: def __init__( self, @@ -14,10 +14,13 @@ def __init__( trace_options=None, trace_names=None, trace_name_width=20, - backend = None, - **kwargs): - - self.plotter = call_plotting_function('Plotter', backend, trace_options=trace_options, **kwargs) + backend=None, + **kwargs, + ): + + self.plotter = call_plotting_function( + "Plotter", backend, trace_options=trace_options, **kwargs + ) self.trace_name_width = trace_name_width @@ -31,7 +34,7 @@ def __call__(self, show=True): @property def traces(self): return self.plotter.traces - + @traces.setter def traces(self, value): self.plotter.traces = value @@ -42,7 +45,9 @@ def add_traces(self, x, y, trace_names): if isinstance(trace_names, str): trace_names = [trace_names] for i, name in enumerate(trace_names): - trace_names[i] = self.wrap_text(name, width=self.trace_name_width, backend=self.plotter.backend) + trace_names[i] = self.wrap_text( + name, width=self.trace_name_width, backend=self.plotter.backend + ) # Parse the data x, y = self.parse_data(x, y) @@ -50,7 +55,6 @@ def add_traces(self, x, y, trace_names): # Add traces self.plotter.add_traces(x, y, trace_names) - def parse_data(self, x, y): """ Check the type and dimensions of the data and convert if necessary to a list @@ -89,12 +93,12 @@ def parse_data(self, x, y): "Input x should have either one data series or the same number as y." ) return x, y - + def create_trace(self, x, y, **trace_options): return self.plotter.create_trace(x, y, **trace_options) @staticmethod - def wrap_text(text, width, backend='matplotlib'): + def wrap_text(text, width, backend="matplotlib"): """ Wrap text to a specified width with HTML line breaks. @@ -111,7 +115,7 @@ def wrap_text(text, width, backend='matplotlib'): The wrapped text. """ wrapped_text = textwrap.fill(text, width=width, break_long_words=False) - if backend == 'plotly': + if backend == "plotly": return wrapped_text.replace("\n", "
") else: return wrapped_text @@ -132,7 +136,7 @@ def remove_brackets(s): char_in_brackets = s[start + 1 : end] return s[:start] + " / " + char_in_brackets + s[end + 1 :] return s - + class StandardSubplot(StandardPlot): """ @@ -178,7 +182,13 @@ def __init__( trace_name_width=40, **kwargs, ): - self.plotter = call_plotting_function('SubplotPlotter', backend, axis_titles=axis_titles, trace_options=trace_options, **kwargs) + self.plotter = call_plotting_function( + "SubplotPlotter", + backend, + axis_titles=axis_titles, + trace_options=trace_options, + **kwargs, + ) self.trace_name_width = trace_name_width @@ -201,7 +211,7 @@ def __init__( def __call__(self, show=True): return self.plotter(show=show, num_rows=self.num_rows, num_cols=self.num_cols) - + def trajectories(x, y, trace_names=None, show=True, backend=None, **layout_kwargs): """ Quickly plot one or more trajectories using Plotly. @@ -225,4 +235,12 @@ def trajectories(x, y, trace_names=None, show=True, backend=None, **layout_kwarg The Plotly figure object for the scatter plot. """ - return call_plotting_function('trajectories', backend, x=x, y=y, trace_names=trace_names, show=show, **layout_kwargs) \ No newline at end of file + return call_plotting_function( + "trajectories", + backend, + x=x, + y=y, + trace_names=trace_names, + show=show, + **layout_kwargs, + ) From 99b0ed0026d6f2eedf86b99cda1c87164b930532 Mon Sep 17 00:00:00 2001 From: u2370093 Date: Wed, 8 Apr 2026 17:12:39 +0100 Subject: [PATCH 05/20] minor edits --- pybop/plot/nyquist.py | 14 +++++++++----- 1 file changed, 9 insertions(+), 5 deletions(-) diff --git a/pybop/plot/nyquist.py b/pybop/plot/nyquist.py index 792991110..bcd609fd1 100644 --- a/pybop/plot/nyquist.py +++ b/pybop/plot/nyquist.py @@ -1,6 +1,7 @@ from pybop.parameters.parameter import Inputs -from pybop.plot.util import call_plotting_function from pybop.plot.standard_plots import StandardPlot +from pybop.plot.util import call_plotting_function + def nyquist(problem, inputs: Inputs = None, show=True, backend=None, **layout_kwargs): """ @@ -43,7 +44,10 @@ def nyquist(problem, inputs: Inputs = None, show=True, backend=None, **layout_kw "nyquist", backend, problem=problem, inputs=inputs, show=show, **layout_kwargs ) -def _nyquist(problem, trace_options_model: dict, trace_options_reference, inputs: Inputs = None): + +def _nyquist( + problem, trace_options_model: dict, trace_options_reference, inputs: Inputs = None +): if not isinstance(inputs, dict): inputs = problem.parameters.to_dict(inputs) @@ -54,9 +58,9 @@ def _nyquist(problem, trace_options_model: dict, trace_options_reference, inputs figure_list = [] for var in problem.target: plot_dict = StandardPlot( - x=domain_data, - y=-model_output[var].data.imag, - trace_names="Model", + x=domain_data, + y=-model_output[var].data.imag, + trace_names="Model", ) plot_dict.traces[0].update(trace_options_model) From ea5af267d52c765086119df3244a668affb18569 Mon Sep 17 00:00:00 2001 From: u2370093 Date: Thu, 9 Apr 2026 14:42:04 +0100 Subject: [PATCH 06/20] some simplifications --- .../ecm_tau_redefined.py | 1 + pybop/plot/__init__.py | 8 +- pybop/plot/{plotly => }/dataset.py | 11 +- pybop/plot/matplotlib/__init__.py | 4 +- pybop/plot/matplotlib/dataset.py | 55 --------- pybop/plot/matplotlib/parameters.py | 71 ----------- pybop/plot/matplotlib/problem.py | 113 ------------------ pybop/plot/matplotlib/standard_plots.py | 35 ++++-- pybop/plot/matplotlib/util.py | 27 +++++ pybop/plot/{plotly => }/parameters.py | 18 +-- pybop/plot/plotly/__init__.py | 5 +- pybop/plot/plotly/standard_plots.py | 11 ++ pybop/plot/plotly/util.py | 24 ++++ pybop/plot/plots.py | 99 --------------- pybop/plot/{plotly => }/problem.py | 67 +++++------ pybop/plot/standard_plots.py | 3 + pybop/plot/util.py | 21 ++++ 17 files changed, 164 insertions(+), 409 deletions(-) rename pybop/plot/{plotly => }/dataset.py (82%) delete mode 100644 pybop/plot/matplotlib/dataset.py delete mode 100644 pybop/plot/matplotlib/parameters.py delete mode 100644 pybop/plot/matplotlib/problem.py create mode 100644 pybop/plot/matplotlib/util.py rename pybop/plot/{plotly => }/parameters.py (81%) create mode 100644 pybop/plot/plotly/util.py rename pybop/plot/{plotly => }/problem.py (70%) diff --git a/examples/scripts/battery_parameterisation/ecm_tau_redefined.py b/examples/scripts/battery_parameterisation/ecm_tau_redefined.py index 4bd754dd9..53fe089c1 100644 --- a/examples/scripts/battery_parameterisation/ecm_tau_redefined.py +++ b/examples/scripts/battery_parameterisation/ecm_tau_redefined.py @@ -138,6 +138,7 @@ print("Identified parameters:", result.x.tolist() + [result.x[2] / result.x[1]]) # Plot the timeseries output +pybop.plot.problem(problem, inputs=result.best_inputs, title="Optimised Comparison", backend='plotly') pybop.plot.problem(problem, inputs=result.best_inputs, title="Optimised Comparison") # Plot the optimisation result diff --git a/pybop/plot/__init__.py b/pybop/plot/__init__.py index 656dbee6c..ce95ef70d 100644 --- a/pybop/plot/__init__.py +++ b/pybop/plot/__init__.py @@ -2,7 +2,7 @@ DEFAULT_BACKEND = 'matplotlib' backend=DEFAULT_BACKEND -from .util import set_backend, call_plotting_function +from .util import set_backend, call_plotting_function, get_default_options # # Import plots @@ -11,17 +11,17 @@ chains, contour, convergence, - dataset, - parameters, posterior, - problem, summary_table, surface, trace ) from .standard_plots import StandardPlot, StandardSubplot, trajectories +from .dataset import dataset from .nyquist import nyquist +from .parameters import parameters +from .problem import problem from .voronoi import voronoi_data, _voronoi_regions from . import matplotlib from . import plotly diff --git a/pybop/plot/plotly/dataset.py b/pybop/plot/dataset.py similarity index 82% rename from pybop/plot/plotly/dataset.py rename to pybop/plot/dataset.py index 899b2ed1a..639bcc88a 100644 --- a/pybop/plot/plotly/dataset.py +++ b/pybop/plot/dataset.py @@ -1,7 +1,8 @@ -from pybop.plot.plotly.standard_plots import StandardPlot, trajectories +from pybop.plot.standard_plots import StandardPlot, trajectories +from pybop.plot.util import update_and_show -def dataset(dataset, signal=None, trace_names=None, show=True, **layout_kwargs): +def dataset(dataset, signal=None, trace_names=None, show=True, backend=None, **layout_kwargs): """ Quickly plot a PyBOP Dataset using Plotly. @@ -50,9 +51,9 @@ def dataset(dataset, signal=None, trace_names=None, show=True, **layout_kwargs): show=False, xaxis_title=StandardPlot.remove_brackets(dataset.domain), yaxis_title=yaxis_title, + backend = backend, ) - fig.update_layout(**layout_kwargs) - if show: - fig.show() + + fig = update_and_show(fig, backend = backend, show=show, **layout_kwargs) return fig diff --git a/pybop/plot/matplotlib/__init__.py b/pybop/plot/matplotlib/__init__.py index 21da2259a..7311edd07 100644 --- a/pybop/plot/matplotlib/__init__.py +++ b/pybop/plot/matplotlib/__init__.py @@ -1,9 +1,7 @@ from .standard_plots import Plotter, SubplotPlotter, trajectories -from .dataset import dataset from .convergence import convergence -from .parameters import parameters -from .problem import problem from .contour import contour from .voronoi import surface from .nyquist import nyquist from .samples import chains, posterior, summary_table, trace +from .util import update_and_show, DEFAULT_PLOT_OPTIONS diff --git a/pybop/plot/matplotlib/dataset.py b/pybop/plot/matplotlib/dataset.py deleted file mode 100644 index 7ade00091..000000000 --- a/pybop/plot/matplotlib/dataset.py +++ /dev/null @@ -1,55 +0,0 @@ -import matplotlib.pyplot as plt - -from pybop.plot.matplotlib.standard_plots import StandardPlot, trajectories - - -def dataset(dataset, signal=None, trace_names=None, show=True): - """ - Quickly plot a PyBOP Dataset using Plotly. - - Parameters - ---------- - dataset : object - A PyBOP dataset. - signal : list or str, optional - The name of the time series to plot (default: "Voltage [V]"). - trace_names : list or str, optional - Name(s) for the trace(s) (default: "Data"). - show : bool, optional - If True, the figure is shown upon creation (default: True). - - Returns - ------- - plotly.graph_objs.Figure - The Plotly figure object for the scatter plot. - """ - - # Get data dictionary - if signal is None: - signal = ["Voltage [V]"] - dataset.check(signal=signal) - - # Compile ydata and labels or legend - y = [dataset[s] for s in signal] - if len(signal) == 1: - yaxis_title = signal[0] - if trace_names is None: - trace_names = ["Data"] - else: - yaxis_title = "Output" - if trace_names is None: - trace_names = StandardPlot.remove_brackets(signal) - - # Create the figure - fig = trajectories( - x=dataset[dataset.domain], - y=y, - trace_names=trace_names, - show=False, - xaxis_title=StandardPlot.remove_brackets(dataset.domain), - yaxis_title=yaxis_title, - ) - if show: - plt.show() - - return fig diff --git a/pybop/plot/matplotlib/parameters.py b/pybop/plot/matplotlib/parameters.py deleted file mode 100644 index eef582f0e..000000000 --- a/pybop/plot/matplotlib/parameters.py +++ /dev/null @@ -1,71 +0,0 @@ -import warnings -from typing import TYPE_CHECKING - -import matplotlib.pyplot as plt - -from pybop.costs.log_likelihoods import GaussianLogLikelihood -from pybop.plot.standard_plots import StandardSubplot - -if TYPE_CHECKING: - from pybop._result import Result - - -def parameters(result: "Result", show=True, **layout_kwargs): - """ - Plot the evolution of parameters during the optimisation process using Plotly. - - Parameters - ---------- - result : pybop.Result - Optimisation result containing the history of parameter values and associated cost. - show : bool, optional - If True, the figure is shown upon creation (default: True). - - Returns - ------- - plotly.graph_objs.Figure - A Plotly figure object showing the parameter evolution over iterations. - """ - - if len(layout_kwargs) > 0: - warnings.warn( - "The following layout argument keys are ignored for the current plotting backend (matplotlib): \n" - f"{list(layout_kwargs.keys())}", - UserWarning, - stacklevel=2, - ) - - # Extract parameters and log from the optimisation object - parameters = result.problem.parameters - x = list(range(len(result.x_model))) - y = [list(item) for item in zip(*result.x_model, strict=False)] - - # Create lists of axis titles and trace names - axis_titles = [] - trace_names = parameters.names - for name in trace_names: - axis_titles.append(("Evaluation", name)) - - if isinstance(result.problem, GaussianLogLikelihood): - axis_titles.append(("Evaluation", "Sigma")) - trace_names.append("Sigma") - - # Create a plot dictionary - plot_dict = StandardSubplot( - x=x, - y=y, - axis_titles=axis_titles, - trace_names=trace_names, - trace_name_width=50, - figsize=(18, 8), - backend="matplotlib", - ) - - plt.suptitle("Parameter Convergence") - - # Generate the figure and update the layout - fig = plot_dict(show=False) - if show: - plt.show() - - return fig diff --git a/pybop/plot/matplotlib/problem.py b/pybop/plot/matplotlib/problem.py deleted file mode 100644 index da080207c..000000000 --- a/pybop/plot/matplotlib/problem.py +++ /dev/null @@ -1,113 +0,0 @@ -import matplotlib.pyplot as plt -import numpy as np - -from pybop.costs.design_cost import DesignCost -from pybop.costs.error_measures import ErrorMeasure -from pybop.parameters.parameter import Inputs -from pybop.plot.matplotlib.standard_plots import StandardPlot -from pybop.problems.meta_problem import MetaProblem -from pybop.problems.problem import Problem -from pybop.simulators.solution import Solution - - -def problem( - problem: Problem, - inputs: Inputs = None, - show: bool = True, - title="Scatter Plot", -): - """ - Produce a quick plot of the target dataset against optimised model output. - - Generates an interactive plot comparing the simulated model output with - an optional target dataset and visualises uncertainty. - - Parameters - ---------- - problem : pybop.Problem - Problem object with dataset and targets attributes. - inputs : Inputs - Optimised (or example) parameter values. - show : bool, optional - If True, the figure is shown upon creation (default: True). - - Returns - ------- - plotly.graph_objs.Figure - The Plotly figure object for the scatter plot. - """ - if inputs is None: - inputs = problem.parameters.to_dict() - elif not isinstance(inputs, dict): - raise TypeError(f"Expecting a dictionary, received {type(inputs)}") - - domain = problem.domain - if problem.domain_data is None: - # Simulate the model for the both the initial and the given inputs - target = problem.target - problem.set_target(target + [domain]) - initial_inputs = problem.simulator.parameters.to_dict("initial") - target_output = problem.simulate(initial_inputs) - target_domain = target_output[domain].data - model_output = problem.simulate(inputs) - model_domain = model_output[domain].data - problem.set_target(target) - else: - # Extract the time data and simulate the model for the given inputs - target_output = Solution() - for target in problem.target: - target_output.set_solution_variable( - target, data=problem.target_data[target] - ) - target_domain = problem.domain_data - model_output = problem.simulate(inputs) - model_domain = target_domain[: len(model_output[target].data)] - - # Create a plot for each output - figure_list = [] - for var in problem.target: - # Create a plot dictionary - plot_dict = StandardPlot() - - plot_dict.create_trace( - x=target_domain, - y=target_output[var].data, - label="Reference", - marker=".", - linestyle="None", - ) - - plot_dict.create_trace( - x=model_domain, - y=model_output[var].data, - label="Optimised" if isinstance(problem.cost, DesignCost) else "Model", - marker="." if isinstance(problem, MetaProblem) else None, - linestyle="None" if isinstance(problem, MetaProblem) else "-", - ) - - if isinstance(problem.cost, ErrorMeasure) and len( - model_output[var].data - ) == len(target_output[var].data): - # Compute the standard deviation as proxy for uncertainty - plot_dict.sigma = np.std(model_output[var].data - target_output[var].data) - - # Convert x and upper and lower limits into lists to create a filled trace - x = target_domain.tolist() - y_upper = (model_output[var].data + plot_dict.sigma).tolist() - y_lower = (model_output[var].data - plot_dict.sigma).tolist() - - plt.fill_between(x, y_upper, y_lower, color=[(1.0, 0.898, 0.800, 0.8)]) - - # Generate the figure and update the layout - fig = plot_dict(show=False) - plt.xlabel("Time / s") - plt.ylabel(StandardPlot.remove_brackets(var)) - plt.title(title) - plt.legend() - plt.tight_layout() - if show: - plt.show() - - figure_list.append(fig) - - return figure_list diff --git a/pybop/plot/matplotlib/standard_plots.py b/pybop/plot/matplotlib/standard_plots.py index e96cf19af..906cda36f 100644 --- a/pybop/plot/matplotlib/standard_plots.py +++ b/pybop/plot/matplotlib/standard_plots.py @@ -34,9 +34,11 @@ def __init__( self, trace_options=None, figsize=(8, 6), + title=None, **kwargs, ): self.backend = "matplotlib" + self.title = title # Warning if layout arguments ignored if len(kwargs) > 0: warnings.warn( @@ -81,6 +83,9 @@ def __call__(self, show=True): **dict(loc="best", fontsize=12), ) + if self.title is not None: + plt.suptitle(self.title) + if show: plt.show() else: @@ -116,7 +121,7 @@ def add_traces(self, x, y, trace_names=None, **trace_options): self.traces.append(self.create_trace(xi, y[i], label, **trace_options)) - def create_trace(self, x, y, label, ax=None, **trace_options): + def create_trace(self, x, y, label=None, ax=None, **trace_options): """ Add line to plot. @@ -129,17 +134,27 @@ def create_trace(self, x, y, label, ax=None, **trace_options): trace = dict(x=x[:size], y=y[:size], label=label, ax=ax) trace.update(trace_options) return trace + + def create_fill_trace(self, x, y_upper, y_lower, **options): + trace = dict(x=x, y=y_upper, plot_type='fill', y_lower=y_lower) + trace.update(options) + return trace - def _plot_trace(self, x, y, label, ax=None, **trace_options): + def _plot_trace(self, x, y, label=None, ax=None, plot_type='plot', **trace_options): if ax is None: ax = plt.gca() - line = ax.plot( - x, - y, - label=label, - **trace_options, - ) + if plot_type=='plot': + line = ax.plot( + x, + y, + label=label, + **trace_options, + ) + elif plot_type=='fill': + line = ax.fill_between(x, y, **trace_options) + else: + raise ValueError('Plot type not recognised') if len(line) > 1: return line @@ -223,6 +238,10 @@ def __call__(self, show=True, num_rows=1, num_cols=1): bbox_to_anchor=(0.99, 0.95), ) plt.tight_layout(rect=[0, 0, 1, 0.95]) + + if self.title is not None: + plt.suptitle(self.title) + if show: plt.show() diff --git a/pybop/plot/matplotlib/util.py b/pybop/plot/matplotlib/util.py new file mode 100644 index 000000000..6f31ce29a --- /dev/null +++ b/pybop/plot/matplotlib/util.py @@ -0,0 +1,27 @@ +import warnings +import matplotlib.pyplot as plt + +def update_and_show(fig, show=True, **layout_kwargs): + if len(layout_kwargs) > 0: + warnings.warn( + "The following layout argument keys are ignored for the current plotting backend (matplotlib): \n" + f"{list(layout_kwargs.keys())}", + UserWarning, + stacklevel=2, + ) + + if show: + plt.show() + + return fig + +DEFAULT_PLOT_OPTIONS = { + 'parameters' : dict(figsize=(18, 8), title="Parameter Convergence"), + 'problem' : { + 'default_trace_options' : dict (label="Model", marker=None, linestyle='-'), + 'design_cost_options' : dict(label = "Optimised"), + 'meta_problem_options' : dict(marker=".", linestyle='none'), + 'reference_options' : dict(label="Reference", marker=".", linestyle='none'), + 'fill_options' : dict(color=[(1.0, 0.898, 0.800, 0.8)]) + } +} \ No newline at end of file diff --git a/pybop/plot/plotly/parameters.py b/pybop/plot/parameters.py similarity index 81% rename from pybop/plot/plotly/parameters.py rename to pybop/plot/parameters.py index e93907248..c1082c6ee 100644 --- a/pybop/plot/plotly/parameters.py +++ b/pybop/plot/parameters.py @@ -2,12 +2,13 @@ from pybop.costs.log_likelihoods import GaussianLogLikelihood from pybop.plot.standard_plots import StandardSubplot +from pybop.plot.util import get_default_options, update_and_show if TYPE_CHECKING: from pybop._result import Result -def parameters(result: "Result", show=True, **layout_kwargs): +def parameters(result: "Result", show=True, backend=None, **layout_kwargs): """ Plot the evolution of parameters during the optimisation process using Plotly. @@ -44,28 +45,21 @@ def parameters(result: "Result", show=True, **layout_kwargs): trace_names.append("Sigma") # Set subplot layout options - layout_options = dict( - title="Parameter Convergence", - width=1024, - height=576, - legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1), - ) + plot_options = get_default_options('paramters', backend) # Create a plot dictionary plot_dict = StandardSubplot( x=x, y=y, axis_titles=axis_titles, - layout_options=layout_options, trace_names=trace_names, trace_name_width=50, - backend="plotly", + backend=backend, + **plot_options, ) # Generate the figure and update the layout fig = plot_dict(show=False) - fig.update_layout(**layout_kwargs) - if show: - fig.show() + fig = update_and_show(fig, backend, **layout_kwargs) return fig diff --git a/pybop/plot/plotly/__init__.py b/pybop/plot/plotly/__init__.py index b3b907abd..de3350225 100644 --- a/pybop/plot/plotly/__init__.py +++ b/pybop/plot/plotly/__init__.py @@ -1,10 +1,9 @@ from .plotly_manager import PlotlyManager from .standard_plots import Plotter, SubplotPlotter, trajectories from .contour import contour -from .dataset import dataset from .convergence import convergence -from .parameters import parameters -from .problem import problem from .nyquist import nyquist from .voronoi import surface from .samples import chains, posterior, summary_table, trace + +from .util import update_and_show, DEFAULT_PLOT_OPTIONS \ No newline at end of file diff --git a/pybop/plot/plotly/standard_plots.py b/pybop/plot/plotly/standard_plots.py index 62cd708a2..adce31af2 100644 --- a/pybop/plot/plotly/standard_plots.py +++ b/pybop/plot/plotly/standard_plots.py @@ -158,6 +158,17 @@ def create_trace(self, x, y, **trace_options): y=y, **trace_options, ) + + def create_fill_trace(self, x, y_upper, y_lower, **options): + return self.create_trace( + x=x + x[::-1], + y=y_upper + y_lower[::-1], + fill="toself", + line=dict(color="rgba(255,255,255,0)"), + hoverinfo="skip", + showlegend=False, + **options + ) class SubplotPlotter(Plotter): diff --git a/pybop/plot/plotly/util.py b/pybop/plot/plotly/util.py new file mode 100644 index 000000000..563dde595 --- /dev/null +++ b/pybop/plot/plotly/util.py @@ -0,0 +1,24 @@ +import warnings + +def update_and_show(fig, show=True, **layout_kwargs): + fig.update_layout(**layout_kwargs) + if show: + fig.show() + return fig + + +DEFAULT_PLOT_OPTIONS = { + 'parameters' : dict(layout_options = dict( + title="Parameter Convergence", + width=1024, + height=576, + legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1), + )), + 'problem' : { + 'default_trace_options' : dict (name="Model", mode="lines", showlegend=True), + 'design_cost_options' : dict(name = "Optimised"), + 'meta_problem_options' : dict(mode = "lines"), + 'reference_options' : dict(name="Reference", mode="markers", showlegend=True), + 'fill_options' : dict(fillcolor ="rgba(255,229,204,0.8)" ) + } +} \ No newline at end of file diff --git a/pybop/plot/plots.py b/pybop/plot/plots.py index 7e6425aee..b51745999 100644 --- a/pybop/plot/plots.py +++ b/pybop/plot/plots.py @@ -103,69 +103,6 @@ def convergence(result: "Result", show=True, backend=None, **layout_kwargs): "convergence", backend, result=result, show=show, **layout_kwargs ) - -def dataset( - dataset, signal=None, trace_names=None, show=True, backend=None, **layout_kwargs -): - """ - Quickly plot a PyBOP Dataset using Plotly. - - Parameters - ---------- - dataset : object - A PyBOP dataset. - signal : list or str, optional - The name of the time series to plot (default: "Voltage [V]"). - trace_names : list or str, optional - Name(s) for the trace(s) (default: "Data"). - show : bool, optional - If True, the figure is shown upon creation (default: True). - **layout_kwargs : optional - Valid Plotly layout keys and their values, - e.g. `xaxis_title="Time / s"` or - `xaxis={"title": "Time [s]", font={"size":14}}` - - Returns - ------- - plotly.graph_objs.Figure - The Plotly figure object for the scatter plot. - """ - call_plotting_function( - "dataset", - backend, - dataset=dataset, - signal=signal, - trace_names=trace_names, - show=show, - **layout_kwargs, - ) - - -def parameters(result: "Result", show=True, backend=None, **layout_kwargs): - """ - Plot the evolution of parameters during the optimisation process using Plotly. - - Parameters - ---------- - result : pybop.Result - Optimisation result containing the history of parameter values and associated cost. - show : bool, optional - If True, the figure is shown upon creation (default: True). - **layout_kwargs : optional - Valid Plotly layout keys and their values, - e.g. `xaxis_title="Time [s]"` or - `xaxis={"title": "Time [s]", font={"size":14}}` - - Returns - ------- - plotly.graph_objs.Figure - A Plotly figure object showing the parameter evolution over iterations. - """ - return call_plotting_function( - "parameters", backend, result=result, show=show, **layout_kwargs - ) - - def posterior(result: "SamplingResult", show=True, backend=None, **kwargs): """ Plot the summed posterior distribution across chains. @@ -173,42 +110,6 @@ def posterior(result: "SamplingResult", show=True, backend=None, **kwargs): return call_plotting_function("posterior", backend, result=result, **kwargs) -def problem( - problem: Problem, - inputs: Inputs = None, - show: bool = True, - backend=None, - **layout_kwargs, -): - """ - Produce a quick plot of the target dataset against optimised model output. - - Generates an interactive plot comparing the simulated model output with - an optional target dataset and visualises uncertainty. - - Parameters - ---------- - problem : pybop.Problem - Problem object with dataset and targets attributes. - inputs : Inputs - Optimised (or example) parameter values. - show : bool, optional - If True, the figure is shown upon creation (default: True). - **layout_kwargs : optional - Valid Plotly layout keys and their values, - e.g. `xaxis_title="Time / s"` or - `xaxis={"title": "Time [s]", font={"size":14}}` - - Returns - ------- - plotly.graph_objs.Figure - The Plotly figure object for the scatter plot. - """ - return call_plotting_function( - "problem", backend, problem=problem, inputs=inputs, show=show, **layout_kwargs - ) - - def summary_table(result: "SamplingResult", backend=None): """ Display summary statistics in a table. diff --git a/pybop/plot/plotly/problem.py b/pybop/plot/problem.py similarity index 70% rename from pybop/plot/plotly/problem.py rename to pybop/plot/problem.py index 9aac2571f..b2fabaaa0 100644 --- a/pybop/plot/plotly/problem.py +++ b/pybop/plot/problem.py @@ -3,7 +3,8 @@ from pybop.costs.design_cost import DesignCost from pybop.costs.error_measures import ErrorMeasure from pybop.parameters.parameter import Inputs -from pybop.plot.plotly.standard_plots import StandardPlot +from pybop.plot.matplotlib.standard_plots import StandardPlot +from pybop.plot.util import get_default_options, update_and_show from pybop.problems.meta_problem import MetaProblem from pybop.problems.problem import Problem from pybop.simulators.solution import Solution @@ -13,7 +14,8 @@ def problem( problem: Problem, inputs: Inputs = None, show: bool = True, - **layout_kwargs, + title="Scatter Plot", + backend = None, ): """ Produce a quick plot of the target dataset against optimised model output. @@ -29,10 +31,6 @@ def problem( Optimised (or example) parameter values. show : bool, optional If True, the figure is shown upon creation (default: True). - **layout_kwargs : optional - Valid Plotly layout keys and their values, - e.g. `xaxis_title="Time / s"` or - `xaxis={"title": "Time [s]", font={"size":14}}` Returns ------- @@ -66,33 +64,39 @@ def problem( model_output = problem.simulate(inputs) model_domain = target_domain[: len(model_output[target].data)] + # Retrieve default layout options + plot_options = get_default_options('problem', backend) + trace_options = plot_options.get('default_trace_options') or {} + design_cost_options = plot_options.get('design_cost_options') or {} + meta_problem_options = plot_options.get('meta_problem_options') or {} + reference_options = plot_options.get('reference_options') or {} + fill_options = plot_options.get('fill_options') or {} + # Create a plot for each output figure_list = [] for var in problem.target: + options = trace_options.copy() + if isinstance(problem, MetaProblem): + options.update(meta_problem_options) + if isinstance(problem.cost, DesignCost): + options.update(design_cost_options) + + print(isinstance(problem, MetaProblem), isinstance(problem.cost, DesignCost), options) + # Create a plot dictionary - plot_dict = StandardPlot( - layout_options=dict( - title="Scatter Plot", - xaxis_title=StandardPlot.remove_brackets(domain), - yaxis_title=StandardPlot.remove_brackets(var), - ) - ) + plot_dict = StandardPlot(backend=backend) model_trace = plot_dict.create_trace( x=model_domain, y=model_output[var].data, - name="Optimised" if isinstance(problem.cost, DesignCost) else "Model", - mode="markers" if isinstance(problem, MetaProblem) else "lines", - showlegend=True, + **options ) plot_dict.traces.append(model_trace) - target_trace = plot_dict.create_trace( + target_trace =plot_dict.create_trace( x=target_domain, y=target_output[var].data, - name="Reference", - mode="markers", - showlegend=True, + **reference_options ) plot_dict.traces.append(target_trace) @@ -107,25 +111,16 @@ def problem( y_upper = (model_output[var].data + plot_dict.sigma).tolist() y_lower = (model_output[var].data - plot_dict.sigma).tolist() - fill_trace = plot_dict.create_trace( - x=x + x[::-1], - y=y_upper + y_lower[::-1], - fill="toself", - fillcolor="rgba(255,229,204,0.8)", - line=dict(color="rgba(255,255,255,0)"), - hoverinfo="skip", - showlegend=False, - ) - plot_dict.traces.append(fill_trace) - - # Reverse the order of the traces to put the model on top + fill_trace = plot_dict.create_fill_trace(x, y_upper, y_lower, **fill_options) plot_dict.traces = plot_dict.traces[::-1] - # Generate the figure and update the layout fig = plot_dict(show=False) - fig.update_layout(**layout_kwargs) - if show: - fig.show() + # plt.xlabel("Time / s") + # plt.ylabel(StandardPlot.remove_brackets(var)) + # plt.title(title) + # plt.legend() + # plt.tight_layout() + fig = update_and_show(fig, backend=backend) figure_list.append(fig) diff --git a/pybop/plot/standard_plots.py b/pybop/plot/standard_plots.py index 0dc0bfddf..db566a533 100644 --- a/pybop/plot/standard_plots.py +++ b/pybop/plot/standard_plots.py @@ -96,6 +96,9 @@ def parse_data(self, x, y): def create_trace(self, x, y, **trace_options): return self.plotter.create_trace(x, y, **trace_options) + + def create_fill_trace(self, x, y_upper, y_lower, **options): + return self.plotter.create_fill_trace(x, y_upper, y_lower, **options) @staticmethod def wrap_text(text, width, backend="matplotlib"): diff --git a/pybop/plot/util.py b/pybop/plot/util.py index 39a1ecfd2..27cf9303c 100644 --- a/pybop/plot/util.py +++ b/pybop/plot/util.py @@ -34,3 +34,24 @@ def call_plotting_function(function_name, backend, **kwargs): except ModuleNotFoundError as error: # Raise an ModuleNotFoundError if the module or attribute is not available raise ModuleNotFoundError(err_msg) from error + + +def update_and_show(fig, backend=None, **kwargs): + return call_plotting_function("update_and_show", backend, fig=fig, **kwargs) + + +def get_default_options(plot_type, backend): + if backend is None: + backend = pybop.plot.backend + + if backend == 'plotly': + opts = pybop.plot.plotly.DEFAULT_PLOT_OPTIONS + elif backend == 'matplotlib': + opts = pybop.plot.matplotlib.DEFAULT_PLOT_OPTIONS + else: + opts = {} + + if plot_type in opts.keys(): + return opts[plot_type] + else: + return {} \ No newline at end of file From 112c231722449b19b5a0020af33a35bc5dbd6eb2 Mon Sep 17 00:00:00 2001 From: u2370093 Date: Mon, 13 Apr 2026 11:44:32 +0100 Subject: [PATCH 07/20] simplify sample plotting --- .../ecm_monte_carlo_sampling.ipynb | 6 +- .../ecm_tau_redefined.py | 1 - pybop/plot/__init__.py | 7 +- pybop/plot/dataset.py | 8 +- pybop/plot/matplotlib/__init__.py | 3 +- pybop/plot/matplotlib/samples.py | 146 ------------------ pybop/plot/matplotlib/standard_plots.py | 114 +++++++++++--- pybop/plot/matplotlib/util.py | 47 ++++-- pybop/plot/parameters.py | 2 +- pybop/plot/plotly/__init__.py | 5 +- pybop/plot/plotly/samples.py | 131 ---------------- pybop/plot/plotly/standard_plots.py | 89 +++++++---- pybop/plot/plotly/util.py | 68 +++++--- pybop/plot/plots.py | 30 ---- pybop/plot/problem.py | 48 +++--- pybop/plot/samples.py | 117 ++++++++++++++ pybop/plot/standard_plots.py | 23 ++- pybop/plot/util.py | 8 +- 18 files changed, 416 insertions(+), 437 deletions(-) delete mode 100644 pybop/plot/matplotlib/samples.py delete mode 100644 pybop/plot/plotly/samples.py create mode 100644 pybop/plot/samples.py diff --git a/examples/notebooks/battery_parameterisation/ecm_monte_carlo_sampling.ipynb b/examples/notebooks/battery_parameterisation/ecm_monte_carlo_sampling.ipynb index addca5b86..23433be03 100644 --- a/examples/notebooks/battery_parameterisation/ecm_monte_carlo_sampling.ipynb +++ b/examples/notebooks/battery_parameterisation/ecm_monte_carlo_sampling.ipynb @@ -46,7 +46,7 @@ "\n", "import pybop\n", "\n", - "pybop.plot.set_backend(\"plotly\")\n", + "pybop.plot.set_backend(\"matplotlib\")\n", "pybop.plot.plotly.PlotlyManager().pio.renderers.default = \"notebook_connected\"\n", "\n", "np.random.seed(8) # users can remove this line" @@ -1104,7 +1104,7 @@ ], "metadata": { "kernelspec": { - "display_name": ".venv", + "display_name": "env-py-3-13", "language": "python", "name": "python3" }, @@ -1118,7 +1118,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.3" + "version": "3.13.11" } }, "nbformat": 4, diff --git a/examples/scripts/battery_parameterisation/ecm_tau_redefined.py b/examples/scripts/battery_parameterisation/ecm_tau_redefined.py index 53fe089c1..4bd754dd9 100644 --- a/examples/scripts/battery_parameterisation/ecm_tau_redefined.py +++ b/examples/scripts/battery_parameterisation/ecm_tau_redefined.py @@ -138,7 +138,6 @@ print("Identified parameters:", result.x.tolist() + [result.x[2] / result.x[1]]) # Plot the timeseries output -pybop.plot.problem(problem, inputs=result.best_inputs, title="Optimised Comparison", backend='plotly') pybop.plot.problem(problem, inputs=result.best_inputs, title="Optimised Comparison") # Plot the optimisation result diff --git a/pybop/plot/__init__.py b/pybop/plot/__init__.py index ce95ef70d..5c6eced49 100644 --- a/pybop/plot/__init__.py +++ b/pybop/plot/__init__.py @@ -8,13 +8,9 @@ # Import plots # from .plots import ( - chains, contour, convergence, - posterior, - summary_table, - surface, - trace + surface ) from .standard_plots import StandardPlot, StandardSubplot, trajectories @@ -22,6 +18,7 @@ from .nyquist import nyquist from .parameters import parameters from .problem import problem +from .samples import chains, posterior, summary_table, trace from .voronoi import voronoi_data, _voronoi_regions from . import matplotlib from . import plotly diff --git a/pybop/plot/dataset.py b/pybop/plot/dataset.py index 639bcc88a..b92d2b78f 100644 --- a/pybop/plot/dataset.py +++ b/pybop/plot/dataset.py @@ -2,7 +2,9 @@ from pybop.plot.util import update_and_show -def dataset(dataset, signal=None, trace_names=None, show=True, backend=None, **layout_kwargs): +def dataset( + dataset, signal=None, trace_names=None, show=True, backend=None, **layout_kwargs +): """ Quickly plot a PyBOP Dataset using Plotly. @@ -51,9 +53,9 @@ def dataset(dataset, signal=None, trace_names=None, show=True, backend=None, **l show=False, xaxis_title=StandardPlot.remove_brackets(dataset.domain), yaxis_title=yaxis_title, - backend = backend, + backend=backend, ) - fig = update_and_show(fig, backend = backend, show=show, **layout_kwargs) + fig = update_and_show(fig, backend=backend, show=show, **layout_kwargs) return fig diff --git a/pybop/plot/matplotlib/__init__.py b/pybop/plot/matplotlib/__init__.py index 7311edd07..ae087d5fc 100644 --- a/pybop/plot/matplotlib/__init__.py +++ b/pybop/plot/matplotlib/__init__.py @@ -1,7 +1,6 @@ -from .standard_plots import Plotter, SubplotPlotter, trajectories +from .standard_plots import Plotter, SubplotPlotter, show_table, trajectories from .convergence import convergence from .contour import contour from .voronoi import surface from .nyquist import nyquist -from .samples import chains, posterior, summary_table, trace from .util import update_and_show, DEFAULT_PLOT_OPTIONS diff --git a/pybop/plot/matplotlib/samples.py b/pybop/plot/matplotlib/samples.py deleted file mode 100644 index 0d2ca526a..000000000 --- a/pybop/plot/matplotlib/samples.py +++ /dev/null @@ -1,146 +0,0 @@ -import warnings -from typing import TYPE_CHECKING - -from matplotlib import pyplot as plt - -if TYPE_CHECKING: - from pybop.samplers.base_pints_sampler import SamplingResult - - -def trace(result: "SamplingResult", show=True, **kwargs): - """ - Plot trace plots for the posterior samples. - """ - # Warning if layout arguments ignored - if len(kwargs) > 0: - warnings.warn( - "The following layout argument keys are ignored for the current plotting backend (matplotlib): \n" - f"{list(kwargs.keys())}", - UserWarning, - stacklevel=2, - ) - figlist = [] - for i in range(result.n_parameters): - fig = plt.figure() - - for j, chain in enumerate(result.chains): - plt.plot(chain[:, i], label=f"Chain {j}") - - plt.title(f"Parameter {i} Trace Plot") - plt.xlabel("Sample Index") - plt.ylabel("Value") - plt.legend(fontsize=12) - figlist.append(fig) - - if show: - plt.show() - else: - return figlist - - -def chains(result: "SamplingResult", show=True, **kwargs): - """ - Plot posterior distributions for each chain. - """ - # Warning if layout arguments ignored - if len(kwargs) > 0: - warnings.warn( - "The following layout argument keys are ignored for the current plotting backend (matplotlib): \n" - f"{list(kwargs.keys())}", - UserWarning, - stacklevel=2, - ) - fig = plt.figure(figsize=(15, 8), dpi=100) - - for i, chain in enumerate(result.chains): - for j in range(chain.shape[1]): - plt.hist( - x=chain[:, j], label=f"Chain {i} - Parameter {j}", alpha=0.5, rwidth=2.0 - ) - - for j in range(chain.shape[1]): - plt.plot( - [result.mean[j], result.mean[j]], - [0, result.max[j]], - "--", - lw=3, - label=f"Mean - Parameter {j}", - ) - - plt.legend(loc="upper left", bbox_to_anchor=(1.01, 1.0)) - plt.grid(axis="y", zorder=-1) - plt.title("Posterior Distribution") - plt.xlabel("Value") - plt.ylabel("Density") - plt.tight_layout() - if show: - plt.show() - else: - return fig - - -def posterior(result: "SamplingResult", show=True, **kwargs): - """ - Plot the summed posterior distribution across chains. - """ - # Warning if layout arguments ignored - if len(kwargs) > 0: - warnings.warn( - "The following layout argument keys are ignored for the current plotting backend (matplotlib): \n" - f"{list(kwargs.keys())}", - UserWarning, - stacklevel=2, - ) - - fig = plt.figure(figsize=(15, 8), dpi=100) - - for j in range(result.all_samples.shape[1]): - plt.hist( - x=result.all_samples[:, j], - label=f"Parameter {j}", - alpha=0.75, - ) - plt.axvline(result.mean[j], ls="--", c="k", lw=3) - - plt.legend(loc="upper left", bbox_to_anchor=(1.01, 1.0)) - plt.grid(axis="y", zorder=-1) - plt.title("Posterior Distribution") - plt.xlabel("Value") - plt.ylabel("Density") - plt.tight_layout() - if show: - plt.show() - else: - return fig - - -def summary_table(result: "SamplingResult"): - """ - Display summary statistics in a table. - """ - - summary_stats = result.get_summary_statistics() - - header = ["Statistic", "Value"] - values = [ - ["Mean", ", ".join(summary_stats["mean"].astype(str))], - ["Median", ", ".join(summary_stats["median"].astype(str))], - ["Standard Deviation", ", ".join(summary_stats["std"].astype(str))], - ["95% CI Lower", ", ".join(summary_stats["ci_lower"].astype(str))], - ["95% CI Upper", ", ".join(summary_stats["ci_upper"].astype(str))], - ] - fig, ax = plt.subplots(figsize=(6, 2), dpi=100) - - # hide axes - ax.axis("off") - ax.axis("tight") - ax.table( - cellText=values, - colLabels=header, - loc="center", - cellLoc="center", - colColours=["lightsteelblue", "lightsteelblue"], - ) - ax.set_title("Summary Statistics") - fig.tight_layout() - plt.show() diff --git a/pybop/plot/matplotlib/standard_plots.py b/pybop/plot/matplotlib/standard_plots.py index 906cda36f..64c31bd66 100644 --- a/pybop/plot/matplotlib/standard_plots.py +++ b/pybop/plot/matplotlib/standard_plots.py @@ -35,6 +35,10 @@ def __init__( trace_options=None, figsize=(8, 6), title=None, + xaxis_title=None, + yaxis_title=None, + grid=None, + axis_bg_color=None, **kwargs, ): self.backend = "matplotlib" @@ -54,6 +58,19 @@ def __init__( self.trace_options.update(trace_options) self.fig = plt.figure(figsize=figsize, dpi=100) + if title is not None: + plt.suptitle(self.title) + if xaxis_title is not None: + plt.xlabel(xaxis_title) + if yaxis_title is not None: + plt.ylabel(yaxis_title) + if grid is not None: + plt.grid(**grid) + if axis_bg_color is not None: + ax = plt.gca() + ax.set_facecolor(axis_bg_color) + ax.set_axisbelow(True) + self.traces = [] def __call__(self, show=True): @@ -83,9 +100,6 @@ def __call__(self, show=True): **dict(loc="best", fontsize=12), ) - if self.title is not None: - plt.suptitle(self.title) - if show: plt.show() else: @@ -121,7 +135,7 @@ def add_traces(self, x, y, trace_names=None, **trace_options): self.traces.append(self.create_trace(xi, y[i], label, **trace_options)) - def create_trace(self, x, y, label=None, ax=None, **trace_options): + def create_trace(self, x=None, y=None, label=None, ax=None, **trace_options): """ Add line to plot. @@ -130,36 +144,62 @@ def create_trace(self, x, y, label=None, ax=None, **trace_options): plotly.graph_objs.Scatter A trace for a Plotly figure. """ - size = min(len(x), len(y)) - trace = dict(x=x[:size], y=y[:size], label=label, ax=ax) + if x is not None and y is not None: + size = min(len(x), len(y)) + trace = dict(x=x[:size], y=y[:size], label=label, ax=ax) + elif y is not None: + trace = dict(y=y, label=label, ax=ax) + trace.update(trace_options) return trace - + def create_fill_trace(self, x, y_upper, y_lower, **options): - trace = dict(x=x, y=y_upper, plot_type='fill', y_lower=y_lower) + trace = dict(x=x, y=y_upper, plot_type="fill", y_lower=y_lower) trace.update(options) return trace - def _plot_trace(self, x, y, label=None, ax=None, plot_type='plot', **trace_options): + def create_histogram(self, x, name, **trace_options): + trace = dict(x=x, label=name, plot_type="hist") + trace.update(trace_options) + return trace + + def create_vline(self, fig, x, **trace_options): + fig.gca() + plt.axvline(x, **trace_options) + + def _plot_trace( + self, x=None, y=None, label=None, ax=None, plot_type="plot", **trace_options + ): if ax is None: ax = plt.gca() - if plot_type=='plot': - line = ax.plot( - x, - y, - label=label, - **trace_options, - ) - elif plot_type=='fill': - line = ax.fill_between(x, y, **trace_options) - else: - raise ValueError('Plot type not recognised') - - if len(line) > 1: - return line + if plot_type == "plot": + if x is not None: + line = ax.plot( + x, + y, + label=label, + **trace_options, + ) + else: + line = ax.plot( + y, + label=label, + **trace_options, + ) + if len(line) > 1: + return line + else: + return line[0] + elif plot_type == "fill": + y_upper = y + y_lower = trace_options["y_lower"] + del trace_options["y_lower"] + return ax.fill_between(x, y_upper, y_lower, **trace_options) + elif plot_type == "hist": + return ax.hist(x=x, label=label, **trace_options) else: - return line[0] + raise ValueError("Plot type not recognised") class SubplotPlotter(Plotter): @@ -241,7 +281,7 @@ def __call__(self, show=True, num_rows=1, num_cols=1): if self.title is not None: plt.suptitle(self.title) - + if show: plt.show() @@ -301,3 +341,27 @@ def trajectories( fig.show() return fig + + +def show_table(header, values, title): + """ + Display data in a table. + """ + for i, val in enumerate(values): + values[i] = [val[0], ", ".join(val[1].astype(str))] + + fig, ax = plt.subplots(figsize=(6, 2), dpi=100) + + # hide axes + ax.axis("off") + ax.axis("tight") + ax.table( + cellText=values, + colLabels=header, + loc="center", + cellLoc="center", + colColours=["lightsteelblue", "lightsteelblue"], + ) + ax.set_title(title) + fig.tight_layout() + plt.show() diff --git a/pybop/plot/matplotlib/util.py b/pybop/plot/matplotlib/util.py index 6f31ce29a..2226c2966 100644 --- a/pybop/plot/matplotlib/util.py +++ b/pybop/plot/matplotlib/util.py @@ -1,6 +1,8 @@ import warnings + import matplotlib.pyplot as plt + def update_and_show(fig, show=True, **layout_kwargs): if len(layout_kwargs) > 0: warnings.warn( @@ -15,13 +17,40 @@ def update_and_show(fig, show=True, **layout_kwargs): return fig + DEFAULT_PLOT_OPTIONS = { - 'parameters' : dict(figsize=(18, 8), title="Parameter Convergence"), - 'problem' : { - 'default_trace_options' : dict (label="Model", marker=None, linestyle='-'), - 'design_cost_options' : dict(label = "Optimised"), - 'meta_problem_options' : dict(marker=".", linestyle='none'), - 'reference_options' : dict(label="Reference", marker=".", linestyle='none'), - 'fill_options' : dict(color=[(1.0, 0.898, 0.800, 0.8)]) - } -} \ No newline at end of file + "parameters": dict(figsize=(18, 8), title="Parameter Convergence"), + "problem": { + "default_trace_options": dict(label="Model", marker=None, linestyle="-"), + "design_cost_options": dict(label="Optimised"), + "meta_problem_options": dict(marker=".", linestyle="none"), + "reference_options": dict(label="Reference", marker=".", linestyle="none"), + "fill_options": dict(color=[(1.0, 0.898, 0.800, 0.8)]), + }, + "posterior": { + "plot_options": { + "figsize": (15, 8), + "grid": dict(axis="y", zorder=-1, color="w"), + "axis_bg_color": ( + 0.6784313725490196, + 0.8470588235294118, + 0.9019607843137255, + 0.3, + ), + }, + "trace_options": dict(alpha=0.75), + "trace_options_vline": dict(linewidth=3, linestyle="--", color="k"), + }, + "trace": { + "plot_options": { + "figsize": (15, 8), + "grid": dict(axis="y", zorder=-10, color="w"), + "axis_bg_color": ( + 0.6784313725490196, + 0.8470588235294118, + 0.9019607843137255, + 0.3, + ), + }, + }, +} diff --git a/pybop/plot/parameters.py b/pybop/plot/parameters.py index c1082c6ee..934ec67a3 100644 --- a/pybop/plot/parameters.py +++ b/pybop/plot/parameters.py @@ -45,7 +45,7 @@ def parameters(result: "Result", show=True, backend=None, **layout_kwargs): trace_names.append("Sigma") # Set subplot layout options - plot_options = get_default_options('paramters', backend) + plot_options = get_default_options("paramters", backend) # Create a plot dictionary plot_dict = StandardSubplot( diff --git a/pybop/plot/plotly/__init__.py b/pybop/plot/plotly/__init__.py index de3350225..ad894f9bb 100644 --- a/pybop/plot/plotly/__init__.py +++ b/pybop/plot/plotly/__init__.py @@ -1,9 +1,8 @@ from .plotly_manager import PlotlyManager -from .standard_plots import Plotter, SubplotPlotter, trajectories +from .standard_plots import Plotter, SubplotPlotter, show_table, trajectories from .contour import contour from .convergence import convergence from .nyquist import nyquist from .voronoi import surface -from .samples import chains, posterior, summary_table, trace -from .util import update_and_show, DEFAULT_PLOT_OPTIONS \ No newline at end of file +from .util import update_and_show, DEFAULT_PLOT_OPTIONS diff --git a/pybop/plot/plotly/samples.py b/pybop/plot/plotly/samples.py deleted file mode 100644 index f5016c325..000000000 --- a/pybop/plot/plotly/samples.py +++ /dev/null @@ -1,131 +0,0 @@ -from typing import TYPE_CHECKING - -from pybop.plot.plotly import PlotlyManager - -if TYPE_CHECKING: - from pybop.samplers.base_pints_sampler import SamplingResult - - -def trace(result: "SamplingResult", **kwargs): - """ - Plot trace plots for the posterior samples. - """ - # Import plotly only when needed - go = PlotlyManager().go - - for i in range(result.n_parameters): - fig = go.Figure() - - for j, chain in enumerate(result.chains): - fig.add_trace(go.Scatter(y=chain[:, i], mode="lines", name=f"Chain {j}")) - - fig.update_layout( - title=f"Parameter {i} Trace Plot", - xaxis_title="Sample Index", - yaxis_title="Value", - ) - fig.update_layout(**kwargs) - fig.show() - - -def chains(result: "SamplingResult", **kwargs): - """ - Plot posterior distributions for each chain. - """ - # Import plotly only when needed - go = PlotlyManager().go - - fig = go.Figure() - - for i, chain in enumerate(result.chains): - for j in range(chain.shape[1]): - fig.add_trace( - go.Histogram( - x=chain[:, j], - name=f"Chain {i} - Parameter {j}", - opacity=0.75, - ) - ) - - fig.add_shape( - type="line", - x0=result.mean[j], - y0=0, - x1=result.mean[j], - y1=result.max[j], - name=f"Mean - Parameter {j}", - line=dict(color="Black", width=1.5, dash="dash"), - ) - - fig.update_layout( - barmode="overlay", - title="Posterior Distribution", - xaxis_title="Value", - yaxis_title="Density", - ) - fig.update_layout(**kwargs) - fig.show() - - -def posterior(result: "SamplingResult", **kwargs): - """ - Plot the summed posterior distribution across chains. - """ - # Import plotly only when needed - go = PlotlyManager().go - - fig = go.Figure() - - for j in range(result.all_samples.shape[1]): - histogram = go.Histogram( - x=result.all_samples[:, j], - name=f"Parameter {j}", - opacity=0.75, - ) - fig.add_trace(histogram) - fig.add_vline( - x=result.mean[j], line_width=3, line_dash="dash", line_color="black" - ) - - fig.update_layout( - barmode="overlay", - title="Posterior Distribution", - xaxis_title="Value", - yaxis_title="Density", - ) - fig.update_layout(**kwargs) - fig.show() - return fig - - -def summary_table(result: "SamplingResult"): - """ - Display summary statistics in a table. - """ - # Import plotly only when needed - go = PlotlyManager().go - - summary_stats = result.get_summary_statistics() - - header = ["Statistic", "Value"] - values = [ - ["Mean", summary_stats["mean"]], - ["Median", summary_stats["median"]], - ["Standard Deviation", summary_stats["std"]], - ["95% CI Lower", summary_stats["ci_lower"]], - ["95% CI Upper", summary_stats["ci_upper"]], - ] - - fig = go.Figure( - data=[ - go.Table( - header=dict(values=header), - cells=dict( - values=[[row[0] for row in values], [row[1] for row in values]] - ), - ) - ] - ) - - fig.update_layout(title="Summary Statistics") - fig.show() diff --git a/pybop/plot/plotly/standard_plots.py b/pybop/plot/plotly/standard_plots.py index adce31af2..537a24286 100644 --- a/pybop/plot/plotly/standard_plots.py +++ b/pybop/plot/plotly/standard_plots.py @@ -1,5 +1,3 @@ -import warnings - from pybop.plot import StandardPlot from pybop.plot.plotly.plotly_manager import PlotlyManager @@ -62,20 +60,13 @@ class Plotter: def __init__( self, + title=None, + xaxis_title=None, + yaxis_title=None, layout=None, layout_options=None, trace_options=None, - **kwargs, ): - # Warning if layout arguments ignored - if len(kwargs) > 0: - warnings.warn( - "The following layout argument keys are ignored for the current plotting backend (plotly): \n" - f"{list(kwargs.keys())}", - UserWarning, - stacklevel=2, - ) - self.backend = "plotly" self.traces = [] @@ -99,6 +90,16 @@ def __init__( if self.layout is None: self.layout = self.go.Layout(**self.layout_options) + title_options = {} + if title is not None: + title_options.update({"title": title}) + if title is not None: + title_options.update({"xaxis_title": xaxis_title}) + if title is not None: + title_options.update({"yaxis_title": yaxis_title}) + + self.layout.update(**title_options) + def __call__(self, show=True): """ Generate and show the figure. @@ -143,7 +144,7 @@ def add_traces(self, x, y, trace_names=None, **trace_options): trace = self.create_trace(xi, y[i], **trace_options) self.traces.append(trace) - def create_trace(self, x, y, **trace_options): + def create_trace(self, x=None, y=None, label=None, **trace_options): """ Create a trace for the Plotly figure. @@ -152,23 +153,36 @@ def create_trace(self, x, y, **trace_options): plotly.graph_objs.Scatter A trace for a Plotly figure. """ + if label is not None: + trace_options.update({"name": label}) + if x is not None and y is not None: + return self.go.Scatter( + x=x, + y=y, + **trace_options, + ) + if x is None and y is not None: + return self.go.Scatter( + y=y, + **trace_options, + ) - return self.go.Scatter( - x=x, - y=y, - **trace_options, - ) - def create_fill_trace(self, x, y_upper, y_lower, **options): return self.create_trace( - x=x + x[::-1], - y=y_upper + y_lower[::-1], - fill="toself", - line=dict(color="rgba(255,255,255,0)"), - hoverinfo="skip", - showlegend=False, - **options - ) + x=x + x[::-1], + y=y_upper + y_lower[::-1], + fill="toself", + line=dict(color="rgba(255,255,255,0)"), + hoverinfo="skip", + showlegend=False, + **options, + ) + + def create_histogram(self, x, name, **trace_options): + return self.go.Histogram(x=x, name=name, **trace_options) + + def create_vline(self, fig, x, **trace_options): + fig.add_vline(x=x, **trace_options) class SubplotPlotter(Plotter): @@ -294,3 +308,24 @@ def trajectories(x, y, trace_names=None, show=True, **layout_kwargs): fig.show() return fig + + +def show_table(header, values, title): + """ + Display data in a table. + """ + # Import plotly only when needed + go = PlotlyManager().go + fig = go.Figure( + data=[ + go.Table( + header=dict(values=header), + cells=dict( + values=[[row[0] for row in values], [row[1] for row in values]] + ), + ) + ] + ) + + fig.update_layout(title=title) + fig.show() diff --git a/pybop/plot/plotly/util.py b/pybop/plot/plotly/util.py index 563dde595..f622890fb 100644 --- a/pybop/plot/plotly/util.py +++ b/pybop/plot/plotly/util.py @@ -1,24 +1,54 @@ -import warnings - def update_and_show(fig, show=True, **layout_kwargs): - fig.update_layout(**layout_kwargs) - if show: - fig.show() + if hasattr(fig, "__len__") and len(fig) > 0: + for f in fig: + f.update_layout(**layout_kwargs) + if show: + f.show() + else: + fig.update_layout(**layout_kwargs) + if show: + fig.show() return fig DEFAULT_PLOT_OPTIONS = { - 'parameters' : dict(layout_options = dict( - title="Parameter Convergence", - width=1024, - height=576, - legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1), - )), - 'problem' : { - 'default_trace_options' : dict (name="Model", mode="lines", showlegend=True), - 'design_cost_options' : dict(name = "Optimised"), - 'meta_problem_options' : dict(mode = "lines"), - 'reference_options' : dict(name="Reference", mode="markers", showlegend=True), - 'fill_options' : dict(fillcolor ="rgba(255,229,204,0.8)" ) - } -} \ No newline at end of file + "parameters": dict( + layout_options=dict( + title="Parameter Convergence", + width=1024, + height=576, + legend=dict( + orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1 + ), + ) + ), + "problem": { + "default_trace_options": dict(name="Model", mode="lines", showlegend=True), + "design_cost_options": dict(name="Optimised"), + "meta_problem_options": dict(mode="lines"), + "reference_options": dict(name="Reference", mode="markers", showlegend=True), + "fill_options": dict(fillcolor="rgba(255,229,204,0.8)"), + }, + "trace": { + "plot_options": { + "layout_options": dict( + width=None, height=None, plot_bgcolor=None, autosize=None, legend=None + ) + }, + "trace_options": dict(mode="lines"), + }, + "posterior": { + "plot_options": { + "layout_options": dict( + barmode="overlay", + width=None, + height=None, + plot_bgcolor=None, + autosize=None, + legend=None, + ) + }, + "trace_options": dict(opacity=0.75), + "trace_options_vline": dict(line_width=3, line_dash="dash", line_color="black"), + }, +} diff --git a/pybop/plot/plots.py b/pybop/plot/plots.py index b51745999..99dc699b7 100644 --- a/pybop/plot/plots.py +++ b/pybop/plot/plots.py @@ -4,20 +4,11 @@ if TYPE_CHECKING: from pybop._result import Result - from pybop.samplers.base_pints_sampler import SamplingResult -from pybop.parameters.parameter import Inputs from pybop.plot.util import call_plotting_function from pybop.problems.problem import Problem -def chains(result: "SamplingResult", show=True, backend=None, **kwargs): - """ - Plot posterior distributions for each chain. - """ - return call_plotting_function("chains", backend, result=result, **kwargs) - - def contour( call_object: "Problem | Result", gradient: bool = False, @@ -103,20 +94,6 @@ def convergence(result: "Result", show=True, backend=None, **layout_kwargs): "convergence", backend, result=result, show=show, **layout_kwargs ) -def posterior(result: "SamplingResult", show=True, backend=None, **kwargs): - """ - Plot the summed posterior distribution across chains. - """ - return call_plotting_function("posterior", backend, result=result, **kwargs) - - -def summary_table(result: "SamplingResult", backend=None): - """ - Display summary statistics in a table. - """ - - return call_plotting_function("summary_table", backend, result=result) - def surface( result: "Result", @@ -159,10 +136,3 @@ def surface( show=show, **layout_kwargs, ) - - -def trace(result: "SamplingResult", backend=None, **kwargs): - """ - Plot trace plots for the posterior samples. - """ - return call_plotting_function("trace", backend, result=result, **kwargs) diff --git a/pybop/plot/problem.py b/pybop/plot/problem.py index b2fabaaa0..27b573641 100644 --- a/pybop/plot/problem.py +++ b/pybop/plot/problem.py @@ -3,7 +3,7 @@ from pybop.costs.design_cost import DesignCost from pybop.costs.error_measures import ErrorMeasure from pybop.parameters.parameter import Inputs -from pybop.plot.matplotlib.standard_plots import StandardPlot +from pybop.plot.standard_plots import StandardPlot from pybop.plot.util import get_default_options, update_and_show from pybop.problems.meta_problem import MetaProblem from pybop.problems.problem import Problem @@ -15,7 +15,7 @@ def problem( inputs: Inputs = None, show: bool = True, title="Scatter Plot", - backend = None, + backend=None, ): """ Produce a quick plot of the target dataset against optimised model output. @@ -65,12 +65,12 @@ def problem( model_domain = target_domain[: len(model_output[target].data)] # Retrieve default layout options - plot_options = get_default_options('problem', backend) - trace_options = plot_options.get('default_trace_options') or {} - design_cost_options = plot_options.get('design_cost_options') or {} - meta_problem_options = plot_options.get('meta_problem_options') or {} - reference_options = plot_options.get('reference_options') or {} - fill_options = plot_options.get('fill_options') or {} + plot_options = get_default_options("problem", backend) + trace_options = plot_options.get("default_trace_options") or {} + design_cost_options = plot_options.get("design_cost_options") or {} + meta_problem_options = plot_options.get("meta_problem_options") or {} + reference_options = plot_options.get("reference_options") or {} + fill_options = plot_options.get("fill_options") or {} # Create a plot for each output figure_list = [] @@ -81,22 +81,21 @@ def problem( if isinstance(problem.cost, DesignCost): options.update(design_cost_options) - print(isinstance(problem, MetaProblem), isinstance(problem.cost, DesignCost), options) - # Create a plot dictionary - plot_dict = StandardPlot(backend=backend) + plot_dict = StandardPlot( + title=title, + xaxis_title=StandardPlot.remove_brackets(domain), + yaxis_title=StandardPlot.remove_brackets(var), + backend=backend, + ) model_trace = plot_dict.create_trace( - x=model_domain, - y=model_output[var].data, - **options + x=model_domain, y=model_output[var].data, **options ) plot_dict.traces.append(model_trace) - target_trace =plot_dict.create_trace( - x=target_domain, - y=target_output[var].data, - **reference_options + target_trace = plot_dict.create_trace( + x=target_domain, y=target_output[var].data, **reference_options ) plot_dict.traces.append(target_trace) @@ -111,15 +110,16 @@ def problem( y_upper = (model_output[var].data + plot_dict.sigma).tolist() y_lower = (model_output[var].data - plot_dict.sigma).tolist() - fill_trace = plot_dict.create_fill_trace(x, y_upper, y_lower, **fill_options) + fill_trace = plot_dict.create_fill_trace( + x, y_upper, y_lower, **fill_options + ) + plot_dict.traces.append(fill_trace) + + # Reverse the order of the traces to put the model on top plot_dict.traces = plot_dict.traces[::-1] + # Generate the figure and update the layout fig = plot_dict(show=False) - # plt.xlabel("Time / s") - # plt.ylabel(StandardPlot.remove_brackets(var)) - # plt.title(title) - # plt.legend() - # plt.tight_layout() fig = update_and_show(fig, backend=backend) figure_list.append(fig) diff --git a/pybop/plot/samples.py b/pybop/plot/samples.py new file mode 100644 index 000000000..fe80746cc --- /dev/null +++ b/pybop/plot/samples.py @@ -0,0 +1,117 @@ +from typing import TYPE_CHECKING + +from pybop.plot import StandardPlot +from pybop.plot.util import update_and_show, get_default_options, call_plotting_function + +if TYPE_CHECKING: + from pybop.samplers.base_pints_sampler import SamplingResult + + +def chains(result: "SamplingResult", show=True, backend=None): + """ + Plot posterior distributions for each chain. + """ + options = get_default_options('posterior', backend) + plot_options = options.get("plot_options") or {} + trace_options = options.get("trace_options") or {} + trace_options_vline = options.get("trace_options_vline") or {} + + plot_dict = StandardPlot( + backend=backend, + title="Posterior Distribution", + xaxis_title="Value", + yaxis_title="Density", + **plot_options + ) + + for i, chain in enumerate(result.chains): + for j in range(chain.shape[1]): + hist = plot_dict.create_histogram( + x=chain[:, j], + name=f"Chain {i} - Parameter {j}", + **trace_options + ) + plot_dict.traces.append(hist) + + fig = plot_dict(show=False) + for j in range(chain.shape[1]): + plot_dict.create_vline(fig, result.mean[j], **trace_options_vline) + + update_and_show(fig, backend=backend) + +def trace(result: "SamplingResult", show = True, backend=None): + """ + Plot trace plots for the posterior samples. + """ + figlist = [] + options = get_default_options('trace', backend) + plot_options = options.get("plot_options") or {} + trace_options = options.get("trace_options") or {} + for i in range(result.n_parameters): + plots = StandardPlot( + title=f"Parameter {i} Trace Plot", + xaxis_title="Sample Index", + yaxis_title="Value", + backend=backend, + **plot_options + ) + + for j, chain in enumerate(result.chains): + plots.traces.append(plots.create_trace(y=chain[:, i], label=f"Chain {j}", **trace_options)) + fig = plots(show=False) + figlist.append(fig) + + update_and_show(figlist, show=show, backend=backend) + + return figlist + +def posterior(result: "SamplingResult", backend=None): + """ + Plot the summed posterior distribution across chains. + """ + options = get_default_options('posterior', backend) + plot_options = options.get("plot_options") or {} + trace_options = options.get("trace_options") or {} + trace_options_vline = options.get("trace_options_vline") or {} + # Import plotly only when needed + plot_dict = StandardPlot( + backend=backend, + title="Posterior Distribution", + xaxis_title="Value", + yaxis_title="Density", + **plot_options + ) + + for j in range(result.all_samples.shape[1]): + hist = plot_dict.create_histogram( + x=result.all_samples[:, j], + name=f"Parameter {j}", + **trace_options + ) + + plot_dict.traces.append(hist) + + + fig = plot_dict(show=False) + for j in range(result.all_samples.shape[1]): + plot_dict.create_vline(fig, result.mean[j], **trace_options_vline) + + update_and_show(fig, backend=backend) + +def summary_table(result: "SamplingResult", backend=None): + """ + Display summary statistics in a table. + """ + + summary_stats = result.get_summary_statistics() + + header = ["Statistic", "Value"] + values = [ + ["Mean", summary_stats["mean"]], + ["Median", summary_stats["median"]], + ["Standard Deviation", summary_stats["std"]], + ["95% CI Lower", summary_stats["ci_lower"]], + ["95% CI Upper", summary_stats["ci_upper"]], + ] + + call_plotting_function('show_table', backend=backend, header=header, values=values, title="Summary Statistics") \ No newline at end of file diff --git a/pybop/plot/standard_plots.py b/pybop/plot/standard_plots.py index db566a533..b1a6f9e33 100644 --- a/pybop/plot/standard_plots.py +++ b/pybop/plot/standard_plots.py @@ -11,6 +11,9 @@ def __init__( self, x=None, y=None, + title=None, + xaxis_title=None, + yaxis_title=None, trace_options=None, trace_names=None, trace_name_width=20, @@ -19,7 +22,13 @@ def __init__( ): self.plotter = call_plotting_function( - "Plotter", backend, trace_options=trace_options, **kwargs + "Plotter", + backend, + title=title, + xaxis_title=xaxis_title, + yaxis_title=yaxis_title, + trace_options=trace_options, + **kwargs, ) self.trace_name_width = trace_name_width @@ -94,12 +103,18 @@ def parse_data(self, x, y): ) return x, y - def create_trace(self, x, y, **trace_options): - return self.plotter.create_trace(x, y, **trace_options) - + def create_trace(self, x=None, y=None, label=None, **trace_options): + return self.plotter.create_trace(x, y, label, **trace_options) + def create_fill_trace(self, x, y_upper, y_lower, **options): return self.plotter.create_fill_trace(x, y_upper, y_lower, **options) + def create_histogram(self, x, name, **trace_options): + return self.plotter.create_histogram(x, name, **trace_options) + + def create_vline(self, fig, x, **trace_options): + return self.plotter.create_vline(fig, x, **trace_options) + @staticmethod def wrap_text(text, width, backend="matplotlib"): """ diff --git a/pybop/plot/util.py b/pybop/plot/util.py index 27cf9303c..a30b0cee1 100644 --- a/pybop/plot/util.py +++ b/pybop/plot/util.py @@ -37,16 +37,16 @@ def call_plotting_function(function_name, backend, **kwargs): def update_and_show(fig, backend=None, **kwargs): - return call_plotting_function("update_and_show", backend, fig=fig, **kwargs) + return call_plotting_function("update_and_show", backend, fig=fig, **kwargs) def get_default_options(plot_type, backend): if backend is None: backend = pybop.plot.backend - if backend == 'plotly': + if backend == "plotly": opts = pybop.plot.plotly.DEFAULT_PLOT_OPTIONS - elif backend == 'matplotlib': + elif backend == "matplotlib": opts = pybop.plot.matplotlib.DEFAULT_PLOT_OPTIONS else: opts = {} @@ -54,4 +54,4 @@ def get_default_options(plot_type, backend): if plot_type in opts.keys(): return opts[plot_type] else: - return {} \ No newline at end of file + return {} From 072e8fab12a4843056628c3e6b189f4286c881a7 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Mon, 13 Apr 2026 10:45:22 +0000 Subject: [PATCH 08/20] style: pre-commit fixes --- pybop/plot/samples.py | 44 ++++++++++++++++++++++++------------------- 1 file changed, 25 insertions(+), 19 deletions(-) diff --git a/pybop/plot/samples.py b/pybop/plot/samples.py index fe80746cc..3ea022cce 100644 --- a/pybop/plot/samples.py +++ b/pybop/plot/samples.py @@ -1,7 +1,7 @@ from typing import TYPE_CHECKING from pybop.plot import StandardPlot -from pybop.plot.util import update_and_show, get_default_options, call_plotting_function +from pybop.plot.util import call_plotting_function, get_default_options, update_and_show if TYPE_CHECKING: from pybop.samplers.base_pints_sampler import SamplingResult @@ -11,7 +11,7 @@ def chains(result: "SamplingResult", show=True, backend=None): """ Plot posterior distributions for each chain. """ - options = get_default_options('posterior', backend) + options = get_default_options("posterior", backend) plot_options = options.get("plot_options") or {} trace_options = options.get("trace_options") or {} trace_options_vline = options.get("trace_options_vline") or {} @@ -21,30 +21,29 @@ def chains(result: "SamplingResult", show=True, backend=None): title="Posterior Distribution", xaxis_title="Value", yaxis_title="Density", - **plot_options + **plot_options, ) for i, chain in enumerate(result.chains): for j in range(chain.shape[1]): hist = plot_dict.create_histogram( - x=chain[:, j], - name=f"Chain {i} - Parameter {j}", - **trace_options + x=chain[:, j], name=f"Chain {i} - Parameter {j}", **trace_options ) plot_dict.traces.append(hist) fig = plot_dict(show=False) for j in range(chain.shape[1]): plot_dict.create_vline(fig, result.mean[j], **trace_options_vline) - + update_and_show(fig, backend=backend) -def trace(result: "SamplingResult", show = True, backend=None): + +def trace(result: "SamplingResult", show=True, backend=None): """ Plot trace plots for the posterior samples. """ figlist = [] - options = get_default_options('trace', backend) + options = get_default_options("trace", backend) plot_options = options.get("plot_options") or {} trace_options = options.get("trace_options") or {} for i in range(result.n_parameters): @@ -53,11 +52,13 @@ def trace(result: "SamplingResult", show = True, backend=None): xaxis_title="Sample Index", yaxis_title="Value", backend=backend, - **plot_options + **plot_options, ) for j, chain in enumerate(result.chains): - plots.traces.append(plots.create_trace(y=chain[:, i], label=f"Chain {j}", **trace_options)) + plots.traces.append( + plots.create_trace(y=chain[:, i], label=f"Chain {j}", **trace_options) + ) fig = plots(show=False) figlist.append(fig) @@ -65,11 +66,12 @@ def trace(result: "SamplingResult", show = True, backend=None): return figlist + def posterior(result: "SamplingResult", backend=None): """ Plot the summed posterior distribution across chains. """ - options = get_default_options('posterior', backend) + options = get_default_options("posterior", backend) plot_options = options.get("plot_options") or {} trace_options = options.get("trace_options") or {} trace_options_vline = options.get("trace_options_vline") or {} @@ -79,25 +81,23 @@ def posterior(result: "SamplingResult", backend=None): title="Posterior Distribution", xaxis_title="Value", yaxis_title="Density", - **plot_options + **plot_options, ) for j in range(result.all_samples.shape[1]): hist = plot_dict.create_histogram( - x=result.all_samples[:, j], - name=f"Parameter {j}", - **trace_options + x=result.all_samples[:, j], name=f"Parameter {j}", **trace_options ) plot_dict.traces.append(hist) - fig = plot_dict(show=False) for j in range(result.all_samples.shape[1]): plot_dict.create_vline(fig, result.mean[j], **trace_options_vline) update_and_show(fig, backend=backend) + def summary_table(result: "SamplingResult", backend=None): """ Display summary statistics in a table. @@ -113,5 +113,11 @@ def summary_table(result: "SamplingResult", backend=None): ["95% CI Lower", summary_stats["ci_lower"]], ["95% CI Upper", summary_stats["ci_upper"]], ] - - call_plotting_function('show_table', backend=backend, header=header, values=values, title="Summary Statistics") \ No newline at end of file + + call_plotting_function( + "show_table", + backend=backend, + header=header, + values=values, + title="Summary Statistics", + ) From d439edfe35fd0aef08640f568caa9333be665e1b Mon Sep 17 00:00:00 2001 From: u2370093 Date: Mon, 13 Apr 2026 17:45:16 +0100 Subject: [PATCH 09/20] simplify contour, nyquist, convergence --- pybop/plot/__init__.py | 4 +- pybop/plot/{matplotlib => }/contour.py | 93 ++++----- pybop/plot/{plotly => }/convergence.py | 18 +- pybop/plot/matplotlib/__init__.py | 5 +- pybop/plot/matplotlib/convergence.py | 51 ----- pybop/plot/matplotlib/nyquist.py | 80 ------- pybop/plot/matplotlib/standard_plots.py | 84 +++++--- pybop/plot/matplotlib/util.py | 36 ++++ pybop/plot/nyquist.py | 22 +- pybop/plot/plotly/__init__.py | 5 +- pybop/plot/plotly/contour.py | 263 ------------------------ pybop/plot/plotly/nyquist.py | 101 --------- pybop/plot/plotly/standard_plots.py | 47 +++-- pybop/plot/plotly/util.py | 124 +++++++++-- pybop/plot/plots.py | 25 --- pybop/plot/standard_plots.py | 3 + 16 files changed, 297 insertions(+), 664 deletions(-) rename pybop/plot/{matplotlib => }/contour.py (75%) rename pybop/plot/{plotly => }/convergence.py (78%) delete mode 100644 pybop/plot/matplotlib/convergence.py delete mode 100644 pybop/plot/matplotlib/nyquist.py delete mode 100644 pybop/plot/plotly/contour.py delete mode 100644 pybop/plot/plotly/nyquist.py diff --git a/pybop/plot/__init__.py b/pybop/plot/__init__.py index 5c6eced49..da5e84d44 100644 --- a/pybop/plot/__init__.py +++ b/pybop/plot/__init__.py @@ -8,12 +8,12 @@ # Import plots # from .plots import ( - contour, - convergence, surface ) from .standard_plots import StandardPlot, StandardSubplot, trajectories +from .contour import contour +from .convergence import convergence from .dataset import dataset from .nyquist import nyquist from .parameters import parameters diff --git a/pybop/plot/matplotlib/contour.py b/pybop/plot/contour.py similarity index 75% rename from pybop/plot/matplotlib/contour.py rename to pybop/plot/contour.py index d0127375a..c6ff92786 100644 --- a/pybop/plot/matplotlib/contour.py +++ b/pybop/plot/contour.py @@ -3,8 +3,9 @@ from typing import TYPE_CHECKING import numpy as np -from matplotlib import pyplot as plt +from pybop.plot.standard_plots import StandardPlot +from pybop.plot.util import call_plotting_function, get_default_options, update_and_show from pybop.problems.problem import Problem if TYPE_CHECKING: @@ -18,7 +19,7 @@ def contour( transformed: bool = False, steps: int = 10, show: bool = True, - title: str = "Cost Landscape", + backend=None, ): """ Plot a 2D visualisation of a cost landscape using Plotly. @@ -143,77 +144,57 @@ def transform_array_of_values(list_of_values, parameter): bounds[0] = transform_array_of_values(bounds[0], parameters[names[0]]) bounds[1] = transform_array_of_values(bounds[1], parameters[names[1]]) - # define levels - exponent = np.floor(np.log10(np.abs(np.max(costs)))) - levels = np.linspace( - np.floor(np.min(costs) / (10**exponent)) * (10**exponent), - np.ceil(np.max(costs) / (10**exponent)) * (10**exponent), - 2 * steps - 1, - ) + # Get options + options = get_default_options("contour", backend) + plot_options = options.get("plot_options") or {} + trace_options_initial = options.get("trace_options_initial") or {} + trace_options_optim = options.get("trace_options_optim") or {} + trace_options_contour = options.get("trace_options_contour") or {} + + plot_dict = StandardPlot( + xaxis_title="Transformed " + names[0] if transformed else names[0], + yaxis_title="Transformed " + names[1] if transformed else names[1], + xaxis_range=bounds[0], + yaxis_range=bounds[1], + backend=backend, + **plot_options + ) # Create contour plot and update the layout - fig = plt.figure(figsize=(6, 6), dpi=100) - plt.contourf(x, y, costs, levels=levels, extend="both", cmap="viridis") - plt.colorbar() - plt.contour( - x, y, costs, levels=levels, colors=("k",), linestyles="solid", linewidths=0.1 - ) - - # Layout - plt.xlabel("Transformed " + names[0] if transformed else names[0], labelpad=15) - plt.ticklabel_format(axis="both", **dict(style="sci", scilimits=(-4, 4))) - plt.ylabel("Transformed " + names[1] if transformed else names[1], labelpad=15) - plt.title(title, pad=40) - plt.xlim(bounds[0]) - plt.ylim(bounds[1]) + plot_dict.create_contour(x=x, y=y, z=costs, **trace_options_contour) if plot_optim: # Plot the optimisation trace optim_trace = np.asarray([item[:2] for item in result.x_model]) optim_trace = optim_trace.reshape(-1, 2) - - plt.scatter( - transform_array_of_values(optim_trace[:, 0], parameters[names[0]]), - transform_array_of_values(optim_trace[:, 1], parameters[names[1]]), - c=[i / len(optim_trace) for i in range(len(optim_trace))], - cmap="Grays", - zorder=1, + call_plotting_function('plot_optimisation_path', backend=backend, + plot_dict=plot_dict, + x=transform_array_of_values(optim_trace[:, 0], parameters[names[0]]), + y=transform_array_of_values(optim_trace[:, 1], parameters[names[1]]), ) # Plot the initial guess if len(result.x_model) > 0: x0 = result.x_model[0] - plt.plot( - transform_array_of_values([x0[0]], parameters[names[0]]), - transform_array_of_values([x0[1]], parameters[names[1]]), - "X", - markersize=14, - markerfacecolor="w", - markeredgecolor="k", - label="Initial values", - linestyle="None", - ) + plot_dict.traces.append(plot_dict.create_trace( + x=transform_array_of_values([x0[0]], parameters[names[0]]), + y=transform_array_of_values([x0[1]], parameters[names[1]]), + **trace_options_initial + )) # Plot optimised value if result.x is not None: x_best = result.x - plt.plot( - transform_array_of_values([x_best[0]], parameters[names[0]]), - transform_array_of_values([x_best[1]], parameters[names[1]]), - "P", - markersize=14, - markerfacecolor="k", - markeredgecolor="w", - label="Final values", - linestyle="None", - ) - - plt.legend(ncols=2, loc="lower center", bbox_to_anchor=(0.5, 1.0)) - - plt.tight_layout() - + plot_dict.traces.append(plot_dict.create_trace( + x=transform_array_of_values([x_best[0]], parameters[names[0]]), + y=transform_array_of_values([x_best[1]], parameters[names[1]]), + **trace_options_optim + )) + + # Update the layout and display the figure + fig = plot_dict(show=False) if show: - plt.show() + update_and_show(fig) # if gradient: # grad_figs = [] diff --git a/pybop/plot/plotly/convergence.py b/pybop/plot/convergence.py similarity index 78% rename from pybop/plot/plotly/convergence.py rename to pybop/plot/convergence.py index 6b190a93c..9e3b1bbb7 100644 --- a/pybop/plot/plotly/convergence.py +++ b/pybop/plot/convergence.py @@ -1,12 +1,13 @@ from typing import TYPE_CHECKING -from pybop.plot.plotly.standard_plots import StandardPlot +from pybop.plot.standard_plots import StandardPlot +from pybop.plot.util import update_and_show if TYPE_CHECKING: from pybop._result import Result -def convergence(result: "Result", show=True, **layout_kwargs): +def convergence(result: "Result", show=True, backend=None): """ Plot the convergence of the optimisation algorithm. @@ -37,18 +38,15 @@ def convergence(result: "Result", show=True, **layout_kwargs): plot_dict = StandardPlot( x=iteration_numbers, y=cost_log, - layout_options=dict( - xaxis_title="Evaluation", - yaxis_title="Cost", - title="Convergence", - ), + xaxis_title="Evaluation", + yaxis_title="Cost", + title="Convergence", trace_names=result.method_name, + backend=backend ) # Generate and display the figure fig = plot_dict(show=False) - fig.update_layout(**layout_kwargs) if show: - fig.show() - + update_and_show(fig, backend=backend) return fig diff --git a/pybop/plot/matplotlib/__init__.py b/pybop/plot/matplotlib/__init__.py index ae087d5fc..2730122df 100644 --- a/pybop/plot/matplotlib/__init__.py +++ b/pybop/plot/matplotlib/__init__.py @@ -1,6 +1,3 @@ -from .standard_plots import Plotter, SubplotPlotter, show_table, trajectories -from .convergence import convergence -from .contour import contour +from .standard_plots import Plotter, SubplotPlotter, show_table, trajectories, plot_optimisation_path from .voronoi import surface -from .nyquist import nyquist from .util import update_and_show, DEFAULT_PLOT_OPTIONS diff --git a/pybop/plot/matplotlib/convergence.py b/pybop/plot/matplotlib/convergence.py deleted file mode 100644 index 2e53e4738..000000000 --- a/pybop/plot/matplotlib/convergence.py +++ /dev/null @@ -1,51 +0,0 @@ -from typing import TYPE_CHECKING - -import matplotlib.pyplot as plt - -from pybop.plot.matplotlib.standard_plots import StandardPlot - -if TYPE_CHECKING: - from pybop._result import Result - - -def convergence(result: "Result", show=True): - """ - Plot the convergence of the optimisation algorithm. - - Parameters - ----------- - result : pybop.Result - Optimisation result containing the history of parameter values and associated cost. - show : bool, optional - If True, the figure is shown upon creation (default: True). - - Returns - --------- - fig : plotly.graph_objs.Figure - The Plotly figure object for the convergence plot. - """ - - # Extract log from the optimisation object - cost_log = result.cost_convergence - - # Generate a list of iteration numbers - iteration_numbers = list(range(1, len(cost_log) + 1)) - - # Create a plot dictionary - plot_dict = StandardPlot( - x=iteration_numbers, - y=cost_log, - trace_names=result.method_name, - ) - - # Generate and display the figure - fig = plot_dict(show=False) - plt.xlabel("Evaluation") - plt.ylabel("Cost") - plt.title("Convergence") - plt.tight_layout() - - if show: - plt.show() - - return fig diff --git a/pybop/plot/matplotlib/nyquist.py b/pybop/plot/matplotlib/nyquist.py deleted file mode 100644 index 5c6a64144..000000000 --- a/pybop/plot/matplotlib/nyquist.py +++ /dev/null @@ -1,80 +0,0 @@ -import warnings - -from matplotlib import pyplot as plt - -from pybop.parameters.parameter import Inputs -from pybop.plot.nyquist import _nyquist - - -def nyquist(problem, inputs: Inputs = None, show=True, **layout_kwargs): - """ - Generates Nyquist plots for the given problem by evaluating the model's output and target values. - - Parameters - ---------- - problem : pybop.Problem - An instance of a problem class that contains the parameters and methods - for evaluation and target retrieval. - inputs : Inputs, optional - Input parameters for the problem. If not provided, the default parameters from the problem - instance will be used. These parameters are verified before use (default is None). - show : bool, optional - If True, the plots will be displayed. - **layout_kwargs : dict, optional - Additional keyword arguments for customising the plot layout. These arguments are passed to - `fig.update_layout()`. - - Returns - ------- - list - A list of plotly `Figure` objects, each representing a Nyquist plot for the model's output and target values. - - Notes - ----- - - The function extracts the real part of the impedance from the model's output and the real and imaginary parts - of the impedance from the target output. - - For each signal in the problem, a Nyquist plot is created with the model's impedance plotted as a scatter plot. - - An additional trace for the reference (target output) is added to the plot. - - The plot layout can be customised using `layout_kwargs`. - - Example - ------- - >>> problem = pybop.EISProblem() - >>> nyquist_figures = nyquist(problem, show=True, title="Nyquist Plot", xaxis_title="Real(Z)", yaxis_title="Imag(Z)") - >>> # The plots will be displayed and nyquist_figures will contain the list of figure objects. - """ - - if len(layout_kwargs) > 0: - warnings.warn( - "The following layout argument keys are ignored for the current plotting backend (matplotlib): \n" - f"{list(layout_kwargs.keys())}", - UserWarning, - stacklevel=2, - ) - trace_options_model = dict( - label="Model", color="#00CC96", linewidth=2, marker=".", markersize=8 - ) - trace_options_reference = dict( - label="Reference", - linestyle="none", - marker="o", - fillstyle="none", - markersize=8, - markeredgecolor="#636EFA", - ) - figure_list = _nyquist( - problem, trace_options_model, trace_options_reference, inputs=inputs - ) - - for fig in figure_list: - plt.sca(fig.gca()) - # Layout - plt.title("Nyquist Plot", fontsize=14, x=0.2) - plt.xlabel(r"$Z_{re} / \Omega$", fontsize=16) - plt.ylabel(r"$-Z_{im} / \Omega$", fontsize=16) - plt.legend(loc="upper right", bbox_to_anchor=(1, 1.08), ncols=2) - - if show: - plt.show() - - return figure_list diff --git a/pybop/plot/matplotlib/standard_plots.py b/pybop/plot/matplotlib/standard_plots.py index 64c31bd66..bd49cd6d5 100644 --- a/pybop/plot/matplotlib/standard_plots.py +++ b/pybop/plot/matplotlib/standard_plots.py @@ -37,6 +37,8 @@ def __init__( title=None, xaxis_title=None, yaxis_title=None, + xaxis_range=None, + yaxis_range=None, grid=None, axis_bg_color=None, **kwargs, @@ -70,7 +72,10 @@ def __init__( ax = plt.gca() ax.set_facecolor(axis_bg_color) ax.set_axisbelow(True) - + if xaxis_range is not None: + plt.xlim(xaxis_range) + if yaxis_range is not None: + plt.ylim(yaxis_range) self.traces = [] def __call__(self, show=True): @@ -146,20 +151,20 @@ def create_trace(self, x=None, y=None, label=None, ax=None, **trace_options): """ if x is not None and y is not None: size = min(len(x), len(y)) - trace = dict(x=x[:size], y=y[:size], label=label, ax=ax) + trace = dict(positional_args=[x[:size], y[:size]], label=label, ax=ax) elif y is not None: - trace = dict(y=y, label=label, ax=ax) + trace = dict(positional_args=[y], label=label, ax=ax) trace.update(trace_options) return trace def create_fill_trace(self, x, y_upper, y_lower, **options): - trace = dict(x=x, y=y_upper, plot_type="fill", y_lower=y_lower) + trace = dict(positional_args=(x, y_upper, y_lower), plot_type="fill_between") trace.update(options) return trace def create_histogram(self, x, name, **trace_options): - trace = dict(x=x, label=name, plot_type="hist") + trace = dict(positional_args=[x], label=name, plot_type="hist") trace.update(trace_options) return trace @@ -167,39 +172,42 @@ def create_vline(self, fig, x, **trace_options): fig.gca() plt.axvline(x, **trace_options) + def create_contour(self, x, y, z, **trace_options): + contour = dict(positional_args=[x, y, z], plot_type="contourf") + contour.update(**trace_options) + self.traces.append(contour) + self.traces.append( + dict( + positional_args=[x, y, z], + colors=("k"), + linestyles="solid", + linewidths=0.2, + plot_type="contour", + ) + ) + + def create_scatter(self, x, y, **trace_options): + scatter = dict(positional_args=[x, y], plot_type="scatter") + scatter.update(**trace_options) + self.traces.append(scatter) + def _plot_trace( - self, x=None, y=None, label=None, ax=None, plot_type="plot", **trace_options + self, ax=None, plot_type="plot", positional_args=None, **trace_options ): + if positional_args is None: + positional_args = [] if ax is None: ax = plt.gca() + try: + plot_function = getattr(ax, plot_type) + except ValueError: + print("Plot type not recognised") - if plot_type == "plot": - if x is not None: - line = ax.plot( - x, - y, - label=label, - **trace_options, - ) - else: - line = ax.plot( - y, - label=label, - **trace_options, - ) - if len(line) > 1: - return line - else: - return line[0] - elif plot_type == "fill": - y_upper = y - y_lower = trace_options["y_lower"] - del trace_options["y_lower"] - return ax.fill_between(x, y_upper, y_lower, **trace_options) - elif plot_type == "hist": - return ax.hist(x=x, label=label, **trace_options) - else: - raise ValueError("Plot type not recognised") + obj = plot_function(*positional_args, **trace_options) + if plot_type == "contourf": + plt.colorbar(obj) + + return obj class SubplotPlotter(Plotter): @@ -365,3 +373,13 @@ def show_table(header, values, title): ax.set_title(title) fig.tight_layout() plt.show() + + +def plot_optimisation_path(plot_dict: StandardPlot, x, y): + plot_dict.plotter.create_scatter( + x, + y, + c=[i / len(x) for i in range(len(x))], + cmap="Grays", + zorder=1, + ) diff --git a/pybop/plot/matplotlib/util.py b/pybop/plot/matplotlib/util.py index 2226c2966..b6a7311d8 100644 --- a/pybop/plot/matplotlib/util.py +++ b/pybop/plot/matplotlib/util.py @@ -19,6 +19,42 @@ def update_and_show(fig, show=True, **layout_kwargs): DEFAULT_PLOT_OPTIONS = { + "contour": { + "plot_options": dict(title="Cost Landscape"), + "trace_options_contour": dict(extend="both", cmap="viridis"), + "trace_options_initial": dict( + marker="X", + markersize=14, + markerfacecolor="w", + markeredgecolor="k", + label="Initial values", + linestyle="None", + ), + "trace_options_optim": dict( + marker="P", + markersize=14, + markerfacecolor="k", + markeredgecolor="w", + label="Final values", + linestyle="None", + ), + }, + "nyquist": { + "plot_options": dict( + xaxis_title=r"$Z_{re} / \Omega$", yaxis_title=r"$-Z_{im} / \Omega$" + ), + "trace_options_model": dict( + label="Model", color="#00CC96", linewidth=2, marker=".", markersize=8 + ), + "trace_options_reference": dict( + label="Reference", + linestyle="none", + marker="o", + fillstyle="none", + markersize=8, + markeredgecolor="#636EFA", + ), + }, "parameters": dict(figsize=(18, 8), title="Parameter Convergence"), "problem": { "default_trace_options": dict(label="Model", marker=None, linestyle="-"), diff --git a/pybop/plot/nyquist.py b/pybop/plot/nyquist.py index bcd609fd1..5d1452f2e 100644 --- a/pybop/plot/nyquist.py +++ b/pybop/plot/nyquist.py @@ -1,9 +1,11 @@ from pybop.parameters.parameter import Inputs from pybop.plot.standard_plots import StandardPlot -from pybop.plot.util import call_plotting_function +from pybop.plot.util import get_default_options, update_and_show -def nyquist(problem, inputs: Inputs = None, show=True, backend=None, **layout_kwargs): +def nyquist( + problem, inputs: Inputs = None, show=True, title="Nyquist Plot", backend=None +): """ Generates Nyquist plots for the given problem by evaluating the model's output and target values. @@ -40,14 +42,11 @@ def nyquist(problem, inputs: Inputs = None, show=True, backend=None, **layout_kw >>> nyquist_figures = nyquist(problem, show=True, title="Nyquist Plot", xaxis_title="Real(Z)", yaxis_title="Imag(Z)") >>> # The plots will be displayed and nyquist_figures will contain the list of figure objects. """ - return call_plotting_function( - "nyquist", backend, problem=problem, inputs=inputs, show=show, **layout_kwargs - ) - -def _nyquist( - problem, trace_options_model: dict, trace_options_reference, inputs: Inputs = None -): + options = get_default_options("nyquist", backend) + plot_options = options.get("plot_options") or {} + trace_options_model = options.get("trace_options_model") or {} + trace_options_reference = options.get("trace_options_reference") or {} if not isinstance(inputs, dict): inputs = problem.parameters.to_dict(inputs) @@ -61,6 +60,8 @@ def _nyquist( x=domain_data, y=-model_output[var].data.imag, trace_names="Model", + title=title, + **plot_options, ) plot_dict.traces[0].update(trace_options_model) @@ -75,4 +76,7 @@ def _nyquist( fig = plot_dict(show=False) figure_list.append(fig) + if show: + update_and_show(figure_list) + return figure_list diff --git a/pybop/plot/plotly/__init__.py b/pybop/plot/plotly/__init__.py index ad894f9bb..4de178480 100644 --- a/pybop/plot/plotly/__init__.py +++ b/pybop/plot/plotly/__init__.py @@ -1,8 +1,5 @@ from .plotly_manager import PlotlyManager -from .standard_plots import Plotter, SubplotPlotter, show_table, trajectories -from .contour import contour -from .convergence import convergence -from .nyquist import nyquist +from .standard_plots import Plotter, SubplotPlotter, show_table, trajectories, plot_optimisation_path from .voronoi import surface from .util import update_and_show, DEFAULT_PLOT_OPTIONS diff --git a/pybop/plot/plotly/contour.py b/pybop/plot/plotly/contour.py deleted file mode 100644 index 4fb1a1156..000000000 --- a/pybop/plot/plotly/contour.py +++ /dev/null @@ -1,263 +0,0 @@ -import warnings -from collections.abc import Callable -from typing import TYPE_CHECKING - -import numpy as np - -from pybop.plot.plotly.plotly_manager import PlotlyManager -from pybop.problems.problem import Problem - -if TYPE_CHECKING: - from pybop._result import Result - - -def contour( - call_object: "Problem | Result", - gradient: bool = False, - bounds: np.ndarray | None = None, - transformed: bool = False, - steps: int = 10, - show: bool = True, - **layout_kwargs, -): - """ - Plot a 2D visualisation of a cost landscape using Plotly. - - This function generates a contour plot representing the cost landscape for a provided - callable cost function over a grid of parameter values within the specified bounds. - - Parameters - ---------- - call_object : pybop.Problem | pybop.Result - Either: - - the cost function to be evaluated. Must accept a list of parameter values and return a cost value. - - an optimiser result which provides a specific optimisation trace overlaid on the cost landscape. - gradient : bool, optional - If True, the gradient is shown (default: False). - bounds : numpy.ndarray | list[list[float]], optional - A 2x2 array specifying the [min, max] bounds for each parameter. If None, uses - `parameters.get_bounds_for_plotly`. - transformed : bool, optional - Uses the transformed parameter values (as seen by the optimiser) for plotting. - steps : int, optional - The number of grid points to divide the parameter space into along each dimension (default: 10). - show : bool, optional - If True, the figure is shown upon creation (default: True). - **layout_kwargs : optional - Valid Plotly layout keys and their values, - e.g. `xaxis_title="Time [s]"` or - `xaxis={"title": "Time [s]", font={"size":14}}` - - Returns - ------- - plotly.graph_objs.Figure - The Plotly figure object containing the cost landscape plot. - - Raises - ------ - ValueError - If the cost function does not return a valid cost when called with a parameter list. - """ - plot_optim = False - problem = call_object - - # Assign input as a cost or optimisation result - if not isinstance(call_object, Callable): - plot_optim = True - result = call_object - problem = result.problem - - parameters = problem.parameters - names = parameters.names - additional_values = [] - - if len(parameters) < 2: - raise ValueError("This cost function takes fewer than 2 parameters.") - - if len(parameters) > 2: - warnings.warn( - "This cost function requires more than 2 parameters. " - "Plotting in 2d with fixed values for the additional parameters.", - UserWarning, - stacklevel=2, - ) - for ( - i, - (name, param), - ) in enumerate(parameters.items()): - if i > 1: - # TODO: Update from the initial to the intended value - additional_values.append(param.get_initial_value()) - print(f"Fixed {name}:", param.get_initial_value()) - - # Set up parameter bounds - if bounds is None: - bounds = parameters.get_bounds_for_plotly() - else: - bounds = np.asarray(bounds) - - # Generate grid - x = np.linspace(bounds[0, 0], bounds[0, 1], steps) - y = np.linspace(bounds[1, 0], bounds[1, 1], steps) - - # Initialize cost matrix - costs = np.zeros((len(y), len(x))) - - if gradient: - grad_parameter_costs = [] - - # Create an array to hold the gradient with respect to each parameter - grads = [np.zeros((len(y), len(x))) for _ in range(len(parameters))] - - # Populate cost matrix - for i, xi in enumerate(x): - for j, yj in enumerate(y): - if gradient: - out = problem.evaluate( - np.asarray([xi, yj] + additional_values), - calculate_sensitivities=True, - ).get_values() - costs[j, i], sensitivities = out[0][0], out[1] - for k, key in enumerate(problem.parameters.names): - grads[k][j, i] = sensitivities[key].item() - else: - costs[j, i] = problem.evaluate( - np.asarray([xi, yj] + additional_values), - ).get_values()[0] - - # Append the arrays to the grad_parameter_costs list - if gradient: - grad_parameter_costs.extend(grads) - - # Apply any transformation if requested - def transform_array_of_values(list_of_values, parameter): - """Apply transformation if requested.""" - if transformed: - return np.asarray( - [parameter.transformation.to_search(value) for value in list_of_values] - ).flatten() - return list_of_values - - x = transform_array_of_values(x, parameters[names[0]]) - y = transform_array_of_values(y, parameters[names[1]]) - bounds[0] = transform_array_of_values(bounds[0], parameters[names[0]]) - bounds[1] = transform_array_of_values(bounds[1], parameters[names[1]]) - - # Import plotly only when needed - go = PlotlyManager().go - - # Set default layout properties - layout_options = dict( - title="Cost Landscape", - title_x=0.5, - title_y=0.905, - width=600, - height=600, - xaxis=dict(range=bounds[0], showexponent="last", exponentformat="e"), - yaxis=dict(range=bounds[1], showexponent="last", exponentformat="e"), - legend=dict(orientation="h", yanchor="bottom", y=1, xanchor="right", x=1), - ) - layout_options["xaxis_title"] = ( - "Transformed " + names[0] if transformed else names[0] - ) - layout_options["yaxis_title"] = ( - "Transformed " + names[1] if transformed else names[1] - ) - layout = go.Layout(layout_options) - - # Create contour plot and update the layout - fig = go.Figure( - data=[go.Contour(x=x, y=y, z=costs, colorscale="Viridis", connectgaps=True)], - layout=layout, - ) - - if plot_optim: - # Plot the optimisation trace - optim_trace = np.asarray([item[:2] for item in result.x_model]) - optim_trace = optim_trace.reshape(-1, 2) - - fig.add_trace( - go.Scatter( - x=transform_array_of_values(optim_trace[:, 0], parameters[names[0]]), - y=transform_array_of_values(optim_trace[:, 1], parameters[names[1]]), - mode="markers", - marker=dict( - color=[i / len(optim_trace) for i in range(len(optim_trace))], - colorscale="Greys", - size=8, - showscale=False, - ), - showlegend=False, - ) - ) - - # Plot the initial guess - if len(result.x_model) > 0: - x0 = result.x_model[0] - fig.add_trace( - go.Scatter( - x=transform_array_of_values([x0[0]], parameters[names[0]]), - y=transform_array_of_values([x0[1]], parameters[names[1]]), - mode="markers", - marker_symbol="x", - marker=dict( - color="white", - line_color="black", - line_width=1, - size=14, - showscale=False, - ), - name="Initial values", - ) - ) - - # Plot optimised value - if result.x is not None: - x_best = result.x - fig.add_trace( - go.Scatter( - x=transform_array_of_values([x_best[0]], parameters[names[0]]), - y=transform_array_of_values([x_best[1]], parameters[names[1]]), - mode="markers", - marker_symbol="cross", - marker=dict( - color="black", - line_color="white", - line_width=1, - size=14, - showscale=False, - ), - name="Final values", - ) - ) - - # Update the layout and display the figure - fig.update_layout(**layout_kwargs) - if show: - fig.show() - - if gradient: - grad_figs = [] - for i, grad_costs in enumerate(grad_parameter_costs): - # Update title for gradient plots - updated_layout_options = layout_options.copy() - updated_layout_options["title"] = f"Gradient for Parameter: {i + 1}" - - # Create contour plot with updated layout options - grad_layout = go.Layout(updated_layout_options) - - # Create fig - grad_fig = go.Figure( - data=[go.Contour(x=x, y=y, z=grad_costs)], layout=grad_layout - ) - grad_fig.update_layout(**layout_kwargs) - - if show: - grad_fig.show() - - # append grad_fig to list - grad_figs.append(grad_fig) - - return fig, grad_figs - - return fig diff --git a/pybop/plot/plotly/nyquist.py b/pybop/plot/plotly/nyquist.py deleted file mode 100644 index 1578d2462..000000000 --- a/pybop/plot/plotly/nyquist.py +++ /dev/null @@ -1,101 +0,0 @@ -from pybop.parameters.parameter import Inputs -from pybop.plot.nyquist import _nyquist - - -def nyquist(problem, inputs: Inputs = None, show=True, **layout_kwargs): - """ - Generates Nyquist plots for the given problem by evaluating the model's output and target values. - - Parameters - ---------- - problem : pybop.Problem - An instance of a problem class that contains the parameters and methods - for evaluation and target retrieval. - inputs : Inputs, optional - Input parameters for the problem. If not provided, the default parameters from the problem - instance will be used. These parameters are verified before use (default is None). - show : bool, optional - If True, the plots will be displayed. - **layout_kwargs : dict, optional - Additional keyword arguments for customising the plot layout. These arguments are passed to - `fig.update_layout()`. - - Returns - ------- - list - A list of plotly `Figure` objects, each representing a Nyquist plot for the model's output and target values. - - Notes - ----- - - The function extracts the real part of the impedance from the model's output and the real and imaginary parts - of the impedance from the target output. - - For each signal in the problem, a Nyquist plot is created with the model's impedance plotted as a scatter plot. - - An additional trace for the reference (target output) is added to the plot. - - The plot layout can be customised using `layout_kwargs`. - - Example - ------- - >>> problem = pybop.EISProblem() - >>> nyquist_figures = nyquist(problem, show=True, title="Nyquist Plot", xaxis_title="Real(Z)", yaxis_title="Imag(Z)") - >>> # The plots will be displayed and nyquist_figures will contain the list of figure objects. - """ - default_layout_options = dict( - title="Nyquist Plot", - font=dict(family="Arial", size=14), - plot_bgcolor="white", - paper_bgcolor="white", - xaxis=dict( - title=dict(text="Zre / Ω", font=dict(size=16), standoff=15), - showline=True, - linewidth=2, - linecolor="black", - mirror=True, - ticks="outside", - tickwidth=2, - tickcolor="black", - ticklen=5, - ), - yaxis=dict( - title=dict(text="-Zim / Ω", font=dict(size=16), standoff=15), - showline=True, - linewidth=2, - linecolor="black", - mirror=True, - ticks="outside", - tickwidth=2, - tickcolor="black", - ticklen=5, - scaleanchor="x", - scaleratio=1, - ), - legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1), - width=600, - height=600, - ) - - # Overwrite with user-kwargs - default_layout_options.update(layout_kwargs) - - trace_options_model = dict( - mode="lines+markers", - line=dict(color="#00CC96", width=2), - marker=dict(size=8, color="#00CC96", symbol="circle"), - ) - - trace_options_reference = dict( - name="Reference", - mode="markers", - marker=dict(size=8, color="#636EFA", symbol="circle-open"), - showlegend=True, - ) - - figure_list = _nyquist( - problem, trace_options_model, trace_options_reference, inputs=inputs - ) - - for fig in figure_list: - fig.update_layout(**default_layout_options) - if show: - fig.show() - - return figure_list diff --git a/pybop/plot/plotly/standard_plots.py b/pybop/plot/plotly/standard_plots.py index 537a24286..80429f0c4 100644 --- a/pybop/plot/plotly/standard_plots.py +++ b/pybop/plot/plotly/standard_plots.py @@ -63,6 +63,8 @@ def __init__( title=None, xaxis_title=None, yaxis_title=None, + xaxis_range=None, + yaxis_range=None, layout=None, layout_options=None, trace_options=None, @@ -77,6 +79,16 @@ def __init__( self.layout_options = DEFAULT_LAYOUT_OPTIONS.copy() if layout_options: self.layout_options.update(layout_options) + if title is not None: + self.layout_options.update({"title": title}) + if xaxis_title is not None: + self.layout_options.update({"xaxis_title": xaxis_title}) + if yaxis_title is not None: + self.layout_options.update({"yaxis_title": yaxis_title}) + if xaxis_range is not None: + self.layout_options["xaxis"].update({"range": xaxis_range}) + if yaxis_range is not None: + self.layout_options["yaxis"].update({"range": yaxis_range}) # Set default trace options and update if provided self.trace_options = DEFAULT_TRACE_OPTIONS.copy() @@ -90,16 +102,6 @@ def __init__( if self.layout is None: self.layout = self.go.Layout(**self.layout_options) - title_options = {} - if title is not None: - title_options.update({"title": title}) - if title is not None: - title_options.update({"xaxis_title": xaxis_title}) - if title is not None: - title_options.update({"yaxis_title": yaxis_title}) - - self.layout.update(**title_options) - def __call__(self, show=True): """ Generate and show the figure. @@ -184,6 +186,9 @@ def create_histogram(self, x, name, **trace_options): def create_vline(self, fig, x, **trace_options): fig.add_vline(x=x, **trace_options) + def create_contour(self, x, y, z, **trace_options): + self.traces.append(self.go.Contour(x=x, y=y, z=z, **trace_options)) + class SubplotPlotter(Plotter): """ @@ -223,9 +228,10 @@ def __init__( layout_options=DEFAULT_LAYOUT_OPTIONS, subplot_options=DEFAULT_SUBPLOT_OPTIONS, trace_options=DEFAULT_SUBPLOT_TRACE_OPTIONS, - **kwargs, ): - super().__init__(layout, layout_options, trace_options, **kwargs) + super().__init__( + layout, layout_options=layout_options, trace_options=trace_options + ) self.subplot_options = subplot_options.copy() if subplot_options is not None: for arg, value in subplot_options.items(): @@ -329,3 +335,20 @@ def show_table(header, values, title): fig.update_layout(title=title) fig.show() + + +def plot_optimisation_path(plot_dict: StandardPlot, x, y): + plot_dict.traces.append( + plot_dict.create_trace( + x, + y, + mode="markers", + marker=dict( + color=[i / len(x) for i in range(len(x))], + colorscale="Greys", + size=8, + showscale=False, + ), + showlegend=False, + ) + ) diff --git a/pybop/plot/plotly/util.py b/pybop/plot/plotly/util.py index f622890fb..9a913b3aa 100644 --- a/pybop/plot/plotly/util.py +++ b/pybop/plot/plotly/util.py @@ -12,6 +12,102 @@ def update_and_show(fig, show=True, **layout_kwargs): DEFAULT_PLOT_OPTIONS = { + "contour": { + "plot_options": dict( + layout_options=dict( + title="Cost Landscape", + title_x=0.5, + title_y=0.905, + width=600, + height=600, + xaxis=dict(showexponent="last", exponentformat="e"), + yaxis=dict(showexponent="last", exponentformat="e"), + legend=dict( + orientation="h", yanchor="bottom", y=1, xanchor="right", x=1 + ), + autosize=None, + showlegend=None, + margin=None, + ) + ), + "trace_options_contour": dict(colorscale="Viridis", connectgaps=True), + "trace_options_initial": dict( + mode="markers", + marker_symbol="x", + marker=dict( + color="white", + line_color="black", + line_width=1, + size=14, + showscale=False, + ), + name="Initial values", + ), + "trace_options_optim": dict( + mode="markers", + marker_symbol="cross", + marker=dict( + color="black", + line_color="white", + line_width=1, + size=14, + showscale=False, + ), + name="Final values", + ), + }, + "nyquist": { + "plot_options": dict( + layout_options=dict( + title="Nyquist Plot", + font=dict(family="Arial", size=14), + plot_bgcolor="white", + paper_bgcolor="white", + xaxis=dict( + title=dict(font=dict(size=16), standoff=15), + showline=True, + linewidth=2, + linecolor="black", + mirror=True, + ticks="outside", + tickwidth=2, + tickcolor="black", + ticklen=5, + ), + yaxis=dict( + title=dict(font=dict(size=16), standoff=15), + showline=True, + linewidth=2, + linecolor="black", + mirror=True, + ticks="outside", + tickwidth=2, + tickcolor="black", + ticklen=5, + scaleanchor="x", + scaleratio=1, + ), + legend=dict( + orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1 + ), + width=600, + height=600, + ), + xaxis_title="Zre / Ω", + yaxis_title="-Zim / Ω", + ), + "trace_options_model": dict( + mode="lines+markers", + line=dict(color="#00CC96", width=2), + marker=dict(size=8, color="#00CC96", symbol="circle"), + ), + "trace_options_reference": dict( + name="Reference", + mode="markers", + marker=dict(size=8, color="#636EFA", symbol="circle-open"), + showlegend=True, + ), + }, "parameters": dict( layout_options=dict( title="Parameter Convergence", @@ -22,6 +118,20 @@ def update_and_show(fig, show=True, **layout_kwargs): ), ) ), + "posterior": { + "plot_options": { + "layout_options": dict( + barmode="overlay", + width=None, + height=None, + plot_bgcolor=None, + autosize=None, + legend=None, + ) + }, + "trace_options": dict(opacity=0.75), + "trace_options_vline": dict(line_width=3, line_dash="dash", line_color="black"), + }, "problem": { "default_trace_options": dict(name="Model", mode="lines", showlegend=True), "design_cost_options": dict(name="Optimised"), @@ -37,18 +147,4 @@ def update_and_show(fig, show=True, **layout_kwargs): }, "trace_options": dict(mode="lines"), }, - "posterior": { - "plot_options": { - "layout_options": dict( - barmode="overlay", - width=None, - height=None, - plot_bgcolor=None, - autosize=None, - legend=None, - ) - }, - "trace_options": dict(opacity=0.75), - "trace_options_vline": dict(line_width=3, line_dash="dash", line_color="black"), - }, } diff --git a/pybop/plot/plots.py b/pybop/plot/plots.py index 99dc699b7..873af49fe 100644 --- a/pybop/plot/plots.py +++ b/pybop/plot/plots.py @@ -70,31 +70,6 @@ def contour( ) -def convergence(result: "Result", show=True, backend=None, **layout_kwargs): - """ - Plot the convergence of the optimisation algorithm. - - Parameters - ----------- - result : pybop.Result - Optimisation result containing the history of parameter values and associated cost. - show : bool, optional - If True, the figure is shown upon creation (default: True). - **layout_kwargs : optional - Valid Plotly layout keys and their values, - e.g. `xaxis_title="Time [s]"` or - `xaxis={"title": "Time [s]", font={"size":14}}` - - Returns - --------- - fig : plotly.graph_objs.Figure - The Plotly figure object for the convergence plot. - """ - return call_plotting_function( - "convergence", backend, result=result, show=show, **layout_kwargs - ) - - def surface( result: "Result", bounds=None, diff --git a/pybop/plot/standard_plots.py b/pybop/plot/standard_plots.py index b1a6f9e33..3287789c5 100644 --- a/pybop/plot/standard_plots.py +++ b/pybop/plot/standard_plots.py @@ -115,6 +115,9 @@ def create_histogram(self, x, name, **trace_options): def create_vline(self, fig, x, **trace_options): return self.plotter.create_vline(fig, x, **trace_options) + def create_contour(self, x, y, z, **trace_options): + return self.plotter.create_contour(x, y, z, **trace_options) + @staticmethod def wrap_text(text, width, backend="matplotlib"): """ From 5c12c725c4edc61289d1d835cb803fe5c0cb63a7 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Mon, 13 Apr 2026 16:46:54 +0000 Subject: [PATCH 10/20] style: pre-commit fixes --- pybop/plot/contour.py | 44 ++++++++++++++++++++++----------------- pybop/plot/convergence.py | 2 +- 2 files changed, 26 insertions(+), 20 deletions(-) diff --git a/pybop/plot/contour.py b/pybop/plot/contour.py index c6ff92786..cdd5b8b59 100644 --- a/pybop/plot/contour.py +++ b/pybop/plot/contour.py @@ -150,15 +150,15 @@ def transform_array_of_values(list_of_values, parameter): trace_options_initial = options.get("trace_options_initial") or {} trace_options_optim = options.get("trace_options_optim") or {} trace_options_contour = options.get("trace_options_contour") or {} - + plot_dict = StandardPlot( - xaxis_title="Transformed " + names[0] if transformed else names[0], - yaxis_title="Transformed " + names[1] if transformed else names[1], - xaxis_range=bounds[0], - yaxis_range=bounds[1], - backend=backend, - **plot_options - ) + xaxis_title="Transformed " + names[0] if transformed else names[0], + yaxis_title="Transformed " + names[1] if transformed else names[1], + xaxis_range=bounds[0], + yaxis_range=bounds[1], + backend=backend, + **plot_options, + ) # Create contour plot and update the layout plot_dict.create_contour(x=x, y=y, z=costs, **trace_options_contour) @@ -167,7 +167,9 @@ def transform_array_of_values(list_of_values, parameter): # Plot the optimisation trace optim_trace = np.asarray([item[:2] for item in result.x_model]) optim_trace = optim_trace.reshape(-1, 2) - call_plotting_function('plot_optimisation_path', backend=backend, + call_plotting_function( + "plot_optimisation_path", + backend=backend, plot_dict=plot_dict, x=transform_array_of_values(optim_trace[:, 0], parameters[names[0]]), y=transform_array_of_values(optim_trace[:, 1], parameters[names[1]]), @@ -176,20 +178,24 @@ def transform_array_of_values(list_of_values, parameter): # Plot the initial guess if len(result.x_model) > 0: x0 = result.x_model[0] - plot_dict.traces.append(plot_dict.create_trace( - x=transform_array_of_values([x0[0]], parameters[names[0]]), - y=transform_array_of_values([x0[1]], parameters[names[1]]), - **trace_options_initial - )) + plot_dict.traces.append( + plot_dict.create_trace( + x=transform_array_of_values([x0[0]], parameters[names[0]]), + y=transform_array_of_values([x0[1]], parameters[names[1]]), + **trace_options_initial, + ) + ) # Plot optimised value if result.x is not None: x_best = result.x - plot_dict.traces.append(plot_dict.create_trace( - x=transform_array_of_values([x_best[0]], parameters[names[0]]), - y=transform_array_of_values([x_best[1]], parameters[names[1]]), - **trace_options_optim - )) + plot_dict.traces.append( + plot_dict.create_trace( + x=transform_array_of_values([x_best[0]], parameters[names[0]]), + y=transform_array_of_values([x_best[1]], parameters[names[1]]), + **trace_options_optim, + ) + ) # Update the layout and display the figure fig = plot_dict(show=False) diff --git a/pybop/plot/convergence.py b/pybop/plot/convergence.py index 9e3b1bbb7..5d3390959 100644 --- a/pybop/plot/convergence.py +++ b/pybop/plot/convergence.py @@ -42,7 +42,7 @@ def convergence(result: "Result", show=True, backend=None): yaxis_title="Cost", title="Convergence", trace_names=result.method_name, - backend=backend + backend=backend, ) # Generate and display the figure From b78af4e6ef701084b01135ba1b001b31510d5e3d Mon Sep 17 00:00:00 2001 From: u2370093 Date: Tue, 21 Apr 2026 12:26:36 +0100 Subject: [PATCH 11/20] Reduce code duplication --- pybop/plot/__init__.py | 2 +- pybop/plot/contour.py | 47 ++- pybop/plot/convergence.py | 2 +- pybop/plot/matplotlib/__init__.py | 6 +- pybop/plot/matplotlib/figure_handling.py | 86 +++++ pybop/plot/matplotlib/plotting_functions.py | 202 ++++++++++ pybop/plot/matplotlib/standard_plots.py | 385 -------------------- pybop/plot/matplotlib/util.py | 14 +- pybop/plot/nyquist.py | 3 +- pybop/plot/parameters.py | 9 +- pybop/plot/plotly/__init__.py | 6 +- pybop/plot/plotly/figure_handling.py | 40 ++ pybop/plot/plotly/plotting_functions.py | 143 ++++++++ pybop/plot/plotly/standard_plots.py | 354 ------------------ pybop/plot/plotly/util.py | 150 ++++---- pybop/plot/plots.py | 71 +--- pybop/plot/samples.py | 7 +- pybop/plot/standard_plots.py | 150 +++++--- pybop/plot/util.py | 37 +- 19 files changed, 762 insertions(+), 952 deletions(-) create mode 100644 pybop/plot/matplotlib/figure_handling.py create mode 100644 pybop/plot/matplotlib/plotting_functions.py delete mode 100644 pybop/plot/matplotlib/standard_plots.py create mode 100644 pybop/plot/plotly/figure_handling.py create mode 100644 pybop/plot/plotly/plotting_functions.py delete mode 100644 pybop/plot/plotly/standard_plots.py diff --git a/pybop/plot/__init__.py b/pybop/plot/__init__.py index da5e84d44..7151009f0 100644 --- a/pybop/plot/__init__.py +++ b/pybop/plot/__init__.py @@ -2,7 +2,7 @@ DEFAULT_BACKEND = 'matplotlib' backend=DEFAULT_BACKEND -from .util import set_backend, call_plotting_function, get_default_options +from .util import set_backend, get_default_options, import_backend, _AxisData # # Import plots diff --git a/pybop/plot/contour.py b/pybop/plot/contour.py index c6ff92786..3ae41ba4d 100644 --- a/pybop/plot/contour.py +++ b/pybop/plot/contour.py @@ -20,6 +20,7 @@ def contour( steps: int = 10, show: bool = True, backend=None, + **layout_options, ): """ Plot a 2D visualisation of a cost landscape using Plotly. @@ -150,15 +151,17 @@ def transform_array_of_values(list_of_values, parameter): trace_options_initial = options.get("trace_options_initial") or {} trace_options_optim = options.get("trace_options_optim") or {} trace_options_contour = options.get("trace_options_contour") or {} - + + plot_options.update(layout_options) + plot_dict = StandardPlot( - xaxis_title="Transformed " + names[0] if transformed else names[0], - yaxis_title="Transformed " + names[1] if transformed else names[1], - xaxis_range=bounds[0], - yaxis_range=bounds[1], - backend=backend, - **plot_options - ) + xaxis_title="Transformed " + names[0] if transformed else names[0], + yaxis_title="Transformed " + names[1] if transformed else names[1], + xaxis_range=bounds[0], + yaxis_range=bounds[1], + backend=backend, + **plot_options, + ) # Create contour plot and update the layout plot_dict.create_contour(x=x, y=y, z=costs, **trace_options_contour) @@ -167,7 +170,9 @@ def transform_array_of_values(list_of_values, parameter): # Plot the optimisation trace optim_trace = np.asarray([item[:2] for item in result.x_model]) optim_trace = optim_trace.reshape(-1, 2) - call_plotting_function('plot_optimisation_path', backend=backend, + call_plotting_function( + "plot_optimisation_path", + backend=backend, plot_dict=plot_dict, x=transform_array_of_values(optim_trace[:, 0], parameters[names[0]]), y=transform_array_of_values(optim_trace[:, 1], parameters[names[1]]), @@ -176,20 +181,24 @@ def transform_array_of_values(list_of_values, parameter): # Plot the initial guess if len(result.x_model) > 0: x0 = result.x_model[0] - plot_dict.traces.append(plot_dict.create_trace( - x=transform_array_of_values([x0[0]], parameters[names[0]]), - y=transform_array_of_values([x0[1]], parameters[names[1]]), - **trace_options_initial - )) + plot_dict.traces.append( + plot_dict.create_trace( + x=transform_array_of_values([x0[0]], parameters[names[0]]), + y=transform_array_of_values([x0[1]], parameters[names[1]]), + **trace_options_initial, + ) + ) # Plot optimised value if result.x is not None: x_best = result.x - plot_dict.traces.append(plot_dict.create_trace( - x=transform_array_of_values([x_best[0]], parameters[names[0]]), - y=transform_array_of_values([x_best[1]], parameters[names[1]]), - **trace_options_optim - )) + plot_dict.traces.append( + plot_dict.create_trace( + x=transform_array_of_values([x_best[0]], parameters[names[0]]), + y=transform_array_of_values([x_best[1]], parameters[names[1]]), + **trace_options_optim, + ) + ) # Update the layout and display the figure fig = plot_dict(show=False) diff --git a/pybop/plot/convergence.py b/pybop/plot/convergence.py index 9e3b1bbb7..5d3390959 100644 --- a/pybop/plot/convergence.py +++ b/pybop/plot/convergence.py @@ -42,7 +42,7 @@ def convergence(result: "Result", show=True, backend=None): yaxis_title="Cost", title="Convergence", trace_names=result.method_name, - backend=backend + backend=backend, ) # Generate and display the figure diff --git a/pybop/plot/matplotlib/__init__.py b/pybop/plot/matplotlib/__init__.py index 2730122df..2ff7aea19 100644 --- a/pybop/plot/matplotlib/__init__.py +++ b/pybop/plot/matplotlib/__init__.py @@ -1,3 +1,7 @@ -from .standard_plots import Plotter, SubplotPlotter, show_table, trajectories, plot_optimisation_path from .voronoi import surface from .util import update_and_show, DEFAULT_PLOT_OPTIONS + +from .plotting_functions import line_plot, contour_plot, scatter_plot, fill_plot, histogram_plot, add_vline, plot_trace, add_traces, show_table, trajectories, plot_optimisation_path +from .figure_handling import create_figure, make_subplots, update_layout + +name="matplotlib" diff --git a/pybop/plot/matplotlib/figure_handling.py b/pybop/plot/matplotlib/figure_handling.py new file mode 100644 index 000000000..30b0ae3f6 --- /dev/null +++ b/pybop/plot/matplotlib/figure_handling.py @@ -0,0 +1,86 @@ +from matplotlib import pyplot as plt + +from pybop.plot.matplotlib import plot_trace +from pybop.plot.util import _AxisData + + +def create_figure(traces, **layout_options): + figsize = (8, 6) + if "figsize" in layout_options: + figsize = layout_options.get("figsize") + + fig = plt.figure(figsize=figsize, dpi=100) + + for trace in traces: + plot_trace(trace, fig) + + update_layout(fig, **layout_options) + return fig + + +def update_layout(fig, axes=None, **layout_options): + if axes is None: + axes = [plt.gca()] + if "title" in layout_options: + plt.suptitle(layout_options.get("title")) + if "xaxis_title" in layout_options: + plt.xlabel(layout_options.get("xaxis_title")) + if "yaxis_title" in layout_options: + plt.ylabel(layout_options.get("yaxis_title")) + if "grid" in layout_options: + grid = layout_options.get("grid") + plt.grid(**grid) + if "xaxis_range" in layout_options: + plt.xlim(layout_options.get("xaxis_range")) + if "yaxis_range" in layout_options: + plt.ylim(layout_options.get("yaxis_range")) + if "figsize" in layout_options: + fig.set_size_inches(layout_options.get("figsize")) + + # plt.tick_params(axis="both", labelsize=12) + # plt.ticklabel_format(axis="both", style="sci", scilimits=(-4, 4)) + + for ax in axes: + if "axis_bg_color" in layout_options: + ax.set_facecolor(layout_options.get("axis_bg_color")) + ax.set_axisbelow(True) + if ( + not ax.get_legend_handles_labels() == ([], []) + and "fig_legend" not in layout_options + ): + ax.legend(layout_options.get("legend") or {}) + + if "fig_legend" in layout_options: + labels_in_fig = False + lines_labels = [] + for ax in axes: + if not ax.get_legend_handles_labels() == ([], []): + lines_labels.append(ax.get_legend_handles_labels()) + labels_in_fig = True + if labels_in_fig: + lines, labels = [sum(lol, []) for lol in zip(*lines_labels, strict=False)] + opts = dict(loc="best", fontsize=12) + opts.update(layout_options.get("fig_legend") or {}) + if opts.get("horizontal"): + opts["ncols"] = len(lines) + del opts["horizontal"] + fig.legend(lines, labels, **opts) + + if "tight_layout" in layout_options: + plt.tight_layout(**layout_options.get("tight_layout")) + + +def make_subplots(axes: list[_AxisData], subplot_options=None): + subplot_options = subplot_options or {} + + num_rows = max(ax.row + ax.row_span - 1 for ax in axes) + num_cols = max(ax.col + ax.col_span - 1 for ax in axes) + + fig = plt.figure() + + for i, ax in enumerate(axes): + idx_start = (ax.row - 1) * num_cols + ax.col + idx_end = (ax.row + ax.row_span - 2) * num_cols + ax.col + ax.col_span - 1 + axes[i] = fig.add_subplot(num_rows, num_cols, (idx_start, idx_end)) + + return fig diff --git a/pybop/plot/matplotlib/plotting_functions.py b/pybop/plot/matplotlib/plotting_functions.py new file mode 100644 index 000000000..0e7fa7856 --- /dev/null +++ b/pybop/plot/matplotlib/plotting_functions.py @@ -0,0 +1,202 @@ +import warnings +from copy import deepcopy + +from matplotlib import pyplot as plt + +from pybop.plot import StandardPlot + + +def add_traces(x, y, trace_names, **trace_options): + color_cycle = None + if trace_options.get("use_color_cycle"): + color_cycle = plt.rcParams["axes.prop_cycle"]() + del trace_options["use_color_cycle"] + traces = [] + xi = x[0] + for i in range(0, len(y)): + if len(x) > 1: + xi = x[i] + label = None + if trace_names is not None: + label = trace_names[i] + if color_cycle is not None: + traces.append( + line_plot(xi, y[i], label, **trace_options, **next(color_cycle)) + ) + else: + traces.append(line_plot(xi, y[i], label, **trace_options)) + + return traces + + +def plot_trace(trace: dict, fig, ax=None, color_cycle=None): + # retrieve axis, plot_type and positional arguments + plot_type = trace.get("plot_type") or "plot" + positional_args = trace.get("positional_args") or None + ax = ax or fig.gca() + + # axis titles + if "xaxis_title" in trace.keys(): + ax.set_xlabel(trace["xaxis_title"]) + if "yaxis_title" in trace.keys(): + ax.set_ylabel(trace["yaxis_title"]) + + # retrieve remaining arguments + trace_options = deepcopy(trace) + for key in ["plot_type", "ax", "positional_args", "xaxis_title", "yaxis_title"]: + if key in trace_options.keys(): + del trace_options[key] + + # update color + if color_cycle is not None and plot_type == "plot": + trace_options.update(**next(color_cycle)) + + # get plotting function + try: + plot_function = getattr(ax, plot_type) + except ValueError: + print("Plot type not recognised") + + obj = plot_function(*positional_args, **trace_options) + if plot_type == "contourf": + plt.colorbar(obj) + + return obj + + +def line_plot(x=None, y=None, label=None, ax=None, **kwargs): + if x is not None and y is not None: + size = min(len(x), len(y)) + trace = dict(positional_args=[x[:size], y[:size]], label=label, ax=ax) + elif y is not None: + trace = dict(positional_args=[y], label=label, ax=ax) + + trace.update(kwargs) + return trace + + +def contour_plot(x, y, z, **kwargs): + contour = dict(positional_args=[x, y, z], plot_type="contourf") + contour.update(**kwargs) + contour_lines = dict( + positional_args=[x, y, z], + colors=("k"), + linestyles="solid", + linewidths=0.2, + plot_type="contour", + ) + return contour, contour_lines + + +def scatter_plot(x, y, **trace_options): + scatter = dict(positional_args=[x, y], plot_type="scatter") + scatter.update(**trace_options) + return scatter + + +def fill_plot(x, y_upper, y_lower, **options): + trace = dict(positional_args=(x, y_upper, y_lower), plot_type="fill_between") + trace.update(options) + return trace + + +def histogram_plot(x, name, **trace_options): + trace = dict(positional_args=[x], label=name, plot_type="hist") + trace.update(trace_options) + return trace + + +def add_vline(fig, x, **trace_options): + fig.gca() + plt.axvline(x, **trace_options) + + +def show_table(header, values, title): + """ + Display data in a table. + """ + for i, val in enumerate(values): + values[i] = [val[0], ", ".join(val[1].astype(str))] + + fig, ax = plt.subplots(figsize=(6, 2), dpi=100) + + # hide axes + ax.axis("off") + ax.axis("tight") + ax.table( + cellText=values, + colLabels=header, + loc="center", + cellLoc="center", + colColours=["lightsteelblue", "lightsteelblue"], + ) + ax.set_title(title) + fig.tight_layout() + plt.show() + + +def plot_optimisation_path(plot_dict: StandardPlot, x, y): + plot_dict.traces.append( + scatter_plot( + x, + y, + c=[i / len(x) for i in range(len(x))], + cmap="Grays", + zorder=1, + ) + ) + + +def trajectories( + x, + y, + trace_names=None, + show=True, + xaxis_title="", + yaxis_title="", + title="", + **layout_kwargs, +): + """ + Quickly plot one or more trajectories using Plotly. + + Parameters + ---------- + x : list or np.ndarray + X-axis data points. + y : list or np.ndarray + Y-axis data points for each trajectory. + trace_names : list or str, optional + Name(s) for the trace(s) (default: None). + **layout_kwargs : optional + This argument is ignored for the matplotlib backend. + Valid Plotly layout keys and their values, + e.g. `xaxis_title="Time / s"` or + `xaxis={"title": "Time [s]", font={"size":14}}` + + Returns + ------- + plotly.graph_objs.Figure + The Plotly figure object for the scatter plot. + """ + + if len(layout_kwargs) > 0: + warnings.warn( + "The following layout argument keys are ignored for the current plotting backend (matplotlib): \n" + f"{list(layout_kwargs.keys())}", + UserWarning, + stacklevel=2, + ) + # Create a plot dictionary + plot_dict = StandardPlot(x=x, y=y, trace_names=trace_names, backend="matplotlib") + + # Generate the figure and update the layout + fig = plot_dict(show=False) + plt.title(title) + plt.xlabel(xaxis_title, fontsize=12) + plt.ylabel(yaxis_title, fontsize=12) + plt.tight_layout() + if show: + fig.show() + + return fig diff --git a/pybop/plot/matplotlib/standard_plots.py b/pybop/plot/matplotlib/standard_plots.py deleted file mode 100644 index bd49cd6d5..000000000 --- a/pybop/plot/matplotlib/standard_plots.py +++ /dev/null @@ -1,385 +0,0 @@ -import warnings - -from matplotlib import pyplot as plt - -from pybop.plot import StandardPlot - -DEFAULT_TRACE_OPTIONS = dict(linewidth=2.0) - - -class Plotter: - """ - A class for creating and displaying interactive Plotly figures. - - Parameters - ---------- - x : list or np.ndarray, optional - X-axis data points. - y : list or np.ndarray, optional - Primary Y-axis data points for simulated model output. - trace_options : dict, optional - Settings to modify the default trace type (default: DEFAULT_TRACE_OPTIONS). - trace_names : str, optional - Name(s) for the primary trace(s) (default: None). - trace_name_width : int, optional - Maximum length of the trace names before text wrapping is used (default: 40). - - Returns - ------- - plotly.graph_objs.Figure - The generated Plotly figure. - """ - - def __init__( - self, - trace_options=None, - figsize=(8, 6), - title=None, - xaxis_title=None, - yaxis_title=None, - xaxis_range=None, - yaxis_range=None, - grid=None, - axis_bg_color=None, - **kwargs, - ): - self.backend = "matplotlib" - self.title = title - # Warning if layout arguments ignored - if len(kwargs) > 0: - warnings.warn( - "The following layout argument keys are ignored for the current plotting backend (matplotlib): \n" - f"{list(kwargs.keys())}", - UserWarning, - stacklevel=2, - ) - - # Set default trace options and update if provided - self.trace_options = DEFAULT_TRACE_OPTIONS.copy() - if trace_options: - self.trace_options.update(trace_options) - - self.fig = plt.figure(figsize=figsize, dpi=100) - if title is not None: - plt.suptitle(self.title) - if xaxis_title is not None: - plt.xlabel(xaxis_title) - if yaxis_title is not None: - plt.ylabel(yaxis_title) - if grid is not None: - plt.grid(**grid) - if axis_bg_color is not None: - ax = plt.gca() - ax.set_facecolor(axis_bg_color) - ax.set_axisbelow(True) - if xaxis_range is not None: - plt.xlim(xaxis_range) - if yaxis_range is not None: - plt.ylim(yaxis_range) - self.traces = [] - - def __call__(self, show=True): - """ - Generate and show the figure. - - Parameters - ---------- - show : bool, optional - If True, the figure is shown upon creation (default: True). - """ - # Add traces - for trace in self.traces: - self._plot_trace(**trace) - - plt.tick_params(axis="both", labelsize=12) - plt.ticklabel_format(axis="both", style="sci", scilimits=(-4, 4)) - - labels_in_fig = True - for ax in self.fig.axes: - if not ax.get_legend_handles_labels() == ([], []): - break - else: - labels_in_fig = False - if labels_in_fig: - plt.legend( - **dict(loc="best", fontsize=12), - ) - - if show: - plt.show() - else: - return self.fig - - def add_traces(self, x, y, trace_names=None, **trace_options): - """ - Add a set of traces to the plot dictionary. - - Parameters - ---------- - x : list or np.ndarray - X-axis data points. - y : list or np.ndarray - Primary Y-axis data points for simulated model output. - trace_names : str or list[str], optional - Name(s) for the primary trace(s) (default: None). - """ - - options = self.trace_options.copy() - options.update(trace_options) - - # Create a trace for each trajectory - xi = x[0] - for i in range(0, len(y)): - trace_options = options.copy() - if len(x) > 1: - xi = x[i] - - label = None - if trace_names is not None: - label = trace_names[i] - - self.traces.append(self.create_trace(xi, y[i], label, **trace_options)) - - def create_trace(self, x=None, y=None, label=None, ax=None, **trace_options): - """ - Add line to plot. - - Returns - ------- - plotly.graph_objs.Scatter - A trace for a Plotly figure. - """ - if x is not None and y is not None: - size = min(len(x), len(y)) - trace = dict(positional_args=[x[:size], y[:size]], label=label, ax=ax) - elif y is not None: - trace = dict(positional_args=[y], label=label, ax=ax) - - trace.update(trace_options) - return trace - - def create_fill_trace(self, x, y_upper, y_lower, **options): - trace = dict(positional_args=(x, y_upper, y_lower), plot_type="fill_between") - trace.update(options) - return trace - - def create_histogram(self, x, name, **trace_options): - trace = dict(positional_args=[x], label=name, plot_type="hist") - trace.update(trace_options) - return trace - - def create_vline(self, fig, x, **trace_options): - fig.gca() - plt.axvline(x, **trace_options) - - def create_contour(self, x, y, z, **trace_options): - contour = dict(positional_args=[x, y, z], plot_type="contourf") - contour.update(**trace_options) - self.traces.append(contour) - self.traces.append( - dict( - positional_args=[x, y, z], - colors=("k"), - linestyles="solid", - linewidths=0.2, - plot_type="contour", - ) - ) - - def create_scatter(self, x, y, **trace_options): - scatter = dict(positional_args=[x, y], plot_type="scatter") - scatter.update(**trace_options) - self.traces.append(scatter) - - def _plot_trace( - self, ax=None, plot_type="plot", positional_args=None, **trace_options - ): - if positional_args is None: - positional_args = [] - if ax is None: - ax = plt.gca() - try: - plot_function = getattr(ax, plot_type) - except ValueError: - print("Plot type not recognised") - - obj = plot_function(*positional_args, **trace_options) - if plot_type == "contourf": - plt.colorbar(obj) - - return obj - - -class SubplotPlotter(Plotter): - """ - A class for creating and displaying a set of interactive Plotly figures in a grid layout. - - Parameters - ---------- - x : list or np.ndarray - X-axis data points. - y : list or np.ndarray - Primary Y-axis data points for simulated model output. - num_rows : int, optional - Number of rows of subplots, can be set automatically (default: None). - num_cols : int, optional - Number of columns of subplots, can be set automatically (default: None). - layout : Plotly layout, optional - A layout for the figure, overrides the layout options (default: None). - trace_options : dict, optional - Settings to modify the default trace type (default: DEFAULT_TRACE_OPTIONS). - trace_names : str, optional - Name(s) for the primary trace(s) (default: None). - trace_name_width : int, optional - Maximum length of the trace names before text wrapping is used (default: 40). - - Returns - ------- - plotly.graph_objs.Figure - The generated Plotly figure. - """ - - def __init__( - self, - axis_titles=None, - trace_options=DEFAULT_TRACE_OPTIONS, - figsize=(8, 6), - **kwargs, - ): - super().__init__(trace_options, figsize, **kwargs) - self.axis_titles = axis_titles - - def __call__(self, show=True, num_rows=1, num_cols=1): - """ - Generate and show the set of figures. - - Parameters - ---------- - show : bool, optional - If True, the figure is shown upon creation (default: True). - """ - - color_cycle = plt.rcParams["axes.prop_cycle"]() - - lines = [] - show_legend = False - for idx, trace in enumerate(self.traces): - ax = self.fig.add_subplot(num_rows, num_cols, idx + 1) - trace["ax"] = ax - if self.axis_titles and idx < len(self.axis_titles): - x_title, y_title = self.axis_titles[idx] - ax.set_xlabel(x_title) - ax.set_ylabel(y_title) - if "label" in trace.keys() and trace["label"] is not None: - show_legend = True - - lines.append(self._plot_trace(**trace, **next(color_cycle))) - - lines_labels = [ax.get_legend_handles_labels() for ax in self.fig.axes] - lines, labels = [sum(lol, []) for lol in zip(*lines_labels, strict=False)] - if show_legend: - self.fig.legend( - lines, - labels, - loc="upper right", - ncol=len(lines), - bbox_to_anchor=(0.99, 0.95), - ) - plt.tight_layout(rect=[0, 0, 1, 0.95]) - - if self.title is not None: - plt.suptitle(self.title) - - if show: - plt.show() - - return self.fig - - -def trajectories( - x, - y, - trace_names=None, - show=True, - xaxis_title="", - yaxis_title="", - title="", - **layout_kwargs, -): - """ - Quickly plot one or more trajectories using Plotly. - - Parameters - ---------- - x : list or np.ndarray - X-axis data points. - y : list or np.ndarray - Y-axis data points for each trajectory. - trace_names : list or str, optional - Name(s) for the trace(s) (default: None). - **layout_kwargs : optional - This argument is ignored for the matplotlib backend. - Valid Plotly layout keys and their values, - e.g. `xaxis_title="Time / s"` or - `xaxis={"title": "Time [s]", font={"size":14}}` - - Returns - ------- - plotly.graph_objs.Figure - The Plotly figure object for the scatter plot. - """ - - if len(layout_kwargs) > 0: - warnings.warn( - "The following layout argument keys are ignored for the current plotting backend (matplotlib): \n" - f"{list(layout_kwargs.keys())}", - UserWarning, - stacklevel=2, - ) - # Create a plot dictionary - plot_dict = StandardPlot(x=x, y=y, trace_names=trace_names, backend="matplotlib") - - # Generate the figure and update the layout - fig = plot_dict(show=False) - plt.title(title) - plt.xlabel(xaxis_title, fontsize=12) - plt.ylabel(yaxis_title, fontsize=12) - plt.tight_layout() - if show: - fig.show() - - return fig - - -def show_table(header, values, title): - """ - Display data in a table. - """ - for i, val in enumerate(values): - values[i] = [val[0], ", ".join(val[1].astype(str))] - - fig, ax = plt.subplots(figsize=(6, 2), dpi=100) - - # hide axes - ax.axis("off") - ax.axis("tight") - ax.table( - cellText=values, - colLabels=header, - loc="center", - cellLoc="center", - colColours=["lightsteelblue", "lightsteelblue"], - ) - ax.set_title(title) - fig.tight_layout() - plt.show() - - -def plot_optimisation_path(plot_dict: StandardPlot, x, y): - plot_dict.plotter.create_scatter( - x, - y, - c=[i / len(x) for i in range(len(x))], - cmap="Grays", - zorder=1, - ) diff --git a/pybop/plot/matplotlib/util.py b/pybop/plot/matplotlib/util.py index b6a7311d8..b5dd31888 100644 --- a/pybop/plot/matplotlib/util.py +++ b/pybop/plot/matplotlib/util.py @@ -19,6 +19,7 @@ def update_and_show(fig, show=True, **layout_kwargs): DEFAULT_PLOT_OPTIONS = { + "standard_plot": {"default_trace_options": dict(linewidth=2.0)}, "contour": { "plot_options": dict(title="Cost Landscape"), "trace_options_contour": dict(extend="both", cmap="viridis"), @@ -55,7 +56,18 @@ def update_and_show(fig, show=True, **layout_kwargs): markeredgecolor="#636EFA", ), }, - "parameters": dict(figsize=(18, 8), title="Parameter Convergence"), + "parameters": dict( + layout_options=dict( + figsize=(18, 8), + title="Parameter Convergence", + fig_legend=dict( + loc="lower right", bbox_to_anchor=(0.98, 0.90), horizontal=True + ), + tight_layout=dict(rect=[0, 0, 1, 0.95]), + ), + subplot_options=dict(wspace=0.3, hspace=0.3), + trace_options=dict(use_color_cycle=True), + ), "problem": { "default_trace_options": dict(label="Model", marker=None, linestyle="-"), "design_cost_options": dict(label="Optimised"), diff --git a/pybop/plot/nyquist.py b/pybop/plot/nyquist.py index 5d1452f2e..399558654 100644 --- a/pybop/plot/nyquist.py +++ b/pybop/plot/nyquist.py @@ -48,6 +48,8 @@ def nyquist( trace_options_model = options.get("trace_options_model") or {} trace_options_reference = options.get("trace_options_reference") or {} + plot_options.update({"title": title}) + if not isinstance(inputs, dict): inputs = problem.parameters.to_dict(inputs) @@ -60,7 +62,6 @@ def nyquist( x=domain_data, y=-model_output[var].data.imag, trace_names="Model", - title=title, **plot_options, ) diff --git a/pybop/plot/parameters.py b/pybop/plot/parameters.py index 934ec67a3..93831abf8 100644 --- a/pybop/plot/parameters.py +++ b/pybop/plot/parameters.py @@ -45,17 +45,22 @@ def parameters(result: "Result", show=True, backend=None, **layout_kwargs): trace_names.append("Sigma") # Set subplot layout options - plot_options = get_default_options("paramters", backend) + opts = get_default_options("parameters", backend) + layout_options = opts.get("layout_options") or {} + subplot_options = opts.get("subplot_options") or {} + trace_options = opts.get("trace_options") or {} # Create a plot dictionary plot_dict = StandardSubplot( x=x, y=y, axis_titles=axis_titles, + trace_options=trace_options, trace_names=trace_names, trace_name_width=50, backend=backend, - **plot_options, + layout_options=layout_options, + subplot_options=subplot_options, ) # Generate the figure and update the layout diff --git a/pybop/plot/plotly/__init__.py b/pybop/plot/plotly/__init__.py index 4de178480..4bd283184 100644 --- a/pybop/plot/plotly/__init__.py +++ b/pybop/plot/plotly/__init__.py @@ -1,5 +1,9 @@ from .plotly_manager import PlotlyManager -from .standard_plots import Plotter, SubplotPlotter, show_table, trajectories, plot_optimisation_path from .voronoi import surface from .util import update_and_show, DEFAULT_PLOT_OPTIONS + +from .plotting_functions import contour_plot, line_plot, fill_plot, histogram_plot, add_vline, show_table, trajectories, plot_optimisation_path, add_traces, plot_trace +from .figure_handling import make_subplots, update_layout, create_figure + +name='plotly' diff --git a/pybop/plot/plotly/figure_handling.py b/pybop/plot/plotly/figure_handling.py new file mode 100644 index 000000000..d6a5a92dc --- /dev/null +++ b/pybop/plot/plotly/figure_handling.py @@ -0,0 +1,40 @@ +from pybop.plot.plotly import PlotlyManager +from pybop.plot.util import _AxisData + + +def _check_empty(specs, row, col): + if specs[row - 1][col - 1] is None or len(specs[row - 1][col - 1]) > 0: + raise ValueError("Overlapping axes are not supported") + + +def create_figure(traces, **layout_options): + go = PlotlyManager().go + layout = go.Layout(**layout_options) + fig = go.Figure(data=traces, layout=layout) + return fig + + +def update_layout(fig, axes=None, **layout_options): + fig.update_layout(**layout_options) + + +def make_subplots(axes: list[_AxisData], subplot_options=None): + subplot_options = subplot_options or {} + + num_rows = max(ax.row + ax.row_span - 1 for ax in axes) + num_cols = max(ax.col + ax.col_span - 1 for ax in axes) + specs = [[{}] * num_cols for _ in range(num_rows)] + + for ax in axes: + _check_empty(specs, ax.row, ax.col) + specs[ax.row - 1][ax.col - 1] = {"colspan": ax.col_span, "rowspan": ax.row_span} + for row in range(ax.row, ax.row + ax.row_span - 1): + for col in range(ax.col, ax.col + ax.col_span - 1): + if row > ax.row or col > ax.col: + _check_empty(specs, row, col) + specs[row - 1, col - 1] = None + + make_subplots = PlotlyManager().make_subplots + fig = make_subplots(rows=num_rows, cols=num_cols, specs=specs, **subplot_options) + + return fig diff --git a/pybop/plot/plotly/plotting_functions.py b/pybop/plot/plotly/plotting_functions.py new file mode 100644 index 000000000..0f1b5a7a9 --- /dev/null +++ b/pybop/plot/plotly/plotting_functions.py @@ -0,0 +1,143 @@ +from copy import deepcopy + +from pybop.plot import StandardPlot +from pybop.plot.plotly.plotly_manager import PlotlyManager + + +def plot_trace(trace, fig, ax=None): + if ax is None: + fig.add_trace(trace) + else: + fig.add_trace(trace, row=ax.row, col=ax.col) + fig.update_xaxes(title_text=ax.xlabel, row=ax.row, col=ax.col) + fig.update_yaxes(title_text=ax.ylabel, row=ax.row, col=ax.col) + + +def add_traces(x, y, trace_names, **trace_options): + traces = [] + xi = x[0] + for i in range(0, len(y)): + opts = deepcopy(trace_options) + if len(x) > 1: + xi = x[i] + label = None + if trace_names is not None: + label = trace_names[i] + + traces.append(line_plot(xi, y[i], label, **opts)) + return traces + + +def line_plot(x=None, y=None, label=None, ax=None, **kwargs): + go = PlotlyManager().go + if label is not None: + kwargs.update({"name": label}) + if x is not None and y is not None: + return go.Scatter( + x=x, + y=y, + **kwargs, + ) + if x is None and y is not None: + return go.Scatter( + y=y, + **kwargs, + ) + + +def contour_plot(x, y, z, **kwargs): + go = PlotlyManager().go + return go.Contour(x=x, y=y, z=z, **kwargs) + + +def fill_plot(x, y_upper, y_lower, **options): + return line_plot( + x=x + x[::-1], + y=y_upper + y_lower[::-1], + fill="toself", + line=dict(color="rgba(255,255,255,0)"), + hoverinfo="skip", + showlegend=False, + **options, + ) + + +def histogram_plot(x, name, **trace_options): + go = PlotlyManager().go + return go.Histogram(x=x, name=name, **trace_options) + + +def add_vline(fig, x, **trace_options): + fig.add_vline(x=x, **trace_options) + + +def trajectories(x, y, trace_names=None, show=True, **layout_kwargs): + """ + Quickly plot one or more trajectories using Plotly. + + Parameters + ---------- + x : list or np.ndarray + X-axis data points. + y : list or np.ndarray + Y-axis data points for each trajectory. + trace_names : list or str, optional + Name(s) for the trace(s) (default: None). + **layout_kwargs : optional + Valid Plotly layout keys and their values, + e.g. `xaxis_title="Time / s"` or + `xaxis={"title": "Time [s]", font={"size":14}}` + + Returns + ------- + plotly.graph_objs.Figure + The Plotly figure object for the scatter plot. + """ + # Create a plot dictionary + plot_dict = StandardPlot(x=x, y=y, trace_names=trace_names, backend="plotly") + + # Generate the figure and update the layout + fig = plot_dict(show=False) + fig.update_layout(**layout_kwargs) + if show: + fig.show() + + return fig + + +def show_table(header, values, title): + """ + Display data in a table. + """ + # Import plotly only when needed + go = PlotlyManager().go + fig = go.Figure( + data=[ + go.Table( + header=dict(values=header), + cells=dict( + values=[[row[0] for row in values], [row[1] for row in values]] + ), + ) + ] + ) + + fig.update_layout(title=title) + fig.show() + + +def plot_optimisation_path(plot_dict: StandardPlot, x, y): + plot_dict.traces.append( + plot_dict.create_trace( + x, + y, + mode="markers", + marker=dict( + color=[i / len(x) for i in range(len(x))], + colorscale="Greys", + size=8, + showscale=False, + ), + showlegend=False, + ) + ) diff --git a/pybop/plot/plotly/standard_plots.py b/pybop/plot/plotly/standard_plots.py deleted file mode 100644 index 80429f0c4..000000000 --- a/pybop/plot/plotly/standard_plots.py +++ /dev/null @@ -1,354 +0,0 @@ -from pybop.plot import StandardPlot -from pybop.plot.plotly.plotly_manager import PlotlyManager - -DEFAULT_LAYOUT_OPTIONS = dict( - title=None, - title_x=0.5, - xaxis=dict( - title=dict(font={"size": 14}), - showexponent="last", - exponentformat="e", - tickfont=dict(size=12), - ), - yaxis=dict( - title=dict(font={"size": 14}), - showexponent="last", - exponentformat="e", - tickfont=dict(size=12), - ), - legend=dict(x=1, y=1, xanchor="right", yanchor="top", font_size=12), - showlegend=True, - autosize=False, - width=600, - height=600, - margin=dict(l=10, r=10, b=10, t=75, pad=4), - plot_bgcolor="white", -) -DEFAULT_SUBPLOT_OPTIONS = dict( - start_cell="bottom-left", -) -DEFAULT_TRACE_OPTIONS = dict(line=dict(width=4), mode="lines") -DEFAULT_SUBPLOT_TRACE_OPTIONS = dict(line=dict(width=2), mode="lines") - - -class Plotter: - """ - A class for creating and displaying interactive Plotly figures. - - Parameters - ---------- - x : list or np.ndarray, optional - X-axis data points. - y : list or np.ndarray, optional - Primary Y-axis data points for simulated model output. - layout : Plotly layout, optional - A layout for the figure, overrides the layout options (default: None). - layout_options : dict, optional - Settings to modify the default layout (default: DEFAULT_LAYOUT_OPTIONS). - trace_options : dict, optional - Settings to modify the default trace type (default: DEFAULT_TRACE_OPTIONS). - trace_names : str, optional - Name(s) for the primary trace(s) (default: None). - trace_name_width : int, optional - Maximum length of the trace names before text wrapping is used (default: 40). - - Returns - ------- - plotly.graph_objs.Figure - The generated Plotly figure. - """ - - def __init__( - self, - title=None, - xaxis_title=None, - yaxis_title=None, - xaxis_range=None, - yaxis_range=None, - layout=None, - layout_options=None, - trace_options=None, - ): - self.backend = "plotly" - - self.traces = [] - self.layout = layout - - # Set default layout options and update if provided - if self.layout is None: - self.layout_options = DEFAULT_LAYOUT_OPTIONS.copy() - if layout_options: - self.layout_options.update(layout_options) - if title is not None: - self.layout_options.update({"title": title}) - if xaxis_title is not None: - self.layout_options.update({"xaxis_title": xaxis_title}) - if yaxis_title is not None: - self.layout_options.update({"yaxis_title": yaxis_title}) - if xaxis_range is not None: - self.layout_options["xaxis"].update({"range": xaxis_range}) - if yaxis_range is not None: - self.layout_options["yaxis"].update({"range": yaxis_range}) - - # Set default trace options and update if provided - self.trace_options = DEFAULT_TRACE_OPTIONS.copy() - if trace_options: - self.trace_options.update(trace_options) - - # Attempt to import plotly when an instance is created - self.go = PlotlyManager().go - - # Create layout - if self.layout is None: - self.layout = self.go.Layout(**self.layout_options) - - def __call__(self, show=True): - """ - Generate and show the figure. - - Parameters - ---------- - show : bool, optional - If True, the figure is shown upon creation (default: True). - """ - fig = self.go.Figure(data=self.traces, layout=self.layout) - if show: - fig.show() - - return fig - - def add_traces(self, x, y, trace_names=None, **trace_options): - """ - Add a set of traces to the plot dictionary. - - Parameters - ---------- - x : list or np.ndarray - X-axis data points. - y : list or np.ndarray - Primary Y-axis data points for simulated model output. - trace_names : str or list[str], optional - Name(s) for the primary trace(s) (default: None). - """ - options = self.trace_options.copy() - options.update(trace_options) - - # Create a trace for each trajectory - xi = x[0] - for i in range(0, len(y)): - trace_options = options.copy() - if len(x) > 1: - xi = x[i] - if trace_names is not None: - trace_options["name"] = trace_names[i] - else: - trace_options["showlegend"] = False - trace = self.create_trace(xi, y[i], **trace_options) - self.traces.append(trace) - - def create_trace(self, x=None, y=None, label=None, **trace_options): - """ - Create a trace for the Plotly figure. - - Returns - ------- - plotly.graph_objs.Scatter - A trace for a Plotly figure. - """ - if label is not None: - trace_options.update({"name": label}) - if x is not None and y is not None: - return self.go.Scatter( - x=x, - y=y, - **trace_options, - ) - if x is None and y is not None: - return self.go.Scatter( - y=y, - **trace_options, - ) - - def create_fill_trace(self, x, y_upper, y_lower, **options): - return self.create_trace( - x=x + x[::-1], - y=y_upper + y_lower[::-1], - fill="toself", - line=dict(color="rgba(255,255,255,0)"), - hoverinfo="skip", - showlegend=False, - **options, - ) - - def create_histogram(self, x, name, **trace_options): - return self.go.Histogram(x=x, name=name, **trace_options) - - def create_vline(self, fig, x, **trace_options): - fig.add_vline(x=x, **trace_options) - - def create_contour(self, x, y, z, **trace_options): - self.traces.append(self.go.Contour(x=x, y=y, z=z, **trace_options)) - - -class SubplotPlotter(Plotter): - """ - A class for creating and displaying a set of interactive Plotly figures in a grid layout. - - Parameters - ---------- - x : list or np.ndarray - X-axis data points. - y : list or np.ndarray - Primary Y-axis data points for simulated model output. - num_rows : int, optional - Number of rows of subplots, can be set automatically (default: None). - num_cols : int, optional - Number of columns of subplots, can be set automatically (default: None). - layout : Plotly layout, optional - A layout for the figure, overrides the layout options (default: None). - layout_options : dict, optional - Settings to modify the default layout (default: DEFAULT_LAYOUT_OPTIONS). - trace_options : dict, optional - Settings to modify the default trace type (default: DEFAULT_TRACE_OPTIONS). - trace_names : str, optional - Name(s) for the primary trace(s) (default: None). - trace_name_width : int, optional - Maximum length of the trace names before text wrapping is used (default: 40). - - Returns - ------- - plotly.graph_objs.Figure - The generated Plotly figure. - """ - - def __init__( - self, - axis_titles=None, - layout=None, - layout_options=DEFAULT_LAYOUT_OPTIONS, - subplot_options=DEFAULT_SUBPLOT_OPTIONS, - trace_options=DEFAULT_SUBPLOT_TRACE_OPTIONS, - ): - super().__init__( - layout, layout_options=layout_options, trace_options=trace_options - ) - self.subplot_options = subplot_options.copy() - if subplot_options is not None: - for arg, value in subplot_options.items(): - self.subplot_options[arg] = value - - # Attempt to import plotly when an instance is created - self.make_subplots = PlotlyManager().make_subplots - - self.axis_titles = axis_titles - - def __call__(self, show=True, num_rows=1, num_cols=1): - """ - Generate and show the set of figures. - - Parameters - ---------- - show : bool, optional - If True, the figure is shown upon creation (default: True). - """ - fig = self.make_subplots( - rows=num_rows, - cols=num_cols, - horizontal_spacing=0.1, - vertical_spacing=0.15, - **self.subplot_options, - ) - fig.update_layout(self.layout_options) - - for idx, trace in enumerate(self.traces): - row = (idx // num_cols) + 1 - col = (idx % num_cols) + 1 - fig.add_trace(trace, row=row, col=col) - - if self.axis_titles and idx < len(self.axis_titles): - x_title, y_title = self.axis_titles[idx] - fig.update_xaxes(title_text=x_title, row=row, col=col) - fig.update_yaxes( - title_text=y_title, - row=row, - col=col, - showexponent="last", - exponentformat="e", - ) - - if show: - fig.show() - - return fig - - -def trajectories(x, y, trace_names=None, show=True, **layout_kwargs): - """ - Quickly plot one or more trajectories using Plotly. - - Parameters - ---------- - x : list or np.ndarray - X-axis data points. - y : list or np.ndarray - Y-axis data points for each trajectory. - trace_names : list or str, optional - Name(s) for the trace(s) (default: None). - **layout_kwargs : optional - Valid Plotly layout keys and their values, - e.g. `xaxis_title="Time / s"` or - `xaxis={"title": "Time [s]", font={"size":14}}` - - Returns - ------- - plotly.graph_objs.Figure - The Plotly figure object for the scatter plot. - """ - # Create a plot dictionary - plot_dict = StandardPlot(x=x, y=y, trace_names=trace_names, backend="plotly") - - # Generate the figure and update the layout - fig = plot_dict(show=False) - fig.update_layout(**layout_kwargs) - if show: - fig.show() - - return fig - - -def show_table(header, values, title): - """ - Display data in a table. - """ - # Import plotly only when needed - go = PlotlyManager().go - fig = go.Figure( - data=[ - go.Table( - header=dict(values=header), - cells=dict( - values=[[row[0] for row in values], [row[1] for row in values]] - ), - ) - ] - ) - - fig.update_layout(title=title) - fig.show() - - -def plot_optimisation_path(plot_dict: StandardPlot, x, y): - plot_dict.traces.append( - plot_dict.create_trace( - x, - y, - mode="markers", - marker=dict( - color=[i / len(x) for i in range(len(x))], - colorscale="Greys", - size=8, - showscale=False, - ), - showlegend=False, - ) - ) diff --git a/pybop/plot/plotly/util.py b/pybop/plot/plotly/util.py index 9a913b3aa..7abe26645 100644 --- a/pybop/plot/plotly/util.py +++ b/pybop/plot/plotly/util.py @@ -12,23 +12,45 @@ def update_and_show(fig, show=True, **layout_kwargs): DEFAULT_PLOT_OPTIONS = { + "standard_plot": { + "default_layout_options": dict( + title=None, + title_x=0.5, + xaxis=dict( + title=dict(font={"size": 14}), + showexponent="last", + exponentformat="e", + tickfont=dict(size=12), + ), + yaxis=dict( + title=dict(font={"size": 14}), + showexponent="last", + exponentformat="e", + tickfont=dict(size=12), + ), + legend=dict(x=1, y=1, xanchor="right", yanchor="top", font_size=12), + showlegend=True, + autosize=False, + width=600, + height=600, + margin=dict(l=10, r=10, b=10, t=75, pad=4), + plot_bgcolor="white", + ), + "default_trace_options": dict(line=dict(width=4), mode="lines"), + }, "contour": { "plot_options": dict( - layout_options=dict( - title="Cost Landscape", - title_x=0.5, - title_y=0.905, - width=600, - height=600, - xaxis=dict(showexponent="last", exponentformat="e"), - yaxis=dict(showexponent="last", exponentformat="e"), - legend=dict( - orientation="h", yanchor="bottom", y=1, xanchor="right", x=1 - ), - autosize=None, - showlegend=None, - margin=None, - ) + title="Cost Landscape", + title_x=0.5, + title_y=0.905, + width=600, + height=600, + xaxis=dict(showexponent="last", exponentformat="e"), + yaxis=dict(showexponent="last", exponentformat="e"), + legend=dict(orientation="h", yanchor="bottom", y=1, xanchor="right", x=1), + autosize=None, + showlegend=None, + margin=None, ), "trace_options_contour": dict(colorscale="Viridis", connectgaps=True), "trace_options_initial": dict( @@ -58,41 +80,39 @@ def update_and_show(fig, show=True, **layout_kwargs): }, "nyquist": { "plot_options": dict( - layout_options=dict( - title="Nyquist Plot", - font=dict(family="Arial", size=14), - plot_bgcolor="white", - paper_bgcolor="white", - xaxis=dict( - title=dict(font=dict(size=16), standoff=15), - showline=True, - linewidth=2, - linecolor="black", - mirror=True, - ticks="outside", - tickwidth=2, - tickcolor="black", - ticklen=5, - ), - yaxis=dict( - title=dict(font=dict(size=16), standoff=15), - showline=True, - linewidth=2, - linecolor="black", - mirror=True, - ticks="outside", - tickwidth=2, - tickcolor="black", - ticklen=5, - scaleanchor="x", - scaleratio=1, - ), - legend=dict( - orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1 - ), - width=600, - height=600, + title="Nyquist Plot", + font=dict(family="Arial", size=14), + plot_bgcolor="white", + paper_bgcolor="white", + xaxis=dict( + title=dict(font=dict(size=16), standoff=15), + showline=True, + linewidth=2, + linecolor="black", + mirror=True, + ticks="outside", + tickwidth=2, + tickcolor="black", + ticklen=5, + ), + yaxis=dict( + title=dict(font=dict(size=16), standoff=15), + showline=True, + linewidth=2, + linecolor="black", + mirror=True, + ticks="outside", + tickwidth=2, + tickcolor="black", + ticklen=5, + scaleanchor="x", + scaleratio=1, ), + legend=dict( + orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1 + ), + width=600, + height=600, xaxis_title="Zre / Ω", yaxis_title="-Zim / Ω", ), @@ -116,19 +136,21 @@ def update_and_show(fig, show=True, **layout_kwargs): legend=dict( orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1 ), - ) + ), + subplot_options=dict( + horizontal_spacing=0.1, + vertical_spacing=0.15, + ), ), "posterior": { - "plot_options": { - "layout_options": dict( - barmode="overlay", - width=None, - height=None, - plot_bgcolor=None, - autosize=None, - legend=None, - ) - }, + "plot_options": dict( + barmode="overlay", + width=None, + height=None, + plot_bgcolor=None, + autosize=None, + legend=None, + ), "trace_options": dict(opacity=0.75), "trace_options_vline": dict(line_width=3, line_dash="dash", line_color="black"), }, @@ -140,11 +162,9 @@ def update_and_show(fig, show=True, **layout_kwargs): "fill_options": dict(fillcolor="rgba(255,229,204,0.8)"), }, "trace": { - "plot_options": { - "layout_options": dict( - width=None, height=None, plot_bgcolor=None, autosize=None, legend=None - ) - }, + "plot_options": dict( + width=None, height=None, plot_bgcolor=None, autosize=None, legend=None + ), "trace_options": dict(mode="lines"), }, } diff --git a/pybop/plot/plots.py b/pybop/plot/plots.py index 873af49fe..3f9dea150 100644 --- a/pybop/plot/plots.py +++ b/pybop/plot/plots.py @@ -1,73 +1,9 @@ from typing import TYPE_CHECKING -import numpy as np - if TYPE_CHECKING: from pybop._result import Result -from pybop.plot.util import call_plotting_function -from pybop.problems.problem import Problem - - -def contour( - call_object: "Problem | Result", - gradient: bool = False, - bounds: np.ndarray | None = None, - transformed: bool = False, - steps: int = 10, - show: bool = True, - backend=None, - **layout_kwargs, -): - """ - Plot a 2D visualisation of a cost landscape using Plotly. - - This function generates a contour plot representing the cost landscape for a provided - callable cost function over a grid of parameter values within the specified bounds. - - Parameters - ---------- - call_object : pybop.Problem | pybop.Result - Either: - - the cost function to be evaluated. Must accept a list of parameter values and return a cost value. - - an optimiser result which provides a specific optimisation trace overlaid on the cost landscape. - gradient : bool, optional - If True, the gradient is shown (default: False). - bounds : numpy.ndarray | list[list[float]], optional - A 2x2 array specifying the [min, max] bounds for each parameter. If None, uses - `parameters.get_bounds_for_plotly`. - transformed : bool, optional - Uses the transformed parameter values (as seen by the optimiser) for plotting. - steps : int, optional - The number of grid points to divide the parameter space into along each dimension (default: 10). - show : bool, optional - If True, the figure is shown upon creation (default: True). - **layout_kwargs : optional - Valid Plotly layout keys and their values, - e.g. `xaxis_title="Time [s]"` or - `xaxis={"title": "Time [s]", font={"size":14}}` - - Returns - ------- - plotly.graph_objs.Figure - The Plotly figure object containing the cost landscape plot. - - Raises - ------ - ValueError - If the cost function does not return a valid cost when called with a parameter list. - """ - return call_plotting_function( - "contour", - backend, - call_object=call_object, - gradient=gradient, - bounds=bounds, - transformed=transformed, - steps=steps, - show=show, - **layout_kwargs, - ) +from pybop.plot.util import import_backend def surface( @@ -101,9 +37,8 @@ def surface( e.g. `xaxis_title="Time [s]"` or `xaxis={"title": "Time [s]", font={"size":14}}` """ - return call_plotting_function( - "surface", - backend, + backend = import_backend(backend) + return backend.surface( result=result, bounds=bounds, normalise=normalise, diff --git a/pybop/plot/samples.py b/pybop/plot/samples.py index 3ea022cce..906be7471 100644 --- a/pybop/plot/samples.py +++ b/pybop/plot/samples.py @@ -1,7 +1,7 @@ from typing import TYPE_CHECKING from pybop.plot import StandardPlot -from pybop.plot.util import call_plotting_function, get_default_options, update_and_show +from pybop.plot.util import get_default_options, import_backend, update_and_show if TYPE_CHECKING: from pybop.samplers.base_pints_sampler import SamplingResult @@ -114,9 +114,8 @@ def summary_table(result: "SamplingResult", backend=None): ["95% CI Upper", summary_stats["ci_upper"]], ] - call_plotting_function( - "show_table", - backend=backend, + backend = import_backend(backend) + backend.show_table( header=header, values=values, title="Summary Statistics", diff --git a/pybop/plot/standard_plots.py b/pybop/plot/standard_plots.py index 3287789c5..c2d4204b5 100644 --- a/pybop/plot/standard_plots.py +++ b/pybop/plot/standard_plots.py @@ -1,9 +1,15 @@ import math import textwrap +from copy import deepcopy import numpy as np -from pybop.plot.util import call_plotting_function +from pybop.plot.util import ( + create_axis, + get_default_options, + import_backend, + update_and_show, +) class StandardPlot: @@ -11,58 +17,67 @@ def __init__( self, x=None, y=None, - title=None, - xaxis_title=None, - yaxis_title=None, trace_options=None, trace_names=None, trace_name_width=20, backend=None, - **kwargs, + **layout_options, ): - self.plotter = call_plotting_function( - "Plotter", - backend, - title=title, - xaxis_title=xaxis_title, - yaxis_title=yaxis_title, - trace_options=trace_options, - **kwargs, - ) - + self.traces = [] self.trace_name_width = trace_name_width + self.backend = import_backend(backend) + + # Set default options and update if provided + opts = deepcopy(get_default_options("standard_plot", backend)) + self.layout_options = opts.get("default_layout_options") or {} + if layout_options: + self.layout_options.update(layout_options) + self.trace_options = opts.get("default_trace_options") or {} + if trace_options: + self.trace_options.update(trace_options) + # Add traces if x is not None and y is not None: self.add_traces(x, y, trace_names) def __call__(self, show=True): - return self.plotter(show=show) + fig = self.backend.create_figure(self.traces, **self.layout_options) + if show: + update_and_show(fig, backend=self.backend.name) + return fig - @property - def traces(self): - return self.plotter.traces + def add_traces(self, x, y, trace_names, **trace_options): + """ + Add a set of traces to the plot dictionary. - @traces.setter - def traces(self, value): - self.plotter.traces = value + Parameters + ---------- + x : list or np.ndarray + X-axis data points. + y : list or np.ndarray + Primary Y-axis data points for simulated model output. + trace_names : str or list[str], optional + Name(s) for the primary trace(s) (default: None). + """ + options = deepcopy(self.trace_options) + options.update(trace_options) - def add_traces(self, x, y, trace_names): # Check and wrap trace names if trace_names is not None: if isinstance(trace_names, str): trace_names = [trace_names] for i, name in enumerate(trace_names): trace_names[i] = self.wrap_text( - name, width=self.trace_name_width, backend=self.plotter.backend + name, width=self.trace_name_width, backend=self.backend.name ) # Parse the data x, y = self.parse_data(x, y) - # Add traces - self.plotter.add_traces(x, y, trace_names) + # Create a trace for each trajectory + self.traces.extend(self.backend.add_traces(x, y, trace_names, **options)) def parse_data(self, x, y): """ @@ -104,19 +119,24 @@ def parse_data(self, x, y): return x, y def create_trace(self, x=None, y=None, label=None, **trace_options): - return self.plotter.create_trace(x, y, label, **trace_options) + return self.backend.line_plot(x=x, y=y, label=label, **trace_options) def create_fill_trace(self, x, y_upper, y_lower, **options): - return self.plotter.create_fill_trace(x, y_upper, y_lower, **options) + return self.backend.fill_plot(x, y_upper, y_lower, **options) def create_histogram(self, x, name, **trace_options): - return self.plotter.create_histogram(x, name, **trace_options) + return self.backend.histogram_plot(x, name, **trace_options) def create_vline(self, fig, x, **trace_options): - return self.plotter.create_vline(fig, x, **trace_options) + return self.backend.add_vline(fig, x, **trace_options) def create_contour(self, x, y, z, **trace_options): - return self.plotter.create_contour(x, y, z, **trace_options) + ct = self.backend.contour_plot(x, y, z, **trace_options) + if hasattr(ct, "__len__"): + for t in ct: + self.traces.append(t) + else: + self.traces.append(ct) @staticmethod def wrap_text(text, width, backend="matplotlib"): @@ -198,26 +218,26 @@ def __init__( num_rows=None, num_cols=None, axis_titles=None, + layout_options=None, + subplot_options=None, trace_options=None, trace_names=None, trace_name_width=40, **kwargs, ): - self.plotter = call_plotting_function( - "SubplotPlotter", - backend, - axis_titles=axis_titles, + if layout_options is None: + layout_options = {} + + super().__init__( + x, + y, trace_options=trace_options, - **kwargs, + trace_names=trace_names, + trace_name_width=trace_name_width, + backend=backend, + **layout_options, ) - - self.trace_name_width = trace_name_width - - # Add traces - if x is not None and y is not None: - self.add_traces(x, y, trace_names) - - self.num_traces = len(self.plotter.traces) + self.num_traces = len(self.traces) self.num_rows = num_rows self.num_cols = num_cols if self.num_rows is None and self.num_cols is None: @@ -229,8 +249,43 @@ def __init__( elif self.num_cols is None: self.num_cols = int(math.ceil(self.num_traces / self.num_rows)) + self.axis_titles = axis_titles + self.subplot_options = {} + if subplot_options is not None: + for arg, value in subplot_options.items(): + self.subplot_options[arg] = value + def __call__(self, show=True): - return self.plotter(show=show, num_rows=self.num_rows, num_cols=self.num_cols) + axes = [ + create_axis(row + 1, col + 1) + for row in range(self.num_rows) + for col in range(self.num_cols) + ] + + fig = self.backend.make_subplots(axes, subplot_options=self.subplot_options) + + for idx, trace in enumerate(self.traces): + if self.axis_titles and idx < len(self.axis_titles): + x_title, y_title = self.axis_titles[idx] + axes[idx].set_xlabel(x_title) + axes[idx].set_ylabel(y_title) + # trace.update({'yaxis_title' : y_title, 'xaxis_title' : x_title}) + # fig.update_xaxes(title_text=x_title, row=row, col=col) + # fig.update_yaxes( + # title_text=y_title, + # row=row, + # col=col, + # showexponent="last", + # exponentformat="e", + # ) + self.backend.plot_trace(trace, fig, ax=axes[idx]) + + self.backend.update_layout(fig, axes=axes, **self.layout_options) + + if show: + self.backend.update_and_show(fig) + else: + return fig def trajectories(x, y, trace_names=None, show=True, backend=None, **layout_kwargs): @@ -256,9 +311,8 @@ def trajectories(x, y, trace_names=None, show=True, backend=None, **layout_kwarg The Plotly figure object for the scatter plot. """ - return call_plotting_function( - "trajectories", - backend, + backend = import_backend(backend) + return backend.trajectories( x=x, y=y, trace_names=trace_names, diff --git a/pybop/plot/util.py b/pybop/plot/util.py index a30b0cee1..9bf7398d2 100644 --- a/pybop/plot/util.py +++ b/pybop/plot/util.py @@ -1,8 +1,30 @@ import importlib.util +from copy import deepcopy +from dataclasses import dataclass import pybop.plot +@dataclass +class _AxisData: + row: int = 1 + col: int = 1 + row_span: int = 1 + col_span: int = 1 + xlabel: str | None = None + ylabel: str | None = None + + def set_xlabel(self, xlabel: str): + self.xlabel = xlabel + + def set_ylabel(self, ylabel: str): + self.ylabel = ylabel + + +def create_axis(row, col, row_span=1, col_span=1): + return _AxisData(row, col, row_span, col_span) + + def set_backend(backend): err_msg = ( f"Plotting backend {backend} is not available. The default backend has not been updated. \n" @@ -36,6 +58,19 @@ def call_plotting_function(function_name, backend, **kwargs): raise ModuleNotFoundError(err_msg) from error +def import_backend(backend): + if backend is None: + backend = pybop.plot.backend + err_msg = f"Plotting backend {backend} is not available." + try: + module = importlib.import_module("pybop.plot." + backend) + except ModuleNotFoundError as error: + # Raise an ModuleNotFoundError if the module or attribute is not available + raise ModuleNotFoundError(err_msg) from error + + return module + + def update_and_show(fig, backend=None, **kwargs): return call_plotting_function("update_and_show", backend, fig=fig, **kwargs) @@ -52,6 +87,6 @@ def get_default_options(plot_type, backend): opts = {} if plot_type in opts.keys(): - return opts[plot_type] + return deepcopy(opts[plot_type]) else: return {} From 1faa02058098b95a7501fdd7de7e8ffe2856abc6 Mon Sep 17 00:00:00 2001 From: u2370093 Date: Tue, 21 Apr 2026 18:48:21 +0100 Subject: [PATCH 12/20] simplify voronoi --- pybop/plot/__init__.py | 15 +- pybop/plot/matplotlib/__init__.py | 3 +- pybop/plot/matplotlib/figure_handling.py | 4 +- pybop/plot/matplotlib/plotting_functions.py | 44 +++- pybop/plot/matplotlib/util.py | 25 +++ pybop/plot/matplotlib/voronoi.py | 141 ------------- pybop/plot/plotly/__init__.py | 3 +- pybop/plot/plotly/plotly_manager.py | 2 + pybop/plot/plotly/plotting_functions.py | 64 +++++- pybop/plot/plotly/util.py | 40 ++++ pybop/plot/plotly/voronoi.py | 216 -------------------- pybop/plot/plots.py | 48 ----- pybop/plot/standard_plots.py | 2 +- pybop/plot/voronoi.py | 127 +++++++++++- 14 files changed, 296 insertions(+), 438 deletions(-) delete mode 100644 pybop/plot/matplotlib/voronoi.py delete mode 100644 pybop/plot/plotly/voronoi.py delete mode 100644 pybop/plot/plots.py diff --git a/pybop/plot/__init__.py b/pybop/plot/__init__.py index 7151009f0..2f6cc3f30 100644 --- a/pybop/plot/__init__.py +++ b/pybop/plot/__init__.py @@ -7,18 +7,17 @@ # # Import plots # -from .plots import ( - surface - ) - from .standard_plots import StandardPlot, StandardSubplot, trajectories from .contour import contour -from .convergence import convergence from .dataset import dataset -from .nyquist import nyquist +from .convergence import convergence from .parameters import parameters from .problem import problem -from .samples import chains, posterior, summary_table, trace -from .voronoi import voronoi_data, _voronoi_regions +from .nyquist import nyquist +from .voronoi import surface +from .samples import trace, chains, posterior, summary_table + + +# Import backend specific plotting functions from . import matplotlib from . import plotly diff --git a/pybop/plot/matplotlib/__init__.py b/pybop/plot/matplotlib/__init__.py index 2ff7aea19..023e82b74 100644 --- a/pybop/plot/matplotlib/__init__.py +++ b/pybop/plot/matplotlib/__init__.py @@ -1,7 +1,6 @@ -from .voronoi import surface from .util import update_and_show, DEFAULT_PLOT_OPTIONS -from .plotting_functions import line_plot, contour_plot, scatter_plot, fill_plot, histogram_plot, add_vline, plot_trace, add_traces, show_table, trajectories, plot_optimisation_path +from .plotting_functions import line_plot, colorbar, contour_plot, scatter_plot, fill_between_plot, fill_plot, histogram_plot, add_vline, plot_trace, add_traces, sample_color_scale, show_table, trajectories, plot_optimisation_path from .figure_handling import create_figure, make_subplots, update_layout name="matplotlib" diff --git a/pybop/plot/matplotlib/figure_handling.py b/pybop/plot/matplotlib/figure_handling.py index 30b0ae3f6..65a581d87 100644 --- a/pybop/plot/matplotlib/figure_handling.py +++ b/pybop/plot/matplotlib/figure_handling.py @@ -20,7 +20,7 @@ def create_figure(traces, **layout_options): def update_layout(fig, axes=None, **layout_options): if axes is None: - axes = [plt.gca()] + axes = [fig.gca()] if "title" in layout_options: plt.suptitle(layout_options.get("title")) if "xaxis_title" in layout_options: @@ -48,7 +48,7 @@ def update_layout(fig, axes=None, **layout_options): not ax.get_legend_handles_labels() == ([], []) and "fig_legend" not in layout_options ): - ax.legend(layout_options.get("legend") or {}) + ax.legend(**layout_options.get("legend") or {}) if "fig_legend" in layout_options: labels_in_fig = False diff --git a/pybop/plot/matplotlib/plotting_functions.py b/pybop/plot/matplotlib/plotting_functions.py index 0e7fa7856..c72dffd94 100644 --- a/pybop/plot/matplotlib/plotting_functions.py +++ b/pybop/plot/matplotlib/plotting_functions.py @@ -1,6 +1,8 @@ import warnings from copy import deepcopy +import matplotlib as mpl +import numpy as np from matplotlib import pyplot as plt from pybop.plot import StandardPlot @@ -43,7 +45,7 @@ def plot_trace(trace: dict, fig, ax=None, color_cycle=None): # retrieve remaining arguments trace_options = deepcopy(trace) - for key in ["plot_type", "ax", "positional_args", "xaxis_title", "yaxis_title"]: + for key in ["plot_type", "positional_args", "xaxis_title", "yaxis_title"]: if key in trace_options.keys(): del trace_options[key] @@ -64,17 +66,29 @@ def plot_trace(trace: dict, fig, ax=None, color_cycle=None): return obj -def line_plot(x=None, y=None, label=None, ax=None, **kwargs): +def line_plot(x=None, y=None, label=None, **kwargs): if x is not None and y is not None: size = min(len(x), len(y)) - trace = dict(positional_args=[x[:size], y[:size]], label=label, ax=ax) + trace = dict(positional_args=[x[:size], y[:size]], label=label) elif y is not None: - trace = dict(positional_args=[y], label=label, ax=ax) + trace = dict(positional_args=[y], label=label) trace.update(kwargs) return trace +def colorbar(fig, data, colorscale="viridis"): + # normalise cost + f_min = np.nanmin(data[np.isfinite(data)]) + f_max = np.nanmax(data[np.isfinite(data)]) + norm = mpl.colors.Normalize(vmin=f_min, vmax=f_max, clip=True) + + # get colours + cmap = mpl.colormaps["viridis"] + + plt.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap), ax=fig.gca()) + + def contour_plot(x, y, z, **kwargs): contour = dict(positional_args=[x, y, z], plot_type="contourf") contour.update(**kwargs) @@ -88,18 +102,24 @@ def contour_plot(x, y, z, **kwargs): return contour, contour_lines -def scatter_plot(x, y, **trace_options): +def scatter_plot(x, y, colors=None, labels=None, **trace_options): scatter = dict(positional_args=[x, y], plot_type="scatter") scatter.update(**trace_options) + if colors is not None: + scatter["c"] = colors return scatter -def fill_plot(x, y_upper, y_lower, **options): +def fill_between_plot(x, y_upper, y_lower, **options): trace = dict(positional_args=(x, y_upper, y_lower), plot_type="fill_between") trace.update(options) return trace +def fill_plot(x, y, color=None, label=None): + return dict(positional_args=(x, y), plot_type="fill", color=color) + + def histogram_plot(x, name, **trace_options): trace = dict(positional_args=[x], label=name, plot_type="hist") trace.update(trace_options) @@ -111,6 +131,18 @@ def add_vline(fig, x, **trace_options): plt.axvline(x, **trace_options) +def sample_color_scale(data, scale="viridis"): + # normalise and clip data + d_min = np.nanmin(data[np.isfinite(data)]) + d_max = np.nanmax(data[np.isfinite(data)]) + norm = mpl.colors.Normalize(vmin=d_min, vmax=d_max, clip=True) + norm_d = norm(data, clip=True) + + # get colours + cmap = mpl.colormaps[scale] + return cmap(norm_d) + + def show_table(header, values, title): """ Display data in a table. diff --git a/pybop/plot/matplotlib/util.py b/pybop/plot/matplotlib/util.py index b5dd31888..60e96c086 100644 --- a/pybop/plot/matplotlib/util.py +++ b/pybop/plot/matplotlib/util.py @@ -101,4 +101,29 @@ def update_and_show(fig, show=True, **layout_kwargs): ), }, }, + "voronoi": { + "layout_options": dict( + legend=dict(ncols=2, loc="lower center", bbox_to_anchor=(0.5, 1.0)) + ), + "outline_opts": dict(color="w", linewidth=0.5), + "scatter_opts": dict(cmap="Grays", zorder=2.5), + "optimised_opts": dict( + marker="P", + markersize=14, + markerfacecolor="k", + markeredgecolor="w", + label="Final values", + linestyle="None", + zorder=2.6, + ), + "initial_opts": dict( + marker="X", + markersize=14, + markerfacecolor="w", + markeredgecolor="k", + label="Initial values", + linestyle="None", + zorder=2.6, + ), + }, } diff --git a/pybop/plot/matplotlib/voronoi.py b/pybop/plot/matplotlib/voronoi.py deleted file mode 100644 index e7502b941..000000000 --- a/pybop/plot/matplotlib/voronoi.py +++ /dev/null @@ -1,141 +0,0 @@ -import warnings -from typing import TYPE_CHECKING - -import matplotlib as mpl -import numpy as np -from matplotlib import pyplot as plt - -if TYPE_CHECKING: - from pybop._result import Result - -from pybop.plot.voronoi import voronoi_data - - -def surface( - result: "Result", - bounds=None, - normalise=True, - title="Voronoi Cost Landscape", - show=True, - **layout_kwargs, -): - """ - Plot a 2D representation of the Voronoi diagram with color-coded regions. - - Parameters: - ----------- - result : pybop.Result - Optimisation result containing the history of parameter values and associated cost. - bounds : numpy.ndarray, optional - A 2x2 array specifying the [min, max] bounds for each parameter. If None, uses - `cost.parameters.get_bounds_for_plotly`. - normalise : bool, optional - If True, the voronoi regions are computed using the Euclidean distance between - points normalised with respect to the bounds (default: True). - resolution : int, optional - Resolution of the plot. Default is 500. - show : bool, optional - If True, the figure is shown upon creation (default: True). - """ - - if len(layout_kwargs) > 0: - warnings.warn( - "The following layout argument keys are ignored for the current plotting backend (matplotlib): \n" - f"{list(layout_kwargs.keys())}", - UserWarning, - stacklevel=2, - ) - - points = result.x_model - parameters = result.problem.parameters - - if points[0].shape[0] != 2: - raise ValueError("This plot method requires two parameters.") - - x_optim, y_optim = map(list, zip(*points, strict=False)) - f = result.cost - - # Translate bounds, taking only the first two elements - xlim, ylim = ( - bounds if bounds is not None else [param.bounds for param in parameters] - )[:2] - - _, _, f, regions, relative_sizes = voronoi_data( - xlim, ylim, x_optim, y_optim, f, normalise - ) - - # Construct figure - plt.figure(figsize=(7, 6), dpi=100) - - # normalise cost - f_min = np.nanmin(f[np.isfinite(f)]) - f_max = np.nanmax(f[np.isfinite(f)]) - norm = mpl.colors.Normalize(vmin=f_min, vmax=f_max, clip=True) - norm_f = norm(f, clip=True) - - # get colours - cmap = mpl.colormaps["viridis"] - colors = cmap(norm_f) - - # Add Voronoi edges and fill Voronoi regions - for j, (region, size) in enumerate(zip(regions, relative_sizes, strict=False)): - x_region = region[:, 0].tolist() + [region[0, 0]] - y_region = region[:, 1].tolist() + [region[0, 1]] - - plt.fill(x_region, y_region, color=colors[j]) - plt.plot(x_region, y_region, color="w", linewidth=0.5 + size * 0.1) - - plt.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap), ax=plt.gca()) - - # Add original points - plt.scatter( - x_optim, - y_optim, - c=[i / len(x_optim) for i in range(len(x_optim))], - cmap="Grays", - zorder=2.5, - ) - - # Plot the initial guess - if len(result.x_model) > 0: - x0 = result.x_model[0] - plt.plot( - [x0[0]], - [x0[1]], - "X", - markersize=14, - markerfacecolor="w", - markeredgecolor="k", - label="Initial values", - linestyle="None", - zorder=2.6, - ) - - # Plot optimised value - if result.x is not None: - x_best = result.x - plt.plot( - [x_best[0]], - [x_best[1]], - "P", - markersize=14, - markerfacecolor="k", - markeredgecolor="w", - label="Final values", - linestyle="None", - zorder=2.6, - ) - - # Layout - names = result.problem.parameters.names - plt.xlabel(names[0], labelpad=15) - plt.ticklabel_format(axis="both", **dict(style="sci", scilimits=(-4, 4))) - plt.ylabel(names[1], labelpad=15) - plt.title(title, pad=40) - plt.legend(ncols=2, loc="lower center", bbox_to_anchor=(0.5, 1.0)) - plt.xlim(xlim[0], xlim[1]) - plt.ylim(ylim[0], ylim[1]) - plt.tight_layout() - - if show: - plt.show() diff --git a/pybop/plot/plotly/__init__.py b/pybop/plot/plotly/__init__.py index 4bd283184..809aa71b0 100644 --- a/pybop/plot/plotly/__init__.py +++ b/pybop/plot/plotly/__init__.py @@ -1,9 +1,8 @@ from .plotly_manager import PlotlyManager -from .voronoi import surface from .util import update_and_show, DEFAULT_PLOT_OPTIONS -from .plotting_functions import contour_plot, line_plot, fill_plot, histogram_plot, add_vline, show_table, trajectories, plot_optimisation_path, add_traces, plot_trace +from .plotting_functions import colorbar, contour_plot, line_plot, fill_between_plot, fill_plot, histogram_plot, add_vline, show_table, scatter_plot, trajectories, plot_optimisation_path, add_traces, plot_trace, sample_color_scale from .figure_handling import make_subplots, update_layout, create_figure name='plotly' diff --git a/pybop/plot/plotly/plotly_manager.py b/pybop/plot/plotly/plotly_manager.py index d8ad14c88..4420b6326 100644 --- a/pybop/plot/plotly/plotly_manager.py +++ b/pybop/plot/plotly/plotly_manager.py @@ -43,12 +43,14 @@ def ensure_plotly_installed(self): Check if Plotly is installed and import necessary modules; prompt for installation if missing. """ try: + import plotly.express as px import plotly.graph_objs as go import plotly.io as pio from plotly.subplots import make_subplots self.go = go self.pio = pio + self.px = px self.make_subplots = make_subplots except ImportError: self.prompt_for_plotly_installation() diff --git a/pybop/plot/plotly/plotting_functions.py b/pybop/plot/plotly/plotting_functions.py index 0f1b5a7a9..18c0c6f0f 100644 --- a/pybop/plot/plotly/plotting_functions.py +++ b/pybop/plot/plotly/plotting_functions.py @@ -1,9 +1,22 @@ from copy import deepcopy +import numpy as np + from pybop.plot import StandardPlot from pybop.plot.plotly.plotly_manager import PlotlyManager +def sample_color_scale(data, scale="viridis"): + px = PlotlyManager().px + # normalise and clip data + d_min = np.nanmin(data[np.isfinite(data)]) + d_max = np.nanmax(data[np.isfinite(data)]) + + d = (data - d_min) / (d_max - d_min) + d = np.clip(np.asarray(data), 0, 1.0) + return px.colors.sample_colorscale("viridis", list(d)) + + def plot_trace(trace, fig, ax=None): if ax is None: fig.add_trace(trace) @@ -50,7 +63,29 @@ def contour_plot(x, y, z, **kwargs): return go.Contour(x=x, y=y, z=z, **kwargs) -def fill_plot(x, y_upper, y_lower, **options): +def colorbar(fig, data, colorscale="viridis"): + go = PlotlyManager().go + d_min = np.nanmin(data[np.isfinite(data)]) + d_max = np.nanmax(data[np.isfinite(data)]) + fig.add_trace( + go.Scatter( + x=[None], + y=[None], + mode="markers", + marker=dict( + colorscale=colorscale, + showscale=True, + cmin=d_min, + cmax=d_max, + colorbar=dict(thickness=25, outlinewidth=0), + ), + showlegend=False, + hoverinfo="none", + ) + ) + + +def fill_between_plot(x, y_upper, y_lower, **options): return line_plot( x=x + x[::-1], y=y_upper + y_lower[::-1], @@ -62,6 +97,16 @@ def fill_plot(x, y_upper, y_lower, **options): ) +def fill_plot(x, y, color=None, label=None): + opts = {} + if color is not None: + opts["fillcolor"] = color + if label is not None: + opts["name"] = label + go = PlotlyManager().go + return go.Scatter(x=x, y=y, fill="toself", mode="text", showlegend=False, **opts) + + def histogram_plot(x, name, **trace_options): go = PlotlyManager().go return go.Histogram(x=x, name=name, **trace_options) @@ -71,6 +116,23 @@ def add_vline(fig, x, **trace_options): fig.add_vline(x=x, **trace_options) +def scatter_plot(x, y, colors, labels=None, colorscale="Greys"): + go = PlotlyManager().go + opts = dict( + mode="markers", + marker=dict( + color=colors, + colorscale=colorscale, + size=8, + showscale=False, + ), + showlegend=False, + ) + if labels is not None: + opts.update({"text": labels, "hoverinfo": "text"}) + return go.Scatter(x=x, y=y, **opts) + + def trajectories(x, y, trace_names=None, show=True, **layout_kwargs): """ Quickly plot one or more trajectories using Plotly. diff --git a/pybop/plot/plotly/util.py b/pybop/plot/plotly/util.py index 7abe26645..7aa709bbf 100644 --- a/pybop/plot/plotly/util.py +++ b/pybop/plot/plotly/util.py @@ -167,4 +167,44 @@ def update_and_show(fig, show=True, **layout_kwargs): ), "trace_options": dict(mode="lines"), }, + "voronoi": { + "layout_options": dict( + title_x=0.5, + title_y=0.905, + width=600, + height=600, + xaxis=dict(showexponent="last", exponentformat="e"), + yaxis=dict(showexponent="last", exponentformat="e"), + legend=dict(orientation="h", yanchor="bottom", y=1, xanchor="right", x=1), + ), + "outline_opts": dict( + mode="lines", + line=dict(color="white", width=0.5), + showlegend=False, + ), + "optimised_opts": dict( + mode="markers", + marker_symbol="cross", + marker=dict( + color="black", + line_color="white", + line_width=1, + size=14, + showscale=False, + ), + name="Final values", + ), + "initial_opts": dict( + mode="markers", + marker_symbol="x", + marker=dict( + color="white", + line_color="black", + line_width=1, + size=14, + showscale=False, + ), + name="Initial values", + ), + }, } diff --git a/pybop/plot/plotly/voronoi.py b/pybop/plot/plotly/voronoi.py deleted file mode 100644 index d60b9597e..000000000 --- a/pybop/plot/plotly/voronoi.py +++ /dev/null @@ -1,216 +0,0 @@ -from typing import TYPE_CHECKING - -import numpy as np -from scipy.spatial import cKDTree - -if TYPE_CHECKING: - from pybop._result import Result -from pybop.plot import voronoi_data -from pybop.plot.plotly.plotly_manager import PlotlyManager - - -def assign_nearest_value(x, y, f, xi, yi): - """ - Computes an array of values given by the score of the nearest point. - - Parameters - ---------- - x : array-like - The x coordinates of points with known scores. - y : array-like - The y coordinates of points with known scores. - f : array-like - The score function at the given x and y coordinates. - xi : array-like - The x coordinates of grid points. - yi : array-like - The y coordinates of grid points. - - Returns - ------- - A numpy array containing the scores corresponding to the grid points. - """ - # Create a KD-tree for efficient nearest neighbor search - tree = cKDTree(np.column_stack((x, y))) - - # Find the nearest point for each grid point - _, indices = tree.query(np.column_stack((xi.ravel(), yi.ravel()))) - zi = f[indices].reshape(xi.shape) - - return zi - - -def surface( - result: "Result", - bounds=None, - normalise=True, - resolution=250, - show=True, - **layout_kwargs, -): - """ - Plot a 2D representation of the Voronoi diagram with color-coded regions. - - Parameters: - ----------- - result : pybop.Result - Optimisation result containing the history of parameter values and associated cost. - bounds : numpy.ndarray, optional - A 2x2 array specifying the [min, max] bounds for each parameter. If None, uses - `cost.parameters.get_bounds_for_plotly`. - normalise : bool, optional - If True, the voronoi regions are computed using the Euclidean distance between - points normalised with respect to the bounds (default: True). - resolution : int, optional - Resolution of the plot. Default is 500. - show : bool, optional - If True, the figure is shown upon creation (default: True). - **layout_kwargs : optional - Valid Plotly layout keys and their values, - e.g. `xaxis_title="Time [s]"` or - `xaxis={"title": "Time [s]", font={"size":14}}` - """ - points = result.x_model - parameters = result.problem.parameters - - if points[0].shape[0] != 2: - raise ValueError("This plot method requires two parameters.") - - x_optim, y_optim = map(list, zip(*points, strict=False)) - f = result.cost - - # Translate bounds, taking only the first two elements - xlim, ylim = ( - bounds if bounds is not None else [param.bounds for param in parameters] - )[:2] - - x, y, f, regions, relative_sizes = voronoi_data( - xlim, ylim, x_optim, y_optim, f, normalise - ) - - # Create a grid for plot - xi = np.linspace(xlim[0], xlim[1], resolution) - yi = np.linspace(ylim[0], ylim[1], resolution) - xi, yi = np.meshgrid(xi, yi) - - if normalise: - # Create a normalised grid - norm_xi = np.linspace(0, 1, resolution) - norm_xi, norm_yi = np.meshgrid(norm_xi, norm_xi) - - # Assign a value to each point in the grid - zi = assign_nearest_value(x, y, f, norm_xi, norm_yi) - else: - # Assign a value to each point in the grid - zi = assign_nearest_value(x, y, f, xi, yi) - - # Calculate the size of each Voronoi region - region_sizes = np.array([len(region) for region in regions]) - relative_sizes = (region_sizes - region_sizes.min()) / ( - region_sizes.max() - region_sizes.min() - ) - - # Construct figure - go = PlotlyManager().go - fig = go.Figure() - - # Heatmap - fig.add_trace( - go.Heatmap( - x=xi[0], - y=yi[:, 0], - z=zi, - colorscale="Viridis", - zsmooth="best", - ) - ) - - # Add Voronoi edges - for region, size in zip(regions, relative_sizes, strict=False): - x_region = region[:, 0].tolist() + [region[0, 0]] - y_region = region[:, 1].tolist() + [region[0, 1]] - - fig.add_trace( - go.Scatter( - x=x_region, - y=y_region, - mode="lines", - line=dict(color="white", width=0.5 + size * 0.1), - showlegend=False, - ) - ) - - # Add original points - fig.add_trace( - go.Scatter( - x=x_optim, - y=y_optim, - mode="markers", - marker=dict( - color=[i / len(x_optim) for i in range(len(x_optim))], - colorscale="Greys", - size=8, - showscale=False, - ), - text=[f"f={val:.2f}" for val in f], - hoverinfo="text", - showlegend=False, - ) - ) - - # Plot the initial guess - if len(result.x_model) > 0: - x0 = result.x_model[0] - fig.add_trace( - go.Scatter( - x=[x0[0]], - y=[x0[1]], - mode="markers", - marker_symbol="x", - marker=dict( - color="white", - line_color="black", - line_width=1, - size=14, - showscale=False, - ), - name="Initial values", - ) - ) - - # Plot optimised value - if result.x is not None: - x_best = result.x - fig.add_trace( - go.Scatter( - x=[x_best[0]], - y=[x_best[1]], - mode="markers", - marker_symbol="cross", - marker=dict( - color="black", - line_color="white", - line_width=1, - size=14, - showscale=False, - ), - name="Final values", - ) - ) - - names = parameters.names - fig.update_layout( - title="Voronoi Cost Landscape", - title_x=0.5, - title_y=0.905, - xaxis_title=names[0], - yaxis_title=names[1], - width=600, - height=600, - xaxis=dict(range=xlim, showexponent="last", exponentformat="e"), - yaxis=dict(range=ylim, showexponent="last", exponentformat="e"), - legend=dict(orientation="h", yanchor="bottom", y=1, xanchor="right", x=1), - ) - fig.update_layout(**layout_kwargs) - if show: - fig.show() diff --git a/pybop/plot/plots.py b/pybop/plot/plots.py deleted file mode 100644 index 3f9dea150..000000000 --- a/pybop/plot/plots.py +++ /dev/null @@ -1,48 +0,0 @@ -from typing import TYPE_CHECKING - -if TYPE_CHECKING: - from pybop._result import Result - -from pybop.plot.util import import_backend - - -def surface( - result: "Result", - bounds=None, - normalise=True, - resolution=250, - show=True, - backend=None, - **layout_kwargs, -): - """ - Plot a 2D representation of the Voronoi diagram with color-coded regions. - - Parameters: - ----------- - result : pybop.Result - Optimisation result containing the history of parameter values and associated cost. - bounds : numpy.ndarray, optional - A 2x2 array specifying the [min, max] bounds for each parameter. If None, uses - `cost.parameters.get_bounds_for_plotly`. - normalise : bool, optional - If True, the voronoi regions are computed using the Euclidean distance between - points normalised with respect to the bounds (default: True). - resolution : int, optional - Resolution of the plot. Default is 500. - show : bool, optional - If True, the figure is shown upon creation (default: True). - **layout_kwargs : optional - Valid Plotly layout keys and their values, - e.g. `xaxis_title="Time [s]"` or - `xaxis={"title": "Time [s]", font={"size":14}}` - """ - backend = import_backend(backend) - return backend.surface( - result=result, - bounds=bounds, - normalise=normalise, - resolution=resolution, - show=show, - **layout_kwargs, - ) diff --git a/pybop/plot/standard_plots.py b/pybop/plot/standard_plots.py index c2d4204b5..9c7b5e9bd 100644 --- a/pybop/plot/standard_plots.py +++ b/pybop/plot/standard_plots.py @@ -122,7 +122,7 @@ def create_trace(self, x=None, y=None, label=None, **trace_options): return self.backend.line_plot(x=x, y=y, label=label, **trace_options) def create_fill_trace(self, x, y_upper, y_lower, **options): - return self.backend.fill_plot(x, y_upper, y_lower, **options) + return self.backend.fill_between_plot(x, y_upper, y_lower, **options) def create_histogram(self, x, name, **trace_options): return self.backend.histogram_plot(x, name, **trace_options) diff --git a/pybop/plot/voronoi.py b/pybop/plot/voronoi.py index 2ac57fada..312e470a1 100644 --- a/pybop/plot/voronoi.py +++ b/pybop/plot/voronoi.py @@ -3,8 +3,10 @@ import numpy as np from scipy.spatial import Voronoi +from pybop.plot.util import get_default_options, import_backend + if TYPE_CHECKING: - pass + from pybop._result import Result def _voronoi_regions(x, y, f, xlim, ylim): @@ -194,7 +196,49 @@ def interpolate_point(p, q, axis, boundary_val): return np.array([boundary_val, s]) if axis == 0 else np.array([s, boundary_val]) -def voronoi_data(xlim, ylim, pts_x, pts_y, f, normalise=True): +def surface( + result: "Result", + bounds=None, + normalise=True, + title="Voronoi Cost Landscape", + show=True, + backend=None, + **layout_kwargs, +): + """ + Plot a 2D representation of the Voronoi diagram with color-coded regions. + + Parameters: + ----------- + result : pybop.Result + Optimisation result containing the history of parameter values and associated cost. + bounds : numpy.ndarray, optional + A 2x2 array specifying the [min, max] bounds for each parameter. If None, uses + `cost.parameters.get_bounds_for_plotly`. + normalise : bool, optional + If True, the voronoi regions are computed using the Euclidean distance between + points normalised with respect to the bounds (default: True). + show : bool, optional + If True, the figure is shown upon creation (default: True). + **layout_kwargs : optional + Valid Plotly layout keys and their values, + e.g. `xaxis_title="Time [s]"` or + `xaxis={"title": "Time [s]", font={"size":14}}` + """ + backend = import_backend(backend) + points = result.x_model + parameters = result.problem.parameters + + if points[0].shape[0] != 2: + raise ValueError("This plot method requires two parameters.") + + x_optim, y_optim = map(list, zip(*points, strict=False)) + f = result.cost + + # Translate bounds, taking only the first two elements + xlim, ylim = ( + bounds if bounds is not None else [param.bounds for param in parameters] + )[:2] if normalise: if xlim[1] <= xlim[0] or ylim[1] <= ylim[0]: @@ -203,11 +247,11 @@ def voronoi_data(xlim, ylim, pts_x, pts_y, f, normalise=True): # Normalise the region x_range = xlim[1] - xlim[0] y_range = ylim[1] - ylim[0] - norm_x_optim = (np.asarray(pts_x) - xlim[0]) / x_range - norm_y_optim = (np.asarray(pts_y) - ylim[0]) / y_range + norm_x_optim = (np.asarray(x_optim) - xlim[0]) / x_range + norm_y_optim = (np.asarray(y_optim) - ylim[0]) / y_range # Compute regions - x, y, f, norm_regions = _voronoi_regions( + norm_x, norm_y, f, norm_regions = _voronoi_regions( norm_x_optim, norm_y_optim, f, (0, 1), (0, 1) ) @@ -221,12 +265,73 @@ def voronoi_data(xlim, ylim, pts_x, pts_y, f, normalise=True): else: # Compute regions - x, y, f, regions = _voronoi_regions(pts_x, pts_y, f, xlim, ylim) + x, y, f, regions = _voronoi_regions(x_optim, y_optim, f, xlim, ylim) + + # Get plotting options + opts = get_default_options("voronoi", backend.name) + outline_opts = opts.get("outline_opts") or {} + layout_options = opts.get("layout_options") or {} + optimised_opts = opts.get("optimised_opts") or {} + inital_opts = opts.get("initial_opts") or {} + scatter_opts = opts.get("scatter_opts") or {} + layout_options.update(layout_kwargs) + names = parameters.names + layout_options.update( + { + "title": title, + "xaxis_range": xlim, + "yaxis_range": ylim, + "xaxis_title": names[0], + "yaxis_title": names[1], + } + ) - # Calculate the size of each Voronoi region - region_sizes = np.array([len(region) for region in regions]) - relative_sizes = (region_sizes - region_sizes.min()) / ( - region_sizes.max() - region_sizes.min() + # Construct figure + fig = backend.create_figure([]) + + # Add Voronoi edges and fill Voronoi regions + print(f) + colors = backend.sample_color_scale(f) + for j, region in enumerate(regions): + x_region = region[:, 0].tolist() + [region[0, 0]] + y_region = region[:, 1].tolist() + [region[0, 1]] + + backend.plot_trace( + backend.fill_plot( + x_region, y_region, color=colors[j], label=f"f={f[j]:.2f}" + ), + fig, + ) + + backend.plot_trace(backend.line_plot(x_region, y_region, **outline_opts), fig) + + backend.colorbar(fig, f) + + # Add original points + backend.plot_trace( + backend.scatter_plot( + x=x_optim, + y=y_optim, + colors=[i / len(x_optim) for i in range(len(x_optim))], + labels=[f"f={val:.2f}" for val in f], + **scatter_opts, + ), + fig, ) - return x, y, f, regions, relative_sizes + # Plot the initial guess + if len(result.x_model) > 0: + x0 = result.x_model[0] + backend.plot_trace(backend.line_plot(x=[x0[0]], y=[x0[1]], **inital_opts), fig) + + # Plot optimised value + if result.x is not None: + x_best = result.x + backend.plot_trace( + backend.line_plot(x=[x_best[0]], y=[x_best[1]], **optimised_opts), fig + ) + + backend.update_layout(fig, **layout_options) + + if show: + backend.update_and_show(fig) From b76bf2be63b9d1b7bdfd75ce3eb1701b7d7b7edc Mon Sep 17 00:00:00 2001 From: u2370093 Date: Wed, 20 May 2026 15:20:15 +0100 Subject: [PATCH 13/20] more simplifications --- pybop/plot/contour.py | 12 +-- pybop/plot/convergence.py | 5 +- pybop/plot/dataset.py | 6 +- pybop/plot/matplotlib/__init__.py | 4 +- .../{util.py => default_options.py} | 53 ++---------- pybop/plot/matplotlib/figure_handling.py | 13 ++- pybop/plot/matplotlib/plotting_functions.py | 12 +-- pybop/plot/nyquist.py | 42 +++++---- pybop/plot/parameters.py | 9 +- pybop/plot/plotly/__init__.py | 4 +- .../plotly/{util.py => default_options.py} | 39 ++------- pybop/plot/plotly/figure_handling.py | 11 ++- pybop/plot/plotly/plotting_functions.py | 23 +++-- pybop/plot/predictive.py | 60 ++++++------- pybop/plot/problem.py | 6 +- pybop/plot/samples.py | 85 +++++++++++-------- pybop/plot/standard_plots.py | 10 +-- pybop/plot/util.py | 4 - pybop/plot/voronoi.py | 2 +- 19 files changed, 189 insertions(+), 211 deletions(-) rename pybop/plot/matplotlib/{util.py => default_options.py} (66%) rename pybop/plot/plotly/{util.py => default_options.py} (86%) diff --git a/pybop/plot/contour.py b/pybop/plot/contour.py index 3ae41ba4d..467c0cfcf 100644 --- a/pybop/plot/contour.py +++ b/pybop/plot/contour.py @@ -5,7 +5,7 @@ import numpy as np from pybop.plot.standard_plots import StandardPlot -from pybop.plot.util import call_plotting_function, get_default_options, update_and_show +from pybop.plot.util import get_default_options, import_backend from pybop.problems.problem import Problem if TYPE_CHECKING: @@ -60,6 +60,8 @@ def contour( ValueError If the cost function does not return a valid cost when called with a parameter list. """ + backend_module = import_backend(backend) + plot_optim = False problem = call_object @@ -170,12 +172,10 @@ def transform_array_of_values(list_of_values, parameter): # Plot the optimisation trace optim_trace = np.asarray([item[:2] for item in result.x_model]) optim_trace = optim_trace.reshape(-1, 2) - call_plotting_function( - "plot_optimisation_path", - backend=backend, + backend_module.plot_optimisation_path( plot_dict=plot_dict, x=transform_array_of_values(optim_trace[:, 0], parameters[names[0]]), - y=transform_array_of_values(optim_trace[:, 1], parameters[names[1]]), + y=transform_array_of_values(optim_trace[:, 1], parameters[names[1]]) ) # Plot the initial guess @@ -203,7 +203,7 @@ def transform_array_of_values(list_of_values, parameter): # Update the layout and display the figure fig = plot_dict(show=False) if show: - update_and_show(fig) + backend_module.show_figure(fig) # if gradient: # grad_figs = [] diff --git a/pybop/plot/convergence.py b/pybop/plot/convergence.py index 5d3390959..e7d54db21 100644 --- a/pybop/plot/convergence.py +++ b/pybop/plot/convergence.py @@ -1,7 +1,7 @@ from typing import TYPE_CHECKING from pybop.plot.standard_plots import StandardPlot -from pybop.plot.util import update_and_show +from pybop.plot.util import import_backend if TYPE_CHECKING: from pybop._result import Result @@ -47,6 +47,7 @@ def convergence(result: "Result", show=True, backend=None): # Generate and display the figure fig = plot_dict(show=False) + backend_module = import_backend(backend) if show: - update_and_show(fig, backend=backend) + backend_module.show_figure(fig) return fig diff --git a/pybop/plot/dataset.py b/pybop/plot/dataset.py index b92d2b78f..b7cada6c9 100644 --- a/pybop/plot/dataset.py +++ b/pybop/plot/dataset.py @@ -1,5 +1,5 @@ from pybop.plot.standard_plots import StandardPlot, trajectories -from pybop.plot.util import update_and_show +from pybop.plot.util import import_backend def dataset( @@ -56,6 +56,8 @@ def dataset( backend=backend, ) - fig = update_and_show(fig, backend=backend, show=show, **layout_kwargs) + backend_module = import_backend(backend) + if show: + backend_module.show_figure(fig) return fig diff --git a/pybop/plot/matplotlib/__init__.py b/pybop/plot/matplotlib/__init__.py index 023e82b74..d56422d53 100644 --- a/pybop/plot/matplotlib/__init__.py +++ b/pybop/plot/matplotlib/__init__.py @@ -1,6 +1,6 @@ -from .util import update_and_show, DEFAULT_PLOT_OPTIONS +from .default_options import DEFAULT_PLOT_OPTIONS from .plotting_functions import line_plot, colorbar, contour_plot, scatter_plot, fill_between_plot, fill_plot, histogram_plot, add_vline, plot_trace, add_traces, sample_color_scale, show_table, trajectories, plot_optimisation_path -from .figure_handling import create_figure, make_subplots, update_layout +from .figure_handling import create_figure, make_subplots, update_layout, show_figure, legend name="matplotlib" diff --git a/pybop/plot/matplotlib/util.py b/pybop/plot/matplotlib/default_options.py similarity index 66% rename from pybop/plot/matplotlib/util.py rename to pybop/plot/matplotlib/default_options.py index 60e96c086..cee06cb1a 100644 --- a/pybop/plot/matplotlib/util.py +++ b/pybop/plot/matplotlib/default_options.py @@ -1,23 +1,3 @@ -import warnings - -import matplotlib.pyplot as plt - - -def update_and_show(fig, show=True, **layout_kwargs): - if len(layout_kwargs) > 0: - warnings.warn( - "The following layout argument keys are ignored for the current plotting backend (matplotlib): \n" - f"{list(layout_kwargs.keys())}", - UserWarning, - stacklevel=2, - ) - - if show: - plt.show() - - return fig - - DEFAULT_PLOT_OPTIONS = { "standard_plot": {"default_trace_options": dict(linewidth=2.0)}, "contour": { @@ -41,14 +21,9 @@ def update_and_show(fig, show=True, **layout_kwargs): ), }, "nyquist": { - "plot_options": dict( - xaxis_title=r"$Z_{re} / \Omega$", yaxis_title=r"$-Z_{im} / \Omega$" - ), - "trace_options_model": dict( - label="Model", color="#00CC96", linewidth=2, marker=".", markersize=8 + "trace_options_model": dict(color="#00CC96", linewidth=2, marker=".", markersize=8 ), "trace_options_reference": dict( - label="Reference", linestyle="none", marker="o", fillstyle="none", @@ -68,6 +43,9 @@ def update_and_show(fig, show=True, **layout_kwargs): subplot_options=dict(wspace=0.3, hspace=0.3), trace_options=dict(use_color_cycle=True), ), + "predictive" : { + "trace_options_pdf": dict(linestyle="--") + }, "problem": { "default_trace_options": dict(label="Model", marker=None, linestyle="-"), "design_cost_options": dict(label="Optimised"), @@ -76,31 +54,10 @@ def update_and_show(fig, show=True, **layout_kwargs): "fill_options": dict(color=[(1.0, 0.898, 0.800, 0.8)]), }, "posterior": { - "plot_options": { - "figsize": (15, 8), - "grid": dict(axis="y", zorder=-1, color="w"), - "axis_bg_color": ( - 0.6784313725490196, - 0.8470588235294118, - 0.9019607843137255, - 0.3, - ), - }, + "plot_options": {"figsize": (15, 8)}, "trace_options": dict(alpha=0.75), "trace_options_vline": dict(linewidth=3, linestyle="--", color="k"), }, - "trace": { - "plot_options": { - "figsize": (15, 8), - "grid": dict(axis="y", zorder=-10, color="w"), - "axis_bg_color": ( - 0.6784313725490196, - 0.8470588235294118, - 0.9019607843137255, - 0.3, - ), - }, - }, "voronoi": { "layout_options": dict( legend=dict(ncols=2, loc="lower center", bbox_to_anchor=(0.5, 1.0)) diff --git a/pybop/plot/matplotlib/figure_handling.py b/pybop/plot/matplotlib/figure_handling.py index 65a581d87..283af944a 100644 --- a/pybop/plot/matplotlib/figure_handling.py +++ b/pybop/plot/matplotlib/figure_handling.py @@ -1,22 +1,26 @@ +import warnings from matplotlib import pyplot as plt from pybop.plot.matplotlib import plot_trace from pybop.plot.util import _AxisData -def create_figure(traces, **layout_options): +def create_figure(traces=None, **layout_options): figsize = (8, 6) if "figsize" in layout_options: figsize = layout_options.get("figsize") fig = plt.figure(figsize=figsize, dpi=100) - for trace in traces: - plot_trace(trace, fig) + if traces is not None: + for trace in traces: + plot_trace(trace, fig) update_layout(fig, **layout_options) return fig +def legend(fig, **kwargs): + fig.legend(**kwargs) def update_layout(fig, axes=None, **layout_options): if axes is None: @@ -84,3 +88,6 @@ def make_subplots(axes: list[_AxisData], subplot_options=None): axes[i] = fig.add_subplot(num_rows, num_cols, (idx_start, idx_end)) return fig + +def show_figure(fig): + plt.show() diff --git a/pybop/plot/matplotlib/plotting_functions.py b/pybop/plot/matplotlib/plotting_functions.py index c72dffd94..9f917ddcf 100644 --- a/pybop/plot/matplotlib/plotting_functions.py +++ b/pybop/plot/matplotlib/plotting_functions.py @@ -77,7 +77,7 @@ def line_plot(x=None, y=None, label=None, **kwargs): return trace -def colorbar(fig, data, colorscale="viridis"): +def colorbar(fig, data, colorscale="viridis", label=None): # normalise cost f_min = np.nanmin(data[np.isfinite(data)]) f_max = np.nanmax(data[np.isfinite(data)]) @@ -86,7 +86,7 @@ def colorbar(fig, data, colorscale="viridis"): # get colours cmap = mpl.colormaps["viridis"] - plt.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap), ax=fig.gca()) + plt.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap), ax=fig.gca(), label=label) def contour_plot(x, y, z, **kwargs): @@ -131,12 +131,14 @@ def add_vline(fig, x, **trace_options): plt.axvline(x, **trace_options) -def sample_color_scale(data, scale="viridis"): +def sample_color_scale(data, scale="viridis", d_min=None, d_max=None): # normalise and clip data - d_min = np.nanmin(data[np.isfinite(data)]) - d_max = np.nanmax(data[np.isfinite(data)]) + d_min = d_min or np.nanmin(data[np.isfinite(data)]) + d_max = d_max or np.nanmax(data[np.isfinite(data)]) norm = mpl.colors.Normalize(vmin=d_min, vmax=d_max, clip=True) norm_d = norm(data, clip=True) + if np.isscalar(norm_d): + norm_d = [norm_d] # get colours cmap = mpl.colormaps[scale] diff --git a/pybop/plot/nyquist.py b/pybop/plot/nyquist.py index 399558654..1be3da8a1 100644 --- a/pybop/plot/nyquist.py +++ b/pybop/plot/nyquist.py @@ -1,6 +1,6 @@ from pybop.parameters.parameter import Inputs from pybop.plot.standard_plots import StandardPlot -from pybop.plot.util import get_default_options, update_and_show +from pybop.plot.util import get_default_options, import_backend def nyquist( @@ -48,8 +48,6 @@ def nyquist( trace_options_model = options.get("trace_options_model") or {} trace_options_reference = options.get("trace_options_reference") or {} - plot_options.update({"title": title}) - if not isinstance(inputs, dict): inputs = problem.parameters.to_dict(inputs) @@ -57,27 +55,39 @@ def nyquist( domain_data = model_output["Impedance"].data.real target_output = problem.target_data figure_list = [] + backend = import_backend(backend) for var in problem.target: - plot_dict = StandardPlot( - x=domain_data, - y=-model_output[var].data.imag, - trace_names="Model", + fig = backend.create_figure( + xaxis_title=r"$Z_{re} / \Omega$", + yaxis_title=r"$-Z_{im} / \Omega$", + title=title, **plot_options, ) - plot_dict.traces[0].update(trace_options_model) - - target_trace = plot_dict.create_trace( - x=target_output[var].real, - y=-target_output[var].imag, - **trace_options_reference, + backend.plot_trace( + backend.line_plot( + x=domain_data, + y=-model_output[var].data.imag, + label="Model", + **trace_options_model + ), + fig ) - plot_dict.traces.append(target_trace) - fig = plot_dict(show=False) + backend.plot_trace( + backend.line_plot( + x=target_output[var].real, + y=-target_output[var].imag, + label="Reference", + **trace_options_reference, + ), + fig + ) + backend.legend(fig) figure_list.append(fig) + if show: - update_and_show(figure_list) + backend.show_figure(fig) return figure_list diff --git a/pybop/plot/parameters.py b/pybop/plot/parameters.py index 93831abf8..cd3c68e0a 100644 --- a/pybop/plot/parameters.py +++ b/pybop/plot/parameters.py @@ -2,13 +2,13 @@ from pybop.costs.log_likelihoods import GaussianLogLikelihood from pybop.plot.standard_plots import StandardSubplot -from pybop.plot.util import get_default_options, update_and_show +from pybop.plot.util import get_default_options, import_backend if TYPE_CHECKING: from pybop._result import Result -def parameters(result: "Result", show=True, backend=None, **layout_kwargs): +def parameters(result: "Result", show=True, backend=None): """ Plot the evolution of parameters during the optimisation process using Plotly. @@ -44,6 +44,9 @@ def parameters(result: "Result", show=True, backend=None, **layout_kwargs): axis_titles.append(("Evaluation", "Sigma")) trace_names.append("Sigma") + # import plotting backend + backend_module = import_backend(backend) + # Set subplot layout options opts = get_default_options("parameters", backend) layout_options = opts.get("layout_options") or {} @@ -65,6 +68,6 @@ def parameters(result: "Result", show=True, backend=None, **layout_kwargs): # Generate the figure and update the layout fig = plot_dict(show=False) - fig = update_and_show(fig, backend, **layout_kwargs) + fig = backend_module.show_figure(fig) return fig diff --git a/pybop/plot/plotly/__init__.py b/pybop/plot/plotly/__init__.py index 809aa71b0..ddde0710d 100644 --- a/pybop/plot/plotly/__init__.py +++ b/pybop/plot/plotly/__init__.py @@ -1,8 +1,8 @@ from .plotly_manager import PlotlyManager -from .util import update_and_show, DEFAULT_PLOT_OPTIONS +from .default_options import DEFAULT_PLOT_OPTIONS from .plotting_functions import colorbar, contour_plot, line_plot, fill_between_plot, fill_plot, histogram_plot, add_vline, show_table, scatter_plot, trajectories, plot_optimisation_path, add_traces, plot_trace, sample_color_scale -from .figure_handling import make_subplots, update_layout, create_figure +from .figure_handling import make_subplots, update_layout, create_figure, show_figure, legend name='plotly' diff --git a/pybop/plot/plotly/util.py b/pybop/plot/plotly/default_options.py similarity index 86% rename from pybop/plot/plotly/util.py rename to pybop/plot/plotly/default_options.py index 7aa709bbf..8bc5e9a92 100644 --- a/pybop/plot/plotly/util.py +++ b/pybop/plot/plotly/default_options.py @@ -1,16 +1,3 @@ -def update_and_show(fig, show=True, **layout_kwargs): - if hasattr(fig, "__len__") and len(fig) > 0: - for f in fig: - f.update_layout(**layout_kwargs) - if show: - f.show() - else: - fig.update_layout(**layout_kwargs) - if show: - fig.show() - return fig - - DEFAULT_PLOT_OPTIONS = { "standard_plot": { "default_layout_options": dict( @@ -80,10 +67,11 @@ def update_and_show(fig, show=True, **layout_kwargs): }, "nyquist": { "plot_options": dict( - title="Nyquist Plot", font=dict(family="Arial", size=14), plot_bgcolor="white", paper_bgcolor="white", + width=600, + height=600, xaxis=dict( title=dict(font=dict(size=16), standoff=15), showline=True, @@ -111,10 +99,6 @@ def update_and_show(fig, show=True, **layout_kwargs): legend=dict( orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1 ), - width=600, - height=600, - xaxis_title="Zre / Ω", - yaxis_title="-Zim / Ω", ), "trace_options_model": dict( mode="lines+markers", @@ -122,7 +106,6 @@ def update_and_show(fig, show=True, **layout_kwargs): marker=dict(size=8, color="#00CC96", symbol="circle"), ), "trace_options_reference": dict( - name="Reference", mode="markers", marker=dict(size=8, color="#636EFA", symbol="circle-open"), showlegend=True, @@ -143,17 +126,13 @@ def update_and_show(fig, show=True, **layout_kwargs): ), ), "posterior": { - "plot_options": dict( - barmode="overlay", - width=None, - height=None, - plot_bgcolor=None, - autosize=None, - legend=None, - ), + "plot_options": dict(barmode="overlay",), "trace_options": dict(opacity=0.75), "trace_options_vline": dict(line_width=3, line_dash="dash", line_color="black"), }, + "predictive" : { + "trace_options_pdf": dict(line={"dash": "dot"}) + }, "problem": { "default_trace_options": dict(name="Model", mode="lines", showlegend=True), "design_cost_options": dict(name="Optimised"), @@ -161,12 +140,6 @@ def update_and_show(fig, show=True, **layout_kwargs): "reference_options": dict(name="Reference", mode="markers", showlegend=True), "fill_options": dict(fillcolor="rgba(255,229,204,0.8)"), }, - "trace": { - "plot_options": dict( - width=None, height=None, plot_bgcolor=None, autosize=None, legend=None - ), - "trace_options": dict(mode="lines"), - }, "voronoi": { "layout_options": dict( title_x=0.5, diff --git a/pybop/plot/plotly/figure_handling.py b/pybop/plot/plotly/figure_handling.py index d6a5a92dc..961403aed 100644 --- a/pybop/plot/plotly/figure_handling.py +++ b/pybop/plot/plotly/figure_handling.py @@ -7,12 +7,14 @@ def _check_empty(specs, row, col): raise ValueError("Overlapping axes are not supported") -def create_figure(traces, **layout_options): +def create_figure(traces=None, **layout_options): go = PlotlyManager().go layout = go.Layout(**layout_options) fig = go.Figure(data=traces, layout=layout) return fig +def legend(fig, **kwargs): + fig.update_layout(showlegend=True, **kwargs) def update_layout(fig, axes=None, **layout_options): fig.update_layout(**layout_options) @@ -38,3 +40,10 @@ def make_subplots(axes: list[_AxisData], subplot_options=None): fig = make_subplots(rows=num_rows, cols=num_cols, specs=specs, **subplot_options) return fig + +def show_figure(fig): + if hasattr(fig, "__len__") and len(fig) > 0: + for f in fig: + f.show() + else: + fig.show() diff --git a/pybop/plot/plotly/plotting_functions.py b/pybop/plot/plotly/plotting_functions.py index 18c0c6f0f..502be9eeb 100644 --- a/pybop/plot/plotly/plotting_functions.py +++ b/pybop/plot/plotly/plotting_functions.py @@ -6,14 +6,16 @@ from pybop.plot.plotly.plotly_manager import PlotlyManager -def sample_color_scale(data, scale="viridis"): +def sample_color_scale(data, scale="viridis", d_min=None, d_max=None): px = PlotlyManager().px # normalise and clip data - d_min = np.nanmin(data[np.isfinite(data)]) - d_max = np.nanmax(data[np.isfinite(data)]) + d_min = d_min or np.nanmin(data[np.isfinite(data)]) + d_max = d_max or np.nanmax(data[np.isfinite(data)]) d = (data - d_min) / (d_max - d_min) d = np.clip(np.asarray(data), 0, 1.0) + if np.isscalar(d): + d = [d] return px.colors.sample_colorscale("viridis", list(d)) @@ -41,10 +43,15 @@ def add_traces(x, y, trace_names, **trace_options): return traces -def line_plot(x=None, y=None, label=None, ax=None, **kwargs): +def line_plot(x=None, y=None, label=None, ax=None, color=None, **kwargs): go = PlotlyManager().go if label is not None: kwargs.update({"name": label}) + if color is not None: + if "line" in kwargs: + kwargs["line"].update(color=color) + else: + kwargs.update({"line": {"color" : color}}) if x is not None and y is not None: return go.Scatter( x=x, @@ -63,10 +70,14 @@ def contour_plot(x, y, z, **kwargs): return go.Contour(x=x, y=y, z=z, **kwargs) -def colorbar(fig, data, colorscale="viridis"): +def colorbar(fig, data, colorscale="viridis", label=None): go = PlotlyManager().go d_min = np.nanmin(data[np.isfinite(data)]) d_max = np.nanmax(data[np.isfinite(data)]) + + colorbar=dict(thickness=25, outlinewidth=1) + if label is not None: + colorbar.update({"title": {"text": label, "side": "right"}}) fig.add_trace( go.Scatter( x=[None], @@ -77,7 +88,7 @@ def colorbar(fig, data, colorscale="viridis"): showscale=True, cmin=d_min, cmax=d_max, - colorbar=dict(thickness=25, outlinewidth=0), + colorbar=colorbar, ), showlegend=False, hoverinfo="none", diff --git a/pybop/plot/predictive.py b/pybop/plot/predictive.py index 72e90a1c5..69f87a610 100644 --- a/pybop/plot/predictive.py +++ b/pybop/plot/predictive.py @@ -2,7 +2,7 @@ import numpy as np -from pybop.plot.plotly.plotly_manager import PlotlyManager +from pybop.plot.util import import_backend, get_default_options from pybop.plot.standard_plots import StandardPlot from pybop.problems.meta_problem import MetaProblem from pybop.simulators.failed_solution import FailedSolution @@ -21,13 +21,11 @@ def predictive( pdf_label: str = "PDF", colour_scale="viridis", show: bool = True, - **layout_kwargs, + backend: str | None = None, ): """ Plot the predictive posterior of a Bayesian optimisation result. """ - # Import plotly only when needed - px = PlotlyManager().px posterior_samples = result.posterior.sample_from_distribution( n_samples=number_of_traces @@ -44,6 +42,9 @@ def predictive( else [result.problem] ) figure_list = [] + options = get_default_options("predictive", backend) + trace_options_pdf = options.get("trace_options_pdf") or {} + backend_module = import_backend(backend) for problem in problems: plot_dict = StandardPlot( @@ -52,51 +53,40 @@ def predictive( xaxis_title=StandardPlot.remove_brackets(problem.domain), yaxis_title=StandardPlot.remove_brackets(problem.target[0]), trace_names=data_legend_entry, - backend="plotly", + backend=backend, ) + fig = plot_dict(show=False) # Simulate the samples and add to plot inputs = [problem.parameters.to_dict(s) for s in posterior_samples] simulations = problem.simulate_batch(inputs=inputs) for pdf, sim in zip(posterior_samples_pdf, simulations, strict=False): if not isinstance(sim, FailedSolution): - plot_dict.add_traces( - x=problem.domain_data, - y=sim[problem.target[0]].data, - line={ - "dash": "dot", - "color": px.colors.sample_colorscale( - colour_scale, - (pdf - pdf_range[0]) / (pdf_range[1] - pdf_range[0]), - )[0], - }, + colors = backend_module.sample_color_scale(pdf, d_min = pdf_range[0], d_max=pdf_range[1] ) + backend_module.plot_trace( + backend_module.line_plot( + x=problem.domain_data, + y=sim[problem.target[0]].data, + color=colors[0], + **trace_options_pdf + + ), + fig ) # Add the colourbar - plot_dict.add_traces( - x=[None], - y=[None], - mode="markers", - marker={ - "size": 0, - "color": pdf_range, - "colorscale": colour_scale, - "showscale": True, - "colorbar": {"title": {"text": "Posterior PDF", "side": "right"}}, - }, - ) + backend_module.colorbar(fig, pdf_range, colorscale=colour_scale, label="Posterior PDF") if pdf_plot is not None: - plot_dict.add_traces( - x=pdf_plot[0], - y=pdf_plot[1], - trace_names=pdf_label, + backend_module.plot_trace( + backend_module.line_plot( + x=pdf_plot[0], + y=pdf_plot[1], + trace_names=pdf_label, + ) ) - - fig = plot_dict(show=False) - fig.update_layout(**layout_kwargs) if show: - fig.show() + backend_module.show_figure(fig) figure_list.append(fig) diff --git a/pybop/plot/problem.py b/pybop/plot/problem.py index 385bda034..eb8caed23 100644 --- a/pybop/plot/problem.py +++ b/pybop/plot/problem.py @@ -4,7 +4,7 @@ from pybop.costs.error_measures import ErrorMeasure from pybop.parameters.parameter import Inputs from pybop.plot.standard_plots import StandardPlot -from pybop.plot.util import get_default_options, update_and_show +from pybop.plot.util import get_default_options, import_backend from pybop.problems.meta_problem import MetaProblem from pybop.problems.problem import Problem from pybop.simulators.solution import Solution @@ -73,6 +73,7 @@ def problem( fill_options = plot_options.get("fill_options") or {} # Create a plot for each output + backend_module = import_backend(backend) figure_list = [] for var in problem.target: options = trace_options.copy() @@ -120,7 +121,8 @@ def problem( # Generate the figure and update the layout fig = plot_dict(show=False) - fig = update_and_show(fig, backend=backend) + if show: + backend_module.show_figure(fig) figure_list.append(fig) diff --git a/pybop/plot/samples.py b/pybop/plot/samples.py index 906be7471..f702032e9 100644 --- a/pybop/plot/samples.py +++ b/pybop/plot/samples.py @@ -1,7 +1,8 @@ from typing import TYPE_CHECKING from pybop.plot import StandardPlot -from pybop.plot.util import get_default_options, import_backend, update_and_show +from pybop.plot.util import get_default_options, import_backend +from pybop.plot.plotly import PlotlyManager if TYPE_CHECKING: from pybop.samplers.base_pints_sampler import SamplingResult @@ -16,58 +17,70 @@ def chains(result: "SamplingResult", show=True, backend=None): trace_options = options.get("trace_options") or {} trace_options_vline = options.get("trace_options_vline") or {} - plot_dict = StandardPlot( - backend=backend, + # Import backend + backend = import_backend(backend) + + + fig = backend.create_figure( title="Posterior Distribution", xaxis_title="Value", yaxis_title="Density", - **plot_options, + **plot_options ) for i, chain in enumerate(result.chains): for j in range(chain.shape[1]): - hist = plot_dict.create_histogram( - x=chain[:, j], name=f"Chain {i} - Parameter {j}", **trace_options + backend.plot_trace( + backend.histogram_plot( + x=chain[:, j], + name=f"Chain {i} - Parameter {j}", + **trace_options + ), + fig ) - plot_dict.traces.append(hist) - - fig = plot_dict(show=False) - for j in range(chain.shape[1]): - plot_dict.create_vline(fig, result.mean[j], **trace_options_vline) - update_and_show(fig, backend=backend) + backend.add_vline( + fig, + result.mean[j], + **trace_options_vline + ) + backend.legend(fig) + + if show: + backend.show_figure(fig) def trace(result: "SamplingResult", show=True, backend=None): """ Plot trace plots for the posterior samples. """ + # Import plotting backend + backend = import_backend(backend) + + figlist = [] - options = get_default_options("trace", backend) - plot_options = options.get("plot_options") or {} - trace_options = options.get("trace_options") or {} for i in range(result.n_parameters): - plots = StandardPlot( + fig = backend.create_figure( title=f"Parameter {i} Trace Plot", xaxis_title="Sample Index", yaxis_title="Value", - backend=backend, - **plot_options, ) - for j, chain in enumerate(result.chains): - plots.traces.append( - plots.create_trace(y=chain[:, i], label=f"Chain {j}", **trace_options) + backend.plot_trace( + backend.line_plot(y=chain[:, i], label=f"Chain {j}"), + fig ) - fig = plots(show=False) + backend.legend(fig) figlist.append(fig) - update_and_show(figlist, show=show, backend=backend) + if show: + backend.show_figure(figlist) return figlist -def posterior(result: "SamplingResult", backend=None): + +def posterior(result: "SamplingResult", backend=None, show=True): """ Plot the summed posterior distribution across chains. """ @@ -75,9 +88,9 @@ def posterior(result: "SamplingResult", backend=None): plot_options = options.get("plot_options") or {} trace_options = options.get("trace_options") or {} trace_options_vline = options.get("trace_options_vline") or {} - # Import plotly only when needed - plot_dict = StandardPlot( - backend=backend, + # Import backend + backend = import_backend(backend) + fig = backend.create_figure( title="Posterior Distribution", xaxis_title="Value", yaxis_title="Density", @@ -85,17 +98,18 @@ def posterior(result: "SamplingResult", backend=None): ) for j in range(result.all_samples.shape[1]): - hist = plot_dict.create_histogram( - x=result.all_samples[:, j], name=f"Parameter {j}", **trace_options + backend.plot_trace( + backend.histogram_plot( + x=result.all_samples[:, j], name=f"Parameter {j}", **trace_options + ), + fig ) + backend.add_vline(fig, result.mean[j], **trace_options_vline) - plot_dict.traces.append(hist) - - fig = plot_dict(show=False) - for j in range(result.all_samples.shape[1]): - plot_dict.create_vline(fig, result.mean[j], **trace_options_vline) + backend.legend(fig) - update_and_show(fig, backend=backend) + if show: + backend.show_figure(fig) def summary_table(result: "SamplingResult", backend=None): @@ -120,3 +134,4 @@ def summary_table(result: "SamplingResult", backend=None): values=values, title="Summary Statistics", ) + diff --git a/pybop/plot/standard_plots.py b/pybop/plot/standard_plots.py index 4faf9245e..f733e5234 100644 --- a/pybop/plot/standard_plots.py +++ b/pybop/plot/standard_plots.py @@ -7,8 +7,7 @@ from pybop.plot.util import ( create_axis, get_default_options, - import_backend, - update_and_show, + import_backend ) @@ -45,7 +44,7 @@ def __init__( def __call__(self, show=True): fig = self.backend.create_figure(self.traces, **self.layout_options) if show: - update_and_show(fig, backend=self.backend.name) + self.backend.show_figure(fig) return fig def add_traces(self, x, y, trace_names=None, **trace_options): @@ -79,7 +78,8 @@ def add_traces(self, x, y, trace_names=None, **trace_options): # Create a trace for each trajectory self.traces.extend(self.backend.add_traces(x, y, trace_names, **options)) - def parse_data(self, x, y): + @staticmethod + def parse_data(x, y): """ Check the type and dimensions of the data and convert if necessary to a list of 'things plotly can take', e.g. numpy arrays or lists of numbers. @@ -283,7 +283,7 @@ def __call__(self, show=True): self.backend.update_layout(fig, axes=axes, **self.layout_options) if show: - self.backend.update_and_show(fig) + self.backend.show_figure(fig) else: return fig diff --git a/pybop/plot/util.py b/pybop/plot/util.py index 9bf7398d2..5c64dc1ab 100644 --- a/pybop/plot/util.py +++ b/pybop/plot/util.py @@ -71,10 +71,6 @@ def import_backend(backend): return module -def update_and_show(fig, backend=None, **kwargs): - return call_plotting_function("update_and_show", backend, fig=fig, **kwargs) - - def get_default_options(plot_type, backend): if backend is None: backend = pybop.plot.backend diff --git a/pybop/plot/voronoi.py b/pybop/plot/voronoi.py index 312e470a1..359ded278 100644 --- a/pybop/plot/voronoi.py +++ b/pybop/plot/voronoi.py @@ -334,4 +334,4 @@ def surface( backend.update_layout(fig, **layout_options) if show: - backend.update_and_show(fig) + backend.show_figure(fig) From 1be7b1471c444506a0a7c96f9d29b47fd6483659 Mon Sep 17 00:00:00 2001 From: u2370093 Date: Thu, 28 May 2026 16:05:11 +0100 Subject: [PATCH 14/20] change structure --- pybop/plot/__init__.py | 7 +- pybop/plot/backends/__init__.py | 4 + pybop/plot/backends/base.py | 70 +++ pybop/plot/backends/matplotlib.py | 243 +++++++++++ pybop/plot/backends/plotly.py | 332 +++++++++++++++ .../{plotly => backends}/plotly_manager.py | 0 pybop/plot/contour.py | 86 ++-- pybop/plot/convergence.py | 26 +- pybop/plot/dataset.py | 10 +- pybop/plot/distribution.py | 63 +-- pybop/plot/matplotlib/__init__.py | 6 - pybop/plot/matplotlib/default_options.py | 86 ---- pybop/plot/matplotlib/figure_handling.py | 93 ---- pybop/plot/matplotlib/plotting_functions.py | 236 ----------- pybop/plot/nyquist.py | 24 +- pybop/plot/parameters.py | 54 +-- pybop/plot/plotly/__init__.py | 8 - pybop/plot/plotly/default_options.py | 183 -------- pybop/plot/plotly/figure_handling.py | 49 --- pybop/plot/plotly/plotting_functions.py | 216 ---------- pybop/plot/predictive.py | 38 +- pybop/plot/problem.py | 76 ++-- pybop/plot/samples.py | 23 +- pybop/plot/standard_plots.py | 401 ++++++------------ pybop/plot/util.py | 148 ++++--- pybop/plot/voronoi.py | 61 +-- tests/unit/test_plots.py | 4 +- 27 files changed, 1125 insertions(+), 1422 deletions(-) create mode 100644 pybop/plot/backends/__init__.py create mode 100644 pybop/plot/backends/base.py create mode 100644 pybop/plot/backends/matplotlib.py create mode 100644 pybop/plot/backends/plotly.py rename pybop/plot/{plotly => backends}/plotly_manager.py (100%) delete mode 100644 pybop/plot/matplotlib/__init__.py delete mode 100644 pybop/plot/matplotlib/default_options.py delete mode 100644 pybop/plot/matplotlib/figure_handling.py delete mode 100644 pybop/plot/matplotlib/plotting_functions.py delete mode 100644 pybop/plot/plotly/__init__.py delete mode 100644 pybop/plot/plotly/default_options.py delete mode 100644 pybop/plot/plotly/figure_handling.py delete mode 100644 pybop/plot/plotly/plotting_functions.py diff --git a/pybop/plot/__init__.py b/pybop/plot/__init__.py index 577acfda0..6406739ab 100644 --- a/pybop/plot/__init__.py +++ b/pybop/plot/__init__.py @@ -2,12 +2,12 @@ DEFAULT_BACKEND = 'matplotlib' backend=DEFAULT_BACKEND -from .util import set_backend, get_default_options, import_backend, _AxisData +from .util import set_backend, import_backend, _AxisData, remove_brackets, wrap_text, parse_data # # Import plots # -from .standard_plots import StandardPlot, StandardSubplot, trajectories +from .standard_plots import trajectories, Subplots from .contour import contour from .dataset import dataset from .convergence import convergence @@ -20,5 +20,4 @@ from .distribution import distribution # Import backend specific plotting functions -from . import matplotlib -from . import plotly +from . import backends diff --git a/pybop/plot/backends/__init__.py b/pybop/plot/backends/__init__.py new file mode 100644 index 000000000..f9e20a618 --- /dev/null +++ b/pybop/plot/backends/__init__.py @@ -0,0 +1,4 @@ +from .base import PlotBackend +from .matplotlib import MatplotlibBackend +from .plotly_manager import PlotlyManager +from .plotly import PlotlyBackend \ No newline at end of file diff --git a/pybop/plot/backends/base.py b/pybop/plot/backends/base.py new file mode 100644 index 000000000..9c0b5ece7 --- /dev/null +++ b/pybop/plot/backends/base.py @@ -0,0 +1,70 @@ +from abc import ABC, abstractmethod +from pybop.plot.util import _AxisData + +class PlotBackend(ABC): + @abstractmethod + def create_figure(self, title: str=None, xaxis_title: str=None, yaxis_title: str=None, traces: list=None, style:dict=None): + raise NotImplementedError + + @abstractmethod + def make_subplots(self, axes: list[_AxisData], title=None, axis_titles_x: list[str] | str = None, axis_titles_y: list[str] | str = None, style=None): + raise NotImplementedError + + @abstractmethod + def legend(self, fig, style: dict=None): + raise NotImplementedError + + @abstractmethod + def show_figure(self, fig): + raise NotImplementedError + + @abstractmethod + def plot_trace(self, traces: dict | list[dict], fig, ax=None, color_cycle=None): + raise NotImplementedError + + @abstractmethod + def sample_color_scale(self, data, scale="viridis", d_min=None, d_max=None): + raise NotImplementedError + + @abstractmethod + def colorbar(self, fig, data, colorscale="viridis", label=None): + raise NotImplementedError + + @abstractmethod + def contour_plot(self, x, y, z, colorscale="viridis"): + raise NotImplementedError + + @abstractmethod + def fill_plot(self, x, y, color=None, label=None): + raise NotImplementedError + + @abstractmethod + def fill_between_plot(self, x, y_upper, y_lower, color): + raise NotImplementedError + + @abstractmethod + def histogram_plot(self, x, name, style=None): + raise NotImplementedError + + @abstractmethod + def line_plot(self, x=None, y=None, label=None, style = None): + raise NotImplementedError + + @abstractmethod + def scatter_plot(self, x, y, colors, labels=None, colorscale="Greys"): + raise NotImplementedError + + @abstractmethod + def show_table(self, header, values, title): + raise NotImplementedError + + @abstractmethod + def add_vline(self, fig, x, style=None): + raise NotImplementedError + + + + + + + diff --git a/pybop/plot/backends/matplotlib.py b/pybop/plot/backends/matplotlib.py new file mode 100644 index 000000000..961de170b --- /dev/null +++ b/pybop/plot/backends/matplotlib.py @@ -0,0 +1,243 @@ +import warnings +from copy import deepcopy + +import matplotlib as mpl +import numpy as np +from matplotlib import pyplot as plt +from pybop.plot.util import _AxisData +from pybop.plot.backends.base import PlotBackend + +class MatplotlibBackend(PlotBackend): + def __init__(self): + self.name = 'matplotlib' + + def create_figure(self, title = None, xaxis_title = None, yaxis_title = None, traces = None, style = None): + style = style or {} + figsize = (np.ceil(style.get("width", 800)/100), np.ceil(style.get("height", 600)/100)) + + fig = plt.figure(figsize=figsize, dpi=100) + + # Set titles + if title is not None: + plt.suptitle(title) + if xaxis_title is not None: + plt.xlabel(xaxis_title) + if yaxis_title is not None: + plt.ylabel(yaxis_title) + + # Apply style + if "xaxis_range" in style: + plt.xlim(style.get("xaxis_range")) + if "yaxis_range" in style: + plt.ylim(style.get("yaxis_range")) + if "bg_color" in style: + ax = fig.gca() + ax.set_facecolor(style.get("bg_color")) + ax.set_axisbelow(True) + + + if traces is not None: + for trace in traces: + self.plot_trace(trace, fig) + return fig + def make_subplots(self, axes: list[_AxisData], title=None, axis_titles_x: list[str] | str = None, axis_titles_y: list[str] | str = None, style=None): + style = style or {} + + num_rows = max(ax.row + ax.row_span - 1 for ax in axes) + num_cols = max(ax.col + ax.col_span - 1 for ax in axes) + + figsize = (np.ceil(style.get("width", 800)/100), np.ceil(style.get("height", 600)/100)) + + fig = plt.figure(figsize=figsize, dpi=100) + axes_dict = {} + + for ax in axes: + print(ax) + idx_start = (ax.row - 1) * num_cols + ax.col + idx_end = (ax.row + ax.row_span - 2) * num_cols + ax.col + ax.col_span - 1 + axes_dict[(ax.row, ax.col)] = fig.add_subplot(num_rows, num_cols, (idx_start, idx_end)) + + + # Set title + if title is not None: + plt.suptitle(title) + + for i, ax in enumerate(fig.axes): + if isinstance(axis_titles_x, str): + ax.set_xlabel(axis_titles_x) + elif isinstance(axis_titles_x, list) and i < len(axis_titles_x): + ax.set_xlabel(axis_titles_x[i]) + if isinstance(axis_titles_y, str): + ax.set_ylabel(axis_titles_y) + elif isinstance(axis_titles_y, list) and i < len(axis_titles_y): + ax.set_ylabel(axis_titles_y[i]) + if "bg_color" in style: + ax.set_facecolor(style.get("bg_color")) + ax.set_axisbelow(True) + + return fig, axes_dict, num_rows, num_cols + + def legend(self, fig, style: dict=None): + style= style or {} + lines_labels = [] + if style.get('fig_legend'): + axes = fig.axes + else: + axes = [fig.gca()] + labels_in_fig = False + lines_labels = [] + opts ={} + for ax in axes: + if not ax.get_legend_handles_labels() == ([], []): + lines_labels.append(ax.get_legend_handles_labels()) + labels_in_fig = True + if labels_in_fig: + lines, labels = [sum(lol, []) for lol in zip(*lines_labels, strict=False)] + if style.get("horizontal"): + opts["ncols"] = len(lines) + if "coords" in style.keys(): + opts["bbox_to_anchor"] = style.get("coords") + if style.get('fig_legend'): + opts["loc"] = style.get("loc", "upper right") + fig.legend(lines, labels, **opts) + else: + opts["loc"] = style.get("loc", "best") + axes[0].legend(lines, labels, **opts) + + def show_figure(self, fig): + fig.tight_layout() + plt.show() + + + def plot_trace(self, traces: dict | list[dict], fig, ax=None, color_cycle=None): + if not isinstance(traces, list): + traces = [traces] + for trace in traces: + # retrieve axis, plot_type and positional arguments + plot_type = trace.get("plot_type") or "plot" + positional_args = trace.get("positional_args") or None + ax = ax or fig.gca() + + # axis titles + if "xaxis_title" in trace.keys(): + ax.set_xlabel(trace["xaxis_title"]) + if "yaxis_title" in trace.keys(): + ax.set_ylabel(trace["yaxis_title"]) + + # retrieve remaining arguments + trace_options = deepcopy(trace) + for key in ["plot_type", "positional_args", "xaxis_title", "yaxis_title"]: + if key in trace_options.keys(): + del trace_options[key] + + # update color + if color_cycle is not None and plot_type == "plot": + trace_options.update(**next(color_cycle)) + + # get plotting function + try: + plot_function = getattr(ax, plot_type) + except ValueError: + print("Plot type not recognised") + + obj = plot_function(*positional_args, **trace_options) + if plot_type == "contourf": + plt.colorbar(obj) + + def sample_color_scale(self, data, scale="viridis", d_min=None, d_max=None): + # normalise and clip data + d_min = d_min or np.nanmin(data[np.isfinite(data)]) + d_max = d_max or np.nanmax(data[np.isfinite(data)]) + norm = mpl.colors.Normalize(vmin=d_min, vmax=d_max, clip=True) + norm_d = norm(data, clip=True) + if np.isscalar(norm_d): + norm_d = [norm_d] + + # get colours + cmap = mpl.colormaps[scale] + return cmap(norm_d) + + def colorbar(self, fig, data, colorscale="viridis", label=None): + # normalise cost + f_min = np.nanmin(data[np.isfinite(data)]) + f_max = np.nanmax(data[np.isfinite(data)]) + norm = mpl.colors.Normalize(vmin=f_min, vmax=f_max, clip=True) + + # get colours + cmap = mpl.colormaps[colorscale] + + plt.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap), ax=fig.gca(), label=label) + + def contour_plot(self, x, y, z, colorscale="viridis"): + contour = dict(positional_args=[x, y, z], plot_type="contourf", cmap=colorscale) + contour_lines = dict( + positional_args=[x, y, z], + colors=("k"), + linestyles="solid", + linewidths=0.2, + plot_type="contour", + ) + return [contour, contour_lines] + + def fill_plot(self, x, y, color=None, label=None): + return dict(positional_args=(x, y), plot_type="fill", color=color) + + + def fill_between_plot(self, x, y_upper, y_lower, color): + trace = dict(positional_args=(x, y_upper, y_lower), plot_type="fill_between", color=color) + return trace + + def histogram_plot(self, x, name, style: dict = None): + trace = dict(positional_args=[x], label=name, plot_type="hist") + trace.update({"alpha" : style.get("alpha")}) + return trace + + def line_plot(self, x=None, y=None, label=None, style=None): + style = style or {} + if x is not None and y is not None: + size = min(len(x), len(y)) + trace = dict(positional_args=[x[:size], y[:size]], label=label) + elif y is not None: + trace = dict(positional_args=[y], label=label) + + trace.update(style) + return trace + + def scatter_plot(self, x, y, colors=None, labels=None, colorscale="Greys"): + scatter = dict(positional_args=[x, y], plot_type="scatter", cmap=colorscale) + if colors is not None: + scatter["c"] = colors + return scatter + + def show_table(self, header, values, title): + """ + Display data in a table. + """ + for i, val in enumerate(values): + values[i] = [val[0], ", ".join(val[1].astype(str))] + + fig, ax = plt.subplots(figsize=(6, 2), dpi=100) + + # hide axes + ax.axis("off") + ax.axis("tight") + ax.table( + cellText=values, + colLabels=header, + loc="center", + cellLoc="center", + colColours=["lightsteelblue", "lightsteelblue"], + ) + ax.set_title(title) + fig.tight_layout() + plt.show() + + def add_vline(self, fig, x, style=None): + fig.gca() + style=style or {} + plt.axvline(x, **style) + + + + + diff --git a/pybop/plot/backends/plotly.py b/pybop/plot/backends/plotly.py new file mode 100644 index 000000000..f3f532fcc --- /dev/null +++ b/pybop/plot/backends/plotly.py @@ -0,0 +1,332 @@ +from copy import deepcopy + +import numpy as np + +from pybop.plot.backends.plotly_manager import PlotlyManager +from pybop.plot.backends.base import PlotBackend +from pybop.plot.util import _AxisData + +LINESTYLE_MAP = { + "solid": "solid", + "dashed": "dash", + "dotted": "dot", + "dashdot": "dashdot" +} + +MARKER_MAP = { + "o" : "circle", + "P" : "cross", + "X" : "x", + "." : None +} + +ANCHOR_MAP = { + "lower" : "bottom", + "upper" : "top", + "left" : "left", + "center" : "center", + "right" : "right" +} + + +class PlotlyBackend(PlotBackend): + + def __init__(self): + self.name = 'plotly' + self.plotly_manager = PlotlyManager() + + def create_figure(self, title = None, xaxis_title = None, yaxis_title = None, traces = None, style = None): + style = style or {} + + layout = self.plotly_manager.go.Layout({ + "title" : title, + "xaxis_title" : xaxis_title, + "yaxis_title" : yaxis_title, + "width" : style.get("width"), + "height" : style.get("height"), + "xaxis_range" : style.get("xaxis_range"), + "yaxis_range" : style.get("yaxis_range"), + "xaxis" : dict( + title=dict(font={"size": 14}), + showexponent="last", + exponentformat="e", + tickfont=dict(size=12), + ), + "yaxis" : dict( + title=dict(font={"size": 14}), + showexponent="last", + exponentformat="e", + tickfont=dict(size=12), + ), + "plot_bgcolor" : style.get("bg_color"), + "barmode": "overlay" + }) + fig = self.plotly_manager.goFigure(data=traces, layout=layout) + return fig + + def _check_empty(self, specs, row, col): + if specs[row - 1][col - 1] is None or len(specs[row - 1][col - 1]) > 0: + raise ValueError("Overlapping axes are not supported") + + def make_subplots(self, axes: list[_AxisData], title=None, axis_titles_x: list[str] | str = None, axis_titles_y: list[str] | str = None, style=None): + style = style or {} + + num_rows = max(ax.row + ax.row_span - 1 for ax in axes) + num_cols = max(ax.col + ax.col_span - 1 for ax in axes) + specs = [[{}] * num_cols for _ in range(num_rows)] + axes_dict={} + + for ax in axes: + self._check_empty(specs, ax.row, ax.col) + axes_dict[(ax.row, ax.col)] = ax + specs[ax.row - 1][ax.col - 1] = {"colspan": ax.col_span, "rowspan": ax.row_span} + for row in range(ax.row, ax.row + ax.row_span - 1): + for col in range(ax.col, ax.col + ax.col_span - 1): + if row > ax.row or col > ax.col: + self._check_empty(specs, row, col) + specs[row - 1, col - 1] = None + + make_subplots = self.plotly_manager.make_subplots + fig = make_subplots(rows=num_rows, cols=num_cols, specs=specs) + + for i, ax in enumerate(axes): + if isinstance(axis_titles_x, str): + fig.update_xaxes(title_text=axis_titles_x, row=ax.row, col=ax.col) + elif isinstance(axis_titles_x, list) and i < len(axis_titles_x): + fig.update_xaxes(title_text=axis_titles_x[i], row=ax.row, col=ax.col) + if isinstance(axis_titles_y, str): + fig.update_yaxes(title_text=axis_titles_y, row=ax.row, col=ax.col) + elif isinstance(axis_titles_y, list) and i < len(axis_titles_y): + fig.update_yaxes(title_text=axis_titles_y[i], row=ax.row, col=ax.col) + + fig.update_layout({ + "title" : title, + "width" : style.get("width"), + "height" : style.get("height"), + "xaxis" : dict( + title=dict(font={"size": 14}), + showexponent="last", + exponentformat="e", + tickfont=dict(size=12), + ), + "yaxis" : dict( + title=dict(font={"size": 14}), + showexponent="last", + exponentformat="e", + tickfont=dict(size=12), + ), + "plot_bgcolor" : style.get("bg_color") + }) + + return fig, axes_dict, num_rows, num_cols + + def legend(self, fig, style: dict=None): + style = style or {} + opts = {} + if style.get("horizontal"): + opts["orientation"] = "h" + if "loc" in style.keys(): + anchors = style.get("loc").split(" ") + if len(anchors) != 2: + raise ValueError("loc property must consist of 2 keywords") + opts["xanchor"] = ANCHOR_MAP.get(anchors[1], "auto") + opts["yanchor"] = ANCHOR_MAP.get(anchors[0], "auto") + if "coords" in style.keys(): + coords = style.get("coords") + opts["x"] = coords[0] + opts["y"] = coords[1] + + fig.update_layout(showlegend=True, legend=opts) + + def show_figure(self, fig): + if hasattr(fig, "__len__") and len(fig) > 0: + for f in fig: + f.show() + else: + fig.show() + + def plot_trace(self, traces, fig, ax=None): + if not isinstance(traces, list): + traces = [traces] + for trace in traces: + if ax is None: + fig.add_trace(trace) + else: + fig.add_trace(trace, row=ax.row, col=ax.col) + + def sample_color_scale(self, data, scale="viridis", d_min=None, d_max=None): + px = self.plotly_manager.px + # normalise and clip data + d_min = d_min or np.nanmin(data[np.isfinite(data)]) + d_max = d_max or np.nanmax(data[np.isfinite(data)]) + + d = (data - d_min) / (d_max - d_min) + if np.isscalar(d): + d = np.array([d]) + np.clip(np.asarray(d), 0, 1.0, out=d) + return px.colors.sample_colorscale(scale, list(d)) + + def colorbar(self, fig, data, colorscale="viridis", label=None): + + d_min = np.nanmin(data[np.isfinite(data)]) + d_max = np.nanmax(data[np.isfinite(data)]) + + colorbar=dict(thickness=25, outlinewidth=1) + if label is not None: + colorbar.update({"title": {"text": label, "side": "right"}}) + fig.add_trace( + self.plotly_manager.goScatter( + x=[None], + y=[None], + mode="markers", + marker=dict( + colorscale=colorscale, + showscale=True, + cmin=d_min, + cmax=d_max, + colorbar=colorbar, + ), + showlegend=False, + hoverinfo="none", + ) + ) + + + def contour_plot(self, x, y, z, colorscale="viridis"): + + return self.plotly_manager.goContour(x=x, y=y, z=z, colorscale=colorscale, connectgaps=True) + + def _get_line_options(style, opts): + linestyle = style.get("linestyle", "solid") + color = style.get("color") + opts["line"] = dict( + width = style.get("linewidth", 4), + dash = LINESTYLE_MAP.get(linestyle, "solid") + ) + if color is not None: + opts["line"].update(color=color) + + def fill_plot(self, x, y, color=None, label=None): + opts = {} + if color is not None: + opts["fillcolor"] = color + if label is not None: + opts["name"] = label + + return self.plotly_manager.goScatter(x=x, y=y, fill="toself", mode="text", showlegend=False, **opts) + + def fill_between_plot(self, x, y_upper, y_lower, color): + + return self.plotly_manager.goScatter( + x=x + x[::-1], + y=y_upper + y_lower[::-1], + fill="toself", + line=dict(color="rgba(255,255,255,0)"), + hoverinfo="skip", + showlegend=False, + fillcolor=color, + ) + + + def histogram_plot(self, x, name, style=None): + style= style or {} + + return self.plotly_manager.goHistogram(x=x, name=name, opacity = style.get("alpha")) + + + def line_plot(self, x=None, y=None, label=None, style = None): + + style = style or {} + linestyle = style.get("linestyle", "solid") + marker = style.get("marker", "none") + opts = {} + if linestyle.lower() == "none": + mode = "markers" + elif marker.lower() == "none": + mode = "lines" + else: + mode ="markers+lines" + + opts["mode"] = mode + if linestyle.lower() != "none": + self._get_line_options(style, opts) + + if marker.lower() != "none": + opts["marker"] = dict( + size = style.get("markersize", 8), + symbol = MARKER_MAP.get(marker), + ) + fillstyle = style.get("fillstyle", "full") + markerfacecolor = style.get("markerfacecolor") + markeredgecolor = style.get("markeredgecolor") + markeredgewidth = style.get("markeredgewidth") + if fillstyle.lower() == 'none': + opts["marker"].update(symbol = MARKER_MAP.get(marker) + "-open") + if markeredgecolor is not None: + opts["marker"].update(color = markeredgecolor) + + if markerfacecolor is not None: + opts["marker"].update(color = markerfacecolor) + if markeredgecolor is not None: + opts["marker"].update(line_color = markeredgecolor, line_width=1 or markeredgewidth) + elif markeredgewidth is not None: + opts["marker"].update(line_width = markeredgewidth) + + if label is None: + opts["showlegend"] = False + + if x is not None and y is not None: + return self.plotly_manager.goScatter( + x=x, + y=y, + name = label, + **opts, + ) + if x is None and y is not None: + return self.plotly_manager.goScatter( + y=y, + name = label, + **opts, + ) + + def scatter_plot(self, x, y, colors, labels=None, colorscale="Greys"): + + opts = dict( + mode="markers", + marker=dict( + color=colors, + colorscale=colorscale, + size=8, + showscale=False, + ), + showlegend=False, + ) + if labels is not None: + opts.update({"text": labels, "hoverinfo": "text"}) + return self.plotly_manager.goScatter(x=x, y=y, **opts) + + def show_table(self, header, values, title): + """ + Display data in a table. + """ + # Import plotly only when needed + + fig = self.plotly_manager.goFigure( + data=[ + self.plotly_manager.goTable( + header=dict(values=header), + cells=dict( + values=[[row[0] for row in values], [row[1] for row in values]] + ), + ) + ] + ) + + fig.update_layout(title=title) + fig.show() + + def add_vline(self, fig, x, style=None): + style = style or {} + opts = {} + self._get_line_options(style, opts) + fig.add_vline(x=x, **opts) \ No newline at end of file diff --git a/pybop/plot/plotly/plotly_manager.py b/pybop/plot/backends/plotly_manager.py similarity index 100% rename from pybop/plot/plotly/plotly_manager.py rename to pybop/plot/backends/plotly_manager.py diff --git a/pybop/plot/contour.py b/pybop/plot/contour.py index 467c0cfcf..c657f5722 100644 --- a/pybop/plot/contour.py +++ b/pybop/plot/contour.py @@ -4,8 +4,7 @@ import numpy as np -from pybop.plot.standard_plots import StandardPlot -from pybop.plot.util import get_default_options, import_backend +from pybop.plot.util import import_backend from pybop.problems.problem import Problem if TYPE_CHECKING: @@ -20,7 +19,6 @@ def contour( steps: int = 10, show: bool = True, backend=None, - **layout_options, ): """ Plot a 2D visualisation of a cost landscape using Plotly. @@ -147,61 +145,87 @@ def transform_array_of_values(list_of_values, parameter): bounds[0] = transform_array_of_values(bounds[0], parameters[names[0]]) bounds[1] = transform_array_of_values(bounds[1], parameters[names[1]]) - # Get options - options = get_default_options("contour", backend) - plot_options = options.get("plot_options") or {} - trace_options_initial = options.get("trace_options_initial") or {} - trace_options_optim = options.get("trace_options_optim") or {} - trace_options_contour = options.get("trace_options_contour") or {} - - plot_options.update(layout_options) + optimised_opts = dict( + marker="P", + markersize=14, + markerfacecolor="black", + markeredgecolor="white", + linestyle="None", + zorder=2.6, + ) + inital_opts = dict( + marker="X", + markersize=14, + markerfacecolor="white", + markeredgecolor="black", + linestyle="None", + zorder=2.6, + ) - plot_dict = StandardPlot( + fig = backend_module.create_figure( + title = "Cost Landscape", xaxis_title="Transformed " + names[0] if transformed else names[0], yaxis_title="Transformed " + names[1] if transformed else names[1], - xaxis_range=bounds[0], - yaxis_range=bounds[1], - backend=backend, - **plot_options, + style ={ + "width" : 600, + "height" : 600, + "xaxis_range" : bounds[0], + "yaxis_range" : bounds[1], + } ) # Create contour plot and update the layout - plot_dict.create_contour(x=x, y=y, z=costs, **trace_options_contour) + backend_module.plot_trace( + backend_module.contour_plot(x=x, y=y, z=costs), + fig + ) + if plot_optim: # Plot the optimisation trace optim_trace = np.asarray([item[:2] for item in result.x_model]) optim_trace = optim_trace.reshape(-1, 2) - backend_module.plot_optimisation_path( - plot_dict=plot_dict, - x=transform_array_of_values(optim_trace[:, 0], parameters[names[0]]), - y=transform_array_of_values(optim_trace[:, 1], parameters[names[1]]) + backend_module.plot_trace( + backend_module.scatter_plot( + transform_array_of_values(optim_trace[:, 0], parameters[names[0]]), + transform_array_of_values(optim_trace[:, 1], parameters[names[1]]), + [i / optim_trace.shape[0] for i in range(optim_trace.shape[0])] + ), + fig ) # Plot the initial guess if len(result.x_model) > 0: x0 = result.x_model[0] - plot_dict.traces.append( - plot_dict.create_trace( + backend_module.plot_trace( + backend_module.line_plot( x=transform_array_of_values([x0[0]], parameters[names[0]]), y=transform_array_of_values([x0[1]], parameters[names[1]]), - **trace_options_initial, - ) + label="Initial values", + style=inital_opts, + ), + fig ) # Plot optimised value if result.x is not None: x_best = result.x - plot_dict.traces.append( - plot_dict.create_trace( + backend_module.plot_trace( + backend_module.line_plot( x=transform_array_of_values([x_best[0]], parameters[names[0]]), y=transform_array_of_values([x_best[1]], parameters[names[1]]), - **trace_options_optim, - ) + style=optimised_opts, + label="Final values" + ), + fig ) - # Update the layout and display the figure - fig = plot_dict(show=False) + backend_module.legend(fig, style={ + "horizontal" : True, + "loc" : "lower right", + "coords" : (1, 1), + }) + # display the figure if show: backend_module.show_figure(fig) diff --git a/pybop/plot/convergence.py b/pybop/plot/convergence.py index e7d54db21..a00a713b9 100644 --- a/pybop/plot/convergence.py +++ b/pybop/plot/convergence.py @@ -1,6 +1,5 @@ from typing import TYPE_CHECKING -from pybop.plot.standard_plots import StandardPlot from pybop.plot.util import import_backend if TYPE_CHECKING: @@ -34,20 +33,27 @@ def convergence(result: "Result", show=True, backend=None): # Generate a list of iteration numbers iteration_numbers = list(range(1, len(cost_log) + 1)) + backend = import_backend(backend) + # Create a plot dictionary - plot_dict = StandardPlot( - x=iteration_numbers, - y=cost_log, + fig= backend.create_figure( xaxis_title="Evaluation", yaxis_title="Cost", title="Convergence", - trace_names=result.method_name, - backend=backend, + style = { + "bg_color" : "white", + "width" : 600, + "height" : 600 + } ) - # Generate and display the figure - fig = plot_dict(show=False) - backend_module = import_backend(backend) + backend.plot_trace(backend.line_plot( + x=iteration_numbers, + y=cost_log, + label=result.method_name, + ), fig) + + # Display figure if show: - backend_module.show_figure(fig) + backend.show_figure(fig) return fig diff --git a/pybop/plot/dataset.py b/pybop/plot/dataset.py index b7cada6c9..bfb7d5481 100644 --- a/pybop/plot/dataset.py +++ b/pybop/plot/dataset.py @@ -1,5 +1,5 @@ -from pybop.plot.standard_plots import StandardPlot, trajectories -from pybop.plot.util import import_backend +from pybop.plot.standard_plots import trajectories +from pybop.plot.util import import_backend, remove_brackets def dataset( @@ -37,13 +37,13 @@ def dataset( # Compile ydata and labels or legend y = [dataset[s] for s in signal] if len(signal) == 1: - yaxis_title = StandardPlot.remove_brackets(signal[0]) + yaxis_title = remove_brackets(signal[0]) if trace_names is None: trace_names = ["Data"] else: yaxis_title = "Output" if trace_names is None: - trace_names = StandardPlot.remove_brackets(signal) + trace_names = remove_brackets(signal) # Create the figure fig = trajectories( @@ -51,7 +51,7 @@ def dataset( y=y, trace_names=trace_names, show=False, - xaxis_title=StandardPlot.remove_brackets(dataset.domain), + xaxis_title=remove_brackets(dataset.domain), yaxis_title=yaxis_title, backend=backend, ) diff --git a/pybop/plot/distribution.py b/pybop/plot/distribution.py index 37aaa0535..bbbfc9c56 100644 --- a/pybop/plot/distribution.py +++ b/pybop/plot/distribution.py @@ -1,7 +1,8 @@ import numpy as np from pybop.parameters.parameter import Parameters -from pybop.plot.standard_plots import StandardSubplot +from pybop.plot.standard_plots import Subplots +from pybop.plot.util import import_backend def distribution( @@ -10,22 +11,24 @@ def distribution( n_samples: int = 100, transformed: bool = False, show: bool = True, - **layout_kwargs, + backend = None, ): """ Plot the posterior on top of the prior distribution for a Bayesian optimisation result. """ # Create lists of axis titles and trace names - axis_titles = [] + axis_titles_x = [] + axis_titles_y = [] trace_names = ( parameters.names if posterior is None - else ["Prior"] * len(parameters) + ["Posterior"] * len(parameters) + else ["Prior"] * len(parameters) ) for name in parameters.names: - axis_titles.append( - (name + " (transformed)" if transformed else name, "Probability density") + axis_titles_x.append( + name + " (transformed)" if transformed else name ) + axis_titles_y.append("Probability density") # Evaluate marginal distributions for each parameter values = [] @@ -37,24 +40,27 @@ def distribution( values.append(parameter_range) probability.append([d.pdf(s) for s in values[-1]]) - # Set subplot layout options - layout_options = dict( - width=1024, - height=576, - legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1), - ) - # Create a plot dictionary - plot_dict = StandardSubplot( + subplots = Subplots( x=values, y=probability, - axis_titles=axis_titles, - layout_options=layout_options, - trace_names=trace_names, - trace_name_width=50, - backend="plotly", + backend=backend + ) + + subplots.create_figure( + axis_titles_x=axis_titles_x, + axis_titles_y=axis_titles_y, + style={ + "width" : 1024, + "height" : 576 + } ) - fig = plot_dict(show=False) + + subplots.plot_lines( + labels=trace_names + ) + + backend = import_backend(backend) if posterior is not None: for idx, p in enumerate(posterior): @@ -64,15 +70,14 @@ def distribution( values.append(parameter_range) probability.append([d.pdf(s) for s in values[-1]]) - trace = plot_dict.create_trace( - values[-1], probability[-1], **plot_dict.trace_options + trace = backend.line_plot( + values[-1], probability[-1], label="Posterior" ) - row = (idx // plot_dict.num_cols) + 1 - col = (idx % plot_dict.num_cols) + 1 - fig.add_trace(trace, row=row, col=col) - - fig.update_layout(**layout_kwargs) + row = (idx // subplots.num_cols) + 1 + col = (idx % subplots.num_cols) + 1 + backend.plot_trace(trace, subplots.fig, ax=subplots.get_axis(row, col)) + backend.legend(subplots.fig, style=dict(horizontal=True, loc="lower right", coords=(1, 1.02)),) if show: - fig.show() + backend.show_figure(subplots.fig) - return fig + return subplots.fig diff --git a/pybop/plot/matplotlib/__init__.py b/pybop/plot/matplotlib/__init__.py deleted file mode 100644 index d56422d53..000000000 --- a/pybop/plot/matplotlib/__init__.py +++ /dev/null @@ -1,6 +0,0 @@ -from .default_options import DEFAULT_PLOT_OPTIONS - -from .plotting_functions import line_plot, colorbar, contour_plot, scatter_plot, fill_between_plot, fill_plot, histogram_plot, add_vline, plot_trace, add_traces, sample_color_scale, show_table, trajectories, plot_optimisation_path -from .figure_handling import create_figure, make_subplots, update_layout, show_figure, legend - -name="matplotlib" diff --git a/pybop/plot/matplotlib/default_options.py b/pybop/plot/matplotlib/default_options.py deleted file mode 100644 index cee06cb1a..000000000 --- a/pybop/plot/matplotlib/default_options.py +++ /dev/null @@ -1,86 +0,0 @@ -DEFAULT_PLOT_OPTIONS = { - "standard_plot": {"default_trace_options": dict(linewidth=2.0)}, - "contour": { - "plot_options": dict(title="Cost Landscape"), - "trace_options_contour": dict(extend="both", cmap="viridis"), - "trace_options_initial": dict( - marker="X", - markersize=14, - markerfacecolor="w", - markeredgecolor="k", - label="Initial values", - linestyle="None", - ), - "trace_options_optim": dict( - marker="P", - markersize=14, - markerfacecolor="k", - markeredgecolor="w", - label="Final values", - linestyle="None", - ), - }, - "nyquist": { - "trace_options_model": dict(color="#00CC96", linewidth=2, marker=".", markersize=8 - ), - "trace_options_reference": dict( - linestyle="none", - marker="o", - fillstyle="none", - markersize=8, - markeredgecolor="#636EFA", - ), - }, - "parameters": dict( - layout_options=dict( - figsize=(18, 8), - title="Parameter Convergence", - fig_legend=dict( - loc="lower right", bbox_to_anchor=(0.98, 0.90), horizontal=True - ), - tight_layout=dict(rect=[0, 0, 1, 0.95]), - ), - subplot_options=dict(wspace=0.3, hspace=0.3), - trace_options=dict(use_color_cycle=True), - ), - "predictive" : { - "trace_options_pdf": dict(linestyle="--") - }, - "problem": { - "default_trace_options": dict(label="Model", marker=None, linestyle="-"), - "design_cost_options": dict(label="Optimised"), - "meta_problem_options": dict(marker=".", linestyle="none"), - "reference_options": dict(label="Reference", marker=".", linestyle="none"), - "fill_options": dict(color=[(1.0, 0.898, 0.800, 0.8)]), - }, - "posterior": { - "plot_options": {"figsize": (15, 8)}, - "trace_options": dict(alpha=0.75), - "trace_options_vline": dict(linewidth=3, linestyle="--", color="k"), - }, - "voronoi": { - "layout_options": dict( - legend=dict(ncols=2, loc="lower center", bbox_to_anchor=(0.5, 1.0)) - ), - "outline_opts": dict(color="w", linewidth=0.5), - "scatter_opts": dict(cmap="Grays", zorder=2.5), - "optimised_opts": dict( - marker="P", - markersize=14, - markerfacecolor="k", - markeredgecolor="w", - label="Final values", - linestyle="None", - zorder=2.6, - ), - "initial_opts": dict( - marker="X", - markersize=14, - markerfacecolor="w", - markeredgecolor="k", - label="Initial values", - linestyle="None", - zorder=2.6, - ), - }, -} diff --git a/pybop/plot/matplotlib/figure_handling.py b/pybop/plot/matplotlib/figure_handling.py deleted file mode 100644 index 283af944a..000000000 --- a/pybop/plot/matplotlib/figure_handling.py +++ /dev/null @@ -1,93 +0,0 @@ -import warnings -from matplotlib import pyplot as plt - -from pybop.plot.matplotlib import plot_trace -from pybop.plot.util import _AxisData - - -def create_figure(traces=None, **layout_options): - figsize = (8, 6) - if "figsize" in layout_options: - figsize = layout_options.get("figsize") - - fig = plt.figure(figsize=figsize, dpi=100) - - if traces is not None: - for trace in traces: - plot_trace(trace, fig) - - update_layout(fig, **layout_options) - return fig - -def legend(fig, **kwargs): - fig.legend(**kwargs) - -def update_layout(fig, axes=None, **layout_options): - if axes is None: - axes = [fig.gca()] - if "title" in layout_options: - plt.suptitle(layout_options.get("title")) - if "xaxis_title" in layout_options: - plt.xlabel(layout_options.get("xaxis_title")) - if "yaxis_title" in layout_options: - plt.ylabel(layout_options.get("yaxis_title")) - if "grid" in layout_options: - grid = layout_options.get("grid") - plt.grid(**grid) - if "xaxis_range" in layout_options: - plt.xlim(layout_options.get("xaxis_range")) - if "yaxis_range" in layout_options: - plt.ylim(layout_options.get("yaxis_range")) - if "figsize" in layout_options: - fig.set_size_inches(layout_options.get("figsize")) - - # plt.tick_params(axis="both", labelsize=12) - # plt.ticklabel_format(axis="both", style="sci", scilimits=(-4, 4)) - - for ax in axes: - if "axis_bg_color" in layout_options: - ax.set_facecolor(layout_options.get("axis_bg_color")) - ax.set_axisbelow(True) - if ( - not ax.get_legend_handles_labels() == ([], []) - and "fig_legend" not in layout_options - ): - ax.legend(**layout_options.get("legend") or {}) - - if "fig_legend" in layout_options: - labels_in_fig = False - lines_labels = [] - for ax in axes: - if not ax.get_legend_handles_labels() == ([], []): - lines_labels.append(ax.get_legend_handles_labels()) - labels_in_fig = True - if labels_in_fig: - lines, labels = [sum(lol, []) for lol in zip(*lines_labels, strict=False)] - opts = dict(loc="best", fontsize=12) - opts.update(layout_options.get("fig_legend") or {}) - if opts.get("horizontal"): - opts["ncols"] = len(lines) - del opts["horizontal"] - fig.legend(lines, labels, **opts) - - if "tight_layout" in layout_options: - plt.tight_layout(**layout_options.get("tight_layout")) - - -def make_subplots(axes: list[_AxisData], subplot_options=None): - subplot_options = subplot_options or {} - - num_rows = max(ax.row + ax.row_span - 1 for ax in axes) - num_cols = max(ax.col + ax.col_span - 1 for ax in axes) - - fig = plt.figure() - - for i, ax in enumerate(axes): - idx_start = (ax.row - 1) * num_cols + ax.col - idx_end = (ax.row + ax.row_span - 2) * num_cols + ax.col + ax.col_span - 1 - axes[i] = fig.add_subplot(num_rows, num_cols, (idx_start, idx_end)) - - return fig - -def show_figure(fig): - plt.show() diff --git a/pybop/plot/matplotlib/plotting_functions.py b/pybop/plot/matplotlib/plotting_functions.py deleted file mode 100644 index 9f917ddcf..000000000 --- a/pybop/plot/matplotlib/plotting_functions.py +++ /dev/null @@ -1,236 +0,0 @@ -import warnings -from copy import deepcopy - -import matplotlib as mpl -import numpy as np -from matplotlib import pyplot as plt - -from pybop.plot import StandardPlot - - -def add_traces(x, y, trace_names, **trace_options): - color_cycle = None - if trace_options.get("use_color_cycle"): - color_cycle = plt.rcParams["axes.prop_cycle"]() - del trace_options["use_color_cycle"] - traces = [] - xi = x[0] - for i in range(0, len(y)): - if len(x) > 1: - xi = x[i] - label = None - if trace_names is not None: - label = trace_names[i] - if color_cycle is not None: - traces.append( - line_plot(xi, y[i], label, **trace_options, **next(color_cycle)) - ) - else: - traces.append(line_plot(xi, y[i], label, **trace_options)) - - return traces - - -def plot_trace(trace: dict, fig, ax=None, color_cycle=None): - # retrieve axis, plot_type and positional arguments - plot_type = trace.get("plot_type") or "plot" - positional_args = trace.get("positional_args") or None - ax = ax or fig.gca() - - # axis titles - if "xaxis_title" in trace.keys(): - ax.set_xlabel(trace["xaxis_title"]) - if "yaxis_title" in trace.keys(): - ax.set_ylabel(trace["yaxis_title"]) - - # retrieve remaining arguments - trace_options = deepcopy(trace) - for key in ["plot_type", "positional_args", "xaxis_title", "yaxis_title"]: - if key in trace_options.keys(): - del trace_options[key] - - # update color - if color_cycle is not None and plot_type == "plot": - trace_options.update(**next(color_cycle)) - - # get plotting function - try: - plot_function = getattr(ax, plot_type) - except ValueError: - print("Plot type not recognised") - - obj = plot_function(*positional_args, **trace_options) - if plot_type == "contourf": - plt.colorbar(obj) - - return obj - - -def line_plot(x=None, y=None, label=None, **kwargs): - if x is not None and y is not None: - size = min(len(x), len(y)) - trace = dict(positional_args=[x[:size], y[:size]], label=label) - elif y is not None: - trace = dict(positional_args=[y], label=label) - - trace.update(kwargs) - return trace - - -def colorbar(fig, data, colorscale="viridis", label=None): - # normalise cost - f_min = np.nanmin(data[np.isfinite(data)]) - f_max = np.nanmax(data[np.isfinite(data)]) - norm = mpl.colors.Normalize(vmin=f_min, vmax=f_max, clip=True) - - # get colours - cmap = mpl.colormaps["viridis"] - - plt.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap), ax=fig.gca(), label=label) - - -def contour_plot(x, y, z, **kwargs): - contour = dict(positional_args=[x, y, z], plot_type="contourf") - contour.update(**kwargs) - contour_lines = dict( - positional_args=[x, y, z], - colors=("k"), - linestyles="solid", - linewidths=0.2, - plot_type="contour", - ) - return contour, contour_lines - - -def scatter_plot(x, y, colors=None, labels=None, **trace_options): - scatter = dict(positional_args=[x, y], plot_type="scatter") - scatter.update(**trace_options) - if colors is not None: - scatter["c"] = colors - return scatter - - -def fill_between_plot(x, y_upper, y_lower, **options): - trace = dict(positional_args=(x, y_upper, y_lower), plot_type="fill_between") - trace.update(options) - return trace - - -def fill_plot(x, y, color=None, label=None): - return dict(positional_args=(x, y), plot_type="fill", color=color) - - -def histogram_plot(x, name, **trace_options): - trace = dict(positional_args=[x], label=name, plot_type="hist") - trace.update(trace_options) - return trace - - -def add_vline(fig, x, **trace_options): - fig.gca() - plt.axvline(x, **trace_options) - - -def sample_color_scale(data, scale="viridis", d_min=None, d_max=None): - # normalise and clip data - d_min = d_min or np.nanmin(data[np.isfinite(data)]) - d_max = d_max or np.nanmax(data[np.isfinite(data)]) - norm = mpl.colors.Normalize(vmin=d_min, vmax=d_max, clip=True) - norm_d = norm(data, clip=True) - if np.isscalar(norm_d): - norm_d = [norm_d] - - # get colours - cmap = mpl.colormaps[scale] - return cmap(norm_d) - - -def show_table(header, values, title): - """ - Display data in a table. - """ - for i, val in enumerate(values): - values[i] = [val[0], ", ".join(val[1].astype(str))] - - fig, ax = plt.subplots(figsize=(6, 2), dpi=100) - - # hide axes - ax.axis("off") - ax.axis("tight") - ax.table( - cellText=values, - colLabels=header, - loc="center", - cellLoc="center", - colColours=["lightsteelblue", "lightsteelblue"], - ) - ax.set_title(title) - fig.tight_layout() - plt.show() - - -def plot_optimisation_path(plot_dict: StandardPlot, x, y): - plot_dict.traces.append( - scatter_plot( - x, - y, - c=[i / len(x) for i in range(len(x))], - cmap="Grays", - zorder=1, - ) - ) - - -def trajectories( - x, - y, - trace_names=None, - show=True, - xaxis_title="", - yaxis_title="", - title="", - **layout_kwargs, -): - """ - Quickly plot one or more trajectories using Plotly. - - Parameters - ---------- - x : list or np.ndarray - X-axis data points. - y : list or np.ndarray - Y-axis data points for each trajectory. - trace_names : list or str, optional - Name(s) for the trace(s) (default: None). - **layout_kwargs : optional - This argument is ignored for the matplotlib backend. - Valid Plotly layout keys and their values, - e.g. `xaxis_title="Time / s"` or - `xaxis={"title": "Time [s]", font={"size":14}}` - - Returns - ------- - plotly.graph_objs.Figure - The Plotly figure object for the scatter plot. - """ - - if len(layout_kwargs) > 0: - warnings.warn( - "The following layout argument keys are ignored for the current plotting backend (matplotlib): \n" - f"{list(layout_kwargs.keys())}", - UserWarning, - stacklevel=2, - ) - # Create a plot dictionary - plot_dict = StandardPlot(x=x, y=y, trace_names=trace_names, backend="matplotlib") - - # Generate the figure and update the layout - fig = plot_dict(show=False) - plt.title(title) - plt.xlabel(xaxis_title, fontsize=12) - plt.ylabel(yaxis_title, fontsize=12) - plt.tight_layout() - if show: - fig.show() - - return fig diff --git a/pybop/plot/nyquist.py b/pybop/plot/nyquist.py index 1be3da8a1..83dd79432 100644 --- a/pybop/plot/nyquist.py +++ b/pybop/plot/nyquist.py @@ -1,6 +1,5 @@ from pybop.parameters.parameter import Inputs -from pybop.plot.standard_plots import StandardPlot -from pybop.plot.util import get_default_options, import_backend +from pybop.plot.util import import_backend def nyquist( @@ -43,10 +42,18 @@ def nyquist( >>> # The plots will be displayed and nyquist_figures will contain the list of figure objects. """ - options = get_default_options("nyquist", backend) - plot_options = options.get("plot_options") or {} - trace_options_model = options.get("trace_options_model") or {} - trace_options_reference = options.get("trace_options_reference") or {} + trace_style_model = dict( + linewidth = 2, + color = "#00CC96", + marker = "o", + markerfacecolor = "#00CC96", + ) + trace_style_reference = dict( + linestyle = "none", + marker = "o", + fillstyle= "none", + markeredgecolor="#636EFA" + ) if not isinstance(inputs, dict): inputs = problem.parameters.to_dict(inputs) @@ -61,7 +68,6 @@ def nyquist( xaxis_title=r"$Z_{re} / \Omega$", yaxis_title=r"$-Z_{im} / \Omega$", title=title, - **plot_options, ) backend.plot_trace( @@ -69,7 +75,7 @@ def nyquist( x=domain_data, y=-model_output[var].data.imag, label="Model", - **trace_options_model + style=trace_style_model ), fig ) @@ -79,7 +85,7 @@ def nyquist( x=target_output[var].real, y=-target_output[var].imag, label="Reference", - **trace_options_reference, + style=trace_style_reference, ), fig ) diff --git a/pybop/plot/parameters.py b/pybop/plot/parameters.py index cd3c68e0a..88a223d3c 100644 --- a/pybop/plot/parameters.py +++ b/pybop/plot/parameters.py @@ -1,8 +1,8 @@ from typing import TYPE_CHECKING from pybop.costs.log_likelihoods import GaussianLogLikelihood -from pybop.plot.standard_plots import StandardSubplot -from pybop.plot.util import get_default_options, import_backend +from pybop.plot.standard_plots import Subplots +from pybop.plot.util import import_backend if TYPE_CHECKING: from pybop._result import Result @@ -35,39 +35,39 @@ def parameters(result: "Result", show=True, backend=None): y = [list(item) for item in zip(*result.x_model, strict=False)] # Create lists of axis titles and trace names - axis_titles = [] + axis_titles_x = [] + axis_titles_y = [] trace_names = parameters.names - for name in trace_names: - axis_titles.append(("Evaluation", name)) - if isinstance(result.problem, GaussianLogLikelihood): - axis_titles.append(("Evaluation", "Sigma")) trace_names.append("Sigma") + print('yay') + + + for name in trace_names: + axis_titles_x.append("Evaluation") + axis_titles_y.append(name) # import plotting backend backend_module = import_backend(backend) - # Set subplot layout options - opts = get_default_options("parameters", backend) - layout_options = opts.get("layout_options") or {} - subplot_options = opts.get("subplot_options") or {} - trace_options = opts.get("trace_options") or {} - # Create a plot dictionary - plot_dict = StandardSubplot( - x=x, - y=y, - axis_titles=axis_titles, - trace_options=trace_options, - trace_names=trace_names, - trace_name_width=50, - backend=backend, - layout_options=layout_options, - subplot_options=subplot_options, - ) + subplots = Subplots(x=x, y=y, backend=backend) + subplots.create_figure( + title="Parameter Convergence", + axis_titles_x=axis_titles_x, + axis_titles_y=axis_titles_y, + style=dict(bg_color="white", width=1024, height=576) + ) + subplots.plot_lines(labels=trace_names) + + # import plotting backend + backend_module = import_backend(backend) + # add legend + backend_module.legend(subplots.fig, style={"fig_legend" : True}) + # Generate the figure and update the layout - fig = plot_dict(show=False) - fig = backend_module.show_figure(fig) + if show: + backend_module.show_figure(subplots.fig) - return fig + return subplots.fig diff --git a/pybop/plot/plotly/__init__.py b/pybop/plot/plotly/__init__.py deleted file mode 100644 index ddde0710d..000000000 --- a/pybop/plot/plotly/__init__.py +++ /dev/null @@ -1,8 +0,0 @@ -from .plotly_manager import PlotlyManager - -from .default_options import DEFAULT_PLOT_OPTIONS - -from .plotting_functions import colorbar, contour_plot, line_plot, fill_between_plot, fill_plot, histogram_plot, add_vline, show_table, scatter_plot, trajectories, plot_optimisation_path, add_traces, plot_trace, sample_color_scale -from .figure_handling import make_subplots, update_layout, create_figure, show_figure, legend - -name='plotly' diff --git a/pybop/plot/plotly/default_options.py b/pybop/plot/plotly/default_options.py deleted file mode 100644 index 8bc5e9a92..000000000 --- a/pybop/plot/plotly/default_options.py +++ /dev/null @@ -1,183 +0,0 @@ -DEFAULT_PLOT_OPTIONS = { - "standard_plot": { - "default_layout_options": dict( - title=None, - title_x=0.5, - xaxis=dict( - title=dict(font={"size": 14}), - showexponent="last", - exponentformat="e", - tickfont=dict(size=12), - ), - yaxis=dict( - title=dict(font={"size": 14}), - showexponent="last", - exponentformat="e", - tickfont=dict(size=12), - ), - legend=dict(x=1, y=1, xanchor="right", yanchor="top", font_size=12), - showlegend=True, - autosize=False, - width=600, - height=600, - margin=dict(l=10, r=10, b=10, t=75, pad=4), - plot_bgcolor="white", - ), - "default_trace_options": dict(line=dict(width=4), mode="lines"), - }, - "contour": { - "plot_options": dict( - title="Cost Landscape", - title_x=0.5, - title_y=0.905, - width=600, - height=600, - xaxis=dict(showexponent="last", exponentformat="e"), - yaxis=dict(showexponent="last", exponentformat="e"), - legend=dict(orientation="h", yanchor="bottom", y=1, xanchor="right", x=1), - autosize=None, - showlegend=None, - margin=None, - ), - "trace_options_contour": dict(colorscale="Viridis", connectgaps=True), - "trace_options_initial": dict( - mode="markers", - marker_symbol="x", - marker=dict( - color="white", - line_color="black", - line_width=1, - size=14, - showscale=False, - ), - name="Initial values", - ), - "trace_options_optim": dict( - mode="markers", - marker_symbol="cross", - marker=dict( - color="black", - line_color="white", - line_width=1, - size=14, - showscale=False, - ), - name="Final values", - ), - }, - "nyquist": { - "plot_options": dict( - font=dict(family="Arial", size=14), - plot_bgcolor="white", - paper_bgcolor="white", - width=600, - height=600, - xaxis=dict( - title=dict(font=dict(size=16), standoff=15), - showline=True, - linewidth=2, - linecolor="black", - mirror=True, - ticks="outside", - tickwidth=2, - tickcolor="black", - ticklen=5, - ), - yaxis=dict( - title=dict(font=dict(size=16), standoff=15), - showline=True, - linewidth=2, - linecolor="black", - mirror=True, - ticks="outside", - tickwidth=2, - tickcolor="black", - ticklen=5, - scaleanchor="x", - scaleratio=1, - ), - legend=dict( - orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1 - ), - ), - "trace_options_model": dict( - mode="lines+markers", - line=dict(color="#00CC96", width=2), - marker=dict(size=8, color="#00CC96", symbol="circle"), - ), - "trace_options_reference": dict( - mode="markers", - marker=dict(size=8, color="#636EFA", symbol="circle-open"), - showlegend=True, - ), - }, - "parameters": dict( - layout_options=dict( - title="Parameter Convergence", - width=1024, - height=576, - legend=dict( - orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1 - ), - ), - subplot_options=dict( - horizontal_spacing=0.1, - vertical_spacing=0.15, - ), - ), - "posterior": { - "plot_options": dict(barmode="overlay",), - "trace_options": dict(opacity=0.75), - "trace_options_vline": dict(line_width=3, line_dash="dash", line_color="black"), - }, - "predictive" : { - "trace_options_pdf": dict(line={"dash": "dot"}) - }, - "problem": { - "default_trace_options": dict(name="Model", mode="lines", showlegend=True), - "design_cost_options": dict(name="Optimised"), - "meta_problem_options": dict(mode="lines"), - "reference_options": dict(name="Reference", mode="markers", showlegend=True), - "fill_options": dict(fillcolor="rgba(255,229,204,0.8)"), - }, - "voronoi": { - "layout_options": dict( - title_x=0.5, - title_y=0.905, - width=600, - height=600, - xaxis=dict(showexponent="last", exponentformat="e"), - yaxis=dict(showexponent="last", exponentformat="e"), - legend=dict(orientation="h", yanchor="bottom", y=1, xanchor="right", x=1), - ), - "outline_opts": dict( - mode="lines", - line=dict(color="white", width=0.5), - showlegend=False, - ), - "optimised_opts": dict( - mode="markers", - marker_symbol="cross", - marker=dict( - color="black", - line_color="white", - line_width=1, - size=14, - showscale=False, - ), - name="Final values", - ), - "initial_opts": dict( - mode="markers", - marker_symbol="x", - marker=dict( - color="white", - line_color="black", - line_width=1, - size=14, - showscale=False, - ), - name="Initial values", - ), - }, -} diff --git a/pybop/plot/plotly/figure_handling.py b/pybop/plot/plotly/figure_handling.py deleted file mode 100644 index 961403aed..000000000 --- a/pybop/plot/plotly/figure_handling.py +++ /dev/null @@ -1,49 +0,0 @@ -from pybop.plot.plotly import PlotlyManager -from pybop.plot.util import _AxisData - - -def _check_empty(specs, row, col): - if specs[row - 1][col - 1] is None or len(specs[row - 1][col - 1]) > 0: - raise ValueError("Overlapping axes are not supported") - - -def create_figure(traces=None, **layout_options): - go = PlotlyManager().go - layout = go.Layout(**layout_options) - fig = go.Figure(data=traces, layout=layout) - return fig - -def legend(fig, **kwargs): - fig.update_layout(showlegend=True, **kwargs) - -def update_layout(fig, axes=None, **layout_options): - fig.update_layout(**layout_options) - - -def make_subplots(axes: list[_AxisData], subplot_options=None): - subplot_options = subplot_options or {} - - num_rows = max(ax.row + ax.row_span - 1 for ax in axes) - num_cols = max(ax.col + ax.col_span - 1 for ax in axes) - specs = [[{}] * num_cols for _ in range(num_rows)] - - for ax in axes: - _check_empty(specs, ax.row, ax.col) - specs[ax.row - 1][ax.col - 1] = {"colspan": ax.col_span, "rowspan": ax.row_span} - for row in range(ax.row, ax.row + ax.row_span - 1): - for col in range(ax.col, ax.col + ax.col_span - 1): - if row > ax.row or col > ax.col: - _check_empty(specs, row, col) - specs[row - 1, col - 1] = None - - make_subplots = PlotlyManager().make_subplots - fig = make_subplots(rows=num_rows, cols=num_cols, specs=specs, **subplot_options) - - return fig - -def show_figure(fig): - if hasattr(fig, "__len__") and len(fig) > 0: - for f in fig: - f.show() - else: - fig.show() diff --git a/pybop/plot/plotly/plotting_functions.py b/pybop/plot/plotly/plotting_functions.py deleted file mode 100644 index 502be9eeb..000000000 --- a/pybop/plot/plotly/plotting_functions.py +++ /dev/null @@ -1,216 +0,0 @@ -from copy import deepcopy - -import numpy as np - -from pybop.plot import StandardPlot -from pybop.plot.plotly.plotly_manager import PlotlyManager - - -def sample_color_scale(data, scale="viridis", d_min=None, d_max=None): - px = PlotlyManager().px - # normalise and clip data - d_min = d_min or np.nanmin(data[np.isfinite(data)]) - d_max = d_max or np.nanmax(data[np.isfinite(data)]) - - d = (data - d_min) / (d_max - d_min) - d = np.clip(np.asarray(data), 0, 1.0) - if np.isscalar(d): - d = [d] - return px.colors.sample_colorscale("viridis", list(d)) - - -def plot_trace(trace, fig, ax=None): - if ax is None: - fig.add_trace(trace) - else: - fig.add_trace(trace, row=ax.row, col=ax.col) - fig.update_xaxes(title_text=ax.xlabel, row=ax.row, col=ax.col) - fig.update_yaxes(title_text=ax.ylabel, row=ax.row, col=ax.col) - - -def add_traces(x, y, trace_names, **trace_options): - traces = [] - xi = x[0] - for i in range(0, len(y)): - opts = deepcopy(trace_options) - if len(x) > 1: - xi = x[i] - label = None - if trace_names is not None: - label = trace_names[i] - - traces.append(line_plot(xi, y[i], label, **opts)) - return traces - - -def line_plot(x=None, y=None, label=None, ax=None, color=None, **kwargs): - go = PlotlyManager().go - if label is not None: - kwargs.update({"name": label}) - if color is not None: - if "line" in kwargs: - kwargs["line"].update(color=color) - else: - kwargs.update({"line": {"color" : color}}) - if x is not None and y is not None: - return go.Scatter( - x=x, - y=y, - **kwargs, - ) - if x is None and y is not None: - return go.Scatter( - y=y, - **kwargs, - ) - - -def contour_plot(x, y, z, **kwargs): - go = PlotlyManager().go - return go.Contour(x=x, y=y, z=z, **kwargs) - - -def colorbar(fig, data, colorscale="viridis", label=None): - go = PlotlyManager().go - d_min = np.nanmin(data[np.isfinite(data)]) - d_max = np.nanmax(data[np.isfinite(data)]) - - colorbar=dict(thickness=25, outlinewidth=1) - if label is not None: - colorbar.update({"title": {"text": label, "side": "right"}}) - fig.add_trace( - go.Scatter( - x=[None], - y=[None], - mode="markers", - marker=dict( - colorscale=colorscale, - showscale=True, - cmin=d_min, - cmax=d_max, - colorbar=colorbar, - ), - showlegend=False, - hoverinfo="none", - ) - ) - - -def fill_between_plot(x, y_upper, y_lower, **options): - return line_plot( - x=x + x[::-1], - y=y_upper + y_lower[::-1], - fill="toself", - line=dict(color="rgba(255,255,255,0)"), - hoverinfo="skip", - showlegend=False, - **options, - ) - - -def fill_plot(x, y, color=None, label=None): - opts = {} - if color is not None: - opts["fillcolor"] = color - if label is not None: - opts["name"] = label - go = PlotlyManager().go - return go.Scatter(x=x, y=y, fill="toself", mode="text", showlegend=False, **opts) - - -def histogram_plot(x, name, **trace_options): - go = PlotlyManager().go - return go.Histogram(x=x, name=name, **trace_options) - - -def add_vline(fig, x, **trace_options): - fig.add_vline(x=x, **trace_options) - - -def scatter_plot(x, y, colors, labels=None, colorscale="Greys"): - go = PlotlyManager().go - opts = dict( - mode="markers", - marker=dict( - color=colors, - colorscale=colorscale, - size=8, - showscale=False, - ), - showlegend=False, - ) - if labels is not None: - opts.update({"text": labels, "hoverinfo": "text"}) - return go.Scatter(x=x, y=y, **opts) - - -def trajectories(x, y, trace_names=None, show=True, **layout_kwargs): - """ - Quickly plot one or more trajectories using Plotly. - - Parameters - ---------- - x : list or np.ndarray - X-axis data points. - y : list or np.ndarray - Y-axis data points for each trajectory. - trace_names : list or str, optional - Name(s) for the trace(s) (default: None). - **layout_kwargs : optional - Valid Plotly layout keys and their values, - e.g. `xaxis_title="Time / s"` or - `xaxis={"title": "Time [s]", font={"size":14}}` - - Returns - ------- - plotly.graph_objs.Figure - The Plotly figure object for the scatter plot. - """ - # Create a plot dictionary - plot_dict = StandardPlot(x=x, y=y, trace_names=trace_names, backend="plotly") - - # Generate the figure and update the layout - fig = plot_dict(show=False) - fig.update_layout(**layout_kwargs) - if show: - fig.show() - - return fig - - -def show_table(header, values, title): - """ - Display data in a table. - """ - # Import plotly only when needed - go = PlotlyManager().go - fig = go.Figure( - data=[ - go.Table( - header=dict(values=header), - cells=dict( - values=[[row[0] for row in values], [row[1] for row in values]] - ), - ) - ] - ) - - fig.update_layout(title=title) - fig.show() - - -def plot_optimisation_path(plot_dict: StandardPlot, x, y): - plot_dict.traces.append( - plot_dict.create_trace( - x, - y, - mode="markers", - marker=dict( - color=[i / len(x) for i in range(len(x))], - colorscale="Greys", - size=8, - showscale=False, - ), - showlegend=False, - ) - ) diff --git a/pybop/plot/predictive.py b/pybop/plot/predictive.py index 69f87a610..132b34f88 100644 --- a/pybop/plot/predictive.py +++ b/pybop/plot/predictive.py @@ -2,8 +2,7 @@ import numpy as np -from pybop.plot.util import import_backend, get_default_options -from pybop.plot.standard_plots import StandardPlot +from pybop.plot.util import import_backend, remove_brackets from pybop.problems.meta_problem import MetaProblem from pybop.simulators.failed_solution import FailedSolution @@ -42,20 +41,28 @@ def predictive( else [result.problem] ) figure_list = [] - options = get_default_options("predictive", backend) - trace_options_pdf = options.get("trace_options_pdf") or {} backend_module = import_backend(backend) for problem in problems: - plot_dict = StandardPlot( - x=problem.domain_data, - y=problem.target_data[problem.target[0]], - xaxis_title=StandardPlot.remove_brackets(problem.domain), - yaxis_title=StandardPlot.remove_brackets(problem.target[0]), - trace_names=data_legend_entry, - backend=backend, + fig = backend_module.create_figure( + xaxis_title=remove_brackets(problem.domain), + yaxis_title=remove_brackets(problem.target[0]), + style={ + "bg_color" : "white", + "width" : 600, + "height" : 600 + } + ) + + backend_module.plot_trace( + backend_module.line_plot( + x=problem.domain_data, + y=problem.target_data[problem.target[0]], + label=data_legend_entry + + ), + fig ) - fig = plot_dict(show=False) # Simulate the samples and add to plot inputs = [problem.parameters.to_dict(s) for s in posterior_samples] @@ -67,9 +74,10 @@ def predictive( backend_module.line_plot( x=problem.domain_data, y=sim[problem.target[0]].data, - color=colors[0], - **trace_options_pdf - + style=dict( + color=colors[0], + linestyle="dotted" + ) ), fig ) diff --git a/pybop/plot/problem.py b/pybop/plot/problem.py index eb8caed23..1693dd3b7 100644 --- a/pybop/plot/problem.py +++ b/pybop/plot/problem.py @@ -3,8 +3,7 @@ from pybop.costs.design_cost import DesignCost from pybop.costs.error_measures import ErrorMeasure from pybop.parameters.parameter import Inputs -from pybop.plot.standard_plots import StandardPlot -from pybop.plot.util import get_default_options, import_backend +from pybop.plot.util import import_backend, remove_brackets from pybop.problems.meta_problem import MetaProblem from pybop.problems.problem import Problem from pybop.simulators.solution import Solution @@ -64,63 +63,70 @@ def problem( model_output = problem.simulate(inputs) model_domain = target_domain[: len(model_output[target].data)] - # Retrieve default layout options - plot_options = get_default_options("problem", backend) - trace_options = plot_options.get("default_trace_options") or {} - design_cost_options = plot_options.get("design_cost_options") or {} - meta_problem_options = plot_options.get("meta_problem_options") or {} - reference_options = plot_options.get("reference_options") or {} - fill_options = plot_options.get("fill_options") or {} - # Create a plot for each output backend_module = import_backend(backend) figure_list = [] for var in problem.target: - options = trace_options.copy() - if isinstance(problem, MetaProblem): - options.update(meta_problem_options) - if isinstance(problem.cost, DesignCost): - options.update(design_cost_options) - # Create a plot dictionary - plot_dict = StandardPlot( + fig = backend_module.create_figure( title=title, - xaxis_title=StandardPlot.remove_brackets(domain), - yaxis_title=StandardPlot.remove_brackets(var), - backend=backend, + xaxis_title=remove_brackets(domain), + yaxis_title=remove_brackets(var), + style = { + "bg_color" : "white", + "width" : 600, + "height" : 600 + } ) + traces = [] - model_trace = plot_dict.create_trace( - x=model_domain, y=model_output[var].data, **options - ) - plot_dict.traces.append(model_trace) + model_trace = backend_module.line_plot( + x=model_domain, + y=model_output[var].data, + label="Optimised" if isinstance(problem.cost, DesignCost) else "Model", + style={ + "linestyle" : "none" if isinstance(problem, MetaProblem) else "solid", + "marker" : "." if isinstance(problem, MetaProblem) else "none" + } - target_trace = plot_dict.create_trace( - x=target_domain, y=target_output[var].data, **reference_options ) - plot_dict.traces.append(target_trace) + traces.append(model_trace) + + target_trace = backend_module.line_plot( + x=target_domain, y=target_output[var].data, + label="Reference", + style={ + "linestyle" : "none", + "marker" : "." + } + ) + traces.append(target_trace) if isinstance(problem.cost, ErrorMeasure) and len( model_output[var].data ) == len(target_output[var].data): # Compute the standard deviation as proxy for uncertainty - plot_dict.sigma = np.std(model_output[var].data - target_output[var].data) + sigma = np.std(model_output[var].data - target_output[var].data) # Convert x and upper and lower limits into lists to create a filled trace x = target_domain.tolist() - y_upper = (model_output[var].data + plot_dict.sigma).tolist() - y_lower = (model_output[var].data - plot_dict.sigma).tolist() + y_upper = (model_output[var].data + sigma).tolist() + y_lower = (model_output[var].data - sigma).tolist() - fill_trace = plot_dict.create_fill_trace( - x, y_upper, y_lower, **fill_options + fill_trace = backend_module.fill_between_plot( + x, y_upper, y_lower, color="#FFE5CC" ) - plot_dict.traces.append(fill_trace) + traces.append(fill_trace) # Reverse the order of the traces to put the model on top - plot_dict.traces = plot_dict.traces[::-1] + traces = traces[::-1] + + for trace in traces: + backend_module.plot_trace(trace, fig) + + backend_module.legend(fig) # Generate the figure and update the layout - fig = plot_dict(show=False) if show: backend_module.show_figure(fig) diff --git a/pybop/plot/samples.py b/pybop/plot/samples.py index f702032e9..5f49ddd5e 100644 --- a/pybop/plot/samples.py +++ b/pybop/plot/samples.py @@ -1,8 +1,6 @@ from typing import TYPE_CHECKING -from pybop.plot import StandardPlot -from pybop.plot.util import get_default_options, import_backend -from pybop.plot.plotly import PlotlyManager +from pybop.plot.util import import_backend if TYPE_CHECKING: from pybop.samplers.base_pints_sampler import SamplingResult @@ -12,11 +10,6 @@ def chains(result: "SamplingResult", show=True, backend=None): """ Plot posterior distributions for each chain. """ - options = get_default_options("posterior", backend) - plot_options = options.get("plot_options") or {} - trace_options = options.get("trace_options") or {} - trace_options_vline = options.get("trace_options_vline") or {} - # Import backend backend = import_backend(backend) @@ -25,7 +18,6 @@ def chains(result: "SamplingResult", show=True, backend=None): title="Posterior Distribution", xaxis_title="Value", yaxis_title="Density", - **plot_options ) for i, chain in enumerate(result.chains): @@ -34,7 +26,7 @@ def chains(result: "SamplingResult", show=True, backend=None): backend.histogram_plot( x=chain[:, j], name=f"Chain {i} - Parameter {j}", - **trace_options + style=dict(alpha=0.75) ), fig ) @@ -42,7 +34,7 @@ def chains(result: "SamplingResult", show=True, backend=None): backend.add_vline( fig, result.mean[j], - **trace_options_vline + style = dict(linewidth=3, linestyle="dashed", color="black") ) backend.legend(fig) @@ -84,27 +76,22 @@ def posterior(result: "SamplingResult", backend=None, show=True): """ Plot the summed posterior distribution across chains. """ - options = get_default_options("posterior", backend) - plot_options = options.get("plot_options") or {} - trace_options = options.get("trace_options") or {} - trace_options_vline = options.get("trace_options_vline") or {} # Import backend backend = import_backend(backend) fig = backend.create_figure( title="Posterior Distribution", xaxis_title="Value", yaxis_title="Density", - **plot_options, ) for j in range(result.all_samples.shape[1]): backend.plot_trace( backend.histogram_plot( - x=result.all_samples[:, j], name=f"Parameter {j}", **trace_options + x=result.all_samples[:, j], name=f"Parameter {j}", style=dict(alpha=0.75) ), fig ) - backend.add_vline(fig, result.mean[j], **trace_options_vline) + backend.add_vline(fig, result.mean[j], style=dict(linewidth=3, linestyle="dashed", color="black")) backend.legend(fig) diff --git a/pybop/plot/standard_plots.py b/pybop/plot/standard_plots.py index f733e5234..8e4888dfd 100644 --- a/pybop/plot/standard_plots.py +++ b/pybop/plot/standard_plots.py @@ -1,294 +1,116 @@ import math -import textwrap from copy import deepcopy import numpy as np from pybop.plot.util import ( - create_axis, - get_default_options, - import_backend + _AxisData, + import_backend, + wrap_text, + parse_data ) +class Subplots(): -class StandardPlot: def __init__( self, x=None, y=None, - trace_options=None, - trace_names=None, - trace_name_width=20, - backend=None, - **layout_options, - ): - - self.traces = [] - self.trace_name_width = trace_name_width - - self.backend = import_backend(backend) - - # Set default options and update if provided - opts = deepcopy(get_default_options("standard_plot", backend)) - self.layout_options = opts.get("default_layout_options") or {} - if layout_options: - self.layout_options.update(layout_options) - self.trace_options = opts.get("default_trace_options") or {} - if trace_options: - self.trace_options.update(trace_options) - - # Add traces - if x is not None and y is not None: - self.add_traces(x, y, trace_names) - - def __call__(self, show=True): - fig = self.backend.create_figure(self.traces, **self.layout_options) - if show: - self.backend.show_figure(fig) - return fig - - def add_traces(self, x, y, trace_names=None, **trace_options): - """ - Add a set of traces to the plot dictionary. - - Parameters - ---------- - x : list or np.ndarray - X-axis data points. - y : list or np.ndarray - Primary Y-axis data points for simulated model output. - trace_names : str or list[str], optional - Name(s) for the primary trace(s) (default: None). - """ - options = deepcopy(self.trace_options) - options.update(trace_options) - - # Check and wrap trace names - if trace_names is not None: - if isinstance(trace_names, str): - trace_names = [trace_names] - for i, name in enumerate(trace_names): - trace_names[i] = self.wrap_text( - name, width=self.trace_name_width, backend=self.backend.name - ) - - # Parse the data - x, y = self.parse_data(x, y) - - # Create a trace for each trajectory - self.traces.extend(self.backend.add_traces(x, y, trace_names, **options)) - - @staticmethod - def parse_data(x, y): - """ - Check the type and dimensions of the data and convert if necessary to a list - of 'things plotly can take', e.g. numpy arrays or lists of numbers. - - Parameters - ---------- - x : list or np.ndarray, optional - X-axis data points. - y : list or np.ndarray, optional - Primary Y-axis data points for simulated model output. - """ - if isinstance(x, list): - # If it's a list of numpy arrays, it's fine - # If it's a list of lists, it's fine - # If it's neither, it's a list of numbers that we need to wrap - if not isinstance(x[0], np.ndarray) and not isinstance(x[0], list): - x = [x] - elif isinstance(x, np.ndarray): - x = np.squeeze(x) - if x.ndim == 1: - x = [x] - else: - x = x.tolist() - if isinstance(y, list): - if not isinstance(y[0], np.ndarray) and not isinstance(y[0], list): - y = [y] - if isinstance(y, np.ndarray): - y = np.squeeze(y) - if y.ndim == 1: - y = [y] - else: - y = y.tolist() - if len(x) > 1 and len(x) != len(y): - raise ValueError( - "Input x should have either one data series or the same number as y." - ) - return x, y - - def create_trace(self, x=None, y=None, label=None, **trace_options): - return self.backend.line_plot(x=x, y=y, label=label, **trace_options) - - def create_fill_trace(self, x, y_upper, y_lower, **options): - return self.backend.fill_between_plot(x, y_upper, y_lower, **options) - - def create_histogram(self, x, name, **trace_options): - return self.backend.histogram_plot(x, name, **trace_options) - - def create_vline(self, fig, x, **trace_options): - return self.backend.add_vline(fig, x, **trace_options) - - def create_contour(self, x, y, z, **trace_options): - ct = self.backend.contour_plot(x, y, z, **trace_options) - if hasattr(ct, "__len__"): - for t in ct: - self.traces.append(t) - else: - self.traces.append(ct) - - @staticmethod - def wrap_text(text, width, backend="matplotlib"): - """ - Wrap text to a specified width with HTML line breaks. - - Parameters - ---------- - text : str - The text to wrap. - width : int - The width to wrap the text to. - - Returns - ------- - str - The wrapped text. - """ - wrapped_text = textwrap.fill(text, width=width, break_long_words=False) - if backend == "plotly": - return wrapped_text.replace("\n", "
") - else: - return wrapped_text - - @staticmethod - def remove_brackets(s): - """ - Remove square brackets from a string and replace with forward slashes - as per section 7.1 of the SI Handbook - """ - # If s is an iterable (but not a string), apply the function recursively to each element - if hasattr(s, "__iter__") and not isinstance(s, str): - return type(s)(StandardPlot.remove_brackets(i) for i in s) - elif isinstance(s, str): - start = s.find("[") - end = s.find("]") - if start != -1 and end != -1: - char_in_brackets = s[start + 1 : end] - return s[:start] + " / " + char_in_brackets + s[end + 1 :] - return s - - -class StandardSubplot(StandardPlot): - """ - A class for creating and displaying a set of interactive Plotly figures in a grid layout. - - Parameters - ---------- - x : list or np.ndarray - X-axis data points. - y : list or np.ndarray - Primary Y-axis data points for simulated model output. - num_rows : int, optional - Number of rows of subplots, can be set automatically (default: None). - num_cols : int, optional - Number of columns of subplots, can be set automatically (default: None). - layout : Plotly layout, optional - A layout for the figure, overrides the layout options (default: None). - layout_options : dict, optional - Settings to modify the default layout (default: DEFAULT_LAYOUT_OPTIONS). - trace_options : dict, optional - Settings to modify the default trace type (default: DEFAULT_TRACE_OPTIONS). - trace_names : str, optional - Name(s) for the primary trace(s) (default: None). - trace_name_width : int, optional - Maximum length of the trace names before text wrapping is used (default: 40). - - Returns - ------- - plotly.graph_objs.Figure - The generated Plotly figure. - """ - - def __init__( - self, - x, - y, - backend=None, num_rows=None, num_cols=None, - axis_titles=None, - layout_options=None, - subplot_options=None, - trace_options=None, - trace_names=None, - trace_name_width=40, - **kwargs, + num_plots=None, + backend=None, ): - if layout_options is None: - layout_options = {} - - super().__init__( - x, - y, - trace_options=trace_options, - trace_names=trace_names, - trace_name_width=trace_name_width, - backend=backend, - **layout_options, - ) - self.num_traces = len(self.traces) self.num_rows = num_rows self.num_cols = num_cols - if self.num_rows is None and self.num_cols is None: - # Work out the number of subplots - self.num_cols = int(math.ceil(math.sqrt(self.num_traces))) - self.num_rows = int(math.ceil(self.num_traces / self.num_cols)) - elif self.num_rows is None: - self.num_rows = int(math.ceil(self.num_traces / self.num_cols)) - elif self.num_cols is None: - self.num_cols = int(math.ceil(self.num_traces / self.num_rows)) - - self.axis_titles = axis_titles - self.subplot_options = {} - if subplot_options is not None: - for arg, value in subplot_options.items(): - self.subplot_options[arg] = value - - def __call__(self, show=True): - axes = [ - create_axis(row + 1, col + 1) - for row in range(self.num_rows) - for col in range(self.num_cols) - ] + self.num_plots = num_plots - fig = self.backend.make_subplots(axes, subplot_options=self.subplot_options) - - for idx, trace in enumerate(self.traces): - if self.axis_titles and idx < len(self.axis_titles): - x_title, y_title = self.axis_titles[idx] - axes[idx].set_xlabel(x_title) - axes[idx].set_ylabel(y_title) - # trace.update({'yaxis_title' : y_title, 'xaxis_title' : x_title}) - # fig.update_xaxes(title_text=x_title, row=row, col=col) - # fig.update_yaxes( - # title_text=y_title, - # row=row, - # col=col, - # showexponent="last", - # exponentformat="e", - # ) - self.backend.plot_trace(trace, fig, ax=axes[idx]) + if x is not None and y is not None: + self.x, self.y = parse_data(x, y) + if self.num_plots is None: + self.num_plots = len(self.y) + else: + self.x = None + self.y = None - self.backend.update_layout(fig, axes=axes, **self.layout_options) - if show: - self.backend.show_figure(fig) + self.backend = import_backend(backend) + + if self.num_rows == None and self.num_cols == None: + if self.num_plots is not None: + # Work out the number of subplots + self.num_cols = int(math.ceil(math.sqrt(self.num_plots))) + self.num_rows = int(math.ceil(self.num_plots/ self.num_cols)) + elif self.num_rows is None: + if self.num_plots is None: + self.num_rows = 1 + self.num_plots = self.num_cols + else: + self.num_rows = int(math.ceil(self.num_plots/ self.num_cols)) + elif self.num_cols is None: + if self.num_plots is None: + self.num_cols = 1 + self.num_plots = self.num_rows + else: + self.num_cols = int(math.ceil(self.num_traces / self.num_rows)) + + if self.num_plots is not None: + self._axes_data = [ + _AxisData(row + 1, col + 1) + for row in range(self.num_rows) + for col in range(self.num_cols) + ] + else: + self._axes_data = [] + + self.fig = None + self.axes = None + + def add_axis_data(self, row, col, row_span=1, col_span=1): + self._axes_data.append(_AxisData(row, col, row_span, col_span)) + + def create_figure(self, title=None, axis_titles_x: list[str] | str = None, axis_titles_y: list[str] | str = None, style=None): + self.fig, self.axes, self.num_rows, self.num_cols = self.backend.make_subplots(self._axes_data, title=title, axis_titles_x=axis_titles_x, axis_titles_y=axis_titles_y, style=style) + + def get_axis(self, row, col): + if self.axes is None: + raise ValueError("Figure contains no axes or has not been created.") + + if (row, col) in self.axes.keys(): + return self.axes[(row, col)] else: - return fig - - -def trajectories(x, y, trace_names=None, show=True, backend=None, **layout_kwargs): + raise KeyError(f"No axes for row={row} and col={col} found.") + + def plot_lines(self, x=None, y=None, labels=None): + if self.fig is None: + self.create_figure() + if x is None: + if self.x is None: + raise ValueError("No data for plotting supplied.") + else: + x = self.x + if y is None: + if self.y is None: + raise ValueError("No data for plotting supplied.") + else: + y = self.y + x, y = parse_data(x, y) + xi = x[0] + for i in range(0, len(y)): + row = (i // self.num_cols) + 1 + col = (i % self.num_cols) + 1 + if row > self.num_rows: + row = (row % self.num_rows) + 1 + if len(x) > 1: + xi = x[i] + label = None + if labels is not None: + label = labels[i] + + self.backend.plot_trace(self.backend.line_plot(xi, y[i], label), self.fig, ax=self.get_axis(row, col)) + + +def trajectories(x, y, xaxis_title: str = None, yaxis_title: str = None, labels=None, title:str = None, show=True, backend=None, label_width=20): """ Quickly plot one or more trajectories using Plotly. @@ -311,11 +133,40 @@ def trajectories(x, y, trace_names=None, show=True, backend=None, **layout_kwarg The Plotly figure object for the scatter plot. """ + # Check and wrap trace names + if labels is not None: + if isinstance(labels, str): + labels = [labels] + for i, name in enumerate(labels): + labels[i] = wrap_text( + name, width=label_width, backend=backend + ) + backend = import_backend(backend) - return backend.trajectories( - x=x, - y=y, - trace_names=trace_names, - show=show, - **layout_kwargs, + fig = backend.create_figure( + title = title, + xaxis_title = xaxis_title, + yaxis_title = yaxis_title, + style = { + "height" : 600, + "width" : 600, + "bg_color" : "white" + } ) + + x, y = parse_data(x, y) + xi = x[0] + for i in range(0, len(y)): + if len(x) > 1: + xi = x[i] + label = None + if labels is not None: + label = labels[i] + + backend.plot_trace(backend.line_plot(xi, y[i], label), fig) + + backend.legend(fig) + + if show: + backend.show_figure(fig) + return fig \ No newline at end of file diff --git a/pybop/plot/util.py b/pybop/plot/util.py index 5c64dc1ab..36c2e48f3 100644 --- a/pybop/plot/util.py +++ b/pybop/plot/util.py @@ -1,6 +1,6 @@ -import importlib.util -from copy import deepcopy +import textwrap from dataclasses import dataclass +import numpy as np import pybop.plot @@ -11,18 +11,6 @@ class _AxisData: col: int = 1 row_span: int = 1 col_span: int = 1 - xlabel: str | None = None - ylabel: str | None = None - - def set_xlabel(self, xlabel: str): - self.xlabel = xlabel - - def set_ylabel(self, ylabel: str): - self.ylabel = ylabel - - -def create_axis(row, col, row_span=1, col_span=1): - return _AxisData(row, col, row_span, col_span) def set_backend(backend): @@ -30,59 +18,97 @@ def set_backend(backend): f"Plotting backend {backend} is not available. The default backend has not been updated. \n" f"The default backend is set to {pybop.plot.backend}" ) - try: - importlib.import_module("pybop.plot." + backend) - pybop.plot.backend = backend - - except ModuleNotFoundError as error: - # Raise an ModuleNotFoundError if the module or attribute is not available - raise ModuleNotFoundError(err_msg) from error - + if backend.lower() in ['matplotlib', 'plotly']: + pybop.plot.backend = backend -def call_plotting_function(function_name, backend, **kwargs): - if backend is None: - backend = pybop.plot.backend - err_msg = f"Plotting backend {backend} is not available." - try: - module = importlib.import_module("pybop.plot." + backend) - if hasattr(module, function_name): - plotting_function = getattr(module, function_name) - # Return the imported attribute - return plotting_function(**kwargs) - else: - err_msg = f"Plotting backend {backend} has no attribute {function_name}." - raise ModuleNotFoundError(err_msg) - - except ModuleNotFoundError as error: - # Raise an ModuleNotFoundError if the module or attribute is not available - raise ModuleNotFoundError(err_msg) from error + else: + raise ModuleNotFoundError(err_msg) def import_backend(backend): if backend is None: backend = pybop.plot.backend err_msg = f"Plotting backend {backend} is not available." - try: - module = importlib.import_module("pybop.plot." + backend) - except ModuleNotFoundError as error: - # Raise an ModuleNotFoundError if the module or attribute is not available - raise ModuleNotFoundError(err_msg) from error - - return module - - -def get_default_options(plot_type, backend): - if backend is None: - backend = pybop.plot.backend - - if backend == "plotly": - opts = pybop.plot.plotly.DEFAULT_PLOT_OPTIONS - elif backend == "matplotlib": - opts = pybop.plot.matplotlib.DEFAULT_PLOT_OPTIONS + if backend.lower() == 'matplotlib': + return pybop.plot.backends.MatplotlibBackend() + elif backend.lower() == 'plotly': + return pybop.plot.backends.PlotlyBackend() else: - opts = {} - - if plot_type in opts.keys(): - return deepcopy(opts[plot_type]) + raise ModuleNotFoundError(err_msg) + +def parse_data(x, y): + """ + Check the type and dimensions of the data and convert if necessary to a list + of 'things plotly can take', e.g. numpy arrays or lists of numbers. + + Parameters + ---------- + x : list or np.ndarray, optional + X-axis data points. + y : list or np.ndarray, optional + Primary Y-axis data points for simulated model output. + """ + if isinstance(x, list): + # If it's a list of numpy arrays, it's fine + # If it's a list of lists, it's fine + # If it's neither, it's a list of numbers that we need to wrap + if not isinstance(x[0], np.ndarray) and not isinstance(x[0], list): + x = [x] + elif isinstance(x, np.ndarray): + x = np.squeeze(x) + if x.ndim == 1: + x = [x] + else: + x = x.tolist() + if isinstance(y, list): + if not isinstance(y[0], np.ndarray) and not isinstance(y[0], list): + y = [y] + if isinstance(y, np.ndarray): + y = np.squeeze(y) + if y.ndim == 1: + y = [y] + else: + y = y.tolist() + if len(x) > 1 and len(x) != len(y): + raise ValueError( + "Input x should have either one data series or the same number as y." + ) + return x, y + +def remove_brackets(s): + """ + Remove square brackets from a string and replace with forward slashes + as per section 7.1 of the SI Handbook + """ + # If s is an iterable (but not a string), apply the function recursively to each element + if hasattr(s, "__iter__") and not isinstance(s, str): + return type(s)(remove_brackets(i) for i in s) + elif isinstance(s, str): + start = s.find("[") + end = s.find("]") + if start != -1 and end != -1: + char_in_brackets = s[start + 1 : end] + return s[:start] + " / " + char_in_brackets + s[end + 1 :] + return s + +def wrap_text(text, width, backend="matplotlib"): + """ + Wrap text to a specified width with HTML line breaks. + + Parameters + ---------- + text : str + The text to wrap. + width : int + The width to wrap the text to. + + Returns + ------- + str + The wrapped text. + """ + wrapped_text = textwrap.fill(text, width=width, break_long_words=False) + if backend == "plotly": + return wrapped_text.replace("\n", "
") else: - return {} + return wrapped_text diff --git a/pybop/plot/voronoi.py b/pybop/plot/voronoi.py index 359ded278..f7dbc564e 100644 --- a/pybop/plot/voronoi.py +++ b/pybop/plot/voronoi.py @@ -3,7 +3,7 @@ import numpy as np from scipy.spatial import Voronoi -from pybop.plot.util import get_default_options, import_backend +from pybop.plot.util import import_backend if TYPE_CHECKING: from pybop._result import Result @@ -202,8 +202,7 @@ def surface( normalise=True, title="Voronoi Cost Landscape", show=True, - backend=None, - **layout_kwargs, + backend=None ): """ Plot a 2D representation of the Voronoi diagram with color-coded regions. @@ -268,29 +267,40 @@ def surface( x, y, f, regions = _voronoi_regions(x_optim, y_optim, f, xlim, ylim) # Get plotting options - opts = get_default_options("voronoi", backend.name) - outline_opts = opts.get("outline_opts") or {} - layout_options = opts.get("layout_options") or {} - optimised_opts = opts.get("optimised_opts") or {} - inital_opts = opts.get("initial_opts") or {} - scatter_opts = opts.get("scatter_opts") or {} - layout_options.update(layout_kwargs) + outline_opts = dict(color="white", linewidth=0.5) + optimised_opts = dict( + marker="P", + markersize=14, + markerfacecolor="black", + markeredgecolor="white", + linestyle="None", + zorder=2.6, + ) + inital_opts = dict( + marker="X", + markersize=14, + markerfacecolor="white", + markeredgecolor="black", + linestyle="None", + zorder=2.6, + ) + names = parameters.names - layout_options.update( - { - "title": title, + + # Construct figure + fig = backend.create_figure( + title = title, + xaxis_title = names[0], + yaxis_title = names[1], + style={ + "width" : 600, + "height" : 600, "xaxis_range": xlim, "yaxis_range": ylim, - "xaxis_title": names[0], - "yaxis_title": names[1], } ) - # Construct figure - fig = backend.create_figure([]) - # Add Voronoi edges and fill Voronoi regions - print(f) colors = backend.sample_color_scale(f) for j, region in enumerate(regions): x_region = region[:, 0].tolist() + [region[0, 0]] @@ -303,7 +313,7 @@ def surface( fig, ) - backend.plot_trace(backend.line_plot(x_region, y_region, **outline_opts), fig) + backend.plot_trace(backend.line_plot(x_region, y_region, style=outline_opts), fig) backend.colorbar(fig, f) @@ -314,7 +324,6 @@ def surface( y=y_optim, colors=[i / len(x_optim) for i in range(len(x_optim))], labels=[f"f={val:.2f}" for val in f], - **scatter_opts, ), fig, ) @@ -322,16 +331,20 @@ def surface( # Plot the initial guess if len(result.x_model) > 0: x0 = result.x_model[0] - backend.plot_trace(backend.line_plot(x=[x0[0]], y=[x0[1]], **inital_opts), fig) + backend.plot_trace(backend.line_plot(x=[x0[0]], y=[x0[1]], label="Initial values", style=inital_opts), fig) # Plot optimised value if result.x is not None: x_best = result.x backend.plot_trace( - backend.line_plot(x=[x_best[0]], y=[x_best[1]], **optimised_opts), fig + backend.line_plot(x=[x_best[0]], y=[x_best[1]], label="Final values", style=optimised_opts), fig ) - backend.update_layout(fig, **layout_options) + backend.legend(fig, style={ + "horizontal" : True, + "loc" : "lower right", + "coords" : (1, 1), + }) if show: backend.show_figure(fig) diff --git a/tests/unit/test_plots.py b/tests/unit/test_plots.py index 22f0d4b9d..89a5dbd8a 100644 --- a/tests/unit/test_plots.py +++ b/tests/unit/test_plots.py @@ -17,7 +17,7 @@ class TestPlots: def test_standard_plot(self, backend): pybop.plot.set_backend(backend) # Test standard plot - trace_names = pybop.plot.StandardPlot.remove_brackets( + trace_names = pybop.plot.remove_brackets( ["Trace [1]", "Trace [2]"] ) plot_dict = pybop.plot.StandardPlot( @@ -77,7 +77,7 @@ def test_dataset_plots(self, dataset, backend): pybop.plot.trajectories( dataset["Time [s]"], dataset["Voltage [V]"], - trace_names=["Time [s]", "Voltage [V]"], + labels=["Time [s]", "Voltage [V]"], ) pybop.plot.dataset(dataset) From 7118a58a391b23445ddda37d2d6372d72ea47aba Mon Sep 17 00:00:00 2001 From: u2370093 Date: Tue, 2 Jun 2026 14:46:27 +0100 Subject: [PATCH 15/20] a bit of tidying --- .../echem_identification_pitfalls.ipynb | 6 +- .../ecm_monte_carlo_sampling.ipynb | 4 +- .../ecm_multipulse_identification.ipynb | 4 +- .../ecm_scipy_constraints.ipynb | 108 +- .../electrode_balancing.ipynb | 92 +- .../lgm50_pulse_validation.ipynb | 215 +- .../pouch_cell_identification.ipynb | 34 +- .../sensitivity_analysis_hessian.ipynb | 18693 +--------------- .../sensitivity_analysis_salib.ipynb | 175 +- .../comparing_cost_functions.ipynb | 4 +- .../optimiser_calibration.ipynb | 4 +- .../energy_based_electrode_design.ipynb | 4 +- .../cost_compute_methods.ipynb | 2 +- .../maximum_a_posteriori.ipynb | 4 +- .../optimising_with_adamw.ipynb | 4 +- .../setting_optimiser_options.ipynb | 2 +- .../using_transformations.ipynb | 4 +- .../bayesian_feature_fitting.py | 14 +- .../battery_parameterisation/gitt_fitting.py | 2 +- .../battery_parameterisation/ocp_averaging.py | 7 +- .../stoichiometry_fitting.py | 2 +- .../comparison_examples/grouped_SPMe.py | 6 +- pybop/plot/__init__.py | 14 +- pybop/plot/backends/__init__.py | 2 +- pybop/plot/backends/base.py | 61 +- pybop/plot/backends/matplotlib.py | 130 +- pybop/plot/backends/plotly.py | 234 +- pybop/plot/contour.py | 79 +- pybop/plot/convergence.py | 45 +- pybop/plot/dataset.py | 8 +- pybop/plot/distribution.py | 69 +- pybop/plot/nyquist.py | 37 +- pybop/plot/parameters.py | 41 +- pybop/plot/predictive.py | 36 +- pybop/plot/problem.py | 31 +- pybop/plot/samples.py | 42 +- pybop/plot/standard_plots.py | 321 +- pybop/plot/trajectories.py | 67 + pybop/plot/util.py | 24 +- pybop/plot/voronoi.py | 78 +- tests/plotting/test_plotly_manager.py | 6 +- tests/unit/test_plots.py | 24 +- 42 files changed, 915 insertions(+), 19824 deletions(-) create mode 100644 pybop/plot/trajectories.py diff --git a/examples/notebooks/battery_parameterisation/echem_identification_pitfalls.ipynb b/examples/notebooks/battery_parameterisation/echem_identification_pitfalls.ipynb index d036d3336..452763924 100644 --- a/examples/notebooks/battery_parameterisation/echem_identification_pitfalls.ipynb +++ b/examples/notebooks/battery_parameterisation/echem_identification_pitfalls.ipynb @@ -32,9 +32,9 @@ "\n", "import pybop\n", "\n", - "pybop.plot.set_backend(\"plotly\")\n", - "go = pybop.plot.plotly.PlotlyManager().go\n", - "pybop.plot.plotly.PlotlyManager().pio.renderers.default = \"notebook_connected\"\n", + "pybop.plot.use_backend(\"plotly\")\n", + "go = pybop.plot.backends.PlotlyManager().go\n", + "pybop.plot.backends.PlotlyManager().pio.renderers.default = \"notebook_connected\"\n", "\n", "np.random.seed(8) # users can remove this line" ] diff --git a/examples/notebooks/battery_parameterisation/ecm_monte_carlo_sampling.ipynb b/examples/notebooks/battery_parameterisation/ecm_monte_carlo_sampling.ipynb index 23433be03..0042427e4 100644 --- a/examples/notebooks/battery_parameterisation/ecm_monte_carlo_sampling.ipynb +++ b/examples/notebooks/battery_parameterisation/ecm_monte_carlo_sampling.ipynb @@ -46,8 +46,8 @@ "\n", "import pybop\n", "\n", - "pybop.plot.set_backend(\"matplotlib\")\n", - "pybop.plot.plotly.PlotlyManager().pio.renderers.default = \"notebook_connected\"\n", + "pybop.plot.use_backend(\"plotly\")\n", + "pybop.plot.backends.PlotlyManager().pio.renderers.default = \"notebook_connected\"\n", "\n", "np.random.seed(8) # users can remove this line" ] diff --git a/examples/notebooks/battery_parameterisation/ecm_multipulse_identification.ipynb b/examples/notebooks/battery_parameterisation/ecm_multipulse_identification.ipynb index 8140c73c1..df18cdc56 100644 --- a/examples/notebooks/battery_parameterisation/ecm_multipulse_identification.ipynb +++ b/examples/notebooks/battery_parameterisation/ecm_multipulse_identification.ipynb @@ -48,8 +48,8 @@ "\n", "import pybop\n", "\n", - "pybop.plot.set_backend(\"plotly\")\n", - "pybop.plot.plotly.PlotlyManager().pio.renderers.default = \"notebook_connected\"\n", + "pybop.plot.use_backend(\"plotly\")\n", + "pybop.plot.backends.PlotlyManager().pio.renderers.default = \"notebook_connected\"\n", "\n", "np.random.seed(8) # users can remove this line" ] diff --git a/examples/notebooks/battery_parameterisation/ecm_scipy_constraints.ipynb b/examples/notebooks/battery_parameterisation/ecm_scipy_constraints.ipynb index 507dd0af0..8dace3f45 100644 --- a/examples/notebooks/battery_parameterisation/ecm_scipy_constraints.ipynb +++ b/examples/notebooks/battery_parameterisation/ecm_scipy_constraints.ipynb @@ -31,8 +31,8 @@ "\n", "import pybop\n", "\n", - "pybop.plot.set_backend(\"plotly\")\n", - "pybop.plot.plotly.PlotlyManager().pio.renderers.default = \"notebook_connected\"\n", + "pybop.plot.use_backend(\"plotly\")\n", + "pybop.plot.backends.PlotlyManager().pio.renderers.default = \"notebook_connected\"\n", "\n", "np.random.seed(8) # users can remove this line" ] @@ -246,13 +246,9 @@ "outputs": [ { "data": { - "text/html": [ - " \n", - " \n", - " " + "image/png": 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8snj/rVl8wJvtK2DsX4czzAxpAN5qVRTvrkwJpjIKpHB2GcqcXYbV/v9mpCQbtc5SDaeQfvsDV8Fd6p5YRmDgRERElMOabN4uyIpPtNoL0CXY6lq7pNMsvoKBARnWUekAgvOYnI7DlpFaa62pHr9tRsoXOG4tinXW6sgXWgdH8tbE51F57IGS7iKQGvVgcYQUyAMjMXAiIiLyIhLYSJF06l5P7s7CcxVkpZ7FJ/VR0jtq4c4YjFy4N83rmjSgXlgxjG5rwlvLUmbuSSZrWNNAVc+0JSYRLy2By6G9xqbdaGzao/pIlTWdR1nTSuDkSrXPQ34hKhsVaa2u6qQuIsjpfasUSITROKvOBRaHExFRTmmyeTedzG+3aHFEpyp4oUUFNWsv5tJVRJ+5iNDAJIT43wSsyYg8kYDH517O8LXz4Qbqm/arjFQT025U16Jh1pzfaL+1dMqwnrWaGuK7gvwYIzPrGmTuzDrOqsvGE0hERJRbOpmbJKvUsQpeaFnB9c4ytGdJwMQVBzB62Yk0D7cN9cWy6CSXTy2AOLXOXuNbgVRV03Gnx626hl16KP4v8QOsimifqQXiDJyy8QQSERHldHcSeMWkk6W6kwWMRSFcvRVI7UYT0x5UNJ3CTmsYuiZ+rNa/k8WGMwvbERAREVGWdzJ3xVVBurjTHlIfdiiDMgVC0e23Bup+UcSiiHZF3ZZFg/s3CzWkLYHqL0VERESUGcJuFaTfzsttwtN9TJ5++aYVJ65a8Hj1lFl051EQe/Vy6rYEZpIBMwJn1REREVGWzfoz3co2pe7HVLFE/nRfQ/Z9d+W1dIf5jOznxMCJiIiIMlXvW+0MbHVRqw+cTzOzr165YLcXLBa2faVJZ3rtF7ID2xG4wOJwIiKirC8wn5lqxt6jDcpg2sa0s/HEV4/VQeFA/7uaGXg7LA4nIiIijy8w750qMyWmbzyRppBcNdwMDTZ0jTr7sRh9AERERJR7hdxa9sUWWI3pVdNpGWBNg6qZ8oSgSbDGiYiIiDxG71tZqC3Rl1XQVLecZ2SabBg4ERERkUcJCcqLh2p7TrDkiEN1RERERG5i4ERERETkJgZORERERG5i4ERERETkJgZORERERG5i4ERERETkJgZORERERG5i4ERERETkJgZORERERG5i4ERERETkJgZORERERG5i4ERERETkJgZORERERG5i4ERERETkJgZORERERG5i4ERERETkJgZORERERG7ycXfH3ETXdXV99epVow+FiIiIspjt9972+58RBk4uXLt2TV2XKVMms78bIiIi8uDf/6CgoAz30XR3wqtcxmq14vTp08ifPz80Tcv0qFYCshMnTqBAgQKZ+trEc+4p+HfO853T8W88Z51zCYUkaCpZsiRMpoyrmJhxckFOWunSpZGV5Etn4JS9eM6zH885z3dOx7/xnHPOb5dpsmFxOBEREZGbGDgRERERuYmBUzbz9/fHiBEj1DXxnOdU/Dvn+c7p+Deee885i8OJiIiI3MSMExEREZGbGDgRERERuYmBExEREZGbGDgRERERuYmBUzb7+uuvERoaijx58qBhw4bYuHFjdh9CjrB69Wp06dJFdXmV7u7z5s1L0wX2vffeQ0hICPLmzYt27drh4MGDTvtcunQJffr0UY3UChYsiGeeeQbXr1/P5k/iPUaPHo369eurjvrFihVD9+7dsX//fqd9bt68icGDB6Nw4cIIDAxEr169cPbsWad9jh8/js6dOyMgIEC9zhtvvIHk5ORs/jSeb8KECahVq5a92V/jxo2xePFi++M811lvzJgx6t+XV155hec9i7z//vvqHDteqlSp4tHnm4FTNpo5cyZeffVVNZ1y69atqF27Nh588EGcO3cuOw8jR4iLi1PnTwJRVz755BOMHz8e3377LTZs2IB8+fKpcy3/J7SRoGn37t34+++/sWDBAhWMPf/889n4KbzLqlWr1D9g69evV+csKSkJ7du3V9+FzdChQ/Hnn3/i999/V/vL0kU9e/a0P26xWNQ/cImJiYiMjMTUqVMxZcoUFeSSM1m9QH64t2zZgs2bN6NNmzbo1q2b+pvluc56mzZtwsSJE1Xw6oh/45mvevXqiImJsV/WrFnj2edb1qqj7NGgQQN98ODB9vsWi0UvWbKkPnr0aH4F90D+jOfOnWu/b7Va9RIlSujjxo2zb4uNjdX9/f316dOnq/t79uxRz9u0aZN9n8WLF+uapumnTp3i9+GGc+fOqXO4atUq+zn29fXVf//9d/s+e/fuVfusW7dO3V+0aJFuMpn0M2fO2PeZMGGCXqBAAT0hIYHn/TaCg4P177//nuc6i127dk2vWLGi/vfff+stW7bUX375ZbWdf+OZb8SIEXrt2rVdPuap55sZp2wi0bD8l6MMGTmuiSf3161bl12HkSscPXoUZ86ccTrXsgaRDI3azrVcy/Dc/fffb99H9pfvRDJUdHtXrlxR14UKFVLX8vctWSjH8y4p97Jlyzqd95o1a6J48eL2fSQTKIt32jIplJb8V/WMGTNUdk+G7Hius5ZkViWL4fi3LHjes4aUUUjZRfny5dVIgAy9efL55iK/2eTChQvqHz/HL1fI/X379mXXYeQKEjQJV+fa9phcy1i4Ix8fHxUE2Pah9FmtVlX30bRpU9SoUcN+Tv38/FRAmtF5d/W9OH5v9K+oqCgVKMkQs9R3zJ07F9WqVcP27dt5rrOIBKhSSiFDdanxbzzzyX/QytBa5cqV1TDdBx98gObNm2PXrl0ee74ZOBHRXf0XufzD5liLQJlPfkwkSJLs3qxZs9CvXz9V50FZ48SJE3j55ZdVDZ9M4KGs17FjR/ttqSeTQKpcuXL47bff1MQeT8ShumxSpEgRmM3mNLMB5H6JEiWy6zByBdv5zOhcy3XqonyZhSEz7fh9ZOzFF19UxfQrVqxQBcyO512GpGNjYzM8766+F8fvjf4l/7UdHh6OevXqqVmNMiHiiy++4LnOIjI0JP8u1K1bV2Wg5SKBqkw0kduSyeDfeNaS7FKlSpVw6NAhj/07Z+CUjf8Ayj9+y5YtcxrukPuSiqfMExYWpv4P43iuZbxbapds51qu5f+M8g+lzfLly9V3Iv/FQ2lJHb4ETTJcJOdKzrMj+fv29fV1Ou/SrkDqFRzPuww/OQat8l/3Mt1ehqAoY/L3mZCQwHOdRdq2bav+PiXLZ7tIHaTU3dhu8288a0lLmMOHD6tWMh77b0qWlJyTSzNmzFAzu6ZMmaJmdT3//PN6wYIFnWYDkPuzXrZt26Yu8mf82WefqdvHjh1Tj48ZM0ad2z/++EPfuXOn3q1bNz0sLEy/ceOG/TU6dOig16lTR9+wYYO+Zs0aNYvmscce41eQjoEDB+pBQUH6ypUr9ZiYGPslPj7evs+AAQP0smXL6suXL9c3b96sN27cWF1skpOT9Ro1aujt27fXt2/fri9ZskQvWrSoHhERwfOeyvDhw9WMxaNHj6q/Ybkvsz7/+usvnuts5DirTvBvPHO99tpr6t8U+Ttfu3at3q5dO71IkSJq1q6nnm8GTtnsyy+/VH8Efn5+qj3B+vXrs/sQcoQVK1aogCn1pV+/fvaWBO+++65evHhxFay2bdtW379/v9NrXLx4UQVKgYGBaupq//79VUBGrrk633KZPHmyfR8JTAcNGqSmzQcEBOg9evRQwZWj6OhovWPHjnrevHnVP5DyD2dSUhJPeypPP/20Xq5cOfVvhfwQyN+wLWjiuTYucOLfeObq3bu3HhISov7OS5Uqpe4fOnTIo8+3Jv+TNbksIiIiopyFNU5EREREbmLgREREROQmBk5EREREbmLgREREROQmBk5EREREbmLgREREROQmBk5EREREbmLgRERERMTAiYhyg6eeegrdu3c3+jCIKJfwMfoAiIjSo2lahidnxIgR+OKLL9QCxJ5EFkCeNGkS2rVrZ/ShEFEmY+BERB4rJibGfnvmzJl477331OroNoGBgeriSXbu3InLly+jZcuWRh8KEWUB1jgRkccqUaKE/RIUFKQyUI7bJGhKPVTXqlUrvPTSS3jllVcQHByM4sWLq+xPXFwc+vfvj/z58yM8PByLFy92eq9du3ahY8eO6jXlOU8++SQuXLhwx8f8xx9/oEOHDvD19U3zmGTG3n//fZQtWxb+/v4oWbIkhgwZcpdnh4iMwMCJiHKcqVOnokiRIti4caMKogYOHIiHH34YTZo0wdatW9G+fXsVGMXHx6v9Y2Nj0aZNG9SpUwebN2/GkiVLcPbsWTzyyCN3/N7z589Ht27dXD42e/ZsfP7555g4cSIOHjyIefPmoWbNmvf8eYko+2i6pxUHEBG5MGXKFJVFkiDHkWScZJsEIbaMk8ViwT///KPuy23JVvXs2RM//fST2nbmzBmEhIRg3bp1aNSoEUaOHKn2X7p0qf11T548iTJlyqihwUqVKrn1nZw6dQrly5dXQVfBggXTPP7ZZ5+poEmyW64yUkTk+ZhxIqIcp1atWvbbZrMZhQsXdsrsyFCcOHfunLresWMHVqxYYa+ZkkuVKlXUY4cPH76jbFOzZs1cBk1Csl43btxQwdVzzz2HuXPnIjk5+a4/JxFlPxaHE1GOkzqbI7VRjttss/WsVqu6vn79Orp06YKxY8emeS3JTN1J4NS1a9d0H7dlsP73v//h77//xqBBgzBu3DisWrWKGSgiL8HAiYhyvbp166r6o9DQUPj43N0/ixJ8SdZqwoQJGe6XN29eFaTJZfDgwSqzFRUVpY6BiDwfh+qIKNeTAObSpUt47LHHsGnTJjU8J/VOMgtPaqTcIQXlUgslwVdGdVo//PCDqnE6cuQIfvnlFxVIlStXLtd/B0TegoETEeV60hZg7dq1KkiSGXdSDyWF6FKrZDKZ3G5DkNEwnZDXk9YITZs2VXVYMmT3559/qhosIvIOnFVHRHSPpMBbCs6lN1SDBg14PolyMGaciIjukQzzDR06FPXr1+e5JMrhmHE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", 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", + "text/plain": [ + "
" ] }, "metadata": {}, diff --git a/examples/notebooks/battery_parameterisation/electrode_balancing.ipynb b/examples/notebooks/battery_parameterisation/electrode_balancing.ipynb index 94b981c61..01a4f19e2 100644 --- a/examples/notebooks/battery_parameterisation/electrode_balancing.ipynb +++ b/examples/notebooks/battery_parameterisation/electrode_balancing.ipynb @@ -29,8 +29,8 @@ "\n", "import pybop\n", "\n", - "pybop.plot.set_backend(\"plotly\")\n", - "pybop.plot.plotly.PlotlyManager().pio.renderers.default = \"notebook_connected\"\n", + "pybop.plot.use_backend(\"plotly\")\n", + "pybop.plot.backends.PlotlyManager().pio.renderers.default = \"notebook_connected\"\n", "\n", "np.random.seed(8) # users can remove this line" ] @@ -198,56 +198,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - " \n", - " \n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
\n", - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "pybop.plot.problem(problem, inputs=result.best_inputs, title=\"Optimised Comparison\");" ] @@ -270,42 +221,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from plotly import graph_objects as go\n", "\n", diff --git a/examples/notebooks/battery_parameterisation/lgm50_pulse_validation.ipynb b/examples/notebooks/battery_parameterisation/lgm50_pulse_validation.ipynb index d722d52af..a53345add 100644 --- a/examples/notebooks/battery_parameterisation/lgm50_pulse_validation.ipynb +++ b/examples/notebooks/battery_parameterisation/lgm50_pulse_validation.ipynb @@ -32,9 +32,9 @@ "\n", "import pybop\n", "\n", - "pybop.plot.set_backend(\"plotly\")\n", - "go = pybop.plot.plotly.PlotlyManager().go\n", - "pybop.plot.plotly.PlotlyManager().pio.renderers.default = \"notebook_connected\"\n", + "pybop.plot.use_backend(\"plotly\")\n", + "go = pybop.plot.backends.PlotlyManager().go\n", + "pybop.plot.backends.PlotlyManager().pio.renderers.default = \"notebook_connected\"\n", "\n", "np.random.seed(8) # users can remove this line" ] @@ -115,56 +115,7 @@ "execution_count": null, "id": "7", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - " \n", - " \n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
\n", - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "go.Figure(\n", " data=go.Scatter(x=df[\"ProgTime\"], y=df[\"Voltage\"]),\n", @@ -335,18 +286,7 @@ "execution_count": null, "id": "19", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1.1493038123679518" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "inputs = problem.parameters.to_dict([0.01, 0.01, 0.01, 10000, 10000])\n", "evaluation = problem.evaluate(inputs)\n", @@ -390,42 +330,7 @@ "execution_count": null, "id": "23", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "pybop.plot.problem(problem, inputs=result.best_inputs, title=\"Optimised Comparison\");" ] @@ -445,76 +350,7 @@ "execution_count": null, "id": "25", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "result.plot_convergence()\n", "result.plot_parameters();" @@ -591,42 +427,7 @@ "execution_count": null, "id": "31", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "pybop.plot.problem(problem, inputs=result.best_inputs, title=\"Parameter Extrapolation\");" ] diff --git a/examples/notebooks/battery_parameterisation/pouch_cell_identification.ipynb b/examples/notebooks/battery_parameterisation/pouch_cell_identification.ipynb index ad152bce6..b6d97c9e3 100644 --- a/examples/notebooks/battery_parameterisation/pouch_cell_identification.ipynb +++ b/examples/notebooks/battery_parameterisation/pouch_cell_identification.ipynb @@ -30,9 +30,9 @@ "\n", "import pybop\n", "\n", - "pybop.plot.set_backend(\"plotly\")\n", - "go = pybop.plot.plotly.PlotlyManager().go\n", - "pybop.plot.plotly.PlotlyManager().pio.renderers.default = \"notebook_connected\"\n", + "pybop.plot.use_backend(\"plotly\")\n", + "go = pybop.plot.backends.PlotlyManager().go\n", + "pybop.plot.backends.PlotlyManager().pio.renderers.default = \"notebook_connected\"\n", "\n", "np.random.seed(8) # users can remove this line" ] @@ -221,8 +221,8 @@ { "data": { "text/plain": [ - "{'Negative electrode active material volume fraction': 0.5461747348834163,\n", - " 'Positive electrode active material volume fraction': 0.606337248991485}" + "{'Negative electrode active material volume fraction': 0.5461747348834938,\n", + " 'Positive electrode active material volume fraction': 0.6063372489914799}" ] }, "execution_count": null, @@ -280,9 +280,9 @@ "data": { "text/html": [ "
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", + "text/plain": [ + "
" ] }, "metadata": {}, @@ -389,77 +355,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "Running model for Saltelli sample of size: (896, 5)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Eval 90/896, elapsed time=0.5 s, last cost=7.59596\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Eval 180/896, elapsed time=0.7 s, last cost=0.430763\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Eval 270/896, elapsed time=0.9 s, last cost=0.461599\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Eval 360/896, elapsed time=1.2 s, last cost=8.88535\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Eval 450/896, elapsed time=1.4 s, last cost=4.74524\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Eval 540/896, elapsed time=1.7 s, last cost=0.127041\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Eval 629/896, elapsed time=2.0 s, last cost=0.367499\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Eval 718/896, elapsed time=2.3 s, last cost=1.30012\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Eval 807/896, elapsed time=2.6 s, last cost=2.07151\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Eval 896/896, elapsed time=2.9 s, last cost=5.21243\n" + "Running model for Saltelli sample of size: (896, 5)\n", + "Eval 90/896, elapsed time=0.1 s, last cost=8.44241\n", + "Eval 180/896, elapsed time=0.2 s, last cost=1.03403\n", + "Eval 270/896, elapsed time=0.3 s, last cost=1.56065\n", + "Eval 360/896, elapsed time=0.4 s, last cost=8.33648\n", + "Eval 450/896, elapsed time=0.4 s, last cost=0.0455716\n", + "Eval 540/896, elapsed time=0.5 s, last cost=0.0415216\n", + "Eval 629/896, elapsed time=0.6 s, last cost=1.48783\n", + "Eval 718/896, elapsed time=0.6 s, last cost=2.03308\n", + "Eval 807/896, elapsed time=0.7 s, last cost=0.932404\n", + "Eval 896/896, elapsed time=0.8 s, last cost=0.036365\n" ] } ], @@ -486,24 +392,19 @@ "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - " ST ST_conf\n", - "R0 [Ohm] 0.955425 0.167954\n", - "R1 [Ohm] 0.025830 0.009102\n", - "R2 [Ohm] 0.000931 0.000467\n", - "Tau1 [s] 0.008554 0.003997\n", - "Tau2 [s] 0.001098 0.000620\n", - " S1 S1_conf\n", - "R0 [Ohm] 0.943294 0.201951\n", - "R1 [Ohm] 0.024291 0.039169\n", - "R2 [Ohm] -0.001352 0.006306\n", - "Tau1 [s] 0.007184 0.022051\n", - "Tau2 [s] 0.001215 0.006819\n", - "Sobol S1: [ 0.94329445 0.02429073 -0.0013523 0.00718427 0.00121541]\n", - "Sobol ST: [9.55424615e-01 2.58303601e-02 9.30643934e-04 8.55433212e-03\n", - " 1.09805930e-03]\n" + "ename": "TypeError", + "evalue": "unique requires a Series, Index, ExtensionArray, np.ndarray or NumpyExtensionArray got list.", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mTypeError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[11]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m Si = \u001b[43msobol\u001b[49m\u001b[43m.\u001b[49m\u001b[43manalyze\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 2\u001b[39m \u001b[43m \u001b[49m\u001b[43msalib_dict\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mY_saltelli\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcalc_second_order\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprint_to_console\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\n\u001b[32m 3\u001b[39m \u001b[43m)\u001b[49m\n\u001b[32m 4\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33mSobol S1:\u001b[39m\u001b[33m\"\u001b[39m, Si[\u001b[33m\"\u001b[39m\u001b[33mS1\u001b[39m\u001b[33m\"\u001b[39m])\n\u001b[32m 5\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33mSobol ST:\u001b[39m\u001b[33m\"\u001b[39m, Si[\u001b[33m\"\u001b[39m\u001b[33mST\u001b[39m\u001b[33m\"\u001b[39m])\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Source/PyBOP/env-py-3-12/lib/python3.12/site-packages/SALib/analyze/sobol.py:121\u001b[39m, in \u001b[36manalyze\u001b[39m\u001b[34m(problem, Y, calc_second_order, num_resamples, conf_level, print_to_console, parallel, n_processors, keep_resamples, seed)\u001b[39m\n\u001b[32m 117\u001b[39m rng = np.random.randint\n\u001b[32m 119\u001b[39m \u001b[38;5;66;03m# Determine if groups are defined and adjusting the number\u001b[39;00m\n\u001b[32m 120\u001b[39m \u001b[38;5;66;03m# of rows in the cross-sampled matrix accordingly\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m121\u001b[39m _, D = \u001b[43mextract_group_names\u001b[49m\u001b[43m(\u001b[49m\u001b[43mproblem\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 123\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m calc_second_order \u001b[38;5;129;01mand\u001b[39;00m Y.size % (\u001b[32m2\u001b[39m * D + \u001b[32m2\u001b[39m) == \u001b[32m0\u001b[39m:\n\u001b[32m 124\u001b[39m N = \u001b[38;5;28mint\u001b[39m(Y.size / (\u001b[32m2\u001b[39m * D + \u001b[32m2\u001b[39m))\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Source/PyBOP/env-py-3-12/lib/python3.12/site-packages/SALib/util/__init__.py:274\u001b[39m, in \u001b[36mextract_group_names\u001b[39m\u001b[34m(p)\u001b[39m\n\u001b[32m 271\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 272\u001b[39m groups = p[\u001b[33m\"\u001b[39m\u001b[33mgroups\u001b[39m\u001b[33m\"\u001b[39m]\n\u001b[32m--> \u001b[39m\u001b[32m274\u001b[39m names = \u001b[38;5;28mlist\u001b[39m(\u001b[43mpd\u001b[49m\u001b[43m.\u001b[49m\u001b[43munique\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgroups\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[32m 275\u001b[39m number = \u001b[38;5;28mlen\u001b[39m(names)\n\u001b[32m 277\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m names, number\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Source/PyBOP/env-py-3-12/lib/python3.12/site-packages/pandas/core/algorithms.py:433\u001b[39m, in \u001b[36munique\u001b[39m\u001b[34m(values)\u001b[39m\n\u001b[32m 327\u001b[39m \u001b[38;5;129m@set_module\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33mpandas\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 328\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34munique\u001b[39m(values):\n\u001b[32m 329\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 330\u001b[39m \u001b[33;03m Return unique values based on a hash table.\u001b[39;00m\n\u001b[32m 331\u001b[39m \n\u001b[32m (...)\u001b[39m\u001b[32m 431\u001b[39m \u001b[33;03m Length: 3, dtype: complex128\u001b[39;00m\n\u001b[32m 432\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m433\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43munique_with_mask\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Source/PyBOP/env-py-3-12/lib/python3.12/site-packages/pandas/core/algorithms.py:461\u001b[39m, in \u001b[36munique_with_mask\u001b[39m\u001b[34m(values, mask)\u001b[39m\n\u001b[32m 459\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34munique_with_mask\u001b[39m(values, mask: npt.NDArray[np.bool_] | \u001b[38;5;28;01mNone\u001b[39;00m = \u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[32m 460\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"See algorithms.unique for docs. Takes a mask for masked arrays.\"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m461\u001b[39m values = \u001b[43m_ensure_arraylike\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunc_name\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43munique\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m 463\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(values.dtype, ExtensionDtype):\n\u001b[32m 464\u001b[39m \u001b[38;5;66;03m# Dispatch to extension dtype's unique.\u001b[39;00m\n\u001b[32m 465\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m values.unique()\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Source/PyBOP/env-py-3-12/lib/python3.12/site-packages/pandas/core/algorithms.py:239\u001b[39m, in \u001b[36m_ensure_arraylike\u001b[39m\u001b[34m(values, func_name)\u001b[39m\n\u001b[32m 232\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\n\u001b[32m 233\u001b[39m values,\n\u001b[32m 234\u001b[39m (ABCIndex, ABCSeries, ABCExtensionArray, np.ndarray, ABCNumpyExtensionArray),\n\u001b[32m 235\u001b[39m ):\n\u001b[32m 236\u001b[39m \u001b[38;5;66;03m# GH#52986\u001b[39;00m\n\u001b[32m 237\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m func_name != \u001b[33m\"\u001b[39m\u001b[33misin-targets\u001b[39m\u001b[33m\"\u001b[39m:\n\u001b[32m 238\u001b[39m \u001b[38;5;66;03m# Make an exception for the comps argument in isin.\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m239\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[32m 240\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m requires a Series, Index, \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 241\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mExtensionArray, np.ndarray or NumpyExtensionArray \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 242\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mgot \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(values).\u001b[34m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 243\u001b[39m )\n\u001b[32m 245\u001b[39m inferred = lib.infer_dtype(values, skipna=\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[32m 246\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m inferred \u001b[38;5;129;01min\u001b[39;00m [\u001b[33m\"\u001b[39m\u001b[33mmixed\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mstring\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mmixed-integer\u001b[39m\u001b[33m\"\u001b[39m]:\n\u001b[32m 247\u001b[39m \u001b[38;5;66;03m# \"mixed-integer\" to ensure we do not cast [\"ss\", 42] to str GH#22160\u001b[39;00m\n", + "\u001b[31mTypeError\u001b[39m: unique requires a Series, Index, ExtensionArray, np.ndarray or NumpyExtensionArray got list." ] } ], diff --git a/examples/notebooks/comparison_examples/comparing_cost_functions.ipynb b/examples/notebooks/comparison_examples/comparing_cost_functions.ipynb index d0f364d59..880745578 100644 --- a/examples/notebooks/comparison_examples/comparing_cost_functions.ipynb +++ b/examples/notebooks/comparison_examples/comparing_cost_functions.ipynb @@ -26,8 +26,8 @@ "\n", "import pybop\n", "\n", - "go = pybop.plot.plotly.PlotlyManager().go\n", - "pybop.plot.plotly.PlotlyManager().pio.renderers.default = \"notebook_connected\"\n", + "go = pybop.plot.backends.PlotlyManager().go\n", + "pybop.plot.backends.PlotlyManager().pio.renderers.default = \"notebook_connected\"\n", "\n", "np.random.seed(8) # users can remove this line" ] diff --git a/examples/notebooks/comparison_examples/optimiser_calibration.ipynb b/examples/notebooks/comparison_examples/optimiser_calibration.ipynb index 89f6dd9ea..358593105 100644 --- a/examples/notebooks/comparison_examples/optimiser_calibration.ipynb +++ b/examples/notebooks/comparison_examples/optimiser_calibration.ipynb @@ -32,8 +32,8 @@ "\n", "import pybop\n", "\n", - "pybop.plot.set_backend(\"plotly\")\n", - "pybop.plot.plotly.PlotlyManager().pio.renderers.default = \"notebook_connected\"\n", + "pybop.plot.use_backend(\"plotly\")\n", + "pybop.plot.backends.PlotlyManager().pio.renderers.default = \"notebook_connected\"\n", "\n", "np.random.seed(8) # users can remove this line" ] diff --git a/examples/notebooks/design_optimisation/energy_based_electrode_design.ipynb b/examples/notebooks/design_optimisation/energy_based_electrode_design.ipynb index e4541c8d8..09092ba4d 100644 --- a/examples/notebooks/design_optimisation/energy_based_electrode_design.ipynb +++ b/examples/notebooks/design_optimisation/energy_based_electrode_design.ipynb @@ -35,8 +35,8 @@ "\n", "import pybop\n", "\n", - "pybop.plot.set_backend(\"plotly\")\n", - "pybop.plot.plotly.PlotlyManager().pio.renderers.default = \"notebook_connected\"\n", + "pybop.plot.use_backend(\"plotly\")\n", + "pybop.plot.backends.PlotlyManager().pio.renderers.default = \"notebook_connected\"\n", "\n", "np.random.seed(8) # users can remove this line" ] diff --git a/examples/notebooks/getting_started/cost_compute_methods.ipynb b/examples/notebooks/getting_started/cost_compute_methods.ipynb index e06c53037..3ba1717c4 100644 --- a/examples/notebooks/getting_started/cost_compute_methods.ipynb +++ b/examples/notebooks/getting_started/cost_compute_methods.ipynb @@ -28,7 +28,7 @@ "\n", "import pybop\n", "\n", - "pybop.plot.plotly.PlotlyManager().pio.renderers.default = \"notebook_connected\"\n", + "pybop.plot.backends.PlotlyManager().pio.renderers.default = \"notebook_connected\"\n", "\n", "np.random.seed(8) # users can remove this line" ] diff --git a/examples/notebooks/getting_started/maximum_a_posteriori.ipynb b/examples/notebooks/getting_started/maximum_a_posteriori.ipynb index 88f8b5580..fc93c39b2 100644 --- a/examples/notebooks/getting_started/maximum_a_posteriori.ipynb +++ b/examples/notebooks/getting_started/maximum_a_posteriori.ipynb @@ -51,8 +51,8 @@ "\n", "import pybop\n", "\n", - "pybop.plot.set_backend(\"plotly\")\n", - "pybop.plot.plotly.PlotlyManager().pio.renderers.default = \"notebook_connected\"\n", + "pybop.plot.use_backend(\"plotly\")\n", + "pybop.plot.backends.PlotlyManager().pio.renderers.default = \"notebook_connected\"\n", "\n", "np.random.seed(8) # users can remove this line" ] diff --git a/examples/notebooks/getting_started/optimising_with_adamw.ipynb b/examples/notebooks/getting_started/optimising_with_adamw.ipynb index cea097acc..6d1c12b5f 100644 --- a/examples/notebooks/getting_started/optimising_with_adamw.ipynb +++ b/examples/notebooks/getting_started/optimising_with_adamw.ipynb @@ -34,8 +34,8 @@ "\n", "import pybop\n", "\n", - "pybop.plot.set_backend(\"plotly\")\n", - "pybop.plot.plotly.PlotlyManager().pio.renderers.default = \"notebook_connected\"\n", + "pybop.plot.use_backend(\"plotly\")\n", + "pybop.plot.backends.PlotlyManager().pio.renderers.default = \"notebook_connected\"\n", "\n", "np.random.seed(8) # users can remove this line" ] diff --git a/examples/notebooks/getting_started/setting_optimiser_options.ipynb b/examples/notebooks/getting_started/setting_optimiser_options.ipynb index 65760ef97..0ae9270db 100644 --- a/examples/notebooks/getting_started/setting_optimiser_options.ipynb +++ b/examples/notebooks/getting_started/setting_optimiser_options.ipynb @@ -32,7 +32,7 @@ "\n", "import pybop\n", "\n", - "pybop.plot.plotly.PlotlyManager().pio.renderers.default = \"notebook_connected\"\n", + "pybop.plot.backends.PlotlyManager().pio.renderers.default = \"notebook_connected\"\n", "\n", "np.random.seed(8) # users can remove this line" ] diff --git a/examples/notebooks/getting_started/using_transformations.ipynb b/examples/notebooks/getting_started/using_transformations.ipynb index 7f55188a3..d8087213c 100644 --- a/examples/notebooks/getting_started/using_transformations.ipynb +++ b/examples/notebooks/getting_started/using_transformations.ipynb @@ -29,8 +29,8 @@ "\n", "import pybop\n", "\n", - "pybop.plot.set_backend(\"plotly\")\n", - "pybop.plot.plotly.PlotlyManager().pio.renderers.default = \"notebook_connected\"\n", + "pybop.plot.use_backend(\"plotly\")\n", + "pybop.plot.backends.PlotlyManager().pio.renderers.default = \"notebook_connected\"\n", "\n", "np.random.seed(8) # users can remove this line" ] diff --git a/examples/scripts/battery_parameterisation/bayesian_feature_fitting.py b/examples/scripts/battery_parameterisation/bayesian_feature_fitting.py index 8c68af73a..b4d5de8f5 100644 --- a/examples/scripts/battery_parameterisation/bayesian_feature_fitting.py +++ b/examples/scripts/battery_parameterisation/bayesian_feature_fitting.py @@ -117,8 +117,18 @@ print("True values:", [original_D_n, original_D_p]) # Plot the optimisation result - result.plot_convergence(yaxis={"type": "log"}) - result.plot_parameters(yaxis={"type": "log"}, yaxis2={"type": "log"}) + pybop.plot.use_backend("plotly") + fig1 = result.plot_convergence(show=False) + fig1.update_layout( + yaxis={"type": "log"} + ) # use ax.set_yscale('log') if using matplotlib (where ax = fig1.gca()) + fig1.show() + + fig2 = result.plot_parameters(show=False) + fig2.update_layout( + yaxis={"type": "log"}, yaxis2={"type": "log"} + ) # use ax.set_yscale('log') if using matplotlib (for ax in fig2.axes) + fig2.show() # Plot the prior and posterior distributions pybop.plot.distribution(result.problem.parameters, result.posterior) diff --git a/examples/scripts/battery_parameterisation/gitt_fitting.py b/examples/scripts/battery_parameterisation/gitt_fitting.py index 3582f8e5c..6abe016f2 100644 --- a/examples/scripts/battery_parameterisation/gitt_fitting.py +++ b/examples/scripts/battery_parameterisation/gitt_fitting.py @@ -91,7 +91,7 @@ fitted_values["Voltage [V]"].data, solution["Voltage [V]"].data, ], - trace_names=["Ground truth", "Fitted GITT Model", "Identified Model"], + labels=["Ground truth", "Fitted GITT Model", "Identified Model"], xaxis_title="Time / s", yaxis_title="Voltage / V", ) diff --git a/examples/scripts/battery_parameterisation/ocp_averaging.py b/examples/scripts/battery_parameterisation/ocp_averaging.py index 9760b1672..6fc13d51c 100644 --- a/examples/scripts/battery_parameterisation/ocp_averaging.py +++ b/examples/scripts/battery_parameterisation/ocp_averaging.py @@ -111,14 +111,12 @@ average_dataset["Voltage [V]"], ] trace_names = ["Discharge", "Charge", "Averaged"] - legend = dict(yanchor="top", y=0.99, xanchor="left", x=0.01) fig = pybop.plot.trajectories( x=stos, y=volt, - trace_names=trace_names, + labels=trace_names, xaxis_title="Stoichiometry", yaxis_title="Voltage [V]", - legend=legend, ) dcap = [ @@ -131,8 +129,7 @@ fig = pybop.plot.trajectories( x=stos, y=dcap, - trace_names=trace_names, + labels=trace_names, xaxis_title="Stoichiometry", yaxis_title="Differential capacity [V-1]", - legend=legend, ) diff --git a/examples/scripts/battery_parameterisation/stoichiometry_fitting.py b/examples/scripts/battery_parameterisation/stoichiometry_fitting.py index adbdc09b1..394f0e3f5 100644 --- a/examples/scripts/battery_parameterisation/stoichiometry_fitting.py +++ b/examples/scripts/battery_parameterisation/stoichiometry_fitting.py @@ -30,7 +30,7 @@ parameter_values["Positive electrode OCP [V]"](stoichiometry), fitted_dataset["Voltage [V]"], ], - trace_names=["Ground truth", "Data vs. stoichiometry"], + labels=["Ground truth", "Data vs. stoichiometry"], xaxis_title="Stoichiometry", yaxis_title="Voltage / V", ) diff --git a/examples/scripts/comparison_examples/grouped_SPMe.py b/examples/scripts/comparison_examples/grouped_SPMe.py index cb1d060e2..66092c12b 100644 --- a/examples/scripts/comparison_examples/grouped_SPMe.py +++ b/examples/scripts/comparison_examples/grouped_SPMe.py @@ -11,8 +11,7 @@ """ # Prepare figure -pybop.plot.set_backend("matplotlib") -plot_dict = pybop.plot.StandardPlot() +fig = plt.figure() plt.xlabel("Time / s") plt.ylabel("Voltage / V") @@ -55,10 +54,9 @@ model, parameter_values=param, experiment=experiment, cache_esoh=False ).solve(initial_soc=init_soc) dataset = pybop.import_pybamm_solution(solution) - plot_dict.create_trace( + plt.plot( dataset["Time [s]"], dataset["Voltage [V]"], label=None, linestyle=linestyle ) -plot_dict() # Set up figure fig, ax = plt.subplots() diff --git a/pybop/plot/__init__.py b/pybop/plot/__init__.py index 6406739ab..3826b4c6d 100644 --- a/pybop/plot/__init__.py +++ b/pybop/plot/__init__.py @@ -1,13 +1,21 @@ # Plotting backend default DEFAULT_BACKEND = 'matplotlib' -backend=DEFAULT_BACKEND +current_default_backend=DEFAULT_BACKEND -from .util import set_backend, import_backend, _AxisData, remove_brackets, wrap_text, parse_data +from .util import ( + AxisData, + use_backend, + get_backend, + parse_data, + remove_brackets, + wrap_text +) # # Import plots # -from .standard_plots import trajectories, Subplots +from .standard_plots import StandardPlot, StandardSubplot +from .trajectories import trajectories from .contour import contour from .dataset import dataset from .convergence import convergence diff --git a/pybop/plot/backends/__init__.py b/pybop/plot/backends/__init__.py index f9e20a618..bb7b9443d 100644 --- a/pybop/plot/backends/__init__.py +++ b/pybop/plot/backends/__init__.py @@ -1,4 +1,4 @@ from .base import PlotBackend from .matplotlib import MatplotlibBackend from .plotly_manager import PlotlyManager -from .plotly import PlotlyBackend \ No newline at end of file +from .plotly import PlotlyBackend diff --git a/pybop/plot/backends/base.py b/pybop/plot/backends/base.py index 9c0b5ece7..195f891ff 100644 --- a/pybop/plot/backends/base.py +++ b/pybop/plot/backends/base.py @@ -1,70 +1,79 @@ from abc import ABC, abstractmethod -from pybop.plot.util import _AxisData + +from pybop.plot.util import AxisData + class PlotBackend(ABC): @abstractmethod - def create_figure(self, title: str=None, xaxis_title: str=None, yaxis_title: str=None, traces: list=None, style:dict=None): + def create_figure( + self, + title: str = None, + xaxis_title: str = None, + yaxis_title: str = None, + traces: list = None, + style: dict = None, + ): raise NotImplementedError - + @abstractmethod - def make_subplots(self, axes: list[_AxisData], title=None, axis_titles_x: list[str] | str = None, axis_titles_y: list[str] | str = None, style=None): + def make_subplots( + self, + axes: list[AxisData], + title=None, + axis_titles_x: list[str] | str = None, + axis_titles_y: list[str] | str = None, + style=None, + ): raise NotImplementedError - + @abstractmethod - def legend(self, fig, style: dict=None): + def legend(self, fig, style: dict = None): raise NotImplementedError - + @abstractmethod def show_figure(self, fig): raise NotImplementedError - + @abstractmethod def plot_trace(self, traces: dict | list[dict], fig, ax=None, color_cycle=None): raise NotImplementedError - + @abstractmethod def sample_color_scale(self, data, scale="viridis", d_min=None, d_max=None): raise NotImplementedError - + @abstractmethod def colorbar(self, fig, data, colorscale="viridis", label=None): raise NotImplementedError - + @abstractmethod def contour_plot(self, x, y, z, colorscale="viridis"): raise NotImplementedError - + @abstractmethod def fill_plot(self, x, y, color=None, label=None): raise NotImplementedError - + @abstractmethod def fill_between_plot(self, x, y_upper, y_lower, color): raise NotImplementedError - + @abstractmethod def histogram_plot(self, x, name, style=None): raise NotImplementedError - + @abstractmethod - def line_plot(self, x=None, y=None, label=None, style = None): + def line(self, x=None, y=None, label=None, style=None): raise NotImplementedError - + @abstractmethod def scatter_plot(self, x, y, colors, labels=None, colorscale="Greys"): raise NotImplementedError - + @abstractmethod def show_table(self, header, values, title): raise NotImplementedError - + @abstractmethod def add_vline(self, fig, x, style=None): raise NotImplementedError - - - - - - - diff --git a/pybop/plot/backends/matplotlib.py b/pybop/plot/backends/matplotlib.py index 961de170b..9e985323b 100644 --- a/pybop/plot/backends/matplotlib.py +++ b/pybop/plot/backends/matplotlib.py @@ -1,20 +1,29 @@ -import warnings from copy import deepcopy import matplotlib as mpl import numpy as np from matplotlib import pyplot as plt -from pybop.plot.util import _AxisData + from pybop.plot.backends.base import PlotBackend +from pybop.plot.util import AxisData, wrap_text + class MatplotlibBackend(PlotBackend): def __init__(self): - self.name = 'matplotlib' - - def create_figure(self, title = None, xaxis_title = None, yaxis_title = None, traces = None, style = None): + self.name = "matplotlib" + self.global_colorcycle = False + self.colorcycle = plt.rcParams["axes.prop_cycle"]() + self.rect = [0, 0, 1, 1] + + def create_figure( + self, title=None, xaxis_title=None, yaxis_title=None, traces=None, style=None + ): style = style or {} - figsize = (np.ceil(style.get("width", 800)/100), np.ceil(style.get("height", 600)/100)) - + figsize = ( + np.ceil(style.get("width", 800) / 100), + np.ceil(style.get("height", 600) / 100), + ) + fig = plt.figure(figsize=figsize, dpi=100) # Set titles @@ -35,28 +44,38 @@ def create_figure(self, title = None, xaxis_title = None, yaxis_title = None, tr ax.set_facecolor(style.get("bg_color")) ax.set_axisbelow(True) - if traces is not None: for trace in traces: self.plot_trace(trace, fig) return fig - def make_subplots(self, axes: list[_AxisData], title=None, axis_titles_x: list[str] | str = None, axis_titles_y: list[str] | str = None, style=None): + + def make_subplots( + self, + axes: list[AxisData], + title=None, + axis_titles_x: list[str] | str = None, + axis_titles_y: list[str] | str = None, + style=None, + ): style = style or {} num_rows = max(ax.row + ax.row_span - 1 for ax in axes) num_cols = max(ax.col + ax.col_span - 1 for ax in axes) - figsize = (np.ceil(style.get("width", 800)/100), np.ceil(style.get("height", 600)/100)) + figsize = ( + np.ceil(style.get("width", 800) / 100), + np.ceil(style.get("height", 600) / 100), + ) fig = plt.figure(figsize=figsize, dpi=100) axes_dict = {} for ax in axes: - print(ax) idx_start = (ax.row - 1) * num_cols + ax.col idx_end = (ax.row + ax.row_span - 2) * num_cols + ax.col + ax.col_span - 1 - axes_dict[(ax.row, ax.col)] = fig.add_subplot(num_rows, num_cols, (idx_start, idx_end)) - + axes_dict[(ax.row, ax.col)] = fig.add_subplot( + num_rows, num_cols, (idx_start, idx_end) + ) # Set title if title is not None: @@ -64,29 +83,59 @@ def make_subplots(self, axes: list[_AxisData], title=None, axis_titles_x: list[s for i, ax in enumerate(fig.axes): if isinstance(axis_titles_x, str): - ax.set_xlabel(axis_titles_x) + ax.set_xlabel( + wrap_text(axis_titles_x, np.floor(50 / num_rows), self.name) + ) elif isinstance(axis_titles_x, list) and i < len(axis_titles_x): - ax.set_xlabel(axis_titles_x[i]) + ax.set_xlabel( + wrap_text(axis_titles_x[i], np.floor(50 / num_rows), self.name) + ) if isinstance(axis_titles_y, str): - ax.set_ylabel(axis_titles_y) + ax.set_ylabel( + wrap_text(axis_titles_y, np.floor(50 / num_rows), self.name) + ) elif isinstance(axis_titles_y, list) and i < len(axis_titles_y): - ax.set_ylabel(axis_titles_y[i]) + ax.set_ylabel( + wrap_text(axis_titles_y[i], np.floor(50 / num_rows), self.name) + ) if "bg_color" in style: ax.set_facecolor(style.get("bg_color")) ax.set_axisbelow(True) + self.global_colorcycle = True + return fig, axes_dict, num_rows, num_cols - def legend(self, fig, style: dict=None): - style= style or {} + def legend(self, fig, style: dict = None): + style = style or {} lines_labels = [] - if style.get('fig_legend'): + if style.get("fig_legend"): axes = fig.axes else: axes = [fig.gca()] + + if "outside" in style.keys(): + side, offset = style.get("outside") + if side == "left": + style["loc"] = "upper left" + style["coords"] = (0.0, 1.0) + self.rect = [offset, 0, 1, 1] + elif side == "top": + style["loc"] = "lower right" + style["coords"] = (1.0, 1.0 - offset) + self.rect = [0, 0, 1, 1 - offset] + elif side == "bottom": + style["loc"] = "lower left" + style["coords"] = (0.0, 0.0) + self.rect = [0, offset, 1, 1] + else: + style["loc"] = "upper right" + style["coords"] = (1.0, 1.0) + self.rect = [0.0, 0, 1 - offset, 1] + labels_in_fig = False lines_labels = [] - opts ={} + opts = {} for ax in axes: if not ax.get_legend_handles_labels() == ([], []): lines_labels.append(ax.get_legend_handles_labels()) @@ -97,18 +146,21 @@ def legend(self, fig, style: dict=None): opts["ncols"] = len(lines) if "coords" in style.keys(): opts["bbox_to_anchor"] = style.get("coords") - if style.get('fig_legend'): + if style.get("fig_legend"): opts["loc"] = style.get("loc", "upper right") fig.legend(lines, labels, **opts) else: opts["loc"] = style.get("loc", "best") - axes[0].legend(lines, labels, **opts) + axes[0].legend(lines, labels, **opts) def show_figure(self, fig): - fig.tight_layout() + if isinstance(fig, list): + for f in fig: + f.tight_layout(rect=self.rect) + else: + fig.tight_layout(rect=self.rect) plt.show() - def plot_trace(self, traces: dict | list[dict], fig, ax=None, color_cycle=None): if not isinstance(traces, list): traces = [traces] @@ -131,8 +183,8 @@ def plot_trace(self, traces: dict | list[dict], fig, ax=None, color_cycle=None): del trace_options[key] # update color - if color_cycle is not None and plot_type == "plot": - trace_options.update(**next(color_cycle)) + if self.global_colorcycle and plot_type == "plot": + trace_options.update(**next(self.colorcycle)) # get plotting function try: @@ -156,7 +208,7 @@ def sample_color_scale(self, data, scale="viridis", d_min=None, d_max=None): # get colours cmap = mpl.colormaps[scale] return cmap(norm_d) - + def colorbar(self, fig, data, colorscale="viridis", label=None): # normalise cost f_min = np.nanmin(data[np.isfinite(data)]) @@ -166,7 +218,9 @@ def colorbar(self, fig, data, colorscale="viridis", label=None): # get colours cmap = mpl.colormaps[colorscale] - plt.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap), ax=fig.gca(), label=label) + plt.colorbar( + mpl.cm.ScalarMappable(norm=norm, cmap=cmap), ax=fig.gca(), label=label + ) def contour_plot(self, x, y, z, colorscale="viridis"): contour = dict(positional_args=[x, y, z], plot_type="contourf", cmap=colorscale) @@ -178,21 +232,22 @@ def contour_plot(self, x, y, z, colorscale="viridis"): plot_type="contour", ) return [contour, contour_lines] - + def fill_plot(self, x, y, color=None, label=None): return dict(positional_args=(x, y), plot_type="fill", color=color) - def fill_between_plot(self, x, y_upper, y_lower, color): - trace = dict(positional_args=(x, y_upper, y_lower), plot_type="fill_between", color=color) + trace = dict( + positional_args=(x, y_upper, y_lower), plot_type="fill_between", color=color + ) return trace def histogram_plot(self, x, name, style: dict = None): trace = dict(positional_args=[x], label=name, plot_type="hist") - trace.update({"alpha" : style.get("alpha")}) + trace.update({"alpha": style.get("alpha")}) return trace - def line_plot(self, x=None, y=None, label=None, style=None): + def line(self, x=None, y=None, label=None, style=None): style = style or {} if x is not None and y is not None: size = min(len(x), len(y)) @@ -234,10 +289,5 @@ def show_table(self, header, values, title): def add_vline(self, fig, x, style=None): fig.gca() - style=style or {} + style = style or {} plt.axvline(x, **style) - - - - - diff --git a/pybop/plot/backends/plotly.py b/pybop/plot/backends/plotly.py index f3f532fcc..4dec1e2e7 100644 --- a/pybop/plot/backends/plotly.py +++ b/pybop/plot/backends/plotly.py @@ -1,138 +1,176 @@ -from copy import deepcopy - import numpy as np -from pybop.plot.backends.plotly_manager import PlotlyManager from pybop.plot.backends.base import PlotBackend -from pybop.plot.util import _AxisData +from pybop.plot.backends.plotly_manager import PlotlyManager +from pybop.plot.util import AxisData, wrap_text LINESTYLE_MAP = { "solid": "solid", "dashed": "dash", "dotted": "dot", - "dashdot": "dashdot" + "dashdot": "dashdot", } -MARKER_MAP = { - "o" : "circle", - "P" : "cross", - "X" : "x", - "." : None -} +MARKER_MAP = {"o": "circle", "P": "cross", "X": "x", ".": None} ANCHOR_MAP = { - "lower" : "bottom", - "upper" : "top", - "left" : "left", - "center" : "center", - "right" : "right" + "lower": "bottom", + "upper": "top", + "left": "left", + "center": "center", + "right": "right", } class PlotlyBackend(PlotBackend): - def __init__(self): - self.name = 'plotly' + self.name = "plotly" self.plotly_manager = PlotlyManager() - def create_figure(self, title = None, xaxis_title = None, yaxis_title = None, traces = None, style = None): + def create_figure( + self, title=None, xaxis_title=None, yaxis_title=None, traces=None, style=None + ): style = style or {} - - layout = self.plotly_manager.go.Layout({ - "title" : title, - "xaxis_title" : xaxis_title, - "yaxis_title" : yaxis_title, - "width" : style.get("width"), - "height" : style.get("height"), - "xaxis_range" : style.get("xaxis_range"), - "yaxis_range" : style.get("yaxis_range"), - "xaxis" : dict( + + layout = self.plotly_manager.go.Layout( + { + "title": title, + "xaxis_title": xaxis_title, + "yaxis_title": yaxis_title, + "width": style.get("width"), + "height": style.get("height"), + "xaxis_range": style.get("xaxis_range"), + "yaxis_range": style.get("yaxis_range"), + "xaxis": dict( title=dict(font={"size": 14}), showexponent="last", exponentformat="e", tickfont=dict(size=12), ), - "yaxis" : dict( + "yaxis": dict( title=dict(font={"size": 14}), showexponent="last", exponentformat="e", tickfont=dict(size=12), ), - "plot_bgcolor" : style.get("bg_color"), - "barmode": "overlay" - }) - fig = self.plotly_manager.goFigure(data=traces, layout=layout) + "plot_bgcolor": style.get("bg_color"), + "barmode": "overlay", + } + ) + fig = self.plotly_manager.go.Figure(data=traces, layout=layout) return fig def _check_empty(self, specs, row, col): if specs[row - 1][col - 1] is None or len(specs[row - 1][col - 1]) > 0: raise ValueError("Overlapping axes are not supported") - def make_subplots(self, axes: list[_AxisData], title=None, axis_titles_x: list[str] | str = None, axis_titles_y: list[str] | str = None, style=None): + def make_subplots( + self, + axes: list[AxisData], + title=None, + axis_titles_x: list[str] | str = None, + axis_titles_y: list[str] | str = None, + style=None, + ): style = style or {} num_rows = max(ax.row + ax.row_span - 1 for ax in axes) num_cols = max(ax.col + ax.col_span - 1 for ax in axes) specs = [[{}] * num_cols for _ in range(num_rows)] - axes_dict={} + axes_dict = {} for ax in axes: self._check_empty(specs, ax.row, ax.col) axes_dict[(ax.row, ax.col)] = ax - specs[ax.row - 1][ax.col - 1] = {"colspan": ax.col_span, "rowspan": ax.row_span} + specs[ax.row - 1][ax.col - 1] = { + "colspan": ax.col_span, + "rowspan": ax.row_span, + } for row in range(ax.row, ax.row + ax.row_span - 1): for col in range(ax.col, ax.col + ax.col_span - 1): if row > ax.row or col > ax.col: self._check_empty(specs, row, col) - specs[row - 1, col - 1] = None + specs[row - 1, col - 1] = None make_subplots = self.plotly_manager.make_subplots - fig = make_subplots(rows=num_rows, cols=num_cols, specs=specs) + fig = make_subplots( + rows=num_rows, + cols=num_cols, + specs=specs, + horizontal_spacing=0.2, + vertical_spacing=0.15, + ) for i, ax in enumerate(axes): if isinstance(axis_titles_x, str): - fig.update_xaxes(title_text=axis_titles_x, row=ax.row, col=ax.col) + fig.update_xaxes( + title_text=wrap_text( + axis_titles_x, np.floor(50 / num_rows), self.name + ), + row=ax.row, + col=ax.col, + ) elif isinstance(axis_titles_x, list) and i < len(axis_titles_x): - fig.update_xaxes(title_text=axis_titles_x[i], row=ax.row, col=ax.col) + fig.update_xaxes( + title_text=wrap_text( + axis_titles_x[i], np.floor(50 / num_rows), self.name + ), + row=ax.row, + col=ax.col, + ) if isinstance(axis_titles_y, str): - fig.update_yaxes(title_text=axis_titles_y, row=ax.row, col=ax.col) + fig.update_yaxes( + title_text=wrap_text( + axis_titles_y, np.floor(50 / num_rows), self.name + ), + row=ax.row, + col=ax.col, + ) elif isinstance(axis_titles_y, list) and i < len(axis_titles_y): - fig.update_yaxes(title_text=axis_titles_y[i], row=ax.row, col=ax.col) - - fig.update_layout({ - "title" : title, - "width" : style.get("width"), - "height" : style.get("height"), - "xaxis" : dict( + fig.update_yaxes( + title_text=wrap_text( + axis_titles_y[i], np.floor(50 / num_rows), self.name + ), + row=ax.row, + col=ax.col, + ) + + fig.update_layout( + { + "title": title, + "width": style.get("width"), + "height": style.get("height"), + "xaxis": dict( title=dict(font={"size": 14}), showexponent="last", exponentformat="e", tickfont=dict(size=12), ), - "yaxis" : dict( + "yaxis": dict( title=dict(font={"size": 14}), showexponent="last", exponentformat="e", tickfont=dict(size=12), ), - "plot_bgcolor" : style.get("bg_color") - }) + "plot_bgcolor": style.get("bg_color"), + } + ) return fig, axes_dict, num_rows, num_cols - def legend(self, fig, style: dict=None): + def legend(self, fig, style: dict = None): style = style or {} opts = {} if style.get("horizontal"): opts["orientation"] = "h" if "loc" in style.keys(): anchors = style.get("loc").split(" ") - if len(anchors) != 2: + if len(anchors) != 2: raise ValueError("loc property must consist of 2 keywords") opts["xanchor"] = ANCHOR_MAP.get(anchors[1], "auto") opts["yanchor"] = ANCHOR_MAP.get(anchors[0], "auto") if "coords" in style.keys(): - coords = style.get("coords") + coords = style.get("coords") opts["x"] = coords[0] opts["y"] = coords[1] @@ -167,15 +205,15 @@ def sample_color_scale(self, data, scale="viridis", d_min=None, d_max=None): return px.colors.sample_colorscale(scale, list(d)) def colorbar(self, fig, data, colorscale="viridis", label=None): - + d_min = np.nanmin(data[np.isfinite(data)]) d_max = np.nanmax(data[np.isfinite(data)]) - colorbar=dict(thickness=25, outlinewidth=1) + colorbar = dict(thickness=25, outlinewidth=1) if label is not None: colorbar.update({"title": {"text": label, "side": "right"}}) fig.add_trace( - self.plotly_manager.goScatter( + self.plotly_manager.go.Scatter( x=[None], y=[None], mode="markers", @@ -191,17 +229,17 @@ def colorbar(self, fig, data, colorscale="viridis", label=None): ) ) - def contour_plot(self, x, y, z, colorscale="viridis"): - - return self.plotly_manager.goContour(x=x, y=y, z=z, colorscale=colorscale, connectgaps=True) - def _get_line_options(style, opts): + return self.plotly_manager.go.Contour( + x=x, y=y, z=z, colorscale=colorscale, connectgaps=True + ) + + def _get_line_options(self, style, opts): linestyle = style.get("linestyle", "solid") color = style.get("color") opts["line"] = dict( - width = style.get("linewidth", 4), - dash = LINESTYLE_MAP.get(linestyle, "solid") + width=style.get("linewidth", 4), dash=LINESTYLE_MAP.get(linestyle, "solid") ) if color is not None: opts["line"].update(color=color) @@ -212,12 +250,14 @@ def fill_plot(self, x, y, color=None, label=None): opts["fillcolor"] = color if label is not None: opts["name"] = label - - return self.plotly_manager.goScatter(x=x, y=y, fill="toself", mode="text", showlegend=False, **opts) + + return self.plotly_manager.go.Scatter( + x=x, y=y, fill="toself", mode="text", showlegend=False, **opts + ) def fill_between_plot(self, x, y_upper, y_lower, color): - - return self.plotly_manager.goScatter( + + return self.plotly_manager.go.Scatter( x=x + x[::-1], y=y_upper + y_lower[::-1], fill="toself", @@ -227,15 +267,15 @@ def fill_between_plot(self, x, y_upper, y_lower, color): fillcolor=color, ) - def histogram_plot(self, x, name, style=None): - style= style or {} - - return self.plotly_manager.goHistogram(x=x, name=name, opacity = style.get("alpha")) + style = style or {} + return self.plotly_manager.go.Histogram( + x=x, name=name, opacity=style.get("alpha") + ) + + def line(self, x=None, y=None, label=None, style=None): - def line_plot(self, x=None, y=None, label=None, style = None): - style = style or {} linestyle = style.get("linestyle", "solid") marker = style.get("marker", "none") @@ -245,7 +285,7 @@ def line_plot(self, x=None, y=None, label=None, style = None): elif marker.lower() == "none": mode = "lines" else: - mode ="markers+lines" + mode = "markers+lines" opts["mode"] = mode if linestyle.lower() != "none": @@ -253,44 +293,46 @@ def line_plot(self, x=None, y=None, label=None, style = None): if marker.lower() != "none": opts["marker"] = dict( - size = style.get("markersize", 8), - symbol = MARKER_MAP.get(marker), + size=style.get("markersize", 8), + symbol=MARKER_MAP.get(marker), ) fillstyle = style.get("fillstyle", "full") markerfacecolor = style.get("markerfacecolor") markeredgecolor = style.get("markeredgecolor") markeredgewidth = style.get("markeredgewidth") - if fillstyle.lower() == 'none': - opts["marker"].update(symbol = MARKER_MAP.get(marker) + "-open") + if fillstyle.lower() == "none": + opts["marker"].update(symbol=MARKER_MAP.get(marker) + "-open") if markeredgecolor is not None: - opts["marker"].update(color = markeredgecolor) - + opts["marker"].update(color=markeredgecolor) + if markerfacecolor is not None: - opts["marker"].update(color = markerfacecolor) + opts["marker"].update(color=markerfacecolor) if markeredgecolor is not None: - opts["marker"].update(line_color = markeredgecolor, line_width=1 or markeredgewidth) + opts["marker"].update( + line_color=markeredgecolor, line_width=1 or markeredgewidth + ) elif markeredgewidth is not None: - opts["marker"].update(line_width = markeredgewidth) - + opts["marker"].update(line_width=markeredgewidth) + if label is None: opts["showlegend"] = False if x is not None and y is not None: - return self.plotly_manager.goScatter( + return self.plotly_manager.go.Scatter( x=x, y=y, - name = label, + name=label, **opts, ) if x is None and y is not None: - return self.plotly_manager.goScatter( + return self.plotly_manager.go.Scatter( y=y, - name = label, + name=label, **opts, ) def scatter_plot(self, x, y, colors, labels=None, colorscale="Greys"): - + opts = dict( mode="markers", marker=dict( @@ -303,17 +345,17 @@ def scatter_plot(self, x, y, colors, labels=None, colorscale="Greys"): ) if labels is not None: opts.update({"text": labels, "hoverinfo": "text"}) - return self.plotly_manager.goScatter(x=x, y=y, **opts) + return self.plotly_manager.go.Scatter(x=x, y=y, **opts) def show_table(self, header, values, title): """ Display data in a table. """ # Import plotly only when needed - - fig = self.plotly_manager.goFigure( + + fig = self.plotly_manager.go.Figure( data=[ - self.plotly_manager.goTable( + self.plotly_manager.go.Table( header=dict(values=header), cells=dict( values=[[row[0] for row in values], [row[1] for row in values]] @@ -324,9 +366,9 @@ def show_table(self, header, values, title): fig.update_layout(title=title) fig.show() - + def add_vline(self, fig, x, style=None): style = style or {} opts = {} self._get_line_options(style, opts) - fig.add_vline(x=x, **opts) \ No newline at end of file + fig.add_vline(x=x, **opts) diff --git a/pybop/plot/contour.py b/pybop/plot/contour.py index c657f5722..6cc5654d1 100644 --- a/pybop/plot/contour.py +++ b/pybop/plot/contour.py @@ -4,7 +4,7 @@ import numpy as np -from pybop.plot.util import import_backend +from pybop.plot.util import get_backend from pybop.problems.problem import Problem if TYPE_CHECKING: @@ -58,7 +58,7 @@ def contour( ValueError If the cost function does not return a valid cost when called with a parameter list. """ - backend_module = import_backend(backend) + backend_module = get_backend(backend) plot_optim = False problem = call_object @@ -146,40 +146,36 @@ def transform_array_of_values(list_of_values, parameter): bounds[1] = transform_array_of_values(bounds[1], parameters[names[1]]) optimised_opts = dict( - marker="P", - markersize=14, - markerfacecolor="black", - markeredgecolor="white", - linestyle="None", - zorder=2.6, - ) + marker="P", + markersize=14, + markerfacecolor="black", + markeredgecolor="white", + linestyle="None", + zorder=2.6, + ) inital_opts = dict( - marker="X", - markersize=14, - markerfacecolor="white", - markeredgecolor="black", - linestyle="None", - zorder=2.6, - ) + marker="X", + markersize=14, + markerfacecolor="white", + markeredgecolor="black", + linestyle="None", + zorder=2.6, + ) fig = backend_module.create_figure( - title = "Cost Landscape", + title="Cost Landscape", xaxis_title="Transformed " + names[0] if transformed else names[0], yaxis_title="Transformed " + names[1] if transformed else names[1], - style ={ - "width" : 600, - "height" : 600, - "xaxis_range" : bounds[0], - "yaxis_range" : bounds[1], - } + style={ + "width": 600, + "height": 600, + "xaxis_range": bounds[0], + "yaxis_range": bounds[1], + }, ) # Create contour plot and update the layout - backend_module.plot_trace( - backend_module.contour_plot(x=x, y=y, z=costs), - fig - ) - + backend_module.plot_trace(backend_module.contour_plot(x=x, y=y, z=costs), fig) if plot_optim: # Plot the optimisation trace @@ -189,42 +185,45 @@ def transform_array_of_values(list_of_values, parameter): backend_module.scatter_plot( transform_array_of_values(optim_trace[:, 0], parameters[names[0]]), transform_array_of_values(optim_trace[:, 1], parameters[names[1]]), - [i / optim_trace.shape[0] for i in range(optim_trace.shape[0])] + [i / optim_trace.shape[0] for i in range(optim_trace.shape[0])], ), - fig + fig, ) # Plot the initial guess if len(result.x_model) > 0: x0 = result.x_model[0] backend_module.plot_trace( - backend_module.line_plot( + backend_module.line( x=transform_array_of_values([x0[0]], parameters[names[0]]), y=transform_array_of_values([x0[1]], parameters[names[1]]), label="Initial values", style=inital_opts, ), - fig + fig, ) # Plot optimised value if result.x is not None: x_best = result.x backend_module.plot_trace( - backend_module.line_plot( + backend_module.line( x=transform_array_of_values([x_best[0]], parameters[names[0]]), y=transform_array_of_values([x_best[1]], parameters[names[1]]), style=optimised_opts, - label="Final values" + label="Final values", ), - fig + fig, ) - backend_module.legend(fig, style={ - "horizontal" : True, - "loc" : "lower right", - "coords" : (1, 1), - }) + backend_module.legend( + fig, + style={ + "horizontal": True, + "loc": "lower right", + "coords": (1, 1), + }, + ) # display the figure if show: backend_module.show_figure(fig) diff --git a/pybop/plot/convergence.py b/pybop/plot/convergence.py index a00a713b9..439bf9e5c 100644 --- a/pybop/plot/convergence.py +++ b/pybop/plot/convergence.py @@ -1,12 +1,12 @@ from typing import TYPE_CHECKING -from pybop.plot.util import import_backend +from pybop.plot.util import get_backend if TYPE_CHECKING: from pybop._result import Result -def convergence(result: "Result", show=True, backend=None): +def convergence(result: "Result", show: bool = True, backend: str = None): """ Plot the convergence of the optimisation algorithm. @@ -16,15 +16,13 @@ def convergence(result: "Result", show=True, backend=None): Optimisation result containing the history of parameter values and associated cost. show : bool, optional If True, the figure is shown upon creation (default: True). - **layout_kwargs : optional - Valid Plotly layout keys and their values, - e.g. `xaxis_title="Time [s]"` or - `xaxis={"title": "Time [s]", font={"size":14}}` + backend : str, optional + Select a plotting backend. If None, the current default backend is used. Returns --------- - fig : plotly.graph_objs.Figure - The Plotly figure object for the convergence plot. + fig : plotly.graph_objs.Figure or matplotlib.figure.Figure + The figure object for the convergence plot. """ # Extract log from the optimisation object @@ -33,27 +31,28 @@ def convergence(result: "Result", show=True, backend=None): # Generate a list of iteration numbers iteration_numbers = list(range(1, len(cost_log) + 1)) - backend = import_backend(backend) + backend = get_backend(backend) - # Create a plot dictionary - fig= backend.create_figure( + # Create figure + fig = backend.create_figure( xaxis_title="Evaluation", yaxis_title="Cost", title="Convergence", - style = { - "bg_color" : "white", - "width" : 600, - "height" : 600 - } + style={"bg_color": "white", "width": 600, "height": 600}, ) - backend.plot_trace(backend.line_plot( - x=iteration_numbers, - y=cost_log, - label=result.method_name, - ), fig) + # Add line plot + backend.plot_trace( + backend.line( + x=iteration_numbers, + y=cost_log, + label=result.method_name, + ), + fig, + ) - # Display figure + # Display or return figure if show: backend.show_figure(fig) - return fig + else: + return fig diff --git a/pybop/plot/dataset.py b/pybop/plot/dataset.py index bfb7d5481..001205c58 100644 --- a/pybop/plot/dataset.py +++ b/pybop/plot/dataset.py @@ -1,5 +1,5 @@ -from pybop.plot.standard_plots import trajectories -from pybop.plot.util import import_backend, remove_brackets +from pybop.plot.trajectories import trajectories +from pybop.plot.util import get_backend, remove_brackets def dataset( @@ -49,14 +49,14 @@ def dataset( fig = trajectories( x=dataset[dataset.domain], y=y, - trace_names=trace_names, + labels=trace_names, show=False, xaxis_title=remove_brackets(dataset.domain), yaxis_title=yaxis_title, backend=backend, ) - backend_module = import_backend(backend) + backend_module = get_backend(backend) if show: backend_module.show_figure(fig) diff --git a/pybop/plot/distribution.py b/pybop/plot/distribution.py index bbbfc9c56..df948662f 100644 --- a/pybop/plot/distribution.py +++ b/pybop/plot/distribution.py @@ -1,8 +1,8 @@ import numpy as np from pybop.parameters.parameter import Parameters -from pybop.plot.standard_plots import Subplots -from pybop.plot.util import import_backend +from pybop.plot.standard_plots import StandardSubplot +from pybop.plot.util import get_backend def distribution( @@ -11,7 +11,7 @@ def distribution( n_samples: int = 100, transformed: bool = False, show: bool = True, - backend = None, + backend=None, ): """ Plot the posterior on top of the prior distribution for a Bayesian optimisation result. @@ -19,15 +19,9 @@ def distribution( # Create lists of axis titles and trace names axis_titles_x = [] axis_titles_y = [] - trace_names = ( - parameters.names - if posterior is None - else ["Prior"] * len(parameters) - ) + trace_names = parameters.names if posterior is None else ["Prior"] * len(parameters) for name in parameters.names: - axis_titles_x.append( - name + " (transformed)" if transformed else name - ) + axis_titles_x.append(name + " (transformed)" if transformed else name) axis_titles_y.append("Probability density") # Evaluate marginal distributions for each parameter @@ -40,27 +34,21 @@ def distribution( values.append(parameter_range) probability.append([d.pdf(s) for s in values[-1]]) + # Get plotting backend + backend = get_backend(backend) + # Create a plot dictionary - subplots = Subplots( + plot_dict = StandardSubplot( x=values, y=probability, - backend=backend + xaxis_titles=axis_titles_x, + yaxis_titles=axis_titles_y, + labels=trace_names, + style={"width": 1024, "height": 576}, + backend=backend, ) - subplots.create_figure( - axis_titles_x=axis_titles_x, - axis_titles_y=axis_titles_y, - style={ - "width" : 1024, - "height" : 576 - } - ) - - subplots.plot_lines( - labels=trace_names - ) - - backend = import_backend(backend) + fig = plot_dict(show=False) if posterior is not None: for idx, p in enumerate(posterior): @@ -70,14 +58,21 @@ def distribution( values.append(parameter_range) probability.append([d.pdf(s) for s in values[-1]]) - trace = backend.line_plot( - values[-1], probability[-1], label="Posterior" - ) - row = (idx // subplots.num_cols) + 1 - col = (idx % subplots.num_cols) + 1 - backend.plot_trace(trace, subplots.fig, ax=subplots.get_axis(row, col)) - backend.legend(subplots.fig, style=dict(horizontal=True, loc="lower right", coords=(1, 1.02)),) + line = backend.line(values[-1], probability[-1], label="Posterior") + row = (idx // plot_dict.num_cols) + 1 + col = (idx % plot_dict.num_cols) + 1 + backend.plot_trace(line, fig, ax=plot_dict.axes[row, col]) + backend.legend( + fig, + style=dict( + horizontal=True, + outside=("top", 0.1), + loc="lower right", + coords=(1, 1.02), + fig_legend=True, + ), + ) if show: - backend.show_figure(subplots.fig) - - return subplots.fig + backend.show_figure(fig) + else: + return fig diff --git a/pybop/plot/nyquist.py b/pybop/plot/nyquist.py index 83dd79432..69fc6fe0c 100644 --- a/pybop/plot/nyquist.py +++ b/pybop/plot/nyquist.py @@ -1,5 +1,5 @@ from pybop.parameters.parameter import Inputs -from pybop.plot.util import import_backend +from pybop.plot.util import get_backend def nyquist( @@ -43,16 +43,13 @@ def nyquist( """ trace_style_model = dict( - linewidth = 2, - color = "#00CC96", - marker = "o", - markerfacecolor = "#00CC96", + linewidth=2, + color="#00CC96", + marker="o", + markerfacecolor="#00CC96", ) trace_style_reference = dict( - linestyle = "none", - marker = "o", - fillstyle= "none", - markeredgecolor="#636EFA" + linestyle="none", marker="o", fillstyle="none", markeredgecolor="#636EFA" ) if not isinstance(inputs, dict): @@ -62,37 +59,37 @@ def nyquist( domain_data = model_output["Impedance"].data.real target_output = problem.target_data figure_list = [] - backend = import_backend(backend) + backend = get_backend(backend) for var in problem.target: fig = backend.create_figure( - xaxis_title=r"$Z_{re} / \Omega$", + xaxis_title=r"$Z_{re} / \Omega$", yaxis_title=r"$-Z_{im} / \Omega$", title=title, + style={"width": 600, "height": 600, "bg_color": "white"}, ) backend.plot_trace( - backend.line_plot( - x=domain_data, - y=-model_output[var].data.imag, - label="Model", - style=trace_style_model + backend.line( + x=domain_data, + y=-model_output[var].data.imag, + label="Model", + style=trace_style_model, ), - fig + fig, ) backend.plot_trace( - backend.line_plot( + backend.line( x=target_output[var].real, y=-target_output[var].imag, label="Reference", style=trace_style_reference, ), - fig + fig, ) backend.legend(fig) figure_list.append(fig) - if show: backend.show_figure(fig) diff --git a/pybop/plot/parameters.py b/pybop/plot/parameters.py index 88a223d3c..463f6d016 100644 --- a/pybop/plot/parameters.py +++ b/pybop/plot/parameters.py @@ -1,8 +1,8 @@ from typing import TYPE_CHECKING from pybop.costs.log_likelihoods import GaussianLogLikelihood -from pybop.plot.standard_plots import Subplots -from pybop.plot.util import import_backend +from pybop.plot.standard_plots import StandardSubplot +from pybop.plot.util import get_backend if TYPE_CHECKING: from pybop._result import Result @@ -40,34 +40,39 @@ def parameters(result: "Result", show=True, backend=None): trace_names = parameters.names if isinstance(result.problem, GaussianLogLikelihood): trace_names.append("Sigma") - print('yay') - for name in trace_names: axis_titles_x.append("Evaluation") axis_titles_y.append(name) # import plotting backend - backend_module = import_backend(backend) + backend = get_backend(backend) # Create a plot dictionary - subplots = Subplots(x=x, y=y, backend=backend) - subplots.create_figure( + plot_dict = StandardSubplot( + x, + y, title="Parameter Convergence", - axis_titles_x=axis_titles_x, - axis_titles_y=axis_titles_y, - style=dict(bg_color="white", width=1024, height=576) - ) - subplots.plot_lines(labels=trace_names) + xaxis_titles=axis_titles_x, + yaxis_titles=axis_titles_y, + style=dict(bg_color="white", width=1600, height=800), + labels=trace_names, + backend=backend, + ) - # import plotting backend - backend_module = import_backend(backend) + fig = plot_dict(show=False) # add legend - backend_module.legend(subplots.fig, style={"fig_legend" : True}) - + backend.legend( + fig, + style={ + "fig_legend": True, + "outside": ("right", 0.18), + }, + ) + # Generate the figure and update the layout if show: - backend_module.show_figure(subplots.fig) + backend.show_figure(fig) - return subplots.fig + return fig diff --git a/pybop/plot/predictive.py b/pybop/plot/predictive.py index 132b34f88..319598257 100644 --- a/pybop/plot/predictive.py +++ b/pybop/plot/predictive.py @@ -2,7 +2,7 @@ import numpy as np -from pybop.plot.util import import_backend, remove_brackets +from pybop.plot.util import get_backend, remove_brackets from pybop.problems.meta_problem import MetaProblem from pybop.simulators.failed_solution import FailedSolution @@ -41,27 +41,22 @@ def predictive( else [result.problem] ) figure_list = [] - backend_module = import_backend(backend) + backend_module = get_backend(backend) for problem in problems: fig = backend_module.create_figure( xaxis_title=remove_brackets(problem.domain), yaxis_title=remove_brackets(problem.target[0]), - style={ - "bg_color" : "white", - "width" : 600, - "height" : 600 - } + style={"bg_color": "white", "width": 600, "height": 600}, ) backend_module.plot_trace( - backend_module.line_plot( + backend_module.line( x=problem.domain_data, y=problem.target_data[problem.target[0]], - label=data_legend_entry - + label=data_legend_entry, ), - fig + fig, ) # Simulate the samples and add to plot @@ -69,25 +64,26 @@ def predictive( simulations = problem.simulate_batch(inputs=inputs) for pdf, sim in zip(posterior_samples_pdf, simulations, strict=False): if not isinstance(sim, FailedSolution): - colors = backend_module.sample_color_scale(pdf, d_min = pdf_range[0], d_max=pdf_range[1] ) + colors = backend_module.sample_color_scale( + pdf, d_min=pdf_range[0], d_max=pdf_range[1] + ) backend_module.plot_trace( - backend_module.line_plot( + backend_module.line( x=problem.domain_data, y=sim[problem.target[0]].data, - style=dict( - color=colors[0], - linestyle="dotted" - ) + style=dict(color=colors[0], linestyle="dotted"), ), - fig + fig, ) # Add the colourbar - backend_module.colorbar(fig, pdf_range, colorscale=colour_scale, label="Posterior PDF") + backend_module.colorbar( + fig, pdf_range, colorscale=colour_scale, label="Posterior PDF" + ) if pdf_plot is not None: backend_module.plot_trace( - backend_module.line_plot( + backend_module.line( x=pdf_plot[0], y=pdf_plot[1], trace_names=pdf_label, diff --git a/pybop/plot/problem.py b/pybop/plot/problem.py index 1693dd3b7..b8c9a60d4 100644 --- a/pybop/plot/problem.py +++ b/pybop/plot/problem.py @@ -3,7 +3,7 @@ from pybop.costs.design_cost import DesignCost from pybop.costs.error_measures import ErrorMeasure from pybop.parameters.parameter import Inputs -from pybop.plot.util import import_backend, remove_brackets +from pybop.plot.util import get_backend, remove_brackets from pybop.problems.meta_problem import MetaProblem from pybop.problems.problem import Problem from pybop.simulators.solution import Solution @@ -64,7 +64,7 @@ def problem( model_domain = target_domain[: len(model_output[target].data)] # Create a plot for each output - backend_module = import_backend(backend) + backend_module = get_backend(backend) figure_list = [] for var in problem.target: # Create a plot dictionary @@ -72,33 +72,26 @@ def problem( title=title, xaxis_title=remove_brackets(domain), yaxis_title=remove_brackets(var), - style = { - "bg_color" : "white", - "width" : 600, - "height" : 600 - } + style={"bg_color": "white", "width": 600, "height": 600}, ) traces = [] - model_trace = backend_module.line_plot( + model_trace = backend_module.line( x=model_domain, y=model_output[var].data, label="Optimised" if isinstance(problem.cost, DesignCost) else "Model", style={ - "linestyle" : "none" if isinstance(problem, MetaProblem) else "solid", - "marker" : "." if isinstance(problem, MetaProblem) else "none" - } - + "linestyle": "none" if isinstance(problem, MetaProblem) else "solid", + "marker": "." if isinstance(problem, MetaProblem) else "none", + }, ) traces.append(model_trace) - target_trace = backend_module.line_plot( - x=target_domain, y=target_output[var].data, + target_trace = backend_module.line( + x=target_domain, + y=target_output[var].data, label="Reference", - style={ - "linestyle" : "none", - "marker" : "." - } + style={"linestyle": "none", "marker": "."}, ) traces.append(target_trace) @@ -114,7 +107,7 @@ def problem( y_lower = (model_output[var].data - sigma).tolist() fill_trace = backend_module.fill_between_plot( - x, y_upper, y_lower, color="#FFE5CC" + x, y_upper, y_lower, color="#FFE5CC" ) traces.append(fill_trace) diff --git a/pybop/plot/samples.py b/pybop/plot/samples.py index 5f49ddd5e..2162cc66e 100644 --- a/pybop/plot/samples.py +++ b/pybop/plot/samples.py @@ -1,6 +1,6 @@ from typing import TYPE_CHECKING -from pybop.plot.util import import_backend +from pybop.plot.util import get_backend if TYPE_CHECKING: from pybop.samplers.base_pints_sampler import SamplingResult @@ -11,8 +11,7 @@ def chains(result: "SamplingResult", show=True, backend=None): Plot posterior distributions for each chain. """ # Import backend - backend = import_backend(backend) - + backend = get_backend(backend) fig = backend.create_figure( title="Posterior Distribution", @@ -26,29 +25,29 @@ def chains(result: "SamplingResult", show=True, backend=None): backend.histogram_plot( x=chain[:, j], name=f"Chain {i} - Parameter {j}", - style=dict(alpha=0.75) + style=dict(alpha=0.75), ), - fig + fig, ) backend.add_vline( - fig, + fig, result.mean[j], - style = dict(linewidth=3, linestyle="dashed", color="black") + style=dict(linewidth=3, linestyle="dashed", color="black"), ) backend.legend(fig) - + if show: backend.show_figure(fig) + def trace(result: "SamplingResult", show=True, backend=None): """ Plot trace plots for the posterior samples. """ # Import plotting backend - backend = import_backend(backend) - + backend = get_backend(backend) figlist = [] for i in range(result.n_parameters): @@ -58,10 +57,7 @@ def trace(result: "SamplingResult", show=True, backend=None): yaxis_title="Value", ) for j, chain in enumerate(result.chains): - backend.plot_trace( - backend.line_plot(y=chain[:, i], label=f"Chain {j}"), - fig - ) + backend.plot_trace(backend.line(y=chain[:, i], label=f"Chain {j}"), fig) backend.legend(fig) figlist.append(fig) @@ -71,13 +67,12 @@ def trace(result: "SamplingResult", show=True, backend=None): return figlist - def posterior(result: "SamplingResult", backend=None, show=True): """ Plot the summed posterior distribution across chains. """ # Import backend - backend = import_backend(backend) + backend = get_backend(backend) fig = backend.create_figure( title="Posterior Distribution", xaxis_title="Value", @@ -87,11 +82,17 @@ def posterior(result: "SamplingResult", backend=None, show=True): for j in range(result.all_samples.shape[1]): backend.plot_trace( backend.histogram_plot( - x=result.all_samples[:, j], name=f"Parameter {j}", style=dict(alpha=0.75) + x=result.all_samples[:, j], + name=f"Parameter {j}", + style=dict(alpha=0.75), ), - fig + fig, + ) + backend.add_vline( + fig, + result.mean[j], + style=dict(linewidth=3, linestyle="dashed", color="black"), ) - backend.add_vline(fig, result.mean[j], style=dict(linewidth=3, linestyle="dashed", color="black")) backend.legend(fig) @@ -115,10 +116,9 @@ def summary_table(result: "SamplingResult", backend=None): ["95% CI Upper", summary_stats["ci_upper"]], ] - backend = import_backend(backend) + backend = get_backend(backend) backend.show_table( header=header, values=values, title="Summary Statistics", ) - diff --git a/pybop/plot/standard_plots.py b/pybop/plot/standard_plots.py index 8e4888dfd..f58f19dc2 100644 --- a/pybop/plot/standard_plots.py +++ b/pybop/plot/standard_plots.py @@ -1,172 +1,217 @@ import math -from copy import deepcopy -import numpy as np +from pybop.plot.backends import PlotBackend +from pybop.plot.util import AxisData, get_backend, parse_data, wrap_text -from pybop.plot.util import ( - _AxisData, - import_backend, - wrap_text, - parse_data -) -class Subplots(): +class StandardPlot: + """ + A class for creating and displaying figures. + + Parameters + ---------- + x : list or np.ndarray, optional + X-axis data points. + y : list or np.ndarray, optional + Primary Y-axis data points for simulated model output. + title: str, optional + The title of the figure + xaxis_title: str, optional + Sets the title/label of the x-axis + yaxis_title: str, optional + Sets the title/label of the y-axis + Settings to modify the default trace type (default: DEFAULT_TRACE_OPTIONS). + labels : str, optional + Name(s) for the primary trace(s) (default: None). + label_width : int, optional + Maximum length of the labels before text wrapping is used (default: 40). + style: dict, optional + backend: str or pybop.backends.PlotBackend, optional + Plotting backend to be used to create plot + + Returns + ------- + plotly.graph_objs.Figure or matplotlib.figure.Figure + The generated Plotly figure. + """ def __init__( self, - x=None, - y=None, - num_rows=None, - num_cols=None, - num_plots=None, + x, + y, + title: str = None, + xaxis_title: str = None, + yaxis_title: str = None, + labels: list[str] = None, + label_width=40, + style: dict = None, backend=None, ): - self.num_rows = num_rows - self.num_cols = num_cols - self.num_plots = num_plots + self.lines = [] + self.backend = backend + self.title = title + self.xaxis_title = xaxis_title + self.yaxis_title = yaxis_title + self.style = style + if not isinstance(self.backend, PlotBackend): + self.backend = get_backend(backend) if x is not None and y is not None: - self.x, self.y = parse_data(x, y) - if self.num_plots is None: - self.num_plots = len(self.y) - else: - self.x = None - self.y = None - - - self.backend = import_backend(backend) - - if self.num_rows == None and self.num_cols == None: - if self.num_plots is not None: - # Work out the number of subplots - self.num_cols = int(math.ceil(math.sqrt(self.num_plots))) - self.num_rows = int(math.ceil(self.num_plots/ self.num_cols)) - elif self.num_rows is None: - if self.num_plots is None: - self.num_rows = 1 - self.num_plots = self.num_cols - else: - self.num_rows = int(math.ceil(self.num_plots/ self.num_cols)) - elif self.num_cols is None: - if self.num_plots is None: - self.num_cols = 1 - self.num_plots = self.num_rows - else: - self.num_cols = int(math.ceil(self.num_traces / self.num_rows)) - - if self.num_plots is not None: - self._axes_data = [ - _AxisData(row + 1, col + 1) - for row in range(self.num_rows) - for col in range(self.num_cols) - ] - else: - self._axes_data = [] - - self.fig = None - self.axes = None - - def add_axis_data(self, row, col, row_span=1, col_span=1): - self._axes_data.append(_AxisData(row, col, row_span, col_span)) - - def create_figure(self, title=None, axis_titles_x: list[str] | str = None, axis_titles_y: list[str] | str = None, style=None): - self.fig, self.axes, self.num_rows, self.num_cols = self.backend.make_subplots(self._axes_data, title=title, axis_titles_x=axis_titles_x, axis_titles_y=axis_titles_y, style=style) - - def get_axis(self, row, col): - if self.axes is None: - raise ValueError("Figure contains no axes or has not been created.") - - if (row, col) in self.axes.keys(): - return self.axes[(row, col)] + self.add_lines(x, y, labels, label_width) + + def __call__(self, show=True): + """ + Generate and show the figure. + + Parameters + ---------- + show : bool, optional + If True, the figure is shown upon creation (default: True). + """ + fig = self.backend.create_figure( + title=self.title, + xaxis_title=self.xaxis_title, + yaxis_title=self.yaxis_title, + style=self.style, + traces=self.lines, + ) + if show: + self.backend.show_figure(fig) else: - raise KeyError(f"No axes for row={row} and col={col} found.") - - def plot_lines(self, x=None, y=None, labels=None): - if self.fig is None: - self.create_figure() - if x is None: - if self.x is None: - raise ValueError("No data for plotting supplied.") - else: - x = self.x - if y is None: - if self.y is None: - raise ValueError("No data for plotting supplied.") - else: - y = self.y + return fig + + def add_lines(self, x, y, labels=None, labelwidth=40): + """ + Add a set of lines. + + Parameters + ---------- + x : list or np.ndarray + X-axis data points. + y : list or np.ndarray + Primary Y-axis data points for simulated model output. + labels : str or list[str], optional + Name(s) for the primary line(s) (default: None). + label_width : int, optional + Maximum length of the labels before text wrapping is used (default: 40). + """ + # Parse the data x, y = parse_data(x, y) xi = x[0] for i in range(0, len(y)): - row = (i // self.num_cols) + 1 - col = (i % self.num_cols) + 1 - if row > self.num_rows: - row = (row % self.num_rows) + 1 if len(x) > 1: xi = x[i] label = None if labels is not None: - label = labels[i] + label = wrap_text(labels[i], 30, backend=self.backend.name) - self.backend.plot_trace(self.backend.line_plot(xi, y[i], label), self.fig, ax=self.get_axis(row, col)) + line = self.backend.line(xi, y[i], label) + self.lines.append(line) -def trajectories(x, y, xaxis_title: str = None, yaxis_title: str = None, labels=None, title:str = None, show=True, backend=None, label_width=20): +class StandardSubplot(StandardPlot): """ - Quickly plot one or more trajectories using Plotly. + A class for creating and displaying a set of figures in a grid layout. Parameters ---------- x : list or np.ndarray X-axis data points. y : list or np.ndarray - Y-axis data points for each trajectory. - trace_names : list or str, optional - Name(s) for the trace(s) (default: None). - **layout_kwargs : optional - Valid Plotly layout keys and their values, - e.g. `xaxis_title="Time / s"` or - `xaxis={"title": "Time [s]", font={"size":14}}` + Primary Y-axis data points for simulated model output. + num_rows : int, optional + Number of rows of subplots, can be set automatically (default: None). + num_cols : int, optional + Number of columns of subplots, can be set automatically (default: None). + title: str, optional + Title of the Figure + xaxis_titles: str or list of str, optional + titles for the x-axes (default: None) + yaxis_titles: str or list of str, optional + titles for the x-axes (default: None) + + labels : str, optional + Name(s) for the primary trace(s) (default: None). + label_width : int, optional + Maximum length of the trace names before text wrapping is used (default: 40). + style: dict, optional + Options for figure layout + backend: str or pybop.plot.backends.PlotBackend Returns ------- - plotly.graph_objs.Figure - The Plotly figure object for the scatter plot. + plotly.graph_objs.Figure or matplotlib.figure.Figure + The generated figure. """ - # Check and wrap trace names - if labels is not None: - if isinstance(labels, str): - labels = [labels] - for i, name in enumerate(labels): - labels[i] = wrap_text( - name, width=label_width, backend=backend - ) - - backend = import_backend(backend) - fig = backend.create_figure( - title = title, - xaxis_title = xaxis_title, - yaxis_title = yaxis_title, - style = { - "height" : 600, - "width" : 600, - "bg_color" : "white" - } - ) - - x, y = parse_data(x, y) - xi = x[0] - for i in range(0, len(y)): - if len(x) > 1: - xi = x[i] - label = None - if labels is not None: - label = labels[i] - - backend.plot_trace(backend.line_plot(xi, y[i], label), fig) - - backend.legend(fig) - - if show: - backend.show_figure(fig) - return fig \ No newline at end of file + def __init__( + self, + x, + y, + num_rows: int = None, + num_cols: int = None, + title: str = None, + xaxis_titles: list[str] | str = None, + yaxis_titles: list[str] | str = None, + labels: list[str] = None, + label_width: int = 40, + style: dict = None, + backend=None, + ): + super().__init__( + x, + y, + title=title, + xaxis_title=xaxis_titles, + yaxis_title=yaxis_titles, + labels=labels, + label_width=label_width, + style=style, + backend=backend, + ) + + self.num_lines = len(self.lines) + self.num_rows = num_rows + self.num_cols = num_cols + if self.num_rows is None and self.num_cols is None: + # Work out the number of subplots + self.num_cols = int(math.ceil(math.sqrt(self.num_lines))) + self.num_rows = int(math.ceil(self.num_lines / self.num_cols)) + elif self.num_rows is None: + self.num_rows = int(math.ceil(self.num_lines / self.num_cols)) + elif self.num_cols is None: + self.num_cols = int(math.ceil(self.num_lines / self.num_rows)) + + self.axes_data = [] + for idx in range(self.num_lines): + row = (idx // self.num_cols) + 1 + col = (idx % self.num_cols) + 1 + self.axes_data.append(AxisData(row, col)) + + def __call__(self, show): + """ + Generate and show the set of figures. + + Parameters + ---------- + show : bool, optional + If True, the figure is shown upon creation (default: True). + """ + + fig, self.axes, self.num_rows, self.num_cols = self.backend.make_subplots( + self.axes_data, + title=self.title, + axis_titles_x=self.xaxis_title, + axis_titles_y=self.yaxis_title, + style=self.style, + ) + + for idx, line in enumerate(self.lines): + row = (idx // self.num_cols) + 1 + col = (idx % self.num_cols) + 1 + self.backend.plot_trace(line, fig, self.axes[(row, col)]) + + if show: + self.backend.show_figure(fig) + else: + return fig diff --git a/pybop/plot/trajectories.py b/pybop/plot/trajectories.py new file mode 100644 index 000000000..a4197d69d --- /dev/null +++ b/pybop/plot/trajectories.py @@ -0,0 +1,67 @@ +from pybop.plot.util import ( + get_backend, + wrap_text, + parse_data +) + + +def trajectories(x, y, xaxis_title: str = None, yaxis_title: str = None, labels=None, title:str = None, show=True, backend=None, label_width=20): + """ + Quickly plot one or more trajectories using Plotly. + + Parameters + ---------- + x : list or np.ndarray + X-axis data points. + y : list or np.ndarray + Y-axis data points for each trajectory. + trace_names : list or str, optional + Name(s) for the trace(s) (default: None). + **layout_kwargs : optional + Valid Plotly layout keys and their values, + e.g. `xaxis_title="Time / s"` or + `xaxis={"title": "Time [s]", font={"size":14}}` + + Returns + ------- + plotly.graph_objs.Figure + The Plotly figure object for the scatter plot. + """ + + # Check and wrap trace names + if labels is not None: + if isinstance(labels, str): + labels = [labels] + for i, name in enumerate(labels): + labels[i] = wrap_text( + name, width=label_width, backend=backend + ) + + backend = get_backend(backend) + fig = backend.create_figure( + title = title, + xaxis_title = xaxis_title, + yaxis_title = yaxis_title, + style = { + "height" : 600, + "width" : 600, + "bg_color" : "white" + } + ) + + x, y = parse_data(x, y) + xi = x[0] + for i in range(0, len(y)): + if len(x) > 1: + xi = x[i] + label = None + if labels is not None: + label = labels[i] + + backend.plot_trace(backend.line(xi, y[i], label), fig) + + backend.legend(fig) + + if show: + backend.show_figure(fig) + return fig \ No newline at end of file diff --git a/pybop/plot/util.py b/pybop/plot/util.py index 36c2e48f3..f4db11438 100644 --- a/pybop/plot/util.py +++ b/pybop/plot/util.py @@ -1,41 +1,43 @@ import textwrap from dataclasses import dataclass + import numpy as np import pybop.plot @dataclass -class _AxisData: +class AxisData: row: int = 1 col: int = 1 row_span: int = 1 col_span: int = 1 -def set_backend(backend): +def use_backend(backend): err_msg = ( f"Plotting backend {backend} is not available. The default backend has not been updated. \n" - f"The default backend is set to {pybop.plot.backend}" + f"The default backend is set to {pybop.plot.current_default_backend}" ) - if backend.lower() in ['matplotlib', 'plotly']: - pybop.plot.backend = backend + if backend.lower() in ["matplotlib", "plotly"]: + pybop.plot.current_default_backend = backend else: raise ModuleNotFoundError(err_msg) -def import_backend(backend): +def get_backend(backend): if backend is None: - backend = pybop.plot.backend + backend = pybop.plot.current_default_backend err_msg = f"Plotting backend {backend} is not available." - if backend.lower() == 'matplotlib': + if backend.lower() == "matplotlib": return pybop.plot.backends.MatplotlibBackend() - elif backend.lower() == 'plotly': + elif backend.lower() == "plotly": return pybop.plot.backends.PlotlyBackend() else: raise ModuleNotFoundError(err_msg) + def parse_data(x, y): """ Check the type and dimensions of the data and convert if necessary to a list @@ -73,7 +75,8 @@ def parse_data(x, y): raise ValueError( "Input x should have either one data series or the same number as y." ) - return x, y + return x, y + def remove_brackets(s): """ @@ -91,6 +94,7 @@ def remove_brackets(s): return s[:start] + " / " + char_in_brackets + s[end + 1 :] return s + def wrap_text(text, width, backend="matplotlib"): """ Wrap text to a specified width with HTML line breaks. diff --git a/pybop/plot/voronoi.py b/pybop/plot/voronoi.py index f7dbc564e..783c7d86b 100644 --- a/pybop/plot/voronoi.py +++ b/pybop/plot/voronoi.py @@ -3,7 +3,7 @@ import numpy as np from scipy.spatial import Voronoi -from pybop.plot.util import import_backend +from pybop.plot.util import get_backend if TYPE_CHECKING: from pybop._result import Result @@ -202,7 +202,7 @@ def surface( normalise=True, title="Voronoi Cost Landscape", show=True, - backend=None + backend=None, ): """ Plot a 2D representation of the Voronoi diagram with color-coded regions. @@ -224,7 +224,7 @@ def surface( e.g. `xaxis_title="Time [s]"` or `xaxis={"title": "Time [s]", font={"size":14}}` """ - backend = import_backend(backend) + backend = get_backend(backend) points = result.x_model parameters = result.problem.parameters @@ -269,35 +269,35 @@ def surface( # Get plotting options outline_opts = dict(color="white", linewidth=0.5) optimised_opts = dict( - marker="P", - markersize=14, - markerfacecolor="black", - markeredgecolor="white", - linestyle="None", - zorder=2.6, - ) + marker="P", + markersize=14, + markerfacecolor="black", + markeredgecolor="white", + linestyle="None", + zorder=2.6, + ) inital_opts = dict( - marker="X", - markersize=14, - markerfacecolor="white", - markeredgecolor="black", - linestyle="None", - zorder=2.6, - ) - + marker="X", + markersize=14, + markerfacecolor="white", + markeredgecolor="black", + linestyle="None", + zorder=2.6, + ) + names = parameters.names # Construct figure fig = backend.create_figure( - title = title, - xaxis_title = names[0], - yaxis_title = names[1], + title=title, + xaxis_title=names[0], + yaxis_title=names[1], style={ - "width" : 600, - "height" : 600, + "width": 600, + "height": 600, "xaxis_range": xlim, "yaxis_range": ylim, - } + }, ) # Add Voronoi edges and fill Voronoi regions @@ -313,7 +313,7 @@ def surface( fig, ) - backend.plot_trace(backend.line_plot(x_region, y_region, style=outline_opts), fig) + backend.plot_trace(backend.line(x_region, y_region, style=outline_opts), fig) backend.colorbar(fig, f) @@ -331,20 +331,34 @@ def surface( # Plot the initial guess if len(result.x_model) > 0: x0 = result.x_model[0] - backend.plot_trace(backend.line_plot(x=[x0[0]], y=[x0[1]], label="Initial values", style=inital_opts), fig) + backend.plot_trace( + backend.line( + x=[x0[0]], y=[x0[1]], label="Initial values", style=inital_opts + ), + fig, + ) # Plot optimised value if result.x is not None: x_best = result.x backend.plot_trace( - backend.line_plot(x=[x_best[0]], y=[x_best[1]], label="Final values", style=optimised_opts), fig + backend.line( + x=[x_best[0]], + y=[x_best[1]], + label="Final values", + style=optimised_opts, + ), + fig, ) - backend.legend(fig, style={ - "horizontal" : True, - "loc" : "lower right", - "coords" : (1, 1), - }) + backend.legend( + fig, + style={ + "horizontal": True, + "loc": "lower right", + "coords": (1, 1), + }, + ) if show: backend.show_figure(fig) diff --git a/tests/plotting/test_plotly_manager.py b/tests/plotting/test_plotly_manager.py index 30bc8e72c..d5dfb9093 100644 --- a/tests/plotting/test_plotly_manager.py +++ b/tests/plotting/test_plotly_manager.py @@ -8,7 +8,7 @@ import pytest import pybop -from pybop.plot.plotly import PlotlyManager +from pybop.plot.backends import PlotlyManager # Find the Python executable python_executable = which("python") @@ -131,7 +131,7 @@ def dataset(plotly_installed): @pytest.mark.unit def test_standard_plot(dataset, plotly_installed): # Set plotting backend - pybop.plot.set_backend("plotly") + pybop.plot.use_backend("plotly") # Check the StandardPlot class pybop.plot.StandardPlot(dataset["Time [s]"], dataset["Voltage [V]"]) @@ -173,7 +173,7 @@ def test_standard_plot(dataset, plotly_installed): @pytest.mark.unit def test_plot_dataset(dataset, plotly_installed): # Set plotting backend - pybop.plot.set_backend("plotly") + pybop.plot.use_backend("plotly") # Test plot of a dataset pybop.plot.dataset(dataset, signal=["Voltage [V]"]) diff --git a/tests/unit/test_plots.py b/tests/unit/test_plots.py index 89a5dbd8a..1b57dd5c5 100644 --- a/tests/unit/test_plots.py +++ b/tests/unit/test_plots.py @@ -15,15 +15,13 @@ class TestPlots: pytestmark = pytest.mark.unit def test_standard_plot(self, backend): - pybop.plot.set_backend(backend) # Test standard plot - trace_names = pybop.plot.remove_brackets( - ["Trace [1]", "Trace [2]"] - ) + labels = pybop.plot.remove_brackets(["Trace [1]", "Trace [2]"]) plot_dict = pybop.plot.StandardPlot( x=np.ones((2, 10)), y=np.ones((2, 10)), - trace_names=trace_names, + labels=labels, + backend=backend, ) plot_dict() @@ -72,7 +70,7 @@ def dataset(self, model): return pybop.import_pybamm_solution(solution) def test_dataset_plots(self, dataset, backend): - pybop.plot.set_backend(backend) + pybop.plot.use_backend(backend) # Test plot of Dataset objects pybop.plot.trajectories( dataset["Time [s]"], @@ -116,7 +114,7 @@ def design_problem(self, model, parameters, experiment): return pybop.Problem(simulator) def test_problem_plots(self, fitting_problem, design_problem, backend): - pybop.plot.set_backend(backend) + pybop.plot.use_backend(backend) # Test plot of Problem objects pybop.plot.problem(fitting_problem, title="Optimised Comparison") pybop.plot.problem(design_problem) @@ -127,7 +125,7 @@ def test_problem_plots(self, fitting_problem, design_problem, backend): ) def test_cost_plots(self, fitting_problem, fitting_problem_no_bounds, backend): - pybop.plot.set_backend(backend) + pybop.plot.use_backend(backend) # Test plot of Cost objects pybop.plot.contour(fitting_problem, gradient=True, steps=5) @@ -149,7 +147,7 @@ def result(self, fitting_problem): return optim.run() def test_optim_plots(self, result, backend): - pybop.plot.set_backend(backend) + pybop.plot.use_backend(backend) bounds = np.asarray([[0.5, 0.8], [0.4, 0.7]]) # Plot convergence @@ -192,7 +190,7 @@ def sampling_result(self, model, parameters, dataset): return sampler.run() def test_posterior_plots(self, sampling_result, backend): - pybop.plot.set_backend(backend) + pybop.plot.use_backend(backend) sampling_result.get_summary_statistics() # Plot trace @@ -218,7 +216,7 @@ def test_posterior_plots(self, sampling_result, backend): def test_with_ipykernel(self, dataset, fitting_problem, result, backend): import ipykernel - pybop.plot.set_backend(backend) + pybop.plot.use_backend(backend) assert version.parse(ipykernel.__version__) >= version.parse("0.6") pybop.plot.dataset(dataset, signal=["Voltage [V]"]) @@ -228,7 +226,7 @@ def test_with_ipykernel(self, dataset, fitting_problem, result, backend): result.plot_contour(steps=5) def test_contour_incorrect_number_of_parameters(self, model, dataset, backend): - pybop.plot.set_backend(backend) + pybop.plot.use_backend(backend) parameter_values = model.default_parameter_values # Test with less than two paramters @@ -270,7 +268,7 @@ def test_contour_incorrect_number_of_parameters(self, model, dataset, backend): pybop.plot.contour(fitting_problem) def test_nyquist(self, backend): - pybop.plot.set_backend(backend) + pybop.plot.use_backend(backend) # Define model model = pybamm.lithium_ion.SPM(options={"surface form": "differential"}) parameter_values = model.default_parameter_values From 0d812d0223d749bb3a7820f62e33addd051114fc Mon Sep 17 00:00:00 2001 From: u2370093 Date: Wed, 3 Jun 2026 14:36:17 +0100 Subject: [PATCH 16/20] improve in code documentation --- .../battery_parameterisation/ocp_averaging.py | 6 +- papers/joss/param_plots.py | 6 +- pybop/plot/__init__.py | 2 +- pybop/plot/backends/base.py | 241 +++++++- pybop/plot/backends/matplotlib.py | 514 ++++++++++++++---- pybop/plot/backends/plotly.py | 511 +++++++++++++---- pybop/plot/contour.py | 103 ++-- pybop/plot/dataset.py | 24 +- pybop/plot/distribution.py | 18 +- pybop/plot/nyquist.py | 10 +- pybop/plot/parameters.py | 32 +- pybop/plot/predictive.py | 2 +- pybop/plot/problem.py | 10 +- pybop/plot/samples.py | 4 +- pybop/plot/standard_plots.py | 11 +- pybop/plot/trajectories.py | 89 ++- pybop/plot/util.py | 47 +- pybop/plot/voronoi.py | 64 ++- 18 files changed, 1268 insertions(+), 426 deletions(-) diff --git a/examples/scripts/battery_parameterisation/ocp_averaging.py b/examples/scripts/battery_parameterisation/ocp_averaging.py index 6fc13d51c..cbf3479a5 100644 --- a/examples/scripts/battery_parameterisation/ocp_averaging.py +++ b/examples/scripts/battery_parameterisation/ocp_averaging.py @@ -110,11 +110,11 @@ charge_dataset["Voltage [V]"], average_dataset["Voltage [V]"], ] - trace_names = ["Discharge", "Charge", "Averaged"] + labels = ["Discharge", "Charge", "Averaged"] fig = pybop.plot.trajectories( x=stos, y=volt, - labels=trace_names, + labels=labels, xaxis_title="Stoichiometry", yaxis_title="Voltage [V]", ) @@ -129,7 +129,7 @@ fig = pybop.plot.trajectories( x=stos, y=dcap, - labels=trace_names, + labels=labels, xaxis_title="Stoichiometry", yaxis_title="Differential capacity [V-1]", ) diff --git a/papers/joss/param_plots.py b/papers/joss/param_plots.py index 298589b51..c65e2b08f 100644 --- a/papers/joss/param_plots.py +++ b/papers/joss/param_plots.py @@ -57,7 +57,7 @@ simulation_plot_dict = pybop.plot.StandardPlot( x=solution["Time [s]"].data, y=[corrupt_values, solution["Battery open-circuit voltage [V]"].data, values], - trace_names=[ + labels=[ "Voltage w. noise", "Open-circuit voltage", "Voltage", @@ -244,7 +244,7 @@ convergence_plot_dict = pybop.plot.StandardPlot( x=iteration_numbers, y=cost_log, - trace_names=[cost.name], + labels=[cost.name], trace_options={"line": {"width": 4, "dash": "dash"}}, ) convergence_traces.extend(convergence_plot_dict.traces) @@ -323,7 +323,7 @@ convergence_plot_dict = pybop.plot.StandardPlot( x=iteration_numbers, y=cost_log, - trace_names=cost.name + labels=cost.name + " " + ( cost.log_likelihood.name if isinstance(cost, pybop.LogPosterior) else "" diff --git a/pybop/plot/__init__.py b/pybop/plot/__init__.py index 3826b4c6d..6e97bc7e5 100644 --- a/pybop/plot/__init__.py +++ b/pybop/plot/__init__.py @@ -1,6 +1,6 @@ # Plotting backend default DEFAULT_BACKEND = 'matplotlib' -current_default_backend=DEFAULT_BACKEND +current_backend=DEFAULT_BACKEND from .util import ( AxisData, diff --git a/pybop/plot/backends/base.py b/pybop/plot/backends/base.py index 195f891ff..029cc3c04 100644 --- a/pybop/plot/backends/base.py +++ b/pybop/plot/backends/base.py @@ -4,6 +4,18 @@ class PlotBackend(ABC): + """ + Abstract base class defining a plotting backend interface. + + Concrete implementations provide plotting functionality for a specific + visualization library (e.g. Plotly, Matplotlib) while exposing a common + API to the rest of the application. + + Methods in this interface are responsible for creating figures, adding + traces and annotations, generating specialised plot types, and rendering + results. + """ + @abstractmethod def create_figure( self, @@ -13,6 +25,27 @@ def create_figure( traces: list = None, style: dict = None, ): + """ + Create and return a new figure. + + Parameters + ---------- + title : str, optional + Figure title. + xaxis_title : str, optional + X-axis label. + yaxis_title : str, optional + Y-axis label. + traces : list, optional + Initial traces to add to the figure. + style : dict, optional + Backend-specific styling options. + + Returns + ------- + object + Backend-specific figure object. + """ raise NotImplementedError @abstractmethod @@ -20,60 +53,256 @@ def make_subplots( self, axes: list[AxisData], title=None, - axis_titles_x: list[str] | str = None, - axis_titles_y: list[str] | str = None, + xaxis_titles: list[str] | str = None, + yaxis_titles: list[str] | str = None, style=None, ): + """ + Create a figure containing multiple subplot axes. + + Parameters + ---------- + axes : list[AxisData] + Definitions describing subplot layout and configuration. + title : str, optional + Figure title. + xaxis_titles : str or list[str], optional + X-axis titles for each subplot. + yaxis_titles : str or list[str], optional + Y-axis titles for each subplot. + style : dict, optional + Backend-specific styling options. + + Returns + ------- + object + Backend-specific figure object. + """ raise NotImplementedError @abstractmethod def legend(self, fig, style: dict = None): + """ + Configure or display a legend for a figure. + + Parameters + ---------- + fig : object + Figure object. + style : dict, optional + Legend styling options. + """ raise NotImplementedError @abstractmethod def show_figure(self, fig): + """ + Render or display a figure. + + Parameters + ---------- + fig : object + Figure to display. + """ raise NotImplementedError @abstractmethod def plot_trace(self, traces: dict | list[dict], fig, ax=None, color_cycle=None): + """ + Add one or more traces to a figure or subplot. + + Parameters + ---------- + traces : dict or list[dict] + Trace definitions to plot. + fig : object + Target figure. + ax : object, optional + Target subplot axis. + color_cycle : iterable, optional + Sequence of colours used when plotting multiple traces. + """ raise NotImplementedError @abstractmethod def sample_color_scale(self, data, scale="viridis", d_min=None, d_max=None): + """ + Map data values onto a colour scale. + + Parameters + ---------- + data : array-like + Values to colour-map. + scale : str, optional + Colour scale name. + d_min : float, optional + Lower bound for normalisation. + d_max : float, optional + Upper bound for normalisation. + + Returns + ------- + array-like + Colours corresponding to the supplied data. + """ raise NotImplementedError @abstractmethod def colorbar(self, fig, data, colorscale="viridis", label=None): + """ + Add a colour bar representing a colour scale. + + Parameters + ---------- + fig : object + Target figure. + data : array-like + Data used for colour scaling. + colorscale : str, optional + Colour scale name. + label : str, optional + Colour bar label. + """ raise NotImplementedError @abstractmethod def contour_plot(self, x, y, z, colorscale="viridis"): + """ + Create a contour plot. + + Parameters + ---------- + x, y : array-like + Coordinate values. + z : array-like + Surface values. + colorscale : str, optional + Colour scale name. + + Returns + ------- + object + Backend-specific contour trace or figure. + """ raise NotImplementedError @abstractmethod - def fill_plot(self, x, y, color=None, label=None): + def fill(self, x, y, color=None, label=None): + """ + Create a filled region plot. + + Parameters + ---------- + x, y : array-like + Coordinates defining the filled area. + color : str, optional + Fill colour. + label : str, optional + Legend label. + """ raise NotImplementedError @abstractmethod - def fill_between_plot(self, x, y_upper, y_lower, color): + def fill_between(self, x, y_upper, y_lower, color): + """ + Create a filled region between upper and lower bounds. + + Parameters + ---------- + x : array-like + X-axis values. + y_upper : array-like + Upper boundary values. + y_lower : array-like + Lower boundary values. + color : str + Fill colour. + """ raise NotImplementedError @abstractmethod def histogram_plot(self, x, name, style=None): + """ + Create a histogram. + + Parameters + ---------- + x : array-like + Data to bin. + name : str + Histogram label. + style : dict, optional + Histogram styling options. + """ raise NotImplementedError @abstractmethod def line(self, x=None, y=None, label=None, style=None): + """ + Create a line plot trace. + + Parameters + ---------- + x, y : array-like, optional + Coordinates of the line. + label : str, optional + Trace label. + style : dict, optional + Line styling options. + + Returns + ------- + object + Backend-specific line trace. + """ raise NotImplementedError @abstractmethod - def scatter_plot(self, x, y, colors, labels=None, colorscale="Greys"): + def scatter(self, x, y, colors, labels=None, colorscale="Greys"): + """ + Create a scatter plot. + + Parameters + ---------- + x, y : array-like + Point coordinates. + colors : array-like + Values or colours associated with each point. + labels : array-like, optional + Point labels. + colorscale : str, optional + Colour scale name. + """ raise NotImplementedError @abstractmethod def show_table(self, header, values, title): + """ + Display tabular data. + + Parameters + ---------- + header : list + Column headers. + values : list + Table contents. + title : str + Table title. + """ raise NotImplementedError @abstractmethod - def add_vline(self, fig, x, style=None): + def vline(self, fig, x, style=None): + """ + Add a vertical reference line to a figure. + + Parameters + ---------- + fig : object + Target figure. + x : float + X-coordinate of the line. + style : dict, optional + Line styling options. + """ raise NotImplementedError diff --git a/pybop/plot/backends/matplotlib.py b/pybop/plot/backends/matplotlib.py index 9e985323b..872ecc8e5 100644 --- a/pybop/plot/backends/matplotlib.py +++ b/pybop/plot/backends/matplotlib.py @@ -1,44 +1,96 @@ -from copy import deepcopy - -import matplotlib as mpl import numpy as np -from matplotlib import pyplot as plt from pybop.plot.backends.base import PlotBackend from pybop.plot.util import AxisData, wrap_text class MatplotlibBackend(PlotBackend): + """ + Matplotlib implementation of the PlotBackend interface. + This backend converts backend-agnostic trace definitions into + Matplotlib figures, axes, and artists. Plot objects are represented + as dictionaries containing plotting arguments and metadata, allowing + higher-level plotting code to remain independent of the underlying + plotting library. + """ + def __init__(self): + # Import matplotlib only when needed + import matplotlib as mpl + from matplotlib import pyplot as plt + + self.mpl = mpl + self.plt = plt + + # Backend identifier used by utility functions and text wrapping. self.name = "matplotlib" + + # Enable automatic colour cycling across subplots when traces do not + # explicitly define a colour. self.global_colorcycle = False - self.colorcycle = plt.rcParams["axes.prop_cycle"]() + + # Matplotlib's default property cycle. + self.colorcycle = self.plt.rcParams["axes.prop_cycle"]() + + # Layout rectangle reserved for tight_layout(). This may be adjusted + # when legends are placed outside the plotting area. self.rect = [0, 0, 1, 1] - def create_figure( - self, title=None, xaxis_title=None, yaxis_title=None, traces=None, style=None - ): - style = style or {} - figsize = ( + def _figsize(self, style): + """ + Convert pixel-based width and height values from a style dictionary + into a Matplotlib figsize (inches). + """ + return ( np.ceil(style.get("width", 800) / 100), np.ceil(style.get("height", 600) / 100), ) - fig = plt.figure(figsize=figsize, dpi=100) + def create_figure( + self, title=None, xaxis_title=None, yaxis_title=None, traces=None, style=None + ): + """ + Create a single-axis figure and optionally populate it with traces. + + Parameters + ---------- + title : str, optional + Figure title. + xaxis_title : str, optional + X-axis label. + yaxis_title : str, optional + Y-axis label. + traces : list[dict], optional + Trace definitions to plot immediately. + style : dict, optional + Figure styling options. + Currently supported options: + - width in pixels + - heith in pixels + - xaxis_range: range of the X-axis + - yaxis_range: range of the Y-axis + - bg_color: background color of the axis + + Returns + ------- + matplotlib.figure.Figure + Configured figure instance. + """ + style = style or {} + fig = self.plt.figure(figsize=self._figsize(style), dpi=100) - # Set titles if title is not None: - plt.suptitle(title) + self.plt.suptitle(title) if xaxis_title is not None: - plt.xlabel(xaxis_title) + self.plt.xlabel(xaxis_title) if yaxis_title is not None: - plt.ylabel(yaxis_title) + self.plt.ylabel(yaxis_title) - # Apply style + # Apply backend-supported figure styling options. if "xaxis_range" in style: - plt.xlim(style.get("xaxis_range")) + self.plt.xlim(style.get("xaxis_range")) if "yaxis_range" in style: - plt.ylim(style.get("yaxis_range")) + self.plt.ylim(style.get("yaxis_range")) if "bg_color" in style: ax = fig.gca() ax.set_facecolor(style.get("bg_color")) @@ -53,60 +105,109 @@ def make_subplots( self, axes: list[AxisData], title=None, - axis_titles_x: list[str] | str = None, - axis_titles_y: list[str] | str = None, + xaxis_titles: list[str] | str = None, + yaxis_titles: list[str] | str = None, style=None, ): + """ + Create a figure containing a custom subplot layout. + + The layout is defined by a collection of AxisData objects, which + specify subplot positions and spans within a grid. + + Parameters + ---------- + title : str, optional + Figure title. + xaxis_title : str, optional + X-axis label. + yaxis_title : str, optional + Y-axis label. + traces : list[dict], optional + Trace definitions to plot immediately. + style : dict, optional + Figure styling options. + Currently supported options: + - width in pixels + - heith in pixels + - bg_color: background color of the axis + + Returns + ------- + matplotlib.figure.Figure + Configured figure instance. + """ + style = style or {} + # Create figure + fig = self.plt.figure(figsize=self._figsize(style), dpi=100) + if title is not None: + self.plt.suptitle(title) + + # Determine the minimum grid size required to accommodate all subplot spans. num_rows = max(ax.row + ax.row_span - 1 for ax in axes) num_cols = max(ax.col + ax.col_span - 1 for ax in axes) - figsize = ( - np.ceil(style.get("width", 800) / 100), - np.ceil(style.get("height", 600) / 100), - ) - - fig = plt.figure(figsize=figsize, dpi=100) axes_dict = {} - for ax in axes: + # Convert row/column span information into Matplotlib subplot indices. idx_start = (ax.row - 1) * num_cols + ax.col idx_end = (ax.row + ax.row_span - 2) * num_cols + ax.col + ax.col_span - 1 axes_dict[(ax.row, ax.col)] = fig.add_subplot( num_rows, num_cols, (idx_start, idx_end) ) - # Set title - if title is not None: - plt.suptitle(title) + # Helper to support either a shared axis title or per-axis titles. + def _get_axis_title(titles, i): + if isinstance(titles, str): + return titles + if isinstance(titles, list) and i < len(titles): + return titles[i] + return None + # Reduce wrapping width as the number of subplot rows increases. + width = np.floor(50 / num_rows) for i, ax in enumerate(fig.axes): - if isinstance(axis_titles_x, str): - ax.set_xlabel( - wrap_text(axis_titles_x, np.floor(50 / num_rows), self.name) - ) - elif isinstance(axis_titles_x, list) and i < len(axis_titles_x): - ax.set_xlabel( - wrap_text(axis_titles_x[i], np.floor(50 / num_rows), self.name) - ) - if isinstance(axis_titles_y, str): - ax.set_ylabel( - wrap_text(axis_titles_y, np.floor(50 / num_rows), self.name) - ) - elif isinstance(axis_titles_y, list) and i < len(axis_titles_y): - ax.set_ylabel( - wrap_text(axis_titles_y[i], np.floor(50 / num_rows), self.name) - ) + if title := _get_axis_title(xaxis_titles, i): + ax.set_xlabel(wrap_text(title, width, self.name)) + if title := _get_axis_title(yaxis_titles, i): + ax.set_ylabel(wrap_text(title, width, self.name)) if "bg_color" in style: ax.set_facecolor(style.get("bg_color")) ax.set_axisbelow(True) + # Use a shared colour cycle across all subplot axes. self.global_colorcycle = True return fig, axes_dict, num_rows, num_cols def legend(self, fig, style: dict = None): + """ + Create an axis-level or figure-level legend. + + Supports legends positioned outside the plotting area and updates + the layout rectangle used by tight_layout() accordingly. + + Parameters + ---------- + fig : matplotlib.figure.Figure + The figure object + style : dict, optional + Legend styling options. + Currently supported options: + - loc: str + - coords: tuple - is translated into bbox_to_anchor + - outside: tuple(side : str, offset: float) places + the legend outside the plot, where the side (left, + right, top, bottom) determines on wich side of the plot + the legend is placed and the offset determines the fraction + of the figure height or width reserved for the legend. + Overrides loc and coords. + - fig_legend: if true, one legend is created for the entire figure, otherwise the legend is created + for the current axis. + + """ style = style or {} lines_labels = [] if style.get("fig_legend"): @@ -114,6 +215,7 @@ def legend(self, fig, style: dict = None): else: axes = [fig.gca()] + # Configure external legend placement and reserve layout space. if "outside" in style.keys(): side, offset = style.get("outside") if side == "left": @@ -133,13 +235,17 @@ def legend(self, fig, style: dict = None): style["coords"] = (1.0, 1.0) self.rect = [0.0, 0, 1 - offset, 1] + # Collect legend entries from all relevant axes. labels_in_fig = False lines_labels = [] opts = {} for ax in axes: - if not ax.get_legend_handles_labels() == ([], []): - lines_labels.append(ax.get_legend_handles_labels()) + # Flatten handles and labels from multiple axes into a single legend. + handles, labels = ax.get_legend_handles_labels() + if handles: + lines_labels.append((handles, labels)) labels_in_fig = True + if labels_in_fig: lines, labels = [sum(lol, []) for lol in zip(*lines_labels, strict=False)] if style.get("horizontal"): @@ -154,75 +260,146 @@ def legend(self, fig, style: dict = None): axes[0].legend(lines, labels, **opts) def show_figure(self, fig): + """ + Apply final layout adjustments and display the figure. + + Parameters + ---------- + fig : matplotlib.figure.Figure + The figure object + """ + if isinstance(fig, list): for f in fig: f.tight_layout(rect=self.rect) else: fig.tight_layout(rect=self.rect) - plt.show() + self.plt.show() + + def plot_trace(self, traces: dict | list[dict], fig, ax=None): + """ + Convert one or more trace definitions into Matplotlib plotting calls. + + Parameters + ---------- + traces: dict or list[dict] + Each trace dictionary specifies a plotting method, positional + arguments, and keyword arguments compatible with a Matplotlib Axes + method. + fig : matplotlib.figure.Figure + The figure object + ax : matplotlib axis object, optional + Specity an axis for plotting. Otherwise current axis is used. + """ - def plot_trace(self, traces: dict | list[dict], fig, ax=None, color_cycle=None): - if not isinstance(traces, list): - traces = [traces] + traces = traces if isinstance(traces, list) else [traces] + + # Extract plotting keyword arguments while removing backend metadata. + ax = ax or fig.gca() for trace in traces: - # retrieve axis, plot_type and positional arguments - plot_type = trace.get("plot_type") or "plot" - positional_args = trace.get("positional_args") or None - ax = ax or fig.gca() - - # axis titles - if "xaxis_title" in trace.keys(): - ax.set_xlabel(trace["xaxis_title"]) - if "yaxis_title" in trace.keys(): - ax.set_ylabel(trace["yaxis_title"]) - - # retrieve remaining arguments - trace_options = deepcopy(trace) - for key in ["plot_type", "positional_args", "xaxis_title", "yaxis_title"]: - if key in trace_options.keys(): - del trace_options[key] - - # update color + if title := trace.get("xaxis_title"): + ax.set_xlabel(title) + if title := trace.get("yaxis_title"): + ax.set_ylabel(title) + + options = { + k: v + for k, v in trace.items() + if k + not in { + "plot_type", + "positional_args", + "xaxis_title", + "yaxis_title", + } + } + plot_type = trace.get("plot_type", "plot") + args = trace.get("positional_args", ()) + + # Apply the global colour cycle when plotting standard line traces. if self.global_colorcycle and plot_type == "plot": - trace_options.update(**next(self.colorcycle)) + options.update(next(self.colorcycle)) + # Resolve the requested plotting method on the target axis. + plot_func = getattr(ax, plot_type) - # get plotting function - try: - plot_function = getattr(ax, plot_type) - except ValueError: - print("Plot type not recognised") + obj = plot_func(*args, **options) - obj = plot_function(*positional_args, **trace_options) + # Automatically attach a colourbar for filled contour plots. if plot_type == "contourf": - plt.colorbar(obj) + self.plt.colorbar(obj) def sample_color_scale(self, data, scale="viridis", d_min=None, d_max=None): - # normalise and clip data + """ + Map data values to RGBA colours using a Matplotlib colormap. + + Parameters + ---------- + data: ndarray + The data to be mapped + scale : str + Name of the colormap + d_min: float, optional + Minimum value to be mapped. Otherwise the minimum of the data is used. + d_max: float, optional + Maximum value to be mapped. Ohterwise maximum of the data is used. + """ + # Normalise values into the range expected by the colormap. d_min = d_min or np.nanmin(data[np.isfinite(data)]) d_max = d_max or np.nanmax(data[np.isfinite(data)]) - norm = mpl.colors.Normalize(vmin=d_min, vmax=d_max, clip=True) + norm = self.mpl.colors.Normalize(vmin=d_min, vmax=d_max, clip=True) norm_d = norm(data, clip=True) if np.isscalar(norm_d): norm_d = [norm_d] - # get colours - cmap = mpl.colormaps[scale] + # Sample colours from the requested colormap. + cmap = self.mpl.colormaps[scale] return cmap(norm_d) def colorbar(self, fig, data, colorscale="viridis", label=None): - # normalise cost + """ + Add colourbar to figure + + Parameters + ---------- + fig: matplotlib.figure.Figure + The figure. + data: array-like + The data to be mapped + scale : str + Name of the colormap + label: str, optional + label to be displayed alongside colorbar + """ + # Create a normalisation matching the supplied data range. f_min = np.nanmin(data[np.isfinite(data)]) f_max = np.nanmax(data[np.isfinite(data)]) - norm = mpl.colors.Normalize(vmin=f_min, vmax=f_max, clip=True) - - # get colours - cmap = mpl.colormaps[colorscale] + norm = self.mpl.colors.Normalize(vmin=f_min, vmax=f_max, clip=True) - plt.colorbar( - mpl.cm.ScalarMappable(norm=norm, cmap=cmap), ax=fig.gca(), label=label + # Create and attach a standalone colourbar. + cmap = self.mpl.colormaps[colorscale] + self.plt.colorbar( + self.mpl.cm.ScalarMappable(norm=norm, cmap=cmap), ax=fig.gca(), label=label ) def contour_plot(self, x, y, z, colorscale="viridis"): + """ + Return trace definitions for a filled contour plot and contour lines. + + Parameters + ---------- + x, y : array-like + Coordinate values. + z : array-like + Surface values. + colorscale : str, optional + Colour scale name. + + Returns + ------- + object + dictionary for contour plot definition and + dictionary for contour line definition + """ contour = dict(positional_args=[x, y, z], plot_type="contourf", cmap=colorscale) contour_lines = dict( positional_args=[x, y, z], @@ -233,47 +410,148 @@ def contour_plot(self, x, y, z, colorscale="viridis"): ) return [contour, contour_lines] - def fill_plot(self, x, y, color=None, label=None): + def fill(self, x, y, color=None, label=None): + """ + Return a trace definition for a filled polygon. + + Parameters + ---------- + x, y : array-like + Coordinates defining the filled area. + color : str, optional + Fill colour. + label : str, optional + Ignored by the matplotlib implementation. + """ return dict(positional_args=(x, y), plot_type="fill", color=color) - def fill_between_plot(self, x, y_upper, y_lower, color): - trace = dict( - positional_args=(x, y_upper, y_lower), plot_type="fill_between", color=color - ) - return trace + def fill_between(self, x, y_upper, y_lower, color): + """ + Return a trace definition for a filled region between two curves. + + Parameters + ---------- + x : array-like + X-axis values. + y_upper : array-like + Upper boundary values. + y_lower : array-like + Lower boundary values. + color : str + Fill colour. + """ + return { + "positional_args": (x, y_upper, y_lower), + "plot_type": "fill_between", + "color": color, + } def histogram_plot(self, x, name, style: dict = None): - trace = dict(positional_args=[x], label=name, plot_type="hist") - trace.update({"alpha": style.get("alpha")}) - return trace + """ + Return a trace definition for a histogram. + + Parameters + ---------- + x : array-like + Data to bin. + name : str + Histogram label. + style : dict, optional + Currently only 'alpha' supported for opacity. + All other style arguments ignored. + """ + style = style or {} + + return { + "positional_args": [x], + "label": name, + "plot_type": "hist", + "alpha": style.get("alpha"), + } def line(self, x=None, y=None, label=None, style=None): + """ + Return a trace definition for a line plot. + + Parameters + ---------- + x, y : array-like, optional + Coordinates of the line. + If both x and y are provided, the shorter sequence determines the + plotted length. + label : str, optional + Trace label. + style : dict, optional + Line styling options. + + Returns + ------- + object + dictionary with positional argumetns, label and style arguments + """ style = style or {} - if x is not None and y is not None: + if y is None: + raise ValueError("y must be provided") + + args = [y] + if x is not None: size = min(len(x), len(y)) - trace = dict(positional_args=[x[:size], y[:size]], label=label) - elif y is not None: - trace = dict(positional_args=[y], label=label) + args = [x[:size], y[:size]] - trace.update(style) - return trace + return { + "positional_args": args, + "label": label, + **style, + } - def scatter_plot(self, x, y, colors=None, labels=None, colorscale="Greys"): - scatter = dict(positional_args=[x, y], plot_type="scatter", cmap=colorscale) + def scatter(self, x, y, colors=None, labels=None, colorscale="Greys"): + """ + Return a trace definition for a scatter plot. + + Parameters + ---------- + x, y : array-like + Point coordinates. + colors : array-like + Values or colours associated with each point. + labels : array-like, optional + Point labels. + Point labels are ignored by matplotlib implementation. + Argument retained for consistency with plotly. + colorscale : str, optional + Colour scale name. + """ + scatter = { + "positional_args": [x, y], + "plot_type": "scatter", + "cmap": colorscale, + } if colors is not None: scatter["c"] = colors return scatter def show_table(self, header, values, title): """ - Display data in a table. + Display tabular data in a standalone Matplotlib figure. + + Array-valued entries are converted to comma-separated strings before + rendering. + + Parameters + ---------- + header : list + Column headers. + values : list + Table contents. + title : str + Table title. """ for i, val in enumerate(values): values[i] = [val[0], ", ".join(val[1].astype(str))] - fig, ax = plt.subplots(figsize=(6, 2), dpi=100) + fig, ax = self.plt.subplots(figsize=(6, 2), dpi=100) - # hide axes + # Remove axis decorations so only the table is displayed. ax.axis("off") ax.axis("tight") ax.table( @@ -285,9 +563,21 @@ def show_table(self, header, values, title): ) ax.set_title(title) fig.tight_layout() - plt.show() + self.plt.show() - def add_vline(self, fig, x, style=None): + def vline(self, fig, x, style=None): + """ + Add a vertical reference line to the current axis. + + Parameters + ---------- + fig: matplotlib.figure.Figure + The figure. + x: float + The position of the vertical line on the axis + style: dict, optional + matplotlib arguments for axvline method + """ fig.gca() style = style or {} - plt.axvline(x, **style) + self.plt.axvline(x, **style) diff --git a/pybop/plot/backends/plotly.py b/pybop/plot/backends/plotly.py index 4dec1e2e7..1d9890165 100644 --- a/pybop/plot/backends/plotly.py +++ b/pybop/plot/backends/plotly.py @@ -4,6 +4,7 @@ from pybop.plot.backends.plotly_manager import PlotlyManager from pybop.plot.util import AxisData, wrap_text +# Mapping from Matplotlib line styles to Plotly dash styles. LINESTYLE_MAP = { "solid": "solid", "dashed": "dash", @@ -11,8 +12,12 @@ "dashdot": "dashdot", } +# Mapping from Matplotlib-style marker definitions to their Plotly +# equivalents. MARKER_MAP = {"o": "circle", "P": "cross", "X": "x", ".": None} +# Translation between Matplotlib legend anchor keywords and Plotly +# anchor names. ANCHOR_MAP = { "lower": "bottom", "upper": "top", @@ -23,44 +28,105 @@ class PlotlyBackend(PlotBackend): + """ + Plotly implementation of the PlotBackend interface. + + This backend converts backend-agnostic plot definitions into Plotly + figures and traces, providing interactive visualisations while + maintaining a common plotting API. + """ + def __init__(self): + """ + Initialise the Plotly backend and associated Plotly manager. + """ self.name = "plotly" self.plotly_manager = PlotlyManager() + def _figure_layout(self, style, figure_title): + axis_layout = dict( + title=dict(font={"size": 14}), + showexponent="last", + exponentformat="e", + tickfont=dict(size=12), + ) + return { + "title": figure_title, + "width": style.get("width"), + "height": style.get("height"), + "xaxis": axis_layout, + "yaxis": axis_layout, + "plot_bgcolor": style.get("bg_color"), + } + def create_figure( - self, title=None, xaxis_title=None, yaxis_title=None, traces=None, style=None + self, + title: str = None, + xaxis_title: str = None, + yaxis_title: str = None, + traces=None, + style: dict = None, ): + """ + Create a Plotly figure. + + Parameters + ---------- + title : str, optional + Figure title. + xaxis_title : str, optional + X-axis label. + yaxis_title : str, optional + Y-axis label. + traces : list, optional + Plotly traces to add to the figure. + style : dict, optional + Currently supported options: + - width in pixels + - heith in pixels + - xaxis_range: range of the X-axis + - yaxis_range: range of the Y-axis + - bg_color: background color of the axis + + Returns + ------- + plotly.graph_objects.Figure + Configured Plotly figure. + """ style = style or {} - - layout = self.plotly_manager.go.Layout( + layout_opts = self._figure_layout(style, title) + layout_opts.update( { - "title": title, "xaxis_title": xaxis_title, "yaxis_title": yaxis_title, - "width": style.get("width"), - "height": style.get("height"), "xaxis_range": style.get("xaxis_range"), "yaxis_range": style.get("yaxis_range"), - "xaxis": dict( - title=dict(font={"size": 14}), - showexponent="last", - exponentformat="e", - tickfont=dict(size=12), - ), - "yaxis": dict( - title=dict(font={"size": 14}), - showexponent="last", - exponentformat="e", - tickfont=dict(size=12), - ), - "plot_bgcolor": style.get("bg_color"), "barmode": "overlay", } ) + layout = self.plotly_manager.go.Layout(layout_opts) + fig = self.plotly_manager.go.Figure(data=traces, layout=layout) return fig def _check_empty(self, specs, row, col): + """ + Validate that a subplot grid location is available. + + Parameters + ---------- + specs : list[list] + Plotly subplot specification grid. + row : int + Row index (1-based). + col : int + Column index (1-based). + + Raises + ------ + ValueError + If the requested subplot location overlaps an existing subplot. + """ if specs[row - 1][col - 1] is None or len(specs[row - 1][col - 1]) > 0: raise ValueError("Overlapping axes are not supported") @@ -68,30 +134,66 @@ def make_subplots( self, axes: list[AxisData], title=None, - axis_titles_x: list[str] | str = None, - axis_titles_y: list[str] | str = None, + xaxis_titles: list[str] | str = None, + yaxis_titles: list[str] | str = None, style=None, ): + """ + Create a figure containing multiple subplots. + + Parameters + ---------- + axes : list[AxisData] + Definitions describing subplot positions and spans. + title : str, optional + Figure title. + xaxis_titles : str or list[str], optional + X-axis titles. + yaxis_titles : str or list[str], optional + Y-axis titles. + style : dict, optional + Figure styling options. + + Returns + ------- + tuple + ( + figure, + axes dictionary, + number of rows, + number of columns + ) + """ style = style or {} + axes_dict = {} + # Determine the minimum grid size required to accommodate all subplot spans. num_rows = max(ax.row + ax.row_span - 1 for ax in axes) num_cols = max(ax.col + ax.col_span - 1 for ax in axes) + + # Plotly subplot layouts are defined using a grid of specification + # dictionaries. Empty dictionaries denote subplot origins, while None + # marks cells occupied by a spanning subplot. specs = [[{}] * num_cols for _ in range(num_rows)] - axes_dict = {} + # Generate subplots data from axes for ax in axes: + # Ensure no subplot occupies the requested grid location. self._check_empty(specs, ax.row, ax.col) axes_dict[(ax.row, ax.col)] = ax specs[ax.row - 1][ax.col - 1] = { "colspan": ax.col_span, "rowspan": ax.row_span, } + # Check space available for full row-/col-span + # Add spec None to covered grid space for row in range(ax.row, ax.row + ax.row_span - 1): for col in range(ax.col, ax.col + ax.col_span - 1): if row > ax.row or col > ax.col: self._check_empty(specs, row, col) specs[row - 1, col - 1] = None + # Create figure with supbplots make_subplots = self.plotly_manager.make_subplots fig = make_subplots( rows=num_rows, @@ -101,75 +203,61 @@ def make_subplots( vertical_spacing=0.15, ) + # Add axis title to each axis in the subplot + def _get_axis_title(titles, width, i): + if isinstance(titles, str): + title = titles + elif isinstance(titles, list) and i < len(titles): + title = titles[i] + else: + return None + + return wrap_text(title, width, self.name) + + # Reduce wrapping width as subplot rows increase to avoid overlapping + # axis titles in dense layouts. + width = np.floor(50 / num_rows) for i, ax in enumerate(axes): - if isinstance(axis_titles_x, str): + if title := _get_axis_title(xaxis_titles, width, i): fig.update_xaxes( - title_text=wrap_text( - axis_titles_x, np.floor(50 / num_rows), self.name - ), - row=ax.row, - col=ax.col, - ) - elif isinstance(axis_titles_x, list) and i < len(axis_titles_x): - fig.update_xaxes( - title_text=wrap_text( - axis_titles_x[i], np.floor(50 / num_rows), self.name - ), + title_text=title, row=ax.row, col=ax.col, ) - if isinstance(axis_titles_y, str): + if title := _get_axis_title(yaxis_titles, width, i): fig.update_yaxes( - title_text=wrap_text( - axis_titles_y, np.floor(50 / num_rows), self.name - ), - row=ax.row, - col=ax.col, - ) - elif isinstance(axis_titles_y, list) and i < len(axis_titles_y): - fig.update_yaxes( - title_text=wrap_text( - axis_titles_y[i], np.floor(50 / num_rows), self.name - ), + title_text=title, row=ax.row, col=ax.col, ) - fig.update_layout( - { - "title": title, - "width": style.get("width"), - "height": style.get("height"), - "xaxis": dict( - title=dict(font={"size": 14}), - showexponent="last", - exponentformat="e", - tickfont=dict(size=12), - ), - "yaxis": dict( - title=dict(font={"size": 14}), - showexponent="last", - exponentformat="e", - tickfont=dict(size=12), - ), - "plot_bgcolor": style.get("bg_color"), - } - ) + fig.update_layout(self._figure_layout(style, title)) return fig, axes_dict, num_rows, num_cols def legend(self, fig, style: dict = None): + """ + Configure and display a figure legend. + + Parameters + ---------- + fig : plotly.graph_objects.Figure + Target figure. + style : dict, optional + Legend styling options including orientation, + location and anchor coordinates. + """ style = style or {} opts = {} if style.get("horizontal"): opts["orientation"] = "h" - if "loc" in style.keys(): + if "loc" in style: anchors = style.get("loc").split(" ") if len(anchors) != 2: raise ValueError("loc property must consist of 2 keywords") opts["xanchor"] = ANCHOR_MAP.get(anchors[1], "auto") opts["yanchor"] = ANCHOR_MAP.get(anchors[0], "auto") - if "coords" in style.keys(): + if "coords" in style: coords = style.get("coords") opts["x"] = coords[0] opts["y"] = coords[1] @@ -177,6 +265,15 @@ def legend(self, fig, style: dict = None): fig.update_layout(showlegend=True, legend=opts) def show_figure(self, fig): + """ + Display one or more Plotly figures. + + Parameters + ---------- + fig : Figure or iterable[Figure] + Figure or collection of figures to display. + """ + # Support displaying either a single figure or a collection of figures. if hasattr(fig, "__len__") and len(fig) > 0: for f in fig: f.show() @@ -184,15 +281,49 @@ def show_figure(self, fig): fig.show() def plot_trace(self, traces, fig, ax=None): - if not isinstance(traces, list): - traces = [traces] - for trace in traces: + """ + Add one or more traces to a figure or subplot. + + Parameters + ---------- + traces : Trace or list[Trace] + Plotly trace objects to add. + fig : plotly.graph_objects.Figure + Target figure. + ax : AxisData, optional + Subplot location. If provided, traces are added + to the specified subplot. + + Returns + ------- + None + """ + for trace in np.atleast_1d(traces): if ax is None: fig.add_trace(trace) else: fig.add_trace(trace, row=ax.row, col=ax.col) def sample_color_scale(self, data, scale="viridis", d_min=None, d_max=None): + """ + Sample colours from a Plotly colour scale. + + Parameters + ---------- + data : array-like + Values to map onto the colour scale. + scale : str, optional + Plotly colour scale name. + d_min : float, optional + Minimum value used for normalisation. + d_max : float, optional + Maximum value used for normalisation. + + Returns + ------- + list + Colours corresponding to the supplied values. + """ px = self.plotly_manager.px # normalise and clip data d_min = d_min or np.nanmin(data[np.isfinite(data)]) @@ -205,13 +336,33 @@ def sample_color_scale(self, data, scale="viridis", d_min=None, d_max=None): return px.colors.sample_colorscale(scale, list(d)) def colorbar(self, fig, data, colorscale="viridis", label=None): - + """ + Add a standalone colour bar to a figure. + + Parameters + ---------- + fig : plotly.graph_objects.Figure + Target figure. + data : array-like + Values defining the colour range. + colorscale : str, optional + Plotly colour scale name. + label : str, optional + Colour bar title. + + Returns + ------- + None + """ d_min = np.nanmin(data[np.isfinite(data)]) d_max = np.nanmax(data[np.isfinite(data)]) colorbar = dict(thickness=25, outlinewidth=1) if label is not None: colorbar.update({"title": {"text": label, "side": "right"}}) + + # Plotly requires a trace to render a standalone colour bar, so an + # invisible scatter trace is added solely to display the scale. fig.add_trace( self.plotly_manager.go.Scatter( x=[None], @@ -230,21 +381,46 @@ def colorbar(self, fig, data, colorscale="viridis", label=None): ) def contour_plot(self, x, y, z, colorscale="viridis"): - + """ + Create a contour plot trace. + + Parameters + ---------- + x, y : array-like + Coordinate values. + z : array-like + Contour values. + colorscale : str, optional + Plotly colour scale. + + Returns + ------- + plotly.graph_objects.Contour + Contour trace. + """ + # Use connectgaps=True to ill small gaps in the input grid to avoid breaks in contour regions. return self.plotly_manager.go.Contour( x=x, y=y, z=z, colorscale=colorscale, connectgaps=True ) - def _get_line_options(self, style, opts): - linestyle = style.get("linestyle", "solid") - color = style.get("color") - opts["line"] = dict( - width=style.get("linewidth", 4), dash=LINESTYLE_MAP.get(linestyle, "solid") - ) - if color is not None: - opts["line"].update(color=color) - - def fill_plot(self, x, y, color=None, label=None): + def fill(self, x, y, color=None, label=None): + """ + Create a filled polygon trace. + + Parameters + ---------- + x, y : array-like + Polygon coordinates. + color : str, optional + Fill colour. + label : str, optional + Legend label. + + Returns + ------- + plotly.graph_objects.Scatter + Filled polygon trace. + """ opts = {} if color is not None: opts["fillcolor"] = color @@ -255,8 +431,29 @@ def fill_plot(self, x, y, color=None, label=None): x=x, y=y, fill="toself", mode="text", showlegend=False, **opts ) - def fill_between_plot(self, x, y_upper, y_lower, color): + def fill_between(self, x, y_upper, y_lower, color): + """ + Create a filled region between two curves. + + Parameters + ---------- + x : array-like + X values. + y_upper : array-like + Upper boundary. + y_lower : array-like + Lower boundary. + color : str + Fill colour. + + Returns + ------- + plotly.graph_objects.Scatter + Filled area trace. + """ + # Construct a closed polygon by traversing the upper curve forwards + # and the lower curve in reverse. return self.plotly_manager.go.Scatter( x=x + x[::-1], y=y_upper + y_lower[::-1], @@ -268,13 +465,82 @@ def fill_between_plot(self, x, y_upper, y_lower, color): ) def histogram_plot(self, x, name, style=None): + """ + Create a histogram trace. + + Parameters + ---------- + x : array-like + Data values. + name : str + Histogram label. + style : dict, optional + Histogram styling options. + + Returns + ------- + plotly.graph_objects.Histogram + Histogram trace. + """ style = style or {} return self.plotly_manager.go.Histogram( x=x, name=name, opacity=style.get("alpha") ) + def _get_line_options(self, style, opts): + """ + Populate Plotly line styling options. + + Parameters + ---------- + style : dict + User-supplied style options. + opts : dict + Trace options dictionary updated in-place. + + Returns + ------- + None + """ + linestyle = style.get("linestyle", "solid") + color = style.get("color") + opts["line"] = dict( + width=style.get("linewidth", 4), dash=LINESTYLE_MAP.get(linestyle, "solid") + ) + if color is not None: + opts["line"].update(color=color) + def line(self, x=None, y=None, label=None, style=None): + """ + Create a line and/or marker trace. + + Parameters + ---------- + x : array-like, optional + X values. + y : array-like + Y values. + label : str, optional + Legend label. + style : dict, optional + Line and marker styling options. + Currently supported: + - linestyle: see LINESTYLE_MAP + - linewidth + - color + - marker: the marker symbol see MARKER_MAP + - markerfacecolor + - markeredgecolor + - markeredgewidth + - fillstyle (for marker) + + Returns + ------- + plotly.graph_objects.Scatter + Scatter trace configured as a line, marker plot, + or combined line-marker plot. + """ style = style or {} linestyle = style.get("linestyle", "solid") @@ -300,6 +566,8 @@ def line(self, x=None, y=None, label=None, style=None): markerfacecolor = style.get("markerfacecolor") markeredgecolor = style.get("markeredgecolor") markeredgewidth = style.get("markeredgewidth") + + # Plotly uses "-open" marker variants to represent unfilled markers. if fillstyle.lower() == "none": opts["marker"].update(symbol=MARKER_MAP.get(marker) + "-open") if markeredgecolor is not None: @@ -309,29 +577,41 @@ def line(self, x=None, y=None, label=None, style=None): opts["marker"].update(color=markerfacecolor) if markeredgecolor is not None: opts["marker"].update( - line_color=markeredgecolor, line_width=1 or markeredgewidth + line_color=markeredgecolor, line_width=markeredgewidth or 1 ) elif markeredgewidth is not None: opts["marker"].update(line_width=markeredgewidth) + # Avoid creating empty legend entries for unnamed traces. if label is None: opts["showlegend"] = False - if x is not None and y is not None: - return self.plotly_manager.go.Scatter( - x=x, - y=y, - name=label, - **opts, - ) - if x is None and y is not None: - return self.plotly_manager.go.Scatter( - y=y, - name=label, - **opts, - ) + kwargs = {"y": y, "name": label, **opts} + if x is not None: + kwargs["x"] = x - def scatter_plot(self, x, y, colors, labels=None, colorscale="Greys"): + return self.plotly_manager.go.Scatter(**kwargs) + + def scatter(self, x, y, colors, labels=None, colorscale="Greys"): + """ + Create a scatter plot trace. + + Parameters + ---------- + x, y : array-like + Point coordinates. + colors : array-like + Values used to colour markers. + labels : array-like, optional + Hover labels. + colorscale : str, optional + Plotly colour scale. + + Returns + ------- + plotly.graph_objects.Scatter + Scatter trace. + """ opts = dict( mode="markers", @@ -349,7 +629,20 @@ def scatter_plot(self, x, y, colors, labels=None, colorscale="Greys"): def show_table(self, header, values, title): """ - Display data in a table. + Display tabular data as a Plotly table. + + Parameters + ---------- + header : list + Column headers. + values : list + Table contents. + title : str + Table title. + + Returns + ------- + None """ # Import plotly only when needed @@ -367,7 +660,23 @@ def show_table(self, header, values, title): fig.update_layout(title=title) fig.show() - def add_vline(self, fig, x, style=None): + def vline(self, fig, x, style=None): + """ + Add a vertical reference line to a figure. + + Parameters + ---------- + fig : plotly.graph_objects.Figure + Target figure. + x : float + X-coordinate of the line. + style : dict, optional + Line styling options. + + Returns + ------- + None + """ style = style or {} opts = {} self._get_line_options(style, opts) diff --git a/pybop/plot/contour.py b/pybop/plot/contour.py index 6cc5654d1..5ca8712e6 100644 --- a/pybop/plot/contour.py +++ b/pybop/plot/contour.py @@ -17,8 +17,9 @@ def contour( bounds: np.ndarray | None = None, transformed: bool = False, steps: int = 10, + title="Cost Landscape", show: bool = True, - backend=None, + backend: str = None, ): """ Plot a 2D visualisation of a cost landscape using Plotly. @@ -32,6 +33,8 @@ def contour( Either: - the cost function to be evaluated. Must accept a list of parameter values and return a cost value. - an optimiser result which provides a specific optimisation trace overlaid on the cost landscape. + title: str, optional + The title of the figure (default: "Cost Landscape") gradient : bool, optional If True, the gradient is shown (default: False). bounds : numpy.ndarray | list[list[float]], optional @@ -43,15 +46,13 @@ def contour( The number of grid points to divide the parameter space into along each dimension (default: 10). show : bool, optional If True, the figure is shown upon creation (default: True). - **layout_kwargs : optional - Valid Plotly layout keys and their values, - e.g. `xaxis_title="Time [s]"` or - `xaxis={"title": "Time [s]", font={"size":14}}` + backend: str, optional + The plotting backend to be used. Returns ------- - plotly.graph_objs.Figure - The Plotly figure object containing the cost landscape plot. + fig : plotly.graph_objs.Figure or matplotlib.figure.Figure + The figure object containing the cost landscape plot. Raises ------ @@ -145,33 +146,18 @@ def transform_array_of_values(list_of_values, parameter): bounds[0] = transform_array_of_values(bounds[0], parameters[names[0]]) bounds[1] = transform_array_of_values(bounds[1], parameters[names[1]]) - optimised_opts = dict( - marker="P", - markersize=14, - markerfacecolor="black", - markeredgecolor="white", - linestyle="None", - zorder=2.6, - ) - inital_opts = dict( - marker="X", - markersize=14, - markerfacecolor="white", - markeredgecolor="black", - linestyle="None", - zorder=2.6, - ) + figure_style = { + "width": 600, + "height": 600, + "xaxis_range": bounds[0], + "yaxis_range": bounds[1], + } fig = backend_module.create_figure( - title="Cost Landscape", + title=title, xaxis_title="Transformed " + names[0] if transformed else names[0], yaxis_title="Transformed " + names[1] if transformed else names[1], - style={ - "width": 600, - "height": 600, - "xaxis_range": bounds[0], - "yaxis_range": bounds[1], - }, + style=figure_style, ) # Create contour plot and update the layout @@ -182,7 +168,7 @@ def transform_array_of_values(list_of_values, parameter): optim_trace = np.asarray([item[:2] for item in result.x_model]) optim_trace = optim_trace.reshape(-1, 2) backend_module.plot_trace( - backend_module.scatter_plot( + backend_module.scatter( transform_array_of_values(optim_trace[:, 0], parameters[names[0]]), transform_array_of_values(optim_trace[:, 1], parameters[names[1]]), [i / optim_trace.shape[0] for i in range(optim_trace.shape[0])], @@ -198,7 +184,14 @@ def transform_array_of_values(list_of_values, parameter): x=transform_array_of_values([x0[0]], parameters[names[0]]), y=transform_array_of_values([x0[1]], parameters[names[1]]), label="Initial values", - style=inital_opts, + style=dict( + marker="X", + markersize=14, + markerfacecolor="white", + markeredgecolor="black", + linestyle="None", + zorder=2.6, + ), ), fig, ) @@ -210,7 +203,14 @@ def transform_array_of_values(list_of_values, parameter): backend_module.line( x=transform_array_of_values([x_best[0]], parameters[names[0]]), y=transform_array_of_values([x_best[1]], parameters[names[1]]), - style=optimised_opts, + style=dict( + marker="P", + markersize=14, + markerfacecolor="black", + markeredgecolor="white", + linestyle="None", + zorder=2.6, + ), label="Final values", ), fig, @@ -228,28 +228,27 @@ def transform_array_of_values(list_of_values, parameter): if show: backend_module.show_figure(fig) - # if gradient: - # grad_figs = [] - # for i, grad_costs in enumerate(grad_parameter_costs): - # # Update title for gradient plots - # updated_layout_options = layout_options.copy() - # updated_layout_options["title"] = f"Gradient for Parameter: {i + 1}" - - # # Create contour plot with updated layout options - # grad_layout = go.Layout(updated_layout_options) + if gradient: + grad_figs = [] + for i, grad_costs in enumerate(grad_parameter_costs): + # Create fig + grad_fig = backend_module.create_figure( + title=f"Gradient for Parameter: {i + 1}", + xaxis_title="Transformed " + names[0] if transformed else names[0], + yaxis_title="Transformed " + names[1] if transformed else names[1], + style=figure_style, + ) - # # Create fig - # grad_fig = go.Figure( - # data=[go.Contour(x=x, y=y, z=grad_costs)], layout=grad_layout - # ) - # grad_fig.update_layout(**layout_kwargs) + backend_module.plot_trace( + backend_module.contour_plot(x=x, y=y, z=grad_costs), grad_fig + ) - # if show: - # grad_fig.show() + if show: + backend_module.show_figure(grad_fig) - # # append grad_fig to list - # grad_figs.append(grad_fig) + # append grad_fig to list + grad_figs.append(grad_fig) - # return fig, grad_figs + return fig, grad_figs return fig diff --git a/pybop/plot/dataset.py b/pybop/plot/dataset.py index 001205c58..2c00f4b35 100644 --- a/pybop/plot/dataset.py +++ b/pybop/plot/dataset.py @@ -2,9 +2,7 @@ from pybop.plot.util import get_backend, remove_brackets -def dataset( - dataset, signal=None, trace_names=None, show=True, backend=None, **layout_kwargs -): +def dataset(dataset, signal=None, labels=None, show=True, backend=None): """ Quickly plot a PyBOP Dataset using Plotly. @@ -14,19 +12,15 @@ def dataset( A PyBOP dataset. signal : list or str, optional The name of the time series to plot (default: "Voltage [V]"). - trace_names : list or str, optional + labels : list or str, optional Name(s) for the trace(s) (default: "Data"). show : bool, optional If True, the figure is shown upon creation (default: True). - **layout_kwargs : optional - Valid Plotly layout keys and their values, - e.g. `xaxis_title="Time / s"` or - `xaxis={"title": "Time [s]", font={"size":14}}` Returns ------- - plotly.graph_objs.Figure - The Plotly figure object for the scatter plot. + fig : plotly.graph_objs.Figure or matplotlib.figure.Figure + The figure object for the scatter plot. """ # Get data dictionary @@ -38,18 +32,18 @@ def dataset( y = [dataset[s] for s in signal] if len(signal) == 1: yaxis_title = remove_brackets(signal[0]) - if trace_names is None: - trace_names = ["Data"] + if labels is None: + labels = ["Data"] else: yaxis_title = "Output" - if trace_names is None: - trace_names = remove_brackets(signal) + if labels is None: + labels = remove_brackets(signal) # Create the figure fig = trajectories( x=dataset[dataset.domain], y=y, - labels=trace_names, + labels=labels, show=False, xaxis_title=remove_brackets(dataset.domain), yaxis_title=yaxis_title, diff --git a/pybop/plot/distribution.py b/pybop/plot/distribution.py index df948662f..6ac6c0529 100644 --- a/pybop/plot/distribution.py +++ b/pybop/plot/distribution.py @@ -11,18 +11,18 @@ def distribution( n_samples: int = 100, transformed: bool = False, show: bool = True, - backend=None, + backend: str = None, ): """ Plot the posterior on top of the prior distribution for a Bayesian optimisation result. """ # Create lists of axis titles and trace names - axis_titles_x = [] - axis_titles_y = [] - trace_names = parameters.names if posterior is None else ["Prior"] * len(parameters) + xaxis_titles = [] + yaxis_titles = [] + labels = parameters.names if posterior is None else ["Prior"] * len(parameters) for name in parameters.names: - axis_titles_x.append(name + " (transformed)" if transformed else name) - axis_titles_y.append("Probability density") + xaxis_titles.append(name + " (transformed)" if transformed else name) + yaxis_titles.append("Probability density") # Evaluate marginal distributions for each parameter values = [] @@ -41,9 +41,9 @@ def distribution( plot_dict = StandardSubplot( x=values, y=probability, - xaxis_titles=axis_titles_x, - yaxis_titles=axis_titles_y, - labels=trace_names, + xaxis_titles=xaxis_titles, + yaxis_titles=yaxis_titles, + labels=labels, style={"width": 1024, "height": 576}, backend=backend, ) diff --git a/pybop/plot/nyquist.py b/pybop/plot/nyquist.py index 69fc6fe0c..a4f5ed04d 100644 --- a/pybop/plot/nyquist.py +++ b/pybop/plot/nyquist.py @@ -13,19 +13,20 @@ def nyquist( problem : pybop.Problem An instance of a problem class that contains the parameters and methods for evaluation and target retrieval. + title: str, optional + The title of the figure inputs : Inputs, optional Input parameters for the problem. If not provided, the default parameters from the problem instance will be used. These parameters are verified before use (default is None). show : bool, optional If True, the plots will be displayed. - **layout_kwargs : dict, optional - Additional keyword arguments for customising the plot layout. These arguments are passed to - `fig.update_layout()`. + backend: str, optional + The plotting backend to be used. Returns ------- list - A list of plotly `Figure` objects, each representing a Nyquist plot for the model's output and target values. + A list of plotly or matplotlib `Figure` objects, each representing a Nyquist plot for the model's output and target values. Notes ----- @@ -33,7 +34,6 @@ def nyquist( of the impedance from the target output. - For each signal in the problem, a Nyquist plot is created with the model's impedance plotted as a scatter plot. - An additional trace for the reference (target output) is added to the plot. - - The plot layout can be customised using `layout_kwargs`. Example ------- diff --git a/pybop/plot/parameters.py b/pybop/plot/parameters.py index 463f6d016..ca171170d 100644 --- a/pybop/plot/parameters.py +++ b/pybop/plot/parameters.py @@ -8,7 +8,7 @@ from pybop._result import Result -def parameters(result: "Result", show=True, backend=None): +def parameters(result: "Result", show: bool = True, backend: str = None): """ Plot the evolution of parameters during the optimisation process using Plotly. @@ -18,15 +18,13 @@ def parameters(result: "Result", show=True, backend=None): Optimisation result containing the history of parameter values and associated cost. show : bool, optional If True, the figure is shown upon creation (default: True). - **layout_kwargs : optional - Valid Plotly layout keys and their values, - e.g. `xaxis_title="Time [s]"` or - `xaxis={"title": "Time [s]", font={"size":14}}` + backend: str, optional + The plotting backend to be used Returns ------- - plotly.graph_objs.Figure - A Plotly figure object showing the parameter evolution over iterations. + plotly.graph_objs.Figure or matplotlib.figure.Figure + A figure object showing the parameter evolution over iterations. """ # Extract parameters and log from the optimisation object @@ -35,15 +33,15 @@ def parameters(result: "Result", show=True, backend=None): y = [list(item) for item in zip(*result.x_model, strict=False)] # Create lists of axis titles and trace names - axis_titles_x = [] - axis_titles_y = [] - trace_names = parameters.names + xaxis_titles = [] + yaxis_titles = [] + labels = parameters.names if isinstance(result.problem, GaussianLogLikelihood): - trace_names.append("Sigma") + labels.append("Sigma") - for name in trace_names: - axis_titles_x.append("Evaluation") - axis_titles_y.append(name) + for name in labels: + xaxis_titles.append("Evaluation") + yaxis_titles.append(name) # import plotting backend backend = get_backend(backend) @@ -53,10 +51,10 @@ def parameters(result: "Result", show=True, backend=None): x, y, title="Parameter Convergence", - xaxis_titles=axis_titles_x, - yaxis_titles=axis_titles_y, + xaxis_titles=xaxis_titles, + yaxis_titles=yaxis_titles, style=dict(bg_color="white", width=1600, height=800), - labels=trace_names, + labels=labels, backend=backend, ) diff --git a/pybop/plot/predictive.py b/pybop/plot/predictive.py index 319598257..cb2848524 100644 --- a/pybop/plot/predictive.py +++ b/pybop/plot/predictive.py @@ -86,7 +86,7 @@ def predictive( backend_module.line( x=pdf_plot[0], y=pdf_plot[1], - trace_names=pdf_label, + labels=pdf_label, ) ) if show: diff --git a/pybop/plot/problem.py b/pybop/plot/problem.py index b8c9a60d4..658812cfa 100644 --- a/pybop/plot/problem.py +++ b/pybop/plot/problem.py @@ -12,9 +12,9 @@ def problem( problem: Problem, inputs: Inputs = None, - show: bool = True, title="Scatter Plot", - backend=None, + show: bool = True, + backend: str = None, ): """ Produce a quick plot of the target dataset against optimised model output. @@ -28,8 +28,12 @@ def problem( Problem object with dataset and targets attributes. inputs : Inputs Optimised (or example) parameter values. + title: str, optional: + The title of the plot (default: "Scatter Plot") show : bool, optional If True, the figure is shown upon creation (default: True). + backend: str, optional + The plotting backend to be used. Returns ------- @@ -106,7 +110,7 @@ def problem( y_upper = (model_output[var].data + sigma).tolist() y_lower = (model_output[var].data - sigma).tolist() - fill_trace = backend_module.fill_between_plot( + fill_trace = backend_module.fill_between( x, y_upper, y_lower, color="#FFE5CC" ) traces.append(fill_trace) diff --git a/pybop/plot/samples.py b/pybop/plot/samples.py index 2162cc66e..d240bc5d5 100644 --- a/pybop/plot/samples.py +++ b/pybop/plot/samples.py @@ -30,7 +30,7 @@ def chains(result: "SamplingResult", show=True, backend=None): fig, ) - backend.add_vline( + backend.vline( fig, result.mean[j], style=dict(linewidth=3, linestyle="dashed", color="black"), @@ -88,7 +88,7 @@ def posterior(result: "SamplingResult", backend=None, show=True): ), fig, ) - backend.add_vline( + backend.vline( fig, result.mean[j], style=dict(linewidth=3, linestyle="dashed", color="black"), diff --git a/pybop/plot/standard_plots.py b/pybop/plot/standard_plots.py index f58f19dc2..462257eaa 100644 --- a/pybop/plot/standard_plots.py +++ b/pybop/plot/standard_plots.py @@ -26,6 +26,7 @@ class StandardPlot: label_width : int, optional Maximum length of the labels before text wrapping is used (default: 40). style: dict, optional + legend_style: dict, optional backend: str or pybop.backends.PlotBackend, optional Plotting backend to be used to create plot @@ -45,6 +46,7 @@ def __init__( labels: list[str] = None, label_width=40, style: dict = None, + legend_style: dict = None, backend=None, ): self.lines = [] @@ -53,6 +55,7 @@ def __init__( self.xaxis_title = xaxis_title self.yaxis_title = yaxis_title self.style = style + self.legend_style = legend_style if not isinstance(self.backend, PlotBackend): self.backend = get_backend(backend) @@ -75,6 +78,10 @@ def __call__(self, show=True): style=self.style, traces=self.lines, ) + + if self.legend_style is not None: + self.backend.legend(fig, style=self.legend_style) + if show: self.backend.show_figure(fig) else: @@ -201,8 +208,8 @@ def __call__(self, show): fig, self.axes, self.num_rows, self.num_cols = self.backend.make_subplots( self.axes_data, title=self.title, - axis_titles_x=self.xaxis_title, - axis_titles_y=self.yaxis_title, + xaxis_titles=self.xaxis_title, + yaxis_titles=self.yaxis_title, style=self.style, ) diff --git a/pybop/plot/trajectories.py b/pybop/plot/trajectories.py index a4197d69d..0c6291777 100644 --- a/pybop/plot/trajectories.py +++ b/pybop/plot/trajectories.py @@ -1,11 +1,17 @@ -from pybop.plot.util import ( - get_backend, - wrap_text, - parse_data -) - - -def trajectories(x, y, xaxis_title: str = None, yaxis_title: str = None, labels=None, title:str = None, show=True, backend=None, label_width=20): +from pybop.plot.standard_plots import StandardPlot + + +def trajectories( + x, + y, + title: str = None, + xaxis_title: str = None, + yaxis_title: str = None, + labels=None, + label_width=20, + show=True, + backend=None, +): """ Quickly plot one or more trajectories using Plotly. @@ -15,53 +21,40 @@ def trajectories(x, y, xaxis_title: str = None, yaxis_title: str = None, labels= X-axis data points. y : list or np.ndarray Y-axis data points for each trajectory. - trace_names : list or str, optional + title: str, optional + The title of the figure + xaxis_title: str, optional + Sets the title/label of the x-axis + yaxis_title: str, optional + Sets the title/label of the y-axis + Settings to modify the default trace type (default: DEFAULT_TRACE_OPTIONS). + labels : list or str, optional Name(s) for the trace(s) (default: None). - **layout_kwargs : optional - Valid Plotly layout keys and their values, - e.g. `xaxis_title="Time / s"` or - `xaxis={"title": "Time [s]", font={"size":14}}` + label_width : int, optional + Maximum length of the labels before text wrapping is used (default: 20). + show : bool, optional + If True, the figure is shown upon creation (default: True). + backend: str, optional + The plotting backend to be used. Returns ------- plotly.graph_objs.Figure The Plotly figure object for the scatter plot. """ - - # Check and wrap trace names - if labels is not None: - if isinstance(labels, str): - labels = [labels] - for i, name in enumerate(labels): - labels[i] = wrap_text( - name, width=label_width, backend=backend - ) - - backend = get_backend(backend) - fig = backend.create_figure( - title = title, - xaxis_title = xaxis_title, - yaxis_title = yaxis_title, - style = { - "height" : 600, - "width" : 600, - "bg_color" : "white" - } + plot_dict = StandardPlot( + x, + y, + title=title, + xaxis_title=xaxis_title, + yaxis_title=yaxis_title, + labels=labels, + label_width=label_width, + style={"height": 600, "width": 600, "bg_color": "white"}, + legend_style={}, + backend=backend, ) - x, y = parse_data(x, y) - xi = x[0] - for i in range(0, len(y)): - if len(x) > 1: - xi = x[i] - label = None - if labels is not None: - label = labels[i] - - backend.plot_trace(backend.line(xi, y[i], label), fig) - - backend.legend(fig) + fig = plot_dict(show=show) - if show: - backend.show_figure(fig) - return fig \ No newline at end of file + return fig diff --git a/pybop/plot/util.py b/pybop/plot/util.py index f4db11438..b0059e159 100644 --- a/pybop/plot/util.py +++ b/pybop/plot/util.py @@ -6,29 +6,37 @@ import pybop.plot -@dataclass -class AxisData: - row: int = 1 - col: int = 1 - row_span: int = 1 - col_span: int = 1 - - def use_backend(backend): + """ + Select a plotting backend to be used for all subsequent plots. + + Parameters + ---------- + backend : str + The plotting backend to be used. + """ err_msg = ( - f"Plotting backend {backend} is not available. The default backend has not been updated. \n" - f"The default backend is set to {pybop.plot.current_default_backend}" + f"Plotting backend {backend} is not available. The current backend has not been updated. \n" + f"The current backend is set to {pybop.plot.current_backend}" ) if backend.lower() in ["matplotlib", "plotly"]: - pybop.plot.current_default_backend = backend + pybop.plot.current_backend = backend else: raise ModuleNotFoundError(err_msg) -def get_backend(backend): +def get_backend(backend=None): + """ + Get instance of PlotBackend class for a given plotting backend + + Parameters + ---------- + backend : str, optional + The plotting backend to be used (default: pybop.plot.current_backend) + """ if backend is None: - backend = pybop.plot.current_default_backend + backend = pybop.plot.current_backend err_msg = f"Plotting backend {backend} is not available." if backend.lower() == "matplotlib": return pybop.plot.backends.MatplotlibBackend() @@ -38,6 +46,19 @@ def get_backend(backend): raise ModuleNotFoundError(err_msg) +@dataclass +class AxisData: + """ + Simple dataclass to store info needed to construct + subplots from specifying size and location of individual axes. + """ + + row: int = 1 + col: int = 1 + row_span: int = 1 + col_span: int = 1 + + def parse_data(x, y): """ Check the type and dimensions of the data and convert if necessary to a list diff --git a/pybop/plot/voronoi.py b/pybop/plot/voronoi.py index 783c7d86b..f79bc882b 100644 --- a/pybop/plot/voronoi.py +++ b/pybop/plot/voronoi.py @@ -198,9 +198,9 @@ def interpolate_point(p, q, axis, boundary_val): def surface( result: "Result", + title="Voronoi Cost Landscape", bounds=None, normalise=True, - title="Voronoi Cost Landscape", show=True, backend=None, ): @@ -211,6 +211,8 @@ def surface( ----------- result : pybop.Result Optimisation result containing the history of parameter values and associated cost. + title: str, optional + The title of the plot (default: "Voronoi Cost Landscape") bounds : numpy.ndarray, optional A 2x2 array specifying the [min, max] bounds for each parameter. If None, uses `cost.parameters.get_bounds_for_plotly`. @@ -219,10 +221,8 @@ def surface( points normalised with respect to the bounds (default: True). show : bool, optional If True, the figure is shown upon creation (default: True). - **layout_kwargs : optional - Valid Plotly layout keys and their values, - e.g. `xaxis_title="Time [s]"` or - `xaxis={"title": "Time [s]", font={"size":14}}` + backend: str, optional + The plotting backend to be used. """ backend = get_backend(backend) points = result.x_model @@ -266,28 +266,8 @@ def surface( # Compute regions x, y, f, regions = _voronoi_regions(x_optim, y_optim, f, xlim, ylim) - # Get plotting options - outline_opts = dict(color="white", linewidth=0.5) - optimised_opts = dict( - marker="P", - markersize=14, - markerfacecolor="black", - markeredgecolor="white", - linestyle="None", - zorder=2.6, - ) - inital_opts = dict( - marker="X", - markersize=14, - markerfacecolor="white", - markeredgecolor="black", - linestyle="None", - zorder=2.6, - ) - - names = parameters.names - # Construct figure + names = parameters.names fig = backend.create_figure( title=title, xaxis_title=names[0], @@ -307,19 +287,20 @@ def surface( y_region = region[:, 1].tolist() + [region[0, 1]] backend.plot_trace( - backend.fill_plot( - x_region, y_region, color=colors[j], label=f"f={f[j]:.2f}" - ), + backend.fill(x_region, y_region, color=colors[j], label=f"f={f[j]:.2f}"), fig, ) - backend.plot_trace(backend.line(x_region, y_region, style=outline_opts), fig) + backend.plot_trace( + backend.line(x_region, y_region, style=dict(color="white", linewidth=0.5)), + fig, + ) backend.colorbar(fig, f) # Add original points backend.plot_trace( - backend.scatter_plot( + backend.scatter( x=x_optim, y=y_optim, colors=[i / len(x_optim) for i in range(len(x_optim))], @@ -333,7 +314,17 @@ def surface( x0 = result.x_model[0] backend.plot_trace( backend.line( - x=[x0[0]], y=[x0[1]], label="Initial values", style=inital_opts + x=[x0[0]], + y=[x0[1]], + label="Initial values", + style=dict( + marker="X", + markersize=14, + markerfacecolor="white", + markeredgecolor="black", + linestyle="None", + zorder=2.6, + ), ), fig, ) @@ -346,7 +337,14 @@ def surface( x=[x_best[0]], y=[x_best[1]], label="Final values", - style=optimised_opts, + style=dict( + marker="P", + markersize=14, + markerfacecolor="black", + markeredgecolor="white", + linestyle="None", + zorder=2.6, + ), ), fig, ) From 9b9d7db37e9db928fdcfa6b05e794dc80a08dd01 Mon Sep 17 00:00:00 2001 From: u2370093 Date: Wed, 3 Jun 2026 15:14:39 +0100 Subject: [PATCH 17/20] restore notebook outputs --- .../sensitivity_analysis_hessian.ipynb | 18693 +++++++++++++++- 1 file changed, 18680 insertions(+), 13 deletions(-) diff --git a/examples/notebooks/battery_parameterisation/sensitivity_analysis_hessian.ipynb b/examples/notebooks/battery_parameterisation/sensitivity_analysis_hessian.ipynb index 87835326d..c56de57c8 100644 --- a/examples/notebooks/battery_parameterisation/sensitivity_analysis_hessian.ipynb +++ b/examples/notebooks/battery_parameterisation/sensitivity_analysis_hessian.ipynb @@ -29,7 +29,7 @@ "\n", "import pybop\n", "\n", - "pybop.plot.use_backend(\"matplotlib\")\n", + "pybop.plot.use_backend('matplotlib')\n", "np.random.seed(8) # users can remove this line" ] }, @@ -136,9 +136,9 @@ "Result:\n", " Best result from 1 run(s).\n", " Initial parameters: [4.82972820e-12 2.43100795e-14]\n", - " Optimised parameters: [3.30183251e-14 3.99643400e-15]\n", - " Best cost: 1.2946725706002727e-06\n", - " Optimisation time: 1.0264408588409424 seconds\n", + " Optimised parameters: [3.29962497e-14 4.00018768e-15]\n", + " Best cost: 2.0583312184097535e-08\n", + " Optimisation time: 4.387915372848511 seconds\n", " Number of iterations: 35\n", " Number of evaluations: 508\n", " Reason for stopping: Maximum number of iterations has been exceeded.\n" @@ -196,10 +196,2281 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] From 7a7ef9c11ad1053c6693ecede3ee336ee047059a Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Wed, 3 Jun 2026 14:15:16 +0000 Subject: [PATCH 18/20] style: pre-commit fixes --- .../sensitivity_analysis_hessian.ipynb | 4202 ++++++++--------- 1 file changed, 2101 insertions(+), 2101 deletions(-) diff --git a/examples/notebooks/battery_parameterisation/sensitivity_analysis_hessian.ipynb b/examples/notebooks/battery_parameterisation/sensitivity_analysis_hessian.ipynb index c56de57c8..511e08596 100644 --- a/examples/notebooks/battery_parameterisation/sensitivity_analysis_hessian.ipynb +++ b/examples/notebooks/battery_parameterisation/sensitivity_analysis_hessian.ipynb @@ -29,7 +29,7 @@ "\n", "import pybop\n", "\n", - "pybop.plot.use_backend('matplotlib')\n", + "pybop.plot.use_backend(\"matplotlib\")\n", "np.random.seed(8) # users can remove this line" ] }, @@ -921,12 +921,12 @@ 3.9715767655798846, 3.9602819415890913, 3.9520863469990504, - 3.9460678756074725, + 3.946067875607473, 3.941628506505509, 3.9383485119515527, - 3.9359178543744724, - 3.9341003297080803, - 3.9327123448580323, + 3.935917854374472, + 3.93410032970808, + 3.932712344858032, 3.931609848112068, 3.9306796641080486, 3.9298329831198062, @@ -935,37 +935,37 @@ 3.927180286638963, 3.92612347339934, 3.92493948188842, - 3.9236155021567365, - 3.9221446647402187, + 3.923615502156737, + 3.9221446647402183, 3.9205248558904016, 3.9187576203002754, - 3.9168473308889746, + 3.916847330888974, 3.9148007251096937, 3.9126264463976232, 3.91033366532152, 3.907932365882042, 3.905432976921607, - 3.9028460940129293, - 3.9001822236086436, + 3.9028460940129297, + 3.900182223608643, 3.8974516117228104, 3.8946641478364215, - 3.8918293274461426, + 3.891829327446142, 3.888956034053371, 3.8860526455531774, - 3.8831268947390685, - 3.8801859668617205, + 3.883126894739069, + 3.880185966861721, 3.877236398377332, 3.874284172949308, 3.871334689117515, 3.868392806721639, 3.865462862415277, - 3.8625486964981635, + 3.862548696498163, 3.8596536837274753, 3.856780776647933, 3.853932516791931, - 3.8511110737379886, - 3.8483182210854645, - 3.8455554616333396, + 3.851111073737989, + 3.848318221085465, + 3.845555461633339, 3.8428240125597344, 3.8401248253055296, 3.8374586127454657, @@ -979,7 +979,7 @@ 3.8173385738908543, 3.8149712643981433, 3.812634915967867, - 3.8103287339734333, + 3.8103287339734337, 3.808051865920859, 3.805803390603401, 3.8035823195733864, @@ -987,50 +987,50 @@ 3.7992182898500735, 3.79707314866406, 3.794951070263993, - 3.7928508777426186, - 3.7907713513683907, - 3.7887112169249173, + 3.792850877742618, + 3.7907713513683903, + 3.7887112169249177, 3.786669191255614, 3.784643945031963, 3.782634098361605, - 3.7806382312463724, - 3.7786548799283404, - 3.7766825257488907, - 3.7747195962568645, + 3.780638231246373, + 3.778654879928341, + 3.7766825257488903, + 3.774719596256865, 3.772764447562899, - 3.7708153639372717, + 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3.6723837053907165e-15, 3.5235789125011637e-15, 3.4408135168515876e-15, 3.7938090774615065e-15, - 3.51275203831113e-15, + 3.5127520383111302e-15, 3.704541594025706e-15, 3.665394276473023e-15, - 3.782856968235664e-15, + 3.7828569682356635e-15, 3.7525859361236776e-15, - 3.682400309635497e-15, + 3.6824003096354965e-15, 3.6767733565469446e-15, 3.840726192852309e-15, 3.759094405334707e-15, @@ -17854,11 +17854,11 @@ 3.867450458911904e-15, 4.0749042832808875e-15, 4.02658731852303e-15, - 4.063257263904799e-15, + 4.0632572639047986e-15, 3.912707186174819e-15, 3.989824528265928e-15, 3.721706450288044e-15, - 4.0128371715866885e-15, + 4.012837171586689e-15, 3.927797803257719e-15, 3.9288514004773055e-15, 3.855993038503655e-15, @@ -17870,12 +17870,12 @@ 4.0681815465555835e-15, 4.134313966704141e-15, 4.089069770715333e-15, - 3.69224490387292e-15, + 3.692244903872921e-15, 4.0581497050773225e-15, 4.132749460257924e-15, 3.976353715970416e-15, 4.152603372059435e-15, - 3.69224490387292e-15, + 3.692244903872921e-15, 3.8807293050747576e-15, 3.868494559961945e-15, 3.992242760497983e-15, @@ -17885,11 +17885,11 @@ 3.940301800186805e-15, 3.996361500139897e-15, 4.046087221538954e-15, - 4.049699473979599e-15, + 4.0496994739795986e-15, 4.065027120509656e-15, 4.056884886839799e-15, - 4.049699473979599e-15, - 4.0239966133285605e-15, + 4.0496994739795986e-15, + 4.023996613328561e-15, 4.003402202239855e-15, 3.920923279669507e-15, 3.8842856732098985e-15, @@ -17901,8 +17901,8 @@ 4.109479941619976e-15, 4.0581497050773225e-15, 4.165728334588918e-15, - 3.986262023710604e-15, - 4.064432409220699e-15, + 3.9862620237106035e-15, + 4.0644324092206985e-15, 4.013499176619174e-15, 4.003402202239855e-15, 3.920923279669507e-15, @@ -17912,28 +17912,28 @@ 3.986142808460733e-15, 3.966009546261489e-15, 4.000167520218089e-15, - 3.948579509008594e-15, + 3.9485795090085936e-15, 4.0054809415648945e-15, 3.908409233043694e-15, 3.951110200014692e-15, 3.919539305510643e-15, 3.998817593087818e-15, 4.037367142835575e-15, - 4.04008573988597e-15, + 4.040085739885971e-15, 4.0054809415648945e-15, 4.026552977625202e-15, - 4.031465236387412e-15, + 4.0314652363874116e-15, 4.0097957245216235e-15, 4.015554059748669e-15, 4.109482949801833e-15, 4.019128040513666e-15, 4.085453198356692e-15, - 4.0934799201444085e-15, + 4.093479920144409e-15, 4.039465580526599e-15, - 4.03515079756987e-15, + 4.0351507975698706e-15, 3.998817593087818e-15, 4.059180549015011e-15, - 4.04008573988597e-15, + 4.040085739885971e-15, 3.994696608615198e-15, 3.986627414404681e-15, 3.982545783299614e-15, @@ -17951,8 +17951,8 @@ 4.016146263333077e-15, 4.011534979442173e-15, 3.981844286309646e-15, - 3.9958534818945005e-15, - 4.0092666631740445e-15, + 3.995853481894501e-15, + 4.009266663174045e-15, 4.0166661526853714e-15, 4.020689747966659e-15, 3.9953188855898295e-15, @@ -17963,16 +17963,16 @@ 3.991600740552098e-15, 4.026853390440812e-15, 3.979593585344583e-15, - 4.019636939023545e-15, + 4.0196369390235446e-15, 4.0226136425810656e-15, 4.0006719931182875e-15, 3.98093727815837e-15, 3.984316541632969e-15, 4.020205910720811e-15, 4.009380791170467e-15, - 4.0444173871709516e-15, + 4.044417387170952e-15, 4.01215928335304e-15, - 4.0016267176623205e-15, + 4.001626717662321e-15, 3.972651496747918e-15, 3.991600740552098e-15, 3.997408902188223e-15, @@ -17983,19 +17983,19 @@ 4.002034410054299e-15, 4.0167461044773035e-15, 4.033131451133207e-15, - 4.014032602966899e-15, + 4.0140326029668986e-15, 3.984096108355372e-15, 3.997520606466085e-15, - 4.0016267176623205e-15, - 3.9888502810385725e-15, + 4.001626717662321e-15, + 3.988850281038573e-15, 3.991600740552098e-15, - 3.968792778498697e-15, + 3.9687927784986966e-15, 4.012722038326999e-15, 3.9870803005685755e-15, 4.018030051421879e-15, 4.003873501942588e-15, - 4.012433619762837e-15, - 3.996565627319624e-15, + 4.0124336197628366e-15, + 3.9965656273196236e-15, 3.99908252441876e-15, 3.999672857104705e-15, 3.998363546801215e-15, @@ -18026,7 +18026,7 @@ 4.007419864607182e-15, 4.003085936830526e-15, 3.999159934125326e-15, - 4.001140658514248e-15, + 4.0011406585142476e-15, 3.992011484530922e-15, 4.002424172004615e-15, 3.991637242862668e-15, @@ -18053,7 +18053,7 @@ "name": "Initial values", "type": "scatter", "x": [ - 4.82972820431812e-12 + 4.8297282043181195e-12 ], "y": [ 2.4310079463581155e-14 @@ -18074,7 +18074,7 @@ "name": "Final values", "type": "scatter", "x": [ - 3.299624967152268e-14 + 3.2996249671522684e-14 ], "y": [ 4.000187680878734e-15 From 798c60930b2950d75ffda9db5ddda7d9bf6d99fd Mon Sep 17 00:00:00 2001 From: u2370093 Date: Wed, 3 Jun 2026 15:31:37 +0100 Subject: [PATCH 19/20] clean up more notebooks --- .../ecm_monte_carlo_sampling.ipynb | 4 +- .../ecm_scipy_constraints.ipynb | 104 ++++++++- .../electrode_balancing.ipynb | 88 +++++++- .../lgm50_pulse_validation.ipynb | 209 +++++++++++++++++- .../pouch_cell_identification.ipynb | 28 +-- .../sensitivity_analysis_salib.ipynb | 171 +++++++++++--- 6 files changed, 536 insertions(+), 68 deletions(-) diff --git a/examples/notebooks/battery_parameterisation/ecm_monte_carlo_sampling.ipynb b/examples/notebooks/battery_parameterisation/ecm_monte_carlo_sampling.ipynb index 0042427e4..9d992aa2d 100644 --- a/examples/notebooks/battery_parameterisation/ecm_monte_carlo_sampling.ipynb +++ b/examples/notebooks/battery_parameterisation/ecm_monte_carlo_sampling.ipynb @@ -1104,7 +1104,7 @@ ], "metadata": { "kernelspec": { - "display_name": "env-py-3-13", + "display_name": ".venv", "language": "python", "name": "python3" }, @@ -1118,7 +1118,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.13.11" + "version": "3.12.3" } }, "nbformat": 4, diff --git a/examples/notebooks/battery_parameterisation/ecm_scipy_constraints.ipynb b/examples/notebooks/battery_parameterisation/ecm_scipy_constraints.ipynb index 8dace3f45..0c42cf490 100644 --- a/examples/notebooks/battery_parameterisation/ecm_scipy_constraints.ipynb +++ b/examples/notebooks/battery_parameterisation/ecm_scipy_constraints.ipynb @@ -246,9 +246,13 @@ "outputs": [ { "data": { - "image/png": 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ixenu36pVK5U2TX3p3LmzU0ruvffeU6k4iTDbtWuHgwcPZtMnIiIib8c1SnMmLZPWnjU0cJL1YMaMGYMtW7Zg8+bNaNOmDbp164bdu3e73H/OnDmIiYmxX3bt2qXSbQ8//LB9n08++QTjx4/Ht99+iw0bNiBfvnx48MEHcfPmzWz8ZERERJQTGRo4denSBZ06dULFihVRqVIlfPzxxwgMDMT69etd7l+oUCFVEW+7/P333yr1ZgucJNv03//+F++8844KwCSb9dNPP6lFe+fNm5fucSQkJKh1ahwvREREucnatWtRs2ZNNXW/e/fuRh+OxzJ5UlHejBkzEBcXp4bs3PHDDz/g0UcfVVklcfToUTUjQobnbIKCgtCwYUOsW7cu3dcZPXq02s92kYX+iIiIvMVTTz1lL1+RwCcsLAxvvvnmHY22vPrqq7jvvvvUb+mUKVOy9Hi9meGBU1RUlMoy+fv7Y8CAAZg7dy6qVat22+dt3LhRDdU9++yz9m0SNInixYs77Sv3bY+5EhERgStXrtgvJ06cuKfPRERElN06dOigyliOHDmCzz//HBMnTsSIESPcfv7hw4dVyYyU0UjPo7uRmJiInM7wwKly5crYvn27qkcaOHAg+vXrhz179riVbZKUYoMGDe75GCRosxWo2y5ERET3IubKDUQevqCus4P8lkkZi4yayFCbjL5ISYuwWq1qdEUyUTJxqnbt2pg1a5Z6LDo6WmWqLl68iKefflrdtmWcJEHRsWNHleCQJMSTTz6JCxcuOE3aevHFF/HKK6+gSJEiqqbY3ecNGTJEZcVsZTjvv/++0+eR1hAvvPCCen6ePHlQo0YNLFiwwP74mjVr0Lx5c/V55DPL68moVY4PnPz8/BAeHo569eqpL1W+zC+++CLD58iJkWG9Z555xmm7nHhx9uxZp+1y3/YYERFRVpu56TiajlmOxydtUNdyPztJ4BIZGal+Y4X8vkrNr0yckglYQ4cOxRNPPIFVq1apoEMyVZI0kDphud27d28VuEgGqk6dOmoC15IlS9Tv6SOPPOL0XlOnTlXvIzVS8vp38jwptZHEiUzs+vDDD50CPQm85DV/+eUXlVCRyWS2/kuSHZMMW69evbBz507MnDlTBVISxGU53cO0bt1a79evX4b7TJ48Wff399cvXLjgtN1qteolSpTQ//Of/9i3XblyRe07ffp0t49BniOnRq6JiCh3uHHjhr5nzx51fS9Ox8brYcMX6OWG/XspP3yh2p5V5HfTbDbr+fLlU7958htmMpn0WbNm6Tdv3tQDAgL0yMhIp+c888wz+mOPPWa/HxQUpH5fbT766CO9ffv2Ts85ceKEeu39+/er+y1bttTr1KnjtI+7z2vWrJnTPvXr19eHDRumbi9dulQdv23/1OTYn3/+eadt//zzj3pOet9fRt/vnfzuG9o5XGqLJKIsW7Ysrl27hmnTpmHlypVYunSperxv374oVaqUipRTD9NJGjJ1Z1dJL0q6cOTIkWqmnqQk3333XZQsWdKjZghI2vbohTiEFcmHkKCUbqZERJQzyL/vVvkZdmDRdURfiM/Sf/Nbt26t+iPKqIzUOPn4+KiMjGSYZMmRBx54IE09kmSF0rNjxw6sWLFCDbeldvjwYTUbXsiI0d08T2a+O5L+i7ZlUaSER2qtbPu6OjbJNP3666/2bTKzXjJVUtxetWpVZBVDAyc5QRIcSVpQZrPJSZSgyfblHj9+HCaT82ji/v37VTrur7/+cvmaMl4qfzTPP/+8Shc2a9ZMpQllfNQTSLo2Yk6U+j+VSQNG96yJ3vXLGn1YRESUSeQ/iuXfd8fgyaxpCC2StR3JZdhLSl/Ejz/+qEpfJNEgtUFi4cKFKhmRui4qPdevX1dtg8aOHZvmsRCH9d5sM9vv9Hky+y918kMCH8clUjI6Nql/krqm1CQZk5UMDZzkC82IZJ9cFZNLVJkeOfEyTioXTyOZJlvQJOT6rTm70KJSUWaeiIhyCMkqyX8Uy7/vkmmSoGlUzxrZ+u+8JB3eeust1WLgwIEDKkCSZETLli3dfo26deti9uzZCA0NVdmrrH6eI0mknDx5Uh27q6yTvIfUPdkCxexkeHF4bpJR+paIiHIOGUlYM7w1pj/XSF0bMbIgzaGlmFraErz++uuqIFwKsmW4bOvWrfjyyy/V/fQMHjwYly5dwmOPPYZNmzap5y1duhT9+/dXvRcz+3mOJMBr0aKFGmqUgnEZfpMl2WQESQwbNkwVv0sxuAzrydJqf/zxR7YUhxuaccptjErfEhFR9pMMk5F1rJLtkUBCZqxJ4FG0aFFVMyx9nqRPk2RtJCuVHqkPllltEqS0b99erbJRrlw5NZstdRlNZjwvNclaScAnAZiU4Eh2SWbW2TJSMiPw7bffVi0JZCSqQoUKajZgVtOkQjzL38XLyJIrUnMlzTAzs6eT1DcNnx0F2wmX9QbHsMaJiMgjSJdtCTBkYpGn1MVS9ny/d/K7z4xTNtc3fezzPeqb9iNGL4QXk15W9U1ERETkHVjjlM31TSW1i6hoOoUW5ijUM+1nfRMREZEXYeCUzfVNY5Ifs2/z1aysbyIiIvIiDJyyeXrqQZTDVmvK9Mkn6xVhGwIiIiIvwhqnbCTTUaWmKd8UP+Ay0DzUuWkYEREReTYGTtlMTU3Nc+u037yS3W9PRERE94BDdUbwvZVp0lNayxMREZF3YOBkBO3WaWcLLSIiIq/CwMkQWsoVM05ERERehYGTEaRluMKm7URE5PlWrlwJTdMQGxvr9nNkkd///ve/yGkYOBkZODHjREREmeCpp55Sgc2AAQNcLrorj8k+dO8YOBnClnEiIiLKHGXKlMGMGTNw48YNp/XZpk2bhrJly/I0ZxIGTkZgcTgREWWyunXrquBpzpw59m1yW4KmOnXq2LclJCRgyJAhKFasmFrstlmzZti0aZPTay1atAiVKlVC3rx50bp1a0RHR6d5vzVr1qB58+ZqH3lfec24uLgc/70ycDICh+qIiLyDzH5OjDPmchczr59++mlMnjzZfv/HH39E//79nfZ58803MXv2bEydOhVbt25FeHg4HnzwQVy6dEk9fuLECfTs2RNdunTB9u3b8eyzz2L48OFOr3H48GF06NABvXr1ws6dOzFz5kwVSL344ovI6dgA08iMExERebakeGBUSWPe+63TgN+drTDxxBNPICIiAseOHVP3165dq4bvpLhbSEZowoQJmDJlCjp27Ki2TZo0CX///Td++OEHvPHGG+rxChUq4NNPP1WPV65cGVFRURg7dqz9fUaPHo0+ffrglVdeUfcrVqyI8ePHo2XLlur5ksnKqRg4GYLF4URElPmKFi2Kzp07q8BI13V1u0iRIk6ZoqSkJDRt2tS+zdfXFw0aNMDevXvVfblu2LCh0+s2btzY6f6OHTtUpunXX3+1b5P3s1qtOHr0KKpWrZpjv14GTkZgOwIiIu/gG5CS+THqve+CDNfZhsy+/vprZIXr16/jhRdeUHVNqeX0QnQGToYWhxvy7kREdCf/oXuHw2VGk9qjxMRE1YJAapccyRCcn5+fGsIrV66c2iYZKCkOtw27SbZo/vz5Ts9bv359mkL0PXv2qPqo3IbFNoa2I+BadURElLnMZrMabpPARm47ypcvHwYOHKhqmZYsWaL2ee655xAfH49nnnlG7SO9oA4ePKj22b9/v2pnIEN/joYNG4bIyEiV2ZICctn/jz/+yBXF4QycjMB2BERElIUKFCigLq6MGTNGzYZ78sknVebo0KFDWLp0KYKDg+1DbTLrbt68eahduza+/fZbjBo1yuk1atWqhVWrVuHAgQOqJYG0O3jvvfdQsqRBhfTZSNOlmoucXL16FUFBQbhy5Uq6f3j3ZEYfYN8CoNlQoN37PPtERB5AmkVKYXNYWFiOnhWWW93M4Pu9k999ZpwMxZiViIjImzBwMrSPEwMnIiIib8LAydDO4QyciIiIvAkDJ0MwcCIiIvJGDJyMwKE6IiKPxTlTOZOeSaM8DJyMwKE6IiKPI0uPCOlpRDlP/K3v1fY93y12DjcCM05ERB5HmkUWLFgQ586dU/cDAgJU923y/kxTfHy8+l7l+03dFPROMXAyBGuciIg8UYkSJdS1LXiinKNgwYL27/deMHAyAhf5JSLySJJhCgkJQbFixdQabpQz+Pr63nOmyYaBkxG45AoRkUeTH9nM+qGlnIXF4YbgUB0REZE3YuBkBBaHExEReSUGTkawTdJg53AiIiKvwsDJ2MjJmLcnIiKiu8LAyQgsDiciIvJKDJyMwHYEREREXomBkyE4q46IiMgbMXAydKjOasjbExER0d1h4GQErn1ERETklRg4GYF9nIiIiLwSAydDa5w4VEdERORNGDgZOVTHNk5ERERehYGTEThUR0RE5JUYOBmCQ3VERETeiIGTETirjoiIyCsxcDK0xonF4URERN6EgZMhuMgvERGRN2LgZGjncEPenYiIiO4SAycjcKiOiIjIKzFwMgSH6oiIiLwRAydDh+o4VkdERORNGDgZ2o6AgRMREZE3YeBkBGaciIiIvBIDJ0Mw40REROSNDA2cJkyYgFq1aqFAgQLq0rhxYyxevDjD58TGxmLw4MEICQmBv78/KlWqhEWLFtkft1gsePfddxEWFoa8efOiQoUK+Oijj6B7Uj2RfVadBx0TERER3ZYPDFS6dGmMGTMGFStWVIHN1KlT0a1bN2zbtg3Vq1dPs39iYiIeeOABFCtWDLNmzUKpUqVw7NgxFCxY0L7P2LFjVUAmryWvsXnzZvTv3x9BQUEYMmQIPGuRXyIiIvImhgZOXbp0cbr/8ccfq6Bn/fr1LgOnH3/8EZcuXUJkZCR8fX3VttDQUKd95DEJvjp37mx/fPr06di4cWO6x5GQkKAuNlevXkXW4pIrRERE3shjUh8yxDZjxgzExcWpITtX5s+frx6TobrixYujRo0aGDVqlHquTZMmTbBs2TIcOHBA3d+xYwfWrFmDjh07pvveo0ePVhkp26VMmTLIUpxVR0RE5JUMzTiJqKgoFQzdvHkTgYGBmDt3LqpVq+Zy3yNHjmD58uXo06ePqms6dOgQBg0ahKSkJIwYMULtM3z4cJUxqlKlCsxmswqqJJMlz0lPREQEXn31Vft9eX6WBk+scSIiIvJKhgdOlStXxvbt23HlyhVVt9SvXz+sWrXKZfBktVpVfdN3332ngqJ69erh1KlTGDdunD1w+u233/Drr79i2rRparhPXvuVV15ByZIl1Wu7IkXmcsk+LA4nIiLyRoYHTn5+fggPD1e3JRDatGkTvvjiC0ycODHNvjKTTmqbJGiyqVq1Ks6cOaMKx+W13njjDZV1evTRR9XjNWvWVAXkMhyXXuBkXHE4Z9URERF5E4+pcXLMKjkWajtq2rSpGp6TfWyklkkCKgmaRHx8PEwm548lgZbjcwzHoToiIiKvZGjgJLVFq1evRnR0tKp1kvsrV6601yP17dtXbbMZOHCgmlX38ssvq4Bp4cKFqjhcisUdZ+pJTZM8Jq8rNVOfffYZevToAc/BBphERETeyNChunPnzqngKCYmRs1mk2aYS5cuVb2axPHjx52yR1KwLY8PHTpU7St9nCSIGjZsmH2fL7/8UjXAlKJxeX2pbXrhhRfw3nvvwWNwyRUiIiKvpOke1VLbM8isOgnkpGBdOppnunVfA0vfAsLbAk/MyfzXJyIioiz53fe4GqdcgRknIiIir8TAyRBsR0BEROSNGDgZgZ3DiYiIvBIDJyOwjxMREZFXYuBkJNblExEReRUGTkZgcTgREZFXYuBkaI2TB3UzJyIiotti4GTorDpj3p2IiIjuDgMnI7A4nIiIyCsxcDJ0kV8O1REREXkTBk6GsNU4ERERkTdh4GTorDpmnIiIiLwJAydDZ9URERGRN2HgZAT2cSIiIvJKDJwMweJwIiIib8TAyQgcqiMiIvJKDJyMwD5OREREXomBk5E4q46IiMirMHAytDjckHcnIiKiu8TAyQhc5JeIiMgrMXAydFYdU05ERETehIGTocXhRERE5E34C24ELvJLRETklRg4GcK25AqH6oiIiLwJAycjcMkVIiIir8TAydChOmaciIiIvAkDJyOwczgREZFXYuBkCGaciIiIvBEDJ0MbYHKojoiIyJswcDICa5yIiIi8EgMnQzDjRERE5I0YOBmB7QiIiIi8EgMnI3CojoiIyCsxcDIEh+qIiIi8EQMno/s4sQkmERGR12DgZAQO1REREXklBk6GYANMIiIib8TAyQhccoWIiMgrMXAyfKiO3cOJiIi8BQMnIzDjRERE5JUYOBmCNU5ERETeiIGT0Yv8sh0BERGR12DgZGiNk9WQtyciIqK7w8DJELbAyf4/RERE5AUYOBmBxeFEREReiYGT0UN1rHEiIiLyGgycDGErDiciIiJvwsDJyKE6NsAkIiLyKgycDG9HYMgREBER0V1g4GQILrlCRETkjRg4GTpUxz5ORERE3oSBk6FDdYJjdURERN6CgZPRfZzYjoCIiMhrMHAyBJdcISIi8kYMnAxtgGn/HyIiIvICDJwMrXFicTgREZE3YeBkdDsC1jgRERF5DQZOhhaHExERkTcx9Bd8woQJqFWrFgoUKKAujRs3xuLFizN8TmxsLAYPHoyQkBD4+/ujUqVKWLRokdM+p06dwhNPPIHChQsjb968qFmzJjZv3gyPXOSXNU5ERERew8fINy9dujTGjBmDihUrQtd1TJ06Fd26dcO2bdtQvXr1NPsnJibigQceQLFixTBr1iyUKlUKx44dQ8GCBe37XL58GU2bNkXr1q1VEFa0aFEcPHgQwcHB8BxccoWIiMgbGRo4denSxen+xx9/rLJQ69evdxk4/fjjj7h06RIiIyPh6+urtoWGhjrtM3bsWJQpUwaTJ0+2bwsLC8vwOBISEtTF5urVq8iezuH2/yEiIiIv4DHFNhaLBTNmzEBcXJwasnNl/vz56jEZqitevDhq1KiBUaNGqec67nP//ffj4YcfVpmpOnXqYNKkSRm+9+jRoxEUFGS/SOCVpThUR0RE5JUMD5yioqIQGBio6pUGDBiAuXPnolq1ai73PXLkiBqik0BJ6preffddfPrppxg5cqTTPpK1kuG/pUuXYuDAgRgyZIgaBkxPREQErly5Yr+cOHECWYtDdURERN5I06W4yEBSt3T8+HEVsEhQ9P3332PVqlUugycpBL958yaOHj0Ks9mstn322WcYN24cYmJi1H0/Pz+VcZLhPBsJnDZt2oR169a5dUwyVCeZJzkmKVrPdNfOAp9WSgmghh8D8gRl/nsQERFRpv/uG55xkkAnPDwc9erVU0NmtWvXxhdffOFyX5lJJ8GTLWgSVatWxZkzZ1QAZtsnddAl+0hw5nkNMFnfRERE5E0MD5xSs1qtToXajmS23KFDh9Q+NgcOHFDBkgRgtn3279/v9DzZp1y5cvDIPk6qJQERERF5A0MDJ6ktWr16NaKjo1Wtk9xfuXIl+vTpox7v27ev2mYj9Uoyq+7ll19WwdDChQtVcbgUi9sMHTpUzcqT7RJkTZs2Dd99953TPsazZZxudQ8nIiIir2BoO4Jz586p4Ejqk2RsUZphSkG39GoSMrxmMv0b28lsN3lcgiPZV/o4SRA1bNgw+z7169dXBeYScH344YeqFcF///tfezDmWUN1zDgRERF5E8OLwz1RlheH37gMjL3Vf+r1g0Bgscx/DyIiIsp5xeG5k0PGCaxxIiIi8hYMnIwuDncodCciIiLPxsDJ8BonjpQSERF5CwZOhuBQHRERkTdi4GT4UB0zTkRERN6CgZPRQ3X4d4FiIiIi8mwMnAzvHM6MExERkbdg4GQINsAkIiLyRgycjMBZdURERF6JgZMRuMgvERGRV2LgZPhQHYvDiYiIvAUDJ8OH6gw5AiIiIroLDJwMD5yYcSIiIvIWDJwMYwuemHIiIiLyFgycjC4Q17nILxERkbdg4GT0cB2XXCEiIvIaDJwMH6pjxomIiMhbMHAyCofqiIiIvA4DJ8Nrw1kcTkRE5C0YOBmFGSciIiKvw8DJ6JQTZ9URERF5DQZOhjfB5FAdERGRt2DgZPhQHQMnIiIib8HAyfChOi65QkRE5C0YOBk9VMeMExERkddg4GT0UB0bYBIREXkNBk6G4ZIrRERE3oaBk+Gz6rjkChERkbdg4GQU1jgRERF5HQZOhmEDTCIiIm/DwMkoXHKFiIjI6zBwMgqH6oiIiLwOAyejsB0BERGR12HgZBi2IyAiIvI2DJyMwnYEREREXoeBk9FDdckJhh0CERER3RkGTkYP1VkSjTsEIiIiuiMMnAyfVWflQr9ERERegoGTUfIVTbm+fhZIvmHYYRAREZH7GDgZpWTdlOtze4GE64YdBhEREbmPgZNRStdPuT67BzEXLiDy8AXEXGHmiYiIyJP5GH0AuVbp+9WV5cIhtJ54ADf1ozBpwOieNdG7flmjj46IiIhcYMbJKAXLweJfEGY9GbVxSG2y6sBbc3Yx80REROShGDgZduZNuFQkJevU3LzTvtmi64i+EG/YYREREVH6GDgZyDe8pbpuYfo3cDJrGkKLBBh4VERERJQeBk4GkULwgwUaq9u1TEdRFLGqJWbv+qWNOiQiIiK6DQZOBpi56TiajlmOh38/h23WcLWto3kDdADTNp5Ak9HL1T5ERETkWRg4GZBpipgTpQrBxQJLI3XdxbzOvo88JPuwPQEREZFnYeCUzY5eiLMHTbbAyaprqG86gBBctG+XfVgkTkRE5KWB065du7L2SHKJsCL5bMv7KmdRCJv0yup2Z/N6+3bp6cQicSIiIi8NnGrVqoWGDRti0qRJuHbtWtYeVQ4WEpQXzzUPc9r2pyWlSLyrOdK+/q80wpR9iYiIyAsDp1WrVqF69ep47bXXEBISgn79+uGff/7J2qPLofo3C1MZJZvFlgZI0s1qdt1PzS4hcnA1dg8nIiLy5sCpefPm+PHHHxETE4Mvv/wS0dHRaNmyJSpVqoSxY8fizJkzWXukOYhkkiSjJD2bRKwWhDNFGqrb9a78jaOnzyEmNs7goyQiIqLUNF3XHUqV78yhQ4cwefJk/Pzzzypw6tChA+bPnw9vd/XqVQQFBeHKlSsoUKBAlr2PzJrbEn1ZSsHROHYBCq8Yhqt6ABokfI1EzZ/r1hEREXnY7/49zaoLDw/HW2+9hXfeeQf58+fHwoUL7+Xlcp3VB85jyIxteHH6DtRfXAonrEVRQItHZ9MGNasuYjZbEhAREXmSuw6cVq9ejaeeegolSpTAG2+8gZ49e2Lt2rWZe3S5qJ+TFSZMt7RWtx/1WX5rG/Dxgr3s50REROSNgdPp06cxatQoVdfUqlUrNVQ3fvx4tV1m2zVqlNLMke68n5P43dISybpJ9XSqqJ1U2xZExagu4+wkTkRE5EWBU8eOHVGuXDlVGN6jRw/s3bsXa9asQf/+/ZEvX76sPcoc2s/JcWadOI9g/M9aT91+3LzMvl0CrLfm7GLmiYiIyFsCJ19fX8yaNQsnT55Us+gqV05p2ngvJkyYoPpDSSGWXBo3bozFixdn+JzY2FgMHjxYtUTw9/dX2a9Fixa53HfMmDHQNA2vvPIKPH1mnVxHdKiI6ZY26n4v82rkww37/hZdZydxIiIig/m4u2NWzJYrXbq0Cm4qVqwImdw3depUdOvWDdu2bVM9o1JLTEzEAw88gGLFiqkgrlSpUjh27BgKFiyYZt9NmzZh4sSJKjDzVL3rl0WLSkVVQCRdwiWYKmhth8Mrf0IFUwz+z7waUy0P2gMrdhInIiLyksApK3Tp0sXp/scff6yyUOvXr3cZOEkfqUuXLiEyMlJlwERoaGia/a5fv44+ffqouquRI0fe9jgSEhLUxXFaYnaRYMmxQ3jvJpVxJf7/gM1f4inzUvxkeQAmzYRRPWuwkzgREZHBPGaRX4vFghkzZiAuLk4N2aWX9ZLHZKiuePHiqFGjhipWl+c6ksc7d+6Mdu3aufXeo0ePVv0bbJcyZcrAMP4FEFT7IcAvH8JMZ7Co2WGsea4cO4kTERF5AMMDp6ioKAQGBqp6pQEDBmDu3LmoVq2ay32PHDmihugkUJK6pnfffReffvqpU1ZJgq+tW7eqYMhdERERqumV7XLixAkYRmqeCoYCVTqpu1Vj5iMkTwKQyE7iREREXhM4vffee9iyZUumH4AUmW/fvh0bNmzAwIED1Rp4e/bscbmv1WpV9U3fffcd6tWrh969e+Ptt9/Gt99+qx6XgOfll1/Gr7/+ijx58rh9DBK02QrUbRdDBRTGuXLdoMvXc2oLcDkauH7W2GMiIiIi9wMnmU0nLQmkoFsCHJn9JsXa98rPz091IJdASLJEtWvXxhdffOFyX5lJJ7PozGazfVvVqlXVci9yLBLYnTt3DnXr1oWPj4+6yOLE0mtKbqce0vNUM7fGoNEsE5ZaUloTHFr9G3DzMpD8bx0WEREReXDgJIXZEqBMnz5dLa8iU/yLFCmCXr164aefflJF25lBskqOhdqOmjZtqppuyj42Bw4cUAGVBGBt27ZVQ3+SwbJd7r//flUoLrcdAy5v6Cg+ObmD2lbqzHKcuXCZWSciIiJvqnEymUxo3rw5PvnkE+zfv18NrzVs2FBN+y9ZsiRatGiB//znPzh16pTbtUWydEt0dLQKeOT+ypUrVaAj+vbtq7bZSKZLAjQZjpOASdbGk+JwKQYXEtBJwbjjRZpzFi5cWN32to7iG/Qq2GMth7xaIhJ2LwDiLwDWZKMPkYiIKNe6p+JwGSZ788031Rp1Ul8k9Un//POPykq5Q4bVJDiSOifJFknvpaVLl6peTeL48eOIiYmx7y+z3eRx2U/6Mw0ZMkQFUcOHD0fO7CiuYVJySpF4yeh5KUN1ceeNPDwiIqJcTdOl8yQ5kT5O0pZAZtgZUSgu69INnx0F+WJ8kIxV/kNRSruIpSUHIf993RFWqQZCCnKZGyIiouz+3Te8HQGlJd3EbZLhgx9uZZ0qnpyDJ+acQ9OxK7noLxERkQEYOHlonZNjGnCGpTVi9XwobzqDB0ybby36G8VFf4mIiLIZAycPrXOylzkBiEcetfSKGOjzJwAdFh1c9JeIiCibMXDyQLJ23ZheNZ22TU1+EDd1X9xnOoxGpr0wa0Bo4X/XuCMiIqKsx8DJQ/WuXxbrItrg8QZlVfbpIoLwu6WleuwF8594s0kgkHAFkYcvcMiOiIgom3BWnQfOqkvdELPpmOWqrqmsdhYr/F6FWdPROWEU9uihqhZK2heM7lmTCwETERHdBc6qy0EcG2Ie14tjgbWxuv2iz1x7AXlKsfguZp6IiIiyGIfqvKohJvBlcndYdQ0dzZtQWTtu327RdRaLExEReWLgFBsbi++//14th2Jbo27r1q1uL7VCd1YoLsNwti/qkF4ai6wN1e2XfObZ9zNrGkKLBPDUEhEReVLgtHPnTlSqVAljx45V69JJECXmzJnjtK4cZW6h+NqINnioZog96yQ6mTYgXDupisdH9aihgiwiIiLyoMDp1VdfxVNPPYWDBw8iT5489u2dOnVSC/ZS1pCg6O2Hqqogab9eFost9WHSdLx4K+vUopwfTz0REZGnBU6ywO4LL7yQZnupUqVw5syZzDouSid4eq55mLr9ZXIPdd3FtA5h2mlEnzgJcNlBIiIizwqc/P391bS91A4cOICiRf9dY42yRv9mYapYXFoR/GWpp1oTSK1TaH4rcCOl3oyIiIg8JHDq2rUrPvzwQyQlJan7mqbh+PHjGDZsGHr16pUVx0guisWlc/j4W1mnbuZIbNxzEAs2HUBMbDzPFxERkacETp9++imuX7+OYsWK4caNG2jZsiXCw8ORP39+fPzxx1lzlJSmWHzNsFZoUqcWlkitE6zw2ToFLy6+hCZjVmDmpn/bFBAREVHm8bnTJ0hH7b///htr1qxRM+wkiKpbty7atWuXiYdFt6WZMGnrdYRrD6O9aTM6mzdiQvIR7NLLI2JOFFpUKspZdkREREYHTjbNmjVTFzKuo7h0Dj+ol8Zca1P0Mq/B6z6/46mkYaqTePSFeAZORERERgdO48ePd7ldap2kPYEM27Vo0QJmszkzjo8y6CgurQkkePpvci90Na1DK/MONEjei416VRw6e0U1xGRvJyIiIgMX+Q0LC8P58+cRHx+P4OBgte3y5csICAhAYGAgzp07h/Lly2PFihUoU6YMvJEnLfKbEallGj47SgVPH/n8iCd9/oeN1sp4JPE9+WpVYDWmFxf/JSIiMmyR31GjRqF+/fqqAebFixfVRVoRNGzYEF988YWaYVeiRAkMHTr0Tl+a7qJIPDKiDb7qFY7k2o/jpu6LBqb9aGXarh6XgErqnWKu3OC5JSIiMiLjVKFCBcyePRv33Xef0/Zt27apdgRHjhxBZGSkuh0TEwNv5C0ZJztdR+SWLdg573MM8FmAfdYy6JQ4GtZbcfE7nauic60QDtsRERFld8ZJgqHk5OQ022WbrXN4yZIlce3atTt9abpbmoZ8BYthQnJXxOr5UMV0Av9nXmV/eOTCvWg6ZjnbFBAREd2jOw6cWrdurZZckQyTjdweOHAg2rRpo+5HRUWpWijKPnEIwBUE2pdiec3ndwTgpv1xmWn31pxdHLYjIiLKzsDphx9+QKFChVCvXj21/Ipc7r//frVNHhNSJC6NMin7RJ26oq5/tjyAY9ZiKK7F4jnzQqd9LLqu2hQQERFRNtU42ezbt08VhYvKlSurS07hbTVOUvwtQ3GSVRKdTOvxjd94xOv+aJnwGc4jZfajWdOwZnhr1joRERHd5e/+XTfArFKlirqQZzTDtAVNYpG1IbZaw1HXdAiv+sxCRPJzamHgUT1rMGgiIiK6B3cVOJ08eRLz589XrQcSExOdHvvss8/u5XjoLpthSmD0b/CkYWTSE5jj/z4eMa/ET5YHMebhOqhdqzjPLxERUXYGTsuWLUPXrl1Vk0sZrqtRowaio6MhI36yZh1lP+kOPrpnTVX8LXVM0vhyq14JCywN8ZB5A0b4TsW+C9VR++oJoFA4vyIiIqLsKg6PiIjA66+/rmbOyRIr0tPpxIkTaNmyJR5++OG7PQ7KhGaYUr80/blGmDe4icpAjU56HDd0PzQy7cWalUvx88bTiDl7nueaiIgouwKnvXv3om/fvuq2j48Pbty4oWbRffjhhxg7duzdHgdlUuapcYXCiEu0qGG7UyiKb5K7qsfe8p2G0SvPo/HnGzFx5SGebyIiouwInPLly2evawoJCcHhw4ftj124cOFujoGyqOZJfGd5CMetRRGiXcJgn3lq2+gl+zFx9b/fGxEREWVR4NSoUSOsWbNG3e7UqRNee+01fPzxx3j66afVY+Q5NU/y5SbADx8lP6m2S1+nUC1lGZyxi/exGSYREVFWB04ya04W9BUffPAB2rZti5kzZyI0NNTeAJM8o+Zp7uAm6vbf1npYaakNP82C93x+VttkKC/6QpzBR0lERJRLGmDmZN7WADMjMzcdx7DZUQjTYrDU700VPD2d+DqWW+vi8XrF8VL76uztREREudrVrFzkV9oQXLx4Mc322NhY9Rh5XuZp3RtNUbtSGH60dFLbRvj8BH8kYtqWs1z8l4iI6A7cceAkPZssFkua7QkJCTh16tSdvhxlg5DCBfFIvVL4Mrk7YvRCKGc6hyE+c9RjXPyXiIgoCxpgSqdwm6VLl6qUlo0EUtIYU+qcyDOFlSmDG9phjEjqh+/8Psfz5oWYb2mC/XpZ++K/UlROREREmRA4de/eXV1rmoZ+/fo5Pebr66uCpk8//dTdl6NsFhKcD8PalsXo/wFLLffjQfNmjPL9Af+XOAKaZkJo4Tz8ToiIiDIrcLJareo6LCwMmzZtQpEiRdx9KnmImqEhAI6rrFMT027UMx1EH/MyBNTuhhDtsnTpMvoQiYiIclaN09GjRxk0eamwoimNMc+gMMYlP6K2vekzA/WDriLmzGkgke0JiIiI7jnjNH78eLhryJAhbu9LRi0GHIVfLA+gh3kt6pgOwbpmPJqsGIrhzZPwQqfGgHbH8TQREVGu4FYfJxmec+vFNA1HjhyBt8tJfZxciYmNw5cLN2Hrrr2Y7/e26u30cuIg/GFthsGNi+GNbvWNPkQiIiKP/N33cXd4jnIQzYTpUXHQURbjk3vidd/f8YHvVKxLqI6v10m1fxTe6FRTLcly9EKcWvuOM+6IiIjuoDjcFVuySjJN5D0kGLKlGb+1dEF782bUMh3FKN/v8WzS6/h69XEcvZSAJbvPqj5PUhclQ3zSTJOIiCg3u6tilp9++gk1a9ZE3rx51aVWrVr4+eeUNdDI80kGSYIhkQwfvJY0EAm6D9qZt6GX6R+1fdGulKBJsEkmERHRPSzyO3DgQHTq1Am//fabunTo0AEDBgzA559/fqcvRwYWidu+/IN6aXye/H/q9gjfn1ACaZfUsTXJJCIiys3ueJFfKRT/4IMP0LdvX6ftU6dOxfvvv58j6qFyenG4zYKdp/HitG3qthkWzPL7QM2yW2WphX5Jw+TPw2n/iE5V8EKLCgYdLRERkRcu8hsTE4MmTZqk2S7b5DHyHvXKBduH7Cww4/WkF3BT90VL8048Yf5fmv3HLtqHHSekUSYREVHudMeBU3h4uBqeS23mzJmoWLFiZh0XZeOQnflWcf9hvRQ+SX5U3X7H5xeEayed9pfe8d2/icTMTcf5/RARUa50x7PqZJiud+/eWL16NZo2baq2rV27Vi3y6yqgIs8mM+VaVCqK6Atx+HPDXkze+SBamnaorNOXvl+he+KHSICffX8Z2H1rzi71HLYoICKi3MbtjNOuXbvUda9evbBhwwa17Mq8efPURW5v3LgRPXr0yMpjpSwiAVDjCkXwUof71IK/rycNwAW9AKqajuNNn5lp9mehOBER5VZuF4ebTCbUr18fzz77LB599FHkz58fOVVuKQ53ZebafXhrwWG00LZhst84ta1f4jCssta27yNDe2uGt2bGiYiIcoQsKQ5ftWoVqlevjtdeew0hISF46qmn8M8/KT1/KOfo3bQK1gyoiOe7t0Fcpe5q28R8E1FMu6JumzVgVM8a6nbk4QuquzgREVFuccftCOLi4lQt05QpU1TgJMXizzzzDPr164cSJUogJ8jNGSfFagHO7QYS44C5A4HLR3EzpD623fc+Qgv6Y/XlYET8sZ9dxYmIKEfI0nYE+fLlQ//+/VUG6sCBA3j44Yfx9ddfo2zZsujateu9HDd5CpMZCC4P+PgBbd8BzH7IE7MJjS/OlfJwe9Ak2FWciIhyk7tacsVGsk1vvfUW3nnnHVXztHDhwsw7MjKWfyAQWAIoFAY0fSll26YfcfHwVnvQZMNicSIiyi3uOnCSdgRS5yTDc2+88QZ69uyp2hJQDpK/JOAbAFTuBFRsD+hWhG0dg6KITfNHFFokwLDDJCIi8sjA6fTp0xg1ahQqVaqEVq1a4dChQxg/frzaPmnSJDRq1CjrjpSynzTGlCE7Gbpr9jKSgkKRL/kyvvD9CibVDjOFJKBWHzjPb4iIiHI8twOnjh07oly5cvjyyy9Vv6a9e/dizZo1qt5J6p7uxoQJE1CrVi1ViCWXxo0bY/HixRk+JzY2FoMHD1Yz+/z9/VUQt2jRIvvjo0ePVm0TZOiwWLFi6N69O/bv339Xx0fSItUfKBgK+ObFrlpv4bqeB03Me/CKzyynwEmaYnKGHRER5XRudw739fXFrFmz8NBDD8FsNmfKm5cuXRpjxoxRS7XI5D5ZKLhbt27Ytm2ban2QWmJiIh544AEVEMmxlCpVCseOHUPBggXt+0jRugRWEjwlJyerGqz27dtjz549dx3g5Xp5g4GEIjAFJyIi6Vl86fcVhvjMw1ZrJay03mevc9oSfRmFAuOQz8+MuEQLworkY68nIiLK3e0IslqhQoUwbtw41eIgtW+//VY9tm/fPhXIueP8+fMq0JKAqkWLFi73SUhIUBfHaYllypTJve0IXJi58Rgi5u5SheEf+kxGX5+/cUUPQNfEkTiml4B2a2TPsXBcFhCWtfBkWRciIqJc2Y4gq1gsFsyYMUP1iZIhO1fmz5+vHpOMUvHixVGjRg1VcyXPTY+cBFtAlh4Z3pMTZrtI0ET/2nHiMobfCprEyOQnsMVaEUFaPCb5for8SGmCmXq2HVsVEBFRTmN44BQVFYXAwEBVrzRgwADMnTsX1apVc7nvkSNH1BCdBEpS1/Tuu+/i008/xciRI13ub7Va8corr6jFiCXISk9ERIQKsGyXEydOZNrn83YzNx1H968j1eK+NonwxYDEV3BGD0Yl0yl8m+9bOdsun89WBURElJMYHjhVrlwZ27dvVwsHDxw4UHUgl3qk9AIhGXb77rvvUK9ePfTu3Rtvv/22GsJzRTJTsjixZLIyIkGbrUDddiGoYu+IOVGq+Du18wjGgMShSNB90NSyCUPM0hwzLVnXjq0KiIgopzA8cPLz81ONNCUQkiGz2rVr44svvnC5r8ykk1l0jsXpVatWxZkzZ1ThuKMXX3wRCxYswIoVK1QROt25oxfi0gy/Odquh+Od5KfV7aG+s9HetMnpcal76l2/tFMgxvXtiIgoV8yqyy6SVXIs1HYkQ27Tpk1T+5hMKTGfLPsiAZUEYEJq3V966SU15Ldy5UqEhYVl6/HnJDIrTgq8Mwqefre0QjXtGPr7LMVnvhPQK7E49uspxeDytGkbT2D6xhPoWbcU5m47xfXtiIjIqxmacZLaIulAHh0drWqd5L4EO3369FGP9+3bV22zkaG8S5cu4eWXX1YBkyzxIsXhMiRnI7d/+eUXFWBJLyfJRsnlxo2UAmZyX0hQXjUrTobbhFxHdKyC55uXV9kkm4+T+yDSUg2B2k384PefNJ3FJYCavTUlaBIsGiciIm9laDsCaTmwbNkyxMTEqNls0gxz2LBhqleTkO7koaGhmDJliv0569atw9ChQ1VdlPRxkteQ59iG77RbP/KpTZ48WS0Rk9nTEnMDGWKLvhCvapUkmLJtm7wmGpP+OaICoyBcx1y/91DedAbbrRXwaOI7uAn/DF93+nON0LhC4Wz6FERERPf+u+9xfZw8AQMn90jw1HTMcnsmKVSLwVy/EQjWrmOhpQFeTBoCPZ2kpmSv1gxvzQaZRERkOK/s40TeXzwerYfghcShSNTN6GzeiNd9fks3aBrVswaDJiIi8joMnOiei8cdbdarYk/VV9TtwT7z8bB5ZZo/uDmDGrObOBEReSUGTpSJxePA6LZBuK9FZ1yrnlLgP9rne7Qybbc/R9pkxida2ZqAiIi8EmucXGCN0z0Uj/snApcOIfL4TZxZOAo9zWsQr/vj8cS3Vd8n0al6MSzZc46tCYiIyCOwxomyPfMks+PUjLs8QUBQGYQF+2J48vNYZamFAC0BP/p9gvLaabX/ot0pQZNgawIiIvImHKqjzJevGEKKl8BHbQphcNIr2G4tj0LadfzkNwbFcDnN7lzPjoiIvAUDJ8oaQWXQu24Ifn2kJJ5JfBNHrCVQWruAqX5jkR/xLtez45IsRETk6Rg4UdaQgvHg8qhdpiDebFsaTyUNxzm9IKqajmOS36fwx79rCw5oGYbVB86rnlCPT9qgrmduOs5vhoiIPA6Lw11gcXgmsiQBF/Zhwd4r+GbpDszw+wgFtBv421IPA5NeRnI6yyWyQSYREWUXFoeT5zD7AoUroV7JvNirh+L5pNdwU/fFA+Yt+Nz3G5hUg4K0WPdERESeiEN1lPV8/LH6YpC6ud5aDQOSXlHdxbuY12OMzyRoLoInW90TERGRJ2HgRFlOir4j5u9XiwGLldY6GJL0Eiy6hkd8VmGEz0+A/VFA2mkOaFleLekizyUiIvIUDJwo29e0E0usDfB60gB1+ymfvzDMZ4Y9eJL//XrlYRaKExGRx2HgRIasaSfLs5Sq2wFvJz2t7g/0+dMpeLKRgCtidhQzT0RE5BEYOJEBa9ppGNWpHF5vkh8v9n0Mu8JfsAdP7/j8kjZ4AjB5TTS/KSIiMhzbEbjAdgTZsKadLM9y9RRw/Qxm7o7HrpWz8ZHvZLXflOT2eD+5361qpxSSsVo7vE3K84iIiAz63XfdRIcoC0jQ4xT45C+JmCs3EbH8DKz6A0iED0b7fK9qnnxhwTvJ/aHfSorKkJ0EXQyciIjISAycyDiahqMJQfbC8ZmW1kjWzRjnOxF9fJbBF8kYnvwcrLeCpxkbjyLAz4S4RIuqm2IQRURE2Y2BExkqrGhK4bgteJptbYHkJBM+852gWhX4a0l4LWmA6jD+x46z6iLkOVI31bt+2TTDgTKLj4EVERFlBRaHk4cUjv+77Q9rM9XnKUk3o5s5EpN8P0UeJDg9TwKtt+bscpptJ+vbcb07IiLKSgycyHCSNVozvA2mP9cQEa1KqCBqobURnkt6FTd0P7Q278DPfqNRAHHpLsuimmzOibJnrmxtDHacuGzERyIiohyKgRN5TOapcYUieOHBulgzqAq+6hCkOow/mTgcV/UA1DcdwEy/j1AUsfbnyHCdbVkWV002pY1B928iVSaKiIgoMzBwIs+iaQgpXR6FChVRdzfrVfBI4ns4rwehquk4fvf7AKW1c+qxR6vlwbkzpxF5+ALy+ZnTNNkUuoshPSIiorvFwIk8j6YhrFw5eyC0Ty+LXonv47i1KEJNZzHb731U0Y5j2u6b6DZ5n1qapcc3kehRp5TLP2jHIT0iIqJ7wcCJvKJo/BSKY9V943A1XyiKa7GY6fchGmh77fvLMN28bafQt3G5NK8lncptQ3pERET3gp3DXWDncA/rNn4qBqHmCwjJb8aGIxdg/usd3G86gATdRy0U/Ke1iX1/WdVFhuccRXSqghdaVMj+gyciohz3u8+ME3l+0Xi18ggpW0nd3345D/okvoXFlvrw15Lxpd9XGGCer9a3kz/m1EGTqFWqYPYfOBER5UgMnMg75C2IGJ9yGBt5HQnww+Ckl/F9ckf10HDfGRjp8yMG1fN3WN0uBYfpiIgoMzFwIq9x9Nq/HcZlGZaRyU/ig6QnYdU1POGzDHV3jkRe3HT64x7Vs4a6LTPvOLOOiIjuFQMn8hqyjErqlgOTLR0xMOkV3NR90ca8DTMcej1JjLVx/wm3uolLUMXgioiIboeBE3ndTLvUf7RLrfXxWOI7uKjnRy3TUczzfxfVtGgVOM3eFevUTdxVTycu1UJERO5i4ERetzzL2og2eL55ead6pm16RfRM/ACHrSEopV3ELL8P8KBpU7o9nWwZJlmSJfVSLWyYSURE6WE7AhfYjsA7LNh5Gi9O2+a0rQCu4yvfL9HCHKXuf5L0CL6xdJM/dfs+nWqWwJJdZ1SQJFtdTMTD9OcaoXGFwln+GYiIyHhsR0C5Qr1ywWlqnq4iEP2T3sTk5AfV/Td9f8Pnvt/AH4n2fRZFpQRNSCdo4kw8IiJKD4fqyKtrnoZ1qJJmuxVmfJDcD28nPY1k3YQe5rWY4TfSaYHgjDzTLDQLjpaIiHICBk7k1WqWDkqzTbJIj9fIg18t7fBkUgRi9XyoYzqEP/zfQS3t8G1f87t/jmY4A4+IiHIvBk6U41oUyPp2LzUqjIimgdigV0e3xI9wyFoSJbVL+N3vQzxsXnnb12WROBERucLAiXLIYsAp0ZNcj+pZEyHla+KFJiWx9qmieOPByuiR+CH+ttSDv5aEcb7fYaTPD/BFcoavbZuBR0REZONjv0XkxS0KWlQqqoKc0CIBKphSCoUjxOckHsp/DjvPFsHz24ZisPUPvOozS3Uar2o6jkGJr+C8FmwvFk/9XxUX4xJU6wL7axIRUa7GdgQusB1BDhN/ATEnjqDplPMqQGpl2obxvl+jgBaP6z7B2FEjAouuV8S0XTecZtnZWhXIUKBktSRAIyKinIftCIgcBRRBSFg1jG5TUNU/rbTWQbfEkTjpUxaByZfRYFsETHv/gJ6qOYHtHuudiIjIhjVOlDv4BaJ3m/pY82wZTO8ZjE71yqP99fexwNIIvpoFH/lOwRe+XyMQ8W7VO3FtOyKi3Ik1TpR7mP0QUr4G4HMIj885CB158GLSS9hhLY9hPjPQzRyJ+7RDGJL0Inbo4c5P1aDqp8TE1YcxZvE+6DqH8YiIchtmnCh30Uw4mlTIYVBOwyTLQ3gk8T2c1IugnOmcWudugHk+NFjtQdOoB0sgJL8vJq46jNGLUoImwWE8IqLchYET5TphRdP2ftqqV8LP4Z9joaWhGrob7jsDP/mOQVFcxoB6AehdCYg5vENlmlJj2wIiotyDgRMht/d+kv8TSLPMmmUKYXDSELyZ9BzidX80N+/CEv/hOLh1NXacScTRSwku17YzOQzjERFRzsZ2BC6wHUHuIAXeqvdT4bxYveMAhi8+ZQ+MKmin8KXvV6hmOqbuz0huhW2hz+L3w6Y0PZ8iOlXBCy0qZP8HICKiTMF2BERuZp4aVygMaBoilvwbNInDeil0T/wQ3yY/BKuu4VGflRh0/FXUwX7V30nIdffqBVEqKI8KwoiIKOfjUB3lekcvxLnsHJ4IX4xJfhyPJb5jLxz/ze9DvO4zE32rp0xInbc7Fi9O344mo7koMBFRbsDAiXI9VwsFO97doFdFx4QxmG1pDrOmY7DPH3jk4JuooJ207yNxV8ScKMTEcm07IqKcjIET5XquFgoe06smItqH2c/NNQTgtaSBGJj4Mi7rgahhisYCv7fxlHmJvW2BZK227NqNyAOn7UN3bJRJRJSzsDjcBRaH5/JicYeFgieuPIQxS/Y71T8Vw2WM852Iluad6n6kpRreTH4eJ/ViTuvb9ahTCnO3nVIBlWx/rnkY+jcL44LBRERe/LvPwOkeTyDlfDtOXEb3ryPTrGT3pPlvvOUzDXm1RMTp/hid/Dh+tbSFnkEilwsGExF5Hs6qI8pEcYkWF/2bNPxsaY8OiWOwwVoF+bQEjPSdjF99R6G0di7d12KncSIi78YaJ6K7KB6X/+MMvj8Ax/QSeDTxHYxI6qeaZjYx78FSv2F4wvy3vfYpo07jrIEiIvIuDJyI3Cwed4ydJANVNjhAdRzXNBOmWh68o+zTzlOxmLnpOJqOWY7HJ21Q13KfiIg8G2ucXGCNE6UmmSEJbhz7Pcniv2terA5cP4cvN13HtF03VJapr/lvDPOZgQAtQdU+zQ16Eu+ea+VU+6RuaUj1ehrWDG/N4nEiomzGGieibGiSadGB6Jv5Mf9UfhU0CQmOUmefnrj6Pab5jkKoFmN/rgzipX29f4fwiIjIMxk6VDdhwgTUqlVLzVyTS+PGjbF48eIMnxMbG4vBgwcjJCQE/v7+qFSpEhYtWuS0z9dff43Q0FDkyZMHDRs2xMaNG7P4k1BurHOSDFGAnwlj/jqSZv/jenFV+/RnkWdgMfujsap9Go5B5j/gg+R030eG8IiIyHMZGjiVLl0aY8aMwZYtW7B582a0adMG3bp1w+7du13un5iYiAceeADR0dGYNWsW9u/fj0mTJqFUqVL2fWbOnIlXX30VI0aMwNatW1G7dm08+OCDOHcu/ZlORHfTJHNUzxrpzLiDPfv00sm2aBU/Fvvz3gd/LQlv+s7En37voJZ22OVzPlm8n+veERF5MI+rcSpUqBDGjRuHZ555Js1j3377rXps37598PX1dfl8yTDVr18fX331lbpvtVpRpkwZvPTSSxg+fLjL5yQkJKiL41inPId9nOh2TTJd1T65okHH+PIb0fTUDyikXYdF1zDZ0gGfJj+MG8jjtO/05xqicYUiPPlERNnEK2ucLBYLZsyYgbi4ODVk58r8+fPVYzJUV7x4cdSoUQOjRo1Sz7VlpCR71a5dO/tzTCaTur9u3bp033v06NHqhNkuEjQRuSLBUuMKhe0F3KkzUalG8+x0aHjpSEO0S/gP5lqaqjXvnvVZjL/8hqGFaYdTwXkoTgMJ1+7qC2B7AyKirJWyxLuBoqKiVDB08+ZNBAYGYu7cuahWrZrLfY8cOYLly5ejT58+qq7p0KFDGDRoEJKSktTQ3IULF1QQJUGVI7kvWar0REREqOG91BknInf0rl8WLSoVVZkoqXlK22X8X5dQAEOTBuMPS1OM9P0RZUzn8ZPfWMyxNMNHSU+gY7ViQPJNxETvxdH4PAgrWw4hhYPcOg5pZyALDUv2ix3KiYhyaOBUuXJlbN++XaXHpG6pX79+WLVqlcvgSYbdihUrhu+++w5msxn16tXDqVOn1PCdBE53S4rM5UJ0tyTzZMtCyQLBw2dHpRs8iZXW+9A+4RO85vM7+puXoKd5DVqZtmPk3ifQZHczlQyW52s4gecaFkH/VtUREhyYYabJFjQ5diiXgM52XEREdO8MH6rz8/NDeHi4CoJkyEyKub/44guX+8pMOplFJ0GTTdWqVXHmzBk1TFekSBH12NmzZ52eJ/dLlCiR5Z+FyJaBioxog4+6VU936E5IbZN/s0HolfgB9lrLqNqnz/y+xVy/EaijHVD7SBz03YYLaDJ2FSb+vQNIpyTRdbsEtjcgIspxgZOrrJJjobajpk2bquE52cfmwIEDKqCSAEwuEoAtW7bM6fXkfnp1U0RZQbI8TzYOVdknx/qnWzdVLdOYtgVQPtgH2/RwdEn8GGOTHsV1PQ/uMx3GHP/38aXveJTCebW/xESjl53ExEVrgZuxaQKo9NolSBE7ERHlkFl1UlvUsWNHlC1bFteuXcO0adMwduxYLF26VLUd6Nu3r2o1IJkoceLECVSvXl0N58ksuYMHD+Lpp5/GkCFD8Pbbb9vbEcjjEydORIMGDfDf//4Xv/32m6pxSl37lB52Dqesmokn1O3CeRHiF4+Yk9Fo8uNZ+7BeUcTiNZ/f8Ih5FUyajgTdF99bOuKb5G6IQ14VfEX2L4qQQgWAoDKAXz6nGicZnpNMk61dgmS/iIgo8373DQ2cpOWAZINiYmLUAUszzGHDhqmgSbRq1Uo1spwyZYr9OTI7bujQoaouSoIqeQ15juPwnbQikLonGcK77777MH78eNWmwF0MnCjbWJIw858oDFvyb1dxUU2Lxjs+v6hFg8V5PQj/SX4Ev1ta4tEaAXioYl6EFfQB8gThaEIBhBUvqPbbHH0JJk1D3XLBrG0iIsppgZOnYuBE2W3B5kN4cdb+VFt1PGDagrd8fkWYKaVub5+1DMYkP4aV1trQblVQpRSR257BGXVERLmijxNRblavYqk0NUoSDp0v2gjtE8epVgVX9ABUMZ3AFL9P8KvvKFTXjtiH+OTadluKxGVW38/rotmFnIgokzHj5AIzTmQExxol+S+aYU0D0bVSXjSZfF4FRUG4jkE+f+Ap81L4aynr3c2zNMF/knvjpF403deN6FgFL7SskGENlszKkwJzti4gotzoKofqsu8EEmVZIXlCPI4ej8YXkRex4XSSfZ/S2nlVQN7DvFbdT9B9MNXyIL5O7oYrcN3raXDrCniiUbk0ARKbZhIRgYHTvWLgREZzDGjSU0M7ggif6WhqTlkUW4byvkrujp8s7ZEAvzT7aw41UMM6VEHJgnnw8oztTu8hs/HWDG/NzBMR5SpXmXHKvhNIlNncXTg4hY5Wph0Y7jNd1T+Jk3oRjEt6BPOtTZAy6Hdnpj/XSK3HR0SUW1xlcTiR93LVBTy1f+vINbV8S6fE0Xgj6XnE6IVQWruAL/y+wZ9+76CZKcqhbPz2JMxi00wiovRxVh2Rh3HVBdzx/7DPtwhD5OtN8FWXf5cRssKE3y2t0DrhU3yS1BvX9LyoYYrGL36jMc33Y9ynHXLrvSXEWn0gpVs5ERGlxVl1LnCojoyWugv4mx0qo1bpgiobJIXdMpwnzS6HTN/uMp9UGFcx2Gce+pj/Z5+Bt9RyP8YlP4JDeukM39uxzokz7ogoN7jKGqfsO4FE2THDzrFNwO0Kx2UdvDebBOKTyOsooZ/HKz6z0dP8D8yaDouuYY6lOf6b3AunUDTDOqfjl+Ls7yMJsOeah6F/szD7sTCoIqKcgoFTNp5AIqMLx22LB8s2CZpGtSmI3tXzIOaaBZN3xGHS1nhU0E7idZ/f0cG8yd7C4FdLO9XC4CKCnN5DXmPOwEboMWF9muBMhhBH96ypbtuCKts2rotHRN6KgVM2nkCi7BR5+AIen7QhzfavHquDwoH+Kdmp/H7A9TOIiTmNplPOOQU/Uuv0hs9MewuD63oe/GDphEnJnXAdASoIG3R/AArm9cXH/1xxeQyOy7vYsI0BEXkzzqojykWF4xK01AsNVi0E1DCayQwUKIWjprJpMkbb9XD0SXobfRIjsNMahkDtJl72mYPV/q/gGfNC+CERX2+OTzdoSr28i43UYm2JvpyJn5SIyDNxVh2RF5HASIbFJFgScj2qZw2XDSvDihVId3beWmtNdE0ciQGJr+CwNQSFtOt41/dXrPB/FY+YV8AHKQXld2LIjG2q/oqIKCfjrDoXOFRH3lo4ntHsPMchNqlj6l4lD+btuwnoFlU8PtRnFkpql9Q+p/VCmJzcATMsbXANAW4fF4fsiMgbscYpG08gkVetfwcgOuY8Qv1iEZInSRWQb4lJxEtLrqhhuifMf2OAzwIU1VKG6qQf1AxLaxVEnUYRt96PnceJyNswcMrGE0jklXQduBkLXD0FWBIw6p+r+G5bvHrIH4noao7Ec+aFqGQ6pbYl6yYstDZSReS79PJp1r9zHPufO7gJapcJzvaPRER0txg43SMGTpSrAqgbFxFz6jia/hiTqs2BFS1NO1UAZZuFJ9ZZquE7S2esttbGm00LqH5RFofnsT0BEXkbBk7ZeAKJcgTdiplr9mL4wmiXnchfCz+N0sfm4iHTevhqFrVNisq1mr0QV64dus6JT9WeAFjzyv2Af34cvRivZgNmVItFROQtv/s+2XZUROS5NBOQJ3+azTL0NqxpILpWqo2mh0viE/1RPOWzBI+Zl6OCKQbY/RUS90/BGz5tMDW5Pc6ikHqeZKBGLojC4kMJ6TbJtC0bo0k7hXLBDKyIyCtwVp0LzDhRbuOqI7mqV3qsBGoXBWbujsfwZVftWaV8uIGHzavQ37wE5Uzn1LYk3YxF1ob4IbkjduoV0ryHykK93hwhhQuo2X7DZ0fZX09qpcb0YvdxIjIGM05EdEeOXohL0yzTCiA+sBxizBZELN/sNBQXh7yYYumAnyzt0da0Fc/4LEYj0150M0eqy2ZrJRVA/WW9HxaY7VmoLVE7ofsGYNifMU7vpd9awqVFpaLMPBGRR+NQHRHZO5I7Bk/Skym0SD6XQZWNFSb8bb0ffyfej+raUTztswRdTJG433QA9/sdwEm9CKYkP4iZltaqH5S0PdDhuiu5vIe0TWAtFBF5MnYOJ6IMO5K7WubFld16GF5LGoimCePxRXIPXNTzo7R2Ae/4/op1/i9ihM9UlNHOpvt8eYsAP5MaNpQ1+eSaiMjTsMbJBdY4UW6VXkfy1B3I00lAOZF+UN3Na/G0eTEqm06qbVZdw/+sdfGjpSPWW6s6LBmcwrG7OdsaEFF2YTuCbDyBRLkxqDp39Sa6fxOp2kDdno5mpl0qgGpj3m7futtaTtVBLbA2RiJ8XT5TAql5bKhJRFmMgVM2nkCi3MoxC+WuCtopNROvl/kf5NUS1bbzehCmWdri1+S2OIe0HcfVjLtuldGiasl0e0JJUCe1WOwXRUR3g4HTPWLgROSeHScuZ5h5kuG2QfXy4evNcU7De0G4jsfNy9HX5y+E3FpYWNoZLLHWV8XkW/RKLofxbEN4wzpUQc3SQSpQWn3gvJqRl16/KCKi22HgdI8YOBG5R4q4H5+0weVjUmc+pkcNtChjRtPxO1zOzPPXkvFO6e2ocnYh6pv2Ow3jTbE8iD8tjXET/um+v2NNlI0Utq8Z3pqz84jIbezjRESGtTGQYOaj7tXRtmpxFbxIcOUqaHq3eSA6hcuQWwjGRjbD+wf24UnzX6qgvLrpGMaZvsN7Pj9jiaU+5libY4O1qmp/4MhVokuGDtnWgIiyCtsREFGmtjGQDuBPNAq1Z3xctTOQLuKdwgOw+ngCmkw+j3n7b2K3Horhyc+jUcJXGJX0GI5biyK/dgMP+6zGdL+PsdZ/CIb7TEMV7XiGxySvHVo4/XXx2O6AiO4F2xG4wKE6osxpY+CqkFz+a+3Z5mHoXKM4eny7Pt3mmhqsuF87gB7mNehsXo8gLd7+2F5rGcyzNMMfliY4g8IOzwHGtC2A3jWDgIDCQN5CgO+/xzNx9WGMWbxP1WSlrpVi402i3OvqHUwKY+B0jyeQiNwPriavicb3a46oYOl2/aA6hfurRYJlHz8kobVpO7qb16CNaZuqjbL1hVpnrYZ51qZYbGmAeARg7iOFEJekI6ygD0LymwGfPEBAEUzcfAWjlxx0+V4sKifK3a4ycMq+E0hEd7+QsCsSUA1vGogX6gUi5poF76y4gmXRKa0LRAFcRyfzRpWJamjaZ99+U/fF/6z1MM/SFCuttWHVfDC6TQH0rh6AHWcS0e23lNl76WFROVHudfUOfve5Vh0RZYv01ryzFZerIbwWYejfNAwhgT5A/AUg/iyWOwRN4ioCMcPSRl1Ka+fR1bQWPc1rEG46jYfM69Xlkh6oZuTNXN4Me8/VxJSom7c9PhaVE5E7GDgRkaELCc8Z1Bjxida09VH5Q3D0rC90nEr3NU/qRfGNpTu+sXRDdS1aZaG6mSNRVLuCfj5/q0v0/uIo6NNUZaKi9RC3i8olQ7Y5+hI0TUO9csGsgSIihTVOLnCojihrOBaJ2xYSzqhZpbvDe47MsKCpaZdqa9DBtAkBWoL9sa3WcMy1NMNCSyNcQgGXReUxyfnx47br+H7tcXsNlnq817+NNdmpnChnYY1TNp5AIsrcGXiugq2I2VGw3sWJDsBNPGDajB7mtWhu2gmzptu7lK+y1lIz8/621kMS/LC2f1HVHmHYsqsuX0uyZWuHt8mwUzkDKiLvxMApG08gEWX/jDzJVnWvUxLztp12e628oohFF/M6NTOvlumofft1PY8Knkrd1w5PbKqAxAwqGD56KBwjFh5KM9woncq59AuR92LglI0nkIiMy1bJ/S3Rl/HS9G0ZtjZwtdiwDOV1N61FGdN5+/ZYPZ9qa/CntTHWW6ul6VSeXguFrx6rgyEztrkMqAQXICbybAycsvEEEpHxRi3cg+/++TeL5D4ddbRDGBS8AbXjIlFMi7U/cl4PwgJLIzU7b5sejpTWnWnJ1hebl8T4f06neez55uXtWTL2iiLyXAycsvEEEpHxXBWRS3ZIVoLJqLBcgp4na+XB1J03YYIVDU170cUUiY7mTQjWrtv3O6UXxnJLHSyz1lUNN5M0P3sTT+HqLVSYlc4swrhEC7uVE3kQBk7ZeAKJyHNn7LWoVFQN5aUeRpOg5ssOQTh5zYLRa/8NkGx8kYympii8UWwTyl1ej0Dt3z5Q8bo/4orVwengBnhuZ2WcQ7DL1gbP3JcP322LS/OYbbgvowwUi8yJshcDp2w8gUTk+TP2XAZV5YPQZNyadGujBt8fgDeaFMD3my4icsNGtDFtRRvzNpTUnDuQ77SGYZmlLpZZ66iFil+qnx9tw/yx41wSRqy8lmHtlQRwcwc3Qe0ywc6zCDlrjyhbMXDKxhNIRN4ZVEUevoDHJ21wue/jNfJgVJuCmLk7HsOXXXUIfnRU046p9fLambfiPtNhp+ed1QtimaUOllvrYq21Om4gj1vHFtExZbHhfH5m9PgmkrP2iLIZA6dsPIFE5L2BVJPRy9NkhGQobd6TYTh+/jJeXhqbYY1UEVxBa/M2FUg1N0U5Dekl6L6ItFZTdVFSH3UaRe76WDOatedOLywiyhjXqiMiug0JOKQb+PDZUf92CNeAnnVKoccvR93qVn4BQfjd0kpd/JCkistVNsq0VbU5aG3eoS7wnYy91rL4360gaodeIU2rg/RIgJS6yFxwbT0iY3DJFReYcSLKPWy9oCQ+KR2cN81Q2d3RUVE7hba36qLqaQfsXcvFBb0AVljuU9moNdYauI4Al68iReajOpZGiyol0fTzjWkK3Mc/Xuee1tFjETpRCg7V3SMGTkS5U0Z1TxKo1A8Lxsajl++o2aYIxlW0NO1EW/NWdV1Ai7c/lqibVbPN5VZpd1AHJ/Tiavu7zQPRKTwvQvKbMXHL9TSz/9Kbnefu4sQZFaET5TZX76BEhxmnezyBRJSz+0Gp1gWP10HdW0FI6uVf7pQPklHftF8N6UlGqrzpjNPjB6ylsMJaF21btkR41dqYuO2Gy5YJjlT26bH7cOrKTYxZtC/dxYkz+pysmaLc7CoDp+w7gUSUs7hqXZBeryWZpbfzVCw+Wbzf7TXzUgvTYlSrg7ambWhg2gcf7d/ljG+a82NJYi01U+8fa03EIv8dv75tcWLHzFN6mbXpzzVC4wqF7+pzEHkzBk7ZeAKJKPf0g8po/4U7YzBy4d40j73brjSSk27gk9UXYblNbFUAcWhh2qnqolqbtjt1L7fqGvbo5VSbg0hrDWy0Vna73cH0Z+ujcXixe8o4sR6KcrKrzDhl3wkkInInGIm5HIcth09hyOyDbg3xmWFBHe0g2t4KoqqYTjg9LrVR2/SKiLRUV8HUDj0cSfBxnXF6qihCCuUH/IMA//yAXz7M3HzSnlmTob5nm4ehf7Mwl4HT3dZDMdgib8HAKRtPIBHRnQzzpewTddvsU2pFEYvGpt1oKhfzLpTWLjg9Hqf7Y6O1CiJvZaT26PK+JoxpWwC9q6eetacB/oGISciDyVti8X3kCfvae881D0PnWiH29fTE3dRDsficvAkDp2w8gUREdzrMd+/1UTrKaufQ1LRLBVISUBXWrjntEW/Oj9hCtWANqYtzBWvDHFQKcclAPl8NcUk6wgqmZKeaTjmfbgZMskvPNgvDd/8cdVkPJZ/x6IW4NAsWs/icvA0bYBIRGUQCiNvVRdn2kULsrrVLulyIOGMajuvFcdxSHNMtbaHBiiraCTQx7VYXacQZaLmGgPNrgfNrURrAKb0wDqhhvRpqaO+iFoxn7wvI8D3lse//OaoCqNQZJwn6+ny/3uXwnQRTbNhJORXbEbjAjBMRGTnMJ0Nm8j+SiJLbj9UpjGA/C77eEOv0HHnsqw5B2HMhCV9vjndqeVBLO5KSkTLvVrVS/lqy03MPWkvdKjSvrvpIXUXKsJwrnSsGYPGheBUMSVPONztUwdgl+9Kv52K7A/IyHKrLxhNIRJQVw3zzd5zGmMX7VPBky+jExiVi7NL99gBmVJuU+qXIEwl4fO7ldF83DxJU7ygZ1mti2oUaWjRMDp3MLbqGKD1M1UZJMLXZWhkJ8HO5+PFL9fNjyzkdLy50rrGyran3UO2S6dZ7tahU1OXQnuPnz+hxoqzCwCkbTyARUWbLKGMj7DVUgT5A4jXEnLuIpt8ecHuoLwjX0ci0J6XQ3LQLFUwxTo8n6D7Yaq1kz0jJ2noWmNVj9xX3wfazztkrGxXgda+G3g3D7J/DdqyrD5xPMzPPMZBy9Thn7lF28ZrAacKECeoSHR2t7levXh3vvfceOnbs6HL/KVOmoH///k7b/P39cfPmvyuSX79+HcOHD8e8efNw8eJFhIWFYciQIRgwYIDbx8XAiYiMdCcNKm1ZmqhTV/DJ4n1qtp5tORZ3Fccl+7Ce1EiFaJecHr+m58UGNWMvJSO1Xy9za6AwLcmEzXm0OOL0vAgrEYyQokUQc92SJhCUZ8v6gLZtqY+ZM/coO3lNcXjp0qUxZswYVKxYERK/TZ06Fd26dcO2bdtUEOWKfKD9+/fb78t6TI5effVVLF++HL/88gtCQ0Px119/YdCgQShZsiS6du2a5Z+JiOheSQbGVUG2ZG4ymvI/rEMV1CpdEAF+pnQXK25cyhfrTiU5bTuLQphjbaEuEr6EaWdUICVBVGPTHtWIs515m7qI83oBbLBWw3prVWywVsUhvSRSukHJsB/QffrZW+voRWN0mwIoU9A/zbHIXcf/bE99qDLEJ9mqjGYm2j67kGsZGpQsFof5KCsZGjh16dLF6f7HH3+sMlDr169PN3CSQKlEiRLpvmZkZCT69euHVq1aqfvPP/88Jk6ciI0bN6YbOCUkJKiLY+RJRGQU+eGXoarUNUKpp/ynDhw+WbJfDee5mtVms+G0c9DkWLs0/0DKunhH9RActYTgF8sDasZeNe2YvfWB1EoV1a7iIfN6dRGX9EBstFZVfaQkkNqrl1WBlBxDxLKr+KBV/jSB4O24ChQduTtzj3VTlKMCJ0cWiwW///474uLi0Lhx43T3k6G4cuXKwWq1om7duhg1apRTkNWkSRPMnz8fTz/9tMoyrVy5EgcOHMDnn3+e7muOHj0aH3zwQaZ/JiKiuyX1PZI9Sa8nVEaBg2Ss0huucxW8dKkahJD8Gl6oF6juOy4qLAHQbj0Muy1h+M7SBX5Iwn3aIdXyQC71TAdRSLuODuZN6iKu6gHYZK1sD6Q+WBkKK3zcHkKUIMsxUHQV/LiTlWMTTsqR7QiioqJUoCR1SoGBgZg2bRo6derkct9169bh4MGDqFWrlhqH/M9//oPVq1dj9+7dathPSOZIskw//fQTfHx8YDKZMGnSJPTt2zfdY3CVcSpTpgyLw4nIY91uyv/EVYcxevE+p+fYChtc1hI5FJo3mXDAZYDjKvDxRTJqakfUAsUSSN1vOoD82o00Xc23WCupIEqCKSk2T4Svy88lA35zexdF7XKF1fIwEzdewpi/DjvNLrQVjWfUqZ0tEShH1jiJypUrY/v27epgZ82apYbZVq1ahWrVqqXZVwIsx2yUZJeqVq2qhuI++ugjte3LL79UQ32SdZLMlARWgwcPVtmndu3auTwGKTCXCxFRThnOe6FlBRXpjF2c0m8pvaDJaQgwbyEcTba6DJqerxuA/rXzYUtMonqtPD4anl0Qq9bH26pXwlZLJXxr6arW2KuqHUPDW4GUDO1JjVQLc5S6iATdF9v0cBVISdH5VmtF3IR/SmDUpgBqFzcDN2Mxce1Jp+yXGvqbHYUWFYsgpGCAy6ycLTt1KS6RTTgpZ2acUpPgpkKFCioYcsfDDz+sMkvTp0/HjRs3VMQ4d+5cdO7c2b7Ps88+i5MnT2LJkiVuvSZn1RFRTlniRR531ZlcZXYGN0HtMsG3zWTJvmtfb4KQvElAwjXM3HIGEctj3apZkhqpStpJe0ZKLlIjlXrB4nMB4SgYWgeJxWrioG9l5AkIRPffLqUTxOXDW21LAX6BgJ8sWhwAaCanobkMs2vsEUXenHFKTWqXHIfNblcXJUN9tqG9pKQkdZHhOUdms1m9LhFRblviRR4rFJi2Hkr+RYxPtLqfySqSEmDFJOdHxHLnruEZkRqp/XpZ7LeUxc+W9mpLeS1GBVC2YKqkdgmlb+wH9splBu7XNezWQ/GWT8qsPamXuoKU+ivx3dY4lCkQg3ZheRCSX/pLaYhJ8EfEnGj7caU+PJmA/WaHyiobZfucLBynu2Fo4BQREaF6NpUtWxbXrl1T9U1SzL106VL1uNQllSpVShVviw8//BCNGjVCeHg4YmNjMW7cOBw7dkxllIREiS1btsQbb7yBvHnzqqE6GfaTeqfPPvvMyI9KROTx7Q3cKUzPaMaeezQc0UviiKWkWmdPQpzS2nk0kkBKSwmkypnOoZZ2FLVMR/EcFsGqa6p3lAzrpQRSVfDuSuC9ldcwpm1K9/Sj565keFwytqI6sd+q1WpesQjWHLpwxw03iQwNnM6dO6eCo5iYGJUik6JvCZoeeOAB9fjx48edskeXL1/Gc889hzNnziA4OBj16tVT7Qcc66FmzJihArI+ffrg0qVLKniSNgd30gCTiCi3tTdw9RxXj7sKwlxxv/2AhpN6McyyFMMstFRbSuDirWxUSiAVbjqNqtpxVDUdx1P4S+1zyFpSFZpHrqiKVkWaIqxg0du+p+5wvfrgv0vGsAcUeXWNkydgjRMR5cZ6KHc5zmYz2ZpZOjwugdmcQY1x4tKNNLVVd6MIrqC+vUZqnwqgUruZtwT2+lbDrxcrqsacJ/Wi6XY3T4+rzuyUO1z1liVXPBUDJyKijKVehy69tgC3C7Lc5djxXNbak9l6tjqpGtpRmB0WLRan9UL29gdyfUQPyTCQkqVi1gwIR0iRQilF5z55VGEU66Byh6sMnLLvBBIRUcbZrPSCLBXGaM5Lr7jSr1Ye/Bx1M93MVSDiVSNOWyBVSzsMP83itM95PUjVSNkCqQN6afsyMWq5miaBqFnMF2EFfVTBecx14MeoBPyw+bJ9lt7wjlVS2jw4fK7UjTnvFAMzz8DAKRtPIBER3RnHQErIbVlfb1P0ZYxcuDfN/u+0DMbIVZfdfv08SEAd0yFVcN4lcD9K3TgAf815qZnLeqCarXe9cA3oJWph+I5iSNLNKkDqGO6PxYcSXGbGItqUxAstwzFx/VmMWbLfZWNOd7Gzuedg4JSNJ5CIiDJHet2+pV4qvUWLb0cCIVkmRrJQtvYH9UwHkE9zbntzXc9zq7t5SlZqZzrdzSVI6lszL6bsvJFmqG/OoCaIS7SkyUC5yiqxs7lnYeCUjSeQiIgyT3rLqDhud3fNOxvt1sXWtcoHyaihReOlMkdQKWkPClzahSAt3uk5N6W7ubUiNuoytFcF26zhuIE8t30fOS6VgepRHb0bhKbJKg3rUAU1SwepzuYvTtuWYYE6h/GyDwOnbDyBRESUPfVSGdZKuRFMyX6D7g9AkhX4flu8PYNlghWVtRMqI9XgVp1U6u7mMowXpYfZl4nZYq2MawjI8L2+71YUz80/7zJTdrvO5hzGy14MnLLxBBIRkTHSW04mI6oc/LY9plx3N3dk0TXs1cvZi82lXuoS7uz3QmXCbh2LHNewW8XnO05cRvdvIp2K5u9kuRhmqnLZkitERETuSG85GZFeM0w1XKffTXfzC2ig/RtIhZnOquG+GqZoPI2UdVAPWEupQGrjrazUWRTK8F3kMLpVLYA/9lxVxzV2yT4cOHsNc7aeSnOIklmTbNvtAidmqrIe+zi5wIwTEZH3F5S7asDpXsbp9orh8q2hvZRLFdOJNPtEW4unBFK36qRO6MXuuCnnnWSc0l2gOaINFza+DWaciIgoVy8nU7tMsLrEJSaneUzYtt2tcwjGAmtjdRHBuKqactoCqepaNEJNZ9XlEaxS+8TohexDexJIHdZLuhVIyR7PNC5924ZXrtYRlEzW5DXReKtz1bv+rOSMGScXmHEiIsqZDTgd2wFIfdRLM7a5jEfcLThPjztNOS/oBVRtlG14b69eFtZbTTlTH4ttceLn7i+I/o1L3+pwng8xVxPsrQ5Ek9HL0xyzDFuuHd5G3b7Xhp05FYvDs/EEEhGRd4o8fAGPT9qQZvu7nauiUy1ZogXpFp9LMNKjSl7M3XvD3ubAnaacDbSUGqm6poPIk6op51U9wCGQqqJm8SW7KEW2zQ5MtAA/bE+ZHaiCquahuJ5owbQNaYcNO9cMweJdMfa2CHfTsDMnu8olV7LvBBIRkXdytwll6vX2nm0Rhv5NwxBSwB87jpxB90nb7jgz5Ytk1NSOqEWLpQXC/aYDyK85N9WM1/2x1Rp+a829qtiuV0AC/DJ8XXczZXcySy83uMrAKftOIBER5byGm3cyFJg6sOpUNRgL9rq/RIwww4Kq2rFbgVTKJVi77rRPgu6DHXoFe0ZKOp3HIW+GbQ4yIs025fNw+A4MnO4VAyciotwjo6Dobl5j/vbTGL143z0dkwYrwrXTaljPVidVXIt12idZN2GXHqqyURJIyTDfFQSqx77qEIQDF5MxflOcy9eXJWLefLASxi49kOHwXW7pCXWVGafsO4FEREQZDf+Jwa0roFl4Eew8fhGf/HUIljuuOtdRTjt7q4/UPjTU9qKM6bzTHlZdw369jAqigsPuw0cHQ3EeBdO8klr6pUkgxkZeTzVMmbLe3vFL8dA0Dadib2Ds4n13XBcV44XBFgOnbDyBREREtys4T70GnWSnAvxMOHHxGobMTFnL7k6VxAXUN+1Dl/wHEHpjD8JNp9Psc9ga4tDdvAo61y2LzuF5sPF0Ij5e4zwUeDupe0LFXLmBzdGXVJBVr1ywVy8Vwz5OREREBpAsS+qu5VI7JUN4NhJg2IIP1WsqCXe1gHEMiqB03fZo1+T/sONMIur/dhT336qPkqxUFe04Kphi1OUxrFDPiT1YFP+Lqor91qoorVXDSb2o25/NsSfUzE3HMXx2lP1Y5biHd6yiup/bPrtcy+dqUSnlPbwtC5Ue9nFygRknIiLK6oJzR45ZqB7fRDoFXo7F3jKc9mAFfyw+lKCCFtuwm1ynziAVwHU1Wy+lTmofamhH4aM5N084qRfBems1rLdWVde3C6RMGjD3iTB0//mo2wHe883L4/s1Rzw6C8Whumw8gURERJlZcO4q8JKsjQqsfIEeE9bf1dBeAG6inukAGpn2oJFpL2ppR+CbqimnBFIyrCeB1Dp7IOXc3bxOCR9sO5Ps9vumzqJ5YisEBk7ZeAKJiIiyK/BKr4bKldsN++VVgdRBeyBVWzt8V4HU3XCs+fKEgnLWOBEREXkxxzqo29VQpee5+sH4YfPldGfw3UAerLHWVJf0AqnS2gWUNv+DXuZ/Mi2QMt+q+bIFS1Gnrthn76V0QA9D/2ZhTkXonlQfxRonF5hxIiIiT+U4lJce23CYLMQXffYS/tx+CtO2Xbij98msjJTmUKOlOq83LILCBfJh7P+OpRsA2mqhRHbM0uNQXTaeQCIiouw2cfVhjFm8z+UCxa4K0necuIzuX0fe9aLF9xJIRTTNj66V8mLyjjh8vy1lbT13qOWOXcxQzIr6KA7VERER5VAydDU2VdAkQcakfvUQ4Oebpi7K1ltJv4s17TJjaE8/WRtnr9fEkp2hsN7B0J7VxQFKlk1qv4wcsku77DIRERF5LKn3SZ21kSBDgibHgmtbkGUb6nIMsuYOboJiBfKoIGTnqVh8snj/rVl8wJvtK2DsX4czzAxpAN5qVRTvrkwJpjIKpHB2GcqcXYbV/v9mpCQbtc5SDaeQfvsDV8Fd6p5YRmDgRERElMOabN4uyIpPtNoL0CXY6lq7pNMsvoKBARnWUekAgvOYnI7DlpFaa62pHr9tRsoXOG4tinXW6sgXWgdH8tbE51F57IGS7iKQGvVgcYQUyAMjMXAiIiLyIhLYSJF06l5P7s7CcxVkpZ7FJ/VR0jtq4c4YjFy4N83rmjSgXlgxjG5rwlvLUmbuSSZrWNNAVc+0JSYRLy2By6G9xqbdaGzao/pIlTWdR1nTSuDkSrXPQ34hKhsVaa2u6qQuIsjpfasUSITROKvOBRaHExFRTmmyeTedzG+3aHFEpyp4oUUFNWsv5tJVRJ+5iNDAJIT43wSsyYg8kYDH517O8LXz4Qbqm/arjFQT025U16Jh1pzfaL+1dMqwnrWaGuK7gvwYIzPrGmTuzDrOqsvGE0hERJRbOpmbJKvUsQpeaFnB9c4ytGdJwMQVBzB62Yk0D7cN9cWy6CSXTy2AOLXOXuNbgVRV03Gnx626hl16KP4v8QOsimifqQXiDJyy8QQSERHldHcSeMWkk6W6kwWMRSFcvRVI7UYT0x5UNJ3CTmsYuiZ+rNa/k8WGMwvbERAREVGWdzJ3xVVBurjTHlIfdiiDMgVC0e23Bup+UcSiiHZF3ZZFg/s3CzWkLYHqL0VERESUGcJuFaTfzsttwtN9TJ5++aYVJ65a8Hj1lFl051EQe/Vy6rYEZpIBMwJn1REREVGWzfoz3co2pe7HVLFE/nRfQ/Z9d+W1dIf5jOznxMCJiIiIMlXvW+0MbHVRqw+cTzOzr165YLcXLBa2faVJZ3rtF7ID2xG4wOJwIiKirC8wn5lqxt6jDcpg2sa0s/HEV4/VQeFA/7uaGXg7LA4nIiIijy8w750qMyWmbzyRppBcNdwMDTZ0jTr7sRh9AERERJR7hdxa9sUWWI3pVdNpGWBNg6qZ8oSgSbDGiYiIiDxG71tZqC3Rl1XQVLecZ2SabBg4ERERkUcJCcqLh2p7TrDkiEN1RERERG5i4ERERETkJgZORERERG5i4ERERETkJgZORERERG5i4ERERETkJgZORERERG5i4ERERETkJgZORERERG5i4ERERETkJgZORERERG5i4ERERETkJgZORERERG5i4ERERETkJgZORERERG5i4ERERETkJgZORERERG7ycXfH3ETXdXV99epVow+FiIiIspjt9972+58RBk4uXLt2TV2XKVMms78bIiIi8uDf/6CgoAz30XR3wqtcxmq14vTp08ifPz80Tcv0qFYCshMnTqBAgQKZ+trEc+4p+HfO853T8W88Z51zCYUkaCpZsiRMpoyrmJhxckFOWunSpZGV5Etn4JS9eM6zH885z3dOx7/xnHPOb5dpsmFxOBEREZGbGDgRERERuYmBUzbz9/fHiBEj1DXxnOdU/Dvn+c7p+Deee885i8OJiIiI3MSMExEREZGbGDgRERERuYmBExEREZGbGDgRERERuYmBUzb7+uuvERoaijx58qBhw4bYuHFjdh9CjrB69Wp06dJFdXmV7u7z5s1L0wX2vffeQ0hICPLmzYt27drh4MGDTvtcunQJffr0UY3UChYsiGeeeQbXr1/P5k/iPUaPHo369eurjvrFihVD9+7dsX//fqd9bt68icGDB6Nw4cIIDAxEr169cPbsWad9jh8/js6dOyMgIEC9zhtvvIHk5ORs/jSeb8KECahVq5a92V/jxo2xePFi++M811lvzJgx6t+XV155hec9i7z//vvqHDteqlSp4tHnm4FTNpo5cyZeffVVNZ1y69atqF27Nh588EGcO3cuOw8jR4iLi1PnTwJRVz755BOMHz8e3377LTZs2IB8+fKpcy3/J7SRoGn37t34+++/sWDBAhWMPf/889n4KbzLqlWr1D9g69evV+csKSkJ7du3V9+FzdChQ/Hnn3/i999/V/vL0kU9e/a0P26xWNQ/cImJiYiMjMTUqVMxZcoUFeSSM1m9QH64t2zZgs2bN6NNmzbo1q2b+pvluc56mzZtwsSJE1Xw6oh/45mvevXqiImJsV/WrFnj2edb1qqj7NGgQQN98ODB9vsWi0UvWbKkPnr0aH4F90D+jOfOnWu/b7Va9RIlSujjxo2zb4uNjdX9/f316dOnq/t79uxRz9u0aZN9n8WLF+uapumnTp3i9+GGc+fOqXO4atUq+zn29fXVf//9d/s+e/fuVfusW7dO3V+0aJFuMpn0M2fO2PeZMGGCXqBAAT0hIYHn/TaCg4P177//nuc6i127dk2vWLGi/vfff+stW7bUX375ZbWdf+OZb8SIEXrt2rVdPuap55sZp2wi0bD8l6MMGTmuiSf3161bl12HkSscPXoUZ86ccTrXsgaRDI3azrVcy/Dc/fffb99H9pfvRDJUdHtXrlxR14UKFVLX8vctWSjH8y4p97Jlyzqd95o1a6J48eL2fSQTKIt32jIplJb8V/WMGTNUdk+G7Hius5ZkViWL4fi3LHjes4aUUUjZRfny5dVIgAy9efL55iK/2eTChQvqHz/HL1fI/X379mXXYeQKEjQJV+fa9phcy1i4Ix8fHxUE2Pah9FmtVlX30bRpU9SoUcN+Tv38/FRAmtF5d/W9OH5v9K+oqCgVKMkQs9R3zJ07F9WqVcP27dt5rrOIBKhSSiFDdanxbzzzyX/QytBa5cqV1TDdBx98gObNm2PXrl0ee74ZOBHRXf0XufzD5liLQJlPfkwkSJLs3qxZs9CvXz9V50FZ48SJE3j55ZdVDZ9M4KGs17FjR/ttqSeTQKpcuXL47bff1MQeT8ShumxSpEgRmM3mNLMB5H6JEiWy6zByBdv5zOhcy3XqonyZhSEz7fh9ZOzFF19UxfQrVqxQBcyO512GpGNjYzM8766+F8fvjf4l/7UdHh6OevXqqVmNMiHiiy++4LnOIjI0JP8u1K1bV2Wg5SKBqkw0kduSyeDfeNaS7FKlSpVw6NAhj/07Z+CUjf8Ayj9+y5YtcxrukPuSiqfMExYWpv4P43iuZbxbapds51qu5f+M8g+lzfLly9V3Iv/FQ2lJHb4ETTJcJOdKzrMj+fv29fV1Ou/SrkDqFRzPuww/OQat8l/3Mt1ehqAoY/L3mZCQwHOdRdq2bav+PiXLZ7tIHaTU3dhu8288a0lLmMOHD6tWMh77b0qWlJyTSzNmzFAzu6ZMmaJmdT3//PN6wYIFnWYDkPuzXrZt26Yu8mf82WefqdvHjh1Tj48ZM0ad2z/++EPfuXOn3q1bNz0sLEy/ceOG/TU6dOig16lTR9+wYYO+Zs0aNYvmscce41eQjoEDB+pBQUH6ypUr9ZiYGPslPj7evs+AAQP0smXL6suXL9c3b96sN27cWF1skpOT9Ro1aujt27fXt2/fri9ZskQvWrSoHhERwfOeyvDhw9WMxaNHj6q/Ybkvsz7/+usvnuts5DirTvBvPHO99tpr6t8U+Ttfu3at3q5dO71IkSJq1q6nnm8GTtnsyy+/VH8Efn5+qj3B+vXrs/sQcoQVK1aogCn1pV+/fvaWBO+++65evHhxFay2bdtW379/v9NrXLx4UQVKgYGBaupq//79VUBGrrk633KZPHmyfR8JTAcNGqSmzQcEBOg9evRQwZWj6OhovWPHjnrevHnVP5DyD2dSUhJPeypPP/20Xq5cOfVvhfwQyN+wLWjiuTYucOLfeObq3bu3HhISov7OS5Uqpe4fOnTIo8+3Jv+TNbksIiIiopyFNU5EREREbmLgREREROQmBk5EREREbmLgREREROQmBk5EREREbmLgREREROQmBk5EREREbmLgRERERMTAiYhyg6eeegrdu3c3+jCIKJfwMfoAiIjSo2lahidnxIgR+OKLL9QCxJ5EFkCeNGkS2rVrZ/ShEFEmY+BERB4rJibGfnvmzJl477331OroNoGBgeriSXbu3InLly+jZcuWRh8KEWUB1jgRkccqUaKE/RIUFKQyUI7bJGhKPVTXqlUrvPTSS3jllVcQHByM4sWLq+xPXFwc+vfvj/z58yM8PByLFy92eq9du3ahY8eO6jXlOU8++SQuXLhwx8f8xx9/oEOHDvD19U3zmGTG3n//fZQtWxb+/v4oWbIkhgwZcpdnh4iMwMCJiHKcqVOnokiRIti4caMKogYOHIiHH34YTZo0wdatW9G+fXsVGMXHx6v9Y2Nj0aZNG9SpUwebN2/GkiVLcPbsWTzyyCN3/N7z589Ht27dXD42e/ZsfP7555g4cSIOHjyIefPmoWbNmvf8eYko+2i6pxUHEBG5MGXKFJVFkiDHkWScZJsEIbaMk8ViwT///KPuy23JVvXs2RM//fST2nbmzBmEhIRg3bp1aNSoEUaOHKn2X7p0qf11T548iTJlyqihwUqVKrn1nZw6dQrly5dXQVfBggXTPP7ZZ5+poEmyW64yUkTk+ZhxIqIcp1atWvbbZrMZhQsXdsrsyFCcOHfunLresWMHVqxYYa+ZkkuVKlXUY4cPH76jbFOzZs1cBk1Csl43btxQwdVzzz2HuXPnIjk5+a4/JxFlPxaHE1GOkzqbI7VRjttss/WsVqu6vn79Orp06YKxY8emeS3JTN1J4NS1a9d0H7dlsP73v//h77//xqBBgzBu3DisWrWKGSgiL8HAiYhyvbp166r6o9DQUPj43N0/ixJ8SdZqwoQJGe6XN29eFaTJZfDgwSqzFRUVpY6BiDwfh+qIKNeTAObSpUt47LHHsGnTJjU8J/VOMgtPaqTcIQXlUgslwVdGdVo//PCDqnE6cuQIfvnlFxVIlStXLtd/B0TegoETEeV60hZg7dq1KkiSGXdSDyWF6FKrZDKZ3G5DkNEwnZDXk9YITZs2VXVYMmT3559/qhosIvIOnFVHRHSPpMBbCs6lN1SDBg14PolyMGaciIjukQzzDR06FPXr1+e5JMrhmHE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", 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" ] }, "metadata": {}, diff --git a/examples/notebooks/battery_parameterisation/electrode_balancing.ipynb b/examples/notebooks/battery_parameterisation/electrode_balancing.ipynb index 01a4f19e2..190a5d569 100644 --- a/examples/notebooks/battery_parameterisation/electrode_balancing.ipynb +++ b/examples/notebooks/battery_parameterisation/electrode_balancing.ipynb @@ -198,7 +198,56 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "pybop.plot.problem(problem, inputs=result.best_inputs, title=\"Optimised Comparison\");" ] @@ -221,7 +270,42 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from plotly import graph_objects as go\n", "\n", diff --git a/examples/notebooks/battery_parameterisation/lgm50_pulse_validation.ipynb b/examples/notebooks/battery_parameterisation/lgm50_pulse_validation.ipynb index a53345add..9509c47cf 100644 --- a/examples/notebooks/battery_parameterisation/lgm50_pulse_validation.ipynb +++ b/examples/notebooks/battery_parameterisation/lgm50_pulse_validation.ipynb @@ -115,7 +115,56 @@ "execution_count": null, "id": "7", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "go.Figure(\n", " data=go.Scatter(x=df[\"ProgTime\"], y=df[\"Voltage\"]),\n", @@ -286,7 +335,18 @@ "execution_count": null, "id": "19", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "1.1493038123679518" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "inputs = problem.parameters.to_dict([0.01, 0.01, 0.01, 10000, 10000])\n", "evaluation = problem.evaluate(inputs)\n", @@ -330,7 +390,42 @@ "execution_count": null, "id": "23", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "pybop.plot.problem(problem, inputs=result.best_inputs, title=\"Optimised Comparison\");" ] @@ -350,7 +445,76 @@ "execution_count": null, "id": "25", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "result.plot_convergence()\n", "result.plot_parameters();" @@ -427,7 +591,42 @@ "execution_count": null, "id": "31", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "pybop.plot.problem(problem, inputs=result.best_inputs, title=\"Parameter Extrapolation\");" ] diff --git a/examples/notebooks/battery_parameterisation/pouch_cell_identification.ipynb b/examples/notebooks/battery_parameterisation/pouch_cell_identification.ipynb index b6d97c9e3..e649fb54b 100644 --- a/examples/notebooks/battery_parameterisation/pouch_cell_identification.ipynb +++ b/examples/notebooks/battery_parameterisation/pouch_cell_identification.ipynb @@ -221,8 +221,8 @@ { "data": { "text/plain": [ - "{'Negative electrode active material volume fraction': 0.5461747348834938,\n", - " 'Positive electrode active material volume fraction': 0.6063372489914799}" + "{'Negative electrode active material volume fraction': 0.5461747348834163,\n", + " 'Positive electrode active material volume fraction': 0.606337248991485}" ] }, "execution_count": null, @@ -280,9 +280,9 @@ "data": { "text/html": [ "
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\n", + "
" ] }, "metadata": {}, @@ -355,17 +389,77 @@ "name": "stdout", "output_type": "stream", "text": [ - "Running model for Saltelli sample of size: (896, 5)\n", - "Eval 90/896, elapsed time=0.1 s, last cost=8.44241\n", - "Eval 180/896, elapsed time=0.2 s, last cost=1.03403\n", - "Eval 270/896, elapsed time=0.3 s, last cost=1.56065\n", - "Eval 360/896, elapsed time=0.4 s, last cost=8.33648\n", - "Eval 450/896, elapsed time=0.4 s, last cost=0.0455716\n", - "Eval 540/896, elapsed time=0.5 s, last cost=0.0415216\n", - "Eval 629/896, elapsed time=0.6 s, last cost=1.48783\n", - "Eval 718/896, elapsed time=0.6 s, last cost=2.03308\n", - "Eval 807/896, elapsed time=0.7 s, last cost=0.932404\n", - "Eval 896/896, elapsed time=0.8 s, last cost=0.036365\n" + "Running model for Saltelli sample of size: (896, 5)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Eval 90/896, elapsed time=0.5 s, last cost=7.59596\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Eval 180/896, elapsed time=0.7 s, last cost=0.430763\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Eval 270/896, elapsed time=0.9 s, last cost=0.461599\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Eval 360/896, elapsed time=1.2 s, last cost=8.88535\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Eval 450/896, elapsed time=1.4 s, last cost=4.74524\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Eval 540/896, elapsed time=1.7 s, last cost=0.127041\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Eval 629/896, elapsed time=2.0 s, last cost=0.367499\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Eval 718/896, elapsed time=2.3 s, last cost=1.30012\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Eval 807/896, elapsed time=2.6 s, last cost=2.07151\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Eval 896/896, elapsed time=2.9 s, last cost=5.21243\n" ] } ], @@ -392,19 +486,24 @@ "metadata": {}, "outputs": [ { - "ename": "TypeError", - "evalue": "unique requires a Series, Index, ExtensionArray, np.ndarray or NumpyExtensionArray got list.", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mTypeError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[11]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m Si = \u001b[43msobol\u001b[49m\u001b[43m.\u001b[49m\u001b[43manalyze\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 2\u001b[39m \u001b[43m \u001b[49m\u001b[43msalib_dict\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mY_saltelli\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcalc_second_order\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprint_to_console\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\n\u001b[32m 3\u001b[39m \u001b[43m)\u001b[49m\n\u001b[32m 4\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33mSobol S1:\u001b[39m\u001b[33m\"\u001b[39m, Si[\u001b[33m\"\u001b[39m\u001b[33mS1\u001b[39m\u001b[33m\"\u001b[39m])\n\u001b[32m 5\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33mSobol ST:\u001b[39m\u001b[33m\"\u001b[39m, Si[\u001b[33m\"\u001b[39m\u001b[33mST\u001b[39m\u001b[33m\"\u001b[39m])\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/Source/PyBOP/env-py-3-12/lib/python3.12/site-packages/SALib/analyze/sobol.py:121\u001b[39m, in \u001b[36manalyze\u001b[39m\u001b[34m(problem, Y, calc_second_order, num_resamples, conf_level, print_to_console, parallel, n_processors, keep_resamples, seed)\u001b[39m\n\u001b[32m 117\u001b[39m rng = np.random.randint\n\u001b[32m 119\u001b[39m \u001b[38;5;66;03m# Determine if groups are defined and adjusting the number\u001b[39;00m\n\u001b[32m 120\u001b[39m \u001b[38;5;66;03m# of rows in the cross-sampled matrix accordingly\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m121\u001b[39m _, D = \u001b[43mextract_group_names\u001b[49m\u001b[43m(\u001b[49m\u001b[43mproblem\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 123\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m calc_second_order \u001b[38;5;129;01mand\u001b[39;00m Y.size % (\u001b[32m2\u001b[39m * D + \u001b[32m2\u001b[39m) == \u001b[32m0\u001b[39m:\n\u001b[32m 124\u001b[39m N = \u001b[38;5;28mint\u001b[39m(Y.size / (\u001b[32m2\u001b[39m * D + \u001b[32m2\u001b[39m))\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/Source/PyBOP/env-py-3-12/lib/python3.12/site-packages/SALib/util/__init__.py:274\u001b[39m, in \u001b[36mextract_group_names\u001b[39m\u001b[34m(p)\u001b[39m\n\u001b[32m 271\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 272\u001b[39m groups = p[\u001b[33m\"\u001b[39m\u001b[33mgroups\u001b[39m\u001b[33m\"\u001b[39m]\n\u001b[32m--> \u001b[39m\u001b[32m274\u001b[39m names = \u001b[38;5;28mlist\u001b[39m(\u001b[43mpd\u001b[49m\u001b[43m.\u001b[49m\u001b[43munique\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgroups\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[32m 275\u001b[39m number = \u001b[38;5;28mlen\u001b[39m(names)\n\u001b[32m 277\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m names, number\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/Source/PyBOP/env-py-3-12/lib/python3.12/site-packages/pandas/core/algorithms.py:433\u001b[39m, in \u001b[36munique\u001b[39m\u001b[34m(values)\u001b[39m\n\u001b[32m 327\u001b[39m \u001b[38;5;129m@set_module\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33mpandas\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 328\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34munique\u001b[39m(values):\n\u001b[32m 329\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 330\u001b[39m \u001b[33;03m Return unique values based on a hash table.\u001b[39;00m\n\u001b[32m 331\u001b[39m \n\u001b[32m (...)\u001b[39m\u001b[32m 431\u001b[39m \u001b[33;03m Length: 3, dtype: complex128\u001b[39;00m\n\u001b[32m 432\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m433\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43munique_with_mask\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/Source/PyBOP/env-py-3-12/lib/python3.12/site-packages/pandas/core/algorithms.py:461\u001b[39m, in \u001b[36munique_with_mask\u001b[39m\u001b[34m(values, mask)\u001b[39m\n\u001b[32m 459\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34munique_with_mask\u001b[39m(values, mask: npt.NDArray[np.bool_] | \u001b[38;5;28;01mNone\u001b[39;00m = \u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[32m 460\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"See algorithms.unique for docs. Takes a mask for masked arrays.\"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m461\u001b[39m values = \u001b[43m_ensure_arraylike\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunc_name\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43munique\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m 463\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(values.dtype, ExtensionDtype):\n\u001b[32m 464\u001b[39m \u001b[38;5;66;03m# Dispatch to extension dtype's unique.\u001b[39;00m\n\u001b[32m 465\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m values.unique()\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/Source/PyBOP/env-py-3-12/lib/python3.12/site-packages/pandas/core/algorithms.py:239\u001b[39m, in \u001b[36m_ensure_arraylike\u001b[39m\u001b[34m(values, func_name)\u001b[39m\n\u001b[32m 232\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\n\u001b[32m 233\u001b[39m values,\n\u001b[32m 234\u001b[39m (ABCIndex, ABCSeries, ABCExtensionArray, np.ndarray, ABCNumpyExtensionArray),\n\u001b[32m 235\u001b[39m ):\n\u001b[32m 236\u001b[39m \u001b[38;5;66;03m# GH#52986\u001b[39;00m\n\u001b[32m 237\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m func_name != \u001b[33m\"\u001b[39m\u001b[33misin-targets\u001b[39m\u001b[33m\"\u001b[39m:\n\u001b[32m 238\u001b[39m \u001b[38;5;66;03m# Make an exception for the comps argument in isin.\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m239\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[32m 240\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m requires a Series, Index, \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 241\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mExtensionArray, np.ndarray or NumpyExtensionArray \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 242\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mgot \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(values).\u001b[34m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 243\u001b[39m )\n\u001b[32m 245\u001b[39m inferred = lib.infer_dtype(values, skipna=\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[32m 246\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m inferred \u001b[38;5;129;01min\u001b[39;00m [\u001b[33m\"\u001b[39m\u001b[33mmixed\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mstring\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mmixed-integer\u001b[39m\u001b[33m\"\u001b[39m]:\n\u001b[32m 247\u001b[39m \u001b[38;5;66;03m# \"mixed-integer\" to ensure we do not cast [\"ss\", 42] to str GH#22160\u001b[39;00m\n", - "\u001b[31mTypeError\u001b[39m: unique requires a Series, Index, ExtensionArray, np.ndarray or NumpyExtensionArray got list." + "name": "stdout", + "output_type": "stream", + "text": [ + " ST ST_conf\n", + "R0 [Ohm] 0.955425 0.167954\n", + "R1 [Ohm] 0.025830 0.009102\n", + "R2 [Ohm] 0.000931 0.000467\n", + "Tau1 [s] 0.008554 0.003997\n", + "Tau2 [s] 0.001098 0.000620\n", + " S1 S1_conf\n", + "R0 [Ohm] 0.943294 0.201951\n", + "R1 [Ohm] 0.024291 0.039169\n", + "R2 [Ohm] -0.001352 0.006306\n", + "Tau1 [s] 0.007184 0.022051\n", + "Tau2 [s] 0.001215 0.006819\n", + "Sobol S1: [ 0.94329445 0.02429073 -0.0013523 0.00718427 0.00121541]\n", + "Sobol ST: [9.55424615e-01 2.58303601e-02 9.30643934e-04 8.55433212e-03\n", + " 1.09805930e-03]\n" ] } ], From 402646f751c8c9c8ac9579f39ad92823ac699d4d Mon Sep 17 00:00:00 2001 From: u2370093 Date: Wed, 3 Jun 2026 15:35:56 +0100 Subject: [PATCH 20/20] clean up notebook output --- .../sensitivity_analysis_hessian.ipynb | 4200 ++++++++--------- 1 file changed, 2100 insertions(+), 2100 deletions(-) diff --git a/examples/notebooks/battery_parameterisation/sensitivity_analysis_hessian.ipynb b/examples/notebooks/battery_parameterisation/sensitivity_analysis_hessian.ipynb index 511e08596..4fc281242 100644 --- a/examples/notebooks/battery_parameterisation/sensitivity_analysis_hessian.ipynb +++ b/examples/notebooks/battery_parameterisation/sensitivity_analysis_hessian.ipynb @@ -921,12 +921,12 @@ 3.9715767655798846, 3.9602819415890913, 3.9520863469990504, - 3.946067875607473, + 3.9460678756074725, 3.941628506505509, 3.9383485119515527, - 3.935917854374472, - 3.93410032970808, - 3.932712344858032, + 3.9359178543744724, + 3.9341003297080803, + 3.9327123448580323, 3.931609848112068, 3.9306796641080486, 3.9298329831198062, @@ -935,37 +935,37 @@ 3.927180286638963, 3.92612347339934, 3.92493948188842, - 3.923615502156737, - 3.9221446647402183, + 3.9236155021567365, + 3.9221446647402187, 3.9205248558904016, 3.9187576203002754, - 3.916847330888974, + 3.9168473308889746, 3.9148007251096937, 3.9126264463976232, 3.91033366532152, 3.907932365882042, 3.905432976921607, - 3.9028460940129297, - 3.900182223608643, + 3.9028460940129293, + 3.9001822236086436, 3.8974516117228104, 3.8946641478364215, - 3.891829327446142, + 3.8918293274461426, 3.888956034053371, 3.8860526455531774, - 3.883126894739069, - 3.880185966861721, + 3.8831268947390685, + 3.8801859668617205, 3.877236398377332, 3.874284172949308, 3.871334689117515, 3.868392806721639, 3.865462862415277, - 3.862548696498163, + 3.8625486964981635, 3.8596536837274753, 3.856780776647933, 3.853932516791931, - 3.851111073737989, - 3.848318221085465, - 3.845555461633339, + 3.8511110737379886, + 3.8483182210854645, + 3.8455554616333396, 3.8428240125597344, 3.8401248253055296, 3.8374586127454657, @@ -979,7 +979,7 @@ 3.8173385738908543, 3.8149712643981433, 3.812634915967867, - 3.8103287339734337, + 3.8103287339734333, 3.808051865920859, 3.805803390603401, 3.8035823195733864, @@ -987,50 +987,50 @@ 3.7992182898500735, 3.79707314866406, 3.794951070263993, - 3.792850877742618, - 3.7907713513683903, - 3.7887112169249177, + 3.7928508777426186, + 3.7907713513683907, + 3.7887112169249173, 3.786669191255614, 3.784643945031963, 3.782634098361605, - 3.780638231246373, - 3.778654879928341, - 3.7766825257488903, - 3.774719596256865, + 3.7806382312463724, + 3.7786548799283404, + 3.7766825257488907, + 3.7747195962568645, 3.772764447562899, - 3.770815363937272, + 3.7708153639372717, 3.7688705464069434, 3.766928101967575, 3.764986031374835, 3.7630422158017174, 3.761094403248126, - 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