diff --git a/csep/core/forecasts.py b/csep/core/forecasts.py index 4b2f863f..e0053a0b 100644 --- a/csep/core/forecasts.py +++ b/csep/core/forecasts.py @@ -13,7 +13,7 @@ from csep.utils.time_utils import decimal_year, datetime_to_utc_epoch from csep.core.catalogs import AbstractBaseCatalog from csep.utils.constants import SECONDS_PER_ASTRONOMICAL_YEAR -from csep.utils.plots import plot_spatial_dataset +from csep.utils.plots import plot_gridded_dataset # idea: should this be a SpatialDataSet and the class below SpaceMagnitudeDataSet, bc of functions like @@ -453,11 +453,11 @@ def plot(self, ax=None, show=False, log=True, extent=None, set_global=False, plo if log: plot_args.setdefault('clabel', f'log10 M{self.min_magnitude}+ rate per cell per {time}') with numpy.errstate(divide='ignore'): - ax = plot_spatial_dataset(numpy.log10(self.spatial_counts(cartesian=True)), self.region, ax=ax, + ax = plot_gridded_dataset(numpy.log10(self.spatial_counts(cartesian=True)), self.region, ax=ax, show=show, extent=extent, set_global=set_global, plot_args=plot_args) else: plot_args.setdefault('clabel', f'M{self.min_magnitude}+ rate per cell per {time}') - ax = plot_spatial_dataset(self.spatial_counts(cartesian=True), self.region, ax=ax,show=show, extent=extent, + ax = plot_gridded_dataset(self.spatial_counts(cartesian=True), self.region, ax=ax, show=show, extent=extent, set_global=set_global, plot_args=plot_args) return ax diff --git a/csep/core/regions.py b/csep/core/regions.py index 04795c01..87aeb249 100644 --- a/csep/core/regions.py +++ b/csep/core/regions.py @@ -674,7 +674,11 @@ def get_cartesian(self, data): """Returns 2d ndrray representation of the data set, corresponding to the bounding box. Args: - data: + data: An array of values, whose indices corresponds to each cell of the region + + Returns: + A 2D array of values, corresponding to the original data projected onto the region. + Values outside the region polygon are represented as np.nan """ assert len(data) == len(self.polygons) results = numpy.zeros(self.bbox_mask.shape[:2]) diff --git a/csep/utils/plots.py b/csep/utils/plots.py index 84bdc8f5..c48ed30d 100644 --- a/csep/utils/plots.py +++ b/csep/utils/plots.py @@ -3,15 +3,14 @@ import warnings from typing import TYPE_CHECKING, Optional, Any, List, Union, Tuple, Sequence, Dict +# Third-party imports +import numpy +import pandas import cartopy import cartopy.crs as ccrs import matplotlib import matplotlib.lines import matplotlib.pyplot as pyplot -# Third-party imports -import numpy -import numpy as np -import pandas as pandas from cartopy.io import img_tiles from cartopy.io.img_tiles import GoogleWTS from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER @@ -66,7 +65,6 @@ "alpha": 0.8, "linewidth": 1, "linestyle": "-", - "secondary_linestyle": "red", "size": 5, "marker": "o", "markersize": 5, @@ -85,7 +83,6 @@ "coastline": True, "coastline_color": "black", "coastline_linewidth": 1.5, - "borders": False, "borders_color": "black", "borders_linewidth": 1.5, # Color bars @@ -97,51 +94,54 @@ ######################## # Data-exploratory plots ######################## -def plot_magnitude_vs_time( +def plot_magnitude_versus_time( catalog: "CSEPCatalog", - ax: Optional[Axes] = None, - color: Optional[str] = "steelblue", - size: Optional[int] = 4, - max_size: Optional[int] = 300, - power: Optional[int] = 4, - alpha: Optional[float] = 0.5, + color: str = "steelblue", + size: int = 4, + max_size: int = 300, + power: int = 4, + alpha: float = 0.5, + ax: Optional[matplotlib.axes.Axes] = None, show: bool = False, **kwargs: Any, ) -> matplotlib.axes.Axes: """ - Plots magnitude versus time for a given catalog. + Scatter plot of the catalog magnitudes and origin times. The size of each event is scaled + exponentially by its magnitude using the parameters ``size``,``max_size`` and ``power``. Args: - catalog (:class:`csep.core.catalogs.CSEPCatalog`): - Catalog of seismic events to be plotted. - ax (Axes): - Matplotlib axis object on which to plot. If not provided, a new figure and axis are - created. - color (str): - Color of the scatter plot points. If not provided, defaults to value in - `DEFAULT_PLOT_ARGS`. - size (int): - Size of the event with the minimum magnitude - max_size (int): - Size of the event with the maximum magnitude - power (int): - Power scaling of the scatter sizing. - alpha (float): - Transparency level for the scatter plot points. If not provided, defaults to value - in `DEFAULT_PLOT_ARGS`. - show (bool): - Whether to display the plot. Defaults to `False`. + catalog (CSEPCatalog): Catalog of seismic events to be plotted. + color (str, optional): Color of the scatter plot. Defaults to `'steelblue'`. + size (int, optional): Marker size for the event with the minimum magnitude. Defaults + to `4`. + max_size (int, optional): Marker size for the event with the maximum magnitude. + Defaults to `300`. + power (int, optional): Power scaling of the scatter sizing. Defaults to `4`. + alpha (float, optional): Transparency level for the scatter points. Defaults to `0.5`. + ax (matplotlib.axes.Axes, optional): Axis object on which to plot. If not provided, a + new figure and axis are created. Defaults to `None`. + show (bool, optional): Whether to display the plot. Defaults to `False`. **kwargs: - Additional keyword arguments for customizing the plot. These are merged with - `DEFAULT_PLOT_ARGS`. - + Additional keyword arguments to customize the plot: + + - **figsize** (`tuple`): The size of the figure. + - **title** (`str`): Plot title. Defaults to `None`. + - **title_fontsize** (`int`): Font size for the plot title. + - **xlabel** (`str`): Label for the X-axis. Defaults to `'Datetime'`. + - **xlabel_fontsize** (`int`): Font size for the X-axis label. + - **ylabel** (`str`): Label for the Y-axis. Defaults to `'Magnitude'`. + - **ylabel_fontsize** (`int`): Font size for the Y-axis label. + - **datetime_locator** (`matplotlib.dates.Locator`): Locator for the + X-axis datetime ticks. + - **datetime_formatter** (`str` or `matplotlib.dates.Formatter`): + Formatter for the datetime axis. Defaults to `'%Y-%m-%d'`. + - **grid** (`bool`): Whether to show grid lines. Defaults to `True`. + - **tight_layout** (`bool`): Whether to use a tight layout for the figure. + Defaults to `True`. Returns: - Axes: - The Matplotlib axes object with the plotted data. - + matplotlib.axes.Axes: The Matplotlib axes object with the plotted data. """ - # Initialize plot plot_args = {**DEFAULT_PLOT_ARGS, **kwargs} fig, ax = pyplot.subplots(figsize=plot_args["figsize"]) if ax is None else (ax.figure, ax) @@ -160,10 +160,8 @@ def plot_magnitude_vs_time( # Set labels and title ax.set_xlabel(plot_args["xlabel"] or "Datetime", fontsize=plot_args["xlabel_fontsize"]) - ax.set_ylabel(plot_args["ylabel"] or "$M$", fontsize=plot_args["ylabel_fontsize"]) - ax.set_title( - plot_args["title"] or "Magnitude vs. Time", fontsize=plot_args["title_fontsize"] - ) + ax.set_ylabel(plot_args["ylabel"] or "Magnitude", fontsize=plot_args["ylabel_fontsize"]) + ax.set_title(plot_args["title"], fontsize=plot_args["title_fontsize"]) # Autoformat ticks and labels ax.xaxis.set_major_locator(plot_args["datetime_locator"]) @@ -184,30 +182,55 @@ def plot_cumulative_events_versus_time( observation: "CSEPCatalog", time_axis: str = "datetime", bins: int = 50, - ax: Optional[matplotlib.axes.Axes] = None, sim_label: Optional[str] = "Simulated", obs_label: Optional[str] = "Observation", + ax: Optional[matplotlib.axes.Axes] = None, show: bool = False, **kwargs, ) -> matplotlib.axes.Axes: """ - Plots the cumulative number of forecasted events versus observed events over time. + Plots the cumulative number of forecasted events from a + :class:`~csep.core.forecasts.CatalogForecast` versus the observed events over time. Args: - catalog_forecast (GriddedForecast): The forecasted catalogs. - observation (CSEPCatalog): The observed catalog of events. - time_axis (str): The type of time axis ('datetime', 'days', 'hours'). Defaults - to 'datetime'. - ax (Optional[pyplot.Axes]): The axes to plot on. If None, a new figure and - axes are created. - bins (int): The number of bins for time slicing. Defaults to 50. - sim_label (str): Label for simulated data. Defaults to 'Simulated'. - obs_label (str): Label for observed data. Defaults to 'Observation'. - show (bool): If True, displays the plot. Defaults to False. - **kwargs: Additional plotting arguments to override `DEFAULT_PLOT_ARGS`. + catalog_forecast (CatalogForecast): The forecasted synthetic catalogs. + observation (CSEPCatalog): The observed catalog. + time_axis (str, optional): The type of time axis (`'datetime'`, `'days'`, `'hours'`). + Defaults to `'datetime'`. + bins (int, optional): The number of bins for time slicing. Defaults to `50`. + sim_label (str, optional): Label for simulated data. Defaults to `'Simulated'`. + obs_label (str, optional): Label for observed data. Defaults to `'Observation'`. + ax (matplotlib.axes.Axes, optional): Axis object on which to plot. If not provided, a + new figure and axis are created. Defaults to `None`. + show (bool, optional): If True, displays the plot. Defaults to `False`. + **kwargs: Additional keyword arguments to customize the plot: + + - **figsize** (`tuple`): The size of the figure. + - **xlabel** (`str`): Label for the X-axis. Defaults to `'Datetime'`, + `'Days after Mainshock'`, or `'Hours after Mainshock'`, depending on + `time_axis`. + - **xlabel_fontsize** (`int`): Font size for the X-axis label. + - **ylabel** (`str`): Label for the Y-axis. Defaults to + `'Cumulative event counts'`. + - **ylabel_fontsize** (`int`): Font size for the Y-axis label. + - **title** (`str`): Title of the plot. Defaults to `None`. + - **title_fontsize** (`int`): Font size for the plot title. + - **color** (`str`): Color for the simulated forecast. + - **linewidth** (`float`): Line width for the plot lines. Defaults to + `1.5`. + - **grid** (`bool`): Whether to show grid lines. Defaults to `True`. + - **legend** (`bool`): Whether to show the legend. Defaults to `True`. + - **legend_loc** (`str`): Location of the legend. Defaults to `'best'`. + - **legend_fontsize** (`int`): Font size of the legend text. + - **tight_layout** (`bool`): Whether to use tight layout for the figure. + Defaults to `True`. + - **datetime_locator** (`matplotlib.dates.Locator`): Locator for the + X-axis datetime ticks. + - **datetime_formatter** (`str` or `matplotlib.dates.Formatter`): + Formatter for the datetime axis. Defaults to ``'%Y-%m-%d'``. Returns: - pyplot.Axes: The axes with the cumulative event plot. + matplotlib.axes.Axes: The Matplotlib axes object with the plotted data. """ # Initialize plot @@ -347,19 +370,41 @@ def plot_magnitude_histogram( event counts. Args: - catalog_forecast (CatalogForecast, List[CSEPCatalog]): A Catalog-based forecast + catalog_forecast (CatalogForecast or list of CSEPCatalog): A catalog-based forecast + or a list of observed catalogs. observation (CSEPCatalog): The observed catalog for comparison. - magnitude_bins (Optional[Union[List[float], numpy.ndarray]]): The bins for magnitude - histograms. If None, defaults to the region magnitudes or standard CSEP bins. - percentile (int): The percentile used for uncertainty intervals (default: 95). - ax (Optional[pyplot.Axes]): The axes object to draw the plot on. If None, a new - figure and axes are created. - show (bool): Whether to display the plot immediately (default: False). - **kwargs: Additional keyword arguments for plot customization. These override defaults. + magnitude_bins (list of float or numpy.ndarray, optional): The bins for magnitude + histograms. If `None`, defaults to the region magnitudes or standard CSEP bins. + Defaults to `None`. + percentile (int, optional): The percentile used for uncertainty intervals. Defaults to + `95`. + ax (matplotlib.axes.Axes, optional): The axes object to draw the plot on. If `None`, a + new figure and axes are created. Defaults to `None`. + show (bool, optional): Whether to display the plot immediately. Defaults to `False`. + **kwargs: Additional keyword arguments to customize the plot: + + - **figsize** (`tuple`): The size of the figure. + - **color** (`str`): Color for the observed histogram points. + - **markersize** (`int`): Size of the markers in the plot. Defaults to `6`. + - **capsize** (`float`): Size of the error bar caps. Defaults to `4`. + - **linewidth** (`float`): Width of the error bar lines. Defaults to `1.5`. + - **xlim** (`tuple`): Limits for the X-axis. + - **xlabel** (`str`): Label for the X-axis. Defaults to `'Magnitude'`. + - **xlabel_fontsize** (`int`): Font size for the X-axis label. + - **ylabel** (`str`): Label for the Y-axis. Defaults to `'Event count'`. + - **ylabel_fontsize** (`int`): Font size for the Y-axis label. + - **title** (`str`): Title of the plot. Defaults to `'Magnitude Histogram'`. + - **title_fontsize** (`int`): Font size for the plot title. + - **grid** (`bool`): Whether to show grid lines. Defaults to `True`. + - **legend_loc** (`str`): Location of the legend. Defaults to `'best'`. + - **legend_fontsize** (`int`): Font size of the legend text. + - **tight_layout** (`bool`): Whether to use tight layout for the figure. + Defaults to `True`. Returns: matplotlib.axes.Axes: The axes object containing the plot. """ + # Initialize plot plot_args = {**DEFAULT_PLOT_ARGS, **kwargs} fig, ax = pyplot.subplots(figsize=plot_args["figsize"]) if ax is None else (ax.figure, ax) @@ -459,349 +504,379 @@ def get_histogram_synthetic_cat(x, mags, normed=True): return ax -##################### -# Single Result plots -##################### -def plot_distribution_test( - evaluation_result: "EvaluationResult", - bins: Union[str, int, List[Any]] = "fd", - percentile: Optional[int] = 95, - ax: Optional[matplotlib.axes.Axes] = None, - auto_annotate: Union[bool, dict] = True, - sim_label: str = "Simulated", - obs_label: str = "Observation", - legend: bool = True, +############### +# Spatial plots +############### +def plot_basemap( + basemap: Optional[str] = None, + extent: Optional[List[float]] = None, + coastline: bool = True, + borders: bool = False, + tile_depth: Union[str, int] = "auto", + set_global: bool = False, + projection: Optional[ccrs.Projection] = None, + ax: Optional[pyplot.Axes] = None, show: bool = False, **kwargs, ) -> matplotlib.axes.Axes: """ - Plots a histogram of a single statistic for stochastic event sets and observations. + Wrapper function for multiple Cartopy base plots, including access to standard raster + web services and filesystem geoTIFF. Args: - evaluation_result (EvaluationResult): Object containing test distributions and - observed statistics. - bins (Union[str, int], optional): Binning strategy for the histogram. Defaults - to 'fd'. - percentile (int, optional): Percentile for shading regions. Defaults to None - (use global setting). - ax (Optional[matplotlib.axes.Axes], optional): Axes object to plot on. If None, - creates a new figure and axes. - auto_annotate (bool, dict): If True, automatically format the plot details based - on the evaluation result. It can be customized by passing the keyword arguments - `xlabel`, `ylabel`, `annotation_text`, `annotation_xy` and `annotation_fontsize`. - sim_label (str, optional): Label for the simulated data. - obs_label (str, optional): Label for the observation data. - legend (Optional[bool], optional): Whether to display the legend. Defaults to - global setting. - show (bool, optional): If True, show the plot. Defaults to False. - **kwargs: Additional keyword arguments for plot customization. + basemap (str): Possible values are: `'stock_img'`, `'google-satellite'`, + `'ESRI_terrain'`, `'ESRI_imagery'`, `'ESRI_relief'`, `'ESRI_topo'`, a custom web + service link, or a GeoTiff filepath. Defaults to `None`. + extent (list of float): `[lon_min, lon_max, lat_min, lat_max]`. Defaults to `None`. + coastline (bool): Flag to plot coastline. Defaults to `True`. + borders (bool): Flag to plot country borders. Defaults to `False`. + tile_depth (str or int): Zoom resolution level (`1-12`) of the basemap tiles. If + `'auto'`, it is automatically derived from extent. Defaults to `'auto'`. + set_global (bool): Display the complete globe as basemap. Required by global forecasts. + Defaults to `False`. + projection (cartopy.crs.Projection): Projection to be used in the basemap. It can be a + cartopy projection instance, or `approx` for a quick approximation of Mercator. + Defaults to :class:`~cartopy.crs.PlateCarree` if `None`. + ax (matplotlib.axes.Axes, optional): Previously defined ax object. If `None`, a new + axis is created. Defaults to `None`. + show (bool): If `True`, displays the plot. Defaults to `False`. + **kwargs: Additional keyword arguments to customize the plot: + + - **figsize** (`tuple`): The size of the figure. + - **coastline_color** (`str`): Color for the coastlines. + - **coastline_linewidth** (`float`): Line width for the coastlines. + - **borders_color** (`str`): Color for the borders. + - **borders_linewidth** (`float`): Line width for the borders. + - **grid** (`bool`): Whether to show grid lines. Defaults to `True`. + - **grid_labels** (`bool`): Whether to show grid labels. + - **grid_fontsize** (`int`): Font size for the grid labels. Returns: - matplotlib.axes.Axes: Matplotlib axes handle. + matplotlib.axes.Axes: The Matplotlib axes object with the plotted data. """ - # Initialize plot plot_args = {**DEFAULT_PLOT_ARGS, **kwargs} - fig, ax = pyplot.subplots(figsize=plot_args["figsize"]) if ax is None else (ax.figure, ax) - - # Get distributions - simulated = evaluation_result.test_distribution - observation = evaluation_result.observed_statistic - - # Remove any potential nans from arrays - simulated = numpy.array(simulated) - simulated = simulated[~numpy.isnan(simulated)] + ax = ax or _create_geo_axes(plot_args["figsize"], extent, projection, set_global) - # Plot forecast statistic histogram - n, bin_edges, patches = ax.hist( - simulated, - bins=bins, - label=sim_label, - color=plot_args["color"], - alpha=plot_args["alpha"], - edgecolor=None, - linewidth=0, + line_autoscaler = cartopy.feature.AdaptiveScaler("110m", (("50m", 50), ("10m", 5))) + tile_autoscaler = cartopy.feature.AdaptiveScaler(5, ((6, 50), (7, 15))) + tile_depth = ( + 4 + if set_global + else (tile_autoscaler.scale_from_extent(extent) if tile_depth == "auto" else tile_depth) ) - - # Plot observation statistic value/distribution - if isinstance(observation, (float, int)): - ax.axvline( - x=observation, - color="black", - linestyle="--", - label=obs_label + numpy.isinf(observation) * " (-inf)", + # Add coastlines and borders + if coastline: + ax.coastlines( + color=plot_args["coastline_color"], linewidth=plot_args["coastline_linewidth"] ) - elif isinstance(observation, (list, np.ndarray)): - observation = observation[~numpy.isnan(observation)] - ax.hist( - observation, - bins=bins, - label=obs_label, - edgecolor=None, - linewidth=0, - alpha=plot_args["alpha"], - color="green", + if borders: + borders = cartopy.feature.NaturalEarthFeature( + "cultural", + "admin_0_boundary_lines_land", + line_autoscaler, + edgecolor=plot_args["borders_color"], + facecolor="never", ) + ax.add_feature(borders, linewidth=plot_args["borders_linewidth"]) - # Annotate statistic analysis - ax = _annotate_distribution_plot(ax, evaluation_result, auto_annotate, plot_args) - - # Format axis object - if plot_args["xlim"] is None: - ax = _autoscale_histogram(ax, bin_edges, simulated, observation) - else: - ax.set_xlim(plot_args["xlim"]) - ax.grid(plot_args["grid"]) - - if legend: - ax.legend(loc=plot_args["legend_loc"], fontsize=plot_args["legend_fontsize"]) + # Add basemap tiles + try: + if basemap == "stock_img": + ax.stock_img() + elif basemap is not None: + basemap_obj = _get_basemap(basemap) + # basemap_obj is a cartopy TILE IMAGE + if isinstance(basemap_obj, GoogleWTS): + ax.add_image(basemap_obj, tile_depth) + # basemap_obj is a rasterio image + elif isinstance(basemap_obj, DatasetReader): + ax = rio_plot.show(basemap_obj, ax=ax) - # Color bars for rejection area (after setting legend) - if percentile is not None: - inc = (100 - percentile) / 2 - inc_high = 100 - inc - inc_low = inc + except Exception as e: + print( + f"Unable to plot basemap. This might be due to no internet access. " + f"Error: {str(e)}" + ) - p_high = numpy.percentile(simulated, inc_high) - idx_high = numpy.digitize(p_high, bin_edges) - p_low = numpy.percentile(simulated, inc_low) - idx_low = numpy.digitize(p_low, bin_edges) - for idx in range(idx_low): - patches[idx].set_fc("red") - for idx in range(idx_high, len(patches)): - patches[idx].set_fc("red") + # Set up Grid-lines + if plot_args["grid"]: + _add_gridlines(ax, plot_args["grid_labels"], plot_args["grid_fontsize"]) - if plot_args["tight_layout"]: - fig.tight_layout() if show: pyplot.show() return ax -def plot_calibration_test( - evaluation_result: "EvaluationResult", - percentile: float = 95, +def plot_catalog( + catalog: "CSEPCatalog", + basemap: Optional[str] = None, + projection: Optional[Union[ccrs.Projection, str]] = None, + extent: Optional[Sequence[float]] = None, + set_global: bool = False, + mag_ticks: Optional[Union[Sequence[float], numpy.ndarray, int]] = None, + size: float = 15, + max_size: float = 300, + power: float = 3, + min_val: Optional[float] = None, + max_val: Optional[float] = None, + plot_region: bool = False, ax: Optional[matplotlib.axes.Axes] = None, - label: Optional[str] = None, show: bool = False, **kwargs, ) -> matplotlib.axes.Axes: """ - Plots a calibration test (QQ plot) with confidence intervals. + Spatial plot of catalog. Can be plotted over a basemap if desired, by passing the keyword + parameters of the function :func:`~csep.utils.plots.plot_basemap`. The size of the events + is automatically scaled according to their magnitude. Fine-tuning of an exponential sizing + function can be set with the parameters ``size``, ``max_size``, ``power``, ``min_val`` and + ``max_val``. Args: - evaluation_result (EvaluationResult): The evaluation result object containing the test - distribution. - percentile (float): Percentile to build confidence interval - ax (Optional[matplotlib.axes.Axes]): Axes object to plot on. If None, creates a new - figure. - show (bool): If True, displays the plot. Default is False. - label (Optional[str]): Label for the plotted data. If None, uses - `evaluation_result.sim_name`. - **kwargs: Additional keyword arguments for customizing the plot. These are merged with - `DEFAULT_PLOT_ARGS`. + catalog (CSEPCatalog): Catalog object to be plotted. + basemap (str): Passed to :func:`~csep.utils.plots.plot_basemap` along with `kwargs`. + Possible values are: `'stock_img'`, `'google-satellite'`, `'ESRI_terrain'`, + `'ESRI_imagery'`, `'ESRI_relief'`, `'ESRI_topo'`, a custom web service link, or a + GeoTiff filepath. Defaults to `None`. + projection (cartopy.crs.Projection or str): Projection to be used in the underlying + basemap. Can be a cartopy projection instance, or `approx` for a quick approximation + of Mercator. Defaults to :class:`~cartopy.crs.PlateCarree` if `None`. + extent (list of float): Defaults to `1.05` * :meth:`catalog.region.get_bbox`. Defaults + to `None`. + set_global (bool): Display the complete globe. Defaults to `False`. + mag_ticks (list of float, int): Ticks to display in the legend. Can be an array/list of + magnitudes, or a number of bins to discretize the magnitude range. Defaults to + `None`. + size (float): Size of the minimum magnitude event. Defaults to `15`. + max_size (float): Size of the maximum magnitude event. Defaults to `300`. + power (float): Power scaling of the scatter sizing. Defaults to `3`. + min_val (float): Override minimum magnitude of the catalog for scatter sizing. Useful + to plot multiple catalogs with different magnitude ranges. Defaults to `None`. + max_val (float): Override maximum magnitude of the catalog for scatter sizing. Useful + to plot multiple catalogs with different magnitude ranges. Defaults to `None`. + plot_region (bool): Flag to plot the catalog region border. Defaults to `False`. + ax (matplotlib.axes.Axes): Previously defined ax object. Defaults to `None`. + show (bool): If `True`, displays the plot. Defaults to `False`. + **kwargs: Additional keyword arguments to customize the plot: + + - **alpha** (`float`): Transparency level for the scatter points. + - **markercolor** (`str`): Color for the scatter points. + - **markeredgecolor** (`str`): Color for the edges of the scatter points. + - **figsize** (`tuple`): The size of the figure. + - **legend** (`bool`): Whether to display a legend. Defaults to `True`. + - **legend_title** (`str`): Title for the legend. + - **legend_labelspacing** (`float`): Spacing between labels in the legend. + - **legend_borderpad** (`float`): Border padding for the legend. + - **legend_framealpha** (`float`): Frame alpha for the legend. + - **region_color** (`str`): Color for the region border. + - **title** (`str`): Title of the plot. + - **title_fontsize** (`int`): Font size of the plot title. + - **tight_layout** (`bool`): Whether to use tight layout for the figure. Returns: - pyplot.Axes: The matplotlib axes object containing the plot. + matplotlib.axes.Axes: The Matplotlib axes object with the plotted data. """ - # Initialize plot - plot_args = {**DEFAULT_PLOT_ARGS, **kwargs} - fig, ax = pyplot.subplots(figsize=plot_args["figsize"]) if ax is None else (ax.figure, ax) - - # Set up QQ plots and KS test - n = len(evaluation_result.test_distribution) - k = numpy.arange(1, n + 1) - # Plotting points for uniform quantiles - pp = k / (n + 1) - # Compute confidence intervals for order statistics using beta distribution - inf = (100 - percentile) / 2 - sup = 100 - (100 - percentile) / 2 - ulow = beta.ppf(inf / 100, k, n - k + 1) - uhigh = beta.ppf(sup / 100, k, n - k + 1) - # Quantiles should be sorted for plotting - sorted_td = numpy.sort(evaluation_result.test_distribution) - - if ax is None: - fig, ax = pyplot.subplots() - else: - ax = ax + plot_args = {**DEFAULT_PLOT_ARGS, **kwargs} + # Get spatial information for plotting + extent = extent or _calculate_spatial_extent(catalog, set_global, plot_region) + # Instantiate GeoAxes object + ax = ax or _create_geo_axes(plot_args["figsize"], extent, projection, set_global) + # chain basemap + ax = plot_basemap(basemap, extent, ax=ax, set_global=set_global, show=False, **plot_args) - # Plot QQ plot - ax.plot( - sorted_td, - pp, - linewidth=0, - label=label or evaluation_result.sim_name, - c=plot_args["color"], - marker=plot_args["marker"], - markersize=plot_args["markersize"], + # Plot catalog + ax.scatter( + catalog.get_longitudes(), + catalog.get_latitudes(), + s=_autosize_scatter( + values=catalog.get_magnitudes(), + min_size=size, + max_size=max_size, + power=power, + min_val=min_val, + max_val=max_val, + ), + transform=ccrs.PlateCarree(), + color=plot_args["markercolor"], + edgecolors=plot_args["markeredgecolor"], + alpha=plot_args["alpha"], ) - # Plot uncertainty on uniform quantiles - ax.plot(pp, pp, "-k") - ax.plot(ulow, pp, ":k") - ax.plot(uhigh, pp, ":k") + # Legend + if plot_args["legend"]: + if isinstance(mag_ticks, (list, numpy.ndarray)): + mag_ticks = numpy.array(mag_ticks) + else: + mw_range = [min(catalog.get_magnitudes()), max(catalog.get_magnitudes())] + mag_ticks = numpy.linspace(mw_range[0], mw_range[1], mag_ticks or 4, endpoint=True) - # Format plot - ax.grid(plot_args["grid"]) - ax.set_title(plot_args["title"] or "Calibration test", fontsize=plot_args["title_fontsize"]) - ax.set_xlabel( - plot_args["xlabel"] or "Quantile scores", fontsize=plot_args["xlabel_fontsize"] - ) - ax.set_ylabel( - plot_args["ylabel"] or "Standard uniform quantiles", - fontsize=plot_args["ylabel_fontsize"], - ) - ax.legend(loc=plot_args["legend_loc"], fontsize=plot_args["legend_fontsize"]) + # Map mag_ticks to marker sizes using the custom size mapping function + legend_sizes = _autosize_scatter( + values=mag_ticks, + min_size=size, + max_size=max_size, + power=power, + min_val=min_val or numpy.min(catalog.get_magnitudes()), + max_val=max_val or numpy.max(catalog.get_magnitudes()), + ) - if plot_args["tight_layout"]: - fig.tight_layout() - if show: - pyplot.show() + # Create custom legend handles + handles = [ + pyplot.Line2D( + [0], + [0], + marker="o", + lw=0, + label=str(m), + markersize=numpy.sqrt(s), + markerfacecolor="gray", + alpha=0.5, + markeredgewidth=0.8, + markeredgecolor="black", + ) + for m, s in zip(mag_ticks, legend_sizes) + ] + + ax.legend( + handles, + numpy.round(mag_ticks, 1), + loc=plot_args["legend_loc"], + handletextpad=5, + title=plot_args.get("legend_title") or "Magnitudes", + fontsize=plot_args["legend_fontsize"], + title_fontsize=plot_args["legend_titlesize"], + labelspacing=plot_args["legend_labelspacing"], + borderpad=plot_args["legend_borderpad"], + framealpha=plot_args["legend_framealpha"], + ) + + # Draw catalog's region border + if plot_region: + try: + pts = catalog.region.tight_bbox() + ax.plot(pts[:, 0], pts[:, 1], lw=1, color=plot_args["region_color"]) + except AttributeError: + pass + + ax.set_title(plot_args["title"], fontsize=plot_args["title_fontsize"], y=1.06) + + if plot_args["tight_layout"]: + ax.figure.tight_layout() + + if show: + pyplot.show() return ax -##################### -# Results batch plots -##################### -def plot_comparison_test( - results_t: List["EvaluationResult"], - results_w: Optional[List["EvaluationResult"]] = None, - percentile: int = 95, - ax: Optional[matplotlib.axes.Axes] = None, +def plot_gridded_dataset( + gridded: numpy.ndarray, + region: "CartesianGrid2D", + basemap: Optional[str] = None, + ax: Optional[pyplot.Axes] = None, + projection: Optional[Union[ccrs.Projection, str]] = None, show: bool = False, + extent: Optional[List[float]] = None, + set_global: bool = False, + plot_region: bool = True, + colorbar: bool = True, + colormap: Union[str, matplotlib.colors.Colormap] = "viridis", + clim: Optional[Tuple[float, float]] = None, + clabel: Optional[str] = None, + alpha: Optional[float] = None, + alpha_exp: float = 0, **kwargs, -) -> pyplot.Axes: +) -> matplotlib.axes.Axes: """ - Plots a list of T-Test (and optional W-Test) results on a single axis. + Plot spatial gridded dataset such as data from a gridded forecast. Can be plotted over a + basemap if desired, by passing the keyword parameters of the function + :func:`~csep.utils.plots.plot_basemap`. The color map can be fine-tuned using the arguments + ``colorbar``, ``colormap``, ``clim``. An exponential transparency function can be set + with ``alpha`` and ``alpha_exp``. Args: - results_t (List[EvaluationResult]): List of T-Test results. - results_w (Optional[List[EvaluationResult]]): List of W-Test results. If provided, they - are plotted alongside the T-Test results. - percentile (int): Percentile for coloring W-Test results. Default is 95. - ax (Optional[matplotlib.axes.Axes]): Matplotlib axes object to plot on. If None, a new - figure and axes are created. - show (bool): If True, the plot is displayed after creation. Default is False. - **kwargs: Additional plotting arguments to override defaults. + gridded (numpy.ndarray): 2D-square array of values corresponding to the region. See + :class:`~csep.core.regions.CartesianGrid2D` and + :meth:`~csep.core.regions.CartesianGrid2D.get_cartesian` to convert a 1D array + (whose indices correspond to each spatial cell) to a 2D-square array. + region (CartesianGrid2D): Region in which gridded values are contained. + basemap (str): Passed to :func:`~csep.utils.plots.plot_basemap` along with `kwargs`. + ax (matplotlib.axes.Axes): Previously defined ax object. Defaults to `None`. + projection (cartopy.crs.Projection or str): Projection to be used in the underlying + basemap. It can be a cartopy projection instance, or `approx` for a quick + approximation of Mercator. Defaults to :class:`~cartopy.crs.PlateCarree` if `None`. + show (bool): If `True`, displays the plot. Defaults to `False`. + extent (list of float): ``[lon_min, lon_max, lat_min, lat_max]``. Defaults to `None`. + set_global (bool): Display the complete globe as basemap. Defaults to `False`. + plot_region (bool): If `True`, plot the dataset region border. Defaults to `True`. + colorbar (bool): If `True`, include a colorbar. Defaults to `True`. + colormap (str or matplotlib.colors.Colormap): Colormap to use. Defaults to `'viridis'`. + clim (tuple of float): Range of the colorbar. Defaults to `None`. + clabel (str): Label of the colorbar. Defaults to `None`. + alpha (float): Transparency level. Defaults to `None`. + alpha_exp (float): Exponent for the alpha function (recommended between `0.4` and `1`). + Defaults to `0`. + **kwargs: Additional keyword arguments to customize the plot: + + - **colorbar_labelsize** (`int`): Font size for the colorbar label. + - **colorbar_ticksize** (`int`): Font size for the colorbar ticks. + - **figsize** (`tuple`): The size of the figure. + - **region_color** (`str`): Color for the region border. + - **title** (`str`): Title of the plot. + - **title_fontsize** (`int`): Font size of the plot title. Returns: - pyplot.Axes: The matplotlib axes object containing the plot. + matplotlib.axes.Axes: Matplotlib axes object. """ - # Initialize plot plot_args = {**DEFAULT_PLOT_ARGS, **kwargs} - fig, ax = pyplot.subplots(figsize=plot_args["figsize"]) if ax is None else (ax.figure, ax) - - # Iterate through T-test results, or jointly through t- and w- test results - Results = zip(results_t, results_w) if results_w else zip(results_t) - for index, result in enumerate(Results): - result_t = result[0] - result_w = result[1] if results_w else None - - # Get confidence bounds - ylow = result_t.observed_statistic - result_t.test_distribution[0] - yhigh = result_t.test_distribution[1] - result_t.observed_statistic - color = _get_marker_t_color(result_t.test_distribution) - if not numpy.isinf(result_t.observed_statistic): - # Plot observed statistic and confidence bounds - ax.errorbar( - index, - result_t.observed_statistic, - yerr=numpy.array([[ylow, yhigh]]).T, - color=color, - linewidth=plot_args["linewidth"], - capsize=plot_args["capsize"], - ) + # Get spatial information for plotting + extent = extent or _calculate_spatial_extent(region, set_global, plot_region) + # Instantiate GeoAxes object + ax = ax or _create_geo_axes(plot_args["figsize"], extent, projection, set_global) - facecolor = "white" - if result_w is not None: - if _get_marker_w_color(result_w.quantile, percentile): - facecolor = _get_marker_t_color(result_t.test_distribution) + # chain basemap + ax = plot_basemap(basemap, extent, ax=ax, set_global=set_global, show=False, **plot_args) - ax.plot( - index, - result_t.observed_statistic, - marker="o", - markerfacecolor=facecolor, - markeredgecolor=color, - markersize=plot_args["markersize"], - ) - else: - print( - f"Diverging information gain for forecast {result_t.sim_name}, index {index}. " - f"Check for zero-valued bins within the forecast" - ) - ax.axvspan(index - 0.5, index + 0.5, alpha=0.5, facecolor="red") + # Define colormap and alpha transparency + colormap, alpha = _get_colormap(colormap, alpha_exp, alpha) - # Format plot - ax.axhline(y=0, linestyle="--", color="black") - ax.set_xticks(numpy.arange(len(results_t))) - ax.set_xticklabels( - [res.sim_name[0] for res in results_t], - rotation=90, - fontsize=plot_args["xlabel_fontsize"], + # Plot spatial dataset + lons, lats = numpy.meshgrid( + numpy.append(region.xs, region.get_bbox()[1]), + numpy.append(region.ys, region.get_bbox()[3]), ) - ax.set_ylabel( - plot_args["ylabel"] or "Information gain per earthquake", - fontsize=plot_args["ylabel_fontsize"], + im = ax.pcolor( + lons, lats, gridded, cmap=colormap, alpha=alpha, snap=True, transform=ccrs.PlateCarree() ) + im.set_clim(clim) - ax.set_title(plot_args["title"] or results_t[0].name) - ax.set_ylim(plot_args["ylim"]) - ax.set_xlim([-0.5, len(results_t) - 0.5]) - - if plot_args["grid"]: - ax.yaxis.grid() - ax.yaxis.set_major_locator(pyplot.MaxNLocator(integer=True)) - - if plot_args["hbars"]: - if len(results_t) > 2: - ax.bar( - ax.get_xticks(), - numpy.array([9999] * len(ax.get_xticks())), - bottom=-2000, - width=(ax.get_xticks()[1] - ax.get_xticks()[0]), - color=["gray", "w"], - alpha=0.2, - ) - - if plot_args["legend"]: - # Add custom legend to explain results - legend_elements = [ - Line2D([0], [0], color="red", lw=2, - label=f"T-test rejected ($\\alpha = {results_t[0].quantile[-1]}$)"), - Line2D([0], [0], color="green", lw=2, label="T-test non-rejected"), - Line2D([0], [0], color="gray", lw=2, label="T-test indistinguishable"), - Line2D( - [0], - [0], - color="gray", - lw=2, - marker="o", - markersize=6, - markerfacecolor="green", - label="W-test non-rejected", - ), - Line2D( - [0], - [0], - color="gray", - lw=2, - marker="o", - markersize=6, - markerfacecolor="white", - label="W-test indistinguishable", - ), - ] - ax.legend(handles=legend_elements, loc="best", fontsize=plot_args["legend_fontsize"]) + # Colorbar options + if colorbar: + cax = ax.get_figure().add_axes( + [ + ax.get_position().x1 + 0.01, + ax.get_position().y0, + 0.025, + ax.get_position().height, + ], + label="Colorbar", + ) + cbar = ax.get_figure().colorbar(im, ax=ax, cax=cax) + cbar.set_label(clabel, fontsize=plot_args["colorbar_labelsize"]) + cbar.ax.tick_params(labelsize=plot_args["colorbar_ticksize"]) + # Draw forecast's region border + if plot_region and not set_global: + try: + pts = region.tight_bbox() + ax.plot(pts[:, 0], pts[:, 1], lw=1, color=plot_args["region_color"]) + except AttributeError: + pass - if plot_args["tight_layout"]: - fig.tight_layout() + ax.set_title(plot_args["title"], fontsize=plot_args["title_fontsize"], y=1.06) if show: pyplot.show() @@ -809,218 +884,234 @@ def plot_comparison_test( return ax -def plot_consistency_test( - eval_results: Union[List["EvaluationResult"], "EvaluationResult"], - normalize: bool = False, - one_sided_lower: bool = False, - percentile: float = 95, - ax: Optional[pyplot.Axes] = None, - plot_mean: bool = False, - color: str = "black", +##################### +# Single Result plots +##################### +def plot_distribution_test( + evaluation_result: "EvaluationResult", + bins: Union[str, int, List[Any]] = "fd", + percentile: Optional[int] = 95, + ax: Optional[matplotlib.axes.Axes] = None, + auto_annotate: Union[bool, dict] = True, + sim_label: str = "Simulated", + obs_label: str = "Observation", + legend: bool = True, show: bool = False, **kwargs, ) -> matplotlib.axes.Axes: """ - Plots the results from ultiple consistency tests. The distribution of score results from - multiple realizations of a model are plotted as a line representing for a given percentile. - The score of the observation under a model is plotted as a marker. The model is assumed - inconsistent when the observation score lies outside from the model distribution for a - two-sided test, or lies to the right of the distribution for a one-sided test. + Plots a histogram of a single statistic for stochastic event sets and observations. Args: - eval_results (Union[List[EvaluationResult], EvaluationResult]): - Evaluation results from one or multiple models - normalize (bool): - Normalize the forecast likelihood by observed likelihood (default: False). - percentile (float): - Percentile for the extent of the model score distribution (default: 95). - one_sided_lower (bool): - Plot for a one-sided test (default: False). - ax (Optional[pyplot.Axes]): - Axes object to plot on (default: None). - plot_mean (bool): - Plot the mean of the test distribution (default: False). - color (str): - Color for the line representing a model score distribution (default: 'black'). - show (bool): - If True, display the plot (default: False). - **kwargs: - Additional keyword arguments for plot customization from DEFAULT_PLOT_ARGS. + evaluation_result (EvaluationResult): Object containing test + distributions and observed statistics. + bins (str, int, or list): Binning strategy for the histogram. See + :func:`matplotlib.pyplot.hist` for details on this parameter. Defaults to `'fd'`. + percentile (int): Percentile for shading regions. Defaults to `95`. + ax (matplotlib.axes.Axes): Axes object to plot on. If `None`, creates a new figure + and axes. Defaults to `None`. + auto_annotate (bool or dict): If `True`, automatically formats the plot details based + on the evaluation result. It can be further customized by passing the keyword + arguments `xlabel`, `ylabel`, `annotation_text`, `annotation_xy`, and + `annotation_fontsize`. Defaults to `True`. + sim_label (str): Label for the simulated data. Defaults to `'Simulated'`. + obs_label (str): Label for the observation data. Defaults to `'Observation'`. + legend (bool): Whether to display the legend. Defaults to `True`. + show (bool): If `True`, shows the plot. Defaults to `False`. + **kwargs: Additional keyword arguments for plot customization. + + - **color** (`str`): Color of the histogram bars. + - **alpha** (`float`): Transparency level for the histogram bars. + - **figsize** (`tuple`): The size of the figure. + - **xlim** (`tuple`): Limits for the X-axis. + - **grid** (`bool`): Whether to display grid lines. Defaults to `True`. + - **legend_loc** (`str`): Location of the legend. Defaults to `'best'`. + - **legend_fontsize** (`int`): Font size of the legend text. + - **xlabel**: Label of the X-axis. If `auto_annotate` is `True`, will be set to the + test statistic name. + - **ylabel**: Label of the Y-axis. + - **annotate_text**: Annotate the plot. If `auto_annotate` is `True`, it will + provide information about the statistics of the test. + - **annotate_xy**: Position for `annotate_text` in axes fraction. Can be override + if `auto_annotate` does not give an optimal position + - **annotate_fontsize**: Size of the annotation. + - **tight_layout** (`bool`): Whether to use tight layout for the figure. + Defaults to `True`. Returns: - matplotlib.axes.Axes: Matplotlib axes object with the consistency test plot. + matplotlib.axes.Axes: Matplotlib axes handle. """ # Initialize plot plot_args = {**DEFAULT_PLOT_ARGS, **kwargs} fig, ax = pyplot.subplots(figsize=plot_args["figsize"]) if ax is None else (ax.figure, ax) - # Ensure eval_results is a list - results = list(eval_results) if isinstance(eval_results, list) else [eval_results] - results.reverse() + # Get distributions + simulated = evaluation_result.test_distribution + observation = evaluation_result.observed_statistic - xlims = [] + # Remove any potential nans from arrays + simulated = numpy.array(simulated) + simulated = simulated[~numpy.isnan(simulated)] - for index, res in enumerate(results): - plow, phigh, mean, observed_statistic = _process_stat_distribution( - res, percentile, normalize, one_sided_lower - ) + # Plot forecast statistic histogram + n, bin_edges, patches = ax.hist( + simulated, + bins=bins, + label=sim_label, + color=plot_args["color"], + alpha=plot_args["alpha"], + edgecolor=None, + linewidth=0, + ) - if not numpy.isinf(observed_statistic): # Check if test result does not diverge - percentile_lims = numpy.abs(numpy.array([[mean - plow, phigh - mean]]).T) - ax.plot( - observed_statistic, - index, - _get_marker_style(observed_statistic, (plow, phigh), one_sided_lower), - ) - ax.errorbar( - mean, - index, - xerr=percentile_lims, - fmt="ko" if plot_mean else "k", - capsize=plot_args["capsize"], - linewidth=plot_args["linewidth"], - ecolor=color, - ) - # Determine the limits to use - xlims.append((plow, phigh, observed_statistic)) + # Plot observation statistic value/distribution + if isinstance(observation, (float, int)): + ax.axvline( + x=observation, + color="black", + linestyle="--", + label=obs_label + numpy.isinf(observation) * " (-inf)", + ) + elif isinstance(observation, (list, numpy.ndarray)): + observation = observation[~numpy.isnan(observation)] + ax.hist( + observation, + bins=bins, + label=obs_label, + edgecolor=None, + linewidth=0, + alpha=plot_args["alpha"], + color="green", + ) - # Extend distribution to +inf, in case it is a one-sided test - if one_sided_lower and observed_statistic >= plow and phigh < observed_statistic: - xt = numpy.linspace(phigh, 99999, 100) - yt = numpy.ones(100) * index - ax.plot(xt, yt, linestyle="--", linewidth=plot_args["linewidth"], color=color) - else: - print( - f"Observed statistic diverges for forecast {res.sim_name}, index {index}. " - f"Check for zero-valued bins within the forecast" - ) - ax.barh(index, 99999, left=-10000, height=1, color=["red"], alpha=0.5) + # Annotate statistic analysis + ax = _annotate_distribution_plot(ax, evaluation_result, auto_annotate, plot_args) + + # Format axis object + if plot_args["xlim"] is None: + ax = _autoscale_histogram(ax, bin_edges, simulated, observation) + else: + ax.set_xlim(plot_args["xlim"]) + ax.grid(plot_args["grid"]) + + if legend: + ax.legend(loc=plot_args["legend_loc"], fontsize=plot_args["legend_fontsize"]) + + # Color bars for rejection area (after setting legend) + if percentile is not None: + inc = (100 - percentile) / 2 + inc_high = 100 - inc + inc_low = inc + + p_high = numpy.percentile(simulated, inc_high) + idx_high = numpy.digitize(p_high, bin_edges) + p_low = numpy.percentile(simulated, inc_low) + idx_low = numpy.digitize(p_low, bin_edges) + for idx in range(idx_low): + patches[idx].set_fc("red") + for idx in range(idx_high, len(patches)): + patches[idx].set_fc("red") - # Plot formatting - try: - ax.set_xlim(*_get_axis_limits(xlims)) - except ValueError: - raise ValueError("All EvaluationResults have infinite observed_statistics") - ax.set_ylim([-0.5, len(results) - 0.5]) - ax.set_yticks(numpy.arange(len(results))) - ax.set_yticklabels([res.sim_name for res in results], fontsize=plot_args["ylabel_fontsize"]) - ax.set_xlabel( - plot_args["xlabel"] or "Statistic distribution", fontsize=plot_args["xlabel_fontsize"] - ) - ax.tick_params(axis="x", labelsize=plot_args["xticks_fontsize"]) - ax.set_title(plot_args["title"], fontsize=plot_args["title_fontsize"]) - if plot_args["hbars"]: - yTickPos = ax.get_yticks() - if len(yTickPos) >= 2: - ax.barh( - yTickPos, - numpy.array([99999] * len(yTickPos)), - left=-10000, - height=(yTickPos[1] - yTickPos[0]), - color=["w", "gray"], - alpha=0.2, - zorder=0, - ) if plot_args["tight_layout"]: fig.tight_layout() - if show: pyplot.show() return ax -################### -# Alarm-based plots -################### -def plot_concentration_ROC_diagram( - forecast: "GriddedForecast", - catalog: "CSEPCatalog", - linear: bool = True, - plot_uniform: bool = True, - show: bool = True, - ax: Optional[pyplot.Axes] = None, +def plot_calibration_test( + evaluation_result: "EvaluationResult", + percentile: float = 95, + ax: Optional[matplotlib.axes.Axes] = None, + label: Optional[str] = None, + show: bool = False, **kwargs, ) -> matplotlib.axes.Axes: """ - Plots the Concentration ROC Diagram for a given forecast and observed catalog. + Plots a calibration test (Quantile-Quantile plot) with confidence intervals. Args: - forecast (GriddedForecast): Forecast object containing spatial forecast data. - catalog (CSEPCatalog): Catalog object containing observed data. - linear (bool): If True, uses a linear scale for the X-axis, otherwise logarithmic. - ax (Optional[pyplot.Axes]): Axes object to plot on (default: None). - plot_uniform (bool): If True, plots the uniform (random) model as a reference - (default: True). - show (bool): If True, displays the plot (default: True). - ax (Optional[pyplot.Axes]): Axes object to plot on (default: None). - **kwargs: Additional keyword arguments for customization. + evaluation_result (EvaluationResult): The evaluation result object containing + the test distribution. + percentile (float): Percentile to build confidence interval. Defaults to `95`. + ax (matplotlib.axes.Axes): Axes object to plot on. If `None`, creates a new figure. + Defaults to `None`. + label (str): Label for the plotted data. If `None`, uses + `evaluation_result.sim_name`. Defaults to `None`. + show (bool): If `True`, displays the plot. Defaults to `False`. + **kwargs: Additional keyword arguments for customizing the plot: + + - **color** (`str`): Color of the plot line and markers. + - **marker** (`str`): Marker style for the data points. + - **markersize** (`float`): Size of the markers. + - **grid** (`bool`): Whether to display grid lines. Defaults to `True`. + - **title** (`str`): Title of the plot. + - **title_fontsize** (`int`): Font size for the plot title. + - **xlabel** (`str`): Label for the X-axis. + - **xlabel_fontsize** (`int`): Font size for the X-axis label. + - **ylabel** (`str`): Label for the Y-axis. + - **ylabel_fontsize** (`int`): Font size for the Y-axis label. + - **legend_loc** (`str`): Location of the legend. Defaults to `'best'`. + - **legend_fontsize** (`int`): Font size of the legend text. + - **tight_layout** (`bool`): Whether to use tight layout for the figure. + Defaults to `True`. Returns: - matplotlib.axes.Axes: The Axes object with the plot. + matplotlib.axes.Axes: The Matplotlib axes object containing the plot. """ - # Initialize plot plot_args = {**DEFAULT_PLOT_ARGS, **kwargs} fig, ax = pyplot.subplots(figsize=plot_args["figsize"]) if ax is None else (ax.figure, ax) - if not catalog.region == forecast.region: - raise AttributeError("catalog region and forecast region must be identical.") - - # Getting data - forecast_label = plot_args.get("forecast_label", forecast.name or "Forecast") - observed_label = plot_args.get("observation_label", "Observations") - - area_km2 = catalog.region.get_cell_area() - obs_counts = catalog.spatial_counts() - rate = forecast.spatial_counts() - - indices = numpy.argsort(rate) - indices = numpy.flip(indices) + # Set up QQ plots and KS test + n = len(evaluation_result.test_distribution) + k = numpy.arange(1, n + 1) + # Plotting points for uniform quantiles + pp = k / (n + 1) + # Compute confidence intervals for order statistics using beta distribution + inf = (100 - percentile) / 2 + sup = 100 - (100 - percentile) / 2 + ulow = beta.ppf(inf / 100, k, n - k + 1) + uhigh = beta.ppf(sup / 100, k, n - k + 1) - fore_norm_sorted = numpy.cumsum(rate[indices]) / numpy.sum(rate) - area_norm_sorted = numpy.cumsum(area_km2[indices]) / numpy.sum(area_km2) - obs_norm_sorted = numpy.cumsum(obs_counts[indices]) / numpy.sum(obs_counts) + # Quantiles should be sorted for plotting + sorted_td = numpy.sort(evaluation_result.test_distribution) - # Plot data - if plot_uniform: - ax.plot(area_norm_sorted, area_norm_sorted, "k--", label="Uniform") + if ax is None: + fig, ax = pyplot.subplots() + else: + ax = ax + # Plot QQ plot ax.plot( - area_norm_sorted, - fore_norm_sorted, - label=forecast_label, - color=plot_args["secondary_color"], + sorted_td, + pp, + linewidth=0, + label=label or evaluation_result.sim_name, + c=plot_args["color"], + marker=plot_args["marker"], + markersize=plot_args["markersize"], ) - ax.step( - area_norm_sorted, - obs_norm_sorted, - label=observed_label, - color=plot_args["color"], - linestyle=plot_args["linestyle"], - ) + # Plot uncertainty on uniform quantiles + ax.plot(pp, pp, "-k") + ax.plot(ulow, pp, ":k") + ax.plot(uhigh, pp, ":k") - # Plot formatting - ax.set_title(plot_args["title"], fontsize=plot_args["title_fontsize"]) + # Format plot ax.grid(plot_args["grid"]) - if not linear: - ax.set_xscale("log") - ax.set_ylabel( - plot_args["ylabel"] or "True Positive Rate", fontsize=plot_args["ylabel_fontsize"] - ) + ax.set_title(plot_args["title"] or "Calibration test", fontsize=plot_args["title_fontsize"]) ax.set_xlabel( - plot_args["xlabel"] or "False Positive Rate (Normalized Area)", - fontsize=plot_args["xlabel_fontsize"], + plot_args["xlabel"] or "Quantile scores", fontsize=plot_args["xlabel_fontsize"] ) - if plot_args["legend"]: - ax.legend( - loc=plot_args["legend_loc"], - shadow=True, - fontsize=plot_args["legend_fontsize"], - framealpha=plot_args["legend_framealpha"], - ) + ax.set_ylabel( + plot_args["ylabel"] or "Standard uniform quantiles", + fontsize=plot_args["ylabel_fontsize"], + ) + ax.legend(loc=plot_args["legend_loc"], fontsize=plot_args["legend_fontsize"]) + if plot_args["tight_layout"]: fig.tight_layout() if show: @@ -1029,106 +1120,157 @@ def plot_concentration_ROC_diagram( return ax -def plot_ROC_diagram( - forecast: "GriddedForecast", - catalog: "CSEPCatalog", - linear: bool = True, - plot_uniform: bool = True, - show: bool = True, - ax: Optional[pyplot.Axes] = None, +##################### +# Results batch plots +##################### +def plot_comparison_test( + results_t: List["EvaluationResult"], + results_w: Optional[List["EvaluationResult"]] = None, + percentile: int = 95, + ax: Optional[matplotlib.axes.Axes] = None, + show: bool = False, **kwargs, -) -> matplotlib.pyplot.Axes: +) -> pyplot.Axes: """ - Plots the ROC (Receiver Operating Characteristic) curve for a given forecast and observed - catalog. + Plots a list of T-Test (and optional W-Test) results on a single axis. Args: - forecast (GriddedForecast): Forecast object containing spatial forecast data. - catalog (CSEPCatalog): Catalog object containing observed data. - linear (bool): If True, uses a linear scale for the X-axis, otherwise logarithmic. - plot_uniform (bool): If True, plots the uniform (random) model as a reference (default: - True). - show (bool): If True, displays the plot (default: True). - ax (Optional[pyplot.Axes]): Axes object to plot on (default: None). - **kwargs: Additional keyword arguments for customization. + results_t (list of EvaluationResult): List of T-Test results. + results_w (list of EvaluationResult, optional): List of W-Test results. If + provided, they are plotted alongside the T-Test results. Defaults to `None`. + percentile (int): Percentile for coloring W-Test results. Defaults to `95`. + ax (matplotlib.axes.Axes): Matplotlib axes object to plot on. If `None`, a new + figure and axes are created. Defaults to `None`. + show (bool): If `True`, the plot is displayed after creation. Defaults to `False`. + **kwargs: Additional keyword arguments for customizing the plot: + + - **linewidth** (`float`): Width of the error bars. + - **capsize** (`float`): Size of the caps on the error bars. + - **markersize** (`float`): Size of the markers. + - **xlabel_fontsize** (`int`): Font size for the X-axis labels. + - **ylabel** (`str`): Label for the Y-axis. Defaults to `'Information gain per earthquake'`. + - **ylabel_fontsize** (`int`): Font size for the Y-axis label. + - **title** (`str`): Title of the plot. Defaults to the name of the first T-Test result. + - **ylim** (`tuple`): Limits for the Y-axis. + - **grid** (`bool`): Whether to display grid lines. Defaults to `True`. + - **hbars** (`bool`): Whether to include horizontal bars for visual separation. + - **legend** (`bool`): Whether to display a legend. Defaults to `True`. + - **legend_fontsize** (`int`): Font size for the legend text. + - **tight_layout** (`bool`): Whether to use tight layout for the figure. Defaults + to `True`. Returns: - pyplot.Axes: The Axes object with the plot. + matplotlib.axes.Axes: The Matplotlib axes object containing the plot. """ # Initialize plot plot_args = {**DEFAULT_PLOT_ARGS, **kwargs} fig, ax = pyplot.subplots(figsize=plot_args["figsize"]) if ax is None else (ax.figure, ax) - if not catalog.region == forecast.region: - raise RuntimeError("catalog region and forecast region must be identical.") - - rate = forecast.spatial_counts() - obs_counts = catalog.spatial_counts() - - indices = numpy.argsort(rate)[::-1] # Sort in descending order + # Iterate through T-test results, or jointly through t- and w- test results + Results = zip(results_t, results_w) if results_w else zip(results_t) + for index, result in enumerate(Results): + result_t = result[0] + result_w = result[1] if results_w else None - thresholds = (rate[indices]) / numpy.sum(rate) - obs_counts = obs_counts[indices] + # Get confidence bounds + ylow = result_t.observed_statistic - result_t.test_distribution[0] + yhigh = result_t.test_distribution[1] - result_t.observed_statistic + color = _get_marker_t_color(result_t.test_distribution) - Table_ROC = pandas.DataFrame({"Threshold": [], "H": [], "F": []}) + if not numpy.isinf(result_t.observed_statistic): + # Plot observed statistic and confidence bounds + ax.errorbar( + index, + result_t.observed_statistic, + yerr=numpy.array([[ylow, yhigh]]).T, + color=color, + linewidth=plot_args["linewidth"], + capsize=plot_args["capsize"], + ) - for threshold in thresholds: - threshold = float(threshold) - binary_forecast = numpy.where(thresholds >= threshold, 1, 0) - forecastedYes_observedYes = obs_counts[(binary_forecast == 1) & (obs_counts > 0)] - forecastedYes_observedNo = obs_counts[(binary_forecast == 1) & (obs_counts == 0)] - forecastedNo_observedYes = obs_counts[(binary_forecast == 0) & (obs_counts > 0)] - forecastedNo_observedNo = obs_counts[(binary_forecast == 0) & (obs_counts == 0)] + facecolor = "white" + if result_w is not None: + if _get_marker_w_color(result_w.quantile, percentile): + facecolor = _get_marker_t_color(result_t.test_distribution) - H = len(forecastedYes_observedYes) / ( - len(forecastedYes_observedYes) + len(forecastedNo_observedYes) - ) - F = len(forecastedYes_observedNo) / ( - len(forecastedYes_observedNo) + len(forecastedNo_observedNo) - ) - - threshold_row = {"Threshold": threshold, "H": H, "F": F} - Table_ROC = pandas.concat( - [Table_ROC, pandas.DataFrame([threshold_row])], ignore_index=True - ) + ax.plot( + index, + result_t.observed_statistic, + marker="o", + markerfacecolor=facecolor, + markeredgecolor=color, + markersize=plot_args["markersize"], + ) + else: + print( + f"Diverging information gain for forecast {result_t.sim_name}, index {index}. " + f"Check for zero-valued bins within the forecast" + ) + ax.axvspan(index - 0.5, index + 0.5, alpha=0.5, facecolor="red") - Table_ROC = pandas.concat( - [pandas.DataFrame({"H": [0], "F": [0]}), Table_ROC], ignore_index=True + # Format plot + ax.axhline(y=0, linestyle="--", color="black") + ax.set_xticks(numpy.arange(len(results_t))) + ax.set_xticklabels( + [res.sim_name[0] for res in results_t], + rotation=90, + fontsize=plot_args["xlabel_fontsize"], ) - - ax.plot( - Table_ROC["F"], - Table_ROC["H"], - label=plot_args.get("forecast_label", forecast.name or "Forecast"), - color=plot_args["color"], - linestyle=plot_args["linestyle"], + ax.set_ylabel( + plot_args["ylabel"] or "Information gain per earthquake", + fontsize=plot_args["ylabel_fontsize"], ) - if plot_uniform: - ax.plot( - numpy.arange(0, 1.001, 0.001), - numpy.arange(0, 1.001, 0.001), - linestyle="--", - color="gray", - label="Uniform", - ) + ax.set_title(plot_args["title"] or results_t[0].name) + ax.set_ylim(plot_args["ylim"]) + ax.set_xlim([-0.5, len(results_t) - 0.5]) + + if plot_args["grid"]: + ax.yaxis.grid() + ax.yaxis.set_major_locator(pyplot.MaxNLocator(integer=True)) + + if plot_args["hbars"]: + if len(results_t) > 2: + ax.bar( + ax.get_xticks(), + numpy.array([9999] * len(ax.get_xticks())), + bottom=-2000, + width=(ax.get_xticks()[1] - ax.get_xticks()[0]), + color=["gray", "w"], + alpha=0.2, + ) - # Plot formatting - ax.set_ylabel(plot_args["ylabel"] or "Hit Rate", fontsize=plot_args["ylabel_fontsize"]) - ax.set_xlabel( - plot_args["xlabel"] or "Fraction of False Alarms", fontsize=plot_args["xlabel_fontsize"] - ) - if not linear: - ax.set_xscale("log") - ax.set_yscale("linear") - ax.tick_params(axis="x", labelsize=plot_args["xticks_fontsize"]) - ax.tick_params(axis="y", labelsize=plot_args["yticks_fontsize"]) if plot_args["legend"]: - ax.legend( - loc=plot_args["legend_loc"], shadow=True, fontsize=plot_args["legend_fontsize"] - ) - ax.set_title(plot_args["title"], fontsize=plot_args["title_fontsize"]) + # Add custom legend to explain results + legend_elements = [ + Line2D([0], [0], color="red", lw=2, + label=f"T-test rejected ($\\alpha = {results_t[0].quantile[-1]}$)"), + Line2D([0], [0], color="green", lw=2, label="T-test non-rejected"), + Line2D([0], [0], color="gray", lw=2, label="T-test indistinguishable"), + Line2D( + [0], + [0], + color="gray", + lw=2, + marker="o", + markersize=6, + markerfacecolor="green", + label="W-test non-rejected", + ), + Line2D( + [0], + [0], + color="gray", + lw=2, + marker="o", + markersize=6, + markerfacecolor="white", + label="W-test indistinguishable", + ), + ] + ax.legend(handles=legend_elements, loc="best", fontsize=plot_args["legend_fontsize"]) + if plot_args["tight_layout"]: fig.tight_layout() @@ -1138,425 +1280,376 @@ def plot_ROC_diagram( return ax -def plot_Molchan_diagram( - forecast: "GriddedForecast", - catalog: "CSEPCatalog", - linear: bool = True, - plot_uniform: bool = True, - show: bool = True, +def plot_consistency_test( + eval_results: Union[List["EvaluationResult"], "EvaluationResult"], + normalize: bool = False, + one_sided_lower: bool = False, + percentile: float = 95, ax: Optional[pyplot.Axes] = None, + plot_mean: bool = False, + color: str = "black", + show: bool = False, **kwargs, ) -> matplotlib.axes.Axes: """ - Plot the Molchan Diagram based on forecast and test catalogs using the contingency table. - The Area Skill score and its error are shown in the legend. - - The Molchan diagram is computed following this procedure: - 1. Obtain spatial rates from the GriddedForecast and the observed events from the catalog. - 2. Rank the rates in descending order (highest rates first). - 3. Sort forecasted rates by ordering found in (2), and normalize rates so their sum is equal - to unity. - 4. Obtain binned spatial rates from the observed catalog. - 5. Sort gridded observed rates by ordering found in (2). - 6. Test each ordered and normalized forecasted rate defined in (3) as a threshold value to - obtain the corresponding contingency table. - 7. Define the "nu" (Miss rate) and "tau" (Fraction of spatial alarmed cells) for each - threshold using the information provided by the corresponding contingency table defined in - (6). - - Note: - 1. The testing catalog and forecast should have exactly the same time-window (duration). - 2. Forecasts should be defined over the same region. - 3. If calling this function multiple times, update the color in the arguments. - 4. The user can choose the x-scale (linear or log). + Plots the results from multiple consistency tests. The distribution of score results from + multiple realizations of a model are plotted as a line representing a given percentile. + The score of the observation under a model is plotted as a marker. The model is assumed + inconsistent when the observation score lies outside the model distribution for a + two-sided test, or lies to the right of the distribution for a one-sided test. Args: - forecast (GriddedForecast): The forecast object. - catalog (CSEPCatalog): The evaluation catalog. - linear (bool): If True, a linear x-axis is used; if False, a logarithmic x-axis is used. - plot_uniform (bool): If True, include a uniform forecast on the plot. - show (bool): If True, displays the plot. - ax (Optional[pyplot.Axes]): Axes object to plot on (default: None). - **kwargs: Additional keyword arguments for customization. + eval_results (list of EvaluationResult or EvaluationResult): Evaluation results from one + or multiple models. + normalize (bool): Normalize the forecast likelihood by observed likelihood. Defaults + to `False`. + one_sided_lower (bool): Plot for a one-sided test. Defaults to `False`. + percentile (float): Percentile for the extent of the model score distribution. Defaults + to `95`. + ax (matplotlib.axes.Axes): Axes object to plot on. If `None`, creates a new figure. + Defaults to `None`. + plot_mean (bool): Plot the mean of the test distribution. Defaults to `False`. + color (str): Color for the line representing a model score distribution. Defaults to + `'black'`. + show (bool): If `True`, displays the plot. Defaults to `False`. + **kwargs: Additional keyword arguments for plot customization: + + - **figsize** (`tuple`): The size of the figure. + - **capsize** (`float`): Size of the caps on the error bars. + - **linewidth** (`float`): Width of the error bars and lines. + - **xlabel** (`str`): Label for the X-axis. + - **xlabel_fontsize** (`int`): Font size for the X-axis label. + - **ylabel_fontsize** (`int`): Font size for the Y-axis labels. + - **xticks_fontsize** (`int`): Font size for the X-axis ticks. + - **title** (`str`): Title of the plot. + - **title_fontsize** (`int`): Font size of the plot title. + - **hbars** (`bool`): Whether to include horizontal bars for visual separation. + - **tight_layout** (`bool`): Whether to use tight layout for the figure. + Defaults to `True`. Returns: - matplotlib.axes.Axes: The Axes object with the plot. - - Raises: - RuntimeError: If the catalog and forecast do not have the same region. + matplotlib.axes.Axes: Matplotlib axes object with the consistency test plot. """ + # Initialize plot plot_args = {**DEFAULT_PLOT_ARGS, **kwargs} fig, ax = pyplot.subplots(figsize=plot_args["figsize"]) if ax is None else (ax.figure, ax) - if not catalog.region == forecast.region: - raise RuntimeError("Catalog region and forecast region must be identical.") - - forecast_label = plot_args.get("forecast_label", forecast.name or "Forecast") - - # Obtain forecast rates (or counts) and observed catalog aggregated in spatial cells - rate = forecast.spatial_counts() - obs_counts = catalog.spatial_counts() - - # Get index of rates (descending sort) - indices = numpy.argsort(rate) - indices = numpy.flip(indices) - - # Order forecast and cells rates by highest rate cells first - thresholds = (rate[indices]) / numpy.sum(rate) - obs_counts = obs_counts[indices] - - Table_molchan = pandas.DataFrame( - { - "Threshold": [], - "Successful_bins": [], - "Obs_active_bins": [], - "tau": [], - "nu": [], - "R_score": [], - } - ) - - # Each forecasted and normalized rate is tested as a threshold value to define the - # contingency table. - for threshold in thresholds: - threshold = float(threshold) + # Ensure eval_results is a list + results = list(eval_results) if isinstance(eval_results, list) else [eval_results] + results.reverse() - binary_forecast = numpy.where(thresholds >= threshold, 1, 0) - forecastedYes_observedYes = obs_counts[(binary_forecast == 1) & (obs_counts > 0)] - forecastedYes_observedNo = obs_counts[(binary_forecast == 1) & (obs_counts == 0)] - forecastedNo_observedYes = obs_counts[(binary_forecast == 0) & (obs_counts > 0)] - forecastedNo_observedNo = obs_counts[(binary_forecast == 0) & (obs_counts == 0)] + xlims = [] - # Creating the DataFrame for the contingency table - data = { - "Observed": [len(forecastedYes_observedYes), len(forecastedNo_observedYes)], - "Not Observed": [len(forecastedYes_observedNo), len(forecastedNo_observedNo)], - } - index = ["Forecasted", "Not Forecasted"] - contingency_df = pandas.DataFrame(data, index=index) - nu = contingency_df.loc["Not Forecasted", "Observed"] / contingency_df["Observed"].sum() - tau = contingency_df.loc["Forecasted"].sum() / ( - contingency_df.loc["Forecasted"].sum() + contingency_df.loc["Not Forecasted"].sum() - ) - R_score = ( - contingency_df.loc["Forecasted", "Observed"] / contingency_df["Observed"].sum() - ) - ( - contingency_df.loc["Forecasted", "Not Observed"] - / contingency_df["Not Observed"].sum() + for index, res in enumerate(results): + plow, phigh, mean, observed_statistic = _process_stat_distribution( + res, percentile, normalize, one_sided_lower ) - threshold_row = { - "Threshold": threshold, - "Successful_bins": contingency_df.loc["Forecasted", "Observed"], - "Obs_active_bins": contingency_df["Observed"].sum(), - "tau": tau, - "nu": nu, - "R_score": R_score, - } - threshold_row_df = pandas.DataFrame([threshold_row]) - - Table_molchan = pandas.concat([Table_molchan, threshold_row_df], ignore_index=True) - - bottom_row = { - "Threshold": "Full alarms", - "tau": 1, - "nu": 0, - "Obs_active_bins": contingency_df["Observed"].sum(), - } - top_row = { - "Threshold": "No alarms", - "tau": 0, - "nu": 1, - "Obs_active_bins": contingency_df["Observed"].sum(), - } - - Table_molchan = pandas.concat( - [pandas.DataFrame([top_row]), Table_molchan], ignore_index=True - ) - Table_molchan = pandas.concat( - [Table_molchan, pandas.DataFrame([bottom_row])], ignore_index=True - ) - - # Computation of Area Skill score (ASS) - Tab_as_score = pandas.DataFrame() - Tab_as_score["Threshold"] = Table_molchan["Threshold"] - Tab_as_score["tau"] = Table_molchan["tau"] - Tab_as_score["nu"] = Table_molchan["nu"] - - ONE = numpy.ones(len(Tab_as_score)) - Tab_as_score["CUM_BAND"] = cumulative_trapezoid( - ONE, Tab_as_score["tau"], initial=0 - ) - cumulative_trapezoid(Tab_as_score["nu"], Tab_as_score["tau"], initial=0) - Tab_as_score["AS_score"] = numpy.divide( - Tab_as_score["CUM_BAND"], - cumulative_trapezoid(ONE, Tab_as_score["tau"], initial=0) + 1e-10, - ) - Tab_as_score.loc[Tab_as_score.index[-1], "AS_score"] = max( - 0.5, Tab_as_score["AS_score"].iloc[-1] - ) - ASscore = numpy.round(Tab_as_score.loc[Tab_as_score.index[-1], "AS_score"], 2) - - bin_size = 0.01 - devstd = numpy.sqrt(1 / (12 * Table_molchan["Obs_active_bins"].iloc[0])) - devstd = devstd * bin_size**-1 - devstd = numpy.ceil(devstd + 0.5) - devstd = devstd / bin_size**-1 - dev_std = numpy.round(devstd, 2) - - # Plot the Molchan trajectory - ax.plot( - Table_molchan["tau"], - Table_molchan["nu"], - label=f"{forecast_label}, ASS={ASscore}±{dev_std} ", - color=plot_args["color"], - linestyle=plot_args["linestyle"], - ) + if not numpy.isinf(observed_statistic): # Check if test result does not diverge + percentile_lims = numpy.abs(numpy.array([[mean - plow, phigh - mean]]).T) + ax.plot( + observed_statistic, + index, + _get_marker_style(observed_statistic, (plow, phigh), one_sided_lower), + ) + ax.errorbar( + mean, + index, + xerr=percentile_lims, + fmt="ko" if plot_mean else "k", + capsize=plot_args["capsize"], + linewidth=plot_args["linewidth"], + ecolor=color, + ) + # Determine the limits to use + xlims.append((plow, phigh, observed_statistic)) - # Plot uniform forecast - if plot_uniform: - x_uniform = numpy.arange(0, 1.001, 0.001) - y_uniform = numpy.arange(1.00, -0.001, -0.001) - ax.plot(x_uniform, y_uniform, linestyle="--", color="gray", label="Uniform") + # Extend distribution to +inf, in case it is a one-sided test + if one_sided_lower and observed_statistic >= plow and phigh < observed_statistic: + xt = numpy.linspace(phigh, 99999, 100) + yt = numpy.ones(100) * index + ax.plot(xt, yt, linestyle="--", linewidth=plot_args["linewidth"], color=color) + else: + print( + f"Observed statistic diverges for forecast {res.sim_name}, index {index}. " + f"Check for zero-valued bins within the forecast" + ) + ax.barh(index, 99999, left=-10000, height=1, color=["red"], alpha=0.5) # Plot formatting - ax.set_ylabel(plot_args["ylabel"] or "Miss Rate", fontsize=plot_args["ylabel_fontsize"]) + try: + ax.set_xlim(*_get_axis_limits(xlims)) + except ValueError: + raise ValueError("All EvaluationResults have infinite observed_statistics") + ax.set_ylim([-0.5, len(results) - 0.5]) + ax.set_yticks(numpy.arange(len(results))) + ax.set_yticklabels([res.sim_name for res in results], fontsize=plot_args["ylabel_fontsize"]) ax.set_xlabel( - plot_args["xlabel"] or "Fraction of area occupied by alarms", - fontsize=plot_args["xlabel_fontsize"], + plot_args["xlabel"] or "Statistic distribution", fontsize=plot_args["xlabel_fontsize"] ) - if not linear: - ax.set_xscale("log") - ax.tick_params(axis="x", labelsize=plot_args["xlabel_fontsize"]) - ax.tick_params(axis="y", labelsize=plot_args["ylabel_fontsize"]) - ax.legend(loc=plot_args["legend_loc"], shadow=True, fontsize=plot_args["legend_fontsize"]) - ax.set_title(plot_args["title"] or "Molchan Diagram", fontsize=plot_args["title_fontsize"]) - + ax.tick_params(axis="x", labelsize=plot_args["xticks_fontsize"]) + ax.set_title(plot_args["title"], fontsize=plot_args["title_fontsize"]) + if plot_args["hbars"]: + yTickPos = ax.get_yticks() + if len(yTickPos) >= 2: + ax.barh( + yTickPos, + numpy.array([99999] * len(yTickPos)), + left=-10000, + height=(yTickPos[1] - yTickPos[0]), + color=["w", "gray"], + alpha=0.2, + zorder=0, + ) if plot_args["tight_layout"]: fig.tight_layout() + if show: pyplot.show() + return ax -############### -# Spatial plots -############### -def plot_basemap( - basemap: Optional[str] = None, - extent: Optional[List[float]] = None, - coastline: bool = True, - borders: bool = False, - tile_depth: Union[str, int] = "auto", - set_global: bool = False, - projection: ccrs.Projection = ccrs.PlateCarree(), +################### +# Alarm-based plots +################### +def plot_concentration_ROC_diagram( + forecast: "GriddedForecast", + catalog: "CSEPCatalog", + linear: bool = True, + plot_uniform: bool = True, + show: bool = True, ax: Optional[pyplot.Axes] = None, - show: bool = False, **kwargs, ) -> matplotlib.axes.Axes: """ - Wrapper function for multiple Cartopy base plots, including access to standard raster - webservices. + Plots the Concentration ROC Diagram for a given forecast and observed catalog. Args: - basemap (str): Possible values are: 'stock_img', 'google-satellite', 'ESRI_terrain', - 'ESRI_imagery', 'ESRI_relief', 'ESRI_topo', a custom webservice link, - or a GeoTiff filepath. Default is None. - extent (list): [lon_min, lon_max, lat_min, lat_max] - ax (matplotlib.Axes): Previously defined ax object. - coastline (bool): Flag to plot coastline. Default True. - borders (bool): Flag to plot country borders. Default False. - tile_depth (str/int): Zoom level (1-12) of the basemap tiles. If 'auto', it is - automatically derived from extent. - set_global (bool): Display the complete globe as basemap. - projection (cartopy.crs.Projection): Projection to be used in the basemap. - show (bool): If True, displays the plot. + forecast (GriddedForecast): Forecast object containing spatial forecast data. + catalog (CSEPCatalog): Catalog object containing observed data. + linear (bool): If True, uses a linear scale for the X-axis, otherwise logarithmic. + Defaults to `True`. + plot_uniform (bool): If True, plots the uniform (random) model as a reference. + Defaults to `True`. + show (bool): If True, displays the plot. Defaults to `True`. + ax (matplotlib.axes.Axes): Axes object to plot on. If `None`, creates a new figure. + Defaults to `None`. + **kwargs: Additional keyword arguments for customization: + + - **figsize** (`tuple`): The size of the figure. + - **forecast_label** (`str`): Label for the forecast data in the plot. + - **observation_label** (`str`): Label for the observation data in the plot. + - **color** (`str`): Color for the observed data line. + - **secondary_color** (`str`): Color for the forecast data line. + - **linestyle** (`str`): Line style for the observed data line. + - **title** (`str`): Title of the plot. + - **title_fontsize** (`int`): Font size of the plot title. + - **xlabel** (`str`): Label for the X-axis. + - **xlabel_fontsize** (`int`): Font size for the X-axis label. + - **ylabel** (`str`): Label for the Y-axis. + - **ylabel_fontsize** (`int`): Font size for the Y-axis label. + - **grid** (`bool`): Whether to show grid lines. Defaults to `True`. + - **legend** (`bool`): Whether to display a legend. Defaults to `True`. + - **legend_loc** (`str`): Location of the legend. Defaults to `'best'`. + - **legend_fontsize** (`int`): Font size of the legend text. + - **legend_framealpha** (`float`): Transparency level for the legend frame. + - **tight_layout** (`bool`): Whether to use tight layout for the figure. + Defaults to `True`. Returns: - matplotlib.Axes: Matplotlib Axes object. + matplotlib.axes.Axes: The Axes object with the plot. """ + # Initialize plot plot_args = {**DEFAULT_PLOT_ARGS, **kwargs} - ax = ax or _create_geo_axes(plot_args["figsize"], extent, projection, set_global) + fig, ax = pyplot.subplots(figsize=plot_args["figsize"]) if ax is None else (ax.figure, ax) - line_autoscaler = cartopy.feature.AdaptiveScaler("110m", (("50m", 50), ("10m", 5))) - tile_autoscaler = cartopy.feature.AdaptiveScaler(5, ((6, 50), (7, 15))) - tile_depth = ( - 4 - if set_global - else (tile_autoscaler.scale_from_extent(extent) if tile_depth == "auto" else tile_depth) - ) - # Add coastlines and borders - if coastline: - ax.coastlines( - color=plot_args["coastline_color"], linewidth=plot_args["coastline_linewidth"] - ) - if borders: - borders = cartopy.feature.NaturalEarthFeature( - "cultural", - "admin_0_boundary_lines_land", - line_autoscaler, - edgecolor=plot_args["borders_color"], - facecolor="never", - ) - ax.add_feature(borders, linewidth=plot_args["borders_linewidth"]) + if not catalog.region == forecast.region: + raise AttributeError("catalog region and forecast region must be identical.") - # Add basemap tiles - try: - if basemap == "stock_img": - ax.stock_img() - elif basemap is not None: - basemap_obj = _get_basemap(basemap) - # basemap_obj is a cartopy TILE IMAGE - if isinstance(basemap_obj, GoogleWTS): - ax.add_image(basemap_obj, tile_depth) - # basemap_obj is a rasterio image - elif isinstance(basemap_obj, DatasetReader): - ax = rio_plot.show(basemap_obj, ax=ax) + # Getting data + forecast_label = plot_args.get("forecast_label", forecast.name or "Forecast") + observed_label = plot_args.get("observation_label", "Observations") - except Exception as e: - print( - f"Unable to plot basemap. This might be due to no internet access. " - f"Error: {str(e)}" - ) + area_km2 = catalog.region.get_cell_area() + obs_counts = catalog.spatial_counts() + rate = forecast.spatial_counts() - # Set up Grid-lines - if plot_args["grid"]: - _add_gridlines(ax, plot_args["grid_labels"], plot_args["grid_fontsize"]) + indices = numpy.argsort(rate) + indices = numpy.flip(indices) + + fore_norm_sorted = numpy.cumsum(rate[indices]) / numpy.sum(rate) + area_norm_sorted = numpy.cumsum(area_km2[indices]) / numpy.sum(area_km2) + obs_norm_sorted = numpy.cumsum(obs_counts[indices]) / numpy.sum(obs_counts) + + # Plot data + if plot_uniform: + ax.plot(area_norm_sorted, area_norm_sorted, "k--", label="Uniform") + + ax.plot( + area_norm_sorted, + fore_norm_sorted, + label=forecast_label, + color=plot_args["secondary_color"], + ) + + ax.step( + area_norm_sorted, + obs_norm_sorted, + label=observed_label, + color=plot_args["color"], + linestyle=plot_args["linestyle"], + ) + # Plot formatting + ax.set_title(plot_args["title"], fontsize=plot_args["title_fontsize"]) + ax.grid(plot_args["grid"]) + if not linear: + ax.set_xscale("log") + ax.set_ylabel( + plot_args["ylabel"] or "True Positive Rate", fontsize=plot_args["ylabel_fontsize"] + ) + ax.set_xlabel( + plot_args["xlabel"] or "False Positive Rate (Normalized Area)", + fontsize=plot_args["xlabel_fontsize"], + ) + if plot_args["legend"]: + ax.legend( + loc=plot_args["legend_loc"], + shadow=True, + fontsize=plot_args["legend_fontsize"], + framealpha=plot_args["legend_framealpha"], + ) + if plot_args["tight_layout"]: + fig.tight_layout() if show: pyplot.show() return ax -def plot_catalog( +def plot_ROC_diagram( + forecast: "GriddedForecast", catalog: "CSEPCatalog", - basemap: Optional[str] = None, - ax: Optional[matplotlib.axes.Axes] = None, - projection: Optional[Union[ccrs.Projection, str]] = ccrs.PlateCarree(), - show: bool = False, - extent: Optional[Sequence[float]] = None, - set_global: bool = False, - mag_ticks: Optional[Union[Sequence[float], np.ndarray, int]] = None, - size: float = 15, - max_size: float = 300, - power: float = 3, - min_val: Optional[float] = None, - max_val: Optional[float] = None, - plot_region: bool = False, + linear: bool = True, + plot_uniform: bool = True, + show: bool = True, + ax: Optional[pyplot.Axes] = None, **kwargs, -) -> matplotlib.axes.Axes: - """Plot catalog in a region. +) -> matplotlib.pyplot.Axes: + """ + Plots the ROC (Receiver Operating Characteristic) curve for a given forecast and observed + catalog. Args: - catalog (:class:`CSEPCatalog`): Catalog object to be plotted. - basemap (str): Optional. Passed to :func:`plot_basemap` along with `kwargs` - ax (:class:`matplotlib.pyplot.ax`): Previously defined ax object. - show (bool): Flag if the figure is displayed. - extent (list): Default 1.05 * :func:`catalog.region.get_bbox()`. - projection (cartopy.crs.Projection): Projection to be used in the underlying basemap - set_global (bool): Display the complete globe as basemap. - size (float): Size of the event with the minimum magnitude - max_size (float): Size of the catalog's maximum magnitude - power (float, list): Power scaling of the scatter sizing. - min_val (float): Override minimum magnitude of the catalog for scatter sizing - max_val (float): Override maximum magnitude of the catalog for scatter sizing - mag_ticks (list, int): Ticks to display in the legend. - plot_region (bool): Flag to plot the catalog region border. - kwargs: alpha, markercolor, markeredgecolor, figsize, legend, - legend_title, legend_labelspacing, legend_borderpad, legend_framealpha + forecast (GriddedForecast): Forecast object containing spatial forecast data. + catalog (CSEPCatalog): Catalog object containing observed data. + linear (bool): If True, uses a linear scale for the X-axis, otherwise logarithmic. + Defaults to `True`. + plot_uniform (bool): If True, plots the uniform (random) model as a reference. + Defaults to `True`. + show (bool): If True, displays the plot. Defaults to `True`. + ax (matplotlib.axes.Axes): Axes object to plot on. If `None`, creates a new figure. + Defaults to `None`. + **kwargs: Additional keyword arguments for customization: + + - **figsize** (`tuple`): The size of the figure. + - **forecast_label** (`str`): Label for the forecast data in the plot. + - **color** (`str`): Color for the ROC curve line. + - **linestyle** (`str`): Line style for the ROC curve. + - **xlabel** (`str`): Label for the X-axis. + - **xlabel_fontsize** (`int`): Font size for the X-axis label. + - **ylabel** (`str`): Label for the Y-axis. + - **ylabel_fontsize** (`int`): Font size for the Y-axis label. + - **xticks_fontsize** (`int`): Font size for the X-axis ticks. + - **yticks_fontsize** (`int`): Font size for the Y-axis ticks. + - **legend** (`bool`): Whether to display a legend. Defaults to `True`. + - **legend_loc** (`str`): Location of the legend. Defaults to `'best'`. + - **legend_fontsize** (`int`): Font size of the legend text. + - **tight_layout** (`bool`): Whether to use tight layout for the figure. + Defaults to `True`. Returns: - :class:`matplotlib.pyplot.ax` object + matplotlib.pyplot.Axes: The Axes object with the plot. """ + # Initialize plot plot_args = {**DEFAULT_PLOT_ARGS, **kwargs} - # Get spatial information for plotting - extent = extent or _calculate_spatial_extent(catalog, set_global, plot_region) - # Instantiate GeoAxes object - ax = ax or _create_geo_axes(plot_args["figsize"], extent, projection, set_global) - # chain basemap - ax = plot_basemap(basemap, extent, ax=ax, set_global=set_global, show=False, **plot_args) + fig, ax = pyplot.subplots(figsize=plot_args["figsize"]) if ax is None else (ax.figure, ax) - # Plot catalog - ax.scatter( - catalog.get_longitudes(), - catalog.get_latitudes(), - s=_autosize_scatter( - values=catalog.get_magnitudes(), - min_size=size, - max_size=max_size, - power=power, - min_val=min_val, - max_val=max_val, - ), - transform=ccrs.PlateCarree(), - color=plot_args["markercolor"], - edgecolors=plot_args["markeredgecolor"], - alpha=plot_args["alpha"], - ) + if not catalog.region == forecast.region: + raise RuntimeError("catalog region and forecast region must be identical.") - # Legend - if plot_args["legend"]: - if isinstance(mag_ticks, (list, np.ndarray)): - mag_ticks = np.array(mag_ticks) - else: - mw_range = [min(catalog.get_magnitudes()), max(catalog.get_magnitudes())] - mag_ticks = np.linspace(mw_range[0], mw_range[1], mag_ticks or 4, endpoint=True) + rate = forecast.spatial_counts() + obs_counts = catalog.spatial_counts() - # Map mag_ticks to marker sizes using the custom size mapping function - legend_sizes = _autosize_scatter( - values=mag_ticks, - min_size=size, - max_size=max_size, - power=power, - min_val=min_val or np.min(catalog.get_magnitudes()), - max_val=max_val or np.max(catalog.get_magnitudes()), - ) + indices = numpy.argsort(rate)[::-1] # Sort in descending order - # Create custom legend handles - handles = [ - pyplot.Line2D( - [0], - [0], - marker="o", - lw=0, - label=str(m), - markersize=np.sqrt(s), - markerfacecolor="gray", - alpha=0.5, - markeredgewidth=0.8, - markeredgecolor="black", - ) - for m, s in zip(mag_ticks, legend_sizes) - ] + thresholds = (rate[indices]) / numpy.sum(rate) + obs_counts = obs_counts[indices] - ax.legend( - handles, - np.round(mag_ticks, 1), - loc=plot_args["legend_loc"], - handletextpad=5, - title=plot_args.get("legend_title") or "Magnitudes", - fontsize=plot_args["legend_fontsize"], - title_fontsize=plot_args["legend_titlesize"], - labelspacing=plot_args["legend_labelspacing"], - borderpad=plot_args["legend_borderpad"], - framealpha=plot_args["legend_framealpha"], + Table_ROC = pandas.DataFrame({"Threshold": [], "H": [], "F": []}) + + for threshold in thresholds: + threshold = float(threshold) + binary_forecast = numpy.where(thresholds >= threshold, 1, 0) + forecastedYes_observedYes = obs_counts[(binary_forecast == 1) & (obs_counts > 0)] + forecastedYes_observedNo = obs_counts[(binary_forecast == 1) & (obs_counts == 0)] + forecastedNo_observedYes = obs_counts[(binary_forecast == 0) & (obs_counts > 0)] + forecastedNo_observedNo = obs_counts[(binary_forecast == 0) & (obs_counts == 0)] + + H = len(forecastedYes_observedYes) / ( + len(forecastedYes_observedYes) + len(forecastedNo_observedYes) + ) + F = len(forecastedYes_observedNo) / ( + len(forecastedYes_observedNo) + len(forecastedNo_observedNo) ) - # Draw catalog's region border - if plot_region: - try: - pts = catalog.region.tight_bbox() - ax.plot(pts[:, 0], pts[:, 1], lw=1, color=plot_args["region_color"]) - except AttributeError: - pass + threshold_row = {"Threshold": threshold, "H": H, "F": F} + Table_ROC = pandas.concat( + [Table_ROC, pandas.DataFrame([threshold_row])], ignore_index=True + ) + + Table_ROC = pandas.concat( + [pandas.DataFrame({"H": [0], "F": [0]}), Table_ROC], ignore_index=True + ) + + ax.plot( + Table_ROC["F"], + Table_ROC["H"], + label=plot_args.get("forecast_label", forecast.name or "Forecast"), + color=plot_args["color"], + linestyle=plot_args["linestyle"], + ) - ax.set_title(plot_args["title"], fontsize=plot_args["title_fontsize"], y=1.06) + if plot_uniform: + ax.plot( + numpy.arange(0, 1.001, 0.001), + numpy.arange(0, 1.001, 0.001), + linestyle="--", + color="gray", + label="Uniform", + ) + # Plot formatting + ax.set_ylabel(plot_args["ylabel"] or "Hit Rate", fontsize=plot_args["ylabel_fontsize"]) + ax.set_xlabel( + plot_args["xlabel"] or "Fraction of False Alarms", fontsize=plot_args["xlabel_fontsize"] + ) + if not linear: + ax.set_xscale("log") + ax.set_yscale("linear") + ax.tick_params(axis="x", labelsize=plot_args["xticks_fontsize"]) + ax.tick_params(axis="y", labelsize=plot_args["yticks_fontsize"]) + if plot_args["legend"]: + ax.legend( + loc=plot_args["legend_loc"], shadow=True, fontsize=plot_args["legend_fontsize"] + ) + ax.set_title(plot_args["title"], fontsize=plot_args["title_fontsize"]) if plot_args["tight_layout"]: - ax.figure.tight_layout() + fig.tight_layout() if show: pyplot.show() @@ -1564,98 +1657,221 @@ def plot_catalog( return ax -def plot_spatial_dataset( - gridded: numpy.ndarray, - region: "CartesianGrid2D", - basemap: Optional[str] = None, +def plot_Molchan_diagram( + forecast: "GriddedForecast", + catalog: "CSEPCatalog", + linear: bool = True, + plot_uniform: bool = True, + show: bool = True, ax: Optional[pyplot.Axes] = None, - projection: Optional[Union[ccrs.Projection, str]] = ccrs.PlateCarree(), - show: bool = False, - extent: Optional[List[float]] = None, - set_global: bool = False, - plot_region: bool = True, - colorbar: bool = True, - colormap: Union[str, matplotlib.colors.Colormap] = "viridis", - clim: Optional[Tuple[float, float]] = None, - clabel: Optional[str] = None, - alpha: Optional[float] = None, - alpha_exp: float = 0, **kwargs, ) -> matplotlib.axes.Axes: + """ - Plot spatial dataset such as data from a gridded forecast. + Plot the Molchan Diagram based on forecast and test catalogs using the contingency table. + The Area Skill score and its error are shown in the legend. + + The Molchan diagram is computed following this procedure: + + 1. Obtain spatial rates from the GriddedForecast and the observed events from the catalog. + 2. Rank the rates in descending order (highest rates first). + 3. Sort forecasted rates by ordering found in (2), and normalize rates so their sum is equal + to unity. + 4. Obtain binned spatial rates from the observed catalog. + 5. Sort gridded observed rates by ordering found in (2). + 6. Test each ordered and normalized forecasted rate defined in (3) as a threshold value to + obtain the corresponding contingency table. + 7. Define the "nu" (Miss rate) and "tau" (Fraction of spatial alarmed cells) for each + threshold using the information provided by the corresponding contingency table defined in + (6). + + Note: + 1. The testing catalog and forecast should have exactly the same time-window (duration). + 2. Forecasts should be defined over the same region. + 3. If calling this function multiple times, update the color in the arguments. + 4. The user can choose the x-scale (linear or log). Args: - gridded (numpy.ndarray): 2D array of values corresponding to the region. - region (CartesianGrid2D): Region in which gridded values are contained. - basemap (str): Optional. Passed to :func:`plot_basemap` along with `kwargs` - ax (Optional[pyplot.Axes]): Previously defined ax object. - projection (cartopy.crs.Projection): Projection to be used in the basemap. - show (bool): If True, displays the plot. - extent (Optional[List[float]]): [lon_min, lon_max, lat_min, lat_max]. - set_global (bool): Display the complete globe as basemap. - plot_region (bool): If True, plot the dataset region border. - colorbar (bool): If True, include a colorbar. - colormap (Union[str, matplotlib.colors.Colormap]): Colormap to use. - clim (Optional[Tuple[float, float]]): Range of the colorbar. - clabel (Optional[str]): Label of the colorbar. - alpha (Optional[float]): Transparency level. - alpha_exp (float): Exponent for the alpha function (recommended between 0.4 and 1). - kwargs: colorbar_labelsize, colorbar_ticksize + forecast (GriddedForecast): The forecast object. + catalog (CSEPCatalog): The evaluation catalog. + linear (bool): If True, a linear x-axis is used; if False, a logarithmic x-axis is used. + Defaults to `True`. + plot_uniform (bool): If True, include a uniform forecast on the plot. Defaults to `True`. + show (bool): If True, displays the plot. Defaults to `True`. + ax (matplotlib.axes.Axes): Axes object to plot on. If `None`, creates a new figure. + Defaults to `None`. + **kwargs: Additional keyword arguments for customization: + + - **figsize** (`tuple`): The size of the figure. + - **forecast_label** (`str`): Label for the forecast data in the plot. + - **color** (`str`): Color for the Molchan diagram line. + - **linestyle** (`str`): Line style for the Molchan diagram line. + - **xlabel** (`str`): Label for the X-axis. + - **xlabel_fontsize** (`int`): Font size for the X-axis label. + - **ylabel** (`str`): Label for the Y-axis. + - **ylabel_fontsize** (`int`): Font size for the Y-axis label. + - **legend_loc** (`str`): Location of the legend. Defaults to `'best'`. + - **legend_fontsize** (`int`): Font size of the legend text. + - **tight_layout** (`bool`): Whether to use tight layout for the figure. + Defaults to `True`. + Returns: - matplotlib.axes.Axes: Matplotlib axes handle. + matplotlib.axes.Axes: The Axes object with the plot. + + Raises: + RuntimeError: If the catalog and forecast do not have the same region. """ plot_args = {**DEFAULT_PLOT_ARGS, **kwargs} + fig, ax = pyplot.subplots(figsize=plot_args["figsize"]) if ax is None else (ax.figure, ax) - # Get spatial information for plotting - extent = extent or _calculate_spatial_extent(region, set_global, plot_region) - # Instantiate GeoAxes object - ax = ax or _create_geo_axes(plot_args["figsize"], extent, projection, set_global) + if not catalog.region == forecast.region: + raise RuntimeError("Catalog region and forecast region must be identical.") - # chain basemap - ax = plot_basemap(basemap, extent, ax=ax, set_global=set_global, show=False, **plot_args) + forecast_label = plot_args.get("forecast_label", forecast.name or "Forecast") - # Define colormap and alpha transparency - colormap, alpha = _get_colormap(colormap, alpha_exp, alpha) + # Obtain forecast rates (or counts) and observed catalog aggregated in spatial cells + rate = forecast.spatial_counts() + obs_counts = catalog.spatial_counts() - # Plot spatial dataset - lons, lats = numpy.meshgrid( - numpy.append(region.xs, region.get_bbox()[1]), - numpy.append(region.ys, region.get_bbox()[3]), - ) - im = ax.pcolor( - lons, lats, gridded, cmap=colormap, alpha=alpha, snap=True, transform=ccrs.PlateCarree() + # Get index of rates (descending sort) + indices = numpy.argsort(rate) + indices = numpy.flip(indices) + + # Order forecast and cells rates by highest rate cells first + thresholds = (rate[indices]) / numpy.sum(rate) + obs_counts = obs_counts[indices] + + Table_molchan = pandas.DataFrame( + { + "Threshold": [], + "Successful_bins": [], + "Obs_active_bins": [], + "tau": [], + "nu": [], + "R_score": [], + } ) - im.set_clim(clim) - # Colorbar options - if colorbar: - cax = ax.get_figure().add_axes( - [ - ax.get_position().x1 + 0.01, - ax.get_position().y0, - 0.025, - ax.get_position().height, - ], - label="Colorbar", + # Each forecasted and normalized rate is tested as a threshold value to define the + # contingency table. + for threshold in thresholds: + threshold = float(threshold) + + binary_forecast = numpy.where(thresholds >= threshold, 1, 0) + forecastedYes_observedYes = obs_counts[(binary_forecast == 1) & (obs_counts > 0)] + forecastedYes_observedNo = obs_counts[(binary_forecast == 1) & (obs_counts == 0)] + forecastedNo_observedYes = obs_counts[(binary_forecast == 0) & (obs_counts > 0)] + forecastedNo_observedNo = obs_counts[(binary_forecast == 0) & (obs_counts == 0)] + + # Creating the DataFrame for the contingency table + data = { + "Observed": [len(forecastedYes_observedYes), len(forecastedNo_observedYes)], + "Not Observed": [len(forecastedYes_observedNo), len(forecastedNo_observedNo)], + } + index = ["Forecasted", "Not Forecasted"] + contingency_df = pandas.DataFrame(data, index=index) + nu = contingency_df.loc["Not Forecasted", "Observed"] / contingency_df["Observed"].sum() + tau = contingency_df.loc["Forecasted"].sum() / ( + contingency_df.loc["Forecasted"].sum() + contingency_df.loc["Not Forecasted"].sum() + ) + R_score = ( + contingency_df.loc["Forecasted", "Observed"] / contingency_df["Observed"].sum() + ) - ( + contingency_df.loc["Forecasted", "Not Observed"] + / contingency_df["Not Observed"].sum() ) - cbar = ax.get_figure().colorbar(im, ax=ax, cax=cax) - cbar.set_label(clabel, fontsize=plot_args["colorbar_labelsize"]) - cbar.ax.tick_params(labelsize=plot_args["colorbar_ticksize"]) - # Draw forecast's region border - if plot_region and not set_global: - try: - pts = region.tight_bbox() - ax.plot(pts[:, 0], pts[:, 1], lw=1, color=plot_args["region_color"]) - except AttributeError: - pass - ax.set_title(plot_args["title"], fontsize=plot_args["title_fontsize"], y=1.06) + threshold_row = { + "Threshold": threshold, + "Successful_bins": contingency_df.loc["Forecasted", "Observed"], + "Obs_active_bins": contingency_df["Observed"].sum(), + "tau": tau, + "nu": nu, + "R_score": R_score, + } + threshold_row_df = pandas.DataFrame([threshold_row]) + + Table_molchan = pandas.concat([Table_molchan, threshold_row_df], ignore_index=True) + + bottom_row = { + "Threshold": "Full alarms", + "tau": 1, + "nu": 0, + "Obs_active_bins": contingency_df["Observed"].sum(), + } + top_row = { + "Threshold": "No alarms", + "tau": 0, + "nu": 1, + "Obs_active_bins": contingency_df["Observed"].sum(), + } + + Table_molchan = pandas.concat( + [pandas.DataFrame([top_row]), Table_molchan], ignore_index=True + ) + Table_molchan = pandas.concat( + [Table_molchan, pandas.DataFrame([bottom_row])], ignore_index=True + ) + + # Computation of Area Skill score (ASS) + Tab_as_score = pandas.DataFrame() + Tab_as_score["Threshold"] = Table_molchan["Threshold"] + Tab_as_score["tau"] = Table_molchan["tau"] + Tab_as_score["nu"] = Table_molchan["nu"] + + ONE = numpy.ones(len(Tab_as_score)) + Tab_as_score["CUM_BAND"] = cumulative_trapezoid( + ONE, Tab_as_score["tau"], initial=0 + ) - cumulative_trapezoid(Tab_as_score["nu"], Tab_as_score["tau"], initial=0) + Tab_as_score["AS_score"] = numpy.divide( + Tab_as_score["CUM_BAND"], + cumulative_trapezoid(ONE, Tab_as_score["tau"], initial=0) + 1e-10, + ) + Tab_as_score.loc[Tab_as_score.index[-1], "AS_score"] = max( + 0.5, Tab_as_score["AS_score"].iloc[-1] + ) + ASscore = numpy.round(Tab_as_score.loc[Tab_as_score.index[-1], "AS_score"], 2) + + bin_size = 0.01 + devstd = numpy.sqrt(1 / (12 * Table_molchan["Obs_active_bins"].iloc[0])) + devstd = devstd * bin_size**-1 + devstd = numpy.ceil(devstd + 0.5) + devstd = devstd / bin_size**-1 + dev_std = numpy.round(devstd, 2) + + # Plot the Molchan trajectory + ax.plot( + Table_molchan["tau"], + Table_molchan["nu"], + label=f"{forecast_label}, ASS={ASscore}±{dev_std} ", + color=plot_args["color"], + linestyle=plot_args["linestyle"], + ) + + # Plot uniform forecast + if plot_uniform: + x_uniform = numpy.arange(0, 1.001, 0.001) + y_uniform = numpy.arange(1.00, -0.001, -0.001) + ax.plot(x_uniform, y_uniform, linestyle="--", color="gray", label="Uniform") + + # Plot formatting + ax.set_ylabel(plot_args["ylabel"] or "Miss Rate", fontsize=plot_args["ylabel_fontsize"]) + ax.set_xlabel( + plot_args["xlabel"] or "Fraction of area occupied by alarms", + fontsize=plot_args["xlabel_fontsize"], + ) + if not linear: + ax.set_xscale("log") + ax.tick_params(axis="x", labelsize=plot_args["xlabel_fontsize"]) + ax.tick_params(axis="y", labelsize=plot_args["ylabel_fontsize"]) + ax.legend(loc=plot_args["legend_loc"], shadow=True, fontsize=plot_args["legend_fontsize"]) + ax.set_title(plot_args["title"] or "Molchan Diagram", fontsize=plot_args["title_fontsize"]) + if plot_args["tight_layout"]: + fig.tight_layout() if show: pyplot.show() - return ax @@ -1862,8 +2078,8 @@ def _autosize_scatter( Returns: numpy.ndarray: The calculated marker sizes. """ - min_val = min_val or np.min(values) - max_val = max_val or np.max(values) + min_val = min_val or numpy.min(values) + max_val = max_val or numpy.max(values) normalized_values = ((values - min_val) / (max_val - min_val)) ** power marker_sizes = min_size + normalized_values * (max_size - min_size) * bool(power) return marker_sizes @@ -1944,7 +2160,7 @@ def _annotate_distribution_plot( ylabel = "Number of Catalogs" title = f"{evaluation_result.name}: {evaluation_result.sim_name}" annotation_xy = (0.5, 0.3) - if isinstance(evaluation_result.quantile, (list, np.ndarray)): + if isinstance(evaluation_result.quantile, (list, tuple, numpy.ndarray)): annotation_text = ( f"$\\delta_1 = P(X \\geq x) = {evaluation_result.quantile[0]:.2f}$\n" f"$\\delta_2 = P(X \\leq x) = {evaluation_result.quantile[1]:.2f}$\n" @@ -2067,6 +2283,10 @@ def _create_geo_axes( # Set plot aspect according to local longitude-latitude ratio in metric units LATKM = 110.574 # length of a ° of latitude [km] --> ignores Earth's flattening ax.set_aspect(LATKM / (111.320 * numpy.cos(numpy.deg2rad(central_latitude)))) + elif projection is None: + projection = ccrs.PlateCarree() + fig = pyplot.figure(figsize=figsize) + ax = fig.add_subplot(111, projection=projection) else: fig = pyplot.figure(figsize=figsize) ax = fig.add_subplot(111, projection=projection) diff --git a/docs/conf.py b/docs/conf.py index 208045ef..6c75e6fa 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -102,7 +102,8 @@ "numpy": ("https://docs.scipy.org/doc/numpy/", None), "pandas": ("http://pandas.pydata.org/pandas-docs/stable/", None), "scipy": ('http://docs.scipy.org/doc/scipy/reference', None), - "matplotlib": ('http://matplotlib.sourceforge.net/', None) + "matplotlib": ('https://matplotlib.org/stable', None), + "cartopy": ('https://scitools.org.uk/cartopy/docs/latest/', None) } html_theme_options = {} diff --git a/docs/reference/api_reference.rst b/docs/reference/api_reference.rst index 6e3ce176..80ef12ac 100644 --- a/docs/reference/api_reference.rst +++ b/docs/reference/api_reference.rst @@ -177,6 +177,17 @@ Grid-based forecast evaluations: paired_t_test w_test +Evaluation result base class + +.. automodule:: csep.models + +.. autosummary:: + :toctree: generated + + EvaluationResult + + +.. currentmodule:: csep.core.regions .. automodule:: csep.core.regions Regions @@ -191,6 +202,7 @@ Region class(es): :toctree: generated CartesianGrid2D + CartesianGrid2D.get_cartesian Testing regions: @@ -199,6 +211,7 @@ Testing regions: california_relm_region italy_csep_region + nz_csep_region global_region Region utilities: @@ -212,55 +225,50 @@ Region utilities: increase_grid_resolution masked_region generate_aftershock_region - california_relm_region +.. currentmodule:: csep.utils.plots +.. automodule:: csep.utils.plots Plotting -------- -.. automodule:: csep.utils.plots - General plotting: .. autosummary:: :toctree: generated - plot_histogram - plot_ecdf + plot_magnitude_versus_time + plot_cumulative_events_versus_time + plot_magnitude_histogram plot_basemap - plot_spatial_dataset - add_labels_for_publication + plot_catalog + plot_gridded_dataset -Plotting from catalogs: +Plotting catalog-based evaluations: .. autosummary:: :toctree: generated - plot_magnitude_versus_time - plot_catalog + plot_distribution_test + plot_calibration_test -Plotting stochastic event sets and evaluations: +Plotting grid-based evaluations: .. autosummary:: :toctree: generated - plot_cumulative_events_versus_time - plot_magnitude_histogram - plot_number_test - plot_magnitude_test - plot_distribution_test - plot_likelihood_test - plot_spatial_test - plot_calibration_test + plot_comparison_test + plot_consistency_test -Plotting gridded forecasts and evaluations: +Plotting alarm-based evaluations: .. autosummary:: :toctree: generated - plot_spatial_dataset - plot_comparison_test - plot_poisson_consistency_test + plot_ROC_diagram + plot_concentration_ROC_diagram + plot_Molchan_diagram + .. automodule:: csep.utils.time_utils diff --git a/examples/tutorials/plot_customizations.py b/examples/tutorials/plot_customizations.py index 7c66aa23..94ec50ca 100644 --- a/examples/tutorials/plot_customizations.py +++ b/examples/tutorials/plot_customizations.py @@ -119,9 +119,9 @@ # To plot a global forecast, we must assign the option ``set_global=True``, which is required by :ref:cartopy to handle # internally the extent of the plot -ax = plots.plot_spatial_dataset(numpy.log10(rate_sum), forecast.region, - show=True, set_global=True, - plot_args=plot_args) +ax = plots.plot_gridded_dataset(numpy.log10(rate_sum), forecast.region, + show=True, set_global=True, + plot_args=plot_args) #################################################################################################################################### diff --git a/examples/tutorials/quadtree_gridded_forecast_evaluation.py b/examples/tutorials/quadtree_gridded_forecast_evaluation.py index d9f58688..792ec536 100644 --- a/examples/tutorials/quadtree_gridded_forecast_evaluation.py +++ b/examples/tutorials/quadtree_gridded_forecast_evaluation.py @@ -192,9 +192,9 @@ stest_result = [spatial_test_single_res_result, spatial_test_multi_res_result] -ax_spatial = plots.plot_poisson_consistency_test(stest_result, +ax_spatial = plots.plot_consistency_test(stest_result, plot_args={'xlabel': 'Spatial likelihood'}) ntest_result = [number_test_single_res_result, number_test_multi_res_result] -ax_number = plots.plot_poisson_consistency_test(ntest_result, +ax_number = plots.plot_consistency_test(ntest_result, plot_args={'xlabel': 'Number of Earthquakes'}) diff --git a/tests/test_plots.py b/tests/test_plots.py index 66ccc5da..c94f552d 100644 --- a/tests/test_plots.py +++ b/tests/test_plots.py @@ -26,7 +26,7 @@ ) from csep.utils.plots import ( plot_cumulative_events_versus_time, - plot_magnitude_vs_time, + plot_magnitude_versus_time, plot_distribution_test, plot_magnitude_histogram, plot_calibration_test, @@ -34,7 +34,7 @@ plot_consistency_test, plot_basemap, plot_catalog, - plot_spatial_dataset, + plot_gridded_dataset, plot_concentration_ROC_diagram, plot_Molchan_diagram, plot_ROC_diagram, @@ -113,18 +113,18 @@ def setUp(self): def test_plot_magnitude_vs_time(self): # Basic test - ax = plot_magnitude_vs_time(catalog=self.observation_m2, show=show_plots) - self.assertEqual(ax.get_title(), "Magnitude vs. Time") + ax = plot_magnitude_versus_time(catalog=self.observation_m2, show=show_plots) + self.assertEqual(ax.get_title(), "") self.assertEqual(ax.get_xlabel(), "Datetime") - self.assertEqual(ax.get_ylabel(), "$M$") + self.assertEqual(ax.get_ylabel(), "Magnitude") # Test with custom color - ax = plot_magnitude_vs_time(catalog=self.observation_m2, color="red", show=show_plots) + ax = plot_magnitude_versus_time(catalog=self.observation_m2, color="red", show=show_plots) scatter_color = ax.collections[0].get_facecolor()[0] self.assertTrue(all(scatter_color[:3] == (1.0, 0.0, 0.0))) # Check if color is red # Test with custom marker size - ax = plot_magnitude_vs_time( + ax = plot_magnitude_versus_time( catalog=self.observation_m2, size=25, max_size=600, show=show_plots ) scatter_sizes = ax.collections[0].get_sizes() @@ -132,18 +132,18 @@ def test_plot_magnitude_vs_time(self): numpy.testing.assert_array_almost_equal(scatter_sizes, func_sizes) # Test with custom alpha - ax = plot_magnitude_vs_time(catalog=self.observation_m2, alpha=0.5, show=show_plots) + ax = plot_magnitude_versus_time(catalog=self.observation_m2, alpha=0.5, show=show_plots) scatter_alpha = ax.collections[0].get_alpha() self.assertEqual(scatter_alpha, 0.5) # Test with custom marker size power - ax = plot_magnitude_vs_time(catalog=self.observation_m2, power=6, show=show_plots) + ax = plot_magnitude_versus_time(catalog=self.observation_m2, power=6, show=show_plots) scatter_sizes = ax.collections[0].get_sizes() func_sizes = _autosize_scatter(self.observation_m2.data["magnitude"], 4, 300, 6) numpy.testing.assert_array_almost_equal(scatter_sizes, func_sizes) # # # Test with show=True (just to ensure no errors occur) - plot_magnitude_vs_time(catalog=self.observation_m2, show=False) + plot_magnitude_versus_time(catalog=self.observation_m2, show=False) plt.close("all") def test_plot_cumulative_events_default(self): @@ -979,19 +979,19 @@ def tight_bbox(): def test_default_plot(self): fig, ax = plt.subplots(subplot_kw={"projection": ccrs.PlateCarree()}) - ax = plot_spatial_dataset(self.gridded_data, self.region, ax=ax) + ax = plot_gridded_dataset(self.gridded_data, self.region, ax=ax) self.assertIsInstance(ax, plt.Axes) def test_extent_setting_w_ax(self): extent = (-30, 30, -20, 20) - ax = plot_spatial_dataset( + ax = plot_gridded_dataset( self.gridded_data, self.region, extent=extent, show=show_plots ) numpy.testing.assert_array_almost_equal(ax.get_extent(crs=ccrs.PlateCarree()), extent) def test_extent_setting(self): extent = (-30, 30, -20, 20) - ax = plot_spatial_dataset( + ax = plot_gridded_dataset( self.gridded_data, self.region, extent=extent, show=show_plots ) numpy.testing.assert_array_almost_equal(ax.get_extent(crs=ccrs.PlateCarree()), extent) @@ -1000,34 +1000,34 @@ def test_extent_setting(self): def test_color_mapping(self): cmap = plt.get_cmap("plasma") fig, ax = plt.subplots(subplot_kw={"projection": ccrs.PlateCarree()}) - ax = plot_spatial_dataset( + ax = plot_gridded_dataset( self.gridded_data, self.region, ax=ax, colormap=cmap, show=show_plots ) self.assertIsInstance(ax.collections[0].cmap, colors.ListedColormap) def test_gridlines(self): fig, ax = plt.subplots(subplot_kw={"projection": ccrs.PlateCarree()}) - ax = plot_spatial_dataset( + ax = plot_gridded_dataset( self.gridded_data, self.region, ax=ax, grid=True, show=show_plots ) self.assertTrue(ax.gridlines()) def test_alpha_transparency(self): fig, ax = plt.subplots(subplot_kw={"projection": ccrs.PlateCarree()}) - ax = plot_spatial_dataset( + ax = plot_gridded_dataset( self.gridded_data, self.region, ax=ax, alpha=0.5, show=show_plots ) self.assertIsInstance(ax, plt.Axes) def test_plot_with_alpha_exp(self): - ax = plot_spatial_dataset( + ax = plot_gridded_dataset( self.gridded_data, self.region, alpha_exp=0.5, include_cbar=True, show=show_plots ) self.assertIsInstance(ax, plt.Axes) def test_include_colorbar(self): fig, ax = plt.subplots(subplot_kw={"projection": ccrs.PlateCarree()}) - ax = plot_spatial_dataset( + ax = plot_gridded_dataset( self.gridded_data, self.region, ax=ax, include_cbar=True, show=show_plots ) colorbars = [ @@ -1039,7 +1039,7 @@ def test_include_colorbar(self): def test_no_region_border(self): fig, ax = plt.subplots(subplot_kw={"projection": ccrs.PlateCarree()}) - ax = plot_spatial_dataset( + ax = plot_gridded_dataset( self.gridded_data, self.region, ax=ax, plot_region=False, show=show_plots ) lines = ax.get_lines() @@ -1048,7 +1048,7 @@ def test_no_region_border(self): def test_plot_spatial_dataset_w_basemap_stream_kwargs(self): projection = ccrs.Mercator() - ax = plot_spatial_dataset( + ax = plot_gridded_dataset( self.gridded_data, self.region, extent=[-20, 40, -5, 25], @@ -1067,7 +1067,7 @@ def test_plot_spatial_dataset_w_basemap_stream_kwargs(self): def test_plot_spatial_dataset_w_approx_projection(self): projection = "approx" - ax = plot_spatial_dataset( + ax = plot_gridded_dataset( self.gridded_data, self.region, basemap="stock_img",