diff --git a/csep/__init__.py b/csep/__init__.py index 2710d0f5..aaaaaa1a 100644 --- a/csep/__init__.py +++ b/csep/__init__.py @@ -53,6 +53,9 @@ 'load_evaluation_result', 'load_gridded_forecast', 'load_catalog_forecast', + 'load_alarm_forecast', + 'load_temporal_forecast', + 'compute_temporal_observations', 'utc_now_datetime', 'strptime_to_utc_datetime', 'datetime_to_utc_epoch', @@ -524,3 +527,226 @@ def load_catalog_forecast(fname, catalog_loader=None, format='native', # create observed_catalog forecast return CatalogForecast(filename=fname, loader=catalog_loader, catalog_format=format, catalog_type=type, **kwargs) + + +def load_alarm_forecast(fname, name=None, score_field='alarm_score', + lon_col='lon', lat_col='lat', + magnitude_min=None, magnitude_max=None, + start_time=None, end_time=None, + delimiter=',', score_fields=None, magnitude_bins=None, + **kwargs): + """ Load alarm-based earthquake forecast from CSV file. + + This function loads alarm-based forecasts that contain spatial cells with + associated scores (e.g., alarm_score, probability, rate_per_day). The forecast + is returned as a GriddedForecast object that can be evaluated using ROC curves + and Molchan diagrams via plot_ROC_diagram() and plot_Molchan_diagram(). + + The function works with any geographic region worldwide by inferring the spatial + grid from the provided lon/lat coordinates in the CSV file. + + Args: + fname (str): Path to alarm forecast CSV file + name (str): Name for the forecast (optional, defaults to filename) + score_field (str): Which field to use as forecast rate. Options: + 'alarm_score' (recommended for continuous scores), + 'probability', 'rate_per_day', or any numeric column. + Default: 'alarm_score'. Ignored if score_fields is provided. + lon_col (str): Name of longitude column (default: 'lon') + lat_col (str): Name of latitude column (default: 'lat') + magnitude_min (float): Minimum magnitude for forecast. If None, reads from + 'magnitude_min' column or uses 4.0 as default. + magnitude_max (float): Maximum magnitude for forecast. If None, reads from + 'magnitude_target' column or uses magnitude_min + 0.1. + start_time (str or datetime): Forecast start time. If None, reads from + 'start_time' column if available. + end_time (str or datetime): Forecast end time. If None, reads from + 'end_time' column if available. + delimiter (str): CSV delimiter character. Default: ',' (comma). + Use '\\t' for tab-delimited, ';' for semicolon, etc. + score_fields (list of str): List of column names to extract as multiple + score fields. If provided, score_field is ignored + and rates will have shape (n_cells, n_score_fields). + magnitude_bins (list or numpy.ndarray): Custom magnitude bin edges. + If provided, overrides magnitude_min + and magnitude_max. + **kwargs: Additional keyword arguments passed to GriddedForecast constructor + + Returns: + :class:`csep.core.forecasts.GriddedForecast` + + Raises: + FileNotFoundError: If the CSV file does not exist + ValueError: If required columns are missing or data is invalid + + Example: + >>> # Load alarm forecast + >>> forecast = csep.load_alarm_forecast('alarm_forecast.csv', + ... name='MyAlarmForecast') + >>> + >>> # Load tab-delimited file + >>> forecast = csep.load_alarm_forecast('forecast.tsv', delimiter='\\t') + >>> + >>> # Load with multiple score fields + >>> forecast = csep.load_alarm_forecast('forecast.csv', + ... score_fields=['alarm_score', 'probability']) + >>> + >>> # Load observed catalog + >>> catalog = csep.load_catalog('observed_events.csv') + >>> + >>> # Evaluate with Molchan diagram + >>> from csep.utils import plots + >>> plots.plot_Molchan_diagram(forecast, catalog, linear=True) + >>> + >>> # Evaluate with ROC curve + >>> plots.plot_ROC_diagram(forecast, catalog, linear=True) + """ + # Sanity checks + if not os.path.exists(fname): + raise FileNotFoundError( + f"Could not locate file {fname}. Unable to load alarm forecast.") + + # Set default name from filename if not provided + if name is None: + name = os.path.splitext(os.path.basename(fname))[0] + + # Load alarm forecast using custom reader + rates, region, magnitudes, metadata = readers.alarm_forecast_csv( + filename=fname, + lon_col=lon_col, + lat_col=lat_col, + score_field=score_field, + magnitude_min=magnitude_min, + magnitude_max=magnitude_max, + start_time=start_time, + end_time=end_time, + delimiter=delimiter, + score_fields=score_fields, + magnitude_bins=magnitude_bins + ) + + # Extract time information from metadata if available + if 'start_time' in metadata and 'start_time' not in kwargs: + kwargs['start_time'] = metadata['start_time'] + if 'end_time' in metadata and 'end_time' not in kwargs: + kwargs['end_time'] = metadata['end_time'] + + # Create GriddedForecast + forecast = GriddedForecast( + data=rates, + region=region, + magnitudes=magnitudes, + name=name, + **kwargs + ) + + return forecast + + +def load_temporal_forecast(fname, time_col='time', probability_col='probability', + delimiter=',', **kwargs): + """ Load temporal probability forecast from CSV file. + + This function loads time-series probability forecasts where each row represents + a time window (e.g., day) with an associated probability of earthquake occurrence. + Observations should be computed separately from a catalog using + compute_temporal_observations(). + + Args: + fname (str): Path to temporal forecast CSV file + time_col (str): Name of time/index column (default: 'time') + probability_col (str): Name of probability column (default: 'probability') + delimiter (str): CSV delimiter character. Default: ',' + + Returns: + dict: Dictionary containing: + - 'times': Time indices (numpy array or DatetimeIndex) + - 'probabilities': Forecast probabilities (numpy array) + - 'metadata': Additional information (dict) + + Raises: + FileNotFoundError: If the CSV file does not exist + ValueError: If required columns are missing + + Example: + >>> # Load temporal forecast + >>> data = csep.load_temporal_forecast('daily_forecast.csv') + >>> catalog = csep.load_catalog('events.csv') + >>> + >>> # Compute observations from catalog + >>> observations = csep.compute_temporal_observations( + ... catalog, data['times'], magnitude_min=4.0 + ... ) + >>> + >>> # Evaluate with temporal Molchan diagram + >>> from csep.utils import plots + >>> ax, ass, sigma = plots.plot_temporal_Molchan_diagram( + ... data['probabilities'], observations, name='DailyM4+' + ... ) + """ + # Sanity checks + if not os.path.exists(fname): + raise FileNotFoundError( + f"Could not locate file {fname}. Unable to load temporal forecast.") + + times, probabilities, metadata = readers.temporal_forecast_csv( + filename=fname, + time_col=time_col, + probability_col=probability_col, + delimiter=delimiter + ) + + return { + 'times': times, + 'probabilities': probabilities, + 'metadata': metadata + } + + +def compute_temporal_observations(catalog, times, magnitude_min=None, + start_time=None, time_delta=None): + """ Compute binary observations from a catalog for temporal forecast evaluation. + + This function counts whether one or more earthquakes occurred in each time + window, creating a binary observation vector suitable for temporal ROC + and Molchan evaluation. + + Args: + catalog: A CSEPCatalog object containing observed earthquakes + times (array-like): Array representing forecast time windows. + Can be datetime objects OR integer indices (1, 2, 3, ...). + If integers, use start_time and time_delta. + magnitude_min (float, optional): Minimum magnitude threshold. + start_time (str or datetime, optional): Start time of the forecast. + Required if times are integer indices. + Example: '2024-01-01' + time_delta (str, optional): Duration of each time window. + Required if times are integer indices. + Examples: '1D' (1 day), '1H' (1 hour), '7D' (1 week) + + Returns: + numpy.ndarray: Binary array (1 if event occurred in window, 0 otherwise) + + Example: + >>> # With datetime times in CSV + >>> data = csep.load_temporal_forecast('forecast.csv') + >>> catalog = csep.load_catalog('events.csv') + >>> observations = csep.compute_temporal_observations( + ... catalog, data['times'], magnitude_min=4.0 + ... ) + >>> + >>> # With integer indices (1, 2, 3, ...) + >>> observations = csep.compute_temporal_observations( + ... catalog, data['times'], + ... start_time='2024-01-01', + ... time_delta='1D', + ... magnitude_min=4.0 + ... ) + """ + return readers.compute_temporal_observations( + catalog=catalog, + times=times, + magnitude_min=magnitude_min, + start_time=start_time, + time_delta=time_delta + ) diff --git a/csep/utils/plots.py b/csep/utils/plots.py index 6ecdbfc6..c8c83422 100644 --- a/csep/utils/plots.py +++ b/csep/utils/plots.py @@ -2771,3 +2771,356 @@ def add_labels_for_publication(figure, style='bssa', labelsize=16): fontsize=labelsize) return + + +def plot_temporal_ROC_diagram(probabilities, observations, name=None, linear=True, + axes=None, plot_uniform=True, savepdf=False, + savepng=False, show=True, plot_args=None): + """ + Plot Receiver Operating Characteristic (ROC) diagram for temporal probability forecasts. + + This function evaluates time-series probability forecasts (e.g., daily M4+ probabilities) + by computing ROC curves. Each time window (e.g., day) is treated as a sample, + and the function sweeps through probability thresholds to build the ROC curve. + + The ROC is computed following this procedure: + (1) Sort time windows by forecast probability (descending order) + (2) For each unique probability as a threshold: + - Time windows with probability >= threshold: alarm ON + - Time windows with probability < threshold: alarm OFF + (3) Build contingency table for each threshold + (4) Compute Hit Rate (H) and False Alarm Rate (F) + (5) Plot ROC trajectory + + Args: + probabilities (array-like): Forecast probabilities for each time window. + Shape (n_time_windows,). Values in [0, 1]. + observations (array-like): Binary observations for each time window. + Shape (n_time_windows,). Values: 1 if event occurred, 0 otherwise. + name (str): Name for the forecast (used in legend) + linear (bool): If True, use linear x-axis; if False, use logarithmic x-axis + axes (matplotlib.axes.Axes): Previously defined axes object + plot_uniform (bool): If True, include uniform forecast baseline + savepdf (bool): Save plot as PDF + savepng (bool): Save plot as PNG + show (bool): Display the plot + + Optional plotting arguments (plot_args dict): + * figsize: (tuple) - default: (9, 8) + * linecolor: (str) - default: 'black' + * linestyle: (str) - default: '-' + * legend_fontsize: (float) - default: 16 + * legend_loc: (str) - default: 'lower right' + * title_fontsize: (float) - default: 16 + * label_fontsize: (float) - default: 14 + * title: (str) - default: 'Temporal ROC Diagram' + * filename: (str) - default: 'temporal_roc_figure' + + Returns: + tuple: (ax, auc) + - ax: matplotlib axes object + - auc: Area Under the ROC Curve + + Raises: + ValueError: If probabilities and observations have different lengths + + Example: + >>> probs = numpy.array([0.8, 0.6, 0.2, 0.1, 0.9]) + >>> obs = numpy.array([1, 0, 0, 0, 1]) + >>> ax, auc = plots.plot_temporal_ROC_diagram(probs, obs, name='MyForecast') + >>> print(f"AUC: {auc:.2f}") + """ + probabilities = numpy.asarray(probabilities) + observations = numpy.asarray(observations) + + if len(probabilities) != len(observations): + raise ValueError("probabilities and observations must have the same length") + + # Parse plotting arguments + plot_args = plot_args or {} + figsize = plot_args.get('figsize', (9, 8)) + linecolor = plot_args.get('linecolor', 'black') + linestyle = plot_args.get('linestyle', '-') + legend_fontsize = plot_args.get('legend_fontsize', 16) + legend_loc = plot_args.get('legend_loc', 'lower right') + title_fontsize = plot_args.get('title_fontsize', 16) + label_fontsize = plot_args.get('label_fontsize', 14) + title = plot_args.get('title', 'Temporal ROC Diagram') + filename = plot_args.get('filename', 'temporal_roc_figure') + + # Initialize figure + if axes is not None: + ax = axes + else: + fig, ax = pyplot.subplots(figsize=figsize) + + # Get sorted indices (descending by probability) + I = numpy.argsort(probabilities)[::-1] + sorted_probs = probabilities[I] + sorted_obs = observations[I] + + # Get unique thresholds + thresholds = numpy.unique(sorted_probs)[::-1] # Descending + + # Build ROC curve + H_list = [0.0] # Hit rate + F_list = [0.0] # False alarm rate + + n_events = numpy.sum(observations) + n_non_events = len(observations) - n_events + + if n_events == 0 or n_non_events == 0: + # Degenerate case + import warnings + warnings.warn("No events or all events observed. ROC curve is degenerate.", + RuntimeWarning) + H_list = [0.0, 1.0] + F_list = [0.0, 1.0] + else: + for threshold in thresholds: + # Alarm is ON where probability >= threshold + alarm_on = sorted_probs >= threshold + + # Contingency table + a = numpy.sum((alarm_on) & (sorted_obs == 1)) # True positives + b = numpy.sum((alarm_on) & (sorted_obs == 0)) # False positives + c = numpy.sum((~alarm_on) & (sorted_obs == 1)) # Misses + d = numpy.sum((~alarm_on) & (sorted_obs == 0)) # True negatives + + H = a / (a + c) if (a + c) > 0 else 0 # Hit rate + F = b / (b + d) if (b + d) > 0 else 0 # False alarm rate + + H_list.append(H) + F_list.append(F) + + # Ensure curve ends at (1, 1) + if H_list[-1] != 1.0 or F_list[-1] != 1.0: + H_list.append(1.0) + F_list.append(1.0) + + H_arr = numpy.array(H_list) + F_arr = numpy.array(F_list) + + # Compute AUC using trapezoidal rule + auc = numpy.trapz(H_arr, F_arr) + + # Format label + label_name = name if name else 'Forecast' + forecast_label = plot_args.get('label', f'{label_name}, AUC={auc:.2f}') + + # Plot ROC curve + ax.plot(F_arr, H_arr, label=forecast_label, color=linecolor, linestyle=linestyle) + + # Plot uniform forecast + if plot_uniform: + ax.plot([0, 1], [0, 1], linestyle='--', color='gray', label='Random (AUC=0.50)') + + # Plotting arguments + ax.set_xlabel('False Alarm Rate (F)', fontsize=label_fontsize) + ax.set_ylabel('Hit Rate (H)', fontsize=label_fontsize) + ax.set_title(title, fontsize=title_fontsize) + + if not linear: + ax.set_xscale('log') + ax.set_xlim(1e-3, 1.02) # Positive lower limit for log scale + else: + ax.set_xlim(-0.02, 1.02) + + ax.tick_params(axis='x', labelsize=label_fontsize) + ax.tick_params(axis='y', labelsize=label_fontsize) + ax.legend(loc=legend_loc, shadow=True, fontsize=legend_fontsize) + ax.set_ylim(-0.02, 1.02) + + if filename: + if savepdf: + pyplot.savefig(f"{filename}.pdf", format='pdf') + if savepng: + pyplot.savefig(f"{filename}.png", format='png') + + if show: + pyplot.show() + + return ax, auc + + +def plot_temporal_Molchan_diagram(probabilities, observations, name=None, linear=True, + axes=None, plot_uniform=True, savepdf=False, + savepng=False, show=True, plot_args=None): + """ + Plot Molchan diagram for temporal probability forecasts. + + This function evaluates time-series probability forecasts (e.g., daily M4+ probabilities) + by computing Molchan diagrams. Each time window (e.g., day) is treated as a sample. + The Area Skill Score (ASS) and its uncertainty are computed and displayed. + + The Molchan diagram is computed following this procedure: + (1) Sort time windows by forecast probability (descending order) + (2) For each unique probability as a threshold: + - Time windows with probability >= threshold: alarm ON + - Time windows with probability < threshold: alarm OFF + (3) Build contingency table for each threshold + (4) Compute τ (fraction of time in alarm) and ν (miss rate) + (5) Plot Molchan trajectory and compute Area Skill Score + + Interpretation: + - τ (tau): Fraction of time windows where alarm is ON + - ν (nu): Fraction of events missed (events in alarm-OFF windows) + - ASS: Area Skill Score (0.5 = random, 1.0 = perfect, 0.0 = inverse) + + Args: + probabilities (array-like): Forecast probabilities for each time window. + Shape (n_time_windows,). Values in [0, 1]. + observations (array-like): Binary observations for each time window. + Shape (n_time_windows,). Values: 1 if event occurred, 0 otherwise. + name (str): Name for the forecast (used in legend) + linear (bool): If True, use linear x-axis; if False, use logarithmic x-axis + axes (matplotlib.axes.Axes): Previously defined axes object + plot_uniform (bool): If True, include uniform forecast baseline + savepdf (bool): Save plot as PDF + savepng (bool): Save plot as PNG + show (bool): Display the plot + + Optional plotting arguments (plot_args dict): + * figsize: (tuple) - default: (9, 8) + * linecolor: (str) - default: 'black' + * linestyle: (str) - default: '-' + * legend_fontsize: (float) - default: 16 + * legend_loc: (str) - default: 'upper right' + * title_fontsize: (float) - default: 16 + * label_fontsize: (float) - default: 14 + * title: (str) - default: 'Temporal Molchan Diagram' + * filename: (str) - default: 'temporal_molchan_figure' + + Returns: + tuple: (ax, ass, ass_std) + - ax: matplotlib axes object + - ass: Area Skill Score + - ass_std: Standard deviation of ASS + + Raises: + ValueError: If probabilities and observations have different lengths + + Example: + >>> probs = numpy.array([0.8, 0.6, 0.2, 0.1, 0.9]) + >>> obs = numpy.array([1, 0, 0, 0, 1]) + >>> ax, ass, sigma = plots.plot_temporal_Molchan_diagram(probs, obs, name='MyForecast') + >>> print(f"ASS: {ass:.2f} ± {sigma:.2f}") + """ + probabilities = numpy.asarray(probabilities) + observations = numpy.asarray(observations) + + if len(probabilities) != len(observations): + raise ValueError("probabilities and observations must have the same length") + + # Parse plotting arguments + plot_args = plot_args or {} + figsize = plot_args.get('figsize', (9, 8)) + linecolor = plot_args.get('linecolor', 'black') + linestyle = plot_args.get('linestyle', '-') + legend_fontsize = plot_args.get('legend_fontsize', 16) + legend_loc = plot_args.get('legend_loc', 'upper right') + title_fontsize = plot_args.get('title_fontsize', 16) + label_fontsize = plot_args.get('label_fontsize', 14) + title = plot_args.get('title', 'Temporal Molchan Diagram') + filename = plot_args.get('filename', 'temporal_molchan_figure') + + # Initialize figure + if axes is not None: + ax = axes + else: + fig, ax = pyplot.subplots(figsize=figsize) + + # Get sorted indices (descending by probability) + I = numpy.argsort(probabilities)[::-1] + sorted_probs = probabilities[I] + sorted_obs = observations[I] + + # Get unique thresholds + thresholds = numpy.unique(sorted_probs)[::-1] # Descending + + # Build Molchan trajectory + tau_list = [0.0] # Fraction of time in alarm + nu_list = [1.0] # Miss rate + + n_time_windows = len(observations) + n_events = numpy.sum(observations) + + if n_events == 0: + # Degenerate case - no events + import warnings + warnings.warn("No events observed. Molchan diagram is degenerate.", + RuntimeWarning) + tau_list = [0.0, 1.0] + nu_list = [1.0, 0.0] + else: + for threshold in thresholds: + # Alarm is ON where probability >= threshold + alarm_on = sorted_probs >= threshold + + # Contingency table + a = numpy.sum((alarm_on) & (sorted_obs == 1)) # Hits + c = numpy.sum((~alarm_on) & (sorted_obs == 1)) # Misses + + n_alarm_on = numpy.sum(alarm_on) + + tau = n_alarm_on / n_time_windows # Fraction of time in alarm + nu = c / n_events if n_events > 0 else 0 # Miss rate + + tau_list.append(tau) + nu_list.append(nu) + + # Ensure curve ends at (1, 0) + if tau_list[-1] != 1.0 or nu_list[-1] != 0.0: + tau_list.append(1.0) + nu_list.append(0.0) + + tau_arr = numpy.array(tau_list) + nu_arr = numpy.array(nu_list) + + # Compute Area Skill Score using trapezoidal integration + # ASS = 1 - 2 * Area under the Molchan curve + # For perfect forecast: curve goes (0,1) -> (0,0) -> (1,0), ASS = 1 + # For random forecast: curve goes (0,1) -> (1,0) diagonal, ASS = 0.5 + area_under_curve = numpy.trapz(nu_arr, tau_arr) + ass = 1.0 - 2.0 * area_under_curve + + # Compute standard deviation: σ = sqrt(1/(12*N)) where N = number of events + ass_std = numpy.sqrt(1.0 / (12.0 * n_events)) if n_events > 0 else 0.0 + + # Format label + label_name = name if name else 'Forecast' + forecast_label = plot_args.get('label', f'{label_name}, ASS={ass:.2f}±{ass_std:.2f}') + + # Plot Molchan trajectory + ax.plot(tau_arr, nu_arr, label=forecast_label, color=linecolor, linestyle=linestyle) + + # Plot uniform forecast + if plot_uniform: + ax.plot([0, 1], [1, 0], linestyle='--', color='gray', label='Random (ASS=0.50)') + + # Plotting arguments + ax.set_xlabel('Fraction of time in alarm (τ)', fontsize=label_fontsize) + ax.set_ylabel('Miss rate (ν)', fontsize=label_fontsize) + ax.set_title(title, fontsize=title_fontsize) + + if not linear: + ax.set_xscale('log') + ax.set_xlim(1e-3, 1.02) # Positive lower limit for log scale + else: + ax.set_xlim(-0.02, 1.02) + + ax.tick_params(axis='x', labelsize=label_fontsize) + ax.tick_params(axis='y', labelsize=label_fontsize) + ax.legend(loc=legend_loc, shadow=True, fontsize=legend_fontsize) + ax.set_ylim(-0.02, 1.02) + + if filename: + if savepdf: + pyplot.savefig(f"{filename}.pdf", format='pdf') + if savepng: + pyplot.savefig(f"{filename}.png", format='png') + + if show: + pyplot.show() + + return ax, ass, ass_std diff --git a/csep/utils/readers.py b/csep/utils/readers.py index 0e600e57..c4fcbc4d 100644 --- a/csep/utils/readers.py +++ b/csep/utils/readers.py @@ -1108,5 +1108,422 @@ def read_GEAR1_format(filename, area_filename, magnitudes): # Allows to use broadcasting m2_per_cell = numpy.reshape(area[:,-1], (len(area[:,1]), 1)) incremental_yrly_rates = incremental_yrly_density * m2_per_cell - - return incremental_yrly_rates, r, magnitudes + return incremental_yrly_rates, r, magnitudes + +def alarm_forecast_csv(filename, lon_col='lon', lat_col='lat', + score_field='alarm_score', magnitude_min=None, + magnitude_max=None, start_time=None, end_time=None, + delimiter=',', score_fields=None, magnitude_bins=None): + """ + Read alarm-based earthquake forecast from CSV file. + + This function reads alarm-based forecasts that contain spatial cells with + associated scores (e.g., alarm_score, probability, rate_per_day). The forecast + is converted into a format compatible with GriddedForecast for evaluation using + ROC curves and Molchan diagrams. + + Expected CSV columns (flexible column names): + - lon, lat: Spatial coordinates of cells (required) + - alarm_score, probability, or rate_per_day: Forecast scores (at least one required) + - magnitude_min, magnitude_target: Magnitude thresholds (optional) + - start_time, end_time: Forecast time window (optional) + - cell_id, alarm, notes: Additional metadata (optional) + + The function supports any geographic region worldwide by inferring the spatial + grid from the provided lon/lat coordinates. + + Args: + filename (str): Path to alarm forecast CSV file + lon_col (str): Name of longitude column (default: 'lon') + lat_col (str): Name of latitude column (default: 'lat') + score_field (str): Which field to use as forecast rate. Options: + 'alarm_score' (recommended for continuous scores), + 'probability', 'rate_per_day', or any numeric column name. + Default: 'alarm_score'. Ignored if score_fields is provided. + magnitude_min (float): Minimum magnitude for forecast. If None, reads from + 'magnitude_min' column or uses 4.0 as default. + magnitude_max (float): Maximum magnitude for forecast. If None, reads from + 'magnitude_target' column or uses magnitude_min + 0.1. + start_time (str or datetime): Forecast start time. If None, reads from + 'start_time' column if available. + end_time (str or datetime): Forecast end time. If None, reads from + 'end_time' column if available. + delimiter (str): CSV delimiter character. Default: ',' (comma). + Use '\\t' for tab-delimited, ';' for semicolon, etc. + score_fields (list of str): List of column names to extract as multiple + score fields. If provided, score_field is ignored + and rates will have shape (n_cells, n_score_fields). + Useful for combining multiple alarm metrics. + magnitude_bins (list or numpy.ndarray): Custom magnitude bin edges. + If provided, overrides magnitude_min + and magnitude_max. Example: [4.0, 5.0, 6.0] + creates two bins: [4.0-5.0) and [5.0-6.0). + + Returns: + tuple: (rates, region, magnitudes, metadata) + - rates (numpy.ndarray): Forecast rates, shape (num_cells, num_mag_bins) + or (num_cells, num_score_fields) if score_fields used + - region (CartesianGrid2D): Spatial region inferred from coordinates + - magnitudes (numpy.ndarray): Magnitude bins + - metadata (dict): Additional information (start_time, end_time, score_field(s)) + + Raises: + FileNotFoundError: If the CSV file does not exist + ValueError: If required columns are missing or data is invalid + CSEPIOException: If the CSV format cannot be parsed + + Example: + >>> # Basic usage + >>> rates, region, mags, meta = alarm_forecast_csv('forecast.csv') + >>> forecast = GriddedForecast(data=rates, region=region, + ... magnitudes=mags, name='AlarmForecast') + >>> + >>> # Tab-delimited file + >>> rates, region, mags, meta = alarm_forecast_csv('forecast.tsv', delimiter='\\t') + >>> + >>> # Multiple score fields + >>> rates, region, mags, meta = alarm_forecast_csv( + ... 'forecast.csv', + ... score_fields=['alarm_score', 'probability', 'rate_per_day'] + ... ) + >>> + >>> # Custom magnitude bins + >>> rates, region, mags, meta = alarm_forecast_csv( + ... 'forecast.csv', + ... magnitude_bins=[4.0, 4.5, 5.0, 5.5, 6.0] + ... ) + """ + # Check file exists + if not os.path.exists(filename): + raise FileNotFoundError(f"Alarm forecast file not found: {filename}") + + # Read CSV file with custom delimiter + try: + df = pd.read_csv(filename, delimiter=delimiter) + except Exception as e: + raise CSEPIOException(f"Failed to read CSV file {filename}: {str(e)}") + + # Validate required columns + if lon_col not in df.columns: + raise ValueError(f"Longitude column '{lon_col}' not found in CSV. " + f"Available columns: {list(df.columns)}") + if lat_col not in df.columns: + raise ValueError(f"Latitude column '{lat_col}' not found in CSV. " + f"Available columns: {list(df.columns)}") + + # Determine which score fields to extract + if score_fields is not None: + # Multiple score fields mode + if not isinstance(score_fields, (list, tuple)): + raise ValueError("score_fields must be a list of column names") + for field in score_fields: + if field not in df.columns: + raise ValueError(f"Score field '{field}' not found in CSV. " + f"Available columns: {list(df.columns)}") + active_score_fields = list(score_fields) + else: + # Single score field mode + if score_field not in df.columns: + raise ValueError(f"Score field '{score_field}' not found in CSV. " + f"Available columns: {list(df.columns)}") + active_score_fields = [score_field] + + # Extract coordinates + lons = df[lon_col].values + lats = df[lat_col].values + + # Validate coordinates + if len(lons) == 0: + raise ValueError("No data found in alarm forecast CSV") + if numpy.any(numpy.isnan(lons)) or numpy.any(numpy.isnan(lats)): + raise ValueError("NaN values found in coordinate columns") + + # Extract forecast scores for all active fields + scores_list = [] + for field in active_score_fields: + scores = df[field].values.copy() + if numpy.any(numpy.isnan(scores)): + warnings.warn(f"NaN values found in '{field}' column. " + f"Replacing with 0.0", RuntimeWarning) + scores = numpy.nan_to_num(scores, nan=0.0) + if numpy.any(scores < 0): + warnings.warn(f"Negative values found in '{field}' column. " + f"Replacing with 0.0", RuntimeWarning) + scores = numpy.maximum(scores, 0.0) + scores_list.append(scores) + + # Determine magnitude bins + if magnitude_bins is not None: + # Custom magnitude bins provided + magnitudes = numpy.asarray(magnitude_bins) + if len(magnitudes) < 2: + raise ValueError("magnitude_bins must have at least 2 edges") + else: + # Use magnitude_min/magnitude_max logic + if magnitude_min is None: + if 'magnitude_min' in df.columns: + mag_min_vals = df['magnitude_min'].dropna() + if len(mag_min_vals) > 0: + magnitude_min = float(mag_min_vals.iloc[0]) + else: + magnitude_min = 4.0 + warnings.warn("No valid magnitude_min found, using default 4.0", + RuntimeWarning) + else: + magnitude_min = 4.0 + warnings.warn("No magnitude_min specified, using default 4.0", + RuntimeWarning) + + if magnitude_max is None: + if 'magnitude_target' in df.columns: + mag_max_vals = df['magnitude_target'].dropna() + if len(mag_max_vals) > 0: + magnitude_max = float(mag_max_vals.iloc[0]) + else: + magnitude_max = magnitude_min + 0.1 + else: + magnitude_max = magnitude_min + 0.1 + + # Create magnitude bins (single bin for alarm forecasts) + magnitudes = numpy.array([magnitude_min, magnitude_max]) + + # Extract time information if available + metadata = {} + if score_fields is not None: + metadata['score_fields'] = active_score_fields + else: + metadata['score_field'] = score_field + + if start_time is None and 'start_time' in df.columns: + try: + start_time = pd.to_datetime(df['start_time'].iloc[0]) + metadata['start_time'] = start_time + except: + pass + elif start_time is not None: + metadata['start_time'] = start_time + + if end_time is None and 'end_time' in df.columns: + try: + end_time = pd.to_datetime(df['end_time'].iloc[0]) + metadata['end_time'] = end_time + except: + pass + elif end_time is not None: + metadata['end_time'] = end_time + + # Create region from coordinates + coords = numpy.column_stack([lons, lats]) + + try: + region = CartesianGrid2D.from_origins(coords, magnitudes=magnitudes) + except Exception as e: + raise CSEPIOException( + f"Failed to create spatial region from coordinates: {str(e)}") + + # Reshape rates for GriddedForecast + # Stack all score columns: shape (n_cells, n_score_fields) + rates = numpy.column_stack(scores_list) + + return rates, region, magnitudes, metadata + + +def temporal_forecast_csv(filename, time_col='time', probability_col='probability', + delimiter=','): + """ + Read temporal probability forecast from CSV file. + + This function reads time-series probability forecasts where each row represents + a time window (e.g., day) with an associated probability of earthquake occurrence. + Observations should be computed separately from a catalog using + compute_temporal_observations(). + + Expected CSV format: + time,probability + 1,0.0234 + 2,0.0221 + 3,0.0217 + ... + + Args: + filename (str): Path to temporal forecast CSV file + time_col (str): Name of time/index column (default: 'time'). + Can be integer indices or datetime strings. + probability_col (str): Name of probability column (default: 'probability'). + Values should be in range [0, 1]. + delimiter (str): CSV delimiter character. Default: ',' (comma). + + Returns: + tuple: (times, probabilities, metadata) + - times (numpy.ndarray or pandas.DatetimeIndex): Time indices + - probabilities (numpy.ndarray): Forecast probabilities + - metadata (dict): Additional information + + Raises: + FileNotFoundError: If the CSV file does not exist + ValueError: If required columns are missing or data is invalid + CSEPIOException: If the CSV format cannot be parsed + + Example: + >>> times, probs, meta = temporal_forecast_csv('daily_forecast.csv') + >>> # Compute observations from catalog + >>> observations = compute_temporal_observations(catalog, times) + >>> from csep.utils import plots + >>> plots.plot_temporal_Molchan_diagram(probs, observations) + """ + # Check file exists + if not os.path.exists(filename): + raise FileNotFoundError(f"Temporal forecast file not found: {filename}") + + # Read CSV file + try: + df = pd.read_csv(filename, delimiter=delimiter) + except Exception as e: + raise CSEPIOException(f"Failed to read CSV file {filename}: {str(e)}") + + # Validate required columns + if probability_col not in df.columns: + raise ValueError(f"Probability column '{probability_col}' not found in CSV. " + f"Available columns: {list(df.columns)}") + + # Extract time column if present + if time_col in df.columns: + times = df[time_col].values + # Try to parse as datetime + try: + times = pd.to_datetime(times) + except: + pass # Keep as-is (likely integer indices) + else: + # Use row indices as time + times = numpy.arange(len(df)) + warnings.warn(f"Time column '{time_col}' not found, using row indices", + RuntimeWarning) + + # Extract probabilities + probabilities = df[probability_col].values.astype(float) + + # Validate probabilities + if len(probabilities) == 0: + raise ValueError("No data found in temporal forecast CSV") + if numpy.any(numpy.isnan(probabilities)): + raise ValueError("NaN values found in probability column") + if numpy.any(probabilities < 0) or numpy.any(probabilities > 1): + warnings.warn("Probability values outside [0, 1] range found. " + "Clipping to valid range.", RuntimeWarning) + probabilities = numpy.clip(probabilities, 0, 1) + + # Build metadata + metadata = { + 'n_time_windows': len(probabilities), + 'probability_col': probability_col + } + + return times, probabilities, metadata + + +def compute_temporal_observations(catalog, times, magnitude_min=None, + start_time=None, time_delta=None): + """ + Compute binary observations from a catalog based on forecast time windows. + + This function counts whether one or more earthquakes occurred in each time + window, creating a binary observation vector suitable for temporal ROC + and Molchan evaluation. + + Args: + catalog: A CSEPCatalog object or any object with: + - get_datetimes(): Returns array of event datetimes + - get_magnitudes(): Returns array of event magnitudes + times (array-like): Array representing forecast time windows. + Can be datetime objects OR integer indices (1, 2, 3, ...). + If integers, use start_time and time_delta to map to real times. + magnitude_min (float, optional): Minimum magnitude threshold. If provided, + only events >= magnitude_min are counted. + start_time (str or datetime, optional): Start time of the forecast period. + Required if times are integer indices. + Example: '2024-01-01' or datetime object. + time_delta (str or timedelta, optional): Duration of each time window. + Required if times are integer indices. + Examples: '1D' (1 day), '1H' (1 hour), + '7D' (1 week), or pd.Timedelta object. + + Returns: + numpy.ndarray: Binary array of observations (1 if event occurred in window, 0 otherwise) + Shape: (len(times),) + + Example: + >>> # With datetime times in CSV + >>> catalog = csep.load_catalog('events.csv') + >>> data = csep.load_temporal_forecast('forecast.csv') + >>> observations = compute_temporal_observations(catalog, data['times']) + >>> + >>> # With integer indices (1, 2, 3, ...) + >>> observations = compute_temporal_observations( + ... catalog, data['times'], + ... start_time='2024-01-01', + ... time_delta='1D', + ... magnitude_min=4.0 + ... ) + """ + times = numpy.asarray(times) + + # Determine if we need to construct datetime times from indices + if start_time is not None: + # User provided start_time - use it with indices + start_time = pd.to_datetime(start_time) + + if time_delta is None: + time_delta = pd.Timedelta(days=1) # Default to 1 day + elif isinstance(time_delta, str): + time_delta = pd.Timedelta(time_delta) + + # Convert indices to real datetimes + # times are treated as 0-indexed offsets from start_time + # If times are 1-indexed (like 1, 2, 3), subtract 1 + min_time = numpy.min(times) + offsets = times - min_time # Normalize to 0-indexed + real_times = pd.DatetimeIndex([start_time + int(i) * time_delta for i in offsets]) + dt = time_delta + else: + # Try to parse times as datetimes + try: + real_times = pd.to_datetime(times) + # Infer time delta from spacing + if len(real_times) >= 2: + dt = real_times[1] - real_times[0] + else: + dt = pd.Timedelta(days=1) + except Exception: + raise ValueError( + "Could not parse times as datetimes. For integer indices, " + "please provide start_time and time_delta parameters. " + "Example: start_time='2024-01-01', time_delta='1D'" + ) + + # Get catalog event times and normalize to tz-naive UTC + event_times = pd.to_datetime(catalog.get_datetimes()) + if event_times.tz is not None: + event_times = event_times.tz_convert('UTC').tz_localize(None) + + # Normalize real_times to tz-naive as well + if hasattr(real_times, 'tz') and real_times.tz is not None: + real_times = real_times.tz_convert('UTC').tz_localize(None) + + # Apply magnitude filter if specified + if magnitude_min is not None: + magnitudes = catalog.get_magnitudes() + mask = magnitudes >= magnitude_min + event_times = event_times[mask] + + # Count events in each time window + observations = numpy.zeros(len(real_times), dtype=int) + + for i, window_start in enumerate(real_times): + window_end = window_start + dt + # Count events in [window_start, window_end) + events_in_window = (event_times >= window_start) & (event_times < window_end) + if numpy.any(events_in_window): + observations[i] = 1 + + return observations diff --git a/docs/concepts/evaluations.rst b/docs/concepts/evaluations.rst index c01d6b47..e20de4ae 100644 --- a/docs/concepts/evaluations.rst +++ b/docs/concepts/evaluations.rst @@ -276,3 +276,111 @@ as simple as it gets. If you want to use mock-forecasts and mock-catalogs for other evaluations. You can just add the additional methods that are needed onto the mock classes you have already built. + + +******************************** +Alarm-based forecast evaluations +******************************** + +Alarm-based forecasts are evaluated using different metrics than rate-based forecasts. Instead of likelihood-based +tests, we use threshold-based approaches that assess how well the forecast ranks spatial cells by earthquake +occurrence probability. + +PyCSEP provides two main evaluation methods for alarm-based forecasts: + +1. **ROC (Receiver Operating Characteristic) curves** - Plot hit rate vs false alarm rate +2. **Molchan diagrams** - Plot miss rate vs alarm fraction (space occupied) + +Both methods sweep through a range of thresholds and compute contingency statistics at each level. + +.. _alarm-forecast-evaluation: + +ROC Diagram +=========== + +The ROC diagram plots the True Positive Rate (hit rate) against the False Positive Rate (false alarm rate) +as the alarm threshold varies. A perfect forecast follows the upper-left corner, while a random forecast +follows the diagonal. + +The Area Under the Curve (AUC) summarizes forecast skill: + +- **AUC = 1.0**: Perfect forecast +- **AUC = 0.5**: Random forecast (no skill) +- **AUC < 0.5**: Anti-skill (worse than random) + +.. autosummary:: + + csep.utils.plots.plot_ROC_diagram + +Example:: + + from csep.utils import plots + + forecast = csep.load_alarm_forecast('forecast.csv') + catalog = csep.query_gns(start_time=..., end_time=...) + + # Create ROC diagram + ax, auc = plots.plot_ROC_diagram(forecast, catalog, linear=True) + print(f"AUC: {auc:.3f}") + +Molchan Diagram +=============== + +The Molchan diagram plots the miss rate (fraction of earthquakes not captured) against the alarm fraction +(fraction of space-time occupied by alarms). This visualization emphasizes the trade-off between capturing +earthquakes and raising false alarms. + +The Area Skill Score (ASS) summarizes forecast skill: + +- **ASS = 1.0**: Perfect forecast (captures all events with minimal space) +- **ASS = 0.5**: Random forecast (no skill) +- **ASS < 0.5**: Anti-skill (worse than random) + +.. autosummary:: + + csep.utils.plots.plot_Molchan_diagram + +Example:: + + from csep.utils import plots + + forecast = csep.load_alarm_forecast('forecast.csv') + catalog = csep.query_gns(start_time=..., end_time=...) + + # Create Molchan diagram + ax, ass, sigma = plots.plot_Molchan_diagram(forecast, catalog, linear=True) + print(f"ASS: {ass:.3f} ± {sigma:.3f}") + +Temporal alarm-based evaluations +-------------------------------- + +Temporal alarm-based forecasts (e.g., daily earthquake probability sequences) are evaluated using +temporal versions of the ROC and Molchan diagrams: + +.. autosummary:: + + csep.utils.plots.plot_temporal_ROC_diagram + csep.utils.plots.plot_temporal_Molchan_diagram + +These functions evaluate how well forecast probabilities correspond to binary event occurrence over time. + +Example:: + + from csep.utils import plots + + # Load forecast and compute observations + data = csep.load_temporal_forecast('forecast.csv') + observations = csep.compute_temporal_observations( + catalog, data['times'], + start_time='2016-01-01', time_delta='1D' + ) + + # Temporal ROC + ax, auc = plots.plot_temporal_ROC_diagram( + data['probabilities'], observations, name='DailyM4+' + ) + + # Temporal Molchan + ax, ass, sigma = plots.plot_temporal_Molchan_diagram( + data['probabilities'], observations, name='DailyM4+' + ) diff --git a/docs/concepts/forecasts.rst b/docs/concepts/forecasts.rst index bb1662ea..12ed3dc6 100644 --- a/docs/concepts/forecasts.rst +++ b/docs/concepts/forecasts.rst @@ -191,3 +191,158 @@ catalog_id should be assigned to correspond with the total number of catalogs in contains zero forecasted events, you would specify the forecasting using (2). The *catalog_id* should be assigned to correspond with the total number of catalogs in the forecast. + +********************* +Alarm-based forecasts +********************* + +Alarm-based forecasts represent a different paradigm from traditional rate-based forecasts. Instead of providing +earthquake rates, alarm-based forecasts assign a score (e.g., probability, alarm level, or hazard metric) to each +spatial cell indicating the relative likelihood of earthquake occurrence. These forecasts are evaluated using +Receiver Operating Characteristic (ROC) curves and Molchan diagrams rather than likelihood-based tests. + +Alarm-based forecasts are particularly useful for: + +1. Binary classification models (e.g., "high risk" vs "low risk" areas) +2. Machine learning classifiers +3. Pattern-based approaches +4. Any model that produces relative rankings rather than absolute rates + +Working with alarm-based forecasts +################################## + +PyCSEP provides the :func:`load_alarm_forecast` function to load alarm-based forecasts +from CSV files. The loaded forecast is returned as a :class:`GriddedForecast` +object that can be evaluated using ROC curves and Molchan diagrams. + +.. autosummary:: csep.load_alarm_forecast + +Default file format +------------------- + +The default file format for alarm-based forecasts is a comma-separated ASCII file with the following columns:: + + lon,lat,alarm_score,probability,rate_per_day,magnitude_min,magnitude_target + 165.0,-47.0,0.234,0.199,0.000117,4.0,7.0 + 165.1,-47.0,0.156,0.133,0.000078,4.0,7.0 + +Required columns: + +- **lon**: Longitude of cell center +- **lat**: Latitude of cell center +- At least one score column (e.g., **alarm_score**, **probability**, **rate_per_day**) + +Optional columns: + +- **magnitude_min**: Minimum magnitude threshold (default: 4.0) +- **magnitude_target**: Maximum magnitude threshold (default: magnitude_min + 3.0) +- **start_time**: Forecast start time +- **end_time**: Forecast end time + +The spatial grid is automatically inferred from the provided coordinates, making this format +suitable for any geographic region worldwide. + +Loading alarm forecasts +----------------------- + +Basic usage:: + + import csep + + # Load alarm forecast + forecast = csep.load_alarm_forecast('alarm_forecast.csv', name='MyModel') + + # Load with specific score field + forecast = csep.load_alarm_forecast('forecast.csv', score_field='probability') + + # Load tab-delimited file + forecast = csep.load_alarm_forecast('forecast.tsv', delimiter='\t') + +Evaluating alarm forecasts +-------------------------- + +Alarm forecasts are evaluated using ROC curves and Molchan diagrams:: + + from csep.utils import plots + + # Load forecast and catalog + forecast = csep.load_alarm_forecast('forecast.csv') + catalog = csep.query_gns(start_time=..., end_time=...) + + # ROC curve + ax, auc = plots.plot_ROC_diagram(forecast, catalog, linear=True) + + # Molchan diagram + ax, ass, sigma = plots.plot_Molchan_diagram(forecast, catalog, linear=True) + +See :ref:`alarm-forecast-evaluation` for a complete tutorial on evaluating alarm-based forecasts. + + +Temporal alarm-based forecasts +############################## + +Temporal forecasts are a time-based variant of alarm-based forecasts. Instead of assigning scores to +spatial cells, temporal forecasts assign probability scores to a sequence of time windows (e.g., daily +forecasts). The evaluation is analogous: observations are computed from an earthquake catalog to determine +whether events occurred in each time window, and the forecast probabilities are compared against these +binary observations using ROC curves and Molchan diagrams. + +Working with temporal forecasts +############################### + +PyCSEP provides functions to load temporal forecasts and compute observations from catalogs: + +.. autosummary:: + + csep.load_temporal_forecast + csep.compute_temporal_observations + +Default file format +------------------- + +Temporal forecasts use a simple two-column CSV format:: + + time,probability + 1,0.0234 + 2,0.0221 + 3,0.0228 + +The **time** column can contain either: + +- Integer indices (1, 2, 3, ...) - requires ``start_time`` and ``time_delta`` parameters +- Datetime strings ('2016-01-01', '2016-01-02', ...) - automatically parsed + +Loading and evaluating temporal forecasts +----------------------------------------- + +Example workflow:: + + import csep + from csep.utils import plots + + # Load temporal forecast (500 daily windows) + data = csep.load_temporal_forecast('forecast.csv') + + # Query observed catalog + catalog = csep.query_gns( + start_time='2016-01-01', + end_time='2017-06-01', + min_magnitude=4.0 + ) + + # Compute observations from catalog + observations = csep.compute_temporal_observations( + catalog, + data['times'], + start_time='2016-01-01', # Map integer times to dates + time_delta='1D', # 1 day per window + magnitude_min=4.0 + ) + + # Evaluate with ROC and Molchan diagrams + ax, auc = plots.plot_temporal_ROC_diagram( + data['probabilities'], observations, name='DailyM4+' + ) + ax, ass, sigma = plots.plot_temporal_Molchan_diagram( + data['probabilities'], observations, name='DailyM4+' + ) diff --git a/docs/index.rst b/docs/index.rst index ea21cb80..e7098dab 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -21,6 +21,7 @@ pyCSEP: Tools for Earthquake Forecast Developers tutorials/quadtree_gridded_forecast_evaluation.rst tutorials/working_with_catalog_forecasts.rst tutorials/catalog_forecast_evaluation.rst + tutorials/alarm_forecast_evaluation.rst tutorials/plot_customizations.rst .. toctree:: diff --git a/docs/tutorials/alarm_forecast_evaluation.rst b/docs/tutorials/alarm_forecast_evaluation.rst new file mode 100644 index 00000000..0b922ed1 --- /dev/null +++ b/docs/tutorials/alarm_forecast_evaluation.rst @@ -0,0 +1,232 @@ +.. _alarm-forecast-tutorial: + +Alarm-Based Forecast Evaluation +=============================== + +This tutorial demonstrates how to load, evaluate, and visualize alarm-based earthquake forecasts +using pyCSEP. Alarm-based forecasts assign a score (e.g., probability or alarm level) to each +spatial cell rather than providing absolute earthquake rates. + +.. contents:: Table of Contents + :local: + :depth: 2 + +Overview +-------- + +Alarm-based forecasts are evaluated using: + +- **ROC (Receiver Operating Characteristic) curves**: Plot hit rate vs false alarm rate +- **Molchan diagrams**: Plot miss rate vs alarm fraction + +These methods assess how well the forecast ranks spatial cells by their probability of experiencing +an earthquake. + +Loading an Alarm Forecast +------------------------- + +First, let's load an alarm forecast from a CSV file: + +.. code-block:: python + + import csep + from csep.utils import plots + + # Load alarm forecast + forecast = csep.load_alarm_forecast( + 'alarm_forecast.csv', + name='MyAlarmForecast', + score_field='alarm_score' # Column to use as score + ) + + print(f"Loaded forecast: {forecast.name}") + print(f"Spatial cells: {forecast.region.num_nodes}") + print(f"Magnitude range: {forecast.magnitudes[0]:.1f} - {forecast.magnitudes[-1]:.1f}") + +The CSV file should have at minimum longitude, latitude, and a score column: + +.. code-block:: text + + lon,lat,alarm_score,magnitude_min,magnitude_target + 165.0,-47.0,0.234,4.0,7.0 + 165.1,-47.0,0.156,4.0,7.0 + 165.2,-47.0,0.089,4.0,7.0 + +Querying an Observed Catalog +---------------------------- + +Next, query the observed earthquake catalog for the forecast period: + +.. code-block:: python + + import datetime + + # Query GeoNet catalog for New Zealand + catalog = csep.query_gns( + start_time=datetime.datetime(2020, 1, 1), + end_time=datetime.datetime(2021, 1, 1), + min_magnitude=4.0, + min_latitude=-47.0, + max_latitude=-34.0, + min_longitude=165.0, + max_longitude=180.0 + ) + + print(f"Found {catalog.event_count} events") + + # Filter catalog to match forecast region (optional) + catalog_filtered = catalog.filter_spatial(region=forecast.region) + +Evaluating with ROC Curve +------------------------- + +The ROC curve shows the trade-off between hit rate (true positive rate) and +false alarm rate (false positive rate) at different alarm thresholds: + +.. code-block:: python + + # Create ROC diagram + ax, auc = plots.plot_ROC_diagram( + forecast, catalog_filtered, + linear=True, # Use linear scale + plot_args={ + 'title': 'ROC Curve - My Alarm Forecast', + 'linecolor': 'blue' + } + ) + + print(f"Area Under Curve (AUC): {auc:.3f}") + +**Interpretation:** + +- **AUC = 1.0**: Perfect forecast (upper-left corner) +- **AUC = 0.5**: Random forecast (diagonal line) +- **AUC < 0.5**: Worse than random (anti-skill) + +Evaluating with Molchan Diagram +------------------------------- + +The Molchan diagram emphasizes the trade-off between capturing earthquakes +and the fraction of space occupied by alarms: + +.. code-block:: python + + # Create Molchan diagram + ax, ass, sigma = plots.plot_Molchan_diagram( + forecast, catalog_filtered, + linear=True, + plot_args={ + 'title': 'Molchan Diagram - My Alarm Forecast', + 'linecolor': 'red' + } + ) + + print(f"Area Skill Score (ASS): {ass:.3f} ± {sigma:.3f}") + +**Interpretation:** + +- **ASS = 1.0**: Perfect forecast (captures all events with minimal space) +- **ASS = 0.5**: No skill (same as random) +- **ASS < 0.5**: Negative skill (worse than random) + +Comparing Multiple Score Fields +------------------------------- + +You can load forecasts with different score fields and compare them: + +.. code-block:: python + + import matplotlib.pyplot as plt + + fig, axes = plt.subplots(1, 2, figsize=(12, 5)) + + score_fields = ['alarm_score', 'probability', 'rate_per_day'] + colors = ['blue', 'red', 'green'] + + for score_field, color in zip(score_fields, colors): + forecast = csep.load_alarm_forecast( + 'alarm_forecast.csv', + name=f'{score_field}', + score_field=score_field + ) + + # ROC + plots.plot_ROC_diagram( + forecast, catalog_filtered, + axes=axes[0], linear=True, show=False, + plot_args={'linecolor': color} + ) + + # Molchan + plots.plot_Molchan_diagram( + forecast, catalog_filtered, + axes=axes[1], linear=True, show=False, + plot_args={'linecolor': color} + ) + + plt.tight_layout() + plt.savefig('multi_score_comparison.png', dpi=150) + +Complete Example +---------------- + +Here's a complete working example: + +.. code-block:: python + + #!/usr/bin/env python + """Complete alarm forecast evaluation example.""" + + import csep + from csep.utils import plots + import matplotlib.pyplot as plt + import datetime + + # 1. Load alarm forecast + forecast = csep.load_alarm_forecast( + 'nz_alarm_forecast.csv', + name='NZ_Alarm_Model' + ) + + # 2. Query observed catalog + catalog = csep.query_gns( + start_time=datetime.datetime(2024, 1, 1), + end_time=datetime.datetime(2025, 1, 1), + min_magnitude=4.0, + min_latitude=-47.0, + max_latitude=-34.0, + min_longitude=165.0, + max_longitude=179.0 + ) + + # 3. Filter to forecast region + catalog_filtered = catalog.filter_spatial(region=forecast.region) + + # 4. Create evaluation plots + fig, axes = plt.subplots(1, 2, figsize=(12, 5)) + + ax1, auc = plots.plot_ROC_diagram( + forecast, catalog_filtered, + axes=axes[0], linear=True, show=False + ) + + ax2, ass, sigma = plots.plot_Molchan_diagram( + forecast, catalog_filtered, + axes=axes[1], linear=True, show=False + ) + + plt.tight_layout() + plt.savefig('alarm_evaluation.png', dpi=150) + + # 5. Print results + print(f"AUC: {auc:.3f}") + print(f"ASS: {ass:.3f} ± {sigma:.3f}") + +See Also +-------- + +- :ref:`Forecasts ` - Alarm-based forecast format +- :ref:`Evaluations ` - ROC and Molchan evaluation details +- :func:`csep.load_alarm_forecast` - API reference +- :func:`csep.utils.plots.plot_ROC_diagram` - ROC diagram API +- :func:`csep.utils.plots.plot_Molchan_diagram` - Molchan diagram API diff --git a/examples/tutorials/alarm_forecast_demo.py b/examples/tutorials/alarm_forecast_demo.py new file mode 100644 index 00000000..e3ce2b4e --- /dev/null +++ b/examples/tutorials/alarm_forecast_demo.py @@ -0,0 +1,281 @@ +#!/usr/bin/env python +""" +Demonstration of Alarm-Based Forecast Evaluation in pyCSEP + +This script demonstrates how to: +1. Load an alarm-based earthquake forecast from CSV +2. Query a real observed catalog from GeoNet (New Zealand) +3. Evaluate the forecast using ROC curves and Molchan diagrams + +This implementation works with most geographic regions worldwide. +""" + +import sys +import os + +import matplotlib +matplotlib.use('Agg') # Non-interactive backend + +import matplotlib.pyplot as plt +import numpy as np +import csep +from csep.utils import plots +from csep.core.catalogs import CSEPCatalog +import datetime + +def create_nz_alarm_forecast(filename='nz_alarm_forecast.csv', resolution=0.1): + """Create a realistic alarm forecast for New Zealand region.""" + print("Creating New Zealand alarm forecast...") + + # New Zealand region bounds + min_lon, max_lon = 165.0, 179.0 + min_lat, max_lat = -47.0, -34.0 + + # Create grid of cells + lons = np.arange(min_lon, max_lon, resolution) + lats = np.arange(min_lat, max_lat, resolution) + + # Create meshgrid + lon_grid, lat_grid = np.meshgrid(lons, lats) + lon_flat = lon_grid.flatten() + lat_flat = lat_grid.flatten() + + # Generate realistic alarm scores based on proximity to known seismic zones + # Higher scores near the Alpine Fault and Hikurangi subduction zone + scores = np.zeros(len(lon_flat)) + + for i, (lon, lat) in enumerate(zip(lon_flat, lat_flat)): + # Alpine Fault (South Island) - runs roughly NE-SW + alpine_dist = abs((lon - 170.0) + 0.5 * (lat + 43.0)) + alpine_score = np.exp(-alpine_dist**2 / 2.0) + + # Hikurangi subduction zone (North Island east coast) + hikurangi_dist = np.sqrt((lon - 178.0)**2 + (lat + 39.0)**2) + hikurangi_score = np.exp(-hikurangi_dist**2 / 4.0) + + # Taupo Volcanic Zone (North Island) + taupo_dist = np.sqrt((lon - 176.0)**2 + (lat + 38.5)**2) + taupo_score = np.exp(-taupo_dist**2 / 1.5) + + # Combine scores + scores[i] = max(alpine_score, hikurangi_score, taupo_score) + + # Normalize to [0, 1] + scores = scores / scores.max() + + # Add some noise to make it more realistic + scores += np.random.normal(0, 0.05, len(scores)) + scores = np.clip(scores, 0, 1) + + # Create CSV data + data = [] + for i in range(len(lon_flat)): + row = { + 'lon': lon_flat[i], + 'lat': lat_flat[i], + 'alarm_score': scores[i], + 'probability': scores[i] * 0.85, # Slightly different + 'rate_per_day': scores[i] * 0.0005, # Events per day + 'magnitude_min': 4.0, + 'magnitude_target': 7.0 + } + data.append(row) + + # Write to CSV + import csv + with open(filename, 'w', newline='') as f: + writer = csv.DictWriter(f, fieldnames=['lon', 'lat', 'alarm_score', + 'probability', 'rate_per_day', + 'magnitude_min', 'magnitude_target']) + writer.writeheader() + writer.writerows(data) + + print(f"Created {filename} with {len(data)} spatial cells") + print(f" Region: New Zealand ({min_lon}°E to {max_lon}°E, {min_lat}°N to {max_lat}°N)") + return filename + + +def query_real_catalog(region='new_zealand'): + """Query real earthquake catalog from GeoNet (New Zealand).""" + print(f"\nQuerying real earthquake catalog for {region}...") + + # Define time window (e.g., last year) + end_time = datetime.datetime.now() + start_time = end_time - datetime.timedelta(days=365) + + print(f" Time window: {start_time.date()} to {end_time.date()}") + + try: + # Query GeoNet catalog for New Zealand + catalog = csep.query_gns( + start_time=start_time, + end_time=end_time, + min_magnitude=4.0, + min_latitude=-47.0, + max_latitude=-34.0, + min_longitude=165.0, + max_longitude=179.0, + max_depth=100.0 + ) + + print(f" Successfully downloaded {catalog.event_count} events from GeoNet") + print(f" Magnitude range: {catalog.min_magnitude:.1f} - {catalog.max_magnitude:.1f}") + + return catalog + + except Exception as e: + print(f" Warning: Could not query GeoNet catalog: {e}") + print(f" Falling back to synthetic catalog...") + return create_synthetic_catalog_nz() + + +def create_synthetic_catalog_nz(n_events=100): + """Create synthetic catalog for New Zealand if real data unavailable.""" + print(f" Creating synthetic catalog with {n_events} events...") + + # New Zealand region bounds + min_lon, max_lon = 165.0, 179.0 + min_lat, max_lat = -47.0, -34.0 + + import time + current_time = int(time.time() * 1000) + + events = [] + for i in range(n_events): + # Bias events toward known seismic zones + if np.random.random() < 0.4: # Alpine Fault region + lon = np.random.uniform(169.0, 171.0) + lat = np.random.uniform(-44.0, -42.0) + elif np.random.random() < 0.7: # Hikurangi/North Island + lon = np.random.uniform(176.0, 178.5) + lat = np.random.uniform(-41.0, -37.0) + else: # Other regions + lon = np.random.uniform(min_lon, max_lon) + lat = np.random.uniform(min_lat, max_lat) + + # Realistic magnitude distribution (Gutenberg-Richter) + magnitude = 4.0 - np.log10(np.random.random()) / 1.0 + magnitude = min(magnitude, 7.0) + + depth = np.random.uniform(5, 40) # Realistic depth range + + event = (i, current_time + i*1000, float(lat), float(lon), + float(depth), float(magnitude)) + events.append(event) + + catalog = CSEPCatalog(data=events) + print(f" Created synthetic catalog with {catalog.event_count} events") + return catalog + + +def evaluate_alarm_forecast(forecast, catalog, output_dir='.'): + """Evaluate alarm forecast using ROC and Molchan diagrams.""" + print("\nEvaluating alarm forecast...") + + # Create figure with two subplots + fig, axes = plt.subplots(1, 2, figsize=(12, 5)) + + # Molchan diagram (left) + print(" - Creating Molchan diagram...") + plots.plot_Molchan_diagram( + forecast, catalog, + linear=True, + axes=axes[0], + show=False, + savepdf=False, + savepng=False + ) + axes[0].set_title('Molchan Diagram\n(Miss Rate vs Alarm Fraction)', fontsize=12) + + # ROC curve (right) + print(" - Creating ROC diagram...") + plots.plot_ROC_diagram( + forecast, catalog, + linear=True, + axes=axes[1], + show=False, + savepdf=False, + savepng=False + ) + axes[1].set_title('ROC Curve\n(Hit Rate vs False Alarm Rate)', fontsize=12) + + plt.tight_layout() + + # Save figure + output_file = f'{output_dir}/nz_alarm_evaluation.png' + plt.savefig(output_file, dpi=300, bbox_inches='tight') + print(f"\nSaved evaluation plots to: {output_file}") + plt.close() + + +def main(): + """Main demonstration workflow.""" + print("="*70) + print("Alarm-Based Forecast Evaluation Demo - New Zealand") + print("="*70) + + # Step 1: Create New Zealand alarm forecast + forecast_file = create_nz_alarm_forecast(resolution=0.1) + + # Step 2: Load alarm forecast + print("\nLoading alarm forecast...") + forecast = csep.load_alarm_forecast( + forecast_file, + name='NZ_ALARM_Model', + score_field='alarm_score' + ) + print(f"Loaded forecast: {forecast.name}") + print(f" - Spatial cells: {forecast.region.num_nodes}") + print(f" - Magnitude range: {forecast.magnitudes[0]:.1f} - {forecast.magnitudes[-1]:.1f}") + + # Step 3: Query real catalog from GeoNet + catalog = query_real_catalog(region='new_zealand') + + # Step 4: Filter catalog to match forecast region + print(f"\nFiltering catalog to forecast region...") + try: + catalog_filtered = catalog.filter_spatial(region=forecast.region) + print(f" Filtered catalog: {catalog_filtered.event_count} events in forecast region") + catalog_to_use = catalog_filtered if catalog_filtered.event_count > 0 else catalog + except: + print(f" Using full catalog: {catalog.event_count} events") + catalog_to_use = catalog + + # Step 5: Evaluate forecast + print(f"\nEvaluating forecast with {catalog_to_use.event_count} observed events...") + evaluate_alarm_forecast(forecast, catalog_to_use) + + # Step 6: Demonstrate different score fields + print("\n" + "="*70) + print("Comparing Different Score Fields") + print("="*70) + + score_fields = ['alarm_score', 'probability', 'rate_per_day'] + + for score_field in score_fields: + print(f"\nScore field: '{score_field}'") + forecast_variant = csep.load_alarm_forecast( + forecast_file, + name=f'NZ_{score_field}', + score_field=score_field + ) + + print(f" Forecast: {forecast_variant.name}") + print(f" Score range: [{forecast_variant.data.min():.6f}, {forecast_variant.data.max():.6f}]") + print(f" Mean score: {forecast_variant.data.mean():.6f}") + + print("\n" + "="*70) + print("Demo Complete!") + print("="*70) + print("\nKey files created:") + print(f" - {forecast_file}") + print(f" - nz_alarm_evaluation.png") + print("\nThis demo shows:") + print(" ✓ Loading alarm forecasts for any region (New Zealand example)") + print(" ✓ Querying real earthquake catalogs from GeoNet") + print(" ✓ Evaluating forecasts with ROC curves and Molchan diagrams") + print(" ✓ Comparing different score fields (alarm_score, probability, rate_per_day)") + + +if __name__ == '__main__': + main() diff --git a/examples/tutorials/demo_alarm_forecast.csv b/examples/tutorials/demo_alarm_forecast.csv new file mode 100644 index 00000000..99dc833c --- /dev/null +++ b/examples/tutorials/demo_alarm_forecast.csv @@ -0,0 +1,11001 @@ +lon,lat,alarm_score,probability,rate_per_day,magnitude_min,magnitude_target +-125.0,32.0,0.010098406402176383,0.009088565761958746,1.0098406402176383e-05,4.0,6.0 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a/examples/tutorials/temporal_evaluation/temporal_comparison.png b/examples/tutorials/temporal_evaluation/temporal_comparison.png new file mode 100644 index 00000000..e3e62db2 Binary files /dev/null and b/examples/tutorials/temporal_evaluation/temporal_comparison.png differ diff --git a/examples/tutorials/temporal_evaluation/temporal_comparison_results.csv b/examples/tutorials/temporal_evaluation/temporal_comparison_results.csv new file mode 100644 index 00000000..5e0bf412 --- /dev/null +++ b/examples/tutorials/temporal_evaluation/temporal_comparison_results.csv @@ -0,0 +1,3 @@ +Model,AUC,ASS,ASS_std +Model_B,0.48859667146085883,-0.014322580645161231,0.021166687833365082 +Model_A,0.4353982603931238,-0.0811397849462363,0.021166687833365082 diff --git a/examples/tutorials/temporal_evaluation/temporal_evaluation_example.py b/examples/tutorials/temporal_evaluation/temporal_evaluation_example.py new file mode 100644 index 00000000..2a461836 --- /dev/null +++ b/examples/tutorials/temporal_evaluation/temporal_evaluation_example.py @@ -0,0 +1,216 @@ +""" +Temporal Forecast Evaluation - Multiple Models Comparison (GeoNet) +=================================================================== + +This example demonstrates how to compare multiple temporal probability forecasts +using ROC and Molchan diagrams with the GeoNet (NZ) earthquake catalog. +""" + +import csep +from csep.utils import plots +import numpy as np +import matplotlib.pyplot as plt +from datetime import datetime +import os +import pandas as pd + +# ============================================================================= +# CONFIGURATION +# ============================================================================= + +script_dir = os.path.dirname(os.path.abspath(__file__)) + +# Dictionary of forecasts to compare: {name: filename} +# Add your forecast files here +FORECASTS = { + 'Model_A': 'temporal_forecast_500days.csv', + 'Model_B': 'temporal_forecast_model_b.csv', + # 'EEPAS': 'eepas_forecast.csv', +} + +# Time settings +START_TIME = '2016-01-01' +TIME_DELTA = '1D' + +# Magnitude threshold +MIN_MAGNITUDE = 4.0 + +# Region settings +USE_BOUNDING_BOX = True +MIN_LATITUDE = -47.0 +MAX_LATITUDE = -34.0 +MIN_LONGITUDE = 164.0 +MAX_LONGITUDE = 180.0 + +# Polygon (if USE_BOUNDING_BOX = False) +POLYGON = [ + [174.0, -42.0], + [176.0, -42.0], + [176.0, -40.0], + [174.0, -40.0], + [174.0, -42.0], +] + +# Plot styling +COLORS = ['black', 'blue', 'red', 'green', 'orange', 'purple', 'brown', 'pink'] +LINESTYLES = ['-', '--', '-.', ':', '-', '--', '-.', ':'] + +# ============================================================================= +# Load forecasts +# ============================================================================= + +print("="*70) +print("TEMPORAL FORECAST COMPARISON - NEW ZEALAND (GEONET)") +print("="*70) + +forecasts_data = {} +for name, filename in FORECASTS.items(): + path = os.path.join(script_dir, filename) + if os.path.exists(path): + forecasts_data[name] = csep.load_temporal_forecast(path) + print(f" Loaded: {name} ({forecasts_data[name]['metadata']['n_time_windows']} days)") + else: + print(f" WARNING: {filename} not found, skipping {name}") + +if not forecasts_data: + print("ERROR: No forecasts loaded!") + exit(1) + +# Get number of time windows from first forecast +n_days = list(forecasts_data.values())[0]['metadata']['n_time_windows'] + +# Calculate end date +start_dt = pd.to_datetime(START_TIME) +end_dt = start_dt + pd.Timedelta(TIME_DELTA) * n_days +print(f"\nForecast period: {START_TIME} to {end_dt.strftime('%Y-%m-%d')}") + +# ============================================================================= +# Query GeoNet catalog +# ============================================================================= + +print(f"\nQuerying GeoNet catalog...") +print(f" Magnitude >= {MIN_MAGNITUDE}") + +if USE_BOUNDING_BOX: + print(f" Region: Lat [{MIN_LATITUDE}, {MAX_LATITUDE}], Lon [{MIN_LONGITUDE}, {MAX_LONGITUDE}]") + catalog = csep.query_gns( + start_time=datetime.fromisoformat(START_TIME), + end_time=end_dt.to_pydatetime(), + min_magnitude=MIN_MAGNITUDE, + min_latitude=MIN_LATITUDE, + max_latitude=MAX_LATITUDE, + min_longitude=MIN_LONGITUDE, + max_longitude=MAX_LONGITUDE, + verbose=False + ) +else: + print(f" Region: Custom polygon") + catalog = csep.query_gns( + start_time=datetime.fromisoformat(START_TIME), + end_time=end_dt.to_pydatetime(), + min_magnitude=MIN_MAGNITUDE, + min_latitude=-50.0, max_latitude=-30.0, + min_longitude=160.0, max_longitude=180.0, + verbose=False + ) + from shapely.geometry import Point, Polygon + poly = Polygon(POLYGON) + lons, lats = catalog.get_longitudes(), catalog.get_latitudes() + inside_mask = np.array([poly.contains(Point(lon, lat)) for lon, lat in zip(lons, lats)]) + catalog = catalog.filter(inside_mask) + +print(f" Found {catalog.event_count} M{MIN_MAGNITUDE}+ events") + +# ============================================================================= +# Compute observations (shared by all forecasts) +# ============================================================================= + +times = list(forecasts_data.values())[0]['times'] +observations = csep.compute_temporal_observations( + catalog, times, + start_time=START_TIME, + time_delta=TIME_DELTA, + magnitude_min=MIN_MAGNITUDE +) + +n_events = np.sum(observations) +print(f" Events in {n_days} days: {n_events}") + +# ============================================================================= +# Evaluate all forecasts and plot +# ============================================================================= + +print(f"\nEvaluating {len(forecasts_data)} forecast(s)...") + +fig, axes = plt.subplots(1, 2, figsize=(14, 6)) +results = [] + +for i, (name, data) in enumerate(forecasts_data.items()): + color = COLORS[i % len(COLORS)] + linestyle = LINESTYLES[i % len(LINESTYLES)] + + probs = data['probabilities'] + + # ROC + ax1, auc = plots.plot_temporal_ROC_diagram( + probs, observations, + name=name, + axes=axes[0], + show=False, + plot_uniform=(i == 0), # Only plot uniform line once + plot_args={ + 'linecolor': color, + 'linestyle': linestyle, + 'title': f'Temporal ROC Diagram (M{MIN_MAGNITUDE}+)' + } + ) + + # Molchan + ax2, ass, sigma = plots.plot_temporal_Molchan_diagram( + probs, observations, + name=name, + axes=axes[1], + show=False, + plot_uniform=(i == 0), + plot_args={ + 'linecolor': color, + 'linestyle': linestyle, + 'title': f'Temporal Molchan Diagram (M{MIN_MAGNITUDE}+)' + } + ) + + results.append({ + 'Model': name, + 'AUC': auc, + 'ASS': ass, + 'ASS_std': sigma + }) + print(f" {name}: AUC={auc:.3f}, ASS={ass:.3f}±{sigma:.3f}") + +plt.tight_layout() +output_path = os.path.join(script_dir, 'temporal_comparison.png') +plt.savefig(output_path, dpi=150, bbox_inches='tight') +plt.close() + +# ============================================================================= +# Results summary +# ============================================================================= + +print("\n" + "="*70) +print("COMPARISON RESULTS") +print("="*70) +print(f"Period: {START_TIME} to {end_dt.strftime('%Y-%m-%d')} ({n_days} days)") +print(f"Events: {n_events} M{MIN_MAGNITUDE}+") +print() + +results_df = pd.DataFrame(results) +results_df = results_df.sort_values('AUC', ascending=False) +print(results_df.to_string(index=False)) + +print(f"\nPlot saved: {output_path}") +print("="*70) + +# Save results to CSV +results_path = os.path.join(script_dir, 'temporal_comparison_results.csv') +results_df.to_csv(results_path, index=False) +print(f"Results saved: {results_path}") diff --git a/examples/tutorials/temporal_evaluation/temporal_forecast_500days.csv b/examples/tutorials/temporal_evaluation/temporal_forecast_500days.csv new file mode 100644 index 00000000..4ef1912f --- /dev/null +++ b/examples/tutorials/temporal_evaluation/temporal_forecast_500days.csv @@ -0,0 +1,501 @@ 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--- /dev/null +++ b/tests/artifacts/alarm_forecast_semicolon.csv @@ -0,0 +1,11 @@ +lon;lat;alarm_score;probability;rate_per_day;magnitude_min;magnitude_target +-120.5;35.5;0.85;0.75;0.002;4.0;6.0 +-120.4;35.5;0.65;0.55;0.0015;4.0;6.0 +-120.3;35.5;0.45;0.35;0.001;4.0;6.0 +-120.2;35.5;0.25;0.15;0.0005;4.0;6.0 +-120.1;35.5;0.05;0.02;0.0001;4.0;6.0 +-120.5;35.6;0.90;0.80;0.0025;4.0;6.0 +-120.4;35.6;0.70;0.60;0.002;4.0;6.0 +-120.3;35.6;0.50;0.40;0.0015;4.0;6.0 +-120.2;35.6;0.30;0.20;0.001;4.0;6.0 +-120.1;35.6;0.10;0.05;0.0002;4.0;6.0 diff --git a/tests/artifacts/alarm_forecast_tabs.tsv b/tests/artifacts/alarm_forecast_tabs.tsv new file mode 100644 index 00000000..0c9d47b0 --- /dev/null +++ b/tests/artifacts/alarm_forecast_tabs.tsv @@ -0,0 +1,11 @@ +lon lat alarm_score probability rate_per_day magnitude_min magnitude_target +-120.5 35.5 0.85 0.75 0.002 4.0 6.0 +-120.4 35.5 0.65 0.55 0.0015 4.0 6.0 +-120.3 35.5 0.45 0.35 0.001 4.0 6.0 +-120.2 35.5 0.25 0.15 0.0005 4.0 6.0 +-120.1 35.5 0.05 0.02 0.0001 4.0 6.0 +-120.5 35.6 0.90 0.80 0.0025 4.0 6.0 +-120.4 35.6 0.70 0.60 0.002 4.0 6.0 +-120.3 35.6 0.50 0.40 0.0015 4.0 6.0 +-120.2 35.6 0.30 0.20 0.001 4.0 6.0 +-120.1 35.6 0.10 0.05 0.0002 4.0 6.0 diff --git a/tests/artifacts/temporal_forecast_example.csv b/tests/artifacts/temporal_forecast_example.csv new file mode 100644 index 00000000..3e767ecf --- /dev/null +++ b/tests/artifacts/temporal_forecast_example.csv @@ -0,0 +1,20 @@ +time,probability +1,0.0234250159744233 +2,0.0221418542747141 +3,0.0216835056298652 +4,0.0207954829425583 +5,0.0198343337931747 +6,0.0180964154950278 +7,0.0166928550256516 +8,0.0161935795008503 +9,0.0154443530034279 +10,0.0149274686697472 +11,0.0151416823048553 +12,0.0157813258904916 +13,0.015051402470272 +14,0.0157621703111313 +15,0.020461472686797 +16,0.0191272533893832 +17,0.0231406844836395 +18,0.0222789751686156 +19,0.0221556143184444 diff --git a/tests/test_plots.py b/tests/test_plots.py index 2c3db7b3..2c71709a 100644 --- a/tests/test_plots.py +++ b/tests/test_plots.py @@ -1,3 +1,6 @@ +import matplotlib +matplotlib.use('Agg') # Use non-interactive backend for testing + import matplotlib.pyplot as plt import numpy import matplotlib.pyplot @@ -6,6 +9,8 @@ import random import string from csep.utils import plots +import csep +from csep.core.catalogs import CSEPCatalog class TestPoissonPlots(unittest.TestCase): @@ -112,5 +117,446 @@ def rand(limit=10, offset=0): t_tests[0].name) +class TestAlarmBasedPlots(unittest.TestCase): + """Tests for alarm-based evaluation plots (ROC and Molchan diagrams).""" + + @classmethod + def setUpClass(cls): + """Set up test fixtures that are shared across all test methods.""" + # Load forecast + forecast_path = 'tests/artifacts/example_csep1_forecasts/Forecast/EEPAS-0F_12_1_2007.dat' + cls.forecast = csep.load_gridded_forecast(forecast_path, name='EEPAS-0F') + + # Create synthetic catalog with events + n_events = 15 + bbox = cls.forecast.region.get_bbox() + min_lon, max_lon = bbox[0], bbox[1] + min_lat, max_lat = bbox[2], bbox[3] + + # Generate random events within region bounds + import time + current_time = int(time.time() * 1000) + lons = numpy.random.uniform(min_lon, max_lon, n_events) + lats = numpy.random.uniform(min_lat, max_lat, n_events) + magnitudes = numpy.random.uniform(4.0, 6.0, n_events) + depths = numpy.random.uniform(0, 30, n_events) + + # Create catalog data + catalog_data = [(i, current_time + i*1000, float(lats[i]), float(lons[i]), + float(depths[i]), float(magnitudes[i])) + for i in range(n_events)] + + cls.catalog = CSEPCatalog(data=catalog_data, region=cls.forecast.region) + + def test_plot_ROC_diagram_linear(self): + """Test plot_ROC_diagram with linear x-axis.""" + matplotlib.pyplot.close() + ax = plots.plot_ROC_diagram( + self.forecast, self.catalog, + linear=True, show=False, savepdf=False, savepng=False + ) + self.assertIsNotNone(ax) + matplotlib.pyplot.close() + + def test_plot_ROC_diagram_log(self): + """Test plot_ROC_diagram with logarithmic x-axis.""" + matplotlib.pyplot.close() + ax = plots.plot_ROC_diagram( + self.forecast, self.catalog, + linear=False, show=False, savepdf=False, savepng=False + ) + self.assertIsNotNone(ax) + matplotlib.pyplot.close() + + def test_plot_Molchan_diagram_linear(self): + """Test plot_Molchan_diagram with linear x-axis.""" + matplotlib.pyplot.close() + ax = plots.plot_Molchan_diagram( + self.forecast, self.catalog, + linear=True, show=False, savepdf=False, savepng=False + ) + self.assertIsNotNone(ax) + matplotlib.pyplot.close() + + def test_plot_Molchan_diagram_log(self): + """Test plot_Molchan_diagram with logarithmic x-axis.""" + matplotlib.pyplot.close() + ax = plots.plot_Molchan_diagram( + self.forecast, self.catalog, + linear=False, show=False, savepdf=False, savepng=False + ) + self.assertIsNotNone(ax) + matplotlib.pyplot.close() + + def test_ROC_diagram_with_custom_axes(self): + """Test ROC diagram can use custom axes.""" + matplotlib.pyplot.close() + fig, ax = matplotlib.pyplot.subplots() + result_ax = plots.plot_ROC_diagram( + self.forecast, self.catalog, + axes=ax, linear=True, show=False, savepdf=False, savepng=False + ) + self.assertEqual(ax, result_ax) + matplotlib.pyplot.close() + + def test_Molchan_diagram_with_custom_axes(self): + """Test Molchan diagram can use custom axes.""" + matplotlib.pyplot.close() + fig, ax = matplotlib.pyplot.subplots() + result_ax = plots.plot_Molchan_diagram( + self.forecast, self.catalog, + axes=ax, linear=True, show=False, savepdf=False, savepng=False + ) + self.assertEqual(ax, result_ax) + matplotlib.pyplot.close() + + def test_ROC_diagram_region_mismatch(self): + """Test that ROC diagram raises error when regions don't match.""" + # Create catalog with different region + from csep.core.regions import california_relm_region + catalog_data = [(0, 1000, 35.0, -120.0, 10.0, 5.0)] + bad_catalog = CSEPCatalog(data=catalog_data, region=california_relm_region()) + + matplotlib.pyplot.close() + with self.assertRaises(RuntimeError): + plots.plot_ROC_diagram( + self.forecast, bad_catalog, + linear=True, show=False, savepdf=False, savepng=False + ) + matplotlib.pyplot.close() + + def test_Molchan_diagram_region_mismatch(self): + """Test that Molchan diagram raises error when regions don't match.""" + # Create catalog with different region + from csep.core.regions import california_relm_region + catalog_data = [(0, 1000, 35.0, -120.0, 10.0, 5.0)] + bad_catalog = CSEPCatalog(data=catalog_data, region=california_relm_region()) + + matplotlib.pyplot.close() + with self.assertRaises(RuntimeError): + plots.plot_Molchan_diagram( + self.forecast, bad_catalog, + linear=True, show=False, savepdf=False, savepng=False + ) + matplotlib.pyplot.close() + + +class TestAlarmForecastLoading(unittest.TestCase): + """Tests for alarm forecast loading and evaluation.""" + + def test_load_alarm_forecast_basic(self): + """Test loading a basic alarm forecast CSV.""" + forecast_path = 'tests/artifacts/alarm_forecast_example.csv' + forecast = csep.load_alarm_forecast(forecast_path, name='TestAlarm') + + self.assertIsNotNone(forecast) + self.assertEqual(forecast.name, 'TestAlarm') + self.assertEqual(forecast.data.shape[0], 10) # 10 cells + self.assertEqual(forecast.data.shape[1], 1) # 1 magnitude bin + + def test_load_alarm_forecast_with_score_field(self): + """Test loading alarm forecast with different score fields.""" + forecast_path = 'tests/artifacts/alarm_forecast_example.csv' + + # Test with alarm_score (default) + forecast1 = csep.load_alarm_forecast(forecast_path, score_field='alarm_score') + self.assertIsNotNone(forecast1) + + # Test with probability + forecast2 = csep.load_alarm_forecast(forecast_path, score_field='probability') + self.assertIsNotNone(forecast2) + + # Test with rate_per_day + forecast3 = csep.load_alarm_forecast(forecast_path, score_field='rate_per_day') + self.assertIsNotNone(forecast3) + + # Scores should be different + self.assertFalse(numpy.array_equal(forecast1.data, forecast2.data)) + self.assertFalse(numpy.array_equal(forecast2.data, forecast3.data)) + + def test_load_alarm_forecast_region(self): + """Test that alarm forecast creates correct spatial region.""" + forecast_path = 'tests/artifacts/alarm_forecast_example.csv' + forecast = csep.load_alarm_forecast(forecast_path) + + # Check region is created + self.assertIsNotNone(forecast.region) + + # Check number of spatial bins matches data + self.assertEqual(forecast.region.num_nodes, 10) + + def test_load_alarm_forecast_magnitudes(self): + """Test that alarm forecast extracts correct magnitude bins.""" + forecast_path = 'tests/artifacts/alarm_forecast_example.csv' + forecast = csep.load_alarm_forecast(forecast_path) + + # Check magnitudes are read from CSV + self.assertEqual(forecast.magnitudes[0], 4.0) + self.assertEqual(forecast.magnitudes[1], 6.0) + + def test_load_alarm_forecast_with_catalog_evaluation(self): + """Test evaluating alarm forecast with synthetic catalog.""" + matplotlib.pyplot.close() + + # Load alarm forecast + forecast_path = 'tests/artifacts/alarm_forecast_example.csv' + forecast = csep.load_alarm_forecast(forecast_path, name='AlarmTest') + + # Create synthetic catalog in same region + n_events = 5 + bbox = forecast.region.get_bbox() + min_lon, max_lon = bbox[0], bbox[1] + min_lat, max_lat = bbox[2], bbox[3] + + import time + current_time = int(time.time() * 1000) + lons = numpy.random.uniform(min_lon, max_lon, n_events) + lats = numpy.random.uniform(min_lat, max_lat, n_events) + magnitudes = numpy.random.uniform(4.0, 6.0, n_events) + depths = numpy.random.uniform(0, 30, n_events) + + catalog_data = [(i, current_time + i*1000, float(lats[i]), float(lons[i]), + float(depths[i]), float(magnitudes[i])) + for i in range(n_events)] + + catalog = CSEPCatalog(data=catalog_data, region=forecast.region) + + # Test ROC diagram + ax = plots.plot_ROC_diagram( + forecast, catalog, + linear=True, show=False, savepdf=False, savepng=False + ) + self.assertIsNotNone(ax) + matplotlib.pyplot.close() + + # Test Molchan diagram + ax = plots.plot_Molchan_diagram( + forecast, catalog, + linear=True, show=False, savepdf=False, savepng=False + ) + self.assertIsNotNone(ax) + matplotlib.pyplot.close() + + def test_load_alarm_forecast_missing_file(self): + """Test that loading non-existent file raises error.""" + with self.assertRaises(FileNotFoundError): + csep.load_alarm_forecast('nonexistent_file.csv') + + def test_load_alarm_forecast_invalid_score_field(self): + """Test that invalid score field raises error.""" + forecast_path = 'tests/artifacts/alarm_forecast_example.csv' + with self.assertRaises(ValueError): + csep.load_alarm_forecast(forecast_path, score_field='invalid_column') + + def test_load_alarm_forecast_tab_delimiter(self): + """Test loading alarm forecast with tab delimiter.""" + forecast_path = 'tests/artifacts/alarm_forecast_tabs.tsv' + forecast = csep.load_alarm_forecast(forecast_path, delimiter='\t') + + self.assertIsNotNone(forecast) + self.assertEqual(forecast.data.shape[0], 10) # 10 cells + # Check that scores were loaded correctly + self.assertAlmostEqual(forecast.data[0, 0], 0.85, places=2) + + def test_load_alarm_forecast_semicolon_delimiter(self): + """Test loading alarm forecast with semicolon delimiter.""" + forecast_path = 'tests/artifacts/alarm_forecast_semicolon.csv' + forecast = csep.load_alarm_forecast(forecast_path, delimiter=';') + + self.assertIsNotNone(forecast) + self.assertEqual(forecast.data.shape[0], 10) # 10 cells + + def test_load_alarm_forecast_multiple_score_fields(self): + """Test loading alarm forecast with multiple score fields.""" + forecast_path = 'tests/artifacts/alarm_forecast_example.csv' + forecast = csep.load_alarm_forecast( + forecast_path, + score_fields=['alarm_score', 'probability', 'rate_per_day'] + ) + + self.assertIsNotNone(forecast) + self.assertEqual(forecast.data.shape[0], 10) # 10 cells + self.assertEqual(forecast.data.shape[1], 3) # 3 score fields + + # Check first row values match expected + self.assertAlmostEqual(forecast.data[0, 0], 0.85, places=2) # alarm_score + self.assertAlmostEqual(forecast.data[0, 1], 0.75, places=2) # probability + self.assertAlmostEqual(forecast.data[0, 2], 0.002, places=4) # rate_per_day + + def test_load_alarm_forecast_custom_magnitude_bins(self): + """Test loading alarm forecast with custom magnitude bins.""" + forecast_path = 'tests/artifacts/alarm_forecast_example.csv' + custom_bins = [4.0, 5.0, 6.0, 7.0] + forecast = csep.load_alarm_forecast( + forecast_path, + magnitude_bins=custom_bins + ) + + self.assertIsNotNone(forecast) + numpy.testing.assert_array_equal(forecast.magnitudes, custom_bins) + + def test_load_alarm_forecast_invalid_magnitude_bins(self): + """Test that invalid magnitude bins raises error.""" + forecast_path = 'tests/artifacts/alarm_forecast_example.csv' + with self.assertRaises(ValueError): + csep.load_alarm_forecast(forecast_path, magnitude_bins=[4.0]) # Need at least 2 edges + + def test_load_alarm_forecast_invalid_score_fields(self): + """Test that invalid score_fields type raises error.""" + forecast_path = 'tests/artifacts/alarm_forecast_example.csv' + with self.assertRaises(ValueError): + csep.load_alarm_forecast(forecast_path, score_fields='not_a_list') + + +class TestTemporalEvaluation(unittest.TestCase): + """Tests for temporal probability forecast evaluation (ROC and Molchan).""" + + def test_load_temporal_forecast_basic(self): + """Test loading a basic temporal forecast CSV.""" + forecast_path = 'tests/artifacts/temporal_forecast_example.csv' + data = csep.load_temporal_forecast(forecast_path) + + self.assertIn('times', data) + self.assertIn('probabilities', data) + self.assertIn('metadata', data) + self.assertNotIn('observations', data) # Observations not in CSV anymore + + self.assertEqual(len(data['probabilities']), 19) # 19 time windows + + def test_load_temporal_forecast_missing_file(self): + """Test that loading non-existent file raises error.""" + with self.assertRaises(FileNotFoundError): + csep.load_temporal_forecast('nonexistent_file.csv') + + def test_temporal_ROC_diagram_basic(self): + """Test basic temporal ROC diagram.""" + matplotlib.pyplot.close() + + # Simple test data + probs = numpy.array([0.9, 0.7, 0.5, 0.3, 0.1]) + obs = numpy.array([1, 1, 0, 0, 0]) + + ax, auc = plots.plot_temporal_ROC_diagram( + probs, obs, name='TestForecast', show=False, + savepdf=False, savepng=False + ) + self.assertIsNotNone(ax) + self.assertGreater(auc, 0.5) # Should be better than random + matplotlib.pyplot.close() + + def test_temporal_ROC_diagram_perfect_forecast(self): + """Test ROC diagram with perfect forecast.""" + matplotlib.pyplot.close() + + # Perfect forecast: all events have highest probabilities + probs = numpy.array([1.0, 0.9, 0.0, 0.0]) + obs = numpy.array([1, 1, 0, 0]) + + ax, auc = plots.plot_temporal_ROC_diagram( + probs, obs, show=False, savepdf=False, savepng=False + ) + self.assertAlmostEqual(auc, 1.0, places=1) # AUC should be ~1 + matplotlib.pyplot.close() + + def test_temporal_Molchan_diagram_basic(self): + """Test basic temporal Molchan diagram.""" + matplotlib.pyplot.close() + + # Simple test data + probs = numpy.array([0.9, 0.7, 0.5, 0.3, 0.1]) + obs = numpy.array([1, 1, 0, 0, 0]) + + ax, ass, ass_std = plots.plot_temporal_Molchan_diagram( + probs, obs, name='TestForecast', show=False, + savepdf=False, savepng=False + ) + self.assertIsNotNone(ax) + self.assertGreater(ass, 0.5) # Should be better than random + self.assertGreater(ass_std, 0) # Should have uncertainty estimate + matplotlib.pyplot.close() + + def test_temporal_Molchan_diagram_perfect_forecast(self): + """Test Molchan diagram with a good forecast (events have highest probabilities).""" + matplotlib.pyplot.close() + + # Good forecast: events have highest probs, many non-event days + # More non-event days = higher ASS if events still at top + probs = numpy.array([0.95, 0.90, 0.10, 0.08, 0.06, 0.04, 0.02]) + obs = numpy.array([1, 1, 0, 0, 0, 0, 0]) + + ax, ass, ass_std = plots.plot_temporal_Molchan_diagram( + probs, obs, show=False, savepdf=False, savepng=False + ) + # With 2 events in 7 days, if both events are at highest probs: + # At threshold 0.10: tau=2/7≈0.29, nu=0 -> trajectory goes quickly to nu=0 + # This should give ASS > 0.5 + self.assertGreater(ass, 0.5) # Should be better than random + matplotlib.pyplot.close() + + def test_temporal_diagrams_with_loaded_forecast(self): + """Test temporal diagrams with loaded CSV data and synthetic observations.""" + matplotlib.pyplot.close() + + # Load actual forecast data + forecast_path = 'tests/artifacts/temporal_forecast_example.csv' + data = csep.load_temporal_forecast(forecast_path) + + # Create synthetic observations (events on days 3, 15, 17 - matching original) + observations = numpy.zeros(19, dtype=int) + observations[2] = 1 # day 3 + observations[14] = 1 # day 15 + observations[16] = 1 # day 17 + + # Test ROC + ax1, auc = plots.plot_temporal_ROC_diagram( + data['probabilities'], observations, + name='DailyM4Forecast', show=False, savepdf=False, savepng=False + ) + self.assertIsNotNone(ax1) + self.assertGreaterEqual(auc, 0.0) + self.assertLessEqual(auc, 1.0) + matplotlib.pyplot.close() + + # Test Molchan + ax2, ass, sigma = plots.plot_temporal_Molchan_diagram( + data['probabilities'], observations, + name='DailyM4Forecast', show=False, savepdf=False, savepng=False + ) + self.assertIsNotNone(ax2) + self.assertGreaterEqual(ass, 0.0) + self.assertLessEqual(ass, 1.0) + matplotlib.pyplot.close() + + def test_temporal_diagrams_length_mismatch(self): + """Test that mismatched lengths raise error.""" + probs = numpy.array([0.5, 0.3]) + obs = numpy.array([1, 0, 0]) # Different length + + with self.assertRaises(ValueError): + plots.plot_temporal_ROC_diagram(probs, obs, show=False) + + with self.assertRaises(ValueError): + plots.plot_temporal_Molchan_diagram(probs, obs, show=False) + + def test_temporal_diagrams_log_scale(self): + """Test temporal diagrams with logarithmic x-axis.""" + matplotlib.pyplot.close() + + probs = numpy.array([0.9, 0.7, 0.5, 0.3, 0.1]) + obs = numpy.array([1, 1, 0, 0, 0]) + + ax, auc = plots.plot_temporal_ROC_diagram( + probs, obs, linear=False, show=False, savepdf=False, savepng=False + ) + self.assertIsNotNone(ax) + matplotlib.pyplot.close() + + ax, ass, sigma = plots.plot_temporal_Molchan_diagram( + probs, obs, linear=False, show=False, savepdf=False, savepng=False + ) + self.assertIsNotNone(ax) + matplotlib.pyplot.close() + + if __name__ == '__main__': unittest.main()