From bd6270988012f44c969e8448e50ade4cdf4510ab Mon Sep 17 00:00:00 2001 From: Martin Reiss Date: Tue, 7 Jul 2026 10:46:06 +0200 Subject: [PATCH] Add ambient solar wind validation workflow --- .gitignore | 22 + README.md | 93 +- assets/fig_layout.py | 73 + camel/skillscores.py | 623 -- example_data/processed/ambsowi_mod.csv | 11782 ++++++++++++++++++++ example_data/processed/bravda_mod.csv | 11782 ++++++++++++++++++++ example_data/processed/omni_obs.csv | 11782 ++++++++++++++++++++ example_data/processed/p27_mod.csv | 11782 ++++++++++++++++++++ example_data/raw/.gitkeep | 0 notebooks/01_api_and_preprocessing.ipynb | 464 + notebooks/02_point_to_point_metrics.ipynb | 421 + notebooks/03_binary_event_metrics.ipynb | 428 + notebooks/04_peak_analysis.ipynb | 667 ++ notebooks/05_multi_model_comparison.ipynb | 336 + requirements.txt | 13 + utils/data_processing.py | 153 + utils/skill_scores.py | 575 + utils/visualizations.py | 3583 ++++++ 18 files changed, 53951 insertions(+), 628 deletions(-) create mode 100644 .gitignore create mode 100644 assets/fig_layout.py delete mode 100644 camel/skillscores.py create mode 100644 example_data/processed/ambsowi_mod.csv create mode 100644 example_data/processed/bravda_mod.csv create mode 100644 example_data/processed/omni_obs.csv create mode 100644 example_data/processed/p27_mod.csv create mode 100644 example_data/raw/.gitkeep create mode 100644 notebooks/01_api_and_preprocessing.ipynb create mode 100644 notebooks/02_point_to_point_metrics.ipynb create mode 100644 notebooks/03_binary_event_metrics.ipynb create mode 100644 notebooks/04_peak_analysis.ipynb create mode 100644 notebooks/05_multi_model_comparison.ipynb create mode 100644 requirements.txt create mode 100644 utils/data_processing.py create mode 100644 utils/skill_scores.py create mode 100644 utils/visualizations.py diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..87ee88f --- /dev/null +++ b/.gitignore @@ -0,0 +1,22 @@ +# macOS +.DS_Store + +# Python +__pycache__/ +*.py[cod] +.venv/ +venv/ +env/ + +# Jupyter +.ipynb_checkpoints/ + +# Raw data generated by Notebook 01 +example_data/raw/*.csv +!example_data/raw/.gitkeep + +# Generated figure outputs +results/output_pdfs/*.pdf + +# Local environment files +.env diff --git a/README.md b/README.md index 23e2b22..e8b994e 100644 --- a/README.md +++ b/README.md @@ -1,9 +1,92 @@ -# CAMEL +# Open Metrics and Visual Diagnostics for Solar Wind Model Validation -The CAMEL framework is an integrated and flexible framework allowing users to seamlessly compare space weather and space science model outputs with observational data sets. CAMEL was developed at [Community Coordinated Modeling Center](https://ccmc.gsfc.nasa.gov) at NASA GSFC. +This repository provides metrics and visual diagnostics for assessing the capabilities of solar wind models. It contains widely used procedures for comparing solar wind model outputs against near-Earth in situ measurements. The repository complements the [CAMEL Ambient Solar Wind Validation Dashboard](https://ccmc.gsfc.nasa.gov/camel/AmbientSolarWind/home), developed and maintained at the [Community Coordinated Modeling Center (CCMC)](https://ccmc.gsfc.nasa.gov), by enabling users to reproduce assessment results and modify procedures. -The CAMEL web application can be viewed at: https://ccmc.gsfc.nasa.gov/tools/CAMEL/ +The validation procedures include point-to-point metrics, threshold-based event validation, and peak-detection-based event validation. This effort is part of ongoing community work through the [ISWAT H1-01 Team](https://iswat-cospar.org/h1-01) to support unified capability assessment for ambient solar wind models across the heliophysics and space weather community. -This repository currently only includes skill score part of CAMEL and does not include the web application and user interface. +## Overview -Example python notebooks will soon be available. +The repository is organized around a set of Python modules and a Jupyter notebook workflow. The modules include data preparation, metrics, and visual diagnostics, while the notebooks provide end-to-end examples of how to use the modules on solar wind model outputs included in the CAMEL Dashboard. + +Core functionality includes: +- preprocessing and alignment of modeled and observed solar wind time series +- point-to-point metrics +- threshold-based event validation with user-selected thresholds +- peak-detection-based event validation focused on timing and amplitude errors +- multi-model comparison against a reference baseline model +- visual diagnostics and PDF figure export + +## Python Modules + +The main code is organized into three Python modules: + +- `data_processing.py` contains functions for cleaning, preprocessing, and interpolating modeled and observed solar wind time series. +- `skill_scores.py` includes the metrics used in the CAMEL Dashboard and throughout the notebooks. +- `visualizations.py` contains the Plotly routines used to reproduce the CAMEL Dashboard visualizations. + +## Jupyter Notebooks + +The notebook workflow provides a step-by-step guide for reproducing the assessment results in the CAMEL Ambient Solar Wind Dashboard: + +1. `01_api_and_preprocessing.ipynb` uses an API to retrieve modeled and observed solar wind time series and saves the example datasets. +2. `02_point_to_point_metrics.ipynb` computes point-to-point metrics and plots visual diagnostics. +3. `03_binary_event_metrics.ipynb` computes threshold-based event metrics and plots visual diagnostics. +4. `04_peak_analysis.ipynb` computes peak-detection-based event metrics and plots visual diagnostics. +5. `05_multi_model_comparison.ipynb` compares multiple model outputs side by side against in situ measurements. + +The notebooks save generated PDF figures under `results/output_pdfs/`. + +## Getting Started + +### 1. Clone the repository + +```bash +git clone +cd +``` + +### 2. Install dependencies + +Create and activate a Python environment, then install the required packages: + +```bash +pip install -r requirements.txt +``` + +The `requirements.txt` file includes `kaleido`, which is required for Plotly figure export. + +### 3. Run the notebooks + +Start Jupyter and open the notebook series: + +```bash +jupyter notebook notebooks/01_api_and_preprocessing.ipynb +``` + +Start with `01_api_and_preprocessing.ipynb` to create the processed example data before computing metrics and generating visualizations in the later notebooks. + +## Citation + +A publication describing the current implementation is in preparation. Until that publication is available, please cite the following prior work: + +Reiss, M. A., et al. (2022), *Unifying the validation of ambient solar wind models*, **Advances in Space Research**. [https://doi.org/10.1016/j.asr.2022.05.026](https://doi.org/10.1016/j.asr.2022.05.026) + +## Acknowledgements + +This effort was developed in collaboration with the [ISWAT H1-01 Team](https://iswat-cospar.org/h1-01) and builds on the CAMEL validation framework developed at the [CCMC](https://ccmc.gsfc.nasa.gov/). + +We are grateful for the feedback and discussions gathered through COSPAR ISWAT meetings. + +## Contributors + +- Martin A. Reiss +- Maya Levisohn +- Rick Mullinix + +## License + +This project is released under the NASA Open Source Agreement, Version 1.3 (NOSA 1.3). See `LICENSE` for the full license text. + +## Contact + +Martin A. Reiss ([email](mailto:martin.a.reiss@outlook.com)) diff --git a/assets/fig_layout.py b/assets/fig_layout.py new file mode 100644 index 0000000..484fe5c --- /dev/null +++ b/assets/fig_layout.py @@ -0,0 +1,73 @@ +import plotly.graph_objects as go + +# FIG LAYOUT +font_style = { + 'color' : '#2b2323' +} + +margin_style = { + 'b': 10, + 'l': 50, + 'r': 8, + 't': 50, + 'pad': 0 +} + +xaxis_style = { + 'linewidth' : 1, + 'linecolor' : 'rgba(0, 0, 0, 0.35%)', + 'showgrid' : False, + 'zeroline' : False +} + +yaxis_style = { + 'linewidth' : 1, + 'linecolor' : 'rgba(0, 0, 0, 0.35%)', + 'showgrid' : True, + 'gridwidth' : 1, + 'gridcolor' : 'rgba(0, 0, 0, 0.11%)', + 'zeroline' : False +} + +my_figlayout = go.Layout( + paper_bgcolor='rgba(0,0,0,0)', + plot_bgcolor='rgba(0,0,0,0)', + font = font_style, + margin = margin_style, + xaxis = xaxis_style, + yaxis = yaxis_style, + height = 300 +) + +# TRACES LAYOUT +obs_linelayout = { + 'width' : 2, + 'color' : 'gray' +} + +my_tablelayout = { + "style_cell": { + "textAlign": "left", + "padding": "10px", + "backgroundColor": "rgba(0,0,0,0)", # transparent + "color": font_style, + "border": "none", + "fontSize": "16px", + }, + "style_header": { + "backgroundColor": "rgba(0,0,0,0.3)", + "color": font_style, + "border": "none", + "borderBottom": "2px solid #d1d5db", # header separator + }, + "style_data": { + "borderBottom": "1px solid #e5e7eb", # row lines + }, + "style_data_conditional": [ + { + "if": {"row_index": "odd"}, + "backgroundColor": "rgba(0,0,0,0.1)", + } + ], + "style_table": {"overflowX": "auto"}, +} \ No newline at end of file diff --git a/camel/skillscores.py b/camel/skillscores.py deleted file mode 100644 index 8899a5e..0000000 --- a/camel/skillscores.py +++ /dev/null @@ -1,623 +0,0 @@ -""" -Copyright © 2023 United States Government as represented by the -Administrator of the National Aeronautics and Space Administration. -No copyright is claimed in the United States under Title 17, U.S. Code. -All Other Rights Reserved. - -@author: remullinix -""" - -import pandas as pd -import numpy as np -from datetime import datetime,timedelta,timezone - -def interpolate_dataframe_to_base(frame, baseFrame, method): - ''' - Returns interpolated panda data frame. - Any method compatible with pandas.DataFrame.interpolate - - Parameters: - frame (DataFrame): dataframe with datatime as index - baseFrame (DataFrame): dataframe with datatime as index - method (string): "linear", "nearest", others - - Returns: - interpolated_frame (DataFrame): dataframe with datatime as index - ''' - - baseTimes = baseFrame.index.values - frameTimes = frame.index.values - baseAndFrameTimes = pd.to_datetime(np.unique(np.hstack((baseTimes, frameTimes))), utc=True) - interpolated_frame = frame.reindex(baseAndFrameTimes).sort_index().interpolate(method, limit_area = 'inside') - interpolated_frame = interpolated_frame.reindex(baseFrame.index) - return interpolated_frame - - -def remove_gaps(modDF, obsDF, maxSeconds): - ''' - Returns observation dataframe with gaps removed. - - Parameters: - modDF (DataFrame): model dataframe with datatime as index - obsDF (DataFrame): observation dataframe with datatime as index - maxSeconds (int): number of seconds allowed before removing gap - - Returns: - obsDF (DataFrame): observation dataframe with gaps removed - ''' - timeDF = pd.DataFrame(index=modDF.index) - timeDF['nearest'] = pd.to_numeric(modDF.index.values) - - modTimes = modDF.index.values - obsTimes = obsDF.index.values - obsModTimes = pd.to_datetime(np.unique(np.hstack((obsTimes, modTimes))), utc=True) - timeDF = timeDF.reindex(obsModTimes).sort_index().interpolate("nearest") - - timeDF['timestamp'] = pd.to_numeric(timeDF.index.values) - timeDF['diff'] = (timeDF.timestamp - timeDF.nearest)/1000000000 - - timeDF = timeDF.reindex(obsDF.index).sort_index() - timeDF = timeDF.dropna() - - - timeDF = timeDF.loc[abs(timeDF['diff']) <= maxSeconds ] - - return obsDF.reindex(timeDF.index) - -def timestamp2datetime(timestamp,format="%Y-%m-%dT%H:%M:%SZ"): - datetimestamp = datetime.strptime(timestamp,format).replace(tzinfo=timezone.utc) - return datetimestamp - -def dBH_dt(df,compute_derivatives=False): - ''' - Computes the magnititude of time derivatives of horizontal magnetic perturbations. - This is the parameter used in Pulkkinen et al (2013) that is compared against threshold values of 0.3 0.7, 1.1 and 1.5 nT/s. - Parameters: dataframe with tiemstampos, DeltaB components (Down, North, East) and (optional) time derivatives of the DeltaB components - Returns: - df_dbH_dt - dataframe with timestamps and dBH_dt. - Timestamps are taken from existing dataframe if derivatives already exist. - Time stamps are calculated as the average of adjacent time stamps if - derivatives are calculated from raw DeltaB data. - ''' - df_size = df.shape[0] - if "Derivative_DeltaB_East" in df.columns and "Derivative_DeltaB_North" in df.columns and not compute_derivatives: - # if dataframes have the time derivatives already calculated - df_dBE_dt = np.array(df['Derivative_DeltaB_East']) - df_dBN_dt = np.array(df['Derivative_DeltaB_North']) - df_timestamp = np.array(df["timestamp"]) - df_datetimes = np.array([timestamp2datetime(timestamp) for timestamp in df_timestamp]) - else: - # dataframes with the raw DeltaB (magnetic perturbation) data only - df_timestamp = np.array(df['timestamp']) - df_datetimes = np.array([timestamp2datetime(timestamp) for timestamp in df_timestamp]) - df_datetime_deltas = df_datetimes[1:df_size] - df_datetimes[0:df_size-1] - dt_seconds = np.array([df_datetime_deltas[i].total_seconds() for i in range(df_size-1)]) - df_dBN_dt = (np.array(df['DeltaB_North'][1:df_size]) - - np.array(df['DeltaB_North'][0:df_size-1]))/dt_seconds - df_dBE_dt = (np.array(df['DeltaB_East'][1:df_size]) - - np.array(df['DeltaB_East'][0:df_size-1]))/dt_seconds - df_datetimes=df_datetimes[0:df_size-1] + df_datetime_deltas/2 - df_dbH_dt = pd.DataFrame(np.sqrt(df_dBN_dt*df_dBN_dt+df_dBE_dt*df_dBE_dt), - columns=['dBH_dt'], index=df_datetimes) - return(df_dbH_dt) - - -def rmsScore(modDF, obsDF): - ''' - Computes Root Mean Square error based on modDF and obsDF. - Dataframes must be same size and have matching indicies. - - Parameters: - modDF (pandas.DataFrame): dataframe with datatime as index - obsDF (pandas.DataFrame): dataframe with datatime as index - - Returns: - score (pandas.Series): series with parameter name(s) as index - ''' - obsDF = obsDF.reindex(modDF.index) - squaredError = (modDF - obsDF)**2 - rms = squaredError.mean()**0.5 - return rms - -def maeScore(modDF, obsDF): - ''' - Computes Mean Absolute Error based on modDF and obsDF. - Dataframes must be same size and have matching indicies. - - Parameters: - modDF (pandas.DataFrame): dataframe with datatime as index - obsDF (pandas.DataFrame): dataframe with datatime as index - - Returns: - score (pandas.Series): series with parameter name(s) as index - ''' - obsDF = obsDF.reindex(modDF.index) - absError = abs(modDF - obsDF) - mae = absError.mean() - return mae - -def meScore(modDF, obsDF): - ''' - Computes Mean Error based on modDF and obsDF. - Dataframes must be same size and have matching indicies. - - Parameters: - modDF (pandas.DataFrame): dataframe with datatime as index - obsDF (pandas.DataFrame): dataframe with datatime as index - - Returns: - score (pandas.Series): series with parameter name(s) as index - ''' - obsDF = obsDF.reindex(modDF.index) - error = modDF - obsDF - me = error.mean() - return me - -def meanSquaredError(modDF, obsDF): - ''' - Computes Mean Squared Error based on modDF and obsDF. - Dataframes must be same size and have matching indicies. - - Parameters: - modDF (pandas.DataFrame): dataframe with datatime as index - obsDF (pandas.DataFrame): dataframe with datatime as index - - Returns: - score (pandas.Series): series with parameter name(s) as index - ''' - obsDF = obsDF.reindex(modDF.index) - mse = ((obsDF - modDF) ** 2).mean() - return mse - -def ccScore(modDF, obsDF): - ''' - Computes Correlation Coefficient based on modDF and obsDF. - Dataframes must be same size and have matching indicies. - - Parameters: - modDF (pandas.DataFrame): dataframe with datatime as index - obsDF (pandas.DataFrame): dataframe with datatime as index - - Returns: - score (pandas.Series): series with parameter name(s) as index - ''' - obsDF = obsDF.reindex(modDF.index) - modelMean = modDF.mean() - obsMean = obsDF.mean() - sigmaObs = sigma(obsDF) - sigmaModel = sigma(modDF) - diffMeanSquare = (obsDF - obsMean) * (modDF - modelMean) - diffMeanSquareSum = diffMeanSquare.sum() - N = diffMeanSquare.shape[0] - score = (diffMeanSquareSum / (N-1)) / (sigmaObs * sigmaModel) - return score - -def peScore(modDF, obsDF): - ''' - Computes Prediction Efficiency based on modDF and obsDF. - Dataframes must be same size and have matching indicies. - - Parameters: - modDF (pandas.DataFrame): dataframe with datatime as index - obsDF (pandas.DataFrame): dataframe with datatime as index - - Returns: - score (pandas.Series): series with parameter name(s) as index - ''' - obsDF = obsDF.reindex(modDF.index) - sigmaObsSquared = sigma(obsDF)**2 - diff = (obsDF - modDF)*(obsDF - modDF) / sigmaObsSquared - return 1 - diff.mean(); - -def sspbScore(modDF, obsDF): - ''' - Computes Symmetric Signed Percentage Bias based on modDF and obsDF. - Dataframes must be same size and have matching indicies. - - Parameters: - modDF (pandas.DataFrame): dataframe with datatime as index - obsDF (pandas.DataFrame): dataframe with datatime as index - - Returns: - score (pandas.Series): series with parameter name(s) as index - ''' - # assumes all values are greater then 0 - # assumes values are already log base 10 - - obsDF = obsDF.reindex(modDF.index) - mod_over_obs = obsDF - modDF - median = mod_over_obs.median() - sign = median.apply(lambda x: np.sign(x)) - return 100 * sign * (10**median - 1) - - -def ssopScore(modDF, obsDF): - ''' - Computes Skill Score of Prediction based on modDF and obsDF. - Dataframes must be same size and have matching indicies. - - Parameters: - modDF (pandas.DataFrame): dataframe with datatime as index - obsDF (pandas.DataFrame): dataframe with datatime as index - - Returns: - score (pandas.Series): series with parameter name(s) as index - ''' - obsDF = obsDF.reindex(modDF.index) - mseRef = variance(obsDF) - msePred = (modDF - obsDF)**2 - ss = msePred / mseRef - ssop = 1 - ss.mean()**0.5 - return ssop - - -def truePostiveRate(mod, obs, threshold): - ''' - Computes true positive rate based on modDF and obsDF. - Dataframes must be same size and have matching indicies. - - Parameters: - modDF (pandas.DataFrame): dataframe with datatime as index - obsDF (pandas.DataFrame): dataframe with datatime as index - threshold (float): threshold to determine an 'event' - - Returns: - score (pandas.Series): series with parameter name(s) as index - ''' - obs = obs.reindex(mod.index) - ct = contingencyTable(mod, obs, threshold) - return ct['tp'] / (ct['tp'] + ct['fn']) - -def truePostiveRate_from_ct(ct): - ''' - Computes true positive rate based on contingency table - Parameters: - ct (pandas.DataFrame): contingecny table with true positive (tp) - and false negative (fn) counts - Returns: - score (pandas.Series): series with parameter name(s) as index - ''' - return ct['tp'] / (ct['tp'] + ct['fn']) - -def falsePostiveRate(mod, obs, threshold): - ''' - Computes false positive rate based on modDF and obsDF. - Dataframes must be same size and have matching indicies. - - Parameters: - modDF (pandas.DataFrame): dataframe with datatime as index - obsDF (pandas.DataFrame): dataframe with datatime as index - threshold (float): threshold to determine an 'event' - - Returns: - score (pandas.Series): series with parameter name(s) as index - ''' - obs = obs.reindex(mod.index) - ct = contingencyTable(mod, obs, threshold) - return ct['fp'] / (ct['fp'] + ct['tn']) - -def falsePostiveRate_from_ct(ct): - ''' - Computes false positive rate based on contingency table - - Parameters: - ct: contingency table with false positive (fp) and true negative (tn) counts - Returns: - score (pandas.Series): series with parameter name(s) as index - ''' - return ct['fp'] / (ct['fp'] + ct['tn']) - -def threatScore(mod, obs, threshold): - ''' - Computes threat score based on modDF and obsDF. - Dataframes must be same size and have matching indicies. - - Parameters: - modDF (pandas.DataFrame): dataframe with datatime as index - obsDF (pandas.DataFrame): dataframe with datatime as index - threshold (float): threshold to determine an 'event' - - Returns: - score (pandas.Series): series with parameter name(s) as index - ''' - obs = obs.reindex(mod.index) - ct = contingencyTable(mod, obs, threshold) - return ct['tp'] / (ct['tp'] + ct['fp'] + ct['fn']) - -def threatScore_from_ct(ct): - ''' - Computes threat score based on contingency table calculated from modDF and obsDF. - Parameters: - ct: contingency table with true positive (tp), false positive (fp) - and false negative (fn) counts - Returns: - score (pandas.Series): series with parameter name(s) as index - ''' - return ct['tp'] / (ct['tp'] + ct['fp'] + ct['fn']) - -def trueSkillStatistics(mod, obs, threshold): - ''' - Computes true skill based on modDF and obsDF. - Dataframes must be same size and have matching indicies. - - Parameters: - modDF (pandas.DataFrame): dataframe with datatime as index - obsDF (pandas.DataFrame): dataframe with datatime as index - threshold (float): threshold to determine an 'event' - - Returns: - score (pandas.Series): series with parameter name(s) as index - ''' - obs = obs.reindex(mod.index) - ct = contingencyTable(mod, obs, threshold) - return ct['tp'] / (ct['tp'] + ct['fn']) - ct['fp'] / (ct['fp'] + ct['tn']) - -def bias(mod, obs, threshold): - ''' - Computes bias based on modDF and obsDF. - Dataframes must be same size and have matching indicies. - - Parameters: - modDF (pandas.DataFrame): dataframe with datatime as index - obsDF (pandas.DataFrame): dataframe with datatime as index - threshold (float): threshold to determine an 'event' - - Returns: - score (pandas.Series): series with parameter name(s) as index - ''' - obs = obs.reindex(mod.index) - ct = contingencyTable(mod, obs, threshold) - return (ct['tp'] + ct['fp']) / (ct['tp'] + ct['fn']) - -def bias_from_ct(ct): - ''' - Computes bias based on contingency table - - Parameters: - ct: contingency table with true positive (tp) and false negative (fn) counts - Returns: - score (pandas.Series): series with parameter name(s) as index - ''' - return (ct['tp'] + ct['fp']) / (ct['tp'] + ct['fn']) - -def pod_from_ct(ct): - ''' - Computes Probability of Detection based on contingency table - - Parameters: - ct: contingency table with true positive (tp) and false negative (fn) counts - Returns: - score (pandas.Series): series with parameter name(s) as index - ''' - return (ct['tp']) / (ct['tp'] + ct['fn']) - -def pofd_from_ct(ct): - ''' - Computes Probability of False Detection based on contingency table - - Parameters: - ct - contingency table with false positive (fp) and true negative (tn) counts - Returns: - score (pandas.Series): series with parameter name(s) as index - ''' - return (ct['fp']) / (ct['fp'] + ct['tn']) - -def HeidkeScore(ct): - ''' - Computes Heidke Skill Score based on modDF and obsDF. - Parameters: - modDF (pandas.DataFrame): dataframe with datatime as index - obsDF (pandas.DataFrame): dataframe with datatime as index - threshold (float): threshold to determine an 'event' - Returns: - score (pandas.Series): series with parameter name(s) as index - ''' - obs = obs.reindex(mod.index) - ct = contingencyTable(mod, obs, threshold) - h = ct['tp'] - f = ct['fp'] - m = ct['fn'] - n = ct['tn'] - return (2 * (h * n - m * f)/( (h + m) * (m + n) + (h + f) * (f + n) ) ) - -def HeidkeScore_from_ct(ct): - ''' - Computes Heidke Skill Score based on contingency table - Parameters: - ct: contingency table with true positive (tp), false positive (fp), - false negative (fn) and true negative (tn) counts - Returns: - score (pandas.Series): series with parameter name(s) as index - ''' - h = ct['tp'] - f = ct['fp'] - m = ct['fn'] - n = ct['tn'] - return (2 * (h * n - m * f)/( (h + m) * (m + n) + (h + f) * (f + n) ) ) - -# internal charging metrics -# Median symmetric accuracy -def msaScore(modDF, obsDF): - ''' - Computes Median Symmetric Accuracy based on modDF and obsDF. - Dataframes must be same size and have matching indicies. - - Parameters: - modDF (pandas.DataFrame): dataframe with datatime as index - obsDF (pandas.DataFrame): dataframe with datatime as index - - Returns: - score (pandas.Series): series with parameter name(s) as index - ''' - obsDF = obsDF.reindex(modDF.index) - Q = modDF / obsDF - medianAbs = (abs(np.log(Q))).median() - msa = 100 * (pow(np.e, medianAbs)- 1); - return msa - - -def sspbScore(modDF, obsDF): - ''' - Computes Symmetric Signed Percentage Bias based on modDF and obsDF. - Dataframes must be same size and have matching indicies. - - Parameters: - modDF (pandas.DataFrame): dataframe with datatime as index - obsDF (pandas.DataFrame): dataframe with datatime as index - - Returns: - score (pandas.Series): series with parameter name(s) as index - ''' - obsDF = obsDF.reindex(modDF.index) - Q = modDF / obsDF - absMedian = abs(np.log(Q).median()) - sspb = 100 * np.sign(np.log(Q).median()) * (pow(np.e, absMedian)- 1); - return sspb - -def mocrScore(modDF, obsDF): - ''' - Computes Mean Observed to Computed (O/C) Ratio based on modDF and obsDF. - Dataframes must be same size and have matching indicies. - - Parameters: - modDF (pandas.DataFrame): dataframe with datatime as index - obsDF (pandas.DataFrame): dataframe with datatime as index - - Returns: - score (pandas.Series): series with parameter name(s) as index - ''' - obsDF = obsDF.reindex(modDF.index) - R = obsDF / modDF - return R.mean() - -def sdocrScore(modDF, obsDF): - ''' - Computes Standard Deviation Observed to computed (O/C) Ratio based on modDF and obsDF. - Dataframes must be same size and have matching indicies. - - Parameters: - modDF (pandas.DataFrame): dataframe with datatime as index - obsDF (pandas.DataFrame): dataframe with datatime as index - - Returns: - score (pandas.Series): series with parameter name(s) as index - ''' - obsDF = obsDF.reindex(modDF.index) - R = obsDF / modDF - A = np.log(R) - B = np.log(R.mean()) - deviation = ((A - B) ** 2).mean() ** 0.5 - return deviation - -# -# Helper functions -# - -def sigma(df): - ''' - Computes sigma of dataframe. Helper function. - - Parameters: - df (pandas.DataFrame): dataframe with datatime as index and single parameter - Returns: - sigma (float): sigma of the dataframe - ''' - mean = df.mean() - squaredMeanDiff = (df - mean) * (df - mean) - squaredMeanDiffSum = squaredMeanDiff.sum() - N = squaredMeanDiff.shape[0] - squaredSigma = squaredMeanDiffSum / (N-1) - return squaredSigma**0.5 - -def variance(df): - ''' - Computes variance of dataframe. Helper function. - - Parameters: - df (pandas.DataFrame): dataframe with datatime as index and single parameter - Returns: - variance (float): variance of the dataframe - ''' - mean = df.mean() - squaredMeanDiff = (df - mean) * (df - mean) - squaredMeanDiffSum = squaredMeanDiff.sum() - N = squaredMeanDiff.shape[0] - return squaredMeanDiffSum / (N-1) - -def contingencyTable(mod, obs, threshold): - ''' - Computes contingency table. Helper function. Columns returned include - true positive, false positive, false negative, and true negative - - Parameters: - modDF (pandas.DataFrame): dataframe with datatime as index - obsDF (pandas.DataFrame): dataframe with datatime as index - - Returns: - contingency table (pandas.DataFrame): - ''' - eventMod = np.full(mod.shape,0) - eventMod[mod > threshold] = 1 - eventObs = np.full(obs.shape,0) - eventObs[obs > threshold] = 1 - - tp = sum(eventMod + eventObs == 2)[0] - fp = sum(eventMod)[0] - tp - fn = sum(eventObs)[0] - tp - tn = sum(eventMod + eventObs == 0)[0] - df = pd.DataFrame([[tp, fp, fn, tn]], columns = ['tp', 'fp', 'fn', 'tn']) - return df - - -def contingencyTable_with_timewindows(mod, obs, threshold, windowlength,use_obs_times=False,use_model_times=False,debug=False): - ''' - Computes contingency table. Helper function. Columns returned include - true positive, false positive, false negative, and true negative - - Parameters: - modDF (pandas.DataFrame): dataframe with datatime as index - obsDF (pandas.DataFrame): dataframe with datatime as index - threshold: numerical value indicating a hit when exceeded - windowlength: length in minutes of time intervals that the observation and modeled time - interval is divided into. Time windows do not overlap. - in each time interval, at least one value exceeding the threshold indicates a hit - missing data in either observation or model will result in missed hits. - Returns: - contingency table (pandas.DataFrame): - ''' - t0m = mod.index[0] - t1m = mod.index[-1] - t0o = obs.index[0] - t1o = obs.index[-1] - t0 = min([t0m,t0o]) - t1 = max([t1m,t1o]) - if use_obs_times: - t0 = t0o - t1 = t1o - if use_model_times: - t0 = t0m - t1 = t1m - dt = timedelta(minutes=windowlength) - n_dt = int(np.ceil((t1-t0)/dt)) - print('t1-t0: ',t1-t0,' dt: ',dt,' number of windows: ',n_dt) - - eventMod = np.full(n_dt,0) - for i in range(n_dt): - t0_ = t0 + i*dt - t1_ = t0 +(i+1)*dt - eventMod[i] = np.any(mod[ np.logical_and(mod.index >= t0_, mod.index < t1_) == True ] > threshold) - - eventObs = np.full(n_dt,0) - for i in range(n_dt): - t0_ = t0 + i*dt - t1_ = t0 +(i+1)*dt - eventObs[i] = np.any(obs[ np.logical_and(obs.index >= t0_, obs.index < t1_) == True ] > threshold) - if debug: - print("OY ON",np.sum(eventObs),n_dt-np.sum(eventObs)) - - tp = sum(eventMod + eventObs == 2) - fp = sum(eventMod) - tp - fn = sum(eventObs) - tp - tn = sum(eventMod + eventObs == 0) - df = pd.DataFrame([[tp, fp, fn, tn]], columns = ['tp', 'fp', 'fn', 'tn']) - return df diff --git a/example_data/processed/ambsowi_mod.csv b/example_data/processed/ambsowi_mod.csv new file mode 100644 index 0000000..bf8c59c --- /dev/null +++ b/example_data/processed/ambsowi_mod.csv @@ -0,0 +1,11782 @@ +Time,Flow_Speed +2009-09-16 07:00:00+00:00, +2009-09-16 08:00:00+00:00, +2009-09-16 09:00:00+00:00,386.43 +2009-09-16 10:00:00+00:00,387.39 +2009-09-16 11:00:00+00:00,388.35 +2009-09-16 12:00:00+00:00,389.31 +2009-09-16 13:00:00+00:00,392.2725 +2009-09-16 14:00:00+00:00,394.275 +2009-09-16 15:00:00+00:00,396.2775 +2009-09-16 16:00:00+00:00,396.102 +2009-09-16 17:00:00+00:00,393.924 +2009-09-16 18:00:00+00:00,391.746 +2009-09-16 19:00:00+00:00,389.568 +2009-09-16 20:00:00+00:00,391.99 +2009-09-16 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The workflow is based on the **CAMEL (Comprehensive Assessment of Models and Events using Library tools)** framework developed at the [Community Coordinated Modeling Center (CCMC)](https://ccmc.gsfc.nasa.gov/).\n", + "\n", + "This effort is part of the international [ISWAT H1-01 Team](https://iswat-cospar.org/h1-01), which aims to establish a unified validation approach for solar wind models across the heliophysics and space weather community.\n", + "\n", + "The validation procedures presented in this notebook series are also available through an interactive web application built with Plotly Dash and Python. The **Ambient Solar Wind Validation Dashboard** can be accessed here:\n", + "\n", + "- [CCMC CAMEL Ambient Solar Wind Validation Dashboard](https://ccmc.gsfc.nasa.gov/camel/AmbientSolarWind/home)\n", + "\n", + "These notebooks provide a reproducible version of the same validation workflow and are intended to help users explore, reproduce, and extend the validation procedures of the CCMC CAMEL dashboard.\n", + "\n", + "**Notebook version:** 1.0\n", + "\n", + "---\n", + "\n", + "## Notebook Scope\n", + "\n", + "### 01 API and Preprocessing\n", + "\n", + "This notebook:\n", + "- retrieves solar wind observations and model outputs through the **CCMC CAMEL API**\n", + "- applies the preprocessing and alignment steps\n", + "- saves ready-to-use example datasets for the subsequent notebooks in this repository\n", + "\n", + "---\n", + "\n", + "## Notebook Series\n", + "\n", + "This notebook is part of the following workflow:\n", + "\n", + "1. `01_api_and_preprocessing.ipynb` — retrieve, preprocess, align, and save example datasets\n", + "2. `02_point_to_point_metrics.ipynb` — compute point-to-point metrics and visual diagnostics\n", + "3. `03_binary_event_metrics.ipynb` — compute threshold-based binary event metrics and visual diagnostics\n", + "4. `04_peak_analysis.ipynb` — compute peak-based event metrics and visual diagnostics\n", + "5. `05_multi_model_comparison.ipynb` — compare multiple model outputs side by side against in situ measurements\n", + "\n", + "---\n", + "\n", + "## References\n", + "\n", + "- Reiss, M. A., et al. (2022), *Unifying the validation of ambient solar wind models*, **Advances in Space Research**. [Open access](https://www.sciencedirect.com/science/article/pii/S0273117722004057)\n", + "- Rastaetter, L., et al. (2019), *Comprehensive Assessment of Models and Events Using Library Tools (CAMEL) Framework: Time Series Comparisons*, **Space Weather**. [Open access](https://doi.org/10.1029/2018SW002043)\n", + "\n", + "---\n", + "\n", + "## Developers\n", + "\n", + "- **Martin Reiss** — Community Coordinated Modeling Center, NASA Goddard Space Flight Center, USA\n", + "- **Maya Levisohn** — Community Coordinated Modeling Center, NASA Goddard Space Flight Center, USA\n", + "\n", + "## Contact\n", + "\n", + "- martin.a.reiss@outlook.com\n", + "- maya.b.levisohn@nasa.gov\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "ce2b59a3", + "metadata": {}, + "source": [ + "### Imports and API helpers\n", + "\n", + "The **CCMC CAMEL API** provides access to solar wind model outputs and in situ measurements. This makes it possible to fetch data directly into Python for analysis without manually downloading files.\n", + "\n", + "⚠️ **Note on warnings** \n", + "The API calls can create warnings that can be ignored:\n", + "\n", + "```\n", + "InsecureRequestWarning: Unverified HTTPS request is being made to host 'ccmc.gsfc.nasa.gov'.\n", + "Adding certificate verification is strongly advised.\n", + "See: https://urllib3.readthedocs.io/en/1.26.x/advanced-usage.html#ssl-warnings" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "85db8926", + "metadata": {}, + "outputs": [], + "source": [ + "import urllib3\n", + "urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)\n", + "\n", + "from pathlib import Path\n", + "import requests\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "#The base url for the API is https://ccmc.gsfc.nasa.gov/camel-api/ \n", + "ccmcURL = \"https://ccmc.gsfc.nasa.gov/camel-api/\"\n" + ] + }, + { + "cell_type": "markdown", + "id": "fb5c12a8", + "metadata": {}, + "source": [ + "### Return data as a JSON object\n", + "\n", + "The getData function below retrieves data from the CAMEL API and returns it as a JSON object.\n", + "\n", + "**Parameters**\n", + "- `studyID` *(str)* — identifier of the validation study (e.g., `\"AmbientSolarWind\"`) \n", + "- `sourceID` *(str)* — model or data source ID (e.g., `\"AmbSoWi-ML\"` or `\"OMNI_PT1H\"`) \n", + "- `locationID` *(str)* — location of measurements (e.g., `\"L1\"`) \n", + "- `parameters` *(list of str)* — list of variables to request (e.g., `[\"Flow_Speed\"]`) \n", + "- `startTime` *(str, optional)* — start time in `YYYY-MM-DD` format \n", + "- `endTime` *(str, optional)* — end time in `YYYY-MM-DD` format \n", + "- `timeperiodID` *(str, optional)* — predefined time period identifier \n", + "\n", + "> Either a `timeperiodID` **or** both `startTime` and `endTime` must be provided. \n", + "\n", + "**Returns** \n", + "- A JSON object containing the requested data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "160e0863", + "metadata": {}, + "outputs": [], + "source": [ + "def getData(studyID, sourceID, locationID, parameters, startTime=None, endTime=None, timeperiodID=None):\n", + " '''Returns a json file of the specified API call'''\n", + " \n", + " url = ccmcURL+'data?study='+studyID+'&source='+sourceID+'&location='+locationID\n", + "\n", + " #timeperiod can be identified either by start and end or by timePeriodID\n", + " if startTime is not None and endTime is not None:\n", + " timeURL = '&start='+startTime+'&end='+endTime\n", + " elif timeperiodID is not None:\n", + " timeURL = '&timeperiod='+timeperiodID\n", + " else:\n", + " timeURL = ''\n", + "\n", + " #the parameters are listed with commas in between \n", + " parametersURL = \"¶meters=\"\n", + " for name in parameters:\n", + " parametersURL += name + \",\"\n", + " parametersURL = parametersURL[:-1]\n", + "\n", + " #the full API call is strung together\n", + " url += timeURL + parametersURL\n", + " r = requests.get(url, verify=False).json()\n", + " return r" + ] + }, + { + "cell_type": "markdown", + "id": "6d71eb63", + "metadata": {}, + "source": [ + "### Return data as a Pandas DataFrame\n", + "\n", + "The following helper method retrieves data from the CAMEL API and returns it as a **Pandas DataFrame**. \n", + "It can be used directly or in combination with the JSON method above.\n", + "\n", + "**Parameters**\n", + "- `studyID` *(str)* — identifier of the validation study (e.g., `\"AmbientSolarWind\"`) \n", + "- `sourceID` *(str)* — model or data source ID (e.g., `\"AmbSoWi-ML\"` or `\"OMNI_PT1H\"`) \n", + "- `locationID` *(str)* — location of measurements (e.g., `\"L1\"`) \n", + "- `parameters` *(list of str)* — list of variables to request (e.g., `[\"Flow_Speed\"]`) \n", + "- `startTime` *(str, optional)* — start time in `YYYY-MM-DD` format \n", + "- `endTime` *(str, optional)* — end time in `YYYY-MM-DD` format \n", + "- `timeperiodID` *(str, optional)* — predefined time period identifier \n", + "\n", + "> Either a `timeperiodID` **or** both `startTime` and `endTime` must be provided. \n", + "\n", + "**Returns** \n", + "- A Pandas `DataFrame` with: \n", + " - **first column** → timestamps \n", + " - **subsequent columns** → requested parameters" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2d2eca42", + "metadata": {}, + "outputs": [], + "source": [ + "def getDataFrame(studyID, sourceID, locationID, parameters, startTime=None, endTime=None, timeperiodID=None):\n", + " '''Returns a pandas dataframe of the specified API call'''\n", + " try:\n", + " #uses helper method above to fetch data\n", + " json_data = getData(studyID, sourceID, locationID, parameters, startTime, endTime, timeperiodID)\n", + " df = pd.DataFrame(json_data['data'])\n", + " except Exception as e:\n", + " return None\n", + "\n", + " #label columns of dataframe\n", + " df.columns = ['timestamp']+parameters\n", + " \n", + " return df" + ] + }, + { + "cell_type": "markdown", + "id": "475cf91d", + "metadata": {}, + "source": [ + "### Retrieve example datasets\n", + "\n", + "This example uses the CAMEL time period `1963-01-TP-01` and the `Flow_Speed` parameter for:\n", + "- `OMNI_PT1H` observations at L1\n", + "- `AmbSoWi-ML`\n", + "- `Bravda`\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eb9326e7", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "# Load data\n", + "df_ambsowi_ml = getDataFrame('AmbientSolarWind', 'AmbSoWi-ML', 'L1', ['Flow_Speed'], timeperiodID='1963-01-TP-01')\n", + "df_omni = getDataFrame('AmbientSolarWind', 'OMNI_PT1H', 'L1', ['Flow_Speed'], timeperiodID='1963-01-TP-01')\n", + "df_bravda = getDataFrame('AmbientSolarWind', 'Bravda', 'L1', ['Flow_Speed'], timeperiodID='1963-01-TP-01')\n", + "\n", + "# Save raw API outputs\n", + "raw_data_dir = Path(\"../example_data/raw\")\n", + "raw_data_dir.mkdir(parents=True, exist_ok=True)\n", + "\n", + "df_ambsowi_ml.to_csv(raw_data_dir / \"ambsowi_raw.csv\", index=False)\n", + "df_omni.to_csv(raw_data_dir / \"omni_raw.csv\", index=False)\n", + "df_bravda.to_csv(raw_data_dir / \"bravda_raw.csv\", index=False)\n", + "\n", + "# Replace 9999.0 with NaN\n", + "df_omni.replace(9999.0, np.nan, inplace=True)\n", + "df_ambsowi_ml.replace(9999.0, np.nan, inplace=True)\n", + "df_bravda.replace(9999.0, np.nan, inplace=True)\n", + "\n", + "# Convert 'timestamp' to datetime\n", + "df_omni['timestamp'] = pd.to_datetime(df_omni['timestamp'], errors='coerce')\n", + "df_ambsowi_ml['timestamp'] = pd.to_datetime(df_ambsowi_ml['timestamp'], errors='coerce')\n", + "df_bravda['timestamp'] = pd.to_datetime(df_bravda['timestamp'], errors='coerce')\n", + "\n", + "print(\"AmbSoWi-ML rows:\", len(df_ambsowi_ml))\n", + "print(\"OMNI rows: \", len(df_omni))\n", + "print(\"BRaVDA rows: \", len(df_bravda))\n", + "print(\"Raw files saved to:\", raw_data_dir.resolve())" + ] + }, + { + "cell_type": "markdown", + "id": "20372efa", + "metadata": {}, + "source": [ + "## Import preprocessing functions\n", + "\n", + "Import the alignment and interpolation functions used to prepare the time series for the jupyter notebooks." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5943f6ef", + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "\n", + "# Allow imports from the repository root and utils folder\n", + "project_root = \"..\"\n", + "utils_path = \"../utils\"\n", + "\n", + "if project_root not in sys.path:\n", + " sys.path.append(project_root)\n", + "if utils_path not in sys.path:\n", + " sys.path.append(utils_path)\n", + "\n", + "from data_processing import align_and_interpolate" + ] + }, + { + "cell_type": "markdown", + "id": "46575793", + "metadata": {}, + "source": [ + "## Define the validation time window\n", + "\n", + "This section defines the time interval used to create the processed example datasets. The Carrington Rotation time window is applied before the processed files are saved. The downstream notebooks therefore load these processed files directly, so they all use the same validation interval defined here. \n", + "\n", + "Note that the selected time interval here corresponds to the default in the CAMEL Ambient Solar Wind dashboard. \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "37abe275", + "metadata": {}, + "outputs": [], + "source": [ + "from sunpy.coordinates.sun import carrington_rotation_time\n", + "import astropy.units as u\n", + "\n", + "cr_start = 2088\n", + "cr_end = 2105\n", + "\n", + "cr_long_start = 360\n", + "cr_long_end = 1\n", + "\n", + "start_date = pd.Timestamp(carrington_rotation_time(cr_start, cr_long_start * u.deg).datetime).tz_localize(\"UTC\")\n", + "end_date = pd.Timestamp(carrington_rotation_time(cr_end, cr_long_end * u.deg).datetime).tz_localize(\"UTC\")\n", + "\n", + "print(\"Start:\", start_date)\n", + "print(\"End: \", end_date)\n" + ] + }, + { + "cell_type": "markdown", + "id": "3add739c", + "metadata": {}, + "source": [ + "## Preprocess and align the time series\n", + "\n", + "This section performs all preprocessing before any files are saved:\n", + "- replace fill values with `NaN`\n", + "- parse timestamps\n", + "- standardize OMNI timestamps to the start of the hour\n", + "- create the 27-day persistence baseline\n", + "- filter to the validation window\n", + "- coerce numeric values and drop invalid rows\n", + "- align all series to the OMNI time grid using linear interpolation\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b891a37c", + "metadata": {}, + "outputs": [], + "source": [ + "for df in (df_omni, df_ambsowi_ml, df_bravda):\n", + " df.replace(9999.0, np.nan, inplace=True)\n", + "\n", + "for df in (df_omni, df_ambsowi_ml, df_bravda):\n", + " df[\"timestamp\"] = pd.to_datetime(df[\"timestamp\"], utc=True, errors=\"coerce\")\n", + "\n", + "df_omni[\"Time\"] = df_omni[\"timestamp\"].dt.floor(\"H\")\n", + "\n", + "df_persistence_27 = df_omni.loc[:, [\"Time\", \"Flow_Speed\"]].copy()\n", + "df_persistence_27[\"Time\"] = df_persistence_27[\"Time\"] + pd.Timedelta(days=27)\n", + "df_persistence_27[\"Name\"] = \"Persistence (27-days)\"\n", + "\n", + "omni_ref = df_omni.loc[\n", + " (df_omni[\"Time\"] >= start_date) & (df_omni[\"Time\"] <= end_date),\n", + " [\"Time\", \"Flow_Speed\"]\n", + "].copy()\n", + "\n", + "ambsowi_ml_pred = df_ambsowi_ml.loc[\n", + " (df_ambsowi_ml[\"timestamp\"] >= start_date) & (df_ambsowi_ml[\"timestamp\"] <= end_date),\n", + " [\"timestamp\", \"Flow_Speed\"]\n", + "].rename(columns={\"timestamp\": \"Time\"}).copy()\n", + "\n", + "bravda_pred = df_bravda.loc[\n", + " (df_bravda[\"timestamp\"] >= start_date) & (df_bravda[\"timestamp\"] <= end_date),\n", + " [\"timestamp\", \"Flow_Speed\"]\n", + "].rename(columns={\"timestamp\": \"Time\"}).copy()\n", + "\n", + "p27_window = df_persistence_27.loc[\n", + " (df_persistence_27[\"Time\"] >= start_date) & (df_persistence_27[\"Time\"] <= end_date),\n", + " [\"Time\", \"Flow_Speed\"]\n", + "].copy()\n", + "\n", + "for d in (omni_ref, ambsowi_ml_pred, bravda_pred, p27_window):\n", + " d[\"Flow_Speed\"] = pd.to_numeric(d[\"Flow_Speed\"], errors=\"coerce\")\n", + " d.dropna(subset=[\"Time\", \"Flow_Speed\"], inplace=True)\n", + " d.sort_values(\"Time\", inplace=True)\n", + " d.drop_duplicates(subset=[\"Time\"], keep=\"first\", inplace=True)\n", + "\n", + "aligned_omni = align_and_interpolate(omni_ref, omni_ref, method=\"linear\")\n", + "aligned_ambsowi = align_and_interpolate(ambsowi_ml_pred, omni_ref, method=\"linear\")\n", + "aligned_bravda = align_and_interpolate(bravda_pred, omni_ref, method=\"linear\")\n", + "aligned_p27 = align_and_interpolate(p27_window, omni_ref, method=\"linear\")" + ] + }, + { + "cell_type": "markdown", + "id": "912aab5d", + "metadata": {}, + "source": [ + "## Prepare load-ready example datasets\n", + "\n", + "The saved files are fully preprocessed and aligned, so later notebooks can load them directly.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f213cad1", + "metadata": {}, + "outputs": [], + "source": [ + "omni_obs = aligned_omni.set_index(\"Time\")[[\"Flow_Speed\"]]\n", + "ambsowi_mod = aligned_ambsowi.set_index(\"Time\")[[\"Flow_Speed\"]]\n", + "bravda_mod = aligned_bravda.set_index(\"Time\")[[\"Flow_Speed\"]]\n", + "p27_mod = aligned_p27.set_index(\"Time\")[[\"Flow_Speed\"]]\n", + "\n", + "print(\"Observed shape: \", omni_obs.shape)\n", + "print(\"AmbSoWi-ML shape: \", ambsowi_mod.shape)\n", + "print(\"Bravda shape: \", bravda_mod.shape)\n", + "print(\"Persistence shape:\", p27_mod.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b9983c54", + "metadata": {}, + "outputs": [], + "source": [ + "output_dir = Path(\"../example_data/processed\")\n", + "output_dir.mkdir(parents=True, exist_ok=True)\n", + "\n", + "omni_obs.to_csv(output_dir / \"omni_obs.csv\")\n", + "ambsowi_mod.to_csv(output_dir / \"ambsowi_mod.csv\")\n", + "bravda_mod.to_csv(output_dir / \"bravda_mod.csv\")\n", + "p27_mod.to_csv(output_dir / \"p27_mod.csv\")\n", + "\n", + "print(\"Saved files to:\", output_dir.resolve())" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/02_point_to_point_metrics.ipynb b/notebooks/02_point_to_point_metrics.ipynb new file mode 100644 index 0000000..6c494a4 --- /dev/null +++ b/notebooks/02_point_to_point_metrics.ipynb @@ -0,0 +1,421 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "1ac5dad6", + "metadata": {}, + "source": [ + "# CAMEL Ambient Solar Wind Validation\n", + "\n", + "## Overview\n", + "\n", + "This notebook demonstrates how to validate ambient solar wind models against in situ measurements near Earth. The workflow is based on the **CAMEL (Comprehensive Assessment of Models and Events using Library tools)** framework developed at the [Community Coordinated Modeling Center (CCMC)](https://ccmc.gsfc.nasa.gov/).\n", + "\n", + "This effort is part of the international [ISWAT H1-01 Team](https://iswat-cospar.org/h1-01), which aims to establish a unified validation approach for solar wind models across the heliophysics and space weather communities.\n", + "\n", + "The validation procedures presented in this notebook series are also available through an interactive web application built with Plotly Dash and Python. The **Ambient Solar Wind Validation Dashboard** can be accessed here:\n", + "\n", + "- [CCMC CAMEL Ambient Solar Wind Validation Dashboard](https://ccmc.gsfc.nasa.gov/camel/AmbientSolarWind/home)\n", + "\n", + "These notebooks provide a reproducible version of the same validation workflow and are intended to help users explore, reproduce, and extend the validation procedures of the CCMC CAMEL dashboard.\n", + "\n", + "**Notebook version:** 1.0\n", + "\n", + "---\n", + "\n", + "## Notebook Scope\n", + "\n", + "### 02 Point-to-Point Metrics\n", + "\n", + "This notebook:\n", + "- loads the preprocessed example datasets generated in `01_api_and_preprocessing.ipynb`\n", + "- computes descriptive statistics and standard point-to-point validation metrics\n", + "- reproduces the point-to-point visualization examples used in the dashboard workflow\n", + "\n", + "---\n", + "\n", + "## Expected Inputs\n", + "\n", + "This notebook expects the following processed datasets:\n", + "\n", + "```text\n", + "../example_data/processed/\n", + "├── omni_obs.csv\n", + "├── ambsowi_mod.csv\n", + "├── bravda_mod.csv\n", + "└── p27_mod.csv\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "7b447cf6", + "metadata": {}, + "source": [ + "## Imports\n", + "\n", + "First, we import the metric and visual diagnostic functions from `utils/`. Note that these functions are an exact copy of the functions that are used in the CAMEL Ambient Solar Wind Dashboard. \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fcf79eb7", + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "from pathlib import Path\n", + "\n", + "project_root = \"..\"\n", + "utils_path = \"../utils\"\n", + "\n", + "if project_root not in sys.path:\n", + " sys.path.append(project_root)\n", + "if utils_path not in sys.path:\n", + " sys.path.append(utils_path)\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "from IPython.display import display\n", + "\n", + "from skill_scores import *\n", + "from utils.visualizations import *" + ] + }, + { + "cell_type": "markdown", + "id": "cbd97694", + "metadata": {}, + "source": [ + "## Load the processed example datasets\n", + "\n", + "These CSV files are assumed to have already been preprocessed and aligned in `01_api_and_preprocessing.ipynb`. Note that if you want to change the selected time window for validation you need to go back to `01_api_and_preprocessing.ipynb`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "214b5dd3", + "metadata": {}, + "outputs": [], + "source": [ + "data_dir = Path(\"../example_data/processed\")\n", + "\n", + "omni_obs = pd.read_csv(data_dir / \"omni_obs.csv\", parse_dates=[\"Time\"], index_col=\"Time\")\n", + "ambsowi_mod = pd.read_csv(data_dir / \"ambsowi_mod.csv\", parse_dates=[\"Time\"], index_col=\"Time\")\n", + "bravda_mod = pd.read_csv(data_dir / \"bravda_mod.csv\", parse_dates=[\"Time\"], index_col=\"Time\")\n", + "p27_mod = pd.read_csv(data_dir / \"p27_mod.csv\", parse_dates=[\"Time\"], index_col=\"Time\")\n", + "\n", + "print(\"Observed shape: \", omni_obs.shape)\n", + "print(\"AmbSoWi-ML shape: \", ambsowi_mod.shape)\n", + "print(\"BRaVDA shape: \", bravda_mod.shape)\n", + "print(\"Persistence shape:\", p27_mod.shape)\n" + ] + }, + { + "cell_type": "markdown", + "id": "fb646a89", + "metadata": {}, + "source": [ + "## Compute standard point-to-point metrics\n", + "\n", + "We compute the same core metrics used in the dashboard workflow:\n", + "- Mean Error (ME)\n", + "- Mean Absolute Error (MAE)\n", + "- Mean Squared Error (MSE)\n", + "- Root Mean Squared Error (RMSE)\n", + "- Correlation Coefficient (CC)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bd4f920a", + "metadata": {}, + "outputs": [], + "source": [ + "def compute_metric_table(models: dict, observed: pd.DataFrame) -> pd.DataFrame:\n", + " rows = {}\n", + " for name, model_df in models.items():\n", + " rows[name] = {\n", + " \"ME\": float(meScore(model_df, observed)),\n", + " \"MAE\": float(maeScore(model_df, observed)),\n", + " \"MSE\": float(meanSquaredError(model_df, observed)),\n", + " \"RMSE\": float(rmsScore(model_df, observed)),\n", + " \"CC\": float(ccScore(model_df, observed)),\n", + " }\n", + " return pd.DataFrame.from_dict(rows, orient=\"index\")\n", + "\n", + "models = {\n", + " \"AmbSoWi-ML\": ambsowi_mod,\n", + " \"BRaVDA\": bravda_mod,\n", + " \"Persistence (27d)\": p27_mod,\n", + "}\n", + "\n", + "metrics_table = compute_metric_table(models, omni_obs)\n", + "display(metrics_table.round(2))\n" + ] + }, + { + "cell_type": "markdown", + "id": "b8c86088", + "metadata": {}, + "source": [ + "## Compute descriptive statistics\n", + "\n", + "We use the same `descriptiveStats` helper used in the dashboard to summarize the distributions of observations and model outputs.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dd2d7619", + "metadata": {}, + "outputs": [], + "source": [ + "stats_df = pd.DataFrame({\n", + " \"Observed\": descriptiveStats(omni_obs[\"Flow_Speed\"]),\n", + " \"AmbSoWi-ML\": descriptiveStats(ambsowi_mod[\"Flow_Speed\"]),\n", + " \"BRaVDA\": descriptiveStats(bravda_mod[\"Flow_Speed\"]),\n", + " \"Persistence (27d)\": descriptiveStats(p27_mod[\"Flow_Speed\"]),\n", + "})\n", + "\n", + "display(stats_df)\n" + ] + }, + { + "cell_type": "markdown", + "id": "b684fddf", + "metadata": {}, + "source": [ + "## Prepare inputs for the visual diagnostics\n", + "\n", + "Convert the processed time series into the long-format structure expected by the CAMEL dashboard visualization functions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a9bdcb40", + "metadata": {}, + "outputs": [], + "source": [ + "def to_pred(df: pd.DataFrame, name: str) -> pd.DataFrame:\n", + " d = df.copy().reset_index()\n", + " d = d.rename(columns={\"Flow_Speed\": \"Model\"})\n", + " d[\"Name\"] = name\n", + " return d[[\"Time\", \"Model\", \"Name\"]]\n", + "\n", + "def to_obs(df: pd.DataFrame, name: str) -> pd.DataFrame:\n", + " d = df.copy().reset_index()\n", + " d = d.rename(columns={\"Flow_Speed\": \"Obs\"})\n", + " d[\"Name\"] = name\n", + " return d[[\"Time\", \"Obs\", \"Name\"]]\n", + "\n", + "observed_data = to_obs(omni_obs, \"OMNI2 | Hourly averaged\")\n", + "\n", + "predicted_ambsowi = to_pred(ambsowi_mod, \"AmbSoWi-ML\")\n", + "predicted_bravda = to_pred(bravda_mod, \"BRaVDA\")\n", + "baseline_data = to_pred(p27_mod, \"Persistence (27-days)\")" + ] + }, + { + "cell_type": "markdown", + "id": "0843fcb2", + "metadata": {}, + "source": [ + "## Visual Diagnostics for Point-to-Point Metrics\n", + "\n", + "These cells mirror the examples from the original notebook:\n", + "- time series plot\n", + "- raincloud plot\n", + "- scatter plot\n", + "- residual plot\n", + "- RMSE timeline\n", + "\n", + "The visualizations below use **AmbSoWi-ML** against **OMNI** with the **27-day persistence** baseline.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e05e2f19", + "metadata": {}, + "outputs": [], + "source": [ + "fig = create_line_chart(predicted_ambsowi, observed_data, baseline_data)\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f7225edc", + "metadata": {}, + "outputs": [], + "source": [ + "fig = create_raincloud_plot(predicted_ambsowi, observed_data)\n", + "fig.update_layout(width=500, height=500)\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "33c846fe", + "metadata": {}, + "outputs": [], + "source": [ + "fig = create_scatter_plot(predicted_ambsowi, observed_data)\n", + "fig.update_layout(width=500, height=500)\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e6eecf5f", + "metadata": {}, + "outputs": [], + "source": [ + "fig = create_residual_plot(predicted_ambsowi, observed_data)\n", + "fig.update_layout(width=500, height=500)\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "13803acb", + "metadata": {}, + "outputs": [], + "source": [ + "fig = create_rmse_timeline(predicted_ambsowi, observed_data, baseline_data, normalize_time=\"naive_utc\", floor=\"H\")\n", + "fig.update_layout(width=1300, height=500)\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "id": "20dd2ec6", + "metadata": {}, + "source": [ + "## PDF export example\n", + "\n", + "If `kaleido` is installed, all figures can be exported as vector PDFs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e6d5263d", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "out_dir = \"../results/output_pdfs\"\n", + "os.makedirs(out_dir, exist_ok=True)\n", + "\n", + "fig = create_scatter_plot(predicted_ambsowi, observed_data)\n", + "fig.update_layout(width=500, height=500)\n", + "\n", + "pdf_path = os.path.join(out_dir, \"scatter_plot_ambsowi.pdf\")\n", + "\n", + "# Requires kaleido:\n", + "fig.write_image(pdf_path)\n", + "\n", + "print(\"Saved:\", pdf_path)" + ] + }, + { + "cell_type": "markdown", + "id": "1b62f1c1", + "metadata": {}, + "source": [ + "Save all visualizations generated in this notebook as PDF files in `../results/output_pdfs/`. This requires `kaleido`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "80c2c878", + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "\n", + "out_dir = Path(\"../results/output_pdfs\")\n", + "out_dir.mkdir(parents=True, exist_ok=True)\n", + "\n", + "figures = {\n", + " \"line_chart_ambsowi\": create_line_chart(\n", + " predicted_ambsowi,\n", + " observed_data,\n", + " baseline_data,\n", + " ),\n", + " \"raincloud_plot_ambsowi\": create_raincloud_plot(\n", + " predicted_ambsowi,\n", + " observed_data,\n", + " ),\n", + " \"scatter_plot_ambsowi\": create_scatter_plot(\n", + " predicted_ambsowi,\n", + " observed_data,\n", + " ),\n", + " \"residual_plot_ambsowi\": create_residual_plot(\n", + " predicted_ambsowi,\n", + " observed_data,\n", + " ),\n", + " \"rmse_timeline_ambsowi\": create_rmse_timeline(\n", + " predicted_ambsowi,\n", + " observed_data,\n", + " baseline_data,\n", + " normalize_time=\"naive_utc\",\n", + " floor=\"H\",\n", + " )\n", + "}\n", + "\n", + "figure_sizes = {\n", + " \"line_chart_ambsowi\": (1200, 500),\n", + " \"raincloud_plot_ambsowi\": (500, 500),\n", + " \"scatter_plot_ambsowi\": (500, 500),\n", + " \"residual_plot_ambsowi\": (500, 500),\n", + " \"rmse_timeline_ambsowi\": (1200, 500),\n", + "}\n", + "\n", + "for name, fig in figures.items():\n", + " width, height = figure_sizes[name]\n", + " fig.update_layout(width=width, height=height)\n", + "\n", + " pdf_path = out_dir / f\"{name}.pdf\"\n", + "\n", + " # Requires kaleido:\n", + " fig.write_image(pdf_path)\n", + "\n", + " print(\"Saved:\", pdf_path)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/03_binary_event_metrics.ipynb b/notebooks/03_binary_event_metrics.ipynb new file mode 100644 index 0000000..c656294 --- /dev/null +++ b/notebooks/03_binary_event_metrics.ipynb @@ -0,0 +1,428 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "6af96ede", + "metadata": {}, + "source": [ + "# CAMEL Ambient Solar Wind Validation\n", + "\n", + "## Overview\n", + "\n", + "This notebook demonstrates how to validate ambient solar wind models against in situ measurements near Earth. The workflow is based on the **CAMEL (Comprehensive Assessment of Models and Events using Library tools)** framework developed at the [Community Coordinated Modeling Center (CCMC)](https://ccmc.gsfc.nasa.gov/).\n", + "\n", + "This effort is part of the international [ISWAT H1-01 Team](https://iswat-cospar.org/h1-01), which aims to establish a unified validation approach for solar wind models across the heliophysics and space weather communities.\n", + "\n", + "The validation procedures presented in this notebook series are also available through an interactive web application built with Plotly Dash and Python. The **Ambient Solar Wind Validation Dashboard** can be accessed here:\n", + "\n", + "- [CCMC CAMEL Ambient Solar Wind Validation Dashboard](https://ccmc.gsfc.nasa.gov/camel/AmbientSolarWind/home)\n", + "\n", + "These notebooks provide a reproducible version of the same validation workflow and are intended to help users explore, reproduce, and extend the validation procedures of the CCMC CAMEL dashboard.\n", + "\n", + "**Notebook version:** 1.0\n", + "\n", + "---\n", + "\n", + "## Notebook Scope\n", + "\n", + "### 03 Binary Event Metrics\n", + "\n", + "Binary event metrics evaluate whether a model correctly predicts the **occurrence or non-occurrence** of events defined by a user-selected threshold.\n", + "\n", + "This notebook:\n", + "- loads the preprocessed example datasets generated in `01_api_and_preprocessing.ipynb`\n", + "- evaluates threshold-based binary event classifications for observed and modeled solar wind speed\n", + "- computes standard binary event metrics from the resulting contingency tables\n", + "- reproduces the binary-event visualization workflow used in the dashboard\n", + "\n", + "---\n", + "\n", + "## Expected Inputs\n", + "\n", + "This notebook expects the following processed datasets:\n", + "\n", + "```text\n", + "../example_data/processed/\n", + "├── omni_obs.csv\n", + "├── ambsowi_mod.csv\n", + "├── bravda_mod.csv\n", + "└── p27_mod.csv\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "9eb248c1", + "metadata": {}, + "source": [ + "## Imports\n", + "\n", + "We import the metric and visual diagnostic functions from `utils/`. Note that these functions are an exact copy of the functions that are used in the CAMEL Ambient Solar Wind Dashboard. \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2f904838", + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "from pathlib import Path\n", + "\n", + "project_root = \"..\"\n", + "utils_path = \"../utils\"\n", + "\n", + "if project_root not in sys.path:\n", + " sys.path.append(project_root)\n", + "if utils_path not in sys.path:\n", + " sys.path.append(utils_path)\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "from IPython.display import display\n", + "\n", + "from skill_scores import *\n", + "from utils.visualizations import *" + ] + }, + { + "cell_type": "markdown", + "id": "d01bc983", + "metadata": {}, + "source": [ + "## Load the processed example datasets\n", + "\n", + "These CSV files are assumed to have already been preprocessed and aligned in `01_api_and_preprocessing.ipynb`. Note that if you want to change the selected time window for validation you need to go back to `01_api_and_preprocessing.ipynb`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "72c403a0", + "metadata": {}, + "outputs": [], + "source": [ + "data_dir = Path(\"../example_data/processed\")\n", + "\n", + "omni_obs = pd.read_csv(data_dir / \"omni_obs.csv\", parse_dates=[\"Time\"], index_col=\"Time\")\n", + "ambsowi_mod = pd.read_csv(data_dir / \"ambsowi_mod.csv\", parse_dates=[\"Time\"], index_col=\"Time\")\n", + "bravda_mod = pd.read_csv(data_dir / \"bravda_mod.csv\", parse_dates=[\"Time\"], index_col=\"Time\")\n", + "p27_mod = pd.read_csv(data_dir / \"p27_mod.csv\", parse_dates=[\"Time\"], index_col=\"Time\")\n", + "\n", + "print(\"Observed shape: \", omni_obs.shape)\n", + "print(\"AmbSoWi-ML shape: \", ambsowi_mod.shape)\n", + "print(\"BRaVDA shape: \", bravda_mod.shape)\n", + "print(\"Persistence shape:\", p27_mod.shape)\n" + ] + }, + { + "cell_type": "markdown", + "id": "7a2c3077", + "metadata": {}, + "source": [ + "## Prepare inputs for the binary-event validation procedures\n", + "\n", + "Convert the processed time series into the long-format structure expected by the CAMEL dashboard functions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bb1bee61", + "metadata": {}, + "outputs": [], + "source": [ + "def to_pred(df: pd.DataFrame, name: str) -> pd.DataFrame:\n", + " d = df.copy().reset_index()\n", + " d = d.rename(columns={\"Flow_Speed\": \"Model\"})\n", + " d[\"Name\"] = name\n", + " return d[[\"Time\", \"Model\", \"Name\"]]\n", + "\n", + "def to_obs(df: pd.DataFrame, name: str) -> pd.DataFrame:\n", + " d = df.copy().reset_index()\n", + " d = d.rename(columns={\"Flow_Speed\": \"Obs\"})\n", + " d[\"Name\"] = name\n", + " return d[[\"Time\", \"Obs\", \"Name\"]]\n", + "\n", + "observed_data = to_obs(omni_obs, \"OMNI2 | Hourly averaged\")\n", + "\n", + "predicted_ambsowi = to_pred(ambsowi_mod, \"AmbSoWi-ML\")\n", + "predicted_bravda = to_pred(bravda_mod, \"BRaVDA\")\n", + "baseline_data = to_pred(p27_mod, \"Persistence (27-days)\")" + ] + }, + { + "cell_type": "markdown", + "id": "b1f98193", + "metadata": {}, + "source": [ + "## Compute binary classification metrics\n", + "\n", + "Binary event metrics evaluate how well models can simulate the **occurrence or non-occurrence** of events defined by a threshold value.\n", + "\n", + "Instead of comparing every point in time, this approach classifies outcomes for each time step into:\n", + "- **Hit**\n", + "- **Miss**\n", + "- **False Alarm**\n", + "- **Correct Rejection**\n", + "\n", + "We compute a contingency table and derive some standard binary event metrics. To do so, we first need to define an **event threshold**." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c31c2dbc", + "metadata": {}, + "outputs": [], + "source": [ + "threshold = 450\n", + "print(\"Binary event threshold:\", threshold)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7d83ff6a", + "metadata": {}, + "outputs": [], + "source": [ + "_, ct_ambsowi = classifyBinaryEvents(predicted_ambsowi, observed_data, threshold)\n", + "\n", + "ambsowi_metrics = pd.DataFrame(\n", + " {\n", + " \"Metric\": [\n", + " \"True Positive Rate\",\n", + " \"False Positive Rate\",\n", + " \"Threat Score\",\n", + " \"True Skill Statistic\",\n", + " \"Bias\",\n", + " ],\n", + " \"Value\": [\n", + " truePositiveRate(ct_ambsowi)[0],\n", + " falsePostiveRate(ct_ambsowi)[0],\n", + " threatScore(ct_ambsowi)[0],\n", + " trueSkillStatistics(ct_ambsowi)[0],\n", + " bias(ct_ambsowi)[0],\n", + " ],\n", + " }\n", + ")\n", + "\n", + "display(ct_ambsowi)\n", + "display(ambsowi_metrics.round(2))" + ] + }, + { + "cell_type": "markdown", + "id": "869e91d9", + "metadata": {}, + "source": [ + "## Compare binary classification metrics across all available models\n", + "\n", + "This table extends the example above to all available series." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9ccdcf28", + "metadata": {}, + "outputs": [], + "source": [ + "def compute_binary_metric_table(models: dict, observed: pd.DataFrame, threshold: float) -> pd.DataFrame:\n", + " rows = {}\n", + " obs_long = to_obs(observed, \"OMNI2 | Hourly averaged\")\n", + " for name, model_df in models.items():\n", + " pred_long = to_pred(model_df, name)\n", + " _, ct = classifyBinaryEvents(pred_long, obs_long, threshold)\n", + " rows[name] = {\n", + " \"tp\": int(ct[\"tp\"].iloc[0]),\n", + " \"fp\": int(ct[\"fp\"].iloc[0]),\n", + " \"fn\": int(ct[\"fn\"].iloc[0]),\n", + " \"tn\": int(ct[\"tn\"].iloc[0]),\n", + " \"TPR\": float(truePositiveRate(ct)[0]),\n", + " \"FPR\": float(falsePostiveRate(ct)[0]),\n", + " \"TS\": float(threatScore(ct)[0]),\n", + " \"TSS\": float(trueSkillStatistics(ct)[0]),\n", + " \"Bias\": float(bias(ct)[0]),\n", + " }\n", + " return pd.DataFrame.from_dict(rows, orient=\"index\")\n", + "\n", + "binary_metric_table = compute_binary_metric_table(\n", + " {\n", + " \"AmbSoWi-ML\": ambsowi_mod,\n", + " \"BRaVDA\": bravda_mod,\n", + " \"Persistence (27d)\": p27_mod,\n", + " },\n", + " omni_obs,\n", + " threshold,\n", + ")\n", + "\n", + "display(binary_metric_table.round(2))" + ] + }, + { + "cell_type": "markdown", + "id": "cf99a143", + "metadata": {}, + "source": [ + "## Visual Diagnostics for Binary Event Metrics\n", + "\n", + "The visualizations in this section include:\n", + "- classification timelines\n", + "- cumulative outcome plots\n", + "- bar charts summarizing event categories\n", + "\n", + "The visualizations below use **AmbSoWi-ML** against **OMNI** with the **27-day persistence** baseline.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "39e10878", + "metadata": {}, + "outputs": [], + "source": [ + "fig = create_binary_classification_plot(predicted_ambsowi, observed_data, threshold=threshold)\n", + "fig.update_layout(width=1200, height=520)\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7d2f5b5c", + "metadata": {}, + "outputs": [], + "source": [ + "fig = create_cumulative_binary_outcomes(predicted_ambsowi, observed_data, threshold=threshold)\n", + "fig.update_layout(width=500, height=500)\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6c82652a", + "metadata": {}, + "outputs": [], + "source": [ + "fig = create_binary_outcome_bars(predicted_ambsowi, observed_data, baseline_data, threshold=threshold)\n", + "fig.update_layout(width=500, height=500)\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "id": "414cb640", + "metadata": {}, + "source": [ + "## PDF export example\n", + "\n", + "If `kaleido` is installed, Plotly figures can be exported as vector PDFs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a0f841ea", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "out_dir = \"../results/output_pdfs\"\n", + "os.makedirs(out_dir, exist_ok=True)\n", + "\n", + "fig = create_binary_outcome_bars(predicted_ambsowi, observed_data, baseline_data, threshold=threshold)\n", + "fig.update_layout(width=900, height=520)\n", + "\n", + "pdf_path = os.path.join(out_dir, \"binary_outcome_bars_ambsowi.pdf\")\n", + "\n", + "# Requires kaleido:\n", + "fig.write_image(pdf_path)\n", + "\n", + "print(\"Saved:\", pdf_path)" + ] + }, + { + "cell_type": "markdown", + "id": "bce5693d", + "metadata": {}, + "source": [ + "Save all visualizations generated in this notebook as PDF files in `../results/output_pdfs/`. This requires `kaleido`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "afb7b0a5", + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "\n", + "out_dir = Path(\"../results/output_pdfs\")\n", + "out_dir.mkdir(parents=True, exist_ok=True)\n", + "\n", + "figures = {\n", + " \"binary_classification_ambsowi\": create_binary_classification_plot(\n", + " predicted_ambsowi,\n", + " observed_data,\n", + " threshold=threshold,\n", + " ),\n", + " \"cumulative_binary_outcomes_ambsowi\": create_cumulative_binary_outcomes(\n", + " predicted_ambsowi,\n", + " observed_data,\n", + " threshold=threshold,\n", + " ),\n", + " \"binary_outcome_bars_ambsowi\": create_binary_outcome_bars(\n", + " predicted_ambsowi,\n", + " observed_data,\n", + " baseline_data,\n", + " threshold=threshold,\n", + " ),\n", + "}\n", + "\n", + "figure_sizes = {\n", + " \"binary_classification_ambsowi\": (1200, 520),\n", + " \"cumulative_binary_outcomes_ambsowi\": (900, 520),\n", + " \"binary_outcome_bars_ambsowi\": (900, 520),\n", + "}\n", + "\n", + "for name, fig in figures.items():\n", + " width, height = figure_sizes[name]\n", + " fig.update_layout(width=width, height=height)\n", + "\n", + " pdf_path = out_dir / f\"{name}.pdf\"\n", + "\n", + " # Requires kaleido:\n", + " fig.write_image(pdf_path)\n", + "\n", + " print(\"Saved:\", pdf_path)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/04_peak_analysis.ipynb b/notebooks/04_peak_analysis.ipynb new file mode 100644 index 0000000..5da8fc0 --- /dev/null +++ b/notebooks/04_peak_analysis.ipynb @@ -0,0 +1,667 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "a14724bc", + "metadata": {}, + "source": [ + "# CAMEL Ambient Solar Wind Validation\n", + "\n", + "## Overview\n", + "\n", + "This notebook demonstrates how to validate ambient solar wind models against in situ measurements near Earth. The workflow is based on the **CAMEL (Comprehensive Assessment of Models and Events using Library tools)** framework developed at the [Community Coordinated Modeling Center (CCMC)](https://ccmc.gsfc.nasa.gov/).\n", + "\n", + "This effort is part of the international [ISWAT H1-01 Team](https://iswat-cospar.org/h1-01), which aims to establish a unified validation approach for solar wind models across the heliophysics and space weather communities.\n", + "\n", + "The validation procedures presented in this notebook series are also available through an interactive web application built with Plotly Dash and Python. The **Ambient Solar Wind Validation Dashboard** can be accessed here:\n", + "\n", + "- [CCMC CAMEL Ambient Solar Wind Validation Dashboard](https://ccmc.gsfc.nasa.gov/camel/AmbientSolarWind/home)\n", + "\n", + "These notebooks provide a reproducible version of the same validation workflow and are intended to help users explore, reproduce, and extend the validation procedures of the CCMC CAMEL dashboard.\n", + "\n", + "**Notebook version:** 1.0\n", + "\n", + "---\n", + "\n", + "## Notebook Scope\n", + "\n", + "### 04 Peak Analysis\n", + "\n", + "Peak analysis evaluates how well models reproduce **significant solar wind speed enhancements**, focusing on event timing and amplitude rather than every individual time step.\n", + "\n", + "This notebook:\n", + "- loads the preprocessed example datasets generated in `01_api_and_preprocessing.ipynb`\n", + "- identifies significant solar wind speed peaks in observed and modeled time series\n", + "- evaluates model performance using event-based peak metrics\n", + "- reproduces the peak-analysis visualization workflow used in the dashboard\n", + "\n", + "---\n", + "\n", + "## Expected Inputs\n", + "\n", + "This notebook expects the following processed datasets:\n", + "\n", + "```text\n", + "../example_data/processed/\n", + "├── omni_obs.csv\n", + "├── ambsowi_mod.csv\n", + "├── bravda_mod.csv\n", + "└── p27_mod.csv\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "b3abd285", + "metadata": {}, + "source": [ + "## Imports\n", + "\n", + "We import the metric and visual diagnostic functions from `utils/`. Note that these functions are an exact copy of the functions that are used in the CAMEL Ambient Solar Wind Dashboard. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d633099c", + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "from pathlib import Path\n", + "\n", + "project_root = \"..\"\n", + "utils_path = \"../utils\"\n", + "\n", + "if project_root not in sys.path:\n", + " sys.path.append(project_root)\n", + "if utils_path not in sys.path:\n", + " sys.path.append(utils_path)\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "from IPython.display import display\n", + "\n", + "from skill_scores import *\n", + "from utils.visualizations import *\n" + ] + }, + { + "cell_type": "markdown", + "id": "0fa1119e", + "metadata": {}, + "source": [ + "## Load the processed example datasets\n", + "\n", + "These CSV files are assumed to have already been preprocessed and aligned in `01_api_and_preprocessing.ipynb`. Note that if you want to change the selected time window for validation you need to go back to `01_api_and_preprocessing.ipynb`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fec38e15", + "metadata": {}, + "outputs": [], + "source": [ + "data_dir = Path(\"../example_data/processed\")\n", + "\n", + "omni_obs = pd.read_csv(data_dir / \"omni_obs.csv\", parse_dates=[\"Time\"], index_col=\"Time\")\n", + "ambsowi_mod = pd.read_csv(data_dir / \"ambsowi_mod.csv\", parse_dates=[\"Time\"], index_col=\"Time\")\n", + "bravda_mod = pd.read_csv(data_dir / \"bravda_mod.csv\", parse_dates=[\"Time\"], index_col=\"Time\")\n", + "p27_mod = pd.read_csv(data_dir / \"p27_mod.csv\", parse_dates=[\"Time\"], index_col=\"Time\")\n", + "\n", + "print(\"Observed shape: \", omni_obs.shape)\n", + "print(\"AmbSoWi-ML shape: \", ambsowi_mod.shape)\n", + "print(\"BRaVDA shape: \", bravda_mod.shape)\n", + "print(\"Persistence shape:\", p27_mod.shape)" + ] + }, + { + "cell_type": "markdown", + "id": "02fa79ff", + "metadata": {}, + "source": [ + "## Prepare inputs for the peak-analysis workflow\n", + "\n", + "Convert the processed time series into the long-format structure expected by the CAMEL dashboard functions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "264ba620", + "metadata": {}, + "outputs": [], + "source": [ + "def to_pred(df: pd.DataFrame, name: str) -> pd.DataFrame:\n", + " d = df.copy().reset_index()\n", + " d = d.rename(columns={\"Flow_Speed\": \"Model\"})\n", + " d[\"Name\"] = name\n", + " return d[[\"Time\", \"Model\", \"Name\"]]\n", + "\n", + "def to_obs(df: pd.DataFrame, name: str) -> pd.DataFrame:\n", + " d = df.copy().reset_index()\n", + " d = d.rename(columns={\"Flow_Speed\": \"Obs\"})\n", + " d[\"Name\"] = name\n", + " return d[[\"Time\", \"Obs\", \"Name\"]]\n", + "\n", + "observed_data = to_obs(omni_obs, \"OMNI2 | Hourly averaged\")\n", + "\n", + "predicted_ambsowi = to_pred(ambsowi_mod, \"AmbSoWi-ML\")\n", + "predicted_bravda = to_pred(bravda_mod, \"BRaVDA\")\n", + "baseline_data = to_pred(p27_mod, \"Persistence (27-days)\")" + ] + }, + { + "cell_type": "markdown", + "id": "2944f529", + "metadata": {}, + "source": [ + "## Compute event-based peak metrics\n", + "\n", + "Peak analysis focuses on the model’s ability to **capture solar wind speed peaks**. First, we compute matched hits, misses, and false alarms from detected peaks, then derive several event-based metrics.\n", + "\n", + "To get started, we need to define an **event threshold** as well as a **time window** for the peak search algorithm. \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c6eee559", + "metadata": {}, + "outputs": [], + "source": [ + "threshold_value = 450 # km/s minimum speed for peak detection\n", + "maximum_dt = 2 # days maximum time difference allowed for matching peaks\n", + "\n", + "print(\"Peak threshold:\", threshold_value)\n", + "print(\"Maximum matching time difference [days]:\", maximum_dt)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d8d36df1", + "metadata": {}, + "outputs": [], + "source": [ + "def compute_peak_metrics(\n", + " predicted: pd.DataFrame,\n", + " observed: pd.DataFrame,\n", + " threshold_value: float,\n", + " maximum_dt: float,\n", + "):\n", + " _, _, hits, misses, false_alarms, _, _ = compute_peaks(\n", + " predicted[\"Time\"],\n", + " observed[\"Obs\"],\n", + " predicted[\"Model\"],\n", + " threshold_value,\n", + " maximum_dt,\n", + " )\n", + "\n", + " contingency_table = pd.DataFrame(\n", + " [[len(hits), len(false_alarms), len(misses), 0]],\n", + " columns=[\"tp\", \"fp\", \"fn\", \"tn\"],\n", + " )\n", + "\n", + " metrics = {\n", + " \"True Positive Rate\": truePositiveRate(contingency_table)[0],\n", + " \"False Negative Rate\": falseNegativeRate(contingency_table)[0],\n", + " \"Positive Predictive Value\": positivePredictiveValue(contingency_table)[0],\n", + " \"False Alarm Ratio\": falseAlarmRatio(contingency_table)[0],\n", + " \"Threat Score\": threatScore(contingency_table)[0],\n", + " \"Bias\": bias(contingency_table)[0],\n", + " }\n", + "\n", + " metrics_table = pd.DataFrame.from_dict(\n", + " metrics,\n", + " orient=\"index\",\n", + " columns=[\"Value\"],\n", + " )\n", + "\n", + " return contingency_table, metrics_table\n", + "\n", + "\n", + "ct_ambsowi, peak_metrics_ambsowi = compute_peak_metrics(\n", + " predicted_ambsowi,\n", + " observed_data,\n", + " threshold_value,\n", + " maximum_dt,\n", + ")\n", + "\n", + "ct_persistence, peak_metrics_persistence = compute_peak_metrics(\n", + " baseline_data,\n", + " observed_data,\n", + " threshold_value,\n", + " maximum_dt,\n", + ")\n", + "\n", + "print(\"AmbSoWi-ML contingency table\")\n", + "display(ct_ambsowi)\n", + "\n", + "print(\"AmbSoWi-ML peak metrics\")\n", + "display(peak_metrics_ambsowi.round(2))\n", + "\n", + "print(\"Persistence baseline contingency table\")\n", + "display(ct_persistence)\n", + "\n", + "print(\"Persistence baseline peak metrics\")\n", + "display(peak_metrics_persistence.round(2))" + ] + }, + { + "cell_type": "markdown", + "id": "3d8450d6", + "metadata": {}, + "source": [ + "## Compare event-based peak metrics across all available models\n", + "\n", + "This table extends the original single-model example to all available model series in `example_data/`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "10a7b165", + "metadata": {}, + "outputs": [], + "source": [ + "def compute_peak_metric_table(models: dict, observed_long: pd.DataFrame, threshold_value: float, maximum_dt: float) -> pd.DataFrame:\n", + " rows = {}\n", + " for name, pred_long in models.items():\n", + " _, _, hits, misses, false_alarms, _, _ = compute_peaks(\n", + " pred_long[\"Time\"],\n", + " observed_long[\"Obs\"],\n", + " pred_long[\"Model\"],\n", + " threshold_value,\n", + " maximum_dt\n", + " )\n", + " ct = pd.DataFrame([[len(hits), len(false_alarms), len(misses), 0]],\n", + " columns=[\"tp\", \"fp\", \"fn\", \"tn\"])\n", + " rows[name] = {\n", + " \"tp\": int(ct[\"tp\"].iloc[0]),\n", + " \"fp\": int(ct[\"fp\"].iloc[0]),\n", + " \"fn\": int(ct[\"fn\"].iloc[0]),\n", + " \"TPR\": float(truePositiveRate(ct)[0]),\n", + " \"FNR\": float(falseNegativeRate(ct)[0]),\n", + " \"PPV\": float(positivePredictiveValue(ct)[0]),\n", + " \"FAR\": float(falseAlarmRatio(ct)[0]),\n", + " \"TS\": float(threatScore(ct)[0]),\n", + " \"Bias\": float(bias(ct)[0]),\n", + " }\n", + " return pd.DataFrame.from_dict(rows, orient=\"index\")\n", + "\n", + "peak_metric_table = compute_peak_metric_table(\n", + " {\n", + " \"AmbSoWi-ML\": predicted_ambsowi,\n", + " \"BRaVDA\": predicted_bravda,\n", + " \"Persistence (27d)\": baseline_data,\n", + " },\n", + " observed_data,\n", + " threshold_value,\n", + " maximum_dt\n", + ")\n", + "\n", + "display(peak_metric_table.round(2))\n" + ] + }, + { + "cell_type": "markdown", + "id": "f5cf3a66", + "metadata": {}, + "source": [ + "## Visual Diagnostics for Peak Analysis\n", + "\n", + "The visualizations in this section include:\n", + "- timelines with peak markers\n", + "- bar charts summarizing hits, misses, and false alarms\n", + "- histograms of timing and magnitude errors\n", + "- scatter plots for event relationships and error diagnostics" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7d2f861c", + "metadata": {}, + "outputs": [], + "source": [ + "fig = create_timeline_with_peaks(\n", + " predicted_ambsowi[\"Time\"],\n", + " observed_data[\"Obs\"],\n", + " predicted_ambsowi[\"Model\"],\n", + " observed_data[\"Name\"][0],\n", + " predicted_ambsowi[\"Name\"][0],\n", + " threshold_value,\n", + " maximum_dt\n", + ")\n", + "\n", + "fig.update_layout(width=1200, height=520)\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ae2d0908", + "metadata": {}, + "outputs": [], + "source": [ + "fig = create_peak_bar_chart(\n", + " predicted_ambsowi[\"Time\"],\n", + " observed_data[\"Obs\"],\n", + " predicted_ambsowi[\"Model\"],\n", + " threshold_value,\n", + " maximum_dt\n", + ")\n", + "\n", + "fig.update_layout(width=400, height=400)\n", + "fig.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "80669e87", + "metadata": {}, + "outputs": [], + "source": [ + "fig = create_histogram_plot(\n", + " predicted_ambsowi[\"Time\"],\n", + " observed_data[\"Obs\"],\n", + " predicted_ambsowi[\"Model\"],\n", + " threshold_value,\n", + " maximum_dt,\n", + " x_label=\"Error [km s⁻¹]\",\n", + " plot_type=\"Data Diff\"\n", + ")\n", + "\n", + "fig.update_layout(width=500, height=500)\n", + "fig.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "332bff48", + "metadata": {}, + "outputs": [], + "source": [ + "fig = create_histogram_plot(\n", + " predicted_ambsowi[\"Time\"],\n", + " observed_data[\"Obs\"],\n", + " predicted_ambsowi[\"Model\"],\n", + " threshold_value,\n", + " maximum_dt,\n", + " x_label=\"Time Error [hours]\",\n", + " plot_type=\"Time Error [hours]\"\n", + ")\n", + "\n", + "fig.update_layout(width=500, height=500)\n", + "fig.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f3c3cc8b", + "metadata": {}, + "outputs": [], + "source": [ + "fig = create_scatter_plot_for_hits(\n", + " predicted_ambsowi[\"Time\"],\n", + " observed_data[\"Obs\"],\n", + " predicted_ambsowi[\"Model\"],\n", + " threshold_value,\n", + " maximum_dt,\n", + " max_points=1000,\n", + " min_points=50,\n", + " opacity=0.5,\n", + " x_label=\"Observed Bulk Speed [km s⁻¹]\",\n", + " y_label=\"Predicted Bulk Speed [km s⁻¹]\",\n", + " plot_title=\"Model Hits: Observed vs Predicted\"\n", + ")\n", + "\n", + "fig.update_layout(width=500, height=500)\n", + "fig.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1d7ac8ec", + "metadata": {}, + "outputs": [], + "source": [ + "fig = create_error_scatter_plot(\n", + " predicted_ambsowi[\"Time\"],\n", + " observed_data[\"Obs\"],\n", + " predicted_ambsowi[\"Model\"],\n", + " threshold_value,\n", + " maximum_dt,\n", + " x_label=\"Time Error [hours]\",\n", + " y_label=\"Speed Error [km s⁻¹]\",\n", + " plot_title=\"Model Event Timing vs Magnitude Errors\",\n", + " max_points=1000,\n", + " min_points=50,\n", + " opacity=0.5,\n", + " time_bounds=(-96, 96),\n", + " data_bounds=(-400, 400)\n", + ")\n", + "\n", + "fig.update_layout(width=500, height=500)\n", + "fig.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a4ee393c", + "metadata": {}, + "outputs": [], + "source": [ + "fig = create_time_error_vs_observed_data_plot(\n", + " predicted_ambsowi[\"Time\"],\n", + " observed_data[\"Obs\"],\n", + " predicted_ambsowi[\"Model\"],\n", + " threshold_value,\n", + " maximum_dt,\n", + " x_label=\"Time Error [hours]\",\n", + " y_label=\"Observed Bulk Speed [km s⁻¹]\",\n", + " plot_title=\"Time Error vs. Observed Bulk Speed\",\n", + " max_points=1000,\n", + " min_points=50,\n", + " opacity=0.5,\n", + " time_bounds=(-96, 96),\n", + " obs_bounds=(200, 800)\n", + ")\n", + "\n", + "fig.update_layout(width=500, height=500)\n", + "fig.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "6bc22256", + "metadata": {}, + "source": [ + "## PDF export example\n", + "\n", + "If `kaleido` is installed, Plotly figures can be exported as vector PDFs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bb898412", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "out_dir = \"../results/output_pdfs\"\n", + "os.makedirs(out_dir, exist_ok=True)\n", + "\n", + "fig = create_peak_bar_chart(\n", + " predicted_ambsowi[\"Time\"],\n", + " observed_data[\"Obs\"],\n", + " predicted_ambsowi[\"Model\"],\n", + " threshold_value,\n", + " maximum_dt\n", + ")\n", + "fig.update_layout(width=400, height=400)\n", + "\n", + "pdf_path = os.path.join(out_dir, \"peak_bar_chart_ambsowi.pdf\")\n", + "\n", + "# Requires kaleido:\n", + "fig.write_image(pdf_path)\n", + "\n", + "print(\"Saved:\", pdf_path)" + ] + }, + { + "cell_type": "markdown", + "id": "09a5def5", + "metadata": {}, + "source": [ + "Save all visualizations generated in this notebook as PDF files in `../results/output_pdfs/`. This requires `kaleido`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c89e0d7c", + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "\n", + "out_dir = Path(\"../results/output_pdfs\")\n", + "out_dir.mkdir(parents=True, exist_ok=True)\n", + "\n", + "figures = {\n", + " \"timeline_with_peaks_ambsowi\": create_timeline_with_peaks(\n", + " predicted_ambsowi[\"Time\"],\n", + " observed_data[\"Obs\"],\n", + " predicted_ambsowi[\"Model\"],\n", + " observed_data[\"Name\"][0],\n", + " predicted_ambsowi[\"Name\"][0],\n", + " threshold_value,\n", + " maximum_dt,\n", + " ),\n", + " \"peak_bar_chart_ambsowi\": create_peak_bar_chart(\n", + " predicted_ambsowi[\"Time\"],\n", + " observed_data[\"Obs\"],\n", + " predicted_ambsowi[\"Model\"],\n", + " threshold_value,\n", + " maximum_dt,\n", + " ),\n", + " \"peak_data_error_histogram_ambsowi\": create_histogram_plot(\n", + " predicted_ambsowi[\"Time\"],\n", + " observed_data[\"Obs\"],\n", + " predicted_ambsowi[\"Model\"],\n", + " threshold_value,\n", + " maximum_dt,\n", + " x_label=\"Error [km s⁻¹]\",\n", + " plot_type=\"Data Diff\",\n", + " ),\n", + " \"peak_time_error_histogram_ambsowi\": create_histogram_plot(\n", + " predicted_ambsowi[\"Time\"],\n", + " observed_data[\"Obs\"],\n", + " predicted_ambsowi[\"Model\"],\n", + " threshold_value,\n", + " maximum_dt,\n", + " x_label=\"Time Error [hours]\",\n", + " plot_type=\"Time Error [hours]\",\n", + " ),\n", + " \"peak_hits_scatter_ambsowi\": create_scatter_plot_for_hits(\n", + " predicted_ambsowi[\"Time\"],\n", + " observed_data[\"Obs\"],\n", + " predicted_ambsowi[\"Model\"],\n", + " threshold_value,\n", + " maximum_dt,\n", + " max_points=1000,\n", + " min_points=50,\n", + " opacity=0.5,\n", + " x_label=\"Observed Bulk Speed [km s⁻¹]\",\n", + " y_label=\"Predicted Bulk Speed [km s⁻¹]\",\n", + " plot_title=\"Model Hits: Observed vs Predicted\",\n", + " ),\n", + " \"peak_error_scatter_ambsowi\": create_error_scatter_plot(\n", + " predicted_ambsowi[\"Time\"],\n", + " observed_data[\"Obs\"],\n", + " predicted_ambsowi[\"Model\"],\n", + " threshold_value,\n", + " maximum_dt,\n", + " x_label=\"Time Error [hours]\",\n", + " y_label=\"Speed Error [km s⁻¹]\",\n", + " plot_title=\"Model Event Timing vs Magnitude Errors\",\n", + " max_points=1000,\n", + " min_points=50,\n", + " opacity=0.5,\n", + " time_bounds=(-96, 96),\n", + " data_bounds=(-400, 400),\n", + " ),\n", + " \"peak_time_error_vs_observed_ambsowi\": create_time_error_vs_observed_data_plot(\n", + " predicted_ambsowi[\"Time\"],\n", + " observed_data[\"Obs\"],\n", + " predicted_ambsowi[\"Model\"],\n", + " threshold_value,\n", + " maximum_dt,\n", + " x_label=\"Time Error [hours]\",\n", + " y_label=\"Observed Bulk Speed [km s⁻¹]\",\n", + " plot_title=\"Time Error vs. Observed Bulk Speed\",\n", + " max_points=1000,\n", + " min_points=50,\n", + " opacity=0.5,\n", + " time_bounds=(-96, 96),\n", + " obs_bounds=(200, 800),\n", + " ),\n", + "}\n", + "\n", + "figure_sizes = {\n", + " \"timeline_with_peaks_ambsowi\": (1200, 520),\n", + " \"peak_bar_chart_ambsowi\": (400, 400),\n", + " \"peak_data_error_histogram_ambsowi\": (500, 500),\n", + " \"peak_time_error_histogram_ambsowi\": (500, 500),\n", + " \"peak_hits_scatter_ambsowi\": (500, 500),\n", + " \"peak_error_scatter_ambsowi\": (500, 500),\n", + " \"peak_time_error_vs_observed_ambsowi\": (500, 500),\n", + "}\n", + "\n", + "for name, fig in figures.items():\n", + " width, height = figure_sizes[name]\n", + " fig.update_layout(width=width, height=height)\n", + "\n", + " pdf_path = out_dir / f\"{name}.pdf\"\n", + "\n", + " # Requires kaleido:\n", + " fig.write_image(pdf_path)\n", + "\n", + " print(\"Saved:\", pdf_path)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/05_multi_model_comparison.ipynb b/notebooks/05_multi_model_comparison.ipynb new file mode 100644 index 0000000..e3c0a92 --- /dev/null +++ b/notebooks/05_multi_model_comparison.ipynb @@ -0,0 +1,336 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "9f5076c5", + "metadata": {}, + "source": [ + "# CAMEL Ambient Solar Wind Validation\n", + "\n", + "## Overview\n", + "\n", + "This notebook demonstrates how to validate ambient solar wind models against in situ measurements near Earth. The workflow is based on the **CAMEL (Comprehensive Assessment of Models and Events using Library tools)** framework developed at the [Community Coordinated Modeling Center (CCMC)](https://ccmc.gsfc.nasa.gov/).\n", + "\n", + "This effort is part of the international [ISWAT H1-01 Team](https://iswat-cospar.org/h1-01), which aims to establish a unified validation approach for solar wind models across the heliophysics and space weather communities.\n", + "\n", + "The validation procedures presented in this notebook series are also available through an interactive web application built with Plotly Dash and Python. The **Ambient Solar Wind Validation Dashboard** can be accessed here:\n", + "\n", + "- [CCMC CAMEL Ambient Solar Wind Validation Dashboard](https://ccmc.gsfc.nasa.gov/camel/AmbientSolarWind/home)\n", + "\n", + "These notebooks provide a reproducible version of the same validation workflow and are intended to help users explore, reproduce, and extend the validation procedures of the CCMC CAMEL dashboard.\n", + "\n", + "**Notebook version:** 1.0\n", + "\n", + "---\n", + "\n", + "## Notebook Scope\n", + "\n", + "### 05 Multi-Model Comparison\n", + "\n", + "This notebook:\n", + "- loads the preprocessed example datasets generated in `01_api_and_preprocessing.ipynb`\n", + "- prepares multiple model outputs for side-by-side comparison against the same observational reference\n", + "- reproduces the multi-model visualization workflow used in the dashboard\n", + "\n", + "Multi-model comparison visualizations help evaluate model performance side by side and provide context for comparing model behavior against a simple persistence baseline.\n", + "\n", + "---\n", + "\n", + "## Expected Inputs\n", + "\n", + "This notebook expects the following processed datasets:\n", + "\n", + "```text\n", + "../example_data/processed/\n", + "├── omni_obs.csv\n", + "├── ambsowi_mod.csv\n", + "├── bravda_mod.csv\n", + "└── p27_mod.csv\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "14f15c5c", + "metadata": {}, + "source": [ + "## Imports\n", + "\n", + "We import the metric and visual diagnostic functions from `utils/`. Note that these functions are an exact copy of the functions that are used in the CAMEL Ambient Solar Wind Dashboard. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0794385a", + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "from pathlib import Path\n", + "\n", + "project_root = \"..\"\n", + "utils_path = \"../utils\"\n", + "\n", + "if project_root not in sys.path:\n", + " sys.path.append(project_root)\n", + "if utils_path not in sys.path:\n", + " sys.path.append(utils_path)\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "from IPython.display import display\n", + "\n", + "from utils.skill_scores import *\n", + "from utils.visualizations import *" + ] + }, + { + "cell_type": "markdown", + "id": "a244c482", + "metadata": {}, + "source": [ + "## Load the processed example datasets\n", + "\n", + "These CSV files are assumed to have already been preprocessed and aligned in `01_api_and_preprocessing.ipynb`. Note that if you want to change the selected time window for validation you need to go back to `01_api_and_preprocessing.ipynb`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2411f7ac", + "metadata": {}, + "outputs": [], + "source": [ + "data_dir = Path(\"../example_data/processed\")\n", + "\n", + "omni_obs = pd.read_csv(data_dir / \"omni_obs.csv\", parse_dates=[\"Time\"], index_col=\"Time\")\n", + "ambsowi_mod = pd.read_csv(data_dir / \"ambsowi_mod.csv\", parse_dates=[\"Time\"], index_col=\"Time\")\n", + "bravda_mod = pd.read_csv(data_dir / \"bravda_mod.csv\", parse_dates=[\"Time\"], index_col=\"Time\")\n", + "p27_mod = pd.read_csv(data_dir / \"p27_mod.csv\", parse_dates=[\"Time\"], index_col=\"Time\")\n", + "\n", + "print(\"Observed shape: \", omni_obs.shape)\n", + "print(\"AmbSoWi-ML shape: \", ambsowi_mod.shape)\n", + "print(\"BRaVDA shape: \", bravda_mod.shape)\n", + "print(\"Persistence shape:\", p27_mod.shape)\n" + ] + }, + { + "cell_type": "markdown", + "id": "afc2962d", + "metadata": {}, + "source": [ + "## Prepare inputs for multi-model comparison\n", + "\n", + "The multi-model visualization helpers expect a list of standardized long-format model DataFrames and one corresponding observational DataFrame.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "67cb005a", + "metadata": {}, + "outputs": [], + "source": [ + "def to_pred(df: pd.DataFrame, name: str) -> pd.DataFrame:\n", + " d = df.copy().reset_index()\n", + " d = d.rename(columns={\"Flow_Speed\": \"Model\"})\n", + " d[\"Name\"] = name\n", + " return d[[\"Time\", \"Model\", \"Name\"]]\n", + "\n", + "def to_obs(df: pd.DataFrame, name: str) -> pd.DataFrame:\n", + " d = df.copy().reset_index()\n", + " d = d.rename(columns={\"Flow_Speed\": \"Obs\"})\n", + " d[\"Name\"] = name\n", + " return d[[\"Time\", \"Obs\", \"Name\"]]\n", + "\n", + "observed_data = to_obs(omni_obs, \"OMNI2 | Hourly averaged\")\n", + "\n", + "predicted_ambsowi = to_pred(ambsowi_mod, \"AmbSoWi-ML\")\n", + "predicted_bravda = to_pred(bravda_mod, \"BRaVDA\")\n", + "baseline_data = to_pred(p27_mod, \"Persistence (27-days)\")\n", + "\n", + "predicted_data_list = [\n", + " predicted_ambsowi,\n", + " predicted_bravda,\n", + " baseline_data,\n", + "]" + ] + }, + { + "cell_type": "markdown", + "id": "acbc8ee5", + "metadata": {}, + "source": [ + "## Summary Metrics Table\n", + "\n", + "This table provides a compact quantitative overview before generating the multi-model visualizations." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bdf1a619", + "metadata": {}, + "outputs": [], + "source": [ + "def compute_metric_table(models: dict, observed: pd.DataFrame) -> pd.DataFrame:\n", + " rows = {}\n", + " for name, model_df in models.items():\n", + " rows[name] = {\n", + " \"ME\": float(np.squeeze(meScore(model_df, observed))),\n", + " \"MAE\": float(np.squeeze(maeScore(model_df, observed))),\n", + " \"MSE\": float(np.squeeze(meanSquaredError(model_df, observed))),\n", + " \"RMSE\": float(np.squeeze(rmsScore(model_df, observed))),\n", + " \"CC\": float(np.squeeze(ccScore(model_df, observed))),\n", + " }\n", + " return pd.DataFrame.from_dict(rows, orient=\"index\")\n", + "\n", + "metrics_table = compute_metric_table(\n", + " {\n", + " \"AmbSoWi-ML\": ambsowi_mod,\n", + " \"BRaVDA\": bravda_mod,\n", + " \"Persistence (27d)\": p27_mod,\n", + " },\n", + " omni_obs,\n", + ")\n", + "\n", + "display(metrics_table.round(2))" + ] + }, + { + "cell_type": "markdown", + "id": "49aac048", + "metadata": {}, + "source": [ + "## Visual Diagnostics for Multi-Model Comparison\n", + "\n", + "Multi-model comparison visualizations allow several model outputs to be evaluated side by side against the same observational dataset.\n", + "\n", + "The examples in this section include:\n", + "- a multi-model time series comparison\n", + "- a multi-model distribution comparison\n", + "- a multi-model RMSE timeline\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fed8869a", + "metadata": {}, + "outputs": [], + "source": [ + "fig = create_mm_line_chart(predicted_data_list, observed_data)\n", + "fig.update_layout(width=1200, height=500)\n", + "fig.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7bf21925", + "metadata": {}, + "outputs": [], + "source": [ + "fig = create_mm_box_plot(predicted_data_list, observed_data)\n", + "fig.update_layout(width=900, height=520)\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fc8813ae", + "metadata": {}, + "outputs": [], + "source": [ + "fig = create_mm_rmse_timeline(\n", + " predicted_data_list,\n", + " observed_data,\n", + " baseline_data,\n", + " normalize_time=\"naive_utc\",\n", + " floor=\"H\",\n", + ")\n", + "fig.update_layout(width=1200, height=500)\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "id": "04ca50b1", + "metadata": {}, + "source": [ + "## Export multi-model figures\n", + "\n", + "Save all multi-model visualizations generated in this notebook as PDF files in `../results/output_pdfs/`. This requires `kaleido` for static Plotly image export.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ff1032b9", + "metadata": {}, + "outputs": [], + "source": [ + "out_dir = Path(\"../results/output_pdfs\")\n", + "out_dir.mkdir(parents=True, exist_ok=True)\n", + "\n", + "figures = {\n", + " \"multi_model_line_chart\": create_mm_line_chart(\n", + " predicted_data_list,\n", + " observed_data,\n", + " ),\n", + " \"multi_model_box_plot\": create_mm_box_plot(\n", + " predicted_data_list,\n", + " observed_data,\n", + " ),\n", + " \"multi_model_rmse_timeline\": create_mm_rmse_timeline(\n", + " predicted_data_list,\n", + " observed_data,\n", + " baseline_data,\n", + " normalize_time=\"naive_utc\",\n", + " floor=\"H\",\n", + " ),\n", + "}\n", + "\n", + "figure_sizes = {\n", + " \"multi_model_line_chart\": (1200, 500),\n", + " \"multi_model_box_plot\": (900, 520),\n", + " \"multi_model_rmse_timeline\": (1200, 500),\n", + "}\n", + "\n", + "for name, fig in figures.items():\n", + " width, height = figure_sizes[name]\n", + " fig.update_layout(width=width, height=height)\n", + "\n", + " pdf_path = out_dir / f\"{name}.pdf\"\n", + "\n", + " # Requires kaleido:\n", + " fig.write_image(pdf_path)\n", + "\n", + " print(\"Saved:\", pdf_path)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..3fe05ec --- /dev/null +++ b/requirements.txt @@ -0,0 +1,13 @@ +numpy +pandas +scipy +plotly +dash +astropy +sunpy +requests +urllib3 +matplotlib +ipython +jupyter +kaleido \ No newline at end of file diff --git a/utils/data_processing.py b/utils/data_processing.py new file mode 100644 index 0000000..1c885d5 --- /dev/null +++ b/utils/data_processing.py @@ -0,0 +1,153 @@ +""" +@author: mareiss +Version: 1.0 +""" + +import pandas as pd +import numpy as np + +def align_and_interpolate(df_to_align, df_reference, method='linear', max_gap='48H', return_mask=False): + """ + Align a DataFrame to match the reference DataFrame's dates using the specified interpolation method. + + """ + + # Ensure datetime index + if not isinstance(df_to_align.index, pd.DatetimeIndex): + if 'Time' not in df_to_align.columns: + raise ValueError("df_to_align must have a DatetimeIndex or a 'Time' column.") + df_to_align = df_to_align.set_index('Time') + + if not isinstance(df_reference.index, pd.DatetimeIndex): + if 'Time' not in df_reference.columns: + raise ValueError("df_reference must have a DatetimeIndex or a 'Time' column.") + df_reference = df_reference.set_index('Time') + + # Normalize max_gap + max_gap = pd.Timedelta(max_gap) + non_numeric_cols = df_to_align.select_dtypes(exclude=[np.number]).columns + numeric_cols = df_to_align.select_dtypes(include=[np.number]).columns + full_index = df_reference.index.union(df_to_align.index).sort_values() + reindexed_numeric = df_to_align[numeric_cols].reindex(full_index) + + # Interpolate + try: + aligned_numeric_full = reindexed_numeric.interpolate(method=method) + except ValueError: + raise ValueError(f"Unsupported interpolation method: {method}") + + original_isna = reindexed_numeric.isna() + + blocked = pd.DataFrame(False, index=full_index, columns=numeric_cols) + + for col in numeric_cols: + na = original_isna[col].to_numpy() + idx = full_index.to_numpy() + + i = 0 + n = len(na) + while i < n: + if not na[i]: + i += 1 + continue + start = i + while i < n and na[i]: + i += 1 + end = i - 1 + + run_start = pd.Timestamp(idx[start]) + run_end = pd.Timestamp(idx[end]) + run_duration = run_end - run_start + + if run_duration > max_gap: + blocked.iloc[start:end + 1, blocked.columns.get_loc(col)] = True + + aligned_numeric_full = aligned_numeric_full.mask(blocked) + + aligned_numeric = aligned_numeric_full.reindex(df_reference.index) + + reindexed_non_numeric = df_to_align[non_numeric_cols].reindex(full_index) if len(non_numeric_cols) else None + if reindexed_non_numeric is not None: + aligned_non_numeric = reindexed_non_numeric.ffill().bfill().reindex(df_reference.index) + aligned_values = pd.concat([aligned_numeric, aligned_non_numeric], axis=1) + else: + aligned_values = aligned_numeric.copy() + + aligned_values.index = df_reference.index + + if len(numeric_cols): + valid_mask = aligned_numeric.notna().all(axis=1) + else: + valid_mask = pd.Series(True, index=df_reference.index) + + out = aligned_values.reset_index() + + if return_mask: + return out, valid_mask.reset_index(drop=True) + return out + + +def apply_time_rolling_average(df, value_col, time_col="Time", rolling_days=0, min_periods=1): + """ + Applies a time-based rolling mean to a selected column in a time series DataFrame. + + Parameters: + df (pandas.DataFrame): + Input data. Must contain: + - time_col (datetime-like): timestamps + - value_col (float): values to smooth + + value_col (str): + Name of the column to smooth (for example, "Model" or "Obs"). + + time_col (str, optional): + Name of the time column. + Default is "Time". + + rolling_days (int, optional): + Width of the rolling window in days. + A value of 0 returns the original series without smoothing. + Values are constrained to the range [0, 5] to match the UI slider. + Default is 0. + + min_periods (int, optional): + Minimum number of samples required within each rolling window + to compute the mean. + Passed directly to pandas rolling(...).mean(). + Default is 1. + + Returns: + pandas.DataFrame: + DataFrame containing: + - time_col: timestamps + - value_col: smoothed values + """ + if df is None or df.empty: + return pd.DataFrame(columns=[time_col, value_col]) + + if time_col not in df.columns: + raise ValueError(f"apply_time_rolling_average: '{time_col}' column not found.") + if value_col not in df.columns: + raise ValueError(f"apply_time_rolling_average: '{value_col}' column not found.") + + rolling_days = int(rolling_days) if rolling_days is not None else 0 + rolling_days = max(0, min(5, rolling_days)) + + tmp = df[[time_col, value_col]].copy() + tmp[time_col] = pd.to_datetime(tmp[time_col]) + + # Sort by time before applying time-based rolling + tmp = tmp.sort_values(time_col) + + if rolling_days == 0: + return tmp.reset_index(drop=True) + + rolled = ( + tmp.set_index(time_col)[value_col] + .rolling(f"{rolling_days}D", min_periods=min_periods) + .mean() + .reset_index() + ) + + rolled.columns = [time_col, value_col] + return rolled.reset_index(drop=True) \ No newline at end of file diff --git a/utils/skill_scores.py b/utils/skill_scores.py new file mode 100644 index 0000000..4e67552 --- /dev/null +++ b/utils/skill_scores.py @@ -0,0 +1,575 @@ +""" +@author: remullinix, mareiss +Version: 1.0 +""" + +import pandas as pd +import numpy as np +from scipy.stats import linregress, skew, kurtosis +from scipy.signal import find_peaks + +def rmsScore(modDF, obsDF): + ''' + Computes Root Mean Square Error based on modDF and obsDF. + Dataframes must be same size and have matching indices. + + Parameters: + modDF (pandas.DataFrame): dataframe with datetime as index + obsDF (pandas.DataFrame): dataframe with datetime as index + + Returns: + score (pandas.Series): series with parameter name(s) as index + ''' + obsDF = obsDF.reindex(modDF.index) + squaredError = (modDF - obsDF)**2 + rms = squaredError.mean()**0.5 + return rms + +def maeScore(modDF, obsDF): + ''' + Computes Mean Absolute Error based on modDF and obsDF. + Dataframes must be same size and have matching indices. + + Parameters: + modDF (pandas.DataFrame): dataframe with datetime as index + obsDF (pandas.DataFrame): dataframe with datetime as index + + Returns: + score (pandas.Series): series with parameter name(s) as index + ''' + obsDF = obsDF.reindex(modDF.index) + absError = abs(modDF - obsDF) + mae = absError.mean() + return mae + +def meScore(modDF, obsDF): + ''' + Computes Mean Error based on modDF and obsDF. + Dataframes must be same size and have matching indices. + + Parameters: + modDF (pandas.DataFrame): dataframe with datetime as index + obsDF (pandas.DataFrame): dataframe with datetime as index + + Returns: + score (pandas.Series): series with parameter name(s) as index + ''' + obsDF = obsDF.reindex(modDF.index) + error = modDF - obsDF + me = error.mean() + return me + +def meanSquaredError(modDF, obsDF): + ''' + Computes Mean Squared Error based on modDF and obsDF. + Dataframes must be same size and have matching indices. + + Parameters: + modDF (pandas.DataFrame): dataframe with datetime as index + obsDF (pandas.DataFrame): dataframe with datetime as index + + Returns: + score (pandas.Series): series with parameter name(s) as index + ''' + obsDF = obsDF.reindex(modDF.index) + mse = ((obsDF - modDF) ** 2).mean() + return mse + +def ccScore(modDF, obsDF): + ''' + Computes Pearson Correlation Coefficient based on modDF and obsDF. + Dataframes must be same size and have matching indices. + + Parameters: + modDF (pandas.DataFrame): dataframe with datetime as index + obsDF (pandas.DataFrame): dataframe with datetime as index + + Returns: + score (pandas.Series): series with parameter name(s) as index + ''' + obsDF = obsDF.reindex(modDF.index) + modelMean = modDF.mean() + obsMean = obsDF.mean() + sigmaObs = sigma(obsDF) + sigmaModel = sigma(modDF) + diffMeanSquare = (obsDF - obsMean) * (modDF - modelMean) + diffMeanSquareSum = diffMeanSquare.sum() + N = diffMeanSquare.shape[0] + score = (diffMeanSquareSum / (N-1)) / (sigmaObs * sigmaModel) + return score + +def peScore(modDF, obsDF): + ''' + Computes Prediction Efficiency based on modDF and obsDF. + Dataframes must be same size and have matching indices. + + Parameters: + modDF (pandas.DataFrame): dataframe with datetime as index + obsDF (pandas.DataFrame): dataframe with datetime as index + + Returns: + score (pandas.Series): series with parameter name(s) as index + ''' + obsDF = obsDF.reindex(modDF.index) + sigmaObsSquared = sigma(obsDF)**2 + diff = (obsDF - modDF)*(obsDF - modDF) / sigmaObsSquared + return 1 - diff.mean() + +def sspbScore(modDF, obsDF): + ''' + Computes Symmetric Signed Percentage Bias based on modDF and obsDF. + Dataframes must be same size and have matching indices. + + Parameters: + modDF (pandas.DataFrame): dataframe with datetime as index + obsDF (pandas.DataFrame): dataframe with datetime as index + + Returns: + score (pandas.Series): series with parameter name(s) as index + ''' + # assumes all values are greater then 0 + # assumes values are already log base 10 + + obsDF = obsDF.reindex(modDF.index) + mod_over_obs = obsDF - modDF + median = mod_over_obs.median() + sign = median.apply(lambda x: np.sign(x)) + return 100 * sign * (10**median - 1) + + +def ssopScore(modDF, obsDF): + ''' + Computes Skill Score of Prediction based on modDF and obsDF. + Dataframes must be same size and have matching indices. + + Parameters: + modDF (pandas.DataFrame): dataframe with datetime as index + obsDF (pandas.DataFrame): dataframe with datetime as index + + Returns: + score (pandas.Series): series with parameter name(s) as index + ''' + obsDF = obsDF.reindex(modDF.index) + mseRef = variance(obsDF) + msePred = (modDF - obsDF)**2 + ss = msePred / mseRef + ssop = 1 - ss.mean()**0.5 + return ssop + + +def truePositiveRate(ct): + ''' + Computes true positive rate based on modDF and obsDF. + Dataframes must be same size and have matching indices. + + Parameters: + modDF (pandas.DataFrame): dataframe with datetime as index + obsDF (pandas.DataFrame): dataframe with datetime as index + threshold (float): threshold to determine an 'event' + + Returns: + score (pandas.Series): series with parameter name(s) as index + ''' + return ct['tp'] / (ct['tp'] + ct['fn']) + +def falsePostiveRate(ct): + ''' + Computes false positive rate based on modDF and obsDF. + Dataframes must be same size and have matching indices. + + Parameters: + modDF (pandas.DataFrame): dataframe with datetime as index + obsDF (pandas.DataFrame): dataframe with datetime as index + threshold (float): threshold to determine an 'event' + + Returns: + score (pandas.Series): series with parameter name(s) as index + ''' + return ct['fp'] / (ct['fp'] + ct['tn']) + +def falseNegativeRate(ct): + ''' + Computes false negative rate based on modDF and obsDF. + Dataframes must be same size and have matching indices. + + Parameters: + modDF (pandas.DataFrame): dataframe with datetime as index + obsDF (pandas.DataFrame): dataframe with datetime as index + threshold (float): threshold to determine an 'event' + + Returns: + score (pandas.Series): series with parameter name(s) as index + ''' + return ct['fn'] / (ct['tp'] + ct['fn']) + +def positivePredictiveValue(ct): + ''' + Computes predictive value rate based on modDF and obsDF. + Dataframes must be same size and have matching indices. + + Parameters: + modDF (pandas.DataFrame): dataframe with datetime as index + obsDF (pandas.DataFrame): dataframe with datetime as index + threshold (float): threshold to determine an 'event' + + Returns: + score (pandas.Series): series with parameter name(s) as index + ''' + return ct['tp'] / (ct['tp'] + ct['fp']) + +def falseAlarmRatio(ct): + ''' + Computes false alarm ratio based on modDF and obsDF. + Dataframes must be same size and have matching indices. + + Parameters: + modDF (pandas.DataFrame): dataframe with datetime as index + obsDF (pandas.DataFrame): dataframe with datetime as index + threshold (float): threshold to determine an 'event' + + Returns: + score (pandas.Series): series with parameter name(s) as index + ''' + return ct['fp'] / (ct['tp'] + ct['fp']) + +def threatScore(ct): + ''' + Computes threat score based on modDF and obsDF. + Dataframes must be same size and have matching indices. + + Parameters: + modDF (pandas.DataFrame): dataframe with datetime as index + obsDF (pandas.DataFrame): dataframe with datetime as index + threshold (float): threshold to determine an 'event' + + Returns: + score (pandas.Series): series with parameter name(s) as index + ''' + return ct['tp'] / (ct['tp'] + ct['fp'] + ct['fn']) + +def trueSkillStatistics(ct): + ''' + Computes true skill based on modDF and obsDF. + Dataframes must be same size and have matching indices. + + Parameters: + modDF (pandas.DataFrame): dataframe with datetime as index + obsDF (pandas.DataFrame): dataframe with datetime as index + threshold (float): threshold to determine an 'event' + + Returns: + score (pandas.Series): series with parameter name(s) as index + ''' + return ct['tp'] / (ct['tp'] + ct['fn']) - ct['fp'] / (ct['fp'] + ct['tn']) + +def bias(ct): + ''' + Computes bias based on modDF and obsDF. + Dataframes must be same size and have matching indices. + + Parameters: + modDF (pandas.DataFrame): dataframe with datetime as index + obsDF (pandas.DataFrame): dataframe with datetime as index + threshold (float): threshold to determine an 'event' + + Returns: + score (pandas.Series): series with parameter name(s) as index + ''' + return (ct['tp'] + ct['fp']) / (ct['tp'] + ct['fn']) + + +# internal charging metrics +# Median symmetric accuracy +def msaScore(modDF, obsDF): + ''' + Computes Median Symmetric Accuracy based on modDF and obsDF. + Dataframes must be same size and have matching indices. + + Parameters: + modDF (pandas.DataFrame): dataframe with datetime as index + obsDF (pandas.DataFrame): dataframe with datetime as index + + Returns: + score (pandas.Series): series with parameter name(s) as index + ''' + obsDF = obsDF.reindex(modDF.index) + Q = modDF / obsDF + medianAbs = (abs(np.log(Q))).median() + msa = 100 * (pow(np.e, medianAbs)- 1) + return msa + + +def sspbScore(modDF, obsDF): + ''' + Computes Symmetric Signed Percentage Bias based on modDF and obsDF. + Dataframes must be same size and have matching indices. + + Parameters: + modDF (pandas.DataFrame): dataframe with datetime as index + obsDF (pandas.DataFrame): dataframe with datetime as index + + Returns: + score (pandas.Series): series with parameter name(s) as index + ''' + obsDF = obsDF.reindex(modDF.index) + Q = modDF / obsDF + absMedian = abs(np.log(Q).median()) + sspb = 100 * np.sign(np.log(Q).median()) * (pow(np.e, absMedian)- 1) + return sspb + +def mocrScore(modDF, obsDF): + ''' + Computes Mean Observed to Computed (O/C) Ratio based on modDF and obsDF. + Dataframes must be same size and have matching indices. + + Parameters: + modDF (pandas.DataFrame): dataframe with datetime as index + obsDF (pandas.DataFrame): dataframe with datetime as index + + Returns: + score (pandas.Series): series with parameter name(s) as index + ''' + obsDF = obsDF.reindex(modDF.index) + R = obsDF / modDF + return R.mean() + +def sdocrScore(modDF, obsDF): + ''' + Computes Standard Deviation Observed to computed (O/C) Ratio based on modDF and obsDF. + Dataframes must be same size and have matching indices. + + Parameters: + modDF (pandas.DataFrame): dataframe with datetime as index + obsDF (pandas.DataFrame): dataframe with datetime as index + + Returns: + score (pandas.Series): series with parameter name(s) as index + ''' + obsDF = obsDF.reindex(modDF.index) + R = obsDF / modDF + A = np.log(R) + B = np.log(R.mean()) + deviation = ((A - B) ** 2).mean() ** 0.5 + return deviation + +def sigma(df): + ''' + Computes sigma of dataframe. Helper function. + + Parameters: + df (pandas.DataFrame): dataframe with datetime as index and single parameter + Returns: + sigma (float): sigma of the dataframe + ''' + mean = df.mean() + squaredMeanDiff = (df - mean) * (df - mean) + squaredMeanDiffSum = squaredMeanDiff.sum() + N = squaredMeanDiff.shape[0] + squaredSigma = squaredMeanDiffSum / (N-1) + return squaredSigma**0.5 + +def variance(df): + ''' + Computes variance of dataframe. Helper function. + + Parameters: + df (pandas.DataFrame): dataframe with datetime as index and single parameter + Returns: + variance (float): variance of the dataframe + ''' + mean = df.mean() + squaredMeanDiff = (df - mean) * (df - mean) + squaredMeanDiffSum = squaredMeanDiff.sum() + N = squaredMeanDiff.shape[0] + return squaredMeanDiffSum / (N-1) + +def regressionStats(modDF, obsDF): + ''' + Computes linear regression statistics between model and observed series. + + Parameters: + modDF (pandas.Series): predicted values + obsDF (pandas.Series): observed values + + Returns: + dict: contains slope, intercept, r_value, r_squared + ''' + + obsDF = obsDF.reindex(modDF.index) + + valid = modDF.notna() & obsDF.notna() + modDF = modDF[valid] + obsDF = obsDF[valid] + + slope, intercept, r_value, _, _ = linregress(obsDF, modDF) + return { + 'slope': slope, + 'intercept': intercept, + 'r_value': r_value, + 'r_squared': r_value ** 2 + } + +def descriptiveStats(series): + ''' + Computes a set of descriptive statistics for a pandas Series. + + Parameters: + series (pandas.Series): Series of numerical data + + Returns: + dict: Dictionary with keys: + - 'mean' + - 'median' + - 'std' + - 'min' + - 'max' + - 'var' + - 'skewness' + - 'kurtosis' + ''' + series = series.dropna() + return { + 'mean': round(series.mean(), 2), + 'median': round(series.median(), 2), + 'std': round(series.std(ddof=0), 2), + 'min': round(series.min(), 2), + 'max': round(series.max(), 2), + 'var': round(series.var(ddof=0), 2), + 'skewness': round(skew(series), 2), + 'kurtosis': round(kurtosis(series), 2) + } + +def classifyBinaryEvents(modDF, obsDF, threshold=400): + ''' + Classifies each prediction–observation pair into binary event categories + and computes the overall contingency table. + + Parameters: + modDF (pandas.DataFrame): Predicted data with a 'Model' column + obsDF (pandas.DataFrame): Observed data with an 'Obs' column + threshold (float, optional): Threshold used to determine an event [default = 400] + + Returns: + tuple: + - labels (pandas.Series): Per-point classification with values: + 'Hit', 'Miss', 'False Alarm', 'Correct Rejection' + (aligned to modDF index) + - table (pandas.DataFrame): 1×4 contingency table with columns: + 'tp' (true positives) + 'fp' (false positives) + 'fn' (false negatives) + 'tn' (true negatives) + ''' + obsDF = obsDF.reindex(modDF.index) + + m = modDF['Model'] + o = obsDF['Obs'] + + m_evt = m >= threshold + o_evt = o >= threshold + + conditions = [ + m_evt & o_evt, # Hit + (~m_evt) & (~o_evt), # Correct Rejection + m_evt & (~o_evt), # False Alarm + (~m_evt) & o_evt # Miss + ] + choices = ['Hit', 'Correct Rejection', 'False Alarm', 'Miss'] + labels = pd.Series(np.select(conditions, choices), index=m.index, name='classification') + + tp = int((m_evt & o_evt).sum()) + fp = int((m_evt & ~o_evt).sum()) + fn = int((~m_evt & o_evt).sum()) + tn = int((~m_evt & ~o_evt).sum()) + table = pd.DataFrame([[tp, fp, fn, tn]], columns=['tp', 'fp', 'fn', 'tn']) + + return labels, table + +def compute_peaks(dates, observed_data, predicted_data, minimum_speed, maximum_dt): + ''' + Detects peaks in observed and predicted time series, matches them within a time tolerance, + and computes event-based statistics (hits, misses, false alarms) along with timing and + magnitude differences. + + Parameters: + dates (pandas.Series): Datetime values corresponding to the data points + observed_data (pandas.Series): Observed values + predicted_data (pandas.Series): Predicted/model values + minimum_speed (float): Minimum peak height (speed) to qualify as an event + maximum_dt (float): Maximum allowed time difference (in days) between matched peaks + + Returns: + tuple: + - peaks_obs (numpy.ndarray): Indices of detected observed peaks + - peaks_pred (numpy.ndarray): Indices of detected predicted peaks + - hit_positions (pandas.Index): Indices of matched predicted peaks ("hits") + - misses_positions (pandas.Index): Indices of unmatched observed peaks ("misses") + - false_alarms_positions (pandas.Index): Indices of unmatched predicted peaks ("false alarms") + - time_diffs (list of float): Time differences [hours] for each matched (hit) peak + - data_diffs (list of float): Value differences [same units as data] for each matched (hit) peak + ''' + dates = pd.to_datetime(dates) + + observed_values = observed_data.to_numpy() + predicted_values = predicted_data.to_numpy() + + # Detect peaks + peaks_obs, _ = find_peaks( + observed_values, height=minimum_speed, prominence=100, distance=48, width=24 + ) + peaks_pred, _ = find_peaks( + predicted_values, height=minimum_speed, prominence=100, distance=48, width=24 + ) + + peaks_obs_dates = dates.iloc[peaks_obs] + peaks_pred_dates = dates.iloc[peaks_pred] + + # Build all valid (obs, pred) pairs within maximum_dt + candidate_pairs = [] + for i, obs_date in enumerate(peaks_obs_dates): + for j, pred_date in enumerate(peaks_pred_dates): + dt_days = abs((pred_date - obs_date).days) + if dt_days <= maximum_dt: + candidate_pairs.append((i, j, dt_days)) + + # Sort pairs by smallest time difference + candidate_pairs.sort(key=lambda x: x[2]) + + matched_obs = set() + matched_pred = set() + matches = [] + + for i, j, _ in candidate_pairs: + if i not in matched_obs and j not in matched_pred: + matched_obs.add(i) + matched_pred.add(j) + matches.append((i, j)) + + # Extract hits + hits = [j for _, j in matches] + hit_positions = peaks_pred_dates.index[hits] + + # Extract misses and false alarms + misses = [i for i in range(len(peaks_obs)) if i not in matched_obs] + false_alarms = [j for j in range(len(peaks_pred)) if j not in matched_pred] + + misses_positions = peaks_obs_dates.index[misses] + false_alarms_positions = peaks_pred_dates.index[false_alarms] + + # Compute time and data diffs for hits + time_diffs, data_diffs = [], [] + for i, j in matches: + time_diff = (peaks_pred_dates.iloc[j] - peaks_obs_dates.iloc[i]).total_seconds() / 3600 + data_diff = predicted_values[peaks_pred[j]] - observed_values[peaks_obs[i]] + time_diffs.append(time_diff) + data_diffs.append(data_diff) + + return ( + peaks_obs, + peaks_pred, + hit_positions, + misses_positions, + false_alarms_positions, + time_diffs, + data_diffs + ) \ No newline at end of file diff --git a/utils/visualizations.py b/utils/visualizations.py new file mode 100644 index 0000000..72777fc --- /dev/null +++ b/utils/visualizations.py @@ -0,0 +1,3583 @@ +""" +@author: mareiss +Version: 1.0 +""" + +import numpy as np +import pandas as pd + +import plotly.express as px +import plotly.graph_objects as go +from plotly.subplots import make_subplots + +import dash +from dash import dash_table +from dash.dash_table.Format import Format, Scheme + +from scipy import stats +from scipy.signal import find_peaks +from scipy.stats import gaussian_kde, kurtosis, skew, linregress + +import astropy.units as u +from sunpy.coordinates.sun import carrington_rotation_time + +from assets.fig_layout import my_figlayout, obs_linelayout, my_tablelayout +from utils.skill_scores import * + +def create_line_chart(predicted_data, observed_data, baseline_data=None, property_type="Bulk Speed"): + """ + Creates a time series comparison chart for predicted, observed, and baseline data. + Observations are plotted as scatter points. + """ + + property_settings = { + "Bulk Speed": { + "yaxis_title": "Bulk Speed [km s⁻¹]", + "title": "Solar Wind Speed Comparison", + "y_range": [200, 900] + }, + "Density": { + "yaxis_title": "Number Density [cm⁻³]", + "title": "Solar Wind Density Comparison" + }, + "Temperature": { + "yaxis_title": "Temperature [MK]", + "title": "Solar Wind Temperature Comparison" + }, + "vr": { + "yaxis_title": "vr [km s⁻¹]", + "title": "Solar Wind vr Comparison", + "y_range": [200, 900] + }, + "vt": { + "yaxis_title": "vt [km s⁻¹]", + "title": "Solar Wind vt Comparison" + }, + "vp": { + "yaxis_title": "vp [km s⁻¹]", + "title": "Solar Wind vp Comparison" + }, + "Br": { + "yaxis_title": "Br", + "title": "Polarity Br Comparison", + }, + "Bt": { + "yaxis_title": "Bt", + "title": "Polarity Bt Comparison", + }, + "Bp": { + "yaxis_title": "Bp", + "title": "Polarity Bp Comparison", + } + } + + settings = property_settings.get(property_type, property_settings["Bulk Speed"]) + + fig = go.Figure() + + fig.add_trace(go.Scatter( + x=predicted_data["Time"], + y=predicted_data["Model"], + name=predicted_data["Name"].iloc[0], + mode="lines", + line=dict(width=2), + hovertemplate="Time=%{x}
Model=%{y:.2f}" + )) + + if baseline_data is not None and not baseline_data.empty: + fig.add_trace(go.Scatter( + x=baseline_data["Time"], + y=baseline_data["Model"], + name=baseline_data["Name"].iloc[0], + mode="lines", + line=dict(color="lightgray", width=1.5, dash="dash"), + hovertemplate="Time=%{x}
Baseline=%{y:.2f}" + )) + + fig.add_trace(go.Scatter( + x=observed_data["Time"], + y=observed_data["Obs"], + name=observed_data["Name"].iloc[0], + mode="markers", + marker=dict( + symbol="circle-open", + size=2, + color="#222222", + line=dict(width=1) + ), + opacity=0.85, + hovertemplate="Time=%{x}
Obs=%{y:.2f}" + )) + + fig.update_layout( + my_figlayout, + title=settings["title"], + xaxis=dict( + range=[ + predicted_data["Time"].iloc[0], + predicted_data["Time"].iloc[-1] + ] + ), + xaxis_title="Time (UTC)", + yaxis_title=settings["yaxis_title"], + legend=dict(orientation="h", x=0.35, y=1.15), + height=400 + ) + + if property_type in {"Br", "Bt", "Bp"}: + fig.update_yaxes( + range=[-1.2, 1.2], + tickmode="array", + tickvals=[-1, 1], + ticktext=["Negative", "Positive"], + zeroline=False, + zerolinecolor="lightgrey", + showgrid=True + ) + + if settings.get("y_range"): + fig.update_yaxes(range=settings["y_range"]) + + return fig + + +def create_raincloud_plot(predicted_data, observed_data, property_type="Bulk Speed"): + """ + Creates a horizontal raincloud-style plot combining violin and box plots + to compare the distribution of predicted and observed values. + + Parameters: + predicted_data (pandas.DataFrame or dict): + Model output to plot. Must contain: + - 'Time' (optional, datetime-like): timestamps + - 'Model' (float): predicted values + - 'Name' (str): model name (used in legend/labels) + If a dictionary is provided, it will be converted to a DataFrame. + + observed_data (pandas.DataFrame or dict): + Observational reference data. Must contain: + - 'Time' (optional, datetime-like): timestamps + - 'Obs' (float): observed values + - 'Name' (str): dataset name (used in legend/labels) + If a dictionary is provided, it will be converted to a DataFrame. + + property_type (str): + The physical property being plotted. + Options: "Bulk Speed", "Density" + + Returns: + plotly.graph_objects.Figure: + Interactive Plotly figure showing: + - Half-violin plots for each dataset (predicted and observed) + - Box plots overlaid on the violins to highlight median, quartiles, and outliers + """ + + # Convert dictionaries to pandas DataFrames if necessary + if not isinstance(predicted_data, pd.DataFrame): + predicted_data = pd.DataFrame(predicted_data) + if not isinstance(observed_data, pd.DataFrame): + observed_data = pd.DataFrame(observed_data) + + # Correctly assign the columns for observed and predicted data + predicted_data = predicted_data.rename(columns={'Model': 'Value'}) + predicted_data['Type'] = predicted_data['Name'][0] + observed_data = observed_data.rename(columns={'Obs': 'Value'}) + observed_data['Type'] = observed_data['Name'][0] + + # Combine the data + combined_data = pd.concat([predicted_data, observed_data], ignore_index=True) + + # Property-specific settings + property_settings = { + "Bulk Speed": { + "title": "Distribution of Bulk Speed", + "xaxis_title": "Bulk Speed [km s⁻¹]", + "tick_spacing": 100 + }, + "Density": { + "title": "Distribution of Number Density", + "xaxis_title": "Number Density [cm⁻³]", + "tick_spacing": 20 + }, + "Temperature": { + "title": "Distribution of Temperature", + "xaxis_title": "Temperature [MK]" + }, + "vr": { + "xaxis_title": "vr [km s⁻¹]", + "title": "Solar Wind vr Comparison", + "tick_spacing": 100 + }, + "vt": { + "xaxis_title": "vt [km s⁻¹]", + "title": "Solar Wind vt Comparison" + }, + "vp": { + "xaxis_title": "vp [km s⁻¹]", + "title": "Solar Wind vp Comparison" + } + } + + settings = property_settings.get(property_type, property_settings["Bulk Speed"]) + + # Raincloud plot + fig = go.Figure() + box_width = 0.15 + + for data_type in combined_data['Type'].unique(): + subset = combined_data[combined_data['Type'] == data_type] + + # Half-violin plot + fig.add_trace(go.Violin( + x=subset['Value'], + y=[data_type] * len(subset), + name=data_type, + points=False, + showlegend=False, + jitter=0.5, + orientation='h', + hoverinfo='skip', + marker=dict(size=2), + opacity=0.7 + )) + + # Box plot + fig.add_trace(go.Box( + x=subset['Value'], + y=[data_type] * len(subset), + name=data_type, + boxmean=True, + showlegend=False, + marker_color='gray', + orientation='h', + boxpoints=False, + width=box_width + )) + + # Layout + fig.update_layout(my_figlayout) + + fig.update_layout( + title=settings["title"], + xaxis_title=settings["xaxis_title"], + height=500, + xaxis=dict( + showgrid=True, + tick0=0, + gridcolor='lightgrey' + ), + yaxis=dict( + showgrid=False, + tickangle=270, + ) + ) + + return fig + + +def create_scatter_plot(predicted_data, observed_data, property_type="Bulk Speed", max_points=1000, opacity=0.4): + """ + Creates a density-enhanced scatter plot comparing predicted vs. observed values. + The plot overlays: + - Kernel density estimation (KDE) contours showing point density + - Downsampled scatter points for performance and visualization + - A diagonal reference line + - An optional linear regression fit with slope, intercept, and R² annotation + + Parameters: + predicted_data (pandas.DataFrame or dict): + Model output to plot. Must contain: + - 'Model' (float): predicted values + - 'Name' (str): model name (optional) + If a dictionary is provided, it will be converted to a DataFrame. + + observed_data (pandas.DataFrame or dict): + Observational reference data. Must contain: + - 'Obs' (float): observed values + - 'Name' (str): dataset name (optional) + If a dictionary is provided, it will be converted to a DataFrame. + + max_points (int, default=1000): + Maximum number of points to plot in the scatter layer. + Data is downsampled if it exceeds this number. + + opacity (float, default=0.4): + Marker opacity for scatter points. + + Returns: + plotly.graph_objects.Figure: + Interactive Plotly figure containing: + - KDE contour density background + - Downsampled scatter points + - diagonal reference line + - Optional linear regression line + - Regression statistics annotation box + """ + + # Convert dictionaries to pandas DataFrames if necessary + if not isinstance(predicted_data, pd.DataFrame): + predicted_data = pd.DataFrame(predicted_data) + if not isinstance(observed_data, pd.DataFrame): + observed_data = pd.DataFrame(observed_data) + + # Combine observed and predicted into one DataFrame + data = pd.DataFrame({ + 'Observed': observed_data['Obs'], + 'Predicted': predicted_data['Model'] + }).dropna() + + # Downsample for scatter plot + if len(data) > max_points: + step = len(data) // max_points + scatter_data = data.iloc[::step].head(max_points) + downsampled = True + else: + scatter_data = data + downsampled = False + + # Downsample for KDE + if len(data) > max_points: + step = len(data) // max_points + kde_data = data.iloc[::step].head(max_points) + else: + kde_data = data + + # Property-specific settings + property_settings = { + "Bulk Speed": { + "title": "Density and Scatter Plot: Bulk Speed", + "unit": "km s⁻¹", + "bounds_mode": "fixed", + "min_val": 200, + "max_val": 800, + "tick0": 200, + "dtick": 100, + }, + "Density": { + "title": "Density and Scatter Plot: Number Density", + "unit": "cm⁻³", + "bounds_mode": "p99", + "min_val": 0, + "tick0": 0, + }, + "Temperature": { + "title": "Density and Scatter Plot: Temperature", + "unit": "MK", + "bounds_mode": "p99", + "min_val": 0, + "fixed_dtick": 0.1, + }, + "vr": { + "title": "Density and Scatter Plot: vr", + "unit": "km s⁻¹", + "bounds_mode": "fixed", + "min_val": 200, + "max_val": 800, + "tick0": 200, + "dtick": 100, + }, + "vt": { + "title": "Density and Scatter Plot: vt", + "unit": "km s⁻¹", + "bounds_mode": "fixed", + "min_val": -100, + "max_val": 100, + "tick0": 0, + "dtick": 50, + }, + "vp": { + "title": "Density and Scatter Plot: vp", + "unit": "km s⁻¹", + "bounds_mode": "fixed", + "min_val": -100, + "max_val": 100, + "tick0": 0, + "dtick": 50, + }, + } + settings = property_settings.get(property_type, property_settings["Bulk Speed"]) + + # Bounds + if settings.get("bounds_mode") == "p99": + # p99 of combined observed and predicted + combined = pd.concat([data["Observed"], data["Predicted"]], ignore_index=True) + combined = combined.astype(float).dropna() + + # guard against empty data + if len(combined) == 0: + min_val, max_val = 0, 1 + else: + p99 = float(combined.quantile(0.99)) + max_val = max(p99, 1.0) + min_val = 0 + + raw = max_val / 10.0 + nice_steps = np.array([1, 2, 5, 10, 20, 50, 100, 200, 500, 1000]) + dtick = float(nice_steps[np.argmin(np.abs(nice_steps - raw))]) + tick0 = 0 + else: + min_val, max_val = settings["min_val"], settings["max_val"] + tick0 = settings["tick0"] + dtick = settings["dtick"] + + + if property_type == "Temperature": + dtick = settings["fixed_dtick"] + tick0 = settings.get("tick0", 0) + + fig = go.Figure() + + # Contour density background + colorscale = [ + [0.0, 'rgba(255,255,255,0)'], + [0.1, 'rgba(255,255,255,0)'], + [0.3, 'rgba(255,200,100,0.2)'], + [0.7, 'rgba(255,140,50,0.5)'], + [1.0, 'rgba(255,100,0,0.8)'] + ] + + # Grid for KDE + xi, yi = np.meshgrid( + np.linspace(min_val, max_val, 100), + np.linspace(min_val, max_val, 100) + ) + + # Evaluate KDE on grid + kde = gaussian_kde(np.vstack([kde_data['Observed'], kde_data['Predicted']])) + zi = kde(np.vstack([xi.ravel(), yi.ravel()])).reshape(xi.shape) + + fig.add_trace(go.Contour( + x=xi[0], + y=yi[:, 0], + z=zi, + showscale=False, + colorscale=colorscale, + hoverinfo='skip', + contours=dict( + showlines=True, + start=zi.min(), + end=zi.max(), + size=(zi.max() - zi.min()) / 11 + ), + line=dict(width=0.5, color='lightgrey') + )) + + # Scatter points + scatter_label = 'Data Points (downsampled)' if downsampled else 'Data Points' + fig.add_trace(go.Scattergl( + x=scatter_data['Observed'], + y=scatter_data['Predicted'], + mode='markers', + marker=dict(size=3, color='black', opacity=opacity), + name=scatter_label, + showlegend=True + )) + + # Diagonal reference line + fig.add_trace(go.Scatter( + x=[min_val, max_val], + y=[min_val, max_val], + mode='lines', + line=dict(color='grey', dash='dash', width=1), + name='Diagonal', + showlegend=False + )) + + # Linear regression line + reg_stats = regressionStats(data['Predicted'], data['Observed']) + slope = reg_stats['slope'] + intercept = reg_stats['intercept'] + r_squared = reg_stats['r_squared'] + + reg_x = np.linspace(min_val, max_val, 100) + reg_y = intercept + slope * reg_x + + fig.add_trace(go.Scatter( + x=reg_x, + y=reg_y, + mode='lines', + line=dict(color='black', dash='dashdot', width=1.5), + name='Linear Regression', + visible='legendonly' + )) + + # Regression stats annotation + fig.add_annotation( + text=f"Linear Regression:
" + f"Slope: {slope:.2f}
" + f"Intercept: {intercept:.2f}
" + f"R²: {r_squared:.2f}", + xref="paper", yref="paper", + x=0.98, y=0.03, + xanchor="right", yanchor="bottom", + showarrow=False, + font=dict(size=11, color="black"), + bgcolor="rgba(255, 255, 255, 0.7)", + bordercolor="lightgrey", + borderwidth=1, + opacity=0.8 + ) + + unit = settings["unit"] + obs_name = observed_data['Name'].iloc[0] if 'Name' in observed_data.columns else "Observation" + pred_name = predicted_data['Name'].iloc[0] if 'Name' in predicted_data.columns else "Prediction" + + # Layout + fig.update_layout( + my_figlayout, + title=settings["title"], + xaxis_title=f"{obs_name} {property_type} [{unit}]", + yaxis_title=f"{pred_name} {property_type} [{unit}]", + xaxis=dict( + range=[min_val, max_val], + tick0=tick0, + dtick=dtick, + showgrid=True, + gridwidth=0.5, + gridcolor='lightgrey' + ), + yaxis=dict( + range=[min_val, max_val], + tick0=tick0, + dtick=dtick, + showgrid=True, + gridwidth=0.5, + gridcolor='lightgrey' + ), + height=700, + legend=dict(orientation='h', x=0.05, y=1.0) + ) + + return fig + + +def create_error_table_ptp(observed_data, predicted_data, baseline_data=None, property_type="Bulk Speed"): + """ + Compute point-to-point error metrics comparing model predictions and (optionally) + a baseline against observed data, and return the results as a Dash DataTable. + + Coverage rows: + - Paired points [#] + - Model points evaluated [%] + - Observations covered [%] + + If baseline_data is None or empty, the Baseline column is not shown. + """ + + # Units depending on property type + property_settings = { + "Bulk Speed": {"unit": "km s⁻¹", "var_unit": "(km s⁻¹)²"}, + "Density": {"unit": "cm⁻³", "var_unit": "(cm⁻³)²"}, + "Temperature": {"unit": "MK", "var_unit": "(MK)²"}, + "vr": {"unit": "km s⁻¹", "var_unit": "(km s⁻¹)²"}, + "vp": {"unit": "km s⁻¹", "var_unit": "(km s⁻¹)²"}, + "vt": {"unit": "km s⁻¹", "var_unit": "(km s⁻¹)²"}, + } + settings = property_settings.get(property_type, property_settings["Bulk Speed"]) + unit = settings["unit"] + var_unit = settings["var_unit"] + + has_baseline = baseline_data is not None and not getattr(baseline_data, "empty", False) + + def _pair_obs_model(obs_df, model_df, obs_col="Obs", model_col="Model"): + """Return paired DataFrame with columns ['Obs','Model'] plus counts and coverages.""" + n_obs_total = int(obs_df[obs_col].notna().sum()) if obs_col in obs_df else 0 + n_model_total = int(model_df[model_col].notna().sum()) if model_col in model_df else 0 + + have_time = ("Time" in obs_df.columns) and ("Time" in model_df.columns) + + if have_time: + obs_s = obs_df[["Time", obs_col]].dropna() + mod_s = model_df[["Time", model_col]].dropna() + paired = pd.merge(obs_s, mod_s, on="Time", how="inner").rename( + columns={obs_col: "Obs", model_col: "Model"} + ) + else: + paired = pd.DataFrame( + {"Obs": obs_df[obs_col], "Model": model_df[model_col]} + ).dropna() + + n_paired = int(len(paired)) + cov_model_points_eval = (100.0 * n_paired / n_model_total) if n_model_total else 0.0 + cov_obs_covered = (100.0 * n_paired / n_obs_total) if n_obs_total else 0.0 + + return paired, n_paired, n_obs_total, n_model_total, cov_model_points_eval, cov_obs_covered + + # Pair Model vs Obs + paired_model, n_paired_m, n_obs_total, n_model_total, cov_m2o, cov_o2m = _pair_obs_model( + observed_data, predicted_data, obs_col="Obs", model_col="Model" + ) + + # Pair Baseline vs Obs (if provided) + if has_baseline: + paired_base, n_paired_b, n_obs_total_b, n_base_total, cov_b2o, cov_o2b = _pair_obs_model( + observed_data, baseline_data, obs_col="Obs", model_col="Model" + ) + if n_obs_total_b and n_obs_total_b != n_obs_total: + n_obs_total = n_obs_total_b + + # Compute metrics on paired data actually compared (Model) + obs_vals = paired_model["Obs"] + model_vals = paired_model["Model"] + + me = meScore(model_vals, obs_vals) + mae = maeScore(model_vals, obs_vals) + mse = meanSquaredError(model_vals, obs_vals) + rms = rmsScore(model_vals, obs_vals) + cc = ccScore(model_vals, obs_vals) + msa = msaScore(model_vals, obs_vals) + sspb = sspbScore(model_vals, obs_vals) + mocr = mocrScore(model_vals, obs_vals) + sdocr = sdocrScore(model_vals, obs_vals) + + # Baseline metrics (if provided) + if has_baseline: + obs_base = paired_base["Obs"] + base_vals = paired_base["Model"] + + meBase = meScore(base_vals, obs_base) + maeBase = maeScore(base_vals, obs_base) + mseBase = meanSquaredError(base_vals, obs_base) + rmsBase = rmsScore(base_vals, obs_base) + ccBase = ccScore(base_vals, obs_base) + msaBase = msaScore(base_vals, obs_base) + sspbBase = sspbScore(base_vals, obs_base) + mocrBase = mocrScore(base_vals, obs_base) + sdocrBase = sdocrScore(base_vals, obs_base) + + table_data = [ + {"Metric": "Paired points [#]", "Model": n_paired_m}, + {"Metric": "Model points evaluated [%]", "Model": round(cov_m2o, 2)}, + {"Metric": "Observations covered [%]", "Model": round(cov_o2m, 2)}, + ] + if has_baseline: + table_data[0]["Baseline"] = n_paired_b + table_data[1]["Baseline"] = round(cov_b2o, 2) + table_data[2]["Baseline"] = round(cov_o2b, 2) + + metric_rows = [ + {'Metric': f'Mean Error [{unit}]', 'Model': round(me, 2)}, + {'Metric': f'Mean Absolute Error [{unit}]', 'Model': round(mae, 2)}, + {'Metric': f'Mean Squared Error [{var_unit}]', 'Model': round(mse, 2)}, + {'Metric': f'Root Mean Squared Error [{unit}]', 'Model': round(rms, 2)}, + {'Metric': 'Pearson Correlation Coefficient', 'Model': round(cc, 2)}, + {'Metric': 'Median Symmetric Accuracy [%]', 'Model': round(msa, 2)}, + {'Metric': 'Symmetric Signed Percentage Bias [%]', 'Model': round(sspb, 2)}, + {'Metric': 'Mean O/C Ratio', 'Model': round(mocr, 2)}, + {'Metric': 'SD of O/C Ratio', 'Model': round(sdocr, 2)} + ] + + if has_baseline: + baseline_vals = [ + round(meBase, 2), + round(maeBase, 2), + round(mseBase, 2), + round(rmsBase, 2), + round(ccBase, 2), + round(msaBase, 2), + round(sspbBase, 2), + round(mocrBase, 2), + round(sdocrBase, 2), + ] + for row, b in zip(metric_rows, baseline_vals): + row['Baseline'] = b + + table_data.extend(metric_rows) + + columns = [ + {'name': 'Metric', 'id': 'Metric'}, + {'name': predicted_data['Name'][0], 'id': 'Model', 'type': 'numeric', + 'format': Format(precision=2, scheme=Scheme.fixed)} + ] + if has_baseline: + columns.append( + {'name': 'Baseline Model', 'id': 'Baseline', 'type': 'numeric', + 'format': Format(precision=2, scheme=Scheme.fixed)} + ) + + style_cell_conditional = [ + {'if': {'column_id': 'Model'}, 'textAlign': 'right'}, + ] + if has_baseline: + style_cell_conditional.append({'if': {'column_id': 'Baseline'}, 'textAlign': 'right'}) + + # Create the data table + error_table = dash_table.DataTable( + data=table_data, + columns=columns, + style_cell_conditional=style_cell_conditional, + **my_tablelayout + ) + + return error_table + + +def create_residual_plot(predicted_data, observed_data, property_type="Bulk Speed", max_points=1000, opacity=0.4): + """ + Creates a residual plot showing the difference between observed and predicted values. + The plot includes: + - Kernel density estimation (KDE) contours for residual distribution + - Downsampled scatter points for large datasets + - A horizontal zero-reference line for bias detection + + Parameters: + predicted_data (pandas.DataFrame or dict): + Predicted model data. Must contain: + - 'Model' (float): predicted values + - 'Name' (str): model name (optional) + If a dictionary is provided, it will be converted to a DataFrame. + + observed_data (pandas.DataFrame or dict): + Observed reference data. Must contain: + - 'Obs' (float): observed values + - 'Name' (str): dataset name (optional) + If a dictionary is provided, it will be converted to a DataFrame. + + max_points (int, default=1000): + Maximum number of points plotted in the scatter layer. + Data is evenly downsampled if it exceeds this number + (improves performance for large datasets). + + opacity (float, default=0.4): + Marker opacity for scatter points. + + Returns: + plotly.graph_objects.Figure: + Interactive Plotly figure containing: + - KDE contour background showing residual density + - Downsampled scatter points of residuals vs. observed values + - Horizontal zero-reference line + - Configurable axis ranges + + Notes: + - Positive residuals mean the model underestimates values; negative residuals indicate overestimation. + """ + + # Convert dictionaries to pandas DataFrames if necessary + if not isinstance(predicted_data, pd.DataFrame): + predicted_data = pd.DataFrame(predicted_data) + if not isinstance(observed_data, pd.DataFrame): + observed_data = pd.DataFrame(observed_data) + + # Combine and compute residuals + data = pd.DataFrame({ + 'Observed': observed_data['Obs'], + 'Predicted': predicted_data['Model'] + }).dropna() + data['Residuals'] = data['Observed'] - data['Predicted'] + + # Downsample for scatter and KDE + if len(data) > max_points: + step = len(data) // max_points + scatter_data = data.iloc[::step].head(max_points) + kde_data = scatter_data + downsampled = True + else: + scatter_data = kde_data = data + downsampled = False + + # Property-specific settings + property_settings = { + "Bulk Speed": { + "title": "Residuals vs. Observed Values: Bulk Speed", + "unit": "km s⁻¹", + "x_mode": "fixed", + "x_min": 200, + "x_max": 800, + "x_tick0": 200, + "x_dtick": 100, + "y_mode": "fixed", + "y_min": -500, + "y_max": 500, + "y_tick0": -500, + "y_dtick": 200, + }, + "Density": { + "title": "Residuals vs. Observed Values: Number Density", + "unit": "cm⁻³", + "x_mode": "p99", + "y_mode": "p99_abs", + "x_min": 0, + "x_tick0": 0, + }, + "Temperature": { + "title": "Residuals vs. Observed Values: Temperature", + "unit": "MK", + "x_mode": "p99", + "y_mode": "p99_abs", + "x_min": 0, + "x_tick0": 0, + "fixed_dtick": 0.1, + }, + "vr": { + "title": "Residuals vs. Observed Values: vr", + "unit": "km s⁻¹", + "x_mode": "fixed", + "x_min": 200, + "x_max": 800, + "x_tick0": 200, + "x_dtick": 100, + "y_mode": "fixed", + "y_min": -500, + "y_max": 500, + "y_tick0": -500, + "y_dtick": 200, + }, + "vt": { + "title": "Residuals vs. Observed Values: vt", + "unit": "km s⁻¹", + "x_mode": "fixed", + "x_min": -100, + "x_max": 100, + "x_tick0": 0, + "x_dtick": 50, + "y_mode": "fixed", + "y_min": -100, + "y_max": 100, + "y_tick0": 0, + "y_dtick": 50, + }, + "vp": { + "title": "Residuals vs. Observed Values: vp", + "unit": "km s⁻¹", + "x_mode": "fixed", + "x_min": -100, + "x_max": 100, + "x_tick0": 0, + "x_dtick": 50, + "y_mode": "fixed", + "y_min": -100, + "y_max": 100, + "y_tick0": 0, + "y_dtick": 50, + }, + } + settings = property_settings.get(property_type, property_settings["Bulk Speed"]) + unit = settings["unit"] + + # Bounds + if settings.get("x_mode") == "p99": + combined_x = pd.concat([data["Observed"], data["Predicted"]], ignore_index=True).astype(float).dropna() + p99_x = float(combined_x.quantile(0.99)) if len(combined_x) else 1.0 + x_min = settings.get("x_min", 0) + x_max = max(p99_x, x_min + 1.0) + + raw = x_max / 10.0 + nice_steps = np.array([1, 2, 5, 10, 20, 50, 100, 200, 500, 1000]) + x_dtick = float(nice_steps[np.argmin(np.abs(nice_steps - raw))]) + x_tick0 = settings.get("x_tick0", 0) + else: + x_min, x_max = settings["x_min"], settings["x_max"] + x_tick0, x_dtick = settings["x_tick0"], settings["x_dtick"] + + if settings.get("y_mode") == "p99_abs": + abs_res = data["Residuals"].astype(float).abs().dropna() + p99_r = float(abs_res.quantile(0.99)) if len(abs_res) else 1.0 + + y_max = max(p99_r, 1.0) + y_min = -y_max + + NICE_STEPS = np.array([1, 2, 5, 10, 20, 50, 100, 200, 500, 1000]) + + target_step = y_max / 6.0 # gives ~8 ticks total (-4 … +4) + y_dtick = float(NICE_STEPS[NICE_STEPS >= target_step][0]) + + y_tick0 = 0 + else: + y_min, y_max = settings["y_min"], settings["y_max"] + y_tick0, y_dtick = settings["y_tick0"], settings["y_dtick"] + + if property_type == "Temperature": + x_dtick = settings["fixed_dtick"] + x_tick0 = settings.get("x_tick0", 0) + y_dtick = settings["fixed_dtick"] + y_tick0 = 0 #symmetry around zero + + # Grid and KDE + xi, yi = np.meshgrid( + np.linspace(x_min, x_max, 100), + np.linspace(y_min, y_max, 100) + ) + values = np.vstack([kde_data['Observed'], kde_data['Residuals']]) + kde = gaussian_kde(values) + zi = kde(np.vstack([xi.ravel(), yi.ravel()])).reshape(xi.shape) + + colorscale = [ + [0.0, 'rgba(255,255,255,0)'], + [0.1, 'rgba(255,255,255,0)'], + [0.3, 'rgba(255,200,100,0.2)'], + [0.7, 'rgba(255,140,50,0.5)'], + [1.0, 'rgba(255,100,0,0.8)'] + ] + + fig = go.Figure() + + # Contour background + fig.add_trace(go.Contour( + x=xi[0], + y=yi[:, 0], + z=zi, + showscale=False, + colorscale=colorscale, + hoverinfo='skip', + contours=dict( + showlines=True, + start=zi.min(), + end=zi.max(), + size=(zi.max() - zi.min()) / 11 + ), + line=dict(width=0.5, color='lightgrey') + )) + + # Scatter points + fig.add_trace(go.Scattergl( + x=scatter_data['Observed'], + y=scatter_data['Residuals'], + mode='markers', + marker=dict(size=3, color='black', opacity=opacity), + name='Data Points (downsampled)' if downsampled else 'Data Points', + showlegend=True + )) + + fig.add_hline(y=0, line_dash="dash", line_color="grey") + + obs_name = observed_data['Name'].iloc[0] if 'Name' in observed_data.columns else "Observation" + + fig.update_layout( + my_figlayout, + title=settings["title"], + xaxis_title=f"{obs_name} {property_type} [{unit}]", + yaxis_title=f"Residuals [{unit}]", + xaxis=dict( + range=[x_min, x_max], + tick0=x_tick0, + dtick=x_dtick, + showgrid=False, + gridwidth=1, + gridcolor='lightgrey' + ), + yaxis=dict( + range=[y_min, y_max], + tick0=y_tick0, + dtick=y_dtick, + showgrid=True, + gridwidth=1, + gridcolor='lightgrey' + ), + height=700, + legend=dict(orientation='h', x=0.05, y=1.0) + ) + + return fig + + +def create_statistics_table(observed_data,predicted_data, baseline_data=None, property_type="Bulk Speed"): + """ + Creates a formatted statistics table comparing observed, predicted, and baseline + distributions using a Dash DataTable. + + Parameters + ---------- + observed_data, predicted_data, baseline_data : pandas.DataFrame + Must include columns: + - observed_data: 'Obs', 'Name' + - predicted_data: 'Model', 'Name' + - baseline_data (optional): 'Model' + property_type : str + "Bulk Speed" or "Density" + + If baseline_data is None or empty, the Baseline column is not shown. + """ + + observed_values = observed_data['Obs'] + predicted_values = predicted_data['Model'] + + has_baseline = baseline_data is not None and not getattr(baseline_data, "empty", False) + + obs_stats = descriptiveStats(observed_values) + pred_stats = descriptiveStats(predicted_values) + + if has_baseline: + baseline_values = baseline_data['Model'] + base_stats = descriptiveStats(baseline_values) + + # Property-specific settings + property_settings = { + "Bulk Speed": { + "unit": "km s⁻¹", + "variance_unit": "(km s⁻¹)²", + }, + "Density": { + "unit": "cm⁻³", + "variance_unit": "(cm⁻³)²", + }, + "Temperature": { + "unit": "MK", + "variance_unit": "(MK)²", + }, + "vr": { + "unit": "km s⁻¹", + "variance_unit": "(km s⁻¹)²", + }, + "vt": { + "unit": "km s⁻¹", + "variance_unit": "(km s⁻¹)²", + }, + "vp": { + "unit": "km s⁻¹", + "variance_unit": "(km s⁻¹)²", + }, + } + settings = property_settings.get(property_type, property_settings["Bulk Speed"]) + unit = settings["unit"] + var_unit = settings["variance_unit"] + + # Labels + label_map = { + 'mean': f'Mean [{unit}]', + 'median': f'Median [{unit}]', + 'std': f'SD [{unit}]', + 'min': f'Minimum [{unit}]', + 'max': f'Maximum [{unit}]', + 'var': f'Variance [{var_unit}]', + 'skewness': 'Skewness', + 'kurtosis': 'Kurtosis' + } + + data = [] + for key, label in label_map.items(): + row = { + '': label, + 'Observation': obs_stats.get(key, 'NaN'), + 'Prediction': pred_stats.get(key, 'NaN'), + } + if has_baseline: + row['Baseline'] = base_stats.get(key, 'NaN') + data.append(row) + + # Build columns + columns = [ + {'name': '', 'id': ''}, + {'name': observed_data['Name'][0], 'id': 'Observation', 'type': 'numeric', + 'format': Format(precision=2, scheme=Scheme.fixed)}, + {'name': predicted_data['Name'][0], 'id': 'Prediction', 'type': 'numeric', + 'format': Format(precision=2, scheme=Scheme.fixed)}, + ] + if has_baseline: + columns.append( + {'name': 'Baseline', 'id': 'Baseline', 'type': 'numeric', + 'format': Format(precision=2, scheme=Scheme.fixed)} + ) + + style_cell_conditional = [ + {'if': {'column_id': 'Observation'}, 'textAlign': 'right'}, + {'if': {'column_id': 'Prediction'}, 'textAlign': 'right'}, + ] + if has_baseline: + style_cell_conditional.append({'if': {'column_id': 'Baseline'}, 'textAlign': 'right'}) + + statistics_table = dash_table.DataTable( + data=data, + columns=columns, + style_cell_conditional=style_cell_conditional, + **my_tablelayout + ) + + return statistics_table + + +def create_rmse_timeline(predicted_data, observed_data, benchmark_data=None, + property_type="Bulk Speed", + normalize_time="none", floor="H"): + """ + Creates an interactive timeline plot showing RMSE of a model and (optionally) a benchmark + over consecutive Carrington rotations. + """ + + # Property-specific settings + property_settings = { + "Bulk Speed": { + "unit": "km s⁻¹", + "abs_dtick": 50, + "abs_tick0": 0, + }, + "Density": { + "unit": "cm⁻³", + "abs_dtick": 20, + "abs_tick0": 0, + }, + "Temperature": { + "unit": "MK", + "abs_dtick": 0.1, + "abs_tick0": 0, + }, + "vr": { + "unit": "km s⁻¹", + "abs_dtick": 50, + "abs_tick0": 0, + }, + "vt": { + "unit": "km s⁻¹", + "abs_dtick": 5, + "abs_tick0": 0, + }, + "vp": { + "unit": "km s⁻¹", + "abs_dtick": 5, + "abs_tick0": 0, + }, + } + settings = property_settings.get(property_type, property_settings["Bulk Speed"]) + unit = settings["unit"] + + def _normalize_time(df, col="Time"): + if normalize_time == "none": + d = df.copy() + d[col] = pd.to_datetime(d[col], errors="coerce") + return d.dropna(subset=[col]).sort_values(col) + + t = pd.to_datetime(df[col], utc=True, errors="coerce") + if floor: + t = t.dt.floor(floor) + + if normalize_time == "naive_utc": + t = t.dt.tz_convert("UTC").dt.tz_localize(None) + elif normalize_time == "aware_utc": + t = t.dt.tz_convert("UTC") + else: + raise ValueError('normalize_time must be "none", "naive_utc", or "aware_utc"') + + d = df.copy() + d[col] = t + return d.dropna(subset=[col]).sort_values(col) + + # Normalize times + predicted_data = _normalize_time(predicted_data, "Time") + observed_data = _normalize_time(observed_data, "Time") + + has_benchmark = benchmark_data is not None and not getattr(benchmark_data, "empty", False) + if has_benchmark: + benchmark_data = _normalize_time(benchmark_data, "Time") + + # Ensure time is datetime + observed_data['Time'] = pd.to_datetime(observed_data['Time']) + + # Merge predictions with observations + merged_main = pd.merge(predicted_data, observed_data, on='Time', how='inner') + if has_benchmark: + merged_bench = pd.merge(benchmark_data, observed_data, on='Time', how='inner') + + # Carrington rotation range + start_time = observed_data['Time'].min() + end_time = observed_data['Time'].max() + cr_start = 1 + int((start_time - pd.Timestamp("1853-11-09")) / pd.Timedelta(days=27.2753)) + cr_end = int((end_time - pd.Timestamp("1853-11-09")) / pd.Timedelta(days=27.2753)) + + model_name = predicted_data['Name'].iloc[0] + benchmark_name = benchmark_data['Name'].iloc[0] if has_benchmark else None + + metrics = ['RMSE'] + error_main = {m: [] for m in metrics} + error_bench = {m: [] for m in metrics} if has_benchmark else None + error_rel = {m: [] for m in metrics} if has_benchmark else None + cr_numbers = [] + + def _rmse(a, b): + a = pd.to_numeric(pd.Series(a), errors="coerce") + b = pd.to_numeric(pd.Series(b), errors="coerce") + m = (a - b) ** 2 + return float(np.sqrt(np.nanmean(m))) + + for cr in range(cr_start, cr_end + 1): + cr_start_time = carrington_rotation_time(cr, 360 * u.deg).datetime + cr_end_time = carrington_rotation_time(cr, 1 * u.deg).datetime + + main = merged_main[(merged_main['Time'] >= cr_start_time) & (merged_main['Time'] < cr_end_time)] + if has_benchmark: + bench = merged_bench[(merged_bench['Time'] >= cr_start_time) & (merged_bench['Time'] < cr_end_time)] + + if not main.empty and not bench.empty: + model, obs, base = main['Model'], main['Obs'], bench['Model'] + + rmse_m = _rmse(model, obs) + rmse_b = _rmse(base, obs) + + error_main['RMSE'].append(rmse_m) + error_bench['RMSE'].append(rmse_b) + error_rel['RMSE'].append(100 * (rmse_b - rmse_m) / rmse_b if rmse_b else np.nan) + + cr_numbers.append(cr) + else: + if not main.empty: + model, obs = main['Model'], main['Obs'] + rmse_m = _rmse(model, obs) + error_main['RMSE'].append(rmse_m) + cr_numbers.append(cr) + + # Build plot + fig = go.Figure() + + if has_benchmark: + for metric in metrics: + fig.add_trace(go.Scatter( + x=cr_numbers, y=error_main[metric], + name=f"{metric} - {model_name}", mode='lines+markers' + )) + fig.add_trace(go.Scatter( + x=cr_numbers, y=error_bench[metric], + name=f"{metric} - {benchmark_name}", mode='lines+markers', + line=dict(dash='dash', color='grey') + )) + fig.add_trace(go.Scatter( + x=cr_numbers, y=error_rel[metric], + name=f"{metric} - Relative [%]", mode='lines+markers' + )) + # zero line for relative view + fig.add_trace(go.Scatter( + x=cr_numbers, y=[0] * len(cr_numbers), + name=f"{benchmark_name}", mode='lines', + line=dict(dash='dash', color='grey'), + showlegend=False + )) + + fig.data[0].visible = True + fig.data[1].visible = True + fig.data[2].visible = False + fig.data[3].visible = False + + abs_button = dict( + label="Absolute", + method="update", + args=[ + {"visible": [True, True, False, False]}, + {"yaxis": { + "title": f"RMSE [{unit}]", + "tick0": settings["abs_tick0"], + "dtick": settings["abs_dtick"], + "showgrid": True, + "gridwidth": 0.5, + "gridcolor": "lightgrey" + }, "annotations": []} + ] + ) + rel_button = dict( + label="Relative [%]", + method="update", + args=[ + {"visible": [False, False, True, True]}, + {"yaxis": { + "title": "Relative Skill [%]", + "tick0": 0, + "dtick": 10, + "showgrid": True, + "gridwidth": 0.5, + "gridcolor": "lightgrey" + }, + "annotations": [dict( + text="Above zero: better than benchmark
Below zero: worse than benchmark", + xref="paper", yref="paper", + x=0.98, y=0.15, + xanchor="right", yanchor="top", + showarrow=False, + font=dict(size=11, color="black"), + bgcolor="rgba(255, 255, 255, 0.8)", + bordercolor="lightgrey", + borderwidth=1, + opacity=0.9 + )]} + ] + ) + + updatemenus = [ + dict( + buttons=[abs_button, rel_button], + direction="down", + showactive=True, + x=0.01, xanchor="left", + y=1.05, yanchor="top" + ) + ] + else: + fig.add_trace(go.Scatter( + x=cr_numbers, y=error_main['RMSE'], + name=f"RMSE - {model_name}", mode='lines+markers' + )) + updatemenus = [] + + fig.update_layout( + my_figlayout, + title=f"Skill Over Time at {observed_data['Name'].iloc[0]}", + xaxis_title="Carrington Rotation Number", + yaxis_title=f"RMSE [{unit}]", + height=500, + xaxis=dict( + tickmode='linear', + tick0=cr_numbers[0] if cr_numbers else 0, + dtick=1, + showgrid=True, + gridwidth=0.5, + gridcolor='lightgrey' + ), + yaxis=dict( + tick0=settings["abs_tick0"], + dtick=settings["abs_dtick"], + showgrid=True, + gridwidth=0.5, + gridcolor='lightgrey' + ), + showlegend=True, + legend=dict(orientation='h', x=0.34, y=1.12), + updatemenus=updatemenus + ) + + return fig + +def create_binary_classification_plot(predicted_data, observed_data, threshold=400, property_type="Bulk Speed"): + """ + Creates a two-row combined plot to compare time-series (predicted vs. observed) + and visualize binary event classifications over the same time window. + + Row 1: + - Lines for predicted model values and observed values + - A horizontal threshold line at the specified speed + + Row 2: + - A categorical timeline of event outcomes (Hit, Miss, False Alarm, Correct Rejection) + placed at their corresponding timestamps + + Parameters: + predicted_data (pandas.DataFrame): + Model predictions. Must contain: + - 'Time' (datetime-like): timestamps (consistent tz handling recommended) + - 'Model' (float): predicted bulk speeds [km s⁻¹] + - 'Name' (str): model name (used in legend) + observed_data (pandas.DataFrame): + Observations. Must contain: + - 'Time' (datetime-like): timestamps aligned to model or later interpolated + - 'Obs' (float): observed bulk speeds [km s⁻¹] + - 'Name' (str, optional): not required here + threshold (float, default=400): + Threshold (in [km s⁻¹]) used by `classifyBinaryEvents()` to define an “event”. + + Returns: + plotly.graph_objects.Figure: + A two-row subplot with shared x-axis: + - Row 1: Time-series lines for Model and Observed + threshold line + - Row 2: Colored markers per timestamp showing the event outcome + """ + + # Property-specific + property_settings = { + "Bulk Speed": { + "yaxis_title": "Bulk Speed [km s⁻¹]", + "title": "Binary Classification Comparison – Solar Wind Speed", + "show_threshold": True, + "y_range": [200, 900], + }, + "Density": { + "yaxis_title": "Number Density [cm⁻³]", + "title": "Binary Classification Comparison – Number Density", + "show_threshold": True, + }, + "Temperature": { + "yaxis_title": "Temperature [MK]", + "title": "Binary Classification Comparison – Temperature", + "show_threshold": True, + }, + "vr": { + "yaxis_title": "vr [km s⁻¹]", + "title": "Binary Classification Comparison – vr", + "show_threshold": True, + "y_range": [200, 900], + }, + "vt": { + "yaxis_title": "vt [km s⁻¹]", + "title": "Binary Classification Comparison – vt", + "show_threshold": True, + }, + "vp": { + "yaxis_title": "vp [km s⁻¹]", + "title": "Binary Classification Comparison – vp", + "show_threshold": True, + }, + "Br": { + "yaxis_title": "Br", + "title": "Binary Classification Comparison – Polarity Br", + "show_threshold": False, + "polarity_axis": True, + }, + "Bt": { + "yaxis_title": "Bt", + "title": "Binary Classification Comparison – Polarity Bt", + "show_threshold": False, + "polarity_axis": True, + }, + "Bp": { + "yaxis_title": "Bp", + "title": "Binary Classification Comparison – Polarity Bp", + "show_threshold": False, + "polarity_axis": True, + } + } + + settings = property_settings.get(property_type, property_settings["Bulk Speed"]) + + outcomes, _ = classifyBinaryEvents(predicted_data, observed_data, threshold) + + outcome_df = pd.DataFrame({ + "Time": predicted_data["Time"], + "Model": predicted_data["Model"], + "Obs": observed_data["Obs"], + "Outcome": outcomes + }) + + outcome_colors = { + "Hit": "green", + "Miss": "red", + "False Alarm": "orange", + "Correct Rejection": "blue", + } + + fig = make_subplots( + rows=2, + cols=1, + shared_xaxes=True, + vertical_spacing=0.05, + row_heights=[0.8, 0.2] + ) + + # Time series + fig.add_trace(go.Scatter( + x=predicted_data["Time"], + y=predicted_data["Model"], + mode="lines", + name=predicted_data["Name"].iloc[0], + line=dict(width=2), + hovertemplate="Time=%{x}
Model=%{y:.2f}" + ), row=1, col=1) + + # Observations + fig.add_trace(go.Scatter( + x=observed_data["Time"], + y=observed_data["Obs"], + mode="markers", + name=observed_data["Name"].iloc[0], + marker=dict( + symbol="circle-open", + size=2, + color="#222222", + line=dict(width=1) + ), + opacity=0.85, + hovertemplate="Time=%{x}
Obs=%{y:.2f}" + ), row=1, col=1) + + # Threshold + if settings.get("show_threshold", True): + fig.add_shape( + type="line", + x0=predicted_data["Time"].iloc[0], + x1=predicted_data["Time"].iloc[-1], + y0=threshold, + y1=threshold, + line=dict(color="black", width=1), + row=1, + col=1 + ) + + # Event outcomes + for outcome in outcome_df["Outcome"].unique(): + df_subset = outcome_df[outcome_df["Outcome"] == outcome] + + fig.add_trace(go.Scatter( + x=df_subset["Time"], + y=[outcome] * len(df_subset), + mode="markers", + name=outcome, + marker=dict( + color=outcome_colors.get(outcome, "grey"), + symbol="circle", + size=5 + ) + ), row=2, col=1) + + fig.update_layout(my_figlayout) + + fig.update_layout( + title=settings["title"], + yaxis_title=settings["yaxis_title"], + xaxis=dict( + range=[ + predicted_data["Time"].iloc[0], + predicted_data["Time"].iloc[-1] + ] + ), + legend=dict(orientation='h', x=0.1, y=1.02), + height=500 + ) + + # Optional y-range + if settings.get("y_range"): + fig.update_yaxes(range=settings["y_range"], row=1, col=1) + + # Polarity axis styling + if property_type in {"Br", "Bt", "Bp"}: + fig.update_yaxes( + range=[-0.2, 1.2], + tickmode="array", + tickvals=[0, 1], + ticktext=["Negative", "Positive"], + zeroline=False, + showgrid=True, + row=1, + col=1 + ) + + # Outcome y-axis + fig.update_yaxes( + tickvals=outcome_df["Outcome"].unique(), + ticktext=outcome_df["Outcome"].unique(), + row=2, + col=1 + ) + return fig + + +def create_cumulative_binary_outcomes(predicted_data, observed_data, threshold=400): + """ + Creates a cumulative stacked area chart showing binary event outcomes + (Hit, Miss, False Alarm, Correct Rejection) over time. + + Parameters: + predicted_data (pandas.DataFrame): + Model predictions. Must contain: + - 'Time' (datetime-like): timestamps + - Model variable column required by `classifyBinaryEvents` + + observed_data (pandas.DataFrame): + Observational reference data. Must contain: + - 'Time' (datetime-like): timestamps + - Observation variable column required by `classifyBinaryEvents` + + threshold (float, optional): + Threshold value for binary event classification. + + Returns: + plotly.graph_objects.Figure: + Interactive stacked area chart with cumulative counts for: + - Hit (true positive) + - Miss (false negative) + - False Alarm (false positive) + - Correct Rejection (true negative) + + Notes: + - 'Correct Rejection' is hidden by default in the legend. + """ + + outcome_colors = { + 'Hit': 'green', + 'Miss': 'red', + 'False Alarm': 'orange', + 'Correct Rejection': 'blue' + } + + outcomes, _ = classifyBinaryEvents(predicted_data, observed_data, threshold) + outcome_df = pd.DataFrame({'Time': predicted_data['Time'], 'Outcome': outcomes}) + + # Ensure all outcomes are represented at each time step, even if they are zero + all_outcomes = ['Hit', 'Miss', 'False Alarm', 'Correct Rejection'] + outcome_df['Count'] = 1 + outcome_counts = outcome_df.pivot_table(index='Time', columns='Outcome', values='Count', aggfunc='sum', fill_value=0) + + for outcome in all_outcomes: + if outcome not in outcome_counts.columns: + outcome_counts[outcome] = 0 + + # Calculate cumulative sums + outcome_counts_cumulative = outcome_counts.cumsum() + outcome_counts_cumulative.reset_index(inplace=True) + + fig = go.Figure() + + # Add a stacked area trace for each outcome category + for outcome in all_outcomes: + fig.add_trace(go.Scatter( + x=outcome_counts_cumulative['Time'], + y=outcome_counts_cumulative[outcome], + mode='lines', + name=outcome, + stackgroup='one', + line_color=outcome_colors[outcome], + visible='legendonly' if outcome == 'Correct Rejection' else True + )) + + # Update the figure with my_figlayout + fig.update_layout(my_figlayout) + + # Apply custom layout if defined + fig.update_layout( + title='Cumulative Outcomes Over Time', + xaxis_title='Time', + yaxis_title='Cumulative Count', + legend=dict(x=0.05, y=1.0), + height=500 + ) + + return fig + + +def create_error_table_binary(observed_data, predicted_data, baseline_data, threshold): + """ + Creates a styled Dash DataTable comparing binary classification + metrics for a model and a baseline, based on thresholded + continuous data. + + Parameters: + observed_data (pandas.DataFrame): + Reference observation dataset. Must contain: + - 'Time' (datetime-like): timestamps + - 'Obs': observed values + - 'Name': dataset name (string) + + predicted_data (pandas.DataFrame): + Model prediction dataset. Must contain: + - 'Time' (datetime-like): timestamps + - 'Model': predicted values + - 'Name': model name (string) + + baseline_data (pandas.DataFrame): + Baseline or benchmark prediction dataset. Must contain: + - 'Time' (datetime-like): timestamps + - 'Model': predicted values + - 'Name': baseline name (string) + + threshold (float): + Threshold applied to observed and predicted data + to classify binary events. + + Returns: + dash_table.DataTable: + A Dash DataTable displaying binary classification + metrics for both the model and the baseline. + Columns include: + - True Positive Rate (TPR) + - False Positive Rate (FPR) + - Threat Score (TS) + - True Skill Statistics (TSS) + - Bias + + Notes: + - All datasets must be time-aligned and share the + same timezone before calling this function. + - The classification metrics are computed from + contingency tables produced by `classifyBinaryEvents`. + - The DataTable uses number formatting with fixed + precision (2 decimal places). + """ + + # Extract names + model_name = predicted_data['Name'][0] + baseline_name = baseline_data['Name'][0] + + # Contingency tables + baseline_as_obs = baseline_data[['Model']].rename(columns={'Model': 'Obs'}) + _, ct = classifyBinaryEvents(predicted_data, observed_data, threshold) + _, ct_base = classifyBinaryEvents(predicted_data, baseline_as_obs, threshold) + + # Model metrics + tpr = truePositiveRate(ct) + fpr = falsePostiveRate(ct) + ts = threatScore(ct) + tss = trueSkillStatistics(ct) + bi = bias(ct) + + # Baseline metrics + tpr_base = truePositiveRate(ct_base) + fpr_base = falsePostiveRate(ct_base) + ts_base = threatScore(ct_base) + tss_base = trueSkillStatistics(ct_base) + bi_base = bias(ct_base) + + # Table data + data = [ + {'Metric': 'True Positive Rate', model_name: tpr[0], baseline_name: tpr_base[0]}, + {'Metric': 'False Positive Rate', model_name: fpr[0], baseline_name: fpr_base[0]}, + {'Metric': 'Threat Score', model_name: ts[0], baseline_name: ts_base[0]}, + {'Metric': 'True Skill Statistics', model_name: tss[0], baseline_name: tss_base[0]}, + {'Metric': 'Bias', model_name: bi[0], baseline_name: bi_base[0]} + ] + + # Create formatted Dash table + error_table = dash_table.DataTable( + data=data, + columns=[ + {'name': 'Metric', 'id': 'Metric'}, + {'name': model_name, 'id': model_name, 'type': 'numeric', 'format': Format(precision=2, scheme=Scheme.fixed)}, + {'name': baseline_name, 'id': baseline_name, 'type': 'numeric', 'format': Format(precision=2, scheme=Scheme.fixed)} + ], + style_cell_conditional=[ + {'if': {'column_id': model_name}, 'textAlign': 'right'}, + {'if': {'column_id': baseline_name}, 'textAlign': 'right'} + ], + **my_tablelayout + ) + + return error_table + + +def create_binary_outcome_bars(predicted_data, observed_data, baseline_data, threshold): + """ + Creates a grouped bar chart comparing binary event classification + outcomes (Hit, False Alarm, Miss, Correct Rejection) for a model + prediction and a baseline (benchmark) prediction. + + Parameters: + predicted_data (pandas.DataFrame): + Model predictions. Must contain: + - 'Time' (datetime-like): timestamps + - 'Model': predicted values + - 'Name': model name (string, used for legend) + + observed_data (pandas.DataFrame): + Observational reference data. Must contain: + - 'Time' (datetime-like): timestamps + - 'Obs': observed values + - 'Name': dataset name + + baseline_data (pandas.DataFrame): + Baseline or benchmark predictions. Must contain: + - 'Time' (datetime-like): timestamps + - 'Model': predicted values + - 'Name': baseline name (string, used for legend) + + threshold(float): + Threshold used by `classifyBinaryEvents` to + classify events as: + - Hit (true positive) + - Miss (false negative) + - False Alarm (false positive) + - Correct Rejection (true negative) + + Returns: + plotly.graph_objects.Figure: + Interactive grouped bar chart showing counts for each + outcome type, comparing model vs. baseline. + """ + + baseline_as_obs = baseline_data[['Model']].rename(columns={'Model': 'Obs'}) + _, ct = classifyBinaryEvents(predicted_data, observed_data, threshold) + _, ct_base = classifyBinaryEvents(predicted_data, baseline_as_obs, threshold) + + # Create the side-by-side bar chart + fig = go.Figure() + + fig.add_trace(go.Bar( + x=['Hit', 'False Alarm', 'Miss', 'Correct Rejection'], + y=ct.values.flatten(), + marker=dict(opacity=0.8), + name=predicted_data['Name'][0] + )) + + fig.add_trace(go.Bar( + x=['Hit', 'False Alarm', 'Miss', 'Correct Rejection'], + y=ct_base.values.flatten(), + marker=dict(opacity=0.8), + name=baseline_data['Name'][0] + )) + + # Update the figure with my_figlayout + fig.update_layout(my_figlayout) + + fig.update_layout( + title='Outcome Counts', + barmode='group', + xaxis_title='Outcome', + legend=dict(x=0.1, y=1.0), + yaxis_title='Count', + height=500 + ) + + return fig + + +def create_timeline_with_peaks(dates, observed_data, predicted_data, observed_name, predicted_name, + minimum_speed, maximum_dt, + x_label='Date', y_label='Bulk Speed [km s⁻¹]', + plot_title='Peak Comparison'): + """ + Create a time series plot highlighting detected peaks in observed and predicted data + for event-based validation. + + This visualization shows: + - Observed and predicted time series + - Automatically detected peaks in both datasets + - Hits (matched peaks), misses, and false alarms + - A horizontal threshold line indicating the event speed threshold + + Parameters: + dates : pandas.Series or array-like + Datetime values corresponding to each observation/prediction. + observed_data : pandas.Series or array-like + Observed data values (e.g., solar wind speed). + predicted_data : pandas.Series or array-like + Predicted data values from the model. + observed_name : list-like or pandas.Series + Dataset name(s) for the observed data (first element used as label). + predicted_name : list-like or pandas.Series + Dataset name(s) for the predicted data (first element used as label). + minimum_speed : float + Minimum speed threshold for peak detection. + maximum_dt : float + Maximum time difference (in hours) allowed when matching peaks between + observations and predictions. + x_label : str, optional + X-axis label for the plot. Default is `'Date'`. + y_label : str, optional + Y-axis label for the plot. Default is `'Bulk Speed [km s⁻¹]'`. + plot_title : str, optional + Plot title. Default is `'Peak Comparison'`. + + Returns: + plotly.graph_objects.Figure + An interactive Plotly figure showing the timeline with detected peaks and event matches. + """ + + # Ensure dates is in datetime format + dates = pd.to_datetime(dates) + + # Compute peaks and peak information + peaks_obs, peaks_pred, hits, misses, false_alarms, time_diff, data_diff = compute_peaks(dates, observed_data, predicted_data, minimum_speed, maximum_dt) + + # Plot timeline for event-based validation using Line plot for observed and predicted data + fig = go.Figure() + fig.add_trace(go.Scatter(x=dates, y=predicted_data, mode='lines', name=predicted_name)) + fig.add_trace(go.Scatter(x=dates, y=observed_data, mode='lines', name=observed_name, line=obs_linelayout)) + + fig.add_trace(go.Scatter(x=dates.iloc[peaks_obs], y=observed_data.iloc[peaks_obs], + mode='markers', + marker=dict(size=7, color='#1f77b4', symbol='circle', line=dict(width=1, color='black')), + name='Peaks in Observation')) + + fig.add_trace(go.Scatter(x=dates.iloc[peaks_pred], y=predicted_data.iloc[peaks_pred], + mode='markers', + marker=dict(size=7, color='#ff7f0e', symbol='circle', line=dict(width=1, color='black')), + name='Peaks in Prediction')) + + fig.add_trace(go.Scatter(x=dates.iloc[hits], y=20+predicted_data.iloc[hits], + mode='markers', + marker=dict(size=10, color='green', symbol='star', line=dict(width=1, color='black')), + name='Hit')) + + fig.add_trace(go.Scatter(x=dates.iloc[misses], y=20+observed_data.iloc[misses], + mode='markers', + marker=dict(size=10, color='red', symbol='x', line=dict(width=1, color='black')), + name='Miss')) + + fig.add_trace(go.Scatter(x=dates.iloc[false_alarms], y=20+predicted_data.iloc[false_alarms], + mode='markers', + marker=dict(size=10, color='#9467bd', symbol='diamond', line=dict(width=1, color='black')), + name='False Alarm')) + + + # Add a horizontal line at the minimum speed threshold + fig.add_shape(type='line', x0=dates.min(), x1=dates.max(), y0=minimum_speed, y1=minimum_speed, line=dict(color='gray', width=2, dash='dash'), name='Minimum Speed Threshold') + + # Update the figure with my_figlayout + fig.update_layout(my_figlayout) + + # Apply custom layout if defined + fig.update_layout( + title=plot_title, + xaxis_title=x_label, + yaxis_title=y_label, + xaxis=dict(range=[dates.iloc[0], dates.iloc[-1]]), + legend=dict(orientation='h', x=0.075, y=1.05), + height=400 + ) + + return fig + + +def create_peak_bar_chart(time, obs, model, + threshold_value, maximum_dt, + baseline_time=None, baseline_model=None, + x_label='Outcome', y_label='Count', plot_title='Outcome Counts'): + ''' + Detects peaks for a model (and optional baseline), computes hits/misses/false alarms, + and creates a grouped bar chart comparing their performance. + + Parameters: + time (pandas.Series): Time values for the model + obs (pandas.Series): Observed data values + model (pandas.Series): Predicted model data values + threshold_value (float): Threshold for event detection + maximum_dt (float): Maximum time difference allowed for peak matching + baseline_time (pandas.Series, optional): Time values for baseline model + baseline_model (pandas.Series, optional): Predicted baseline model values + x_label (str): Label for the x-axis [default = 'Outcome'] + y_label (str): Label for the y-axis [default = 'Count'] + plot_title (str): Title for the plot [default = 'Outcome Counts'] + + Returns: + plotly.graph_objects.Figure: Grouped bar chart figure + ''' + + # Compute peaks for model + peaks_obs, peaks_pred, hits, misses, false_alarms, _, _ = compute_peaks( + time, obs, model, threshold_value, maximum_dt + ) + + # Prepare counts for the model + categories = ['Hit', 'Miss', 'False Alarm'] + model_counts = [len(hits), len(misses), len(false_alarms)] + + fig = go.Figure() + + # Add model bars + fig.add_trace(go.Bar( + x=categories, + y=model_counts, + name='Model', + marker=dict(opacity=0.8) + )) + + # Optional: compute peaks for baseline + if baseline_time is not None and baseline_model is not None: + _, _, hits_base, misses_base, false_alarms_base, _, _ = compute_peaks( + baseline_time, obs, baseline_model, threshold_value, maximum_dt + ) + baseline_counts = [len(hits_base), len(misses_base), len(false_alarms_base)] + fig.add_trace(go.Bar( + x=categories, + y=baseline_counts, + name='Baseline Model', + marker=dict(opacity=0.8) + )) + + # Layout + fig.update_layout( + my_figlayout, + title=plot_title, + barmode='group', + xaxis_title=x_label, + yaxis_title=y_label, + height=500, + legend=dict(orientation='h', x=0.5, y=1.05) + ) + + return fig + + +def create_error_table_events(time, observed_data, model_data, + threshold_value, maximum_dt, + baseline_time=None, baseline_model=None, + model_name='Model', baseline_name='Baseline Model'): + """ + Compute event-based binary classification metrics (from compute_peaks) and + return a formatted Dash DataTable for model vs (optional) baseline. + + Parameters: + time (pandas.Series): Time values aligned with observed/model + observed_data (pandas.Series): Observed values + model_data (pandas.Series): Model values + threshold_value (float): Threshold used in compute_peaks + maximum_dt (float): Max allowed time diff for peak matching + baseline_time (pandas.Series, optional): Time for baseline model + baseline_model (pandas.Series, optional): Baseline model values + model_name (str): Column header for the model metrics + baseline_name (str): Column header for the baseline metrics + + Returns: + dash_table.DataTable: Metrics table (Model and optional Baseline columns) + """ + # Contingency table entries + _, _, hits, misses, false_alarms, _, _ = compute_peaks( + time, observed_data, model_data, threshold_value, maximum_dt + ) + ct = pd.DataFrame( + [[len(hits), len(false_alarms), len(misses), 0]], + columns=['tp', 'fp', 'fn', 'tn'] + ) + + # Baseline contingency + ct_base = None + if baseline_time is not None and baseline_model is not None: + _, _, hits_b, misses_b, false_alarms_b, _, _ = compute_peaks( + baseline_time, observed_data, baseline_model, threshold_value, maximum_dt + ) + ct_base = pd.DataFrame( + [[len(hits_b), len(false_alarms_b), len(misses_b), 0]], + columns=['tp', 'fp', 'fn', 'tn'] + ) + + # Metrics for model + tpr = truePositiveRate(ct)[0] + fnr = falseNegativeRate(ct)[0] + ppv = positivePredictiveValue(ct)[0] + far = falseAlarmRatio(ct)[0] + ts = threatScore(ct)[0] + bi = bias(ct)[0] + + # Metrics for baseline (if provided) + if ct_base is not None: + tpr_b = truePositiveRate(ct_base)[0] + fnr_b = falseNegativeRate(ct_base)[0] + ppv_b = positivePredictiveValue(ct_base)[0] + far_b = falseAlarmRatio(ct_base)[0] + ts_b = threatScore(ct_base)[0] + bi_b = bias(ct_base)[0] + + rows = [ + {'Metric': 'True Positive Rate', model_name: tpr}, + {'Metric': 'False Negative Rate', model_name: fnr}, + {'Metric': 'Positive Predictive Value', model_name: ppv}, + {'Metric': 'False Alarm Ratio', model_name: far}, + {'Metric': 'Threat Score', model_name: ts}, + {'Metric': 'Bias', model_name: bi} + ] + + if ct_base is not None: + rows[0][baseline_name] = tpr_b + rows[1][baseline_name] = fnr_b + rows[2][baseline_name] = ppv_b + rows[3][baseline_name] = far_b + rows[4][baseline_name] = ts_b + rows[5][baseline_name] = bi_b + + columns = [ + {'name': 'Metric', 'id': 'Metric'}, + {'name': model_name, 'id': model_name, 'type': 'numeric', + 'format': Format(precision=2, scheme=Scheme.fixed)}, + ] + if ct_base is not None: + columns.append( + {'name': baseline_name, 'id': baseline_name, 'type': 'numeric', + 'format': Format(precision=2, scheme=Scheme.fixed)} + ) + + error_table = dash_table.DataTable( + data=rows, + columns=columns, + style_cell_conditional=[ + {'if': {'column_id': model_name}, 'textAlign': 'right'}, + {'if': {'column_id': baseline_name}, 'textAlign': 'right'} if ct_base is not None else {} + ], + **my_tablelayout + ) + + return error_table + + +def create_histogram_plot(time, obs, model, threshold_value, maximum_dt, + x_label='Value', y_label='Count', plot_title='Histogram Plot', + num_bins=20, show_zero_line=True, add_stats=True, + plot_type='Data Diff'): + """ + Creates an enhanced histogram plot for given input data. + + Parameters: + time (pandas.Series): Time values for the data points + obs (pandas.Series): Observed data values + model (pandas.Series): Predicted model data values + threshold_value (float): Threshold for event detection + maximum_dt (float): Maximum time difference allowed for peak matching + x_label (str): Label for the x-axis + y_label (str): Label for the y-axis + plot_title (str): Title for the plot + num_bins (int): Number of histogram bins + show_zero_line (bool): Whether to draw a zero reference line + add_stats (bool): Whether to display statistics box + plot_type (str): Type of plot: 'Data Diff' or 'Time Diff' + + Returns: + plotly.graph_objects.Figure: The histogram plot figure + """ + + fig = go.Figure() + + peaks_obs, peaks_pred, hits, misses, false_alarms, time_diff, data_diff = compute_peaks( + time, obs, model, threshold_value, maximum_dt + ) + + if plot_type == 'Data Diff': + input_data = data_diff + else: + input_data = time_diff + + # Histogram + hist_data = np.histogram(input_data, bins=num_bins) + max_y = max(hist_data[0]) + + fig.add_trace(go.Histogram( + x=input_data, + nbinsx=num_bins, + marker_color='black', + opacity=0.5, + name='Histogram', + showlegend=False + )) + + # Zero line + if show_zero_line and np.min(input_data) < 0 < np.max(input_data): + fig.add_trace(go.Scatter( + x=[0, 0], + y=[0, max_y * 100], + mode='lines', + line=dict(color='gray', dash='dash', width=1), + name='Zero Line', + showlegend=False + )) + + # Statistics box + if add_stats: + mean_val = np.mean(input_data) + std_val = np.std(input_data) + median_val = np.median(input_data) + skew_val = skew(input_data) + + stats_text = (f"Statistics
" + f"Mean: {mean_val:.2f}
" + f"Median: {median_val:.2f}
" + f"Std: {std_val:.2f}
" + f"Skew: {skew_val:.2f}") + + fig.add_annotation( + text=stats_text, + xref="paper", yref="paper", + x=0.98, y=0.95, + xanchor="right", yanchor="top", + showarrow=False, + font=dict(size=11, color="black"), + bgcolor="rgba(255, 255, 255, 0.8)", + bordercolor="lightgrey", + borderwidth=1, + opacity=0.9 + ) + + # Symmetric x-axis + abs_max = max(abs(np.min(input_data)), abs(np.max(input_data))) + + fig.update_layout( + my_figlayout, + title=plot_title, + xaxis_title=x_label, + yaxis_title=y_label, + height=500, + bargap=0.05, + xaxis=dict( + range=[-abs_max, abs_max], + zeroline=False, + showgrid=True, + gridwidth=0.5, + gridcolor='lightgrey' + ), + yaxis=dict( + range=[0, max_y*1.1], + showgrid=True, + gridwidth=0.5, + gridcolor='lightgrey' + ), + legend=dict(orientation='h', x=0.05, y=1.0) + ) + + return fig + + +def create_scatter_plot_for_hits(time, observed_data, predicted_data, + threshold_value, maximum_dt, + max_points=1000, min_points=50, opacity=0.5, + x_label='Observed Bulk Speed [km s⁻¹]', + y_label='Modeled Bulk Speed [km s⁻¹]', + plot_title='Scatter Plot for Hits', + bounds=(200, 800)): + ''' + Detects peaks (using compute_peaks), extracts matched "hit" pairs, and + creates a density+scatter plot of observed vs. predicted hit values. + + Parameters: + time (pandas.Series): Time values aligned with observed/predicted + observed_data (pandas.Series): Observed values + predicted_data (pandas.Series): Predicted/model values + threshold_value (float): Threshold used inside compute_peaks + maximum_dt (float): Max allowed time difference for peak matching + max_points (int): Max plotted points (downsampling applied if exceeded) + min_points (int): Minimum points required to show KDE contours + opacity (float): Marker opacity for scatter points + x_label (str): X-axis label + y_label (str): Y-axis label + plot_title (str): Plot title + bounds (tuple): (min_val, max_val) for both axes + + Returns: + plotly.graph_objects.Figure: Scatter/KDE figure for hits + ''' + # Compute peaks + (peaks_obs, peaks_pred, + hits, misses, false_alarms, + time_diff, data_diff) = compute_peaks(time, observed_data, predicted_data, + threshold_value, maximum_dt) + + # If there are no hits, return an empty figure + if len(hits) == 0: + min_val, max_val = bounds + fig = go.Figure() + fig.update_layout( + my_figlayout, + title=plot_title + ' (no hits found)', + xaxis_title=x_label, + yaxis_title=y_label, + xaxis=dict(range=[min_val, max_val], showgrid=True, gridwidth=0.5, gridcolor='lightgrey'), + yaxis=dict(range=[min_val, max_val], showgrid=True, gridwidth=0.5, gridcolor='lightgrey'), + height=500, + legend=dict(orientation='h', x=0.05, y=1.0) + ) + return fig + + if isinstance(hits[0], (pd.Timestamp, np.datetime64)): + idxer = time.index.get_indexer(hits) + hit_indices = [i for i in idxer if i != -1] + else: + n = len(time) + hit_indices = [i for i in hits if 0 <= int(i) < n] + + hit_obs = observed_data.iloc[hit_indices] + hit_pred = predicted_data.iloc[hit_indices] + + data = pd.DataFrame({'Observed': hit_obs, 'Predicted': hit_pred}).dropna() + + # Downsample if needed + if len(data) > max_points: + step = max(1, len(data) // max_points) + scatter_data = data.iloc[::step].head(max_points) + downsampled_label = ' (downsampled)' + else: + scatter_data = data + downsampled_label = '' + + min_val, max_val = bounds + + fig = go.Figure() + + # KDE contours + if len(data) > min_points: + values = np.vstack([data['Observed'].values, data['Predicted'].values]) + kde = gaussian_kde(values) + xi, yi = np.meshgrid( + np.linspace(min_val, max_val, 100), + np.linspace(min_val, max_val, 100) + ) + zi = kde(np.vstack([xi.ravel(), yi.ravel()])).reshape(xi.shape) + + colorscale = [ + [0.0, 'rgba(255,255,255,0)'], + [0.1, 'rgba(255,255,255,0)'], + [0.3, 'rgba(255,200,100,0.2)'], + [0.7, 'rgba(255,140,50,0.5)'], + [1.0, 'rgba(255,100,0,0.8)'] + ] + fig.add_trace(go.Contour( + x=xi[0], + y=yi[:, 0], + z=zi, + showscale=False, + colorscale=colorscale, + hoverinfo='skip', + contours=dict(showlines=True), + line=dict(width=0.5, color='lightgrey') + )) + + # Scatter points + fig.add_trace(go.Scatter( + x=scatter_data['Observed'], + y=scatter_data['Predicted'], + mode='markers', + marker=dict(size=5, color='black', opacity=opacity), + name='Hits' + downsampled_label, + showlegend=True + )) + + # Diagonal y=x line + fig.add_trace(go.Scatter( + x=[min_val, max_val], + y=[min_val, max_val], + mode='lines', + line=dict(color='grey', dash='dash', width=1), + name='Diagonal', + showlegend=False + )) + + # Regression + if len(data) >= 2: + slope, intercept, r_value, p_value, std_err = linregress(data['Observed'], data['Predicted']) + r_squared = r_value ** 2 + reg_x = np.linspace(min_val, max_val, 100) + reg_y = intercept + slope * reg_x + + fig.add_trace(go.Scatter( + x=reg_x, + y=reg_y, + mode='lines', + line=dict(color='black', dash='dashdot', width=1.5), + name='Linear Regression', + visible='legendonly' + )) + + fig.add_annotation( + text=f"Linear Regression:
Slope: {slope:.2f}
Intercept: {intercept:.2f}
R²: {r_squared:.2f}", + xref="paper", yref="paper", + x=0.98, y=0.03, + xanchor="right", yanchor="bottom", + showarrow=False, + font=dict(size=11, color="black"), + bgcolor="rgba(255, 255, 255, 0.7)", + bordercolor="lightgrey", + borderwidth=1, + opacity=0.8 + ) + + # Layout + fig.update_layout( + my_figlayout, + title=plot_title, + xaxis_title=x_label, + yaxis_title=y_label, + xaxis=dict( + range=[min_val, max_val], + tick0=min_val, + dtick=100, + showgrid=True, + gridwidth=0.5, + gridcolor='lightgrey' + ), + yaxis=dict( + range=[min_val, max_val], + tick0=min_val, + dtick=100, + showgrid=True, + gridwidth=0.5, + gridcolor='lightgrey' + ), + height=500, + legend=dict(orientation='h', x=0.05, y=1.0) + ) + + return fig + + +def create_error_scatter_plot(time, observed_data, predicted_data, + threshold_value, maximum_dt, + x_label='Time Error [hours]', y_label='Error [km s⁻¹]', + plot_title='Error Plot for Hits', + max_points=1000, min_points=50, opacity=0.5, + time_bounds=(-96, 96), data_bounds=(-400, 400)): + ''' + Runs compute_peaks to find matched "hit" events, then plots the per-hit + time error vs data error, with optional KDE contours and helpful guides. + + Parameters: + time (pandas.Series): Time values aligned with observed/predicted + observed_data (pandas.Series): Observed values + predicted_data (pandas.Series): Predicted/model values + threshold_value (float): Threshold used inside compute_peaks + maximum_dt (float): Max allowed time difference for peak matching (same units as time) + x_label (str): X-axis label [default = 'Time Error [hours]'] + y_label (str): Y-axis label [default = 'Error [km s⁻¹]'] + plot_title (str): Plot title + max_points (int): Max number of scatter points (downsample if exceeded) + min_points (int): Min points required to draw KDE contours + opacity (float): Marker opacity + time_bounds (tuple): (min,max) range for time error axis + data_bounds (tuple): (min,max) range for data error axis + + Returns: + plotly.graph_objects.Figure: Error scatter/KDE plot for hits + ''' + # Compute peaks + (_, _, hits, _, _, time_diff, data_diff) = compute_peaks( + time, observed_data, predicted_data, threshold_value, maximum_dt + ) + + # Handle case with zero hits + if len(hits) == 0: + fig = go.Figure() + fig.update_layout( + my_figlayout, + title=plot_title + ' (no hits found)', + xaxis_title=x_label, + yaxis_title=y_label, + xaxis=dict(range=list(time_bounds), tick0=0, dtick=24, showgrid=True, gridwidth=0.5, gridcolor='lightgrey'), + yaxis=dict(range=list(data_bounds), tick0=0, dtick=100, showgrid=True, gridwidth=0.5, gridcolor='lightgrey'), + height=500, + legend=dict(orientation='h', x=0.05, y=1.0) + ) + # zero reference lines + fig.add_trace(go.Scatter(x=list(time_bounds), y=[0, 0], mode='lines', + line=dict(color='gray', dash='dash', width=1), showlegend=False)) + fig.add_trace(go.Scatter(x=[0, 0], y=list(data_bounds), mode='lines', + line=dict(color='gray', dash='dash', width=1), showlegend=False)) + return fig + + # Build hover labels + try: + if isinstance(hits[0], (pd.Timestamp, np.datetime64)): + hover_dates = [str(h) for h in hits] + else: + hover_dates = [str(time.iloc[int(h)]) for h in hits] + except Exception: + hover_dates = [str(h) for h in hits] + + data = pd.DataFrame({ + 'Time Error': np.asarray(time_diff), + 'Data Error': np.asarray(data_diff), + 'Date': hover_dates + }).dropna() + + # Downsample if needed + if len(data) > max_points: + step = max(1, len(data) // max_points) + scatter_data = data.iloc[::step].head(max_points) + label_suffix = ' (downsampled)' + else: + scatter_data = data + label_suffix = '' + + # Prepare figure + fig = go.Figure() + + # KDE contour if enough data + if len(data) > min_points: + values = np.vstack([data['Time Error'].values, data['Data Error'].values]) + kde = gaussian_kde(values) + + # Add padding to avoid cutoff + pad_x = (data['Time Error'].max() - data['Time Error'].min()) * 0.25 or 1.0 + pad_y = (data['Data Error'].max() - data['Data Error'].min()) * 0.25 or 1.0 + + xi, yi = np.meshgrid( + np.linspace(data['Time Error'].min() - pad_x, data['Time Error'].max() + pad_x, 100), + np.linspace(data['Data Error'].min() - pad_y, data['Data Error'].max() + pad_y, 100) + ) + zi = kde(np.vstack([xi.ravel(), yi.ravel()])).reshape(xi.shape) + + colorscale = [ + [0.0, 'rgba(255,255,255,0)'], + [0.1, 'rgba(255,255,255,0)'], + [0.3, 'rgba(255,200,100,0.2)'], + [0.7, 'rgba(255,140,50,0.5)'], + [1.0, 'rgba(255,100,0,0.8)'] + ] + fig.add_trace(go.Contour( + x=xi[0], + y=yi[:, 0], + z=zi, + showscale=False, + colorscale=colorscale, + hoverinfo='skip', + contours=dict(showlines=True), + line=dict(width=0.5, color='lightgrey') + )) + + # Scatter points + fig.add_trace(go.Scatter( + x=scatter_data['Time Error'], + y=scatter_data['Data Error'], + mode='markers', + marker=dict(size=5, color='black', opacity=opacity), + name='Error Points' + label_suffix, + text=scatter_data['Date'], + hovertemplate="Date: %{text}" + "
Time Error [h]: %{x}" + "
Error [km s⁻¹]: %{y}", + showlegend=False + )) + + # Zero reference lines + fig.add_trace(go.Scatter( + x=list(time_bounds), + y=[0, 0], + mode='lines', + line=dict(color='gray', dash='dash', width=1), + name='Zero Data Error', + showlegend=False + )) + fig.add_trace(go.Scatter( + x=[0, 0], + y=list(data_bounds), + mode='lines', + line=dict(color='gray', dash='dash', width=1), + name='Zero Time Error', + showlegend=False + )) + + # Quadrant annotations + annotations = [ + dict(x=(time_bounds[0] + time_bounds[1]) * 0.25, y=data_bounds[1] * 0.9, + text="Overestimated peaks", showarrow=False, + font=dict(size=11, color='grey'), align="left"), + dict(x=(time_bounds[0] + time_bounds[1]) * 0.25, y=data_bounds[0] * 0.9, + text="Underestimated peaks", showarrow=False, + font=dict(size=11, color='grey'), align="left") + ] + + # Layout + fig.update_layout( + my_figlayout, + title=plot_title, + xaxis_title=x_label, + yaxis_title=y_label, + xaxis=dict( + range=list(time_bounds), + tick0=0, + dtick=24, + showgrid=True, + gridwidth=0.5, + gridcolor='lightgrey' + ), + yaxis=dict( + range=list(data_bounds), + tick0=0, + dtick=100, + showgrid=True, + gridwidth=0.5, + gridcolor='lightgrey' + ), + height=500, + legend=dict(orientation='h', x=0.05, y=1.0), + annotations=annotations + ) + + return fig + + +def create_time_error_vs_observed_data_plot(time, observed_data, predicted_data, + threshold_value, maximum_dt, + x_label='Time Error [hours]', + y_label='Observed Bulk Speed [km s⁻¹]', + plot_title='Time Error vs. Observed Bulk Speed', + max_points=1000, min_points=50, opacity=0.5, + time_bounds=(-96, 96), obs_bounds=(200, 800)): + ''' + Runs compute_peaks, then plots per-hit time error vs observed peak value, + with optional KDE contours, zero line, and quadrant annotations. + + Parameters: + time (pandas.Series): Time values aligned with observed/predicted + observed_data (pandas.Series): Observed values + predicted_data (pandas.Series): Predicted/model values + threshold_value (float): Threshold used inside compute_peaks + maximum_dt (float): Max allowed time difference for peak matching + x_label (str): X-axis label [default = 'Time Error [hours]'] + y_label (str): Y-axis label [default = 'Observed Bulk Speed [km s⁻¹]'] + plot_title (str): Plot title + max_points (int): Max number of scatter points (downsample if exceeded) + min_points (int): Min points required to draw KDE contours + opacity (float): Marker opacity + time_bounds (tuple): (min,max) range for time error axis + obs_bounds (tuple): (min,max) range for observed axis + + Returns: + plotly.graph_objects.Figure: Scatter/KDE plot + ''' + # Compute peaks + (peaks_obs, peaks_pred, + hits, misses, false_alarms, + time_diff, data_diff) = compute_peaks(time, observed_data, predicted_data, + threshold_value, maximum_dt) + + # Handle no hits + if len(hits) == 0: + fig = go.Figure() + fig.update_layout( + my_figlayout, + title=plot_title + ' (no hits found)', + xaxis_title=x_label, + yaxis_title=y_label, + xaxis=dict(range=list(time_bounds), tick0=0, dtick=24, showgrid=True, gridwidth=0.5, gridcolor='lightgrey'), + yaxis=dict(range=list(obs_bounds), tick0=obs_bounds[0], dtick=100, showgrid=True, gridwidth=0.5, gridcolor='lightgrey'), + height=500, + legend=dict(orientation='h', x=0.05, y=1.0) + ) + # zero time-error line + fig.add_trace(go.Scatter(x=[0, 0], y=list(obs_bounds), mode='lines', + line=dict(color='gray', dash='dash', width=1), showlegend=False)) + return fig + + if isinstance(hits[0], (pd.Timestamp, np.datetime64)): + idxer = time.index.get_indexer(hits) + hit_indices = [i for i in idxer if i != -1] + hover_dates = [str(h) for h in hits] + else: + n = len(time) + hit_indices = [i for i in hits if 0 <= int(i) < n] + hover_dates = [str(time.iloc[int(h)]) for h in hit_indices] + + # Observed values at hit positions + hits_observed_data = observed_data.iloc[hit_indices] + + # Assemble frame (assumes time_diff aligns 1:1 with hits) + data = pd.DataFrame({ + 'Time Error': np.asarray(time_diff), + 'Observed Data': np.asarray(hits_observed_data), + 'Date': hover_dates[:len(hit_indices)] + }).dropna() + + # Downsample + if len(data) > max_points: + step = max(1, len(data) // max_points) + scatter_data = data.iloc[::step].head(max_points) + label_suffix = ' (downsampled)' + else: + scatter_data = data + label_suffix = '' + + fig = go.Figure() + + # KDE contour if enough data + if len(data) > min_points: + values = np.vstack([data['Time Error'].values, data['Observed Data'].values]) + kde = gaussian_kde(values) + + pad_x = (data['Time Error'].max() - data['Time Error'].min()) * 0.25 or 1.0 + xi, yi = np.meshgrid( + np.linspace(data['Time Error'].min() - pad_x, data['Time Error'].max() + pad_x, 100), + np.linspace(obs_bounds[0], obs_bounds[1], 100) + ) + zi = kde(np.vstack([xi.ravel(), yi.ravel()])).reshape(xi.shape) + + colorscale = [ + [0.0, 'rgba(255,255,255,0)'], + [0.1, 'rgba(255,255,255,0)'], + [0.3, 'rgba(255,200,100,0.2)'], + [0.7, 'rgba(255,140,50,0.5)'], + [1.0, 'rgba(255,100,0,0.8)'] + ] + fig.add_trace(go.Contour( + x=xi[0], + y=yi[:, 0], + z=zi, + showscale=False, + colorscale=colorscale, + hoverinfo='skip', + contours=dict(showlines=True), + line=dict(width=0.5, color='lightgrey') + )) + + # Scatter points + fig.add_trace(go.Scatter( + x=scatter_data['Time Error'], + y=scatter_data['Observed Data'], + mode='markers', + marker=dict(size=5, color='black', opacity=opacity), + name='Hit Points' + label_suffix, + text=scatter_data['Date'], + hovertemplate="Date: %{text}" + "
Time Error [h]: %{x}" + "
Observed [km s⁻¹]: %{y}", + showlegend=False + )) + + # Vertical zero line + fig.add_trace(go.Scatter( + x=[0, 0], + y=list(obs_bounds), + mode='lines', + line=dict(color='gray', dash='dash', width=1), + name='Zero Time Error', + showlegend=False + )) + + # Quadrant annotations (positions scaled to bounds) + xq = (time_bounds[1] - time_bounds[0]) * 0.76 / 2 # ~±73 for default bounds + annotations = [ + dict(x=-xq, y=obs_bounds[1] * 0.96, text="Late predictions
High peak values", showarrow=False, + font=dict(size=11, color='grey'), align="left"), + dict(x= xq, y=obs_bounds[1] * 0.96, text="Early predictions
High peak values", showarrow=False, + font=dict(size=11, color='grey'), align="right"), + dict(x=-xq, y=obs_bounds[0] + (obs_bounds[1]-obs_bounds[0]) * 0.15, text="Late predictions
Low peak values", showarrow=False, + font=dict(size=11, color='grey'), align="left"), + dict(x= xq, y=obs_bounds[0] + (obs_bounds[1]-obs_bounds[0]) * 0.15, text="Early predictions
Low peak values", showarrow=False, + font=dict(size=11, color='grey'), align="right") + ] + + # Layout + fig.update_layout( + my_figlayout, + title=plot_title, + xaxis_title=x_label, + yaxis_title=y_label, + xaxis=dict( + range=list(time_bounds), + tick0=0, + dtick=24, + showgrid=True, + gridwidth=0.5, + gridcolor='lightgrey' + ), + yaxis=dict( + range=list(obs_bounds), + tick0=obs_bounds[0], + dtick=100, + showgrid=True, + gridwidth=0.5, + gridcolor='lightgrey' + ), + height=500, + legend=dict(orientation='h', x=0.05, y=1.0), + annotations=annotations + ) + + return fig + + +def create_mm_line_chart(predicted_data_list, observed_data): + """ + Create a multi-model time series line chart comparing predicted bulk speeds + from multiple models against observed data. + + Parameters: + predicted_data_list : list of pandas.DataFrame + A list where each DataFrame contains the predicted time series for one + model. Each DataFrame must include: + - 'Time' (datetime64) : Time stamps for the predictions. + - 'Model' (float) : Predicted bulk speed values [km s⁻¹]. + - 'Name' (str) : Model display name (same value for all rows). + All 'Time' columns should use the same timezone convention (UTC recommended). + + observed_data : pandas.DataFrame + DataFrame containing the observed data with: + - 'Time' (datetime64) : Observation timestamps. + - 'Obs' (float) : Observed bulk speed values [km s⁻¹]. + - 'Name' (str) : Observations display name. + + Returns: + plotly.graph_objects.Figure + A Plotly Figure object containing the line chart. + """ + + fig = go.Figure() + + # Define a list of colors + colors = px.colors.qualitative.Plotly + + # Add traces for each predicted model + for i, predicted_data in enumerate(predicted_data_list): + fig.add_trace(go.Scatter( + x=predicted_data["Time"], + y=predicted_data["Model"], + mode="lines", + line=dict(color=colors[i % len(colors)], width=2), + name=predicted_data["Name"].iloc[0], + hovertemplate="Time=%{x}
Model=%{y:.2f}", + )) + + # Add observed data trace + fig.add_trace(go.Scatter( + x=observed_data["Time"], + y=observed_data["Obs"], + name=observed_data["Name"].iloc[0], + mode="markers", + marker=dict( + symbol="circle-open", + size=2, + color="#222222", + line=dict(width=1), + ), + opacity=0.85, + hovertemplate="Time=%{x}
Obs=%{y:.2f}", + )) + x_start = pd.to_datetime(observed_data["Time"].iloc[0]) + x_end = pd.to_datetime(observed_data["Time"].iloc[-1]) + + # Update layout + fig.update_layout( + my_figlayout, + title="Time Series Comparison", + xaxis_title="Date", + yaxis_title="Bulk Speed [km s⁻¹]", + legend=dict(orientation="h", x=0.03, y=1.05), + xaxis=dict(range=[x_start, x_end]), + height=400, + ) + + return fig + + +def create_mm_box_plot(aligned_predicted_data_list, aligned_observed_data): + """ + Create a comparative box plot showing the statistical distribution of predicted + bulk speeds from multiple models alongside observed data. + + Parameters: + aligned_predicted_data_list : list of pandas.DataFrame + A list where each DataFrame contains time-aligned predicted bulk speed data + for one model, with: + - 'Time' (datetime64) : Timestamps of predictions (alignment already done). + - 'Model' (float) : Predicted bulk speed values [km s⁻¹]. + - 'Name' (str) : Model display name (identical for all rows in that DF). + + aligned_observed_data : pandas.DataFrame + DataFrame containing the observed bulk speed data, with: + - 'Time' (datetime64) : Observation timestamps (aligned to models). + - 'Obs' (float) : Observed bulk speed values [km s⁻¹]. + - 'Name' (str) : Observations display name. + + Returns: + plotly.graph_objects.Figure + A Plotly Figure object containing the box plots. + """ + + combined_data = [] + + # Add predicted data + for aligned_predicted_data in aligned_predicted_data_list: + df = aligned_predicted_data.rename(columns={'Model': 'Value'}) + df['Type'] = aligned_predicted_data['Name'][0] + df['Color'] = 'default' + combined_data.append(df) + + # Add observed data + observed_name = aligned_observed_data['Name'][0] + obs_df = aligned_observed_data.rename(columns={'Obs': 'Value'}) + obs_df['Type'] = observed_name + obs_df['Color'] = 'gray' + combined_data.append(obs_df) + + # Combine all + combined_data = pd.concat(combined_data, ignore_index=True) + + fig = go.Figure() + + types = combined_data['Type'].unique() + for t in types: + subset = combined_data[combined_data['Type'] == t] + color = 'gray' if t == observed_name else None + + fig.add_trace(go.Box( + y=subset['Value'], + name=t, + marker_color=color if color else None, + boxmean=True, + boxpoints=False, + )) + + # Layout updates + fig.update_layout( + my_figlayout, + title='Statistical Distribution', + yaxis_title='Bulk Speed [km s⁻¹]', + xaxis_title='Category', + legend=dict(orientation='h', x=0.03, y=1.05), + legend_title='', + height=360 + ) + + return fig + + +def create_mm_rmse_timeline(predicted_data_list, observed_data, benchmark_data,normalize_time="naive_utc", floor="H"): + """ + Creates a multi-model RMSE timeline across Carrington Rotations, + including optional relative skill vs a benchmark. + + Parameters: + predicted_data_list : list[pd.DataFrame] + Each DF must contain: 'Time' (datetime-like), 'Model' (float), 'Name' (str) + observed_data : pd.DataFrame + Must contain: 'Time' (datetime-like), 'Obs' (float), 'Name' (str) + benchmark_data : pd.DataFrame + Must contain: 'Time' (datetime-like), 'Model' (float), 'Name' (str) + normalize_time : {"naive_utc", "aware_utc"}, default "naive_utc" + - "naive_utc": make timestamps tz-naive in UTC (consistent with earlier notebook code) + - "aware_utc": make timestamps tz-aware (UTC) + floor : str or None, default "H" + Pandas offset alias to floor timestamps (e.g., "H", "30min"). Use None to skip. + + Returns: + plotly.graph_objects.Figure + Figure with Absolute RMSE (default) and toggle for Relative Skill vs benchmark. + """ + + def _norm_time_col(df, time_col="Time"): + d = df.copy() + d[time_col] = pd.to_datetime(d[time_col], errors="coerce", utc=True) + if normalize_time == "naive_utc": + d[time_col] = d[time_col].dt.tz_convert("UTC").dt.tz_localize(None) + else: + d[time_col] = d[time_col].dt.tz_convert("UTC") + if floor: + d[time_col] = d[time_col].dt.floor(floor) + return d + + # Normalize all inputs + observed_data = _norm_time_col(observed_data, "Time") + benchmark_data = _norm_time_col(benchmark_data, "Time") + predicted_data_list = [_norm_time_col(df, "Time") for df in predicted_data_list] + + # Names + model_names = [] + for df in predicted_data_list: + try: + model_names.append(str(df["Name"].iloc[0])) + except Exception: + model_names.append("Model") + try: + benchmark_name = str(benchmark_data["Name"].iloc[0]) + except Exception: + benchmark_name = "Benchmark" + + def _cr_bounds(cr): + start = carrington_rotation_time(cr, 360 * u.deg).datetime + end = carrington_rotation_time(cr, 1 * u.deg).datetime + if normalize_time == "naive_utc": + return pd.Timestamp(start), pd.Timestamp(end) + else: + return pd.Timestamp(start, tz="UTC"), pd.Timestamp(end, tz="UTC") + + epoch = pd.Timestamp("1853-11-09") + if normalize_time != "naive_utc": + epoch = epoch.tz_localize("UTC") + start_time = observed_data["Time"].min() + end_time = observed_data["Time"].max() + + if pd.isna(start_time) or pd.isna(end_time): + fig = go.Figure() + fig.update_layout(title="RMSE Comparison Over Time (no data)") + return fig + + cr_len_days = 27.2753 + cr_start = 1 + int((start_time - epoch) / pd.Timedelta(days=cr_len_days)) + cr_end = int((end_time - epoch) / pd.Timedelta(days=cr_len_days)) + + cr_numbers = [] + rmse_values = {name: [] for name in model_names} + rel_skill = {name: [] for name in model_names} + benchmark_rmse = [] + + # Compute per-CR RMSEs + for cr in range(cr_start, cr_end + 1): + cr_start_time, cr_end_time = _cr_bounds(cr) + + obs_cr = observed_data[(observed_data["Time"] >= cr_start_time) & (observed_data["Time"] < cr_end_time)] + bench_cr = benchmark_data[(benchmark_data["Time"] >= cr_start_time) & (benchmark_data["Time"] < cr_end_time)] + + if obs_cr.empty or bench_cr.empty: + continue + + # Merge benchmark vs obs on Time + merged_bench = pd.merge(bench_cr[["Time","Model"]], + obs_cr[["Time","Obs"]], + on="Time", how="inner") + if merged_bench.empty: + continue + + # Compute benchmark RMSE + # rmsScore expects DF with identical indices/columns; align via column rename + rmse_b = rmsScore( + merged_bench[["Model"]], + merged_bench[["Obs"]].rename(columns={"Obs": "Model"}) + ).iloc[0] + benchmark_rmse.append(rmse_b) + + for df, model_name in zip(predicted_data_list, model_names): + model_cr = df[(df["Time"] >= cr_start_time) & (df["Time"] < cr_end_time)] + merged = pd.merge(model_cr[["Time","Model"]], + obs_cr[["Time","Obs"]], + on="Time", how="inner") + if merged.empty: + rmse_values[model_name].append(np.nan) + rel_skill[model_name].append(np.nan) + continue + + rmse_m = rmsScore( + merged[["Model"]], + merged[["Obs"]].rename(columns={"Obs": "Model"}) + ).iloc[0] + + rmse_values[model_name].append(rmse_m) + rel_skill[model_name].append(100 * (rmse_b - rmse_m) / rmse_b if rmse_b else np.nan) + + cr_numbers.append(cr) + + fig = go.Figure() + + # Absolute RMSE traces + for model_name in model_names: + fig.add_trace(go.Scatter( + x=cr_numbers, y=rmse_values[model_name], + mode='lines+markers', name=model_name + )) + + # Benchmark trace + fig.add_trace(go.Scatter( + x=cr_numbers, y=benchmark_rmse, + mode='lines+markers', name=benchmark_name, + line=dict(dash='dash', color='grey') + )) + + # Relative skill traces + for model_name in model_names: + fig.add_trace(go.Scatter( + x=cr_numbers, y=rel_skill[model_name], + mode='lines+markers', name=f"{benchmark_name} vs. {model_name}", + line=dict(dash='dot'), + visible=False + )) + + # Zero line for relative skill + fig.add_trace(go.Scatter( + x=cr_numbers, y=[0] * len(cr_numbers), + mode='lines+markers', line=dict(color='grey', dash='dash'), + name='Benchmark Reference', visible=False, showlegend = False + )) + + # Dropdown menus + num_models = len(model_names) + num_traces = len(fig.data) + + abs_vis = [True] * (num_models + 1) + [False] * (num_models + 1) + rel_vis = [False] * (num_models + 1) + [True] * num_models + [True] + + # Annotation for Relative Skill + annotation_explainer = dict( + text="Above zero: better than benchmark
Below zero: worse than benchmark", + xref="paper", yref="paper", + x=0.98, y=0.2, + xanchor="right", yanchor="top", + showarrow=False, + font=dict(size=11, color="black"), + bgcolor="rgba(255, 255, 255, 0.8)", + bordercolor="lightgrey", + borderwidth=1, + opacity=0.9 + ) + + # Add annotation + fig.update_layout(annotations=[{**annotation_explainer, "visible": False}]) + + fig.update_layout( + my_figlayout, + title="RMSE Comparison Over Time", + xaxis_title="Carrington Rotation Number", + yaxis_title="RMSE [km s⁻¹]", + height=500, + xaxis=dict( + range=[cr_start-0.25, cr_end+0.25], + tickmode='linear', + tick0=cr_numbers[0], + showgrid=True, + gridwidth=0.5, + gridcolor='lightgrey' + ), + yaxis=dict( + dtick = 50, + tick0 = 0, + showgrid = True, + gridwidth = 0.5, + gridcolor = 'lightgrey' + ), + legend=dict( + orientation='h', + x=0, + y=-0.25, + xanchor='left', + yanchor='top', + traceorder='normal', + title_text='' + ), + updatemenus=[ + dict( + buttons=[ + dict( + label="Absolute Values", + method="update", + args=[ + {"visible": abs_vis}, + { + "yaxis": { + "title": "RMSE [km s⁻¹]", + "tick0": 0, + "dtick": 50, + "showgrid": True, + "gridwidth": 0.5, + "gridcolor": 'lightgrey' + }, + "xaxis": { + "range": [cr_start-0.25, cr_end+0.25], + "title": "Carrington Rotation Number", + "tickmode": "linear", + "tick0": cr_numbers[0], + "showgrid": True, + "gridwidth": 0.5, + "gridcolor": "lightgrey" + }, + "annotations": [{**annotation_explainer, "visible": False}] + } + ] + ), + dict( + label="Relative Skill [%]", + method="update", + args=[ + {"visible": rel_vis}, + { + "yaxis": { + "title": "Relative Skill [%]", + "tick0": 0, + "showgrid": True, + "gridwidth": 0.5, + "gridcolor": 'lightgrey' + }, + "xaxis": { + "range": [cr_start-0.25, cr_end+0.25], + "title": "Carrington Rotation Number", + "tickmode": "linear", + "tick0": cr_numbers[0], + "showgrid": True, + "gridwidth": 0.5, + "gridcolor": "lightgrey" + }, + "annotations": [{**annotation_explainer, "visible": True}] + } + ] + ) + ], + direction="down", + showactive=True, + x=0.26, + xanchor="left", + y=1.15, + yanchor="top" + ) + ] + ) + return fig + + +def create_mm_error_table_ptp(observed_data, predicted_data_list): + """ + Build a point‑to‑point error summary table comparing multiple models + against observed bulk speed time series. This function computes common + continuous‑variable metrics for each model and returns a formatted Dash + DataTable suitable for display in the dashboard. + + Metrics: + • Mean Error (ME) [km s⁻¹] + • Mean Absolute Error (MAE) [km s⁻¹] + • Mean Squared Error (MSE) [(km s⁻¹)²] + • Root Mean Squared Error (RMSE) [km s⁻¹] + • Correlation Coefficient (CC) [unitless] + • Median Symmetric Accuracy (MSA) [%] + • Symmetric Signed Percentage Bias (SSPB) [%] + + Parameters: + observed_data : pandas.DataFrame + Observation frame with columns: + - 'Time' (datetime64): timestamps + - 'Obs' (float) : observed bulk speed [km s⁻¹] + - 'Name' (str) : label for observations (single value repeated) + Note: Timestamps should already be normalized (same tz/naivety) and, + if needed, floored to the desired cadence before calling. + + predicted_data_list : list[pandas.DataFrame] + List of model frames; each must contain: + - 'Time' (datetime64): timestamps (aligned with observations) + - 'Model' (float) : predicted bulk speed [km s⁻¹] + - 'Name' (str) : model display name (single value repeated) + All frames must be aligned to the same time grid as `observed_data` + (e.g., via `align_and_interpolate`), so point‑to‑point metrics make sense. + + Returns: + dash_table.DataTable + A Dash DataTable with one row per metric and one column per model. + Numeric cells are right‑aligned and formatted to two decimals, using the + global `my_tablelayout` styling. + """ + + observed_values = observed_data['Obs'] + + metrics = [ + 'Mean Error [km s⁻¹]', + 'Mean Absolute Error [km s⁻¹]', + 'Mean Squared Error [(km s⁻¹)²]', + 'Root Mean Squared Error [km s⁻¹]', + 'Correlation Coefficient', + 'Median Symmetric Accuracy [%]', + 'Symmetric Signed Percentage Bias [%]' + ] + + model_names = [predicted_data['Name'][0] for predicted_data in predicted_data_list] + error_metrics = {metric: [] for metric in metrics} + + for predicted_data in predicted_data_list: + model_values = predicted_data['Model'] + error_metrics['Mean Error [km s⁻¹]'].append(round(meScore(model_values, observed_values), 2)) + error_metrics['Mean Absolute Error [km s⁻¹]'].append(round(maeScore(model_values, observed_values), 2)) + error_metrics['Mean Squared Error [(km s⁻¹)²]'].append(round(meanSquaredError(model_values, observed_values), 2)) + error_metrics['Root Mean Squared Error [km s⁻¹]'].append(round(rmsScore(model_values, observed_values), 2)) + error_metrics['Correlation Coefficient'].append(round(ccScore(model_values, observed_values), 2)) + error_metrics['Median Symmetric Accuracy [%]'].append(round(msaScore(model_values, observed_values), 2)) + error_metrics['Symmetric Signed Percentage Bias [%]'].append(round(sspbScore(model_values, observed_values), 2)) + + # Format table data + data = [ + {'Metric': metric, **{model: error_metrics[metric][i] for i, model in enumerate(model_names)}} + for metric in metrics + ] + + columns = [{'name': 'Metric', 'id': 'Metric'}] + [ + {'name': model, 'id': model, 'type': 'numeric', 'format': Format(precision=2, scheme=Scheme.fixed)} + for model in model_names + ] + + style_cell_conditional = [ + {'if': {'column_id': model}, 'textAlign': 'right'} for model in model_names + ] + + error_table = dash_table.DataTable( + data=data, + columns=columns, + style_cell_conditional=style_cell_conditional, + **my_tablelayout + ) + + return error_table + + +def create_mm_statistics_table(observed_data, predicted_data_list): + """ + Build an extended statistics summary table for observed and predicted + bulk speed time series. + + The function applies the `descriptiveStats` helper to the observation + series and to each model series, then formats the results into a Dash + DataTable for side-by-side comparison. + + Statistics: + • Mean + • Median + • Standard Deviation (SD) + • Minimum + • Maximum + • Variance + • Skewness + • Kurtosis + + Parameters: + observed_data : pandas.DataFrame + Observation frame with columns: + - 'Time' (datetime64): timestamps + - 'Obs' (float) : observed bulk speed [km s⁻¹] + - 'Name' (str) : label for observations (single value repeated) + Timestamps should be normalized and aligned to match predictions. + + predicted_data_list : list[pandas.DataFrame] + List of model frames; each must contain: + - 'Time' (datetime64): timestamps (aligned with observations) + - 'Model' (float) : predicted bulk speed [km s⁻¹] + - 'Name' (str) : model display name (single value repeated) + All series must be on the same time grid as `observed_data`. + + Returns: + dash_table.DataTable + A Dash DataTable with one row per statistic and columns for + "Observed" and each model. Numeric values are formatted to two + decimals and right-aligned, using global `my_tablelayout`. + """ + + # Prepare column order: Observed first, then each model + observed_values = observed_data['Obs'] + model_names = [pred_df['Name'][0] for pred_df in predicted_data_list] + model_series = [pred_df['Model'] for pred_df in predicted_data_list] + column_order = ['Observed'] + model_names + + # Compute stats + stats_map = { + 'Observed': descriptiveStats(observed_values) + } + for name, series in zip(model_names, model_series): + stats_map[name] = descriptiveStats(series) + + stat_rows = [ + ('mean', 'Mean'), + ('median', 'Median'), + ('std', 'SD'), + ('min', 'Minimum'), + ('max', 'Maximum'), + ('var', 'Variance'), + ('skewness', 'Skewness'), + ('kurtosis', 'Kurtosis') + ] + + # Build DataTable rows + data = [] + for key, label in stat_rows: + row = {'Statistic': label} + for col in column_order: + row[col] = stats_map[col][key] + data.append(row) + + # Define columns with formatting + columns = [{'name': 'Statistic', 'id': 'Statistic'}] + [ + {'name': col, 'id': col, 'type': 'numeric', 'format': Format(precision=2, scheme=Scheme.fixed)} + for col in column_order + ] + + # Align numeric columns right + style_cell_conditional = [ + {'if': {'column_id': col}, 'textAlign': 'right'} for col in column_order + ] + + # Create Dash DataTable + statistics_table = dash_table.DataTable( + data=data, + columns=columns, + style_cell_conditional=style_cell_conditional, + **my_tablelayout + ) + + return statistics_table + + +def create_mm_peak_bias_scatter(predicted_data_list, observed_data, threshold_value=450, maximum_dt=2): + """ + Compute and visualize the relationship between peak prediction bias + and prediction accuracy for multiple models. + + Parameters: + predicted_data_list : list[pandas.DataFrame] + List of model prediction DataFrames, each containing: + - 'Time' (datetime64): timestamps + - 'Model' (float) : predicted bulk speed [km s⁻¹] + - 'Name' (str) : model display name (single value repeated) + Timestamps should be aligned with `observed_data`. + + observed_data : pandas.DataFrame + Observation DataFrame containing: + - 'Time' (datetime64): timestamps + - 'Obs' (float) : observed bulk speed [km s⁻¹] + - 'Name' (str) : label for observations (single value repeated) + + threshold_value : float, optional + Bulk speed threshold [km s⁻¹] used by `compute_peaks` to detect + significant events (default = 450). + + maximum_dt : float, optional + Maximum allowed time difference (in days) for matching predicted + and observed peaks (default = 2 days). + + Returns: + plotly.graph_objects.Figure + Scatter plot with: + - One point per model, labeled with model name + - X-axis = peak bias (Predicted ÷ Observed) + - Y-axis = hit rate (%) + - Quadrant annotations for interpretation + """ + + observed_data['Time'] = pd.to_datetime(observed_data['Time']) + model_names = [df['Name'][0] for df in predicted_data_list] + + bias_list = [] + hit_percentages = [] + labels = [] + + for df in predicted_data_list: + model_name = df['Name'][0] + df['Time'] = pd.to_datetime(df['Time']) + + # Merge predictions with observations + merged = pd.merge(df, observed_data, on='Time', how='inner') + if merged.empty: + continue + + # Compute peaks and associations + peaks_obs, peaks_pred, hits, misses, false_alarms, time_diffs, data_diffs = compute_peaks( + merged['Time'], merged['Obs'], merged['Model'], threshold_value, maximum_dt + ) + + num_predicted = len(peaks_pred) + num_observed = len(peaks_obs) + + if num_predicted == 0 or num_observed == 0: + continue + + bias = num_predicted / num_observed + hit_rate = (len(hits) / num_predicted) * 100 + + bias_list.append(bias) + hit_percentages.append(hit_rate) + labels.append(model_name) + + # Generate distinct colors + color_map = px.colors.qualitative.Plotly + point_colors = [color_map[i % len(color_map)] for i in range(len(labels))] + + # Create scatter plot with colored points + fig = go.Figure() + + for i, label in enumerate(labels): + fig.add_trace(go.Scatter( + x=[bias_list[i]], + y=[hit_percentages[i]], + mode='markers+text', + text=[label], + textposition='bottom center', + marker=dict(size=10, color=point_colors[i]), + name=label, + showlegend=False, + hovertemplate=f"Model: {label}
Bias: {bias_list[i]:.2f}
Hit Rate: {hit_percentages[i]:.1f}%" + )) + + # Compute axis limits + x_min = 0 + x_max = 2 + y_min = 0 + y_max = 101 + + # Annotations for quadrants + annotations = [ + dict(x=x_min + 0.01 * (x_max - x_min), y=y_max - 5, text="Few peaks
Well-targeted", showarrow=False, xanchor='left', font=dict(size=11, color='grey')), + dict(x=x_max - 0.01 * (x_max - x_min), y=y_max - 5, text="Too many peaks
Many matches", showarrow=False, xanchor='right', font=dict(size=11, color='grey')), + dict(x=x_min + 0.01 * (x_max - x_min), y=y_min + 5, text="Few peaks
Mostly wrong", showarrow=False, xanchor='left', font=dict(size=11, color='grey')), + dict(x=x_max - 0.01 * (x_max - x_min), y=y_min + 5, text="Too many peaks
Mostly wrong", showarrow=False, xanchor='right', font=dict(size=11, color='grey')) + ] + + # Add annotations + for ann in annotations: + fig.add_annotation(ann) + + fig.update_layout( + my_figlayout, + title="Peak Prediction Coverage and Accuracy", + xaxis_title="Bias (Predicted ÷ Observed)", + yaxis_title="Success Rate of Predicted Peaks [%]", + xaxis=dict(showgrid=True, gridcolor='lightgrey', range=[0, max(2, max(bias_list, default=1.5))]), + yaxis=dict(showgrid=True, gridcolor='lightgrey', range=[0, y_max]), + height=700 + ) + + return fig + + +def create_mm_overall_error_vs_detection_scatter(predicted_data_list, observed_data, threshold_value=450, maximum_dt=2): + """ + Compute and visualize the trade-off between overall time series accuracy (RMSE) + and event detection sensitivity (POD) for multiple models. + + Parameters: + predicted_data_list : list[pandas.DataFrame] + List of model prediction DataFrames, each containing: + - 'Time' (datetime64): timestamps + - 'Model' (float) : predicted bulk speed [km s⁻¹] + - 'Name' (str) : model display name (single value repeated) + Timestamps should be aligned with `observed_data`. + + observed_data : pandas.DataFrame + Observation DataFrame containing: + - 'Time' (datetime64): timestamps + - 'Obs' (float) : observed bulk speed [km s⁻¹] + - 'Name' (str) : label for observations (single value repeated) + + threshold_value : float, optional + Bulk speed threshold [km s⁻¹] used by `compute_peaks` to identify events. + Default = 450. + + maximum_dt : float, optional + Maximum allowed time difference (in days) for matching predicted and observed peaks. + Default = 2 days. + + Returns: + plotly.graph_objects.Figure + Scatter plot with: + - One point per model, labeled with model name + - X-axis = RMSE [km s⁻¹] + - Y-axis = POD [%] + - Quadrant annotations for interpretation + """ + + observed_data['Time'] = pd.to_datetime(observed_data['Time']) + model_names = [df['Name'][0] for df in predicted_data_list] + + rmse_list = [] + pod_list = [] + labels = [] + + for df in predicted_data_list: + model_name = df['Name'][0] + df['Time'] = pd.to_datetime(df['Time']) + + merged = pd.merge(df, observed_data, on='Time', how='inner') + if merged.empty: + continue + + obs = merged['Obs'] + pred = merged['Model'] + + # Compute RMSE + rmse = np.sqrt(((pred - obs) ** 2).mean()) + + # Compute peak associations + peaks_obs, peaks_pred, hits, misses, false_alarms, time_diffs, data_diffs = compute_peaks( + merged['Time'], merged['Obs'], merged['Model'], threshold_value, maximum_dt + ) + + TP = len(hits) + FN = len(misses) + + if TP + FN == 0: + continue + + pod = 100 * (TP / (TP + FN)) + + rmse_list.append(rmse) + pod_list.append(pod) + labels.append(model_name) + + # Generate distinct colors + color_map = px.colors.qualitative.Plotly + point_colors = [color_map[i % len(color_map)] for i in range(len(labels))] + + # Axis ranges + x_min = 0 + x_max = max(rmse_list, default=50) * 1.2 + y_min = 0 + y_max = 101 + + # Scatter plot + fig = go.Figure() + + for i, label in enumerate(labels): + fig.add_trace(go.Scatter( + x=[rmse_list[i]], + y=[pod_list[i]], + mode='markers+text', + text=[label], + textposition='bottom center', + marker=dict(size=10, color=point_colors[i]), + name=label, + showlegend=False, + hovertemplate=f"Model: {label}
RMSE: {rmse_list[i]:.1f} km/s
POD: {pod_list[i]:.2f}" + )) + + # Annotations for interpretation + annotations = [ + dict(x=x_min + 0.02 * (x_max - x_min), y=y_max - 5, text="Accurate
Sensitive", showarrow=False, xanchor='left', font=dict(size=11, color='grey')), + dict(x=x_max - 0.02 * (x_max - x_min), y=y_max - 5, text="Sensitive
Inaccurate", showarrow=False, xanchor='right', font=dict(size=11, color='grey')), + dict(x=x_min + 0.02 * (x_max - x_min), y=y_min + 5, text="Accurate
Insensitive", showarrow=False, xanchor='left', font=dict(size=11, color='grey')), + dict(x=x_max - 0.02 * (x_max - x_min), y=y_min + 5, text="Inaccurate
Insensitive", showarrow=False, xanchor='right', font=dict(size=11, color='grey')) + ] + + for ann in annotations: + fig.add_annotation(ann) + + fig.update_layout( + my_figlayout, + title="Time Series Accuracy and Event Sensitivity", + xaxis_title="RMSE [km s⁻¹]", + yaxis_title="Correctly Predicted Observed Peaks [%]", + xaxis=dict(showgrid=True, gridcolor='lightgrey', range=[x_min, x_max]), + yaxis=dict(showgrid=True, gridcolor='lightgrey', range=[y_min, y_max]), + height=700 + ) + + return fig \ No newline at end of file