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[USGS-R#184] updates to python plot scripts
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143 changes: 143 additions & 0 deletions
143
3_visualize/src/python_scripts/plot_pred_performance_manuscript.py
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| Original file line number | Diff line number | Diff line change |
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| # --- | ||
| # jupyter: | ||
| # jupytext: | ||
| # formats: ipynb,py:percent | ||
| # text_representation: | ||
| # extension: .py | ||
| # format_name: percent | ||
| # format_version: '1.3' | ||
| # jupytext_version: 1.14.4 | ||
| # kernelspec: | ||
| # display_name: Python 3 (ipykernel) | ||
| # language: python | ||
| # name: python3 | ||
| # --- | ||
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| # %% | ||
| import pandas as pd | ||
| import seaborn as sns | ||
| import matplotlib.pyplot as plt | ||
| import plot_utils | ||
| import numpy as np | ||
| import seaborn.objects as so | ||
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| # %% | ||
| df_comb_reach = plot_utils.read_and_filter_df("reach", "val") | ||
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| # %% | ||
| df_comb_reach.model_id.unique() | ||
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| # %% | ||
| models = ['0_baseline_LSTM', '1_metab_multitask', '2_multitask_dense'] | ||
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| # %% | ||
| df_comb_month = plot_utils.read_and_filter_df('month', 'val') | ||
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| # %% | ||
| ######## Barplot by site ###################################################### | ||
| df_reach = df_comb_reach[df_comb_reach.model_id != '1a_multitask_do_gpp_er'] | ||
| reach_groups = df_reach.groupby(['site_id', 'variable', 'model_id']) | ||
| reach_means = reach_groups.mean() | ||
| reach_stds = reach_groups.std() | ||
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| ######## Overall barplot ###################################################### | ||
| model_groups = df_reach.groupby(['variable', 'model_id']) | ||
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| model_means = model_groups.mean() | ||
| model_stds = model_groups.std() | ||
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| ######## Barplot by month ###################################################### | ||
| month_groups = df_comb_month.groupby(['date', 'model_id']) | ||
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| month_means = month_groups.mean() | ||
| month_stds = month_groups.std() | ||
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| # %% | ||
| def plot_bars_overall(means, stds, ax): | ||
| variables = ['do_min', 'do_mean', 'do_max'] | ||
| x = np.arange(len(variables)) # the label locations | ||
| width = 0.25 # the width of the bars | ||
| multiplier = 0 | ||
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| var_means = means.reset_index().pivot(index='variable', columns='model_id', values='rmse').loc[variables] | ||
| var_stds = stds.reset_index().pivot(index='variable', columns='model_id', values='rmse').loc[variables] | ||
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| for col in var_means.columns: | ||
| data = var_means[col] | ||
| offset = width * multiplier | ||
| rects = ax.bar(x + offset, data, width, label=col, yerr=var_stds[col]) | ||
| # ax.bar_label(rects, padding=3, fmt='%.2f', label_type='center') | ||
| multiplier += 1 | ||
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| ax.set_xticks(x + width, variables) | ||
| ax.grid() | ||
| ax.set_axisbelow(True) | ||
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| def plot_bars_reach(means, stds, variable, ax): | ||
| sites = means.reset_index()['site_id'].unique() | ||
| x = np.arange(len(sites)) # the label locations | ||
| width = 0.25 # the width of the bars | ||
| multiplier = 0 | ||
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| var_means = means.query(f"variable == '{variable}'").reset_index().pivot(index='site_id', columns='model_id', values='rmse') | ||
| var_stds = stds.query(f"variable == '{variable}'").reset_index().pivot(index='site_id', columns='model_id', values='rmse') | ||
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| for col in var_means.columns: | ||
| data = var_means[col] | ||
| offset = width * multiplier | ||
| rects = ax.bar(x + offset, data, width, label=col, yerr=var_stds[col]) | ||
| multiplier += 1 | ||
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| ax.set_xticks(x + width, sites) | ||
| ax.grid() | ||
| ax.set_axisbelow(True) | ||
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| def plot_bars_months(means, stds, variable, ax): | ||
| sites = means.reset_index()['date'].unique() | ||
| x = np.arange(len(sites)) # the label locations | ||
| width = 0.25 # the width of the bars | ||
| multiplier = 0 | ||
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| var_means = means.query(f"variable == '{variable}'").reset_index().pivot(index='date', columns='model_id', values='rmse') | ||
| var_stds = stds.query(f"variable == '{variable}'").reset_index().pivot(index='date', columns='model_id', values='rmse') | ||
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| for col in var_means.columns: | ||
| data = var_means[col] | ||
| offset = width * multiplier | ||
| rects = ax.bar(x + offset, data, width, label=col, yerr=var_stds[col]) | ||
| multiplier += 1 | ||
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| ax.set_xticks(x + width, sites) | ||
| ax.grid() | ||
| ax.set_axisbelow(True) | ||
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| # %% | ||
| fig = plt.figure(constrained_layout=True, figsize=(12, 8)) | ||
| subfigs = fig.subfigures(2, 1, wspace=0.07) | ||
| subfigsTop = subfigs[0].subfigures(1, 2, wspace=0.07, width_ratios=[1, 2]) | ||
| axsTopRight = subfigsTop[1].subplots(1, 3, sharey=True) | ||
| axsTopLeft = subfigsTop[0].subplots() | ||
| subfigsTop[0].suptitle('A') | ||
| subfigsTop[1].suptitle('B') | ||
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| plot_bars_overall(model_means, model_stds, axsTopLeft) | ||
| # axsTopLeft.bar([0, 1, 2, 3], [0, 1, 2, 3]) | ||
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| variables = ['do_min', 'do_mean', 'do_max'] | ||
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| for i, ax in enumerate(axsTopRight): | ||
| plot_bars_reach(reach_means, reach_stds, variables[i], ax) | ||
| ax.set_title(variables[i]) | ||
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| axsBottom = subfigs[1].subplots(1, 3) | ||
| for i, ax in enumerate(axsBottom): | ||
| plot_bars_months(month_means, month_stds, variables[i], ax) | ||
| ax.set_title(variables[i]) | ||
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| # %% |
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