Update changes from dev branch.

42b9c572 · Frisinghelli Daniel · 49f8a7f8 · 42b9c572 · 42b9c572 · 42b9c572
Commit 42b9c572 authored 3 years ago by Frisinghelli Daniel
--- a/.gitignore
+++ b/.gitignore
@@ -8,5 +8,7 @@ __pycache__/

 # jupyter
 .ipynb_checkpoints/
-Notebooks/Figures/
-*.nc
+
+# netcdf files
+Notebooks/*.nc
+
--- a/Notebooks/Figures/bootstrap/pr_members_vs_ensemble_extremes.png
+++ b/Notebooks/Figures/bootstrap/pr_members_vs_ensemble_extremes.png
--- a/Notebooks/Figures/bootstrap/pr_members_vs_ensemble_mean.png
+++ b/Notebooks/Figures/bootstrap/pr_members_vs_ensemble_mean.png
--- a/Notebooks/Figures/bootstrap/pr_only_members_vs_ensemble_extremes.png
+++ b/Notebooks/Figures/bootstrap/pr_only_members_vs_ensemble_extremes.png
--- a/Notebooks/Figures/bootstrap/pr_only_members_vs_ensemble_mean.png
+++ b/Notebooks/Figures/bootstrap/pr_only_members_vs_ensemble_mean.png
--- a/Notebooks/Figures/bootstrap/tasmax_members_vs_ensemble_extremes.png
+++ b/Notebooks/Figures/bootstrap/tasmax_members_vs_ensemble_extremes.png
--- a/Notebooks/Figures/bootstrap/tasmax_members_vs_ensemble_mean.png
+++ b/Notebooks/Figures/bootstrap/tasmax_members_vs_ensemble_mean.png
--- a/Notebooks/plot_figures_paper.ipynb
+++ b/Notebooks/plot_figures_paper.ipynb
@@ -47,7 +47,7 @@
   "outputs": [],
   "source": [
    "# predictand\n",
-    "PREDICTAND = 'tasmin'"
+    "PREDICTAND = 'pr'"
   ]
  },
  {
@@ -58,7 +58,7 @@
   "outputs": [],
   "source": [
    "# whether only precipitation was used as predictor\n",
-    "PR_ONLY = False"
+    "PR_ONLY = True"
   ]
  },
  {
@@ -181,7 +181,9 @@
    "ax4.legend(bbox_to_anchor=(1.3, 1.05), loc=2, frameon=False, fontsize=14);\n",
    "\n",
    "# save figure\n",
-    "fig.savefig('./Figures/bootstrap/{}_members_vs_ensemble_mean.png'.format(PREDICTAND), dpi=300, bbox_inches='tight')"
+    "filename = ('./Figures/bootstrap/pr_only_members_vs_ensemble_mean.png'.format(PREDICTAND) if\n",
+    "            PREDICTAND == 'pr' and PR_ONLY else './Figures/bootstrap/{}_members_vs_ensemble_mean.png'.format(PREDICTAND))\n",
+    "fig.savefig(filename, dpi=300, bbox_inches='tight')"
   ]
  },
  {
@@ -224,10 +226,10 @@
    "\n",
    "# axes limits and ticks\n",
    "y_lim_bias = (-50, 50) if PREDICTAND == 'pr' else (-1, 1)\n",
-    "y_lim_mae = (0, 2) if PREDICTAND == 'pr' else (0, 2)\n",
+    "y_lim_mae = (0, 20) if PREDICTAND == 'pr' else (0, 2)\n",
    "y_ticks_bias = (np.arange(y_lim_bias[0], y_lim_bias[1] + 10, 10) if PREDICTAND == 'pr' else\n",
    "                np.arange(y_lim_bias[0], y_lim_bias[1] + 0.2, 0.2))\n",
-    "y_ticks_mae = np.arange(y_lim_mae[0], y_lim_mae[1] + 0.2, 0.2)\n",
+    "y_ticks_mae = np.arange(y_lim_mae[0], y_lim_mae[1] + 2, 2) if PREDICTAND == 'pr' else np.arange(y_lim_mae[0], y_lim_mae[1] + 0.2, 0.2)\n",
    "\n",
    "# axis for bias\n",
    "ax1.set_ylabel('Bias (%)' if PREDICTAND == 'pr' else 'Bias (°C)')\n",
@@ -259,7 +261,9 @@
    "ax4.legend(bbox_to_anchor=(1.3, 1.05), loc=2, frameon=False, fontsize=14);\n",
    "\n",
    "# save figure\n",
-    "fig.savefig('./Figures/bootstrap/{}_members_vs_ensemble_extremes.png'.format(PREDICTAND), dpi=300, bbox_inches='tight')"
+    "filename = ('./Figures/bootstrap/pr_only_members_vs_ensemble_extremes.png'.format(PREDICTAND) if\n",
+    "            PREDICTAND == 'pr' and PR_ONLY else './Figures/bootstrap/{}_members_vs_ensemble_extremes.png'.format(PREDICTAND))\n",
+    "fig.savefig(filename, dpi=300, bbox_inches='tight')"
   ]
  },
  {

 %% Cell type:markdown id:b6344366-bbd8-4141-b229-2964a5875f76 tags:

 # Plot bootstrapped model results

 %% Cell type:code id:255d7312-58cd-41c3-a25b-f7c86bb8fe19 tags:

 ``` python
 # builtins
 import pathlib

 # externals
 import numpy as np
 import pandas as pd
 import seaborn as sns
 import matplotlib.pyplot as plt
 from matplotlib import gridspec
 ```

 %% Cell type:code id:b357b52a-0093-42ff-b934-e6318f93ecca tags:

 ``` python
 # path to bootstrapped model results
 RESULTS = pathlib.Path('/mnt/CEPH_PROJECTS/FACT_CLIMAX/ERA5_PRED/bootstrap')
 ```

 %% Cell type:code id:f2f9414a-2041-4b5a-8370-eb772ec5c31b tags:

 ``` python
 # predictand
-PREDICTAND = 'tasmin'
+PREDICTAND = 'pr'
 ```

 %% Cell type:code id:e5e7a63c-3d3c-4e29-94af-c21f0f63ba6c tags:

 ``` python
 # whether only precipitation was used as predictor
-PR_ONLY = False
+PR_ONLY = True
 ```

 %% Cell type:markdown id:354f45a0-0d3e-43db-bf0d-ede327b9e77c tags:

 ## Load model results

 %% Cell type:code id:448c695b-e55e-4530-8f9b-661c58f8d81b tags:

 ``` python
 # model results computed by notebook eval_bootstrap.ipynb
 df_refe = pd.read_csv(RESULTS.joinpath(PREDICTAND, 'reference.csv'))
 df_pred = pd.read_csv(RESULTS.joinpath(PREDICTAND, 'prediction_pr-only.csv' if PREDICTAND == 'pr' and PR_ONLY else
                                       'prediction.csv'))
 ```

 %% Cell type:code id:a6f2b9ea-3ede-40f8-a448-de2833b5d180 tags:

 ``` python
 # dataframe of single members and ensembles only
 members = df_pred[np.isin(df_pred['product'], ['Member-{}'.format(i) for i in range(10)])]
 ensemble = df_pred[~np.isin(df_pred['product'], ['Member-{}'.format(i) for i in range(10)])]
 ```

 %% Cell type:code id:1d9535d3-dbfc-4e65-b5df-728a98e2715f tags:

 ``` python
 # create a sequential colormap: for reference data, single ensemble members, and ensemble mean predictions
 palette = sns.color_palette('Blues', len(df_pred['loss'].unique()))
 palette
 ```

 %% Cell type:markdown id:f08e3d29-1692-441b-aee4-25c360dd6e8b tags:

 ### Mean values: single members vs. ensemble

 %% Cell type:code id:c7bd9c53-4a96-41e7-b8bd-7d438d4dbe73 tags:

 ``` python
 # initialize figure
 fig = plt.figure(figsize=(16, 5))

 # create grid for different subplots
 grid = gridspec.GridSpec(ncols=5, nrows=1, width_ratios=[3, 1, 1, 3, 1], wspace=0.05, hspace=0)

 # add subplots
 ax1 = fig.add_subplot(grid[0])
 ax2 = fig.add_subplot(grid[1], sharey=ax1)
 ax3 = fig.add_subplot(grid[3])
 ax4 = fig.add_subplot(grid[4], sharey=ax3)
 axes = [ax1, ax2, ax3, ax4]

 # plot bias: single members vs. ensemble
 sns.barplot(x='product', y='bias', hue='loss', data=members, palette=palette, ax=ax1);
 sns.barplot(x='product', y='bias', hue='loss', data=ensemble, palette=palette, ax=ax2);

 # plot mae: single members vs. ensemble
 sns.barplot(x='product', y='mae', hue='loss', data=members, palette=palette, ax=ax3);
 sns.barplot(x='product', y='mae', hue='loss', data=ensemble, palette=palette, ax=ax4);

 # axes limits and ticks
 y_lim_bias = (-50, 50) if PREDICTAND == 'pr' else (-1, 1)
 y_lim_mae = (0, 2) if PREDICTAND == 'pr' else (0, 1)
 y_ticks_bias = (np.arange(y_lim_bias[0], y_lim_bias[1] + 10, 10) if PREDICTAND == 'pr' else
                np.arange(y_lim_bias[0], y_lim_bias[1] + 0.2, 0.2))
 y_ticks_mae = np.arange(y_lim_mae[0], y_lim_mae[1] + 0.2, 0.2)

 # axis for bias
 ax1.set_ylabel('Bias (%)' if PREDICTAND == 'pr' else 'Bias (°C)')
 ax1.set_ylim(y_lim_bias)
 ax1.set_yticks(y_ticks_bias)

 # axis for mae
 ax3.set_ylabel('Mean absolute error (mm)' if PREDICTAND == 'pr' else 'Mean absolute error (°C)')
 ax3.set_ylim(y_lim_mae)
 ax3.set_yticks(y_ticks_mae)

 # adjust axis for ensemble predictions
 for ax in [ax2, ax4]:
    ax.yaxis.tick_right()
    ax.set_ylabel('')

 # axis fontsize and legend
 for ax in axes:
    ax.tick_params('both', labelsize=14)
    ax.set_xticklabels(ax.get_xticklabels(), rotation=90)
    ax.yaxis.label.set_size(14)
    ax.set_xlabel('')

    # adjust legend
    h, _ = ax.get_legend_handles_labels()
    ax.get_legend().remove()

 # show single legend
 ax4.legend(bbox_to_anchor=(1.3, 1.05), loc=2, frameon=False, fontsize=14);

 # save figure
-fig.savefig('./Figures/bootstrap/{}_members_vs_ensemble_mean.png'.format(PREDICTAND), dpi=300, bbox_inches='tight')
+filename = ('./Figures/bootstrap/pr_only_members_vs_ensemble_mean.png'.format(PREDICTAND) if
+            PREDICTAND == 'pr' and PR_ONLY else './Figures/bootstrap/{}_members_vs_ensemble_mean.png'.format(PREDICTAND))
+fig.savefig(filename, dpi=300, bbox_inches='tight')
 ```

 %% Cell type:markdown id:233f4239-1cca-4ad5-8581-782e42948503 tags:

 ### Extreme values: single members vs. ensemble

 %% Cell type:code id:72aadc32-a1c8-40a1-8cbe-e7166e18b6e0 tags:

 ``` python
 # initialize figure
 fig = plt.figure(figsize=(16, 5))

 # create grid for different subplots
 grid = gridspec.GridSpec(ncols=5, nrows=1, width_ratios=[3, 1, 1, 3, 1], wspace=0.05, hspace=0)

 # add subplots
 ax1 = fig.add_subplot(grid[0])
 ax2 = fig.add_subplot(grid[1], sharey=ax1)
 ax3 = fig.add_subplot(grid[3])
 ax4 = fig.add_subplot(grid[4], sharey=ax3)
 axes = [ax1, ax2, ax3, ax4]

 # plot bias: single members vs. ensemble
 sns.barplot(x='product', y='bias_ex', hue='loss', data=members, palette=palette, ax=ax1);
 sns.barplot(x='product', y='bias_ex', hue='loss', data=ensemble, palette=palette, ax=ax2);

 # plot mae: single members vs. ensemble
 sns.barplot(x='product', y='mae_ex', hue='loss', data=members, palette=palette, ax=ax3);
 sns.barplot(x='product', y='mae_ex', hue='loss', data=ensemble, palette=palette, ax=ax4);

 # axes limits and ticks
 y_lim_bias = (-50, 50) if PREDICTAND == 'pr' else (-1, 1)
-y_lim_mae = (0, 2) if PREDICTAND == 'pr' else (0, 2)
+y_lim_mae = (0, 20) if PREDICTAND == 'pr' else (0, 2)
 y_ticks_bias = (np.arange(y_lim_bias[0], y_lim_bias[1] + 10, 10) if PREDICTAND == 'pr' else
                np.arange(y_lim_bias[0], y_lim_bias[1] + 0.2, 0.2))
-y_ticks_mae = np.arange(y_lim_mae[0], y_lim_mae[1] + 0.2, 0.2)
+y_ticks_mae = np.arange(y_lim_mae[0], y_lim_mae[1] + 2, 2) if PREDICTAND == 'pr' else np.arange(y_lim_mae[0], y_lim_mae[1] + 0.2, 0.2)

 # axis for bias
 ax1.set_ylabel('Bias (%)' if PREDICTAND == 'pr' else 'Bias (°C)')
 ax1.set_ylim(y_lim_bias)
 ax1.set_yticks(y_ticks_bias)

 # axis for mae
 ax3.set_ylabel('Mean absolute error (mm)' if PREDICTAND == 'pr' else 'Mean absolute error (°C)')
 ax3.set_ylim(y_lim_mae)
 ax3.set_yticks(y_ticks_mae)

 # adjust axis for ensemble predictions
 for ax in [ax2, ax4]:
    ax.yaxis.tick_right()
    ax.set_ylabel('')

 # axis fontsize and legend
 for ax in axes:
    ax.tick_params('both', labelsize=14)
    ax.set_xticklabels(ax.get_xticklabels(), rotation=90)
    ax.yaxis.label.set_size(14)
    ax.set_xlabel('')

    # adjust legend
    h, _ = ax.get_legend_handles_labels()
    ax.get_legend().remove()

 # show single legend
 ax4.legend(bbox_to_anchor=(1.3, 1.05), loc=2, frameon=False, fontsize=14);

 # save figure
-fig.savefig('./Figures/bootstrap/{}_members_vs_ensemble_extremes.png'.format(PREDICTAND), dpi=300, bbox_inches='tight')
+filename = ('./Figures/bootstrap/pr_only_members_vs_ensemble_extremes.png'.format(PREDICTAND) if
+            PREDICTAND == 'pr' and PR_ONLY else './Figures/bootstrap/{}_members_vs_ensemble_extremes.png'.format(PREDICTAND))
+fig.savefig(filename, dpi=300, bbox_inches='tight')
 ```

 %% Cell type:markdown id:f13dce92-e895-48de-97ef-17b3d7b7553d tags:

 ### Predictions vs. reference

 %% Cell type:code id:3b63c13d-b32a-4a24-8290-e209db490b88 tags:

 ``` python
 df_refe
 ```

 %% Cell type:code id:a5fea8e6-48f6-42b4-b72e-739e5eb9bf5c tags:

 ``` python
 ensemble
 ```