From ed0666d30eddd08221e3b85a3c9770faff76b78b Mon Sep 17 00:00:00 2001
From: Jerry Lin <jlin96@br011.ib.bridges2.psc.edu>
Date: Mon, 21 Aug 2023 19:15:33 -0400
Subject: [PATCH] made edits to bias calc notebook

---
 climsim_utils/data_utils.py       |   3 +-
 evaluation/bias_calculation.ipynb | 399 ++----------------------------
 2 files changed, 24 insertions(+), 378 deletions(-)

diff --git a/climsim_utils/data_utils.py b/climsim_utils/data_utils.py
index 5a35cbd..b6f2623 100644
--- a/climsim_utils/data_utils.py
+++ b/climsim_utils/data_utils.py
@@ -200,7 +200,8 @@ def find_keys(dictionary, value):
         self.metrics_dict = {'MAE': self.calc_MAE,
                              'RMSE': self.calc_RMSE,
                              'R2': self.calc_R2,
-                             'CRPS': self.calc_CRPS
+                             'CRPS': self.calc_CRPS,
+                             'bias': self.calc_bias
                             }
         self.linecolors = ['#0072B2', 
                            '#E69F00', 
diff --git a/evaluation/bias_calculation.ipynb b/evaluation/bias_calculation.ipynb
index ebc3a3f..86cec15 100644
--- a/evaluation/bias_calculation.ipynb
+++ b/evaluation/bias_calculation.ipynb
@@ -9,19 +9,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2023-08-21 14:36:29.941637: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
-      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
-      "2023-08-21 14:36:30.922437: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from climsim_utils.data_utils import *"
    ]
@@ -35,12 +25,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
     "grid_path = '/ocean/projects/atm200007p/jlin96/neurips_proj/ClimSim/grid_info/ClimSim_low-res_grid-info.nc'\n",
-    "norm_path = '/ocean/projects/atm200007p/jlin96/neurips_proj/ClimSim/normalizations/'\n",
+    "norm_path = '/ocean/projects/atm200007p/jlin96/neurips_proj/ClimSim/preprocessing/normalizations/'\n",
     "\n",
     "grid_info = xr.open_dataset(grid_path)\n",
     "input_mean = xr.open_dataset(norm_path + 'inputs/input_mean.nc')\n",
@@ -64,7 +54,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -120,45 +110,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/ocean/projects/atm200007p/jlin96/neurips_proj/ClimSim/climsim_utils/data_utils.py:649: RuntimeWarning: divide by zero encountered in divide\n",
-      "  r_squared = 1 - sq_diff.sum(axis = 0)/tss_time.sum(axis = 0) # sum over time\n",
-      "/ocean/projects/atm200007p/jlin96/neurips_proj/ClimSim/climsim_utils/data_utils.py:649: RuntimeWarning: invalid value encountered in divide\n",
-      "  r_squared = 1 - sq_diff.sum(axis = 0)/tss_time.sum(axis = 0) # sum over time\n",
-      "/ocean/projects/atm200007p/jlin96/neurips_proj/ClimSim/climsim_utils/data_utils.py:649: RuntimeWarning: divide by zero encountered in divide\n",
-      "  r_squared = 1 - sq_diff.sum(axis = 0)/tss_time.sum(axis = 0) # sum over time\n",
-      "/ocean/projects/atm200007p/jlin96/neurips_proj/ClimSim/climsim_utils/data_utils.py:649: RuntimeWarning: divide by zero encountered in divide\n",
-      "  r_squared = 1 - sq_diff.sum(axis = 0)/tss_time.sum(axis = 0) # sum over time\n",
-      "/ocean/projects/atm200007p/jlin96/neurips_proj/ClimSim/climsim_utils/data_utils.py:649: RuntimeWarning: divide by zero encountered in divide\n",
-      "  r_squared = 1 - sq_diff.sum(axis = 0)/tss_time.sum(axis = 0) # sum over time\n",
-      "/ocean/projects/atm200007p/jlin96/neurips_proj/ClimSim/climsim_utils/data_utils.py:649: RuntimeWarning: divide by zero encountered in divide\n",
-      "  r_squared = 1 - sq_diff.sum(axis = 0)/tss_time.sum(axis = 0) # sum over time\n",
-      "/ocean/projects/atm200007p/jlin96/neurips_proj/ClimSim/climsim_utils/data_utils.py:649: RuntimeWarning: invalid value encountered in divide\n",
-      "  r_squared = 1 - sq_diff.sum(axis = 0)/tss_time.sum(axis = 0) # sum over time\n",
-      "/ocean/projects/atm200007p/jlin96/neurips_proj/ClimSim/climsim_utils/data_utils.py:649: RuntimeWarning: divide by zero encountered in divide\n",
-      "  r_squared = 1 - sq_diff.sum(axis = 0)/tss_time.sum(axis = 0) # sum over time\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "data.reweight_target(data_split = 'scoring')\n",
-    "data.reweight_preds(data_split = 'scoring')\n",
-    "\n",
-    "data.metrics_names = ['MAE', 'RMSE', 'R2']\n",
-    "data.create_metrics_df(data_split = 'scoring')"
+    "data.reweight_preds(data_split = 'scoring')"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Create plots"
+    "## Calculate biases"
    ]
   },
   {
@@ -167,350 +131,38 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%config InlineBackend.figure_format = 'retina'\n",
-    "letters = string.ascii_lowercase"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dict_idx = data.metrics_var_scoring\n",
-    "dict_var = data.metrics_idx_scoring\n",
-    "\n",
-    "plot_df_byvar = {}\n",
-    "for metric in data.metrics_names:\n",
-    "    plot_df_byvar[metric] = pd.DataFrame([dict_idx[model][metric] for model in data.model_names],\n",
-    "                                               index=data.model_names)\n",
-    "    plot_df_byvar[metric] = plot_df_byvar[metric].rename(columns = data.var_short_names).transpose()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'MAE':                 CNN       cVAE         ED        HSR        MLP        RPN\n",
-       " variable                                                                  \n",
-       " $dT/dt$    2.585418   2.732051   2.864516   2.844789   2.682765   2.685253\n",
-       " $d1/dt$    4.400639   4.679666   4.673036   4.784426   4.494752   4.592410\n",
-       " NETSW     18.845029  19.729191  14.967681  19.816967  13.360914  18.878685\n",
-       " FLWDS      8.597703   6.588051   6.893821   6.266635   5.224468   6.017693\n",
-       " PRECSC     3.364180   3.321645   3.045900   3.511187   2.683915   3.328444\n",
-       " PRECC     37.825799  38.805963  37.250200  42.379195  34.333065  37.455143\n",
-       " SOLS      10.830984  10.939536   8.553734  11.314418   7.970805  10.355971\n",
-       " SOLL      13.152971  13.461330  10.923703  13.602788  10.299178  12.955313\n",
-       " SOLSD      5.816831   6.158717   5.074686   6.330680   4.533141   5.845594\n",
-       " SOLLD      5.678992   6.065650   5.136236   6.214983   4.806307   5.701871,\n",
-       " 'RMSE':                 CNN       cVAE         ED        HSR        MLP        RPN\n",
-       " variable                                                                  \n",
-       " $dT/dt$    4.369106   4.720812   4.696491   4.824681   4.420904   4.481825\n",
-       " $d1/dt$    7.283705   7.779619   7.642989   7.895550   7.321821   7.518002\n",
-       " NETSW     36.906747  38.364034  28.537017  37.773019  26.707550  33.596484\n",
-       " FLWDS     10.854747   8.529763   9.070148   8.219851   6.968642   7.913915\n",
-       " PRECSC     6.000881   6.182026   5.078154   6.094945   4.733680   5.511482\n",
-       " PRECC     85.311686  88.711142  76.681968  90.640413  72.876357  76.578238\n",
-       " SOLS      22.916411  23.264985  17.998650  23.614026  17.403315  20.606768\n",
-       " SOLL      27.251214  27.812571  22.540191  27.778323  21.954437  25.218968\n",
-       " SOLSD     12.126842  12.639114   9.916940  12.395840   9.420306  10.996472\n",
-       " SOLLD     12.102328  12.624935  10.416563  12.474274  10.118702  11.247108,\n",
-       " 'R2':                CNN      cVAE         ED        HSR        MLP        RPN\n",
-       " variable                                                                \n",
-       " $dT/dt$   0.627260  0.589693   0.541714   0.567524   0.588987   0.617206\n",
-       " $d1/dt$       -inf      -inf       -inf       -inf       -inf       -inf\n",
-       " NETSW     0.943800  0.956531   0.980463   0.959136   0.982684   0.968036\n",
-       " FLWDS     0.827836  0.883237   0.801872   0.904115   0.924299   0.912173\n",
-       " PRECSC         NaN      -inf       -inf       -inf        NaN       -inf\n",
-       " PRECC     0.076930 -0.926289 -17.908577 -68.347502 -38.685671 -67.943146\n",
-       " SOLS      0.927029  0.929375   0.959621   0.928777   0.961380   0.942723\n",
-       " SOLL      0.915685  0.915267   0.945472   0.916143   0.947612   0.928029\n",
-       " SOLSD     0.927229  0.921261   0.950717   0.922621   0.955713   0.939980\n",
-       " SOLLD     0.812537  0.796327   0.857180   0.797273   0.866159   0.836630}"
-      ]
-     },
-     "execution_count": 54,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "plot_df_byvar"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'CNN':                       MAE       RMSE        R2\n",
-       " variable                                      \n",
-       " ptend_t          2.585418   4.369106   0.62726\n",
-       " ptend_q0001      4.400639   7.283705      -inf\n",
-       " cam_out_NETSW   18.845029  36.906747    0.9438\n",
-       " cam_out_FLWDS    8.597703  10.854747  0.827836\n",
-       " cam_out_PRECSC    3.36418   6.000881       NaN\n",
-       " cam_out_PRECC   37.825799  85.311686   0.07693\n",
-       " cam_out_SOLS    10.830984  22.916411  0.927029\n",
-       " cam_out_SOLL    13.152971  27.251214  0.915685\n",
-       " cam_out_SOLSD    5.816831  12.126842  0.927229\n",
-       " cam_out_SOLLD    5.678992  12.102328  0.812537,\n",
-       " 'cVAE':                       MAE       RMSE        R2\n",
-       " variable                                      \n",
-       " ptend_t          2.732051   4.720812  0.589693\n",
-       " ptend_q0001      4.679666   7.779619      -inf\n",
-       " cam_out_NETSW   19.729191  38.364034  0.956531\n",
-       " cam_out_FLWDS    6.588051   8.529763  0.883237\n",
-       " cam_out_PRECSC   3.321645   6.182026      -inf\n",
-       " cam_out_PRECC   38.805963  88.711142 -0.926289\n",
-       " cam_out_SOLS    10.939536  23.264985  0.929375\n",
-       " cam_out_SOLL     13.46133  27.812571  0.915267\n",
-       " cam_out_SOLSD    6.158717  12.639114  0.921261\n",
-       " cam_out_SOLLD     6.06565  12.624935  0.796327,\n",
-       " 'HSR':                       MAE       RMSE         R2\n",
-       " variable                                       \n",
-       " ptend_t          2.844789   4.824681   0.567524\n",
-       " ptend_q0001      4.784426    7.89555       -inf\n",
-       " cam_out_NETSW   19.816967  37.773019   0.959136\n",
-       " cam_out_FLWDS    6.266635   8.219851   0.904115\n",
-       " cam_out_PRECSC   3.511187   6.094945       -inf\n",
-       " cam_out_PRECC   42.379195  90.640413 -68.347502\n",
-       " cam_out_SOLS    11.314418  23.614026   0.928777\n",
-       " cam_out_SOLL    13.602788  27.778323   0.916143\n",
-       " cam_out_SOLSD     6.33068   12.39584   0.922621\n",
-       " cam_out_SOLLD    6.214983  12.474274   0.797273,\n",
-       " 'MLP':                       MAE       RMSE         R2\n",
-       " variable                                       \n",
-       " ptend_t          2.682765   4.420904   0.588987\n",
-       " ptend_q0001      4.494752   7.321821       -inf\n",
-       " cam_out_NETSW   13.360914   26.70755   0.982684\n",
-       " cam_out_FLWDS    5.224468   6.968642   0.924299\n",
-       " cam_out_PRECSC   2.683915    4.73368        NaN\n",
-       " cam_out_PRECC   34.333065  72.876357 -38.685671\n",
-       " cam_out_SOLS     7.970805  17.403315    0.96138\n",
-       " cam_out_SOLL    10.299178  21.954437   0.947612\n",
-       " cam_out_SOLSD    4.533141   9.420306   0.955713\n",
-       " cam_out_SOLLD    4.806307  10.118702   0.866159,\n",
-       " 'RPN':                       MAE       RMSE         R2\n",
-       " variable                                       \n",
-       " ptend_t          2.685253   4.481825   0.617206\n",
-       " ptend_q0001       4.59241   7.518002       -inf\n",
-       " cam_out_NETSW   18.878685  33.596484   0.968036\n",
-       " cam_out_FLWDS    6.017693   7.913915   0.912173\n",
-       " cam_out_PRECSC   3.328444   5.511482       -inf\n",
-       " cam_out_PRECC   37.455143  76.578238 -67.943146\n",
-       " cam_out_SOLS    10.355971  20.606768   0.942723\n",
-       " cam_out_SOLL    12.955313  25.218968   0.928029\n",
-       " cam_out_SOLSD    5.845594  10.996472    0.93998\n",
-       " cam_out_SOLLD    5.701871  11.247108    0.83663,\n",
-       " 'ED':                       MAE       RMSE         R2\n",
-       " variable                                       \n",
-       " ptend_t          2.864516   4.696491   0.541714\n",
-       " ptend_q0001      4.673036   7.642989       -inf\n",
-       " cam_out_NETSW   14.967681  28.537017   0.980463\n",
-       " cam_out_FLWDS    6.893821   9.070148   0.801872\n",
-       " cam_out_PRECSC     3.0459   5.078154       -inf\n",
-       " cam_out_PRECC     37.2502  76.681968 -17.908577\n",
-       " cam_out_SOLS     8.553734   17.99865   0.959621\n",
-       " cam_out_SOLL    10.923703  22.540191   0.945472\n",
-       " cam_out_SOLSD    5.074686    9.91694   0.950717\n",
-       " cam_out_SOLLD    5.136236  10.416563    0.85718}"
-      ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dict_idx"
+    "results_complete = {}\n",
+    "for model in data.model_names:\n",
+    "    results_temp = {}\n",
+    "    for var in data.target_vars:\n",
+    "        results_temp[var] = data.calc_bias(data.preds_weighted_scoring[model][var], \n",
+    "                                           data.target_weighted_scoring[var],\n",
+    "                                           avg_grid = False)\n",
+    "    results_complete[model] = results_temp"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Plot 1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 700x800 with 3 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 789,
-       "width": 685
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, axes = plt.subplots(nrows  = len(data.metrics_names), sharex = True)\n",
-    "for i in range(len(data.metrics_names)):\n",
-    "    plot_df_byvar[data.metrics_names[i]].plot.bar(\n",
-    "        legend = False,\n",
-    "        ax = axes[i])\n",
-    "    if data.metrics_names[i] != 'R2':\n",
-    "        axes[i].set_ylabel('$W/m^2$')\n",
-    "        axes[i].set_yscale('log')\n",
-    "    else:\n",
-    "        axes[i].set_ylim(0,1)\n",
-    "\n",
-    "    axes[i].set_title(f'({letters[i]}) {data.metrics_names[i]}')\n",
-    "axes[i].set_xlabel('Output variable')\n",
-    "axes[i].set_xticklabels(plot_df_byvar[data.metrics_names[i]].index, \\\n",
-    "    rotation=0, ha='center')\n",
-    "\n",
-    "axes[0].legend(columnspacing = .9, \n",
-    "               labelspacing = .3,\n",
-    "               handleheight = .07,\n",
-    "               handlelength = 1.5,\n",
-    "               handletextpad = .2,\n",
-    "               borderpad = .2,\n",
-    "               ncol = 3,\n",
-    "               loc = 'upper right')\n",
-    "fig.set_size_inches(7,8)\n",
-    "fig.tight_layout()"
+    "## Make plots"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
-    "hmm = data.load_npy_file(load_path = '/ocean/projects/atm200007p/behrens/ED_Behrens_2022/ED_ClimSIM_1_3_pred.npy')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1681920, 128)"
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hmm.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1681920, 128)"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data.target_scoring.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "3.5.2\n"
-     ]
-    }
-   ],
-   "source": [
-    "import matplotlib\n",
-    "print(matplotlib.__version__)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 413,
-       "width": 534
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots()\n",
-    "ax.plot([1, 2, 3], [4, 5, 6], label='Line 1')\n",
-    "ax.plot([1, 2, 3], [6, 5, 4], label='Line 2')\n",
-    "ax.plot([1, 2, 3], [7, 8, 9], label='Line 3')\n",
-    "ax.legend()\n",
-    "\n",
-    "# set the number of columns in the legend to 2\n",
-    "ax.legend(ncol=2)\n",
-    "\n",
-    "# show the plot\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Plot 2"
+    "%config InlineBackend.figure_format = 'retina'\n",
+    "letters = string.ascii_lowercase"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Plot 3"
+    "### Lat-lev plot"
    ]
   },
   {
@@ -520,13 +172,6 @@
    "outputs": [],
    "source": []
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Plot 4"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,