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   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import io as sio \n",
    "import xarray as xr\n",
    "import io\n",
    "import urllib3\n",
    "import zipfile\n",
    "from tqdm import tqdm\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from glob import glob\n",
    "import time\n",
    "import plotly.express as px\n",
    "\n",
    "# Modules to plot\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from data.traces import build_trace_library_production\n",
    "trace_library_prod = build_trace_library_production()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'long', 'medium', 'short'}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataFolder = '/Users/an1009/Ecommons/HFC/PyMC/Data/Processed/'\n",
    "\n",
    "# ------------------------------------------------------------\n",
    "# Load Atmospheric Concentration calculated from AGAGE\n",
    "# ------------------------------------------------------------\n",
    "observations_file = 'AGAGE_AnnualMean_concentration.parquet'\n",
    "atmosConc = pd.read_parquet(f\"{dataFolder}{observations_file}\")\n",
    "gases = atmosConc.columns.values\n",
    "\n",
    "\n",
    "# ------------------------------------------------------------\n",
    "# Reported Production data\n",
    "# ------------------------------------------------------------\n",
    "prod = {}\n",
    "for c in gases:\n",
    "   gn = c.replace(\"-\",\"\")\n",
    "   file = f\"{dataFolder}priors_{gn}.parquet\"\n",
    "   prod[c] = pd.read_parquet(file).loc[1956:2023]\n",
    "   if 'shortProd1' in prod[c].columns:\n",
    "      prod[c]['shortProd'] =  prod[c]['shortProd1'] + prod[c]['shortProd2']\n",
    "      prod[c] = prod[c].drop(['shortProd1','shortProd2'],axis=1)\n",
    "   file = f\"{dataFolder}posterior_{gn}.zarr\"\n",
    "   if gn=='cfc11':\n",
    "      eprod = xr.open_zarr(file)\n",
    "      prod_vars = set(trace_library_prod.keys()) & set(eprod.data_vars)\n",
    "      for v in prod_vars:\n",
    "         prod[c][v+'_median'] = eprod[v].sel(metric='median')\n",
    "         prod[c][v+'_std'] = eprod[v].sel(metric='stddev')\n",
    "   prod[c].attrs['title'] = 'Reported & Estimted Production'\n",
    "   prod[c].attrs['units'] = 'Gg'\n",
    "   prod[c].attrs['long_name'] = 'Production'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "rep_prod_list = ['shortProd','mediumProd','longProd','feedStock','prod']\n",
    "est_prod_list = ['short','medium','long']\n",
    "uncert_list = np.concat([np.zeros(len(rep_prod_list)),\\\n",
    "                np.ones(len(est_prod_list))])\n",
    "prod_label = ['Reported Short Bank','Reported Medium Bank','Reported Long Bank',\\\n",
    "            'Reported Feedstock','Reported Production',\\\n",
    "            'Estimated Short Bank','Estimated Medium Bank', 'Estimated Long Bank']\n",
    "prod_list = rep_prod_list + est_prod_list\n",
    "line_colors = px.colors.qualitative.Dark2[:len(rep_prod_list)]\n",
    "line_colors += line_colors\n",
    "line_styles = ['dot' if unc else 'solid' for unc in uncert_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
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     "metadata": {},
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   "source": [
    "line_colors"
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   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
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     "output_type": "error",
     "traceback": [
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      "\u001b[31mIndexError\u001b[39m                                Traceback (most recent call last)",
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    "colors[10]"
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  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_trace_library_production():\n",
    "    rep_prod_list = ['shortProd','mediumProd','longProd','feedStock','prod']\n",
    "    est_prod_list = ['short','medium','long']\n",
    "    uncert_list = np.concat([np.zeros(len(rep_prod_list),dtype='int'),\\\n",
    "                    np.ones(len(est_prod_list),dtype='int')])\n",
    "    prod_label = ['Reported Short Bank','Reported Medium Bank','Reported Long Bank',\\\n",
    "                'Reported Feedstock','Reported Production',\\\n",
    "                'Estimated Short Bank','Estimated Medium Bank', 'Estimated Long Bank']\n",
    "    prod_list = rep_prod_list + est_prod_list\n",
    "    line_colors = px.colors.qualitative.Dark2[:len(rep_prod_list)]\n",
    "    line_colors += line_colors\n",
    "    line_styles = ['dot' if unc else 'solid' for unc in uncert_list]\n",
    "    \n",
    "    lib = {}\n",
    "    for i in range(len(prod_list)):\n",
    "        lib[prod_list[i]] = {\n",
    "            \"label\": prod_label[i],\n",
    "            \"line_color\": line_colors[i],\\\n",
    "            \"line_style\": line_styles[i],\\\n",
    "            \"uncertainty\": uncert_list[i]\n",
    "        }\n",
    "    return lib"
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   "metadata": {},
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            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.graph_objects as go\n",
    "\n",
    "# Add data\n",
    "month = ['January', 'February', 'March', 'April', 'May', 'June', 'July',\n",
    "         'August', 'September', 'October', 'November', 'December']\n",
    "high_2000 = [32.5, 37.6, 49.9, 53.0, 69.1, 75.4, 76.5, 76.6, 70.7, 60.6, 45.1, 29.3]\n",
    "low_2000 = [13.8, 22.3, 32.5, 37.2, 49.9, 56.1, 57.7, 58.3, 51.2, 42.8, 31.6, 15.9]\n",
    "high_2007 = [36.5, 26.6, 43.6, 52.3, 71.5, 81.4, 80.5, 82.2, 76.0, 67.3, 46.1, 35.0]\n",
    "low_2007 = [23.6, 14.0, 27.0, 36.8, 47.6, 57.7, 58.9, 61.2, 53.3, 48.5, 31.0, 23.6]\n",
    "high_2014 = [28.8, 28.5, 37.0, 56.8, 69.7, 79.7, 78.5, 77.8, 74.1, 62.6, 45.3, 39.9]\n",
    "low_2014 = [12.7, 14.3, 18.6, 35.5, 49.9, 58.0, 60.0, 58.6, 51.7, 45.2, 32.2, 29.1]\n",
    "\n",
    "fig = go.Figure()\n",
    "# Create and style traces\n",
    "fig.add_trace(go.Scatter(x=month, y=high_2014, name='High 2014',\n",
    "                         line=dict(color='firebrick', width=4, dash='solid')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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