GHCN - Climatologic Time Serie exploration_20.07.27_majeur.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GHCN - Climatologic Daily Time Series Exploration (Precipitation, temperature min/min, ...)\n",
    "GHCN data: Global Historical Climatology Network https://www.ncdc.noaa.gov/ghcnd-data-access\n",
    "\n",
    "Python Script:  July 2020, L. Duffar https://github.com/loicduffar\n",
    "\n",
    "This notebook reads the 3 inventory files (not the measurements themselves), and display the informations in a single browser (station, country, period by parameter), allowing to apply filter (e.g. select country and/or stations). In addition a graph is plotted representing the number of stations for each year, and a map is displayed to show the station locations. Outputs can be saved in an excel file.\n",
    "\n",
    "The 3 inventory files (see below) should be downloaded from ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/daily/, and stored to your local drive.\n",
    "\n",
    "- 1) Run the 1st cell below to read the informations and display the browser. \n",
    "     Browse the stations or apply filter to select a contry, and select the desired stations\n",
    "- 2) Run the 2nd cell to plot a graph representing Number of stations in operation, by parameter (only the ones filtered/selected)\n",
    "- 3) Run the 3rd cell to display a map with the location of the stations (only the ones filtered/selected)\n",
    "  If needed, run the cell 3bis to display the map in a separated big window\n",
    "- 4) If needed, save the selected list and the graph in an excel file"
   ]
  },
  {
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    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1) Files reading (stations, inventory, country), informations merging and interactive Browsing in a single table\n",
    "\n",
    "(Library qgrid v 1.3.1, compatible with pandas v 1)\n",
    "\n",
    "Inventory files to be downloaded and stored on your local drive:\n",
    "- ghcnd-stations.txt: Station ID, name, lon, lat, altitude, GSN network (plus an integer whose meaning is unknown)\n",
    "- ghcnd-inventory.txt: Period of measurement for each parameter\n",
    "- ghcnd-countries.txt: Extended country name, according the 2 first letters of the Station Id\n",
    "\n",
    "Tutorial:\n",
    "- a) Customize the files path (e.g. path= r\"C:\\Users\\...\")\n",
    "- b) Run the cell below to display the browser (period of measurement is indicated for each parameter by the year of beginning and ending)\n",
    "- c) Apply filter(s) by column (e.g. CTRY to select one country and ID to select station(s)) ![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "adbd61712b1a4da89765839fee74befd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "QgridWidget(grid_options={'fullWidthRows': True, 'syncColumnCellResize': True, 'forceFitColumns': True, 'defau…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Created on Jul 2020\n",
    "@author: Loïc Duffar\n",
    "\"\"\"\n",
    "import pandas as pd\n",
    "import os\n",
    "from IPython.display import display\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.express as px\n",
    "import qgrid\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "pd.set_option(\"display.max_rows\", None, \"display.max_columns\", None)\n",
    "\n",
    "#********** Customization **************************\n",
    "path= r\"C:\\Users\\DUFFAR\\Documents\\A\\Download\\Hydro-Climato\\NCEI (ex NCDC)\\GHCN\\Daily V3\".replace('\\\\', '/')\n",
    "path_online = r\"https://www.ncei.noaa.gov/data/global-historical-climatology-network-daily/access\"\n",
    "\n",
    "#********** Files reading **************************\n",
    "# --------------- STATIONS\n",
    "file_in= os.path.join(path,\"ghcnd-stations.txt\")\n",
    "header = ['ID', 'LAT', 'LON', 'ALT', 'STATE', 'NAME', 'GSN', 'Unknown']\n",
    "df = pd.read_fwf(file_in, colspecs='infer', infer_nrows=26000, names = header, index_col='ID')\n",
    "\n",
    "# --------------- INVENTORY (PARAMETER & PERIOD)\n",
    "file_in= os.path.join(path,\"ghcnd-inventory.txt\")\n",
    "header = ['ID', 'LAT', 'LON', 'PARAM', 'BEG', 'END']\n",
    "df2 = pd.read_fwf(file_in, colspecs='infer', infer_nrows=100, names = header, index_col='ID')\n",
    "cols = ['PARAM', 'BEG', 'END' ]\n",
    "lparam = ['PRCP', 'TMIN', 'TMAX', 'EVAP', 'SNOW', 'SNWD']# can be completed if needed (additionnal parameters are numerous)\n",
    "for param in lparam:\n",
    "    for col in cols[1:]:\n",
    "        df[param+'_'+col] = df2.loc[df2['PARAM']==param, col]\n",
    "\n",
    "# --------------- COUNTRY\n",
    "file_in= os.path.join(path,\"ghcnd-countries.txt\")\n",
    "header = ['CTRY_ID', 'CTRY']\n",
    "df2 = pd.read_fwf(file_in, colspecs='infer', infer_nrows=100, names = header, index_col='CTRY_ID')\n",
    "list = []\n",
    "for id in df.index:\n",
    "    list.append( df2.loc[ id[:2], 'CTRY'] )\n",
    "df['CTRY'] = list\n",
    "\n",
    "#********** Display Informations in a browser ******************\n",
    "widget = qgrid.show_grid(df, show_toolbar=True)\n",
    "display(widget)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ## 2) Graph representing Number of stations in operation, by parameter\n",
    " (library matplotlib)\n",
    "\n",
    "- a) Run the cell below to display the graph representing \"Number of stations in operation\"\n",
    "- b) Re run the cell below after each filter application in the browser, to update the graphe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\" ----------- Not functional\\n# Automatic update of the graph according to the filter(s) applied in the browser above\\n# To be used in conjunction with the instruction '%matplotlib notebook' above (disabled at this time)\\nwidget.on('filter_changed', handle_filter_changed)    \\n\\ndef handle_filter_changed(event, widget):\\n    stat_numbers = pd.DataFrame(None, index=years, columns=lparam)\\n    for param in lparam:\\n        for year in years:\\n            stat_numbers.loc[year, param] = qg_df.loc[ (qg_df[param+'_BEG']<=year) & (qg_df[param+'_END']>=year),  param+'_BEG'].count()\\n    lines.set_data(stat_number)\\n    fig = plt.gcf()\\n    fig.canvas.draw()\\n\""
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ******************** Work in progress\n",
    "qg_df = widget.get_changed_df()\n",
    "year_min = 9999\n",
    "year_max = 0\n",
    "for param in lparam:\n",
    "    if qg_df[param+'_BEG'].min()<year_min: year_min = qg_df[param+'_BEG'].min()\n",
    "    if qg_df[param+'_END'].max()>year_max: year_max = qg_df[param+'_END'].max()\n",
    "years = np.arange(year_min, year_max+1 )\n",
    "\n",
    "stat_numbers = pd.DataFrame(None, index=years, columns=lparam)\n",
    "for param in lparam:\n",
    "    for year in years:\n",
    "        stat_numbers.loc[year, param] = qg_df.loc[ (qg_df[param+'_BEG']<=year) & (qg_df[param+'_END']>=year),  param+'_BEG'].count()\n",
    "\n",
    "lines = stat_numbers.plot(figsize=(20, 4))\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Number of stations')\n",
    "plt.title('Number of stations in operation, by parameter')\n",
    "if os.path.exists('../out')==False: os.mkdir('../out')\n",
    "plt.savefig('../out/GTHCN - Stations number.png', dpi=300)\n",
    "\n",
    "\"\"\" ----------- Not functional\n",
    "# Automatic update of the graph according to the filter(s) applied in the browser above\n",
    "# To be used in conjunction with the instruction '%matplotlib notebook' above (disabled at this time)\n",
    "widget.on('filter_changed', handle_filter_changed)    \n",
    "\n",
    "def handle_filter_changed(event, widget):\n",
    "    stat_numbers = pd.DataFrame(None, index=years, columns=lparam)\n",
    "    for param in lparam:\n",
    "        for year in years:\n",
    "            stat_numbers.loc[year, param] = qg_df.loc[ (qg_df[param+'_BEG']<=year) & (qg_df[param+'_END']>=year),  param+'_BEG'].count()\n",
    "    lines.set_data(stat_number)\n",
    "    fig = plt.gcf()\n",
    "    fig.canvas.draw()\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3) Display the stations map\n",
    "(Library Plotly - Scattermapbox)\n",
    "- a) Run the cell below to display a map of the stations (all parameters combined)\n",
    "- b) Re run the cell below after each filter application in the browser, to update the map "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "hoverinfo": "text",
         "lat": [
          4.217,
          3.62,
          1.483,
          3.2,
          5.3,
          5.9,
          5.297,
          6.167,
          3.131,
          2.746,
          2.263,
          2.45,
          2.262,
          4.322,
          5.937,
          4.313
         ],
         "lon": [
          100.7,
          103.22,
          110.333,
          113.033,
          115.25,
          118.067,
          100.277,
          102.293,
          101.549,
          101.71,
          102.252,
          103.833,
          111.985,
          113.987,
          116.051,
          118.122
         ],
         "marker": {
          "color": "red",
          "size": 5
         },
         "mode": "markers",
         "text": [
          "MY000048620 - SITIAWAN",
          "MY000048657 - KUANTAN",
          "MY000096413 - KUCHING",
          "MY000096441 - BINTULU",
          "MY000096465 - LABUAN",
          "MY000096491 - SANDAKAN",
          "MYM00048601 - PENANG INTL",
          "MYM00048615 - SULTAN ISMAIL PETRA",
          "MYM00048647 - SULTAN ABDUL AZIZ SHAH INTL",
          "MYM00048650 - KUALA LUMPUR INTL",
          "MYM00048665 - MALACCA",
          "MYM00048674 - MERSING",
          "MYM00096421 - SIBU",
          "MYM00096449 - MIRI",
          "MYM00096471 - KOTA KINABALU INTL",
          "MYM00096481 - TAWAU"
         ],
         "type": "scattermapbox"
        }
       ],
       "layout": {
        "mapbox": {
         "center": {
          "lat": 7.5,
          "lon": 80.9
         },
         "style": "open-street-map",
         "zoom": 1
        },
        "margin": {
         "b": 0,
         "l": 0,
         "r": 0,
         "t": 0
        },
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
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\"plot_bgcolor\": \"#E5ECF6\", \"polar\": {\"angularaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"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}}}},\n",
       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('af58dbfd-1418-4a10-ba7f-242db1df2161');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.graph_objects as go\n",
    "\n",
    "qg_df = widget.get_changed_df()\n",
    "\n",
    "fig = go.Figure(go.Scattermapbox(\n",
    "                        mode = \"markers\",\n",
    "                        lon = qg_df['LON'],\n",
    "                        lat = qg_df['LAT'],\n",
    "                        marker = dict(size= 5, color= 'red'),\n",
    "                        text = qg_df.index+' - '+qg_df['NAME'],\n",
    "                        hoverinfo  = 'text' \n",
    "                                 ))\n",
    "\n",
    "fig.update_layout(  margin = dict(t=0, b=0, r=0, l=0),\n",
    "                    mapbox = dict(\n",
    "                                style= \"open-street-map\",    # open-street-map, carto-positron, stamen-terrain, stamen-toner\n",
    "                                center= dict(lon= 80.9, lat= 7.5),\n",
    "                                zoom= 1) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 bis) Display the map on a separated html file (optionnal)\n",
    "(Library Plotly - Mapbox)\n",
    "- a) Run the cell below to display the map in a separate web explorer window\n",
    "- b) Re run the cell after a filter application, to update the map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'../out/GHCN.html'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Created on Jul 2020\n",
    "@author: Loïc Duffar\n",
    "\"\"\"\n",
    "import plotly.graph_objects as go\n",
    "import plotly\n",
    "\n",
    "qg_df = widget.get_changed_df()\n",
    "\n",
    "# Open mapbox account and pass your access token over here\n",
    "# Mapbox account sign up and access token is free. \n",
    "# Mapbox information: https://www.mapbox.com/help/how-access-tokens-work/#rotating-tokens\n",
    "mapbox_access_token = 'pk.eyJ1IjoibG9pYy1zYWxzZXJvIiwiYSI6ImNrY2syODZvMjFwd2YydG84ZDR2ZXZ2NncifQ.2JN19jp_EvWCpmSi7QgF5A'\n",
    "\n",
    "data = [go.Scattermapbox(\n",
    "    mode = \"markers\",\n",
    "    lon = qg_df['LON'],\n",
    "    lat = qg_df['LAT'],\n",
    "    marker = dict(size= 5, color= 'red'),\n",
    "    text = qg_df.index+' - '+qg_df['NAME'],\n",
    "    textposition='middle right',\n",
    "    hoverinfo  = 'text'\n",
    "        )]\n",
    "\n",
    "layout = go.Layout(\n",
    "    margin ={'l':0,'t':0,'b':0,'r':0},\n",
    "    mapbox = {\n",
    "        'style': \"light\",   # basic, streets, outdoors, light, dark, satellite, satellite-streets\n",
    "        'center': {'lon': 80.9, 'lat': 8},\n",
    "        'zoom': 2,\n",
    "        'accesstoken': mapbox_access_token,\n",
    "        'bearing': 0,\n",
    "        'pitch': 0\n",
    "        }\n",
    "    )                  \n",
    "\n",
    "if os.path.exists('../out')==False: os.mkdir('../out')\n",
    "plotly.offline.plot({\"data\":data, \"layout\":layout}, filename = '../out/GHCN.html')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4) Save an excel file containing the list and the graph for the selected stations\n",
    "WARNING: Don't do that without filtering the stations in the browser above, otherwise the number of lines (115 000) will exceed the excel limit !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "qg_df = widget.get_changed_df()\n",
    "\n",
    "if os.path.exists('../out')==False: os.mkdir('../out')\n",
    "writer = pd.ExcelWriter(\"../out/GHCN - Output.xlsx\")\n",
    "workbook  = writer.book\n",
    "qg_df.to_excel(writer, sheet_name='Summary', startrow=18)\n",
    "if os.path.exists('../out/GTHCN - Stations number.png'):\n",
    "    worksheet = writer.sheets['Summary']\n",
    "    worksheet.insert_image('A1', '../out/GTHCN - Stations number.png')\n",
    "writer.save()"
   ]
  }
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