{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "favorite-commercial",
   "metadata": {},
   "source": [
    "# Seasonal Analisis of discharges in the Mälaren catchement.\n",
    "\n",
    "\n",
    "Jacopo Cantoni\n",
    "\n",
    "Discharge data are a valuable resource and a key dataset. \n",
    "Discharge data across sweden are freely available from the Swedish Metereological and Hidrological Institute (SMHI). Such data are obteained from a series of actual measuring stations that are futher expanded with the use of the HYPE model that give a full coverage of the entire territory. \n",
    "\n",
    "The objectictive of this work is to create an automatic rutine to retrieve, in an eficent way, discharge data and transform them in an manageable data structure for futher analisis. The final step is to use the obtained dataset to identify boundaries of seasonal patterns.\n",
    "\n",
    "The structure of the SMHI dataset is based on a fine catchement division and the whole territory is devided in more than 55000 areas. For the closing point of each of these catchements is available a time series of  Modelled discharges (m³/s) with the time resolution of the day starting from year 1981. \n",
    "\n",
    "Such data can be directly obtained, selecting a specific area (https://vattenwebb.smhi.se/modelarea/) or listing catchement IDs (https://vattenwebb.smhi.se/nadia/). The firs method method is suitable to obtain information for only a small number of catchements while the second is better suited for our scope.\n",
    "\n",
    "The first step is then to obtain the IDs of the catchement that we are interested. Relevant shapefiles can be obtined also from SMHI:\n",
    "(https://www.smhi.se/data/hydrologi/sjoar-och-vattendrag/ladda-ner-data-fran-svenskt-vattenarkiv-1.20127)\n",
    "\n",
    "Specifically the files:\n",
    "\n",
    "Huvudavrinningsområden_SVAR_2016_3 \n",
    "\n",
    "Delavrinningsområden SVAR_2016_3 \n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "included-female",
   "metadata": {},
   "source": [
    "### Find ID's for all the Mälaren's Catchement sub-catchements.\n",
    "1. from the Huvudavrinningsområden_SVAR_2016_3 file is possible to identify the Mälaren catchement shape. Knowing that the Mälaren catchement have the HARO field = to 61000 is possible to select the correct subcatchements from Delavrinningsområden SVAR_2016_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "attended-tribute",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/user/SeasonAnalisis\n",
      "<class 'pandas.core.series.Series'>\n",
      "2419\n"
     ]
    }
   ],
   "source": [
    "# Cell 1\n",
    "%cd /home/user/SeasonAnalisis\n",
    "\n",
    "#inpunt time period \n",
    "SYear = 2000\n",
    "EYear = 2020\n",
    "\n",
    "\n",
    "#from osgeo import ogr\n",
    "import geopandas as gpd\n",
    "\n",
    "catchements = gpd.read_file(\"aro_y_2016_3.shp\")\n",
    "#print (catchements)\n",
    "#catchements.plot()\n",
    "#MalarenCatchement = catchements[(catchements.HARO == 61000) & (catchements.YTKOD != 80)]\n",
    "MalarenCatchement = catchements[(catchements.HARO == 61000) ]\n",
    "#MalarenCatchement = catchements[MalarenCatchement.YTKOD != 80]\n",
    "# MalarenCatchemnt is the base map for our region.\n",
    "#MalarenCatchement.plot()\n",
    "\n",
    "# the array CatchID conatains all the AROID that of the area in which \n",
    "# we are interested\n",
    "\n",
    "CatchID=MalarenCatchement.AROID\n",
    "print(type(CatchID))\n",
    "\n",
    "\n",
    "L=len(CatchID)\n",
    "print(L)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "strategic-geneva",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "sexual-cover",
   "metadata": {},
   "source": [
    "Catchements IDs can be used to get discharge data from the SMHI website. The website is not capable of downloading big data for a big number of stations. considering this and for script semplification each station is downloaded singularly and information are stored in a pandas.dataframe\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "streaming-request",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cell 2\n",
    "import pandas as pd\n",
    "#creating the dataframe (df) that will host all data\n",
    "\n",
    "TIME = pd.date_range(start=str(SYear)+'-01-01',end=str(EYear)+'-12-31')\n",
    "Ltime = len(TIME)\n",
    "df1=pd.DataFrame(index=TIME, columns=CatchID)#total Q\n",
    "df2=pd.DataFrame(index=TIME, columns=CatchID)#area specific Q\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "continuing-hierarchy",
   "metadata": {},
   "source": [
    "Run the following cell if interested in downloading tha data ex-novo alternativelly run the next one to read previously downloaded data stored in a CSV fie.\n",
    "This part is quite time consuming and if the data have been yet downoladed use the next box code to upload them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "working-layer",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cell 3\n",
    "import requests as re\n",
    "\n",
    "for i in CatchID:\n",
    "        \n",
    "    s = re.session()\n",
    "\n",
    "#payload are the input to give to the SMHI website for the retriving data\n",
    "    payload = {\n",
    "        'idlist' : i,\n",
    "        'aggregation' : 'day',\n",
    "        'startYear' : str(SYear),\n",
    "        'endYear' : str(EYear),\n",
    "    }\n",
    "\n",
    "# get data from the website \n",
    "    response = s.post('https://vattenwebb.smhi.se/nadia/rest/all',\n",
    "                      data=payload)\n",
    "    #print (response.status_code)\n",
    "    data = (response.content)\n",
    "    data = data.decode(\"utf-8\")\n",
    "    data = data.splitlines()\n",
    "    Head1 = data[0]\n",
    "    Head2 = data[1]\n",
    "#extracting and storing relevant information\n",
    "    for j in data:\n",
    "        if j==Head1 or j==Head2:\n",
    "            continue\n",
    "        DATA=j.split(';')\n",
    "        Date = pd.to_datetime(DATA[2],format='%Y-%m-%d')\n",
    "        \n",
    "        Qtot=DATA[5]\n",
    "        Qtot=Qtot.replace(',','.')\n",
    "        df1.at[Date,i] = Qtot\n",
    "        \n",
    "        QArea=DATA[3]\n",
    "        QArea=QArea.replace(',','.')\n",
    "        df2.at[Date,i]=QArea\n",
    "        #print(Date)\n",
    "        #print(df.at[i,Date])\n",
    "            \n",
    "#print(df2.head())\n",
    "df1.to_csv('Q_'+str(SYear)+'-'+str(EYear)+'.csv')\n",
    "df2.to_csv('Qarea_'+str(SYear)+'-'+str(EYear)+'.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "several-messenger",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Cell 4\n",
    "df1 = pd.read_csv('Q_'+str(SYear)+'-'+str(EYear)+'.csv')\n",
    "df2 = pd.read_csv('Qarea_'+str(SYear)+'-'+str(EYear)+'.csv')\n",
    "\n",
    "df1 = df1.iloc[:,1:]\n",
    "df1 = pd.DataFrame(df1.values, columns = CatchID, index =TIME)\n",
    "\n",
    "df2 = df2.iloc[:,1:]\n",
    "df2 = pd.DataFrame(df2.values, columns = CatchID, index =TIME)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "innocent-grammar",
   "metadata": {},
   "source": [
    "Due to difference in size of different subcatchements, the magnitude of discharges can vary greatly. To solve this issue the dataframe is pretreated using a normalization method. is also shown the disctribution of the data that is strongly skewed on the left."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "modern-singapore",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------\n",
      "Max value of observed Q:1.0\n",
      "Min value of observed Q:0.0\n",
      "----------------------------------\n",
      "after normalization\n",
      "Max value of observed Q:1.0\n",
      "Min value of observed Q:0.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Cell 5\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import MinMaxScaler \n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "for i in CatchID:\n",
    "    df1[i]=pd.to_numeric(df1[i],downcast='float')\n",
    "    df2[i]=pd.to_numeric(df2[i],downcast='float')\n",
    "    \n",
    "M1 = df1.values.max()\n",
    "m1 = df1.values.min()\n",
    "#n=100\n",
    "#dBin=(M1-m1)/n\n",
    "#bins1={'bins':np.arange(m1,(M1+(0.1*dBin)),dBin)}\n",
    "\n",
    "M2 = df2.values.max()\n",
    "m2 = df2.values.min()\n",
    "#n=100\n",
    "#dBin=(M1-m1)/n\n",
    "#bins1={'bins':np.arange(m1,(M1+(0.1*dBin)),dBin)}\n",
    "#print('----------------------------------')\n",
    "#print('Max value of Q generated by a spefic area:'+str(M2))\n",
    "#print('Min value of Q generated by a spefic area:'+str(m2))\n",
    "#plt.hist(df2.values)\n",
    "#plt.show()\n",
    "print('----------------------------------')\n",
    "print('Max value of observed Q:'+str(M1))\n",
    "print('Min value of observed Q:'+str(m1))\n",
    "#plt.hist(df1.values)\n",
    "#plt.show()\n",
    "print('----------------------------------')\n",
    "print ('after normalization')\n",
    "\n",
    "\n",
    "sc = MinMaxScaler(feature_range = (0,1))\n",
    "dataset = sc.fit_transform(df1.values)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "df1Prova=pd.DataFrame(index=TIME)\n",
    "Tdf1=df1.transpose()\n",
    "for i in CatchID:\n",
    "    sc = MinMaxScaler(feature_range = (0,1))\n",
    "    df1Prova[i] = sc.fit_transform(Tdf1.loc[i].values.reshape(-1, 1))\n",
    "\n",
    "df1 = pd.DataFrame(dataset, columns = CatchID, index =TIME)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "M1 = df1.values.max()\n",
    "m1 = df1.values.min()\n",
    "print('Max value of observed Q:'+str(M1))\n",
    "print('Min value of observed Q:'+str(m1))\n",
    "\n",
    "plt.hist(df1Prova.values)\n",
    "plt.show\n",
    "#print (df1.head)\n",
    "\n",
    "dataset = sc.fit_transform(df2.values)\n",
    "df2 = pd.DataFrame(dataset, columns = CatchID, index =TIME)\n",
    "plt.hist(df2.values)\n",
    "plt.show\n",
    "\n",
    "df1.to_csv('NormQ_'+str(SYear)+'-'+str(EYear)+'.csv')\n",
    "df2.to_csv('NormQarea_'+str(SYear)+'-'+str(EYear)+'.csv')\n",
    "\n",
    "df1Prova.to_csv('ProvaNormQ_'+str(SYear)+'-'+str(EYear)+'.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "nominated-penetration",
   "metadata": {},
   "source": [
    "The following code cell create a color coded map of the discharge in the selected area for each day of the interested period. This are then colected and save in a gif format for visualization. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cultural-channels",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cell 6\n",
    "import matplotlib.pyplot as plt\n",
    "import mapclassify \n",
    "import glob\n",
    "from moviepy.editor import *\n",
    "from PIL import Image\n",
    "\n",
    "\n",
    "Timedf1=df1.transpose()\n",
    "Timedf2=df2.transpose()\n",
    "\n",
    "for i in TIME: \n",
    "    #create a tif file for each day color map on Q'\n",
    "    dayCatch = MalarenCatchement.set_index('AROID')\n",
    "    dayCatch = dayCatch.merge(Timedf1[i], on='AROID')\n",
    "\n",
    "    plt.figure()\n",
    "    dayCatch.plot(column=i,\n",
    "                  cmap='viridis',\n",
    "                  scheme='natural_breaks'\n",
    "                 )\n",
    "    plt.savefig(str(i)+'.png')\n",
    "    plt.show()\n",
    "    plt.close()\n",
    "    prova=dayCatch[i]\n",
    "    \n",
    "    prova=prova.isnull()\n",
    "    b=np.where(prova[0])\n",
    "    if np.size==0:\n",
    "        print (i)\n",
    "    \n",
    "    \n",
    "\n",
    "imgs = glob.glob('*.png')\n",
    "clip= ImageSequenceClip(imgs, fps = 3)\n",
    "clip.write_gif('MoviolatotNoLake.gif')\n",
    "clip.close\n",
    "    \n",
    "    \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ordered-immigration",
   "metadata": {},
   "source": [
    "The following cell import the data about the first clusterization perform to reduce the subcatchement dimension."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "compact-scott",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "               GeoClass\n",
      "AROID                  \n",
      "652430-143251         1\n",
      "653065-143452         1\n",
      "653173-143752         1\n",
      "653474-143283         1\n",
      "653976-143263         1\n",
      "...                 ...\n",
      "658533-552637         9\n",
      "658609-557258         9\n",
      "662890-552741         5\n",
      "662932-551197         5\n",
      "664180-542606         4\n",
      "\n",
      "[2419 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "#Cell 7\n",
    "GeoClass = pd.read_csv('GeoClass.csv')\n",
    "GeoClass = GeoClass.iloc[:,1:]\n",
    "GeoClass = pd.DataFrame(GeoClass.values, \n",
    "                        columns = ['GeoClass'], \n",
    "                        index = CatchID,\n",
    "                       )\n",
    "print(type(GeoClass))\n",
    "print(GeoClass)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "solved-halifax",
   "metadata": {},
   "source": [
    "The cluster are showed on the map of the Mälaren catchement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "great-election",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ET_ID', 'YTKOD', 'NAMN', 'VDRID', 'INOBJ', 'UTOBJ', 'OMRID_NED', 'OMRTYP', 'HARO', 'DISTRICT', 'LANDSKOD', 'AREAL', 'AVM', 'HAVAVM', 'HORDNING', 'AAUEB', 'AA_ANT_ARO', 'MEDHOJD', 'VERSION', 'geometry', 'GeoClass']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Cell 8\n",
    "import matplotlib.pyplot as plt\n",
    "import mapclassify \n",
    "\n",
    "ClusterCatch = MalarenCatchement.set_index('AROID')\n",
    "ClusterCatch = ClusterCatch.merge(GeoClass, on='AROID')\n",
    "\n",
    "print (list(ClusterCatch.columns))\n",
    "plt.figure()\n",
    "ClusterCatch.plot(column='GeoClass',\n",
    "              cmap='Dark2',\n",
    "             )\n",
    "plt.savefig('Clusters15.png')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "welsh-turning",
   "metadata": {},
   "source": [
    "Cell 9 uses the obtained cluster to resize the original dataframe, discharge from homogeneous areas are collapsed in a single value (average)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "tropical-australian",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AROID\n",
      "<bound method NDFrame.head of           2000-01-01  2000-01-02  2000-01-03  2000-01-04  2000-01-05  \\\n",
      "GeoClass                                                               \n",
      "1           0.289802    0.290587    0.297117    0.302977    0.309661   \n",
      "2           0.104935    0.103917    0.104844    0.107146    0.116531   \n",
      "3           0.188470    0.196418    0.203662    0.240717    0.268206   \n",
      "4           0.062980    0.060610    0.062335    0.059395    0.065730   \n",
      "5           0.103323    0.098710    0.104661    0.116903    0.142725   \n",
      "6           0.104400    0.106156    0.238806    0.186725    0.167980   \n",
      "7           0.779957    0.776497    0.772959    0.771627    0.769829   \n",
      "8           0.083394    0.085077    0.082251    0.166874    0.093452   \n",
      "9           0.210893    0.207328    0.211135    0.218442    0.221667   \n",
      "10          0.272583    0.267118    0.264807    0.300864    0.322964   \n",
      "\n",
      "          2000-01-06  2000-01-07  2000-01-08  2000-01-09  2000-01-10  ...  \\\n",
      "GeoClass                                                              ...   \n",
      "1           0.315120    0.325586    0.344750    0.360273    0.365982  ...   \n",
      "2           0.120837    0.138544    0.175712    0.220876    0.236622  ...   \n",
      "3           0.294777    0.352815    0.465375    0.510422    0.483107  ...   \n",
      "4           0.064943    0.099990    0.114731    0.171528    0.162205  ...   \n",
      "5           0.151006    0.193678    0.284130    0.357757    0.341724  ...   \n",
      "6           0.139077    0.135680    0.129757    0.122505    0.100264  ...   \n",
      "7           0.769352    0.770562    0.777954    0.783539    0.781898  ...   \n",
      "8           0.134066    0.153920    0.330980    0.217727    0.134988  ...   \n",
      "9           0.228594    0.245882    0.273273    0.302698    0.310382  ...   \n",
      "10          0.335716    0.377143    0.444976    0.460547    0.433944  ...   \n",
      "\n",
      "          2020-12-22  2020-12-23  2020-12-24  2020-12-25  2020-12-26  \\\n",
      "GeoClass                                                               \n",
      "1           0.235587    0.245190    0.246067    0.247354    0.249532   \n",
      "2           0.208994    0.220661    0.218893    0.214630    0.210146   \n",
      "3           0.262564    0.269207    0.247796    0.228356    0.210662   \n",
      "4           0.139369    0.157713    0.140641    0.123267    0.110595   \n",
      "5           0.171814    0.189979    0.175547    0.160954    0.148650   \n",
      "6           0.066906    0.099577    0.078980    0.064942    0.055313   \n",
      "7           0.799509    0.797248    0.795268    0.795545    0.794346   \n",
      "8           0.144254    0.076247    0.068168    0.064579    0.060205   \n",
      "9           0.301611    0.307876    0.302288    0.293868    0.287493   \n",
      "10          0.234387    0.244009    0.224491    0.208249    0.193700   \n",
      "\n",
      "          2020-12-27  2020-12-28  2020-12-29  2020-12-30  2020-12-31  \n",
      "GeoClass                                                              \n",
      "1           0.252383    0.261583    0.275868    0.290820    0.302004  \n",
      "2           0.212076    0.237391    0.270039    0.291850    0.302532  \n",
      "3           0.220810    0.247161    0.271759    0.289250    0.301082  \n",
      "4           0.106144    0.137478    0.188559    0.210342    0.190320  \n",
      "5           0.156550    0.204309    0.244516    0.263061    0.267967  \n",
      "6           0.050994    0.072354    0.091236    0.146465    0.179095  \n",
      "7           0.793626    0.798802    0.805803    0.812665    0.815793  \n",
      "8           0.097410    0.169879    0.132415    0.091815    0.092327  \n",
      "9           0.290527    0.307564    0.331786    0.356734    0.373203  \n",
      "10          0.202974    0.251311    0.296449    0.316843    0.323494  \n",
      "\n",
      "[10 rows x 7671 columns]>\n"
     ]
    }
   ],
   "source": [
    "#Cell 9\n",
    "\n",
    "Tdf1=df1.transpose()\n",
    "print(Tdf1.index.name)\n",
    "#print (df1.head)\n",
    "df1GeoClass = Tdf1.merge(GeoClass, on='AROID')\n",
    "df1GeoClass = df1GeoClass.groupby('GeoClass').mean()\n",
    "df1GeoClass.to_csv('df1GeoClass.csv')\n",
    "print(df1GeoClass.head)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sixth-diana",
   "metadata": {},
   "source": [
    "Data from the clusterization through SOM performed in the R environment are imported."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "confidential-dating",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            Clusters\n",
      "2000-01-01         5\n",
      "2000-01-02         5\n",
      "2000-01-03         1\n",
      "2000-01-04         4\n",
      "2000-01-05         4\n",
      "...              ...\n",
      "2020-12-27         3\n",
      "2020-12-28         4\n",
      "2020-12-29         4\n",
      "2020-12-30         4\n",
      "2020-12-31         4\n",
      "\n",
      "[7671 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "# Cell 10\n",
    "DataCluster = pd.read_csv('DataCluster.csv')\n",
    "DataCluster = DataCluster.iloc[:,1:]\n",
    "DataCluster = pd.DataFrame(DataCluster.values, \n",
    "                        columns = ['Clusters'], \n",
    "                        index = TIME,\n",
    "                      )\n",
    "\n",
    "print(DataCluster)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acting-trainer",
   "metadata": {},
   "source": [
    "The clusterization is plotted divide by each year each day is plotted at a different hight in the graph base on the cluster (Note: the point high refer to the random cluster but is not about the discharge magnitude)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "employed-prescription",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1440x3600 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Cell 11\n",
    "plt.style.use('seaborn')\n",
    "SelYear=df1[df1.index.year == 2000]\n",
    "Stime = TIME[TIME.year==2000]\n",
    "SelCluster=DataCluster[DataCluster.index.year == 2000]\n",
    "Year=2000\n",
    "fig, ax = plt.subplots(nrows=20, ncols=1,figsize=(20,50))\n",
    "for i in range (20):\n",
    "    SelYear=df1[df1.index.year == Year]\n",
    "    Stime = TIME[TIME.year==Year]\n",
    "    SelCluster=DataCluster[DataCluster.index.year == Year]\n",
    "    Year = Year+1\n",
    "    ax[i].plot_date(Stime,SelCluster)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "tracked-richards",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "A more complex analysis of the results is needed. Nevertheless yet from the last kind of graph is possible to conclude that a meaningful clusterization has not been reached.\n",
    "This is more clear if the whole 20 year period is taken into consideration. Indeed if only one year is analyzed the last figure is of the kind expected. where we can observe some stable continuous periods with the possibility of interruption due to extreme events. Never the less if more than one year is taken into consideration that is not possible to observe good stability on the type of cluster in similar seasons together with strong variability on which cluster is showing a stable/event type behavior (Seasonal/extreme events). \n",
    "An indication that the model has not been able of well classify different discharge regimes is also the uniformity of the SOM distance matrix.\n",
    "Possible improvements can be done by changing the first clusterization method (used to reduce dimensionality) possibly with one that does not require a pre-definition of the number of clusters.\n",
    "Also, a possible issue is with input dataset to the SOM and as it is may still carrying stronger geographical information (relation of importance between catchments) then temporal and as a such a better pretreatment of data may also be a possible way to make emerge different discharge regimes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "considerable-eating",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "massive-denver",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "surgical-sleep",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "classical-supplement",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aquatic-invite",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}