{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "#Loading data and packages\n", "import pandas as pd\n", "import numpy as np\n", "path_eklima = \"/Users/ainajohannessen/Documents/Aina/skole/master/precipitation/input_data/eklima/\"\n", "path_norm=path_eklima +\"month_normals.txt\"\n", "\n", "def load_normals():\n", " data_norm = pd.read_csv(path_norm, sep=\";\")\n", " \n", " def clean_star(stno):\n", " \n", " cleanstno = stno.replace(\"*\",\"\") #string.replace(\"1\",\"\")\n", " cleanstno = cleanstno.replace(\" \",\"\")\n", " return cleanstno\n", " #data_norm.set_index('St.no', inplace=True, drop = False) # data_comb[\"2016_sept\"] = RR_mnth_septred\n", " data_norm.index = data_norm[\"St.no\"].apply(clean_star)\n", " \n", " return data_norm\n", "\n", "def load_hourly():\n", " def extract_valid_data(group):\n", " #if (group[\"RR_1\"].any() != \"-9999\") and (group[\"RR_1\"].any() != \"x\"):\n", " if (\"-9999\" not in group[\"RR_1\"].values ) and (\"x\" not in group[\"RR_1\"].values):\n", " RR1 = pd.DataFrame(index = group[\"St.no\"].values.astype(str) )\n", " RR1[\"year\"] = group[\"Year\"].values\n", " RR1[\"mnth\"] = group[\"Mnth\"].values\n", " RR1[\"day\"] = group[\"Date\"].values\n", " RR1[\"Time(UTC)\"] = group[\"Time(UTC)\"].values\n", " RR1[\"RR_1\"] = group[\"RR_1\"].values.astype(float)\n", " RR1[\"fRR_1\"] = group[\"fRR_1\"].values\n", " RR1[\"RT_1\"] = group[\"RT_1\"].values.astype(float)\n", " RR1[\"fRT_1\"] = group[\"fRT_1\"].values\n", " return RR1\n", "\n", " districts = [\"Hordaland\", \"Rogaland\", \"Vest_agder\"]\n", " dates = [\"2016sept\"]\n", " hourly_precip = pd.DataFrame()\n", " for dis in districts:\n", " for date in dates:\n", " path_hourly = path_eklima + date + \"_\"+ dis + \".txt\"\n", " print(path_hourly)\n", " data_hourly = pd.read_csv(path_hourly, sep=\";\")\n", " grdata_hourly = data_hourly.groupby([\"St.no\"])\n", " RR1 = grdata_hourly.apply(extract_valid_data);\n", " hourly_precip = hourly_precip.append(RR1)\n", " hourly_precip.index=hourly_precip.index.droplevel(level=0) \n", " return hourly_precip\n", "\n", "def load_24hourly():\n", " def extract_valid_data(group):\n", " idx = np.where(group[\"Time(UTC)\"].values == 6 ) #all days at 6.. array\n", " if (\"-9999\" not in group[\"RR_24\"].values[idx]) and (\"x\" not in group[\"RR_24\"].values):\n", " RR = pd.DataFrame(index = group[\"St.no\"].values[idx].astype(str) )\n", " RR[\"year\"] = group[\"Year\"].values[idx]\n", " RR[\"mnth\"] = group[\"Mnth\"].values[idx]\n", " RR[\"day\"] = group[\"Date\"].values[idx]\n", " RR[\"Time(UTC)\"] = group[\"Time(UTC)\"].values[idx]\n", " RR[\"RR_24\"] = group[\"RR_24\"].values[idx].astype(float)\n", " RR[\"fRR_24\"] = group[\"fRR_24\"].values[idx]\n", " return RR\n", "\n", " districts = [\"Hordaland\", \"Rogaland\", \"Vest_agder\", \"sognogfjordane\"]\n", " dates = [\"2016sept\"]\n", " daily24h_precip = pd.DataFrame()\n", " for dis in districts:\n", " for date in dates:\n", " path_hourly = path_eklima + date + \"_\"+ dis + \".txt\"\n", " data_hourly = pd.read_csv(path_hourly, sep=\";\")\n", " grdata_hourly = data_hourly.groupby([\"St.no\"])\n", " RR = grdata_hourly.apply(extract_valid_data);\n", " daily24h_precip = daily24h_precip.append(RR)\n", " \n", " \n", " daily24h_precip.index=daily24h_precip.index.droplevel(level=0)\n", " return daily24h_precip\n", "\n", "def load_septsums():\n", " \n", " def split_date( df_date, index ):\n", " date = str( df_date ).split('.')[index]\n", " return date\n", " \n", " def get_mnthly( group ):\n", " cleaned = group #[~group.RR.isin([\"-9999\", -9999])]\n", " mnth = cleaned[\"Month\"].apply(split_date, index=0)\n", " yr = cleaned[\"Month\"].apply(split_date, index=1)\n", " RR1 = pd.DataFrame()#column=cleaned[\"St.no\"].values.astype(str))#index = group[\"St.no\"].values )\n", " RR1[\"St.no\"] = cleaned[\"St.no\"].values.astype(str)\n", " RR1[\"year\"] = yr.values\n", " RR1[\"month\"] = mnth.values\n", " RR1[\"RR_month\"] = cleaned[\"RR\"].values\n", " RR1[\"quality\"] = cleaned[\"fRR\"].values\n", " return RR1\n", " \n", " districts = [\"westCoast\"]#, \"Rogaland\", \"Vest_agder\"]\n", " dates = [\"2016_mnthly\"] #2016_mnthly_Hordaland.txt#2016_mnthly_westCoast\n", " \n", " for dis in districts:\n", " for date in dates:\n", " #path_tot_mnth = path_eklima + date + \"_\" + dis + \".txt\"#\"2016totsept_Hordaland.txt\"\n", " path_tot_mnth = path_eklima + \"mndhele2016_helenorge.txt\"\n", " data_tot_hord = pd.read_csv(path_tot_mnth, sep=\";\")\n", " grdata_hord = data_tot_hord.groupby([\"St.no\"])\n", " RR_mnth = grdata_hord.apply(get_mnthly);\n", " RR_mnth.index=RR_mnth.index.droplevel(level=1)\n", " \n", " RR_mnth.replace(to_replace=-9999, value=np.nan, inplace=True)\n", " #idx = RR_mnth[(RR_mnth[\"RR_month\"].isna()) | (RR_mnth[\"quality\"].isna())].index.unique()\n", " #RR_mnth.drop( idx, inplace=True )\n", " RR_mnth_sept = RR_mnth[RR_mnth[\"month\"]==\"9\"].copy() #get all precip only in sept\n", " RR_mnth_sept.set_index('St.no', inplace=True, drop = False)#data_comb[\"2016_sept\"] = RR_mnth_septred\n", " \n", " sum2016 = RR_mnth.groupby(\"St.no\").sum()\n", " sum2016.rename(index=str, columns={\"RR_month\": \"yr2016_sum\"}, inplace=True);\n", " RR_mnth_sept.rename(index=str, columns={\"RR_month\": \"Sept2016_sum\"}, inplace=True);\n", " \n", " \n", " return sum2016, RR_mnth_sept\n", "\n", "def load_eventSum():\n", " RR24 = load_24hourly()\n", "\n", " RR24_event = RR24[(RR24.mnth==9) & (RR24.day.isin([28,29,30]))].copy()\n", " #RR24_event.index=RR24_event.index.droplevel(level=1)\n", " RR24_event_sum = pd.DataFrame()\n", " RR24_event_sum[\"EvenSumfrom24\"]=RR24_event.groupby(RR24_event.index)[\"RR_24\"].sum()\n", " #RR24_event_sum[\n", " return RR24_event_sum\n", " \n", "\n", "def load_eventdaily():\n", " #RR24.loc[\"50865\",:]\n", " #RR24_max = RR24.groupby([RR24.index])[\"RR_24\"].max()\n", " RR24 = load_24hourly()\n", "\n", " daily_28 = pd.DataFrame(RR24[RR24[\"day\"]==28][\"RR_24\"].copy())\n", " daily_28.columns = [\"20160928\"]\n", " daily_29 = pd.DataFrame(RR24[RR24[\"day\"]==29][\"RR_24\"].copy())\n", " daily_29.columns = [\"20160929\"]\n", " daily_30 = pd.DataFrame(RR24[RR24[\"day\"]==30][\"RR_24\"].copy())\n", " daily_30.columns = [\"20160930\"]\n", " #Max_daily_sept = RR24[RR24[\"RR_24\"].isin(RR24_max)].copy()\n", " return daily_28, daily_29, daily_30\n", "\n", "def load_returnval(file):\n", " \n", " path_returneklima = \"/Users/ainajohannessen/Documents/Aina/skole/master/precipitation/input_data/eklima/\"+ file\n", " \n", " #READ FILE CORRECTLY\n", " all_returnP = pd.DataFrame( columns = [\"Return period (years)\", \"Method\", \"All year\", \"Dec\", \"Jan, Feb, Dec\", \"Sep\", \"Sep, Oct, Nov\", \"stid\"])\n", " for i in range(0,91):\n", " skiprows = range(0,i*10)\n", " firstline = 1 + i*10\n", " nrows = 7*(i+1)\n", " header = firstline+1\n", " return_stationinfo = pd.read_csv(path_returneklima, sep=\";\", nrows=1, header = None, skiprows = skiprows).values[0][0]\n", " #print(return_stationinfo)\n", " data_tot_hord = pd.read_csv(path_returneklima, sep=\";\", header = [header], nrows = 7)\n", " \n", " stid = return_stationinfo.split(\" \")[0]\n", " stname = return_stationinfo.split(\" \")[1]\n", " period= return_stationinfo.split(\" \")[-1]\n", " data_tot_hord[\"stid\"] = stid\n", " all_returnP = all_returnP.append(data_tot_hord, ignore_index=True) #Contain returnval for all stations in file\n", " \n", " #statIhave = all_returnP[all_returnP.stid.isin(comb_all.index.values)].copy() \n", " return all_returnP\n", " \n", " \n", "def load_station_info():\n", " #path_info = \"/Users/ainajohannessen/Documents/Aina/skole/master/precipitation/input_data/station_info/ALL_land_stat_info_for_walp\"\n", " path_info = path_eklima+\"metainfo_opersep2016_alllandnorw.txt\"\n", " df_info = pd.read_table(path_info, sep= \";\")#,header = [2])\n", " #df_info = df_info[:-2]\n", " #df_info = df_info[:664]\n", " df_info.index = df_info.Stnr.astype(str)\n", " \n", " #df_info.Longitude = df_info.Longitude.apply(lambda Lon: Lon.replace(',','.')).values.astype(float)\n", " #df_info.Latitude = df_info.Latitude.apply(lambda Lat: Lat.replace(',','.')).values.astype(float)\n", " #all_res = pd.DataFrame(columns =['Latitude', 'Longitude', 'Municipality', 'County'])\n", " \n", " return df_info\n", "\n", "def load_maximum(period=24):\n", " acc_colName = str(period) +\"acc\"\n", "\n", " hourly_precip = load_hourly()\n", " hourly_precip[\"stno\"] = hourly_precip.index.values\n", " hourly_precip = hourly_precip[(hourly_precip[\"mnth\"].isin([9])) &\n", " (hourly_precip[\"day\"].isin( [25, 26, 27, 28, 29, 30, 31])) ]\n", " \n", " def select(station):\n", " fillme = pd.DataFrame()\n", " roll = station[ \"RR_1\" ].rolling(period).sum()\n", " fillme[\"Time(UTC)\"] = station[\"Time(UTC)\"].values\n", " fillme[\"mnth\"] = station.mnth.values\n", " fillme[\"day\"] = station.day.values\n", " fillme[\"year\"] = station.year.values\n", " fillme[acc_colName] = roll.values\n", " fillme[\"RR_1\"] = station[ \"RR_1\" ].values\n", " return fillme\n", " gro_hourly_precip = hourly_precip.groupby([\"stno\"])\n", " data = gro_hourly_precip.apply(select)\n", " data.index = data.index.droplevel(level =1)\n", " data[\"stnr\"] = data.index\n", " data.groupby(\"stno\")[acc_colName].max()\n", " data['count_max'] = data.groupby(['stno'])[acc_colName].transform(max)\n", " #new = pd.DataFrame()\n", " #new[\"date\"] = data[data[\"24acc\"]data['count_max']]#.loc[:,[\"Time(UTC)\", \"mnth\"]]\n", " idx = data.groupby(['stno'])[acc_colName].transform(max) == data[acc_colName]\n", " only_max = data.groupby(['stno'])[acc_colName].max()\n", " \n", " return pd.DataFrame(only_max)\n", "\n", "def max_intensity():\n", " hourly_precip = load_hourly()\n", " hourly_precip[\"stno\"] = hourly_precip.index.values\n", " hourly_precip[\"intensity\"] = hourly_precip[\"RR_1\"].values/hourly_precip[\"RT_1\"].values\n", " hourly_precip.loc[ :,\"intensity\" ][ hourly_precip[\"RT_1\"].values < 0.1] = 0.0\n", " hourly_precip.loc[ :,\"intensity\" ][ (hourly_precip[\"intensity\"].isnull()) ] = 0.0\n", " only_max = hourly_precip.groupby(['stno'])[\"intensity\"].max()\n", " \n", " return only_max\n", "\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "def combine_all_data():\n", " \n", " #----CALLING AND SETTING VARIABLE---------------------\n", " #calling functions and setting variables: \n", " maximum24 = load_maximum(24)\n", " maximum48 = load_maximum(48)\n", " maximum72 = load_maximum(72)\n", " \n", " info = load_station_info() #info off all station operational of the time of Walpurga\n", " data_norm = load_normals()\n", " hourly_precip = load_hourly()\n", " maxintensity = max_intensity()\n", " sum2016, sum_mnth_sept2016 = load_septsums()\n", " daily_28, daily_29, daily_30 = load_eventdaily()\n", " \n", " RR24_event_sum = load_eventSum( ) \n", " return1d = load_returnval( file = \"1dreturnWCGUM.txt\" )\n", " return2d = load_returnval( file = \"2dreturnWCGUM.txt\" ) #, \"3dreturnWCGUM.txt\" ]\n", " return3d = load_returnval( file = \"3dreturnWCGUM.txt\" ) \n", " \n", " \n", " #--------SELECTION----------Stnr\n", " #select only information I want from info\n", " selected_info = info.loc[: , ['Latitude', 'Longitude', 'Municipality', 'County', \"Stnr\", \"Name\"] ].copy()\n", " selected_info = selected_info.drop_duplicates()\n", " #selected_info.index = selected_info.Stnr\n", " #Select only operational station at the time of walpurga( those inside station_info)\n", " #Select only those that has data values for period long enough to make a norm value(those inside data_norm)\n", " #data_norm = data_norm[data_norm.index.isin( selected_info.index)]\n", " #selected_info = selected_info[selected_info.index.isin( data_norm.index )]\n", " sum2016 = sum2016[ sum2016.index.isin( data_norm.index) ]\n", " sum_mnth_sept2016 = sum_mnth_sept2016[ sum_mnth_sept2016.index.isin(data_norm.index) ]\n", " comb_all = pd.concat( [ selected_info, data_norm, sum2016, sum_mnth_sept2016, \n", " daily_28, daily_29, daily_30, RR24_event_sum, \n", " maximum24, maximum48, maximum72, maxintensity], axis = 1 )\n", " \n", " \n", " \n", " #-------RETURNPERIODS----------\n", " #statIhave = returnP.copy()#[all_returnP.stid.isin(comb_all.index.values)].copy()\n", " for idx in comb_all.index:\n", " if (return1d.stid.values==idx).any():\n", " comb_all.loc[idx,\"1DR5_wholeYr\"] = return1d[ (return1d.stid == idx) & ( return1d[\"Return period (years)\"] == 5 ) ][\"All year\"].values[0]\n", " #comb_all.loc[idx,\"1DR10_wholeYr\"] = return1d[ (return1d.stid == idx) & ( return1d[\"Return period (years)\"] == 10 ) ][\"All year\"].values[0]\n", " #comb_all.loc[idx,\"1DR25_wholeYr\"] = return1d[ (return1d.stid == idx) & ( return1d[\"Return period (years)\"] == 25 ) ][\"All year\"].values[0]\n", " #comb_all.loc[idx,\"1DR50_wholeYr\"] = return1d[ (return1d.stid == idx) & ( return1d[\"Return period (years)\"] == 50 ) ][\"All year\"].values[0]\n", " \n", " comb_all.loc[idx,\"1DR5_Sep\"] = return1d[ (return1d.stid == idx) & ( return1d[\"Return period (years)\"] == 5 ) ][\"Sep\"].values[0]\n", " #comb_all.loc[idx,\"1DR10_Sep\"] = return1d[ (return1d.stid == idx) & ( return1d[\"Return period (years)\"] == 10 ) ][\"Sep\"].values[0]\n", " #comb_all.loc[idx,\"1DR25_Sep\"] = return1d[ (return1d.stid == idx) & ( return1d[\"Return period (years)\"] == 25 ) ][\"Sep\"].values[0]\n", " #comb_all.loc[idx,\"1DR50_Sep\"] = return1d[ (return1d.stid == idx) & ( return1d[\"Return period (years)\"] == 50 ) ][\"Sep\"].values[0]\n", " \n", " comb_all.loc[idx,\"1DR5_SepOctNov\"] = return1d[ (return1d.stid == idx) & ( return1d[\"Return period (years)\"] == 5 ) ][\"Sep, Oct, Nov\"].values[0]\n", " #comb_all.loc[idx,\"1DR10_SepOctNov\"] = return1d[ (return1d.stid == idx) & ( return1d[\"Return period (years)\"] == 10 ) ][\"Sep, Oct, Nov\"].values[0]\n", " #comb_all.loc[idx,\"1DR25_SepOctNov\"] = return1d[ (return1d.stid == idx) & ( return1d[\"Return period (years)\"] == 25 ) ][\"Sep, Oct, Nov\"].values[0]\n", " #comb_all.loc[idx,\"1DR50_SepOctNov\"] = return1d[ (return1d.stid == idx) & ( return1d[\"Return period (years)\"] == 50 ) ][\"Sep, Oct, Nov\"].values[0]\n", " \n", " #2d\n", " comb_all.loc[idx,\"2DR5_wholeYr\"] = return2d[ (return2d.stid == idx) & ( return2d[\"Return period (years)\"] == 5 ) ][\"All year\"].values[0]\n", " #comb_all.loc[idx,\"2DR10_wholeYr\"] = return2d[ (return2d.stid == idx) & ( return2d[\"Return period (years)\"] == 10 ) ][\"All year\"].values[0]\n", " #comb_all.loc[idx,\"2DR25_wholeYr\"] = return2d[ (return2d.stid == idx) & ( return2d[\"Return period (years)\"] == 25 ) ][\"All year\"].values[0]\n", " #comb_all.loc[idx,\"2DR50_wholeYr\"] = return2d[ (return2d.stid == idx) & ( return2d[\"Return period (years)\"] == 50 ) ][\"All year\"].values[0]\n", " \n", " comb_all.loc[idx,\"2DR5_Sep\"] = return2d[ (return2d.stid == idx) & ( return2d[\"Return period (years)\"] == 5 ) ][\"Sep\"].values[0]\n", " #comb_all.loc[idx,\"2DR10_Sep\"] = return2d[ (return2d.stid == idx) & ( return2d[\"Return period (years)\"] == 10 ) ][\"Sep\"].values[0]\n", " #comb_all.loc[idx,\"2DR25_Sep\"] = return2d[ (return2d.stid == idx) & ( return2d[\"Return period (years)\"] == 25 ) ][\"Sep\"].values[0]\n", " #comb_all.loc[idx,\"2DR50_Sep\"] = return2d[ (return2d.stid == idx) & ( return2d[\"Return period (years)\"] == 50 ) ][\"Sep\"].values[0]\n", " \n", " comb_all.loc[idx,\"2DR5_SepOctNov\"] = return2d[ (return2d.stid == idx) & ( return2d[\"Return period (years)\"] == 5 ) ][\"Sep, Oct, Nov\"].values[0]\n", " #comb_all.loc[idx,\"2DR10_SepOctNov\"] = return2d[ (return2d.stid == idx) & ( return2d[\"Return period (years)\"] == 10 ) ][\"Sep, Oct, Nov\"].values[0]\n", " #comb_all.loc[idx,\"2DR25_SepOctNov\"] = return2d[ (return2d.stid == idx) & ( return2d[\"Return period (years)\"] == 25 ) ][\"Sep, Oct, Nov\"].values[0]\n", " #comb_all.loc[idx,\"2DR50_SepOctNov\"] = return2d[ (return2d.stid == idx) & ( return2d[\"Return period (years)\"] == 50 ) ][\"Sep, Oct, Nov\"].values[0]\n", " \n", " #3d\n", " comb_all.loc[idx,\"3DR5_wholeYr\"] = return3d[ (return3d.stid == idx) & ( return3d[\"Return period (years)\"] == 5 ) ][\"All year\"].values[0]\n", " #comb_all.loc[idx,\"3DR10_wholeYr\"] = return3d[ (return3d.stid == idx) & ( return3d[\"Return period (years)\"] == 10 ) ][\"All year\"].values[0]\n", " #comb_all.loc[idx,\"3DR25_wholeYr\"] = return3d[ (return3d.stid == idx) & ( return3d[\"Return period (years)\"] == 25 ) ][\"All year\"].values[0]\n", " #comb_all.loc[idx,\"3DR50_wholeYr\"] = return3d[ (return3d.stid == idx) & ( return3d[\"Return period (years)\"] == 50 ) ][\"All year\"].values[0]\n", " \n", " comb_all.loc[idx,\"3DR5_Sep\"] = return3d[ (return3d.stid == idx) & ( return3d[\"Return period (years)\"] == 5 ) ][\"Sep\"].values[0]\n", " #comb_all.loc[idx,\"3DR10_Sep\"] = return3d[ (return3d.stid == idx) & ( return3d[\"Return period (years)\"] == 10 ) ][\"Sep\"].values[0]\n", " #comb_all.loc[idx,\"3DR25_Sep\"] = return3d[ (return3d.stid == idx) & ( return3d[\"Return period (years)\"] == 25 ) ][\"Sep\"].values[0]\n", " #comb_all.loc[idx,\"3DR50_Sep\"] = return3d[ (return3d.stid == idx) & ( return3d[\"Return period (years)\"] == 50 ) ][\"Sep\"].values[0]\n", " \n", " comb_all.loc[idx,\"3DR5_SepOctNov\"] = return3d[ (return3d.stid == idx) & ( return3d[\"Return period (years)\"] == 5 ) ][\"Sep, Oct, Nov\"].values[0]\n", " #comb_all.loc[idx,\"3DR10_SepOctNov\"] = return3d[ (return3d.stid == idx) & ( return3d[\"Return period (years)\"] == 10 ) ][\"Sep, Oct, Nov\"].values[0]\n", " #comb_all.loc[idx,\"3DR25_SepOctNov\"] = return3d[ (return3d.stid == idx) & ( return3d[\"Return period (years)\"] == 25 ) ][\"Sep, Oct, Nov\"].values[0]\n", " #comb_all.loc[idx,\"3DR50_SepOctNov\"] = return3d[ (return3d.stid == idx) & ( return3d[\"Return period (years)\"] == 50 ) ][\"Sep, Oct, Nov\"].values[0]\n", " \n", " return comb_all\n", " \n", " \n", " " ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/Users/ainajohannessen/Documents/Aina/skole/master/precipitation/input_data/eklima/2016sept_Hordaland.txt\n", "/Users/ainajohannessen/Documents/Aina/skole/master/precipitation/input_data/eklima/2016sept_Rogaland.txt\n", "/Users/ainajohannessen/Documents/Aina/skole/master/precipitation/input_data/eklima/2016sept_Vest_agder.txt\n", "/Users/ainajohannessen/Documents/Aina/skole/master/precipitation/input_data/eklima/2016sept_Hordaland.txt\n", "/Users/ainajohannessen/Documents/Aina/skole/master/precipitation/input_data/eklima/2016sept_Rogaland.txt\n", "/Users/ainajohannessen/Documents/Aina/skole/master/precipitation/input_data/eklima/2016sept_Vest_agder.txt\n", "/Users/ainajohannessen/Documents/Aina/skole/master/precipitation/input_data/eklima/2016sept_Hordaland.txt\n", "/Users/ainajohannessen/Documents/Aina/skole/master/precipitation/input_data/eklima/2016sept_Rogaland.txt\n", "/Users/ainajohannessen/Documents/Aina/skole/master/precipitation/input_data/eklima/2016sept_Vest_agder.txt\n", "/Users/ainajohannessen/Documents/Aina/skole/master/precipitation/input_data/eklima/2016sept_Hordaland.txt\n", "/Users/ainajohannessen/Documents/Aina/skole/master/precipitation/input_data/eklima/2016sept_Rogaland.txt\n", "/Users/ainajohannessen/Documents/Aina/skole/master/precipitation/input_data/eklima/2016sept_Vest_agder.txt\n", "/Users/ainajohannessen/Documents/Aina/skole/master/precipitation/input_data/eklima/2016sept_Hordaland.txt\n", "/Users/ainajohannessen/Documents/Aina/skole/master/precipitation/input_data/eklima/2016sept_Rogaland.txt\n", "/Users/ainajohannessen/Documents/Aina/skole/master/precipitation/input_data/eklima/2016sept_Vest_agder.txt\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ainajohannessen/anaconda/envs/3point6/lib/python3.6/site-packages/ipykernel/__main__.py:218: RuntimeWarning: divide by zero encountered in true_divide\n", "/Users/ainajohannessen/anaconda/envs/3point6/lib/python3.6/site-packages/ipykernel/__main__.py:218: RuntimeWarning: invalid value encountered in true_divide\n", "/Users/ainajohannessen/anaconda/envs/3point6/lib/python3.6/site-packages/ipykernel/__main__.py:219: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", "/Users/ainajohannessen/anaconda/envs/3point6/lib/python3.6/site-packages/ipykernel/__main__.py:220: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", "/Users/ainajohannessen/anaconda/envs/3point6/lib/python3.6/site-packages/ipykernel/__main__.py:113: FutureWarning: 'St.no' is both a column name and an index level.\n", "Defaulting to column but this will raise an ambiguity error in a future version\n" ] } ], "source": [ "comb_all = combine_all_data()\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/html": [ "
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LatitudeLongitudeMunicipalityCountyStnrNameSt.noJanFebMar...intensity1DR5_wholeYr1DR5_Sep1DR5_SepOctNov2DR5_wholeYr2DR5_Sep2DR5_SepOctNov3DR5_wholeYr3DR5_Sep3DR5_SepOctNov
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1007062.500311.1723OsHedmark10070.0FV30 MOSENGETNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1030062.469811.7752RørosTrøndelag10300.0HÅSJØEN - SOLGLØTTNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1038062.577311.3518RørosTrøndelag10380.0RØROS LUFTHAVNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1060062.673711.4534RørosTrøndelag10600.0AURSUNDNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
108059.036711.0444HvalerØstfold1080.0HVALERNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1080062.679311.8153RørosTrøndelag10800.0SØLENDETNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1100062.871511.8077TydalTrøndelag11000.0FV705 LANGSVOLANaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1112560.285211.1358EidsvollAkershus11125.0E6 ANDELVANaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1134060.454810.6430GranOppland11340.0RV4 LYGNANaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
113558.990011.5408HaldenØstfold1135.0FV101 PRESTEBAKKENaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1144060.587811.1470EidsvollAkershus11440.0FV33 FEIRINGNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1145060.394011.2300EidsvollAkershus11450.0MINNESUND JERNBANESTASJONNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
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1190060.951810.5954GjøvikOppland11900.0BIRINaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
12061.217712.6982TrysilHedmark120.0FV569 FLERMOBERGETNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
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9954069.147029.2440Sør-VarangerFinnmark99540.0NYRUDNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
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9973578.250822.8225-9999Svalbard99735.0EDGEØYA - KAPP HEUGLINNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
9974078.910828.8920-9999Svalbard99740.0KONGSØYANaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
9975276.477716.5488-9999Svalbard99752.0SØRKAPPØYANaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
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1477 rows × 43 columns

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" ], "text/plain": [ " Latitude Longitude Municipality County Stnr \\\n", "100 61.1349 12.5039 Trysil Hedmark 100.0 \n", "10070 62.5003 11.1723 Os Hedmark 10070.0 \n", "10300 62.4698 11.7752 Røros Trøndelag 10300.0 \n", "10380 62.5773 11.3518 Røros Trøndelag 10380.0 \n", "10600 62.6737 11.4534 Røros Trøndelag 10600.0 \n", "1080 59.0367 11.0444 Hvaler Østfold 1080.0 \n", "10800 62.6793 11.8153 Røros Trøndelag 10800.0 \n", "11000 62.8715 11.8077 Tydal Trøndelag 11000.0 \n", "11125 60.2852 11.1358 Eidsvoll Akershus 11125.0 \n", "11340 60.4548 10.6430 Gran Oppland 11340.0 \n", "1135 58.9900 11.5408 Halden Østfold 1135.0 \n", "11440 60.5878 11.1470 Eidsvoll Akershus 11440.0 \n", "11450 60.3940 11.2300 Eidsvoll Akershus 11450.0 \n", "11463 60.3977 11.2303 Eidsvoll Akershus 11463.0 \n", "11500 60.7002 10.8695 Østre Toten Oppland 11500.0 \n", "11690 60.6125 10.6308 Vestre Toten Oppland 11690.0 \n", "11710 60.5952 10.6403 Vestre Toten Oppland 11710.0 \n", "11790 60.7910 10.3998 Gjøvik Oppland 11790.0 \n", "11880 61.0073 10.1908 Nordre Land Oppland 11880.0 \n", "11900 60.9518 10.5954 Gjøvik Oppland 11900.0 \n", "120 61.2177 12.6982 Trysil Hedmark 120.0 \n", "12030 60.5545 11.2502 Stange Hedmark 12030.0 \n", "12050 60.6215 11.2997 Stange Hedmark 12050.0 \n", "12123 -9999.0000 -9999.0000 Hamar Hedmark 12123.0 \n", "12180 60.8028 11.2028 Hamar Hedmark 12180.0 \n", "12290 60.8010 11.0903 Hamar Hedmark 12290.0 \n", "12291 60.8048 11.1113 Hamar Hedmark 12291.0 \n", "1230 59.1223 11.3865 Halden Østfold 1230.0 \n", "12320 60.8180 11.0697 Hamar Hedmark 12320.0 \n", "12520 60.7908 10.9583 Ringsaker Hedmark 12520.0 \n", "... ... ... ... ... ... \n", "98790 70.0653 29.8352 Vadsø Finnmark 98790.0 \n", "99340 69.6820 29.1483 Sør-Varanger Finnmark 99340.0 \n", "99370 69.7255 29.8977 Sør-Varanger Finnmark 99370.0 \n", "99380 69.7100 29.8695 Sør-Varanger Finnmark 99380.0 \n", "99420 69.6873 30.0050 Sør-Varanger Finnmark 99420.0 \n", "99460 69.4552 30.0410 Sør-Varanger Finnmark 99460.0 \n", "99500 69.3698 29.6885 Sør-Varanger Finnmark 99500.0 \n", "99540 69.1470 29.2440 Sør-Varanger Finnmark 99540.0 \n", "99700 74.5167 19.0167 Bjørnøya Svalbard 99700.0 \n", "99710 74.5167 19.0050 Bjørnøya Svalbard 99710.0 \n", "99720 76.5097 25.0133 Hopen Svalbard 99720.0 \n", "99735 78.2508 22.8225 -9999 Svalbard 99735.0 \n", "99740 78.9108 28.8920 -9999 Svalbard 99740.0 \n", "99752 76.4777 16.5488 -9999 Svalbard 99752.0 \n", "99754 77.0017 15.5358 Spitsbergen Svalbard 99754.0 \n", "99760 77.8953 16.7200 Spitsbergen Svalbard 99760.0 \n", "99765 77.6902 14.7632 Spitsbergen Svalbard 99765.0 \n", "99790 78.0625 13.6192 Spitsbergen Svalbard 99790.0 \n", "99840 78.2453 15.5015 Spitsbergen Svalbard 99840.0 \n", "99841 78.2342 15.3595 Spitsbergen Svalbard 99841.0 \n", "99842 78.2293 15.3950 Spitsbergen Svalbard 99842.0 \n", "99880 78.6557 16.3603 Spitsbergen Svalbard 99880.0 \n", "99902 78.9273 11.8837 Spitsbergen Svalbard 99902.0 \n", "99904 78.9232 11.9231 Spitsbergen Svalbard 99904.0 \n", "99910 78.9243 11.9312 Spitsbergen Svalbard 99910.0 \n", "99927 80.0592 16.2500 Spitsbergen Svalbard 99927.0 \n", "99935 80.6530 25.0080 -9999 Svalbard 99935.0 \n", "99938 80.1058 31.4643 -9999 Svalbard 99938.0 \n", "99940 70.9396 -8.6677 Jan Mayen Jan Mayen 99940.0 \n", "99950 70.9394 -8.6690 Jan Mayen Jan Mayen 99950.0 \n", "\n", " Name St.no Jan Feb Mar ... \\\n", "100 PLASSEN NaN NaN NaN NaN ... \n", "10070 FV30 MOSENGET NaN NaN NaN NaN ... \n", "10300 HÅSJØEN - SOLGLØTT NaN NaN NaN NaN ... \n", "10380 RØROS LUFTHAVN NaN NaN NaN NaN ... \n", "10600 AURSUND NaN NaN NaN NaN ... \n", "1080 HVALER NaN NaN NaN NaN ... \n", "10800 SØLENDET NaN NaN NaN NaN ... \n", "11000 FV705 LANGSVOLA NaN NaN NaN NaN ... \n", "11125 E6 ANDELVA NaN NaN NaN NaN ... \n", "11340 RV4 LYGNA NaN NaN NaN NaN ... \n", "1135 FV101 PRESTEBAKKE NaN NaN NaN NaN ... \n", "11440 FV33 FEIRING NaN NaN NaN NaN ... \n", "11450 MINNESUND JERNBANESTASJON NaN NaN NaN NaN ... \n", "11463 E6 MINNESUND NaN NaN NaN NaN ... \n", "11500 ØSTRE TOTEN - APELSVOLL NaN NaN NaN NaN ... \n", "11690 RV4 EINA NaN NaN NaN NaN ... \n", "11710 EINAVATN NaN NaN NaN NaN ... \n", "11790 FV33 VARDALSÅSEN NaN NaN NaN NaN ... \n", "11880 TORPA - KRÅKHUGUKAMPEN NaN NaN NaN NaN ... \n", "11900 BIRI NaN NaN NaN NaN ... \n", "120 FV569 FLERMOBERGET NaN NaN NaN NaN ... \n", "12030 E6 ESPA NaN NaN NaN NaN ... \n", "12050 E6 VIKSELV NaN NaN NaN NaN ... \n", "12123 E6 AKERSVIKA NaN NaN NaN NaN ... \n", "12180 ILSENG NaN NaN NaN NaN ... \n", "12290 HAMAR II NaN NaN NaN NaN ... \n", "12291 E6 ÅKERSVIKA NaN NaN NaN NaN ... \n", "1230 HALDEN NaN NaN NaN NaN ... \n", "12320 HAMAR - STAVSBERG NaN NaN NaN NaN ... \n", "12520 NES PÅ HEDMARK NaN NaN NaN NaN ... \n", "... ... ... ... ... ... ... \n", "98790 VADSØ LUFTHAVN NaN NaN NaN NaN ... \n", "99340 ØVRE NEIDEN NaN NaN NaN NaN ... \n", "99370 KIRKENES LUFTHAVN NaN NaN NaN NaN ... \n", "99380 KIRKENES LUFTHAVN - HØYDE 212 NaN NaN NaN NaN ... \n", "99420 E6 KIRKENES SYD NaN NaN NaN NaN ... \n", "99460 PASVIK - SVANVIK NaN NaN NaN NaN ... \n", "99500 SKOGFOSS NaN NaN NaN NaN ... \n", "99540 NYRUD NaN NaN NaN NaN ... \n", "99700 BJØRNØYA RADIOSONDESTASJON NaN NaN NaN NaN ... \n", "99710 BJØRNØYA NaN NaN NaN NaN ... \n", "99720 HOPEN NaN NaN NaN NaN ... \n", "99735 EDGEØYA - KAPP HEUGLIN NaN NaN NaN NaN ... \n", "99740 KONGSØYA NaN NaN NaN NaN ... \n", "99752 SØRKAPPØYA NaN NaN NaN NaN ... \n", "99754 HORNSUND NaN NaN NaN NaN ... \n", "99760 SVEAGRUVA NaN NaN NaN NaN ... \n", "99765 AKSELØYA NaN NaN NaN NaN ... \n", "99790 ISFJORD RADIO NaN NaN NaN NaN ... \n", "99840 SVALBARD LUFTHAVN NaN NaN NaN NaN ... \n", "99841 SVALBARD LH - PLATÅBERGET NaN NaN NaN NaN ... \n", "99842 SPITSBERGEN - PLATÅBERGET II NaN NaN NaN NaN ... \n", "99880 PYRAMIDEN NaN NaN NaN NaN ... \n", "99902 NY-ÅLESUND - RABBEN FLYPLASS NaN NaN NaN NaN ... \n", "99904 NY-ÅLESUND RADIOSONDESTASJON NaN NaN NaN NaN ... \n", "99910 NY-ÅLESUND NaN NaN NaN NaN ... \n", "99927 VERLEGENHUKEN NaN NaN NaN NaN ... \n", "99935 KARL XII-ØYA NaN NaN NaN NaN ... \n", "99938 KVITØYA NaN NaN NaN NaN ... \n", "99940 JAN MAYEN RADIOSONDESTASJON NaN NaN NaN NaN ... \n", "99950 JAN MAYEN NaN NaN NaN NaN ... \n", "\n", " intensity 1DR5_wholeYr 1DR5_Sep 1DR5_SepOctNov 2DR5_wholeYr \\\n", "100 NaN NaN NaN NaN NaN \n", "10070 NaN NaN NaN NaN NaN \n", "10300 NaN NaN NaN NaN NaN \n", "10380 NaN NaN NaN NaN NaN \n", "10600 NaN NaN NaN NaN NaN \n", "1080 NaN NaN NaN NaN NaN \n", "10800 NaN NaN NaN NaN NaN \n", "11000 NaN NaN NaN NaN NaN \n", "11125 NaN NaN NaN NaN NaN \n", "11340 NaN NaN NaN NaN NaN \n", "1135 NaN NaN NaN NaN NaN \n", "11440 NaN NaN NaN NaN NaN \n", "11450 NaN NaN NaN NaN NaN \n", "11463 NaN NaN NaN NaN NaN \n", "11500 NaN NaN NaN NaN NaN \n", "11690 NaN NaN NaN NaN NaN \n", "11710 NaN NaN NaN NaN NaN \n", "11790 NaN NaN NaN NaN NaN \n", "11880 NaN NaN NaN NaN NaN \n", "11900 NaN NaN NaN NaN NaN \n", "120 NaN NaN NaN NaN NaN \n", "12030 NaN NaN NaN NaN NaN \n", "12050 NaN NaN NaN NaN NaN \n", "12123 NaN NaN NaN NaN NaN \n", "12180 NaN NaN NaN NaN NaN \n", "12290 NaN NaN NaN NaN NaN \n", "12291 NaN NaN NaN NaN NaN \n", "1230 NaN NaN NaN NaN NaN \n", "12320 NaN NaN NaN NaN NaN \n", "12520 NaN NaN NaN NaN NaN \n", "... ... ... ... ... ... \n", "98790 NaN NaN NaN NaN NaN \n", "99340 NaN NaN NaN NaN NaN \n", "99370 NaN NaN NaN NaN NaN \n", "99380 NaN NaN NaN NaN NaN \n", "99420 NaN NaN NaN NaN NaN \n", "99460 NaN NaN NaN NaN NaN \n", "99500 NaN NaN NaN NaN NaN \n", "99540 NaN NaN NaN NaN NaN \n", "99700 NaN NaN NaN NaN NaN \n", "99710 NaN NaN NaN NaN NaN \n", "99720 NaN NaN NaN NaN NaN \n", "99735 NaN NaN NaN NaN NaN \n", "99740 NaN NaN NaN NaN NaN \n", "99752 NaN NaN NaN NaN NaN \n", "99754 NaN NaN NaN NaN NaN \n", "99760 NaN NaN NaN NaN NaN \n", "99765 NaN NaN NaN NaN NaN \n", "99790 NaN NaN NaN NaN NaN \n", "99840 NaN NaN NaN NaN NaN \n", "99841 NaN NaN NaN NaN NaN \n", "99842 NaN NaN NaN NaN NaN \n", "99880 NaN NaN NaN NaN NaN \n", "99902 NaN NaN NaN NaN NaN \n", "99904 NaN NaN NaN NaN NaN \n", "99910 NaN NaN NaN NaN NaN \n", "99927 NaN NaN NaN NaN NaN \n", "99935 NaN NaN NaN NaN NaN \n", "99938 NaN NaN NaN NaN NaN \n", "99940 NaN NaN NaN NaN NaN \n", "99950 NaN NaN NaN NaN NaN \n", "\n", " 2DR5_Sep 2DR5_SepOctNov 3DR5_wholeYr 3DR5_Sep 3DR5_SepOctNov \n", "100 NaN NaN NaN NaN NaN \n", "10070 NaN NaN NaN NaN NaN \n", "10300 NaN NaN NaN NaN NaN \n", "10380 NaN NaN NaN NaN NaN \n", "10600 NaN NaN NaN NaN NaN \n", "1080 NaN NaN NaN NaN NaN \n", "10800 NaN NaN NaN NaN NaN \n", "11000 NaN NaN NaN NaN NaN \n", "11125 NaN NaN NaN NaN NaN \n", "11340 NaN NaN NaN NaN NaN \n", "1135 NaN NaN NaN NaN NaN \n", "11440 NaN NaN NaN NaN NaN \n", "11450 NaN NaN NaN NaN NaN \n", "11463 NaN NaN NaN NaN NaN \n", "11500 NaN NaN NaN NaN NaN \n", "11690 NaN NaN NaN NaN NaN \n", "11710 NaN NaN NaN NaN NaN \n", "11790 NaN NaN NaN NaN NaN \n", "11880 NaN NaN NaN NaN NaN \n", "11900 NaN NaN NaN NaN NaN \n", "120 NaN NaN NaN NaN NaN \n", "12030 NaN NaN NaN NaN NaN \n", "12050 NaN NaN NaN NaN NaN \n", "12123 NaN NaN NaN NaN NaN \n", "12180 NaN NaN NaN NaN NaN \n", "12290 NaN NaN NaN NaN NaN \n", "12291 NaN NaN NaN NaN NaN \n", "1230 NaN NaN NaN NaN NaN \n", "12320 NaN NaN NaN NaN NaN \n", "12520 NaN NaN NaN NaN NaN \n", "... ... ... ... ... ... \n", "98790 NaN NaN NaN NaN NaN \n", "99340 NaN NaN NaN NaN NaN \n", "99370 NaN NaN NaN NaN NaN \n", "99380 NaN NaN NaN NaN NaN \n", "99420 NaN NaN NaN NaN NaN \n", "99460 NaN NaN NaN NaN NaN \n", "99500 NaN NaN NaN NaN NaN \n", "99540 NaN NaN NaN NaN NaN \n", "99700 NaN NaN NaN NaN NaN \n", "99710 NaN NaN NaN NaN NaN \n", "99720 NaN NaN NaN NaN NaN \n", "99735 NaN NaN NaN NaN NaN \n", "99740 NaN NaN NaN NaN NaN \n", "99752 NaN NaN NaN NaN NaN \n", "99754 NaN NaN NaN NaN NaN \n", "99760 NaN NaN NaN NaN NaN \n", "99765 NaN NaN NaN NaN NaN \n", "99790 NaN NaN NaN NaN NaN \n", "99840 NaN NaN NaN NaN NaN \n", "99841 NaN NaN NaN NaN NaN \n", "99842 NaN NaN NaN NaN NaN \n", "99880 NaN NaN NaN NaN NaN \n", "99902 NaN NaN NaN NaN NaN \n", "99904 NaN NaN NaN NaN NaN \n", "99910 NaN NaN NaN NaN NaN \n", "99927 NaN NaN NaN NaN NaN \n", "99935 NaN NaN NaN NaN NaN \n", "99938 NaN NaN NaN NaN NaN \n", "99940 NaN NaN NaN NaN NaN \n", "99950 NaN NaN NaN NaN NaN \n", "\n", "[1477 rows x 43 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "comb_all[~comb_all.County.isnull()].County.unique()\n", "comb_all#[~comb_all.Sep.isnull()] " ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/html": [ "
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County1DR5_Sep1DR5_SepOctNov1DR5_wholeYrintensity24acc201609282016092920160930
39040Vest-Agder55.069.078.00.4000009.99.84.52.4
39220Vest-Agder65.084.096.0NaNNaN18.67.92.4
41200Vest-Agder53.073.080.0NaNNaN18.012.95.0
41480Vest-Agder55.069.076.00.23833341.341.010.011.1
41550Vest-Agder53.071.078.0NaNNaN38.06.410.7
41770Vest-Agder40.053.066.0NaNNaN6.05.83.5
41820Vest-Agder60.080.084.0NaNNaN24.623.510.4
41860Vest-Agder71.0105.0115.0NaNNaN43.739.615.8
42160Vest-Agder43.055.066.00.2200007.95.05.94.3
42520Vest-Agder52.070.079.0NaNNaN42.218.821.5
42650Vest-Agder66.085.096.0NaNNaN29.833.510.9
42720Vest-Agder58.081.095.0NaNNaN46.524.59.2
42810Vest-Agder50.073.083.0NaNNaN42.520.016.5
42950Vest-Agder42.059.068.0NaNNaN37.816.624.5
43360Rogaland51.061.073.0NaNNaN14.015.56.6
43810Rogaland71.0105.0118.0NaNNaN61.741.528.1
44160Rogaland45.056.064.0NaNNaN19.512.01.5
44480Rogaland64.088.0101.0NaNNaN53.721.515.2
44520Rogaland59.085.094.0NaNNaN65.524.217.0
44640Rogaland42.050.059.0NaNNaN23.310.45.4
44760Rogaland48.073.082.0NaNNaN30.916.013.2
44800Rogaland52.069.080.0NaNNaN30.019.010.0
44900Rogaland52.088.0104.0NaNNaN41.222.813.4
45350Rogaland57.089.0115.00.15000046.536.727.637.3
46150Rogaland64.090.0104.0NaNNaN58.836.439.1
46300Rogaland54.077.094.0NaNNaN40.041.447.5
46450Hordaland47.063.077.0NaNNaN41.221.048.3
46850Rogaland75.0100.0111.0NaNNaN59.136.137.7
47300Rogaland40.046.056.00.37166711.68.40.93.7
47600Hordaland58.089.099.0NaNNaN56.116.735.9
..............................
52750Hordaland68.085.095.0NaNNaN64.78.828.3
52860Sogn Og Fjordane95.0120.0131.0NaNNaN86.211.742.8
52930Sogn Og Fjordane98.0121.0135.0NaNNaN75.013.453.0
52990Sogn Og Fjordane41.056.066.0NaNNaN43.715.427.6
53070Sogn Og Fjordane36.047.058.0NaNNaN36.215.427.0
53130Sogn Og Fjordane34.048.054.0NaNNaN40.210.017.8
53160Hordaland63.079.095.0NaNNaN68.717.837.6
53700Sogn Og Fjordane28.037.043.0NaNNaN37.010.011.4
54600Sogn Og Fjordane20.033.042.0NaNNaN21.612.515.1
54780Sogn Og Fjordane25.039.049.0NaNNaN14.06.020.5
55550Sogn Og Fjordane32.044.054.0NaNNaN38.711.820.9
55670Sogn Og Fjordane51.066.072.0NaNNaN48.514.433.0
55730Sogn Og Fjordane49.063.069.0NaNNaN51.618.134.5
56010Sogn Og Fjordane66.092.0116.0NaNNaN60.813.757.2
56320Sogn Og Fjordane66.092.0116.0NaNNaN52.319.636.8
56400Sogn Og Fjordane49.066.074.0NaNNaN29.65.911.3
56420Sogn Og Fjordane55.076.084.0NaNNaN47.44.127.7
56520Sogn Og Fjordane97.0127.0142.0NaNNaN69.522.647.4
56960Sogn Og Fjordane65.083.099.0NaNNaN50.020.060.2
57390Sogn Og Fjordane57.070.079.0NaNNaN41.319.439.9
57480Sogn Og Fjordane72.095.0109.0NaNNaN57.021.451.0
57660Sogn Og Fjordane77.0105.0125.0NaNNaN65.516.054.6
57810Sogn Og Fjordane70.089.096.0NaNNaN62.014.736.1
57940Sogn Og Fjordane66.079.087.0NaNNaN64.214.643.8
57990Sogn Og Fjordane73.093.0107.0NaNNaN55.812.850.7
58070Sogn Og Fjordane43.055.060.0NaNNaN32.010.234.2
58480Sogn Og Fjordane57.077.089.0NaNNaN33.416.536.4
58780Sogn Og Fjordane55.070.085.0NaNNaN38.813.038.1
58960Sogn Og Fjordane53.066.074.0NaNNaN45.012.642.6
59450Sogn Og Fjordane67.084.092.0NaNNaN26.110.429.4
\n", "

73 rows × 9 columns

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" ], "text/plain": [ " County 1DR5_Sep 1DR5_SepOctNov 1DR5_wholeYr intensity \\\n", "39040 Vest-Agder 55.0 69.0 78.0 0.400000 \n", "39220 Vest-Agder 65.0 84.0 96.0 NaN \n", "41200 Vest-Agder 53.0 73.0 80.0 NaN \n", "41480 Vest-Agder 55.0 69.0 76.0 0.238333 \n", "41550 Vest-Agder 53.0 71.0 78.0 NaN \n", "41770 Vest-Agder 40.0 53.0 66.0 NaN \n", "41820 Vest-Agder 60.0 80.0 84.0 NaN \n", "41860 Vest-Agder 71.0 105.0 115.0 NaN \n", "42160 Vest-Agder 43.0 55.0 66.0 0.220000 \n", "42520 Vest-Agder 52.0 70.0 79.0 NaN \n", "42650 Vest-Agder 66.0 85.0 96.0 NaN \n", "42720 Vest-Agder 58.0 81.0 95.0 NaN \n", "42810 Vest-Agder 50.0 73.0 83.0 NaN \n", "42950 Vest-Agder 42.0 59.0 68.0 NaN \n", "43360 Rogaland 51.0 61.0 73.0 NaN \n", "43810 Rogaland 71.0 105.0 118.0 NaN \n", "44160 Rogaland 45.0 56.0 64.0 NaN \n", "44480 Rogaland 64.0 88.0 101.0 NaN \n", "44520 Rogaland 59.0 85.0 94.0 NaN \n", "44640 Rogaland 42.0 50.0 59.0 NaN \n", "44760 Rogaland 48.0 73.0 82.0 NaN \n", "44800 Rogaland 52.0 69.0 80.0 NaN \n", "44900 Rogaland 52.0 88.0 104.0 NaN \n", "45350 Rogaland 57.0 89.0 115.0 0.150000 \n", "46150 Rogaland 64.0 90.0 104.0 NaN \n", "46300 Rogaland 54.0 77.0 94.0 NaN \n", "46450 Hordaland 47.0 63.0 77.0 NaN \n", "46850 Rogaland 75.0 100.0 111.0 NaN \n", "47300 Rogaland 40.0 46.0 56.0 0.371667 \n", "47600 Hordaland 58.0 89.0 99.0 NaN \n", "... ... ... ... ... ... \n", "52750 Hordaland 68.0 85.0 95.0 NaN \n", "52860 Sogn Og Fjordane 95.0 120.0 131.0 NaN \n", "52930 Sogn Og Fjordane 98.0 121.0 135.0 NaN \n", "52990 Sogn Og Fjordane 41.0 56.0 66.0 NaN \n", "53070 Sogn Og Fjordane 36.0 47.0 58.0 NaN \n", "53130 Sogn Og Fjordane 34.0 48.0 54.0 NaN \n", "53160 Hordaland 63.0 79.0 95.0 NaN \n", "53700 Sogn Og Fjordane 28.0 37.0 43.0 NaN \n", "54600 Sogn Og Fjordane 20.0 33.0 42.0 NaN \n", "54780 Sogn Og Fjordane 25.0 39.0 49.0 NaN \n", "55550 Sogn Og Fjordane 32.0 44.0 54.0 NaN \n", "55670 Sogn Og Fjordane 51.0 66.0 72.0 NaN \n", "55730 Sogn Og Fjordane 49.0 63.0 69.0 NaN \n", "56010 Sogn Og Fjordane 66.0 92.0 116.0 NaN \n", "56320 Sogn Og Fjordane 66.0 92.0 116.0 NaN \n", "56400 Sogn Og Fjordane 49.0 66.0 74.0 NaN \n", "56420 Sogn Og Fjordane 55.0 76.0 84.0 NaN \n", "56520 Sogn Og Fjordane 97.0 127.0 142.0 NaN \n", "56960 Sogn Og Fjordane 65.0 83.0 99.0 NaN \n", "57390 Sogn Og Fjordane 57.0 70.0 79.0 NaN \n", "57480 Sogn Og Fjordane 72.0 95.0 109.0 NaN \n", "57660 Sogn Og Fjordane 77.0 105.0 125.0 NaN \n", "57810 Sogn Og Fjordane 70.0 89.0 96.0 NaN \n", "57940 Sogn Og Fjordane 66.0 79.0 87.0 NaN \n", "57990 Sogn Og Fjordane 73.0 93.0 107.0 NaN \n", "58070 Sogn Og Fjordane 43.0 55.0 60.0 NaN \n", "58480 Sogn Og Fjordane 57.0 77.0 89.0 NaN \n", "58780 Sogn Og Fjordane 55.0 70.0 85.0 NaN \n", "58960 Sogn Og Fjordane 53.0 66.0 74.0 NaN \n", "59450 Sogn Og Fjordane 67.0 84.0 92.0 NaN \n", "\n", " 24acc 20160928 20160929 20160930 \n", "39040 9.9 9.8 4.5 2.4 \n", "39220 NaN 18.6 7.9 2.4 \n", "41200 NaN 18.0 12.9 5.0 \n", "41480 41.3 41.0 10.0 11.1 \n", "41550 NaN 38.0 6.4 10.7 \n", "41770 NaN 6.0 5.8 3.5 \n", "41820 NaN 24.6 23.5 10.4 \n", "41860 NaN 43.7 39.6 15.8 \n", "42160 7.9 5.0 5.9 4.3 \n", "42520 NaN 42.2 18.8 21.5 \n", "42650 NaN 29.8 33.5 10.9 \n", "42720 NaN 46.5 24.5 9.2 \n", "42810 NaN 42.5 20.0 16.5 \n", "42950 NaN 37.8 16.6 24.5 \n", "43360 NaN 14.0 15.5 6.6 \n", "43810 NaN 61.7 41.5 28.1 \n", "44160 NaN 19.5 12.0 1.5 \n", "44480 NaN 53.7 21.5 15.2 \n", "44520 NaN 65.5 24.2 17.0 \n", "44640 NaN 23.3 10.4 5.4 \n", "44760 NaN 30.9 16.0 13.2 \n", "44800 NaN 30.0 19.0 10.0 \n", "44900 NaN 41.2 22.8 13.4 \n", "45350 46.5 36.7 27.6 37.3 \n", "46150 NaN 58.8 36.4 39.1 \n", "46300 NaN 40.0 41.4 47.5 \n", "46450 NaN 41.2 21.0 48.3 \n", "46850 NaN 59.1 36.1 37.7 \n", "47300 11.6 8.4 0.9 3.7 \n", "47600 NaN 56.1 16.7 35.9 \n", "... ... ... ... ... \n", "52750 NaN 64.7 8.8 28.3 \n", "52860 NaN 86.2 11.7 42.8 \n", "52930 NaN 75.0 13.4 53.0 \n", "52990 NaN 43.7 15.4 27.6 \n", "53070 NaN 36.2 15.4 27.0 \n", "53130 NaN 40.2 10.0 17.8 \n", "53160 NaN 68.7 17.8 37.6 \n", "53700 NaN 37.0 10.0 11.4 \n", "54600 NaN 21.6 12.5 15.1 \n", "54780 NaN 14.0 6.0 20.5 \n", "55550 NaN 38.7 11.8 20.9 \n", "55670 NaN 48.5 14.4 33.0 \n", "55730 NaN 51.6 18.1 34.5 \n", "56010 NaN 60.8 13.7 57.2 \n", "56320 NaN 52.3 19.6 36.8 \n", "56400 NaN 29.6 5.9 11.3 \n", "56420 NaN 47.4 4.1 27.7 \n", "56520 NaN 69.5 22.6 47.4 \n", "56960 NaN 50.0 20.0 60.2 \n", "57390 NaN 41.3 19.4 39.9 \n", "57480 NaN 57.0 21.4 51.0 \n", "57660 NaN 65.5 16.0 54.6 \n", "57810 NaN 62.0 14.7 36.1 \n", "57940 NaN 64.2 14.6 43.8 \n", "57990 NaN 55.8 12.8 50.7 \n", "58070 NaN 32.0 10.2 34.2 \n", "58480 NaN 33.4 16.5 36.4 \n", "58780 NaN 38.8 13.0 38.1 \n", "58960 NaN 45.0 12.6 42.6 \n", "59450 NaN 26.1 10.4 29.4 \n", "\n", "[73 rows x 9 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mnth = comb_all.loc[:,[\"County\",\"Sep\",\"Sept2016_sum\", \"EvenSumfrom24\"]]\n", "mnth[~mnth[\"Sept2016_sum\"].isnull()]#, \"Year\", \"yr2016_sum\", \"\"]]\n", "\n", "#mnth = comb_all.loc[:,[\"County\",\"Sep\", \"EvenSumfrom24\"]]\n", "#mnth[~mnth[\"EvenSumfrom24\"].isnull()]#, \"Year\", \"yr2016_sum\", \"\"]]\n", "mnth = comb_all.loc[:,[\"County\",\"1DR5_Sep\",\"1DR5_SepOctNov\", \"1DR5_wholeYr\", \"intensity\", \"24acc\", \"20160928\", \"20160929\", \"20160930\"]]\n", "mnth = mnth[~mnth[\"1DR5_Sep\"].isnull()]#, \"Year\", \"yr2016_sum\", \"\"]]\n", "mnth = mnth[~mnth[\"20160928\"].isnull()]#, \"Year\", \"yr2016_sum\", \"\"]]\n", "#mnth = mnth[(mnth[\"20160928\"] >= 50) | (mnth[\"20160929\"] >= 50) | (mnth[\"20160930\"] >= 50)]\n", "#mnth[(mnth[\"20160928\"] < mnth[\"20160930\"])]\n", "mnth" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Making tables for thesis" ] }, { "cell_type": "raw", "metadata": { "deletable": true, "editable": true }, "source": [ "Index(['Latitude', 'Longitude', 'Municipality', 'County', 'Stnr', 'St.no',\n", " 'Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct',\n", " 'Nov', 'Dec', 'Year', 'yr2016_sum', 'St.no', 'year', 'month',\n", " 'Sept2016_sum', 'quality', '20160928', '20160929', '20160930',\n", " 'EvenSumfrom24', '24acc', '48acc', '72acc', 'intensity', '1DR5_wholeYr',\n", " '1DR5_Sep', '1DR5_SepOctNov', '2DR5_wholeYr', '2DR5_Sep',\n", " '2DR5_SepOctNov', '3DR5_wholeYr', '3DR5_Sep', '3DR5_SepOctNov'],\n", " dtype='object')\n", " \n", "array(['Hedmark', 'Trøndelag', 'Østfold', 'Akershus', 'Oppland', 'Oslo',\n", " 'Buskerud', 'Hordaland', 'Vestfold', 'Telemark', 'Aust-Agder',\n", " 'Vest-Agder', 'Rogaland', 'Sogn Og Fjordane', 'Møre Og Romsdal',\n", " 'Nordland', 'Troms', 'Finnmark', 'Svalbard', 'Jan Mayen'], dtype=object)\n", " \n", " " ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Were did it precipitate the most for the entire event 72 h" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/html": [ "
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LatitudeLongitudeCountyNameMunicipalityEvenSumfrom24201609282016092920160930
5260160.83475.5833HordalandHAUKELAND - STOREVATNMasfjorden188.3103.622.462.3
5086560.38285.5420HordalandGULLFJELLETBergen184.5116.621.146.8
4782059.85946.2786HordalandEIKEMOEtne174.765.047.762.0
5031060.38875.9640HordalandKVAMSKOGEN - JONSHØGDIKvam172.577.935.159.5
5011060.33636.2182HordalandKVAM - AKSNESETKvam168.669.246.053.4
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" ], "text/plain": [ " Latitude Longitude County Name Municipality \\\n", "52601 60.8347 5.5833 Hordaland HAUKELAND - STOREVATN Masfjorden \n", "50865 60.3828 5.5420 Hordaland GULLFJELLET Bergen \n", "47820 59.8594 6.2786 Hordaland EIKEMO Etne \n", "50310 60.3887 5.9640 Hordaland KVAMSKOGEN - JONSHØGDI Kvam \n", "50110 60.3363 6.2182 Hordaland KVAM - AKSNESET Kvam \n", "\n", " EvenSumfrom24 20160928 20160929 20160930 \n", "52601 188.3 103.6 22.4 62.3 \n", "50865 184.5 116.6 21.1 46.8 \n", "47820 174.7 65.0 47.7 62.0 \n", "50310 172.5 77.9 35.1 59.5 \n", "50110 168.6 69.2 46.0 53.4 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mnth = comb_all.loc[:,[\"Latitude\",\"Longitude\",\"County\", \"Name\" ,\"Municipality\", \"EvenSumfrom24\", '20160928', '20160929', '20160930'] ] \n", "\n", "mnth = mnth[~mnth[\"EvenSumfrom24\"].isnull()]#, \"Year\", \"yr2016_sum\", \"\"]]\n", "all_lon,all_lat = mnth.Longitude.values, mnth.Latitude.values\n", "#mnth = mnth[mnth[\"EvenSumfrom24\"] > 150]#, \"Year\", \"yr2016_sum\", \"\"]]\n", "mnth.sort_values(\"EvenSumfrom24\", axis=0, ascending=False, inplace=True, kind='quicksort', na_position='last')\n", "mnth = mnth[0:5]\n", "import matplotlib.pyplot as plt \n", "from mpl_toolkits.basemap import Basemap\n", "fig, ax = plt.subplots(figsize=(5,5))\n", "\n", "map = Basemap( llcrnrlon = 3., llcrnrlat = 58., urcrnrlon = 9., urcrnrlat=62.,\\\n", " resolution = 'l', area_thresh = 10000., projection = 'merc' )\n", "map.drawcoastlines( linewidth = 1.3, linestyle = 'solid', color = \"0.2\", zorder=5)#[ 75./255., 75/255., 75/255. ] )\n", "parallels = np.arange(50,70,1.)\n", "# labels = [left,right,top,bottom]\n", "map.drawparallels(parallels,labels=[False,True,True,False])\n", "meridians = np.arange(-10.,30.,1.)\n", "map.drawmeridians(meridians,labels=[True,False,False,True])\n", "lon,lat = map(mnth.Longitude.values, mnth.Latitude.values)\n", "all_lon,all_lat = map(all_lon,all_lat )\n", "\n", "display(mnth)\n", "\n", "#lon_R,lat_R = map(mnth.Longitude.values,mnth.Latitude.values)\n", "\n", "CS = map.plot(all_lon,all_lat, \"ob\")\n", "\n", "CS = map.plot(lon,lat, \"or\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/ainajohannessen/anaconda/envs/3point6/lib/python3.6/site-packages/ipykernel/__main__.py:4: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", "/Users/ainajohannessen/anaconda/envs/3point6/lib/python3.6/site-packages/ipykernel/__main__.py:5: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n" ] }, { "data": { "text/html": [ "
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5199060.86556.4733HordalandMYRKDALEN-VETLEBOTNVoss104.176.325.640.6101.966.2
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" ], "text/plain": [ " Latitude Longitude County Name Municipality \\\n", "50110 60.3363 6.2182 Hordaland KVAM - AKSNESET Kvam \n", "50310 60.3887 5.9640 Hordaland KVAMSKOGEN - JONSHØGDI Kvam \n", "46430 59.8315 6.7330 Hordaland RØLDALSFJELLET Odda \n", "53530 60.6563 7.2755 Hordaland MIDTSTOVA Ulvik \n", "51990 60.8655 6.4733 Hordaland MYRKDALEN-VETLEBOTN Voss \n", "\n", " 48acc 20160928 20160929 20160930 2829 2930 \n", "50110 131.4 69.2 46.0 53.4 115.2 99.4 \n", "50310 130.4 77.9 35.1 59.5 113.0 94.6 \n", "46430 114.6 50.0 24.4 62.8 74.4 87.2 \n", "53530 108.7 54.4 22.8 54.6 77.2 77.4 \n", "51990 104.1 76.3 25.6 40.6 101.9 66.2 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#mnth = comb_all.loc[:,[\"County\", \"Municipality\", \"48acc\"]]\n", "mnth = comb_all.loc[:,[\"Latitude\",\"Longitude\",\"County\", \"Name\" ,\"Municipality\",\"48acc\", '20160928', '20160929', '20160930'] ] \n", "\n", "mnth[\"2829\"] = mnth[\"20160928\"].values + mnth[\"20160929\"].values\n", "mnth[\"2930\"] = mnth[\"20160929\"].values + mnth[\"20160930\"].values\n", "mnth = mnth[~mnth[\"2829\"].isnull()]#, \"Year\", \"yr2016_sum\", \"\"]]\n", "mnth.sort_values([\"48acc\",\"20160928\", \"20160929\", \"20160930\" ], axis=0, ascending=False, inplace=True, kind='quicksort', na_position='last')\n", "mnth = mnth[0:5]\n", "#mnth = mnth[(mnth[\"48acc\"] > 100) | (mnth[\"2829\"] > 100) | (mnth[\"2930\"] > 100) ]#, \"Year\", \"yr2016_sum\", \"\"]]\n", "display(mnth)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/html": [ "
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LatitudeLongitudeCountyNameMunicipality24acc201609282016092920160930
5011060.33636.2182HordalandKVAM - AKSNESETKvam83.269.246.053.4
5031060.38875.9640HordalandKVAMSKOGEN - JONSHØGDIKvam79.077.935.159.5
5054060.38305.3327HordalandBERGEN - FLORIDABergen70.666.97.817.9
5057060.40405.3358HordalandSKREDDERDALENBergen71.163.38.216.6
5086560.38285.5420HordalandGULLFJELLETBergenNaN116.621.146.8
5101060.52055.7243HordalandFOSSMARKVaksdal78.073.520.844.0
5180060.70226.9373HordalandMJØLFJELL UHVoss73.066.216.837.7
5199060.86556.4733HordalandMYRKDALEN-VETLEBOTNVoss78.476.325.640.6
5260160.83475.5833HordalandHAUKELAND - STOREVATNMasfjordenNaN103.622.462.3
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" ], "text/plain": [ " Latitude Longitude County Name Municipality \\\n", "50110 60.3363 6.2182 Hordaland KVAM - AKSNESET Kvam \n", "50310 60.3887 5.9640 Hordaland KVAMSKOGEN - JONSHØGDI Kvam \n", "50540 60.3830 5.3327 Hordaland BERGEN - FLORIDA Bergen \n", "50570 60.4040 5.3358 Hordaland SKREDDERDALEN Bergen \n", "50865 60.3828 5.5420 Hordaland GULLFJELLET Bergen \n", "51010 60.5205 5.7243 Hordaland FOSSMARK Vaksdal \n", "51800 60.7022 6.9373 Hordaland MJØLFJELL UH Voss \n", "51990 60.8655 6.4733 Hordaland MYRKDALEN-VETLEBOTN Voss \n", "52601 60.8347 5.5833 Hordaland HAUKELAND - STOREVATN Masfjorden \n", "\n", " 24acc 20160928 20160929 20160930 \n", "50110 83.2 69.2 46.0 53.4 \n", "50310 79.0 77.9 35.1 59.5 \n", "50540 70.6 66.9 7.8 17.9 \n", "50570 71.1 63.3 8.2 16.6 \n", "50865 NaN 116.6 21.1 46.8 \n", "51010 78.0 73.5 20.8 44.0 \n", "51800 73.0 66.2 16.8 37.7 \n", "51990 78.4 76.3 25.6 40.6 \n", "52601 NaN 103.6 22.4 62.3 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#mnth = comb_all.loc[:,[\"County\",\"Municipality\", \"24acc\"]]\n", "mnth = comb_all.loc[:,[\"Latitude\",\"Longitude\",\"County\", \"Name\" ,\"Municipality\",\"24acc\", '20160928', '20160929', '20160930'] ] \n", "\n", "mnth = mnth[~mnth[\"20160928\"].isnull()]#, \"Year\", \"yr2016_sum\", \"\"]]\n", "mnth = mnth[(mnth[\"24acc\"] > 70) | (mnth[\"20160928\"] > 100) | (mnth[\"20160929\"] > 100) | (mnth[\"20160930\"] > 100) ]#, \"Year\", \"yr2016_sum\", \"\"]]\n", "display(mnth)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Where did Walpurga effect the most ? " ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/html": [ "
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LatitudeLongitudeCountyMunicipality2DR5_wholeYr2DR5_Sep2DR5_SepOctNov201609282016092920160930
4782059.85946.2786HordalandEtne163.0105.0142.065.047.762.0
4789059.85476.0167HordalandKvinnherad200.0121.0170.061.541.564.5
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" ], "text/plain": [ " Latitude Longitude County Municipality 2DR5_wholeYr 2DR5_Sep \\\n", "47820 59.8594 6.2786 Hordaland Etne 163.0 105.0 \n", "47890 59.8547 6.0167 Hordaland Kvinnherad 200.0 121.0 \n", "\n", " 2DR5_SepOctNov 20160928 20160929 20160930 \n", "47820 142.0 65.0 47.7 62.0 \n", "47890 170.0 61.5 41.5 64.5 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mnth = comb_all.loc[:,[\"Latitude\",\"Longitude\",\"County\", \"Municipality\", '2DR5_wholeYr', '2DR5_Sep',\n", " '2DR5_SepOctNov','20160928', '20160929', '20160930'] ] \n", "mnth = mnth[~mnth[\"20160929\"].isnull()]#, \"Year\", \"yr2016_sum\", \"\"]]\n", "mnth = mnth[mnth[\"20160929\"] + mnth[\"20160930\"] >100]\n", "all_lon,all_lat = map(mnth.Longitude.values, mnth.Latitude.values)\n", "\n", "import matplotlib.pyplot as plt \n", "from mpl_toolkits.basemap import Basemap\n", "fig, ax = plt.subplots(figsize=(5,5))\n", "\n", "map = Basemap( llcrnrlon = 3., llcrnrlat = 58., urcrnrlon = 9., urcrnrlat=62.,\\\n", " resolution = 'l', area_thresh = 10000., projection = 'merc' )\n", "map.drawcoastlines( linewidth = 1.3, linestyle = 'solid', color = \"0.2\", zorder=5)#[ 75./255., 75/255., 75/255. ] )\n", "parallels = np.arange(50,70,1.)\n", "# labels = [left,right,top,bottom]\n", "map.drawparallels(parallels,labels=[False,True,True,False])\n", "meridians = np.arange(-10.,30.,1.)\n", "map.drawmeridians(meridians,labels=[True,False,False,True])\n", "lon,lat = map(mnth.Longitude.values, mnth.Latitude.values)\n", "display(mnth)\n", "\n", "#lon_R,lat_R = map(mnth.Longitude.values,mnth.Latitude.values)\n", "\n", "CS = map.plot(all_lon,all_lat, \"ob\")\n", "\n", "CS = map.plot(lon,lat, \"or\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Where was it most extreme" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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LatitudeLongitudeCountyMunicipality2DR5_wholeYr2DR5_Sep2DR5_SepOctNov2016092820160929
5086560.38285.5420HordalandBergenNaNNaNNaN116.621.1
5260160.83475.5833HordalandMasfjordenNaNNaNNaN103.622.4
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" ], "text/plain": [ " Latitude Longitude County Municipality 2DR5_wholeYr 2DR5_Sep \\\n", "50865 60.3828 5.5420 Hordaland Bergen NaN NaN \n", "52601 60.8347 5.5833 Hordaland Masfjorden NaN NaN \n", "\n", " 2DR5_SepOctNov 20160928 20160929 \n", "50865 NaN 116.6 21.1 \n", "52601 NaN 103.6 22.4 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mnth = comb_all.loc[:,[\"Latitude\",\"Longitude\",\"County\", \"Municipality\", '2DR5_wholeYr', '2DR5_Sep',\n", " '2DR5_SepOctNov', '20160928', '20160929'] ] \n", "mnth = mnth[~mnth[\"20160929\"].isnull()]#, \"Year\", \"yr2016_sum\", \"\"]]\n", "mnth = mnth[mnth[\"20160928\"] >100]\n", "all_lon,all_lat = map(mnth.Longitude.values, mnth.Latitude.values)\n", "\n", "import matplotlib.pyplot as plt \n", "from mpl_toolkits.basemap import Basemap\n", "fig, ax = plt.subplots(figsize=(5,5))\n", "\n", "map = Basemap( llcrnrlon = 3., llcrnrlat = 58., urcrnrlon = 9., urcrnrlat=62.,\\\n", " resolution = 'l', area_thresh = 10000., projection = 'merc' )\n", "map.drawcoastlines( linewidth = 1.3, linestyle = 'solid', color = \"0.2\", zorder=5)#[ 75./255., 75/255., 75/255. ] )\n", "parallels = np.arange(50,70,1.)\n", "# labels = [left,right,top,bottom]\n", "map.drawparallels(parallels,labels=[False,True,True,False])\n", "meridians = np.arange(-10.,30.,1.)\n", "map.drawmeridians(meridians,labels=[True,False,False,True])\n", "lon,lat = map(mnth.Longitude.values, mnth.Latitude.values)\n", "display(mnth)\n", "\n", "#lon_R,lat_R = map(mnth.Longitude.values,mnth.Latitude.values)\n", "\n", "CS = map.plot(all_lon,all_lat, \"ob\")\n", "\n", "CS = map.plot(lon,lat, \"or\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### compared to norm for entire event" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/html": [ "
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LatitudeLongitudeCountyMunicipalityEvenSumfrom2472accSepOctNovSept2016_sum
4381058.76456.3675RogalandGjesdal131.3NaN305.0362.0355.0253.7
4615059.47916.2760RogalandSuldal134.3NaN261.0275.0258.0298.7
4630059.58876.8090RogalandSuldal128.9NaN213.0227.0215.0240.8
4645059.83046.8238HordalandOdda110.5NaN190.0213.0191.0211.9
4782059.85946.2786HordalandEtne174.7NaN317.0339.0316.0329.8
4789059.85476.0167HordalandKvinnherad167.5NaN359.0367.0345.0322.6
4850059.99136.0263HordalandKvinnherad127.5NaN220.0214.0197.0260.8
4935160.11886.5547HordalandOdda102.6NaN176.0199.0183.0163.1
4949060.31856.6538HordalandUllensvang78.782.5157.0181.0163.0160.4
4963160.46477.0685HordalandEidfjord72.0NaN122.0139.0124.0126.7
4980060.40857.2798HordalandEidfjord79.5NaN113.0113.0101.0132.3
5007060.36316.2805HordalandKvam101.2NaN267.0280.0258.0200.3
5031060.38875.9640HordalandKvam172.5177.6392.0402.0350.0312.3
5125060.68875.9647HordalandVoss141.5NaN365.0369.0330.0277.9
5147060.64556.2220HordalandVoss104.3109.2221.0230.0211.0201.4
5180060.70226.9373HordalandVoss120.7129.9215.0205.0185.0214.2
5316060.90046.7243HordalandVoss124.1NaN231.0232.0210.0253.4
\n", "
" ], "text/plain": [ " Latitude Longitude County Municipality EvenSumfrom24 72acc \\\n", "43810 58.7645 6.3675 Rogaland Gjesdal 131.3 NaN \n", "46150 59.4791 6.2760 Rogaland Suldal 134.3 NaN \n", "46300 59.5887 6.8090 Rogaland Suldal 128.9 NaN \n", "46450 59.8304 6.8238 Hordaland Odda 110.5 NaN \n", "47820 59.8594 6.2786 Hordaland Etne 174.7 NaN \n", "47890 59.8547 6.0167 Hordaland Kvinnherad 167.5 NaN \n", "48500 59.9913 6.0263 Hordaland Kvinnherad 127.5 NaN \n", "49351 60.1188 6.5547 Hordaland Odda 102.6 NaN \n", "49490 60.3185 6.6538 Hordaland Ullensvang 78.7 82.5 \n", "49631 60.4647 7.0685 Hordaland Eidfjord 72.0 NaN \n", "49800 60.4085 7.2798 Hordaland Eidfjord 79.5 NaN \n", "50070 60.3631 6.2805 Hordaland Kvam 101.2 NaN \n", "50310 60.3887 5.9640 Hordaland Kvam 172.5 177.6 \n", "51250 60.6887 5.9647 Hordaland Voss 141.5 NaN \n", "51470 60.6455 6.2220 Hordaland Voss 104.3 109.2 \n", "51800 60.7022 6.9373 Hordaland Voss 120.7 129.9 \n", "53160 60.9004 6.7243 Hordaland Voss 124.1 NaN \n", "\n", " Sep Oct Nov Sept2016_sum \n", "43810 305.0 362.0 355.0 253.7 \n", "46150 261.0 275.0 258.0 298.7 \n", "46300 213.0 227.0 215.0 240.8 \n", "46450 190.0 213.0 191.0 211.9 \n", "47820 317.0 339.0 316.0 329.8 \n", "47890 359.0 367.0 345.0 322.6 \n", "48500 220.0 214.0 197.0 260.8 \n", "49351 176.0 199.0 183.0 163.1 \n", "49490 157.0 181.0 163.0 160.4 \n", "49631 122.0 139.0 124.0 126.7 \n", "49800 113.0 113.0 101.0 132.3 \n", "50070 267.0 280.0 258.0 200.3 \n", "50310 392.0 402.0 350.0 312.3 \n", "51250 365.0 369.0 330.0 277.9 \n", "51470 221.0 230.0 211.0 201.4 \n", "51800 215.0 205.0 185.0 214.2 \n", "53160 231.0 232.0 210.0 253.4 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt \n", "from mpl_toolkits.basemap import Basemap\n", "fig, ax = plt.subplots(figsize=(5,5))\n", "\n", "map = Basemap( llcrnrlon = 3., llcrnrlat = 58., urcrnrlon = 9., urcrnrlat=62.,\\\n", " resolution = 'l', area_thresh = 10000., projection = 'merc' )\n", "map.drawcoastlines( linewidth = 1.3, linestyle = 'solid', color = \"0.2\", zorder=5)#[ 75./255., 75/255., 75/255. ] )\n", "\n", "mnth = comb_all.loc[:,[\"Latitude\",\"Longitude\",\"County\",\"Municipality\", \"EvenSumfrom24\", \"72acc\", \"Sep\", \"Oct\",\"Nov\", \"Sept2016_sum\" ]]\n", "mnth = mnth[~mnth[\"Sep\"].isnull()]#, \"Year\", \"yr2016_sum\", \"\"]]\n", "mnth = mnth[~mnth[\"EvenSumfrom24\"].isnull()]#, \"Year\", \"yr2016_sum\", \"\"]]\n", "lon,lat = map(mnth.Longitude.values, mnth.Latitude.values)\n", "\n", "mnth = mnth[(mnth[\"EvenSumfrom24\"]/mnth[\"Sept2016_sum\"] >0.5) | (mnth[\"EvenSumfrom24\"]/mnth[\"Sep\"] >0.5)]\n", "#mnth = mnth[(mnth[\"20160928\"]/mnth[\"1DR5_SepOctNov\"] >1) | (mnth[\"20160929\"]/mnth[\"1DR5_SepOctNov\"] >1)| (mnth[\"20160930\"]/mnth[\"1DR5_SepOctNov\"] >1)]\n", "display(mnth)\n", "\n", "lon_R,lat_R = map(mnth.Longitude.values,mnth.Latitude.values)\n", "CS = map.plot(lon,lat, \"ob\")\n", "CS = map.plot(lon_R,lat_R, \"or\")\n", "#display(fig)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Returnperiods" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "#### 24 hours" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/html": [ "
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NameCountyLongitudeLatitudeMunicipality24acc2016092820160929201609301DR5_Sep1DR5_SepOctNov1DR5_wholeYr
53700AURLANDSogn Og Fjordane7.200860.9027AurlandNaN37.010.011.428.037.043.0
\n", "
" ], "text/plain": [ " Name County Longitude Latitude Municipality 24acc \\\n", "53700 AURLAND Sogn Og Fjordane 7.2008 60.9027 Aurland NaN \n", "\n", " 20160928 20160929 20160930 1DR5_Sep 1DR5_SepOctNov 1DR5_wholeYr \n", "53700 37.0 10.0 11.4 28.0 37.0 43.0 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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NameCountyLongitudeLatitudeMunicipality24acc2016092820160929201609301DR5_Sep1DR5_SepOctNov1DR5_wholeYr
53700AURLANDSogn Og Fjordane7.200860.9027AurlandNaN37.010.011.428.037.043.0
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" ], "text/plain": [ " Name County Longitude Latitude Municipality 24acc \\\n", "53700 AURLAND Sogn Og Fjordane 7.2008 60.9027 Aurland NaN \n", "\n", " 20160928 20160929 20160930 1DR5_Sep 1DR5_SepOctNov 1DR5_wholeYr \n", "53700 37.0 10.0 11.4 28.0 37.0 43.0 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mnth = comb_all.loc[:,[\"Name\",\"County\",\"Longitude\",\"Latitude\",\"Municipality\", \"24acc\", \"20160928\",\"20160929\", \"20160930\",\"1DR5_Sep\", \"1DR5_SepOctNov\", \"1DR5_wholeYr\" ]]\n", "mnth = mnth[~mnth[\"1DR5_Sep\"].isnull()]#, \"Year\", \"yr2016_sum\", \"\"]]\n", "mnth = mnth[~mnth[\"20160928\"].isnull()]#, \"Year\", \"yr2016_sum\", \"\"]]\n", "all_lon,all_lat = map(mnth.Longitude.values, mnth.Latitude.values)\n", "\n", "#mnth = mnth[(mnth[\"20160928\"]/mnth[\"1DR5_Sep\"] >1) | (mnth[\"20160929\"]/mnth[\"1DR5_Sep\"] >1)| (mnth[\"20160930\"]/mnth[\"1DR5_Sep\"] >1)]\n", "mnth = mnth[(mnth[\"20160928\"]/mnth[\"1DR5_wholeYr\"] >0.8) | (mnth[\"20160929\"]/mnth[\"1DR5_wholeYr\"] >0.8)| (mnth[\"20160930\"]/mnth[\"1DR5_wholeYr\"] >0.8)]\n", "\n", "import matplotlib.pyplot as plt \n", "from mpl_toolkits.basemap import Basemap\n", "fig, ax = plt.subplots(figsize=(5,5))\n", "\n", "map = Basemap( llcrnrlon = 3., llcrnrlat = 58., urcrnrlon = 9., urcrnrlat=62.,\\\n", " resolution = 'l', area_thresh = 10000., projection = 'merc' )\n", "map.drawcoastlines( linewidth = 1.3, linestyle = 'solid', color = \"0.2\", zorder=5)#[ 75./255., 75/255., 75/255. ] )\n", "parallels = np.arange(50,70,1.)\n", "# labels = [left,right,top,bottom]\n", "map.drawparallels(parallels,labels=[False,True,True,False])\n", "meridians = np.arange(-10.,30.,1.)\n", "map.drawmeridians(meridians,labels=[True,False,False,True])\n", "lon,lat = map(mnth.Longitude.values, mnth.Latitude.values)\n", "display(mnth)\n", "\n", "#lon_R,lat_R = map(mnth.Longitude.values,mnth.Latitude.values)\n", "\n", "CS = map.plot(all_lon,all_lat, \"ob\")\n", "\n", "CS = map.plot(lon,lat, \"or\")\n", "plt.show()\n", "display(mnth)\n" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "#### 48 hours" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/html": [ "
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NameCountyLongitudeLatitudeMunicipality48acc2DR5_SepOctNov2DR5_Sep2DR5_wholeYr28292930
44520HELLAND I GJESDALRogaland6.013558.7550GjesdalNaN105.080.0112.089.741.2
53700AURLANDSogn Og Fjordane7.200860.9027AurlandNaN47.034.056.047.021.4
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" ], "text/plain": [ " Name County Longitude Latitude Municipality \\\n", "44520 HELLAND I GJESDAL Rogaland 6.0135 58.7550 Gjesdal \n", "53700 AURLAND Sogn Og Fjordane 7.2008 60.9027 Aurland \n", "\n", " 48acc 2DR5_SepOctNov 2DR5_Sep 2DR5_wholeYr 2829 2930 \n", "44520 NaN 105.0 80.0 112.0 89.7 41.2 \n", "53700 NaN 47.0 34.0 56.0 47.0 21.4 " ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "tada\n" ] }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mnth = comb_all.loc[:,[\"Name\",\"County\",\"Longitude\",\"Latitude\",\"Municipality\", \"48acc\", \"20160928\", \"20160929\", \"20160930\",\"2DR5_SepOctNov\",\"2DR5_Sep\", \"2DR5_wholeYr\" ]].copy()\n", "mnth = mnth[~mnth[\"20160928\"].isnull()]#, \"Year\", \"yr2016_sum\", \"\"]]\n", "\n", "mnth[\"2829\"] = mnth[\"20160928\"].values + mnth[\"20160929\"].values\n", "mnth[\"2930\"] = mnth[\"20160929\"].values + mnth[\"20160930\"].values\n", "mnth.drop(columns=[\"20160928\", \"20160929\", \"20160930\"], inplace = True)\n", "mnth = mnth[~mnth[\"2DR5_Sep\"].isnull()]#, \"Year\", \"yr2016_sum\", \"\"]]\n", "all_lon,all_lat = mnth.Longitude.values, mnth.Latitude.values\n", "\n", "#mnth = mnth[(mnth[\"48acc\"]/mnth[\"2DR5_Sep\"] >1) | (mnth[\"2829\"]/mnth[\"2DR5_Sep\"] >1) | (mnth[\"2930\"]/mnth[\"2DR5_Sep\"] >1) ]\n", "\n", "mnth = mnth[(mnth[\"48acc\"]/mnth[\"2DR5_wholeYr\"] >0.8) | (mnth[\"2829\"]/mnth[\"2DR5_wholeYr\"] >0.8) | (mnth[\"2930\"]/mnth[\"2DR5_wholeYr\"] >0.8) ]\n", "display(mnth)\n", "\n", "#mnth = mnth[(mnth[\"20160928\"]/mnth[\"1DR5_SepOctNov\"] >1) | (mnth[\"20160929\"]/mnth[\"1DR5_SepOctNov\"] >1)| (mnth[\"20160930\"]/mnth[\"1DR5_SepOctNov\"] >1)]\n", "print(\"tada\")\n", "\n", "import matplotlib.pyplot as plt \n", "from mpl_toolkits.basemap import Basemap\n", "fig, ax = plt.subplots(figsize=(5,5))\n", "\n", "map = Basemap( llcrnrlon = 3., llcrnrlat = 58., urcrnrlon = 9., urcrnrlat=62.,\\\n", " resolution = 'l', area_thresh = 10000., projection = 'merc' )\n", "map.drawcoastlines( linewidth = 1.3, linestyle = 'solid', color = \"0.2\", zorder=5)#[ 75./255., 75/255., 75/255. ] )\n", "parallels = np.arange(50,70,1.)\n", "# labels = [left,right,top,bottom]\n", "map.drawparallels(parallels,labels=[False,True,True,False])\n", "meridians = np.arange(-10.,30.,1.)\n", "map.drawmeridians(meridians,labels=[True,False,False,True])\n", "lon,lat = map(mnth.Longitude.values, mnth.Latitude.values)\n", "all_lon,all_lat = map(all_lon,all_lat)\n", "\n", "CS = map.plot(all_lon,all_lat, \"ob\")\n", "\n", "CS = map.plot(lon,lat, \"or\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "#### 72 hours\n" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tada\n" ] }, { "data": { "text/html": [ "
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NameCountyLongitudeLatitudeMunicipality72accEvenSumfrom243DR5_SepOctNov3DR5_wholeYr201609282016092920160930
48500ROSENDALHordaland6.026359.9913KvinnheradNaN127.5118.0138.061.423.642.5
51470BULKENHordaland6.222060.6455Voss109.2104.3106.0120.054.318.831.2
55730SOGNDAL - SELSENGSogn Og Fjordane6.933561.3348SogndalNaN104.2101.0110.051.618.134.5
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" ], "text/plain": [ " Name County Longitude Latitude Municipality \\\n", "48500 ROSENDAL Hordaland 6.0263 59.9913 Kvinnherad \n", "51470 BULKEN Hordaland 6.2220 60.6455 Voss \n", "55730 SOGNDAL - SELSENG Sogn Og Fjordane 6.9335 61.3348 Sogndal \n", "\n", " 72acc EvenSumfrom24 3DR5_SepOctNov 3DR5_wholeYr 20160928 20160929 \\\n", "48500 NaN 127.5 118.0 138.0 61.4 23.6 \n", "51470 109.2 104.3 106.0 120.0 54.3 18.8 \n", "55730 NaN 104.2 101.0 110.0 51.6 18.1 \n", "\n", " 20160930 \n", "48500 42.5 \n", "51470 31.2 \n", "55730 34.5 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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I7r59+2jcuHHEsiz16dOH1qxZU+k+NUFf/02NjLemJakVwSm9LJpi9JxPr0YUZcO9cuUKDh48iDt37qBevXro2bMnevfuDUdHx1qZx9bSIhBV9KOVyZTDjd7XHpri03Ox92pk5syZcgtQHDt2DLdv34aoxOKSkpJw5coVnDp1Cg8fPkRhYaFa+6Gnlyp3uzLBOiNHigftRCLxsypr7v39w2XWCNTmWgBFcLH3FeGUXo1Ulvc+Pz8fERERePDgATIzM5GRkYGkpCTk5uaCYRi0atUKn332Gdq1ayd9trCwUGruvjyrVr3C4sUWyM8vVdiPXc9OnTX1iouLERYWhgsXLuDo0aPV+k04pZfTD1WNnpunL0VR3vt///0XYWFhuHHjhsz2evXqQSQSKbxbMjQ0xGeffSZ9SC4IZQtWyqNHjx4YPvwUVq40Vcttek1Q1/RfbGwslixZgvj4ePj7+2PJkiXV6gc3Ty+nH5zSq4/ySv/gwQOEhYXh5s2b4PF4mDx5Mlq3bo309HSkp6cjLS0NaWlp0tdlt1cWr29ubi69AHTo0AE+Pj4yYwM1nadXJ5LqPOVRNgNvUVERtmzZgvDwcIhEIjRp0gQHDhyo8oJXHk7p5fSDU3r1IVH6vLw8bN26Fbdu3YKzszMmT55c7b4VFhbKvRiUf52bm4stW7bI/FNXJyJPnQN3ZVFF6e/fv48lS5YgIyMDXbt2xYULF/D777/D3d292v3glF4Oqg7/c/P0pZw+fZqCg4OJZVnq3r07LV++nHbt2kUXLlz4qP1QNkeeuqfoyh/bwEBYrWMXFBTQ6tWricfj0ffff08vXryg3r1709KlS2vcD26enpunrzXCwsKIZVm5j59++umj9kXZHHnqDsYpT6tW/yFLS5FSUYF3796lAQMGkI+PD507d45EIhHNnz+f+vXrR7m5uTXuAzdPXwtGzym9mLi4OAoLC6MrV67QmjVryN3dnfz9/enSpUsfPSpNWaVXd9hteZSJyCsqKqJff/2VeDwe/fDDD5SWlkZE4uzCLMtSVFSUSn3glJ5T+lolICCAvvvuO3J1daX169dTfn5+nfRDU5RemYi8ixcvkrOzs4wLlJGRQb6+vrRy5UqV+8ApPaf0tcqzZ89o7ty5FBwcrNbjikQipWP6iTTDpydSTuklWYQl/RWJRDR79mzq37+/Wi6anNJXfHAReWpk5syZsLW1RXR0NHJycqq9PxEhNTUVN2/exJ49e7B06VJMmDAB3t7e8PLyUvqY5SvcKELdlW+uf7UHKTrWEDFaSNGxxpYec6vMhtuggbi8V3Z2NgAgIiICf//9NxYvXgxDQ8OadaQMXEReRbh5ejUSFxeHxo0b4/PPP8e8efPg7+8vt53EuOPj46WPly9fIj4+XmZ+Xl9fH3Z2dmBZFm5ubujcubNS/aiLefrrX+2B4+YgGKNUBPJghJjgrei2SfFVJCUlBQMGDMD+/ftRv359DB06FJ9//jlmzJihln5x8/Ry+qGq0XPz9KVI5uk3bNgAXV1d/PLLL3j79m0F43758qXc4BtdXV106dIFPB4PPB4Ptra20NPTq3Y/apo5RxVSdKzRUlhxYj5F2xItBXIm7EvIy8tDjx49EBoaij179iA5ORnh4eFyQ5kVUVmsATdPXxEuG64a6dFjC7y9zZGf7w0rq2Vwd+8BPl+sfMbGxhUMXUdHB7a2tjJGXp1/dkWEhoYqlU5bnVgI5afptRDKFv8ob6A//2wEXV1dhIeH48aNG/jjjz+qbfBlU3wlJorfA2LD57LhVoRTejWxZw8wdmwhhEIDaGkVoHnzrTAyeoWOHTOhpZWGlJQUaGlpoXPnzmBZFjweD3Z2dmrxW8ujqUqvKAdfx45i8Rs/fjy+/vrrap23qsg/Tunl9IPz6dWDtXUhMjPvwMRE/DAyegqAgUDQEZMmiZXc3t7+o9wZ1YZPLxQKkZmZiYYNG8pd5aaMT6/IQFlWbAf//PMP9PX1q9WvqmL8OZ9eDqoO/3Pz9GJatvyNnJx4ZGMzilq0WEumptdISytXbYEu1UEdVWv37dtHc+bMofHjx5O/v7+0+m54JfN514LDKVnbioRgKFnbir5p+LnM1KGiYKAuXfyJZVmKiIiodj+rijXg5ulrYcpOU3z6Dh061GkfjI1bQSAww5Mnu/Dq1TTk5HSDSFSvysQV5YtRqiPRhKo+vUgkwpo1a5CRkQF7e3uMHj0aX331FQBUOoPQbdNItBQkQItEaClIwJT/rpBZ/WdpCXTp4o8OHWSn0EQiBwDAxo0bla70I2HZMrGLUJay5bU4n74i3Dy9mggOdoKubhYMDOKl28rXdiuPxMeVVJuVDEKpavhTp07Ftm35Nb6Y5ObmQiQSITg4GNOmTcOwYcNQUFCA5s2bw87OTunjlK9a26/fZujpvYNQWE+6zcgI6Nq1AaytrfH+/XscO3ZM+Y6i6lgDbp5eDqreKnAReWKEQiG5uXlR+/YHlE47XVthsDXNhishISGBWJal+Ph4IhJHyQUGBtKGDRuq1Y+yEXmSY7IsS1ZWsotwwsLCaOjQobRhwwbq1atXpX9LkUhEKSkpdObMGVq7dm2VbgwXkcdF5NUaWlpa0NLiw87uEM6fj0BkZGqVkW3K1HivCQsWaCM/XwgzsyvQ0xPn7MvPF0+VKYMkmk/iIjx8+BCvXr1Cnz59qtUPidILBAJ88cUXAIBTp04hIYGRycHXoEEDZGZmYuzYsRAKhQgPD5ceQygU4smTJ9i/fz/mzp0Lf39/9O/fHwsXLsR///tfCASCSvvAKX1FuHl6NfLjjz/i8OHDWLRoEfh8PrS0tODr6wsnJyc4ODigTZs2MiPfiopRqlJttri4GCYm/6Bdu13Q13+F168n4c2bqQCUv5hkZ2eDYRiYmJgAAP766y/Uq1cP7969Q/PmzZX+m0uq1o4dOxYA8M0336B58+YV2tWvXx9ZWVkwNjbGhAkTEBoaCgDS8mB5eXnQ19dHUVERAHHmoMmTJ+Pzzz+vcoaC8+krwlWtVSOhoaEICQlBy5Yt4eHhAZFIhIiICERERMi0s7a2xpw5c7BsGa/KGu/Kkp+fj6NHjyI8PBytWuXi/fuBaNToLASCBtI2lpZiFc/IyECbNm0UHisrKwumpqZSg5KsJ/j222+hra0tE2tgb2+vMJhm5MiR+PXXX/Ho0SNoaWlh3Lhxcts1aNAAQqEQHz58wJAhQ3DixAlERETA3t4eXl5eiIuLw6VLl2BoaIjx48dj8ODBSk/tcVVrK8LN06uRsjnyzp8/j/nz5+PLL7+EhYUFbty4gaioKGlbAwMDXL9+XeV0VVlZWThw4AAOHDgAkUiEL7/8EunpXli5sgNsbNwRH78CWVk+MDHJxIgR4Xj06BDy8/Ph5+eHadOmyb1D2rVrF44fP46jR49KtwkEAuzduxcmJiZ4/vw57ty5gxcvXlSIKuzSpYvUIK9fv47p06cDEOf+r1evXoVzAcDLly8xZMgQHD16FJYltzkCgQAnT57Etm3bkJeXh1GjRmHEiBHV/htz8/RyUHVQgJunL6V8hZsFCxZQ7969KTMzs0JbVRNrpKam0po1a6hbt27k5+dHO3fulGaY6d69O61ZIx44q1//MnXsuJZcXT2oT58+tG/fPrp27RoNGjSIunXrRjt27KDi4mKZY//+++80fvx4mW2SzEA+Pj507NgxEgqFlJ6eThcuXKCVK1fS4MGDiWVZcnNzo8mTJ9OWLVukA3fXrl2r9LtkZGQQy7J07949EgqFdP78eRowYAC5ublRSEiI3N9PWbh5+ooPTunVSPlsuHl5eRgxYgTatm2L1atXq6XSS3JyMnbt2oXTp0+jSZMmGD16NAICAmRusR8/fgwiwpgxY6ClpYUmTZpg3LhxCAwMlKown8/H/v37ERYWhsaNG+OHH36Am5sbAGDp0qXIyMhASEgIAODZs2cYM2YMvvnmG2RnZ2P37t3o2LEj5syZIxMfkZaWhujoaERHR+P8+fPIz8+Hi4sLNm3aJPMd+Hw+Hj16hFu3buH+/ft48uQJcnJysHjxYuzduxcvXrxAYGAgJk2apPJYDaf0clD1qsEpfSnyatn9+++/5OLiQgcPHlTqGOHhJLfa7LNnz+jHH38kZ2dnGjJkCJ0+fVpuoozCwkLy9PSkx48f08SJE2n27CNkZVWkcBrx3bt3NH/+fGJZlmbOnEkpKSn0/fff0+LFi4mIqLi4mIYNG0bBwcHSu5P4+HiaMmUKOTs702+//VYhh51IJKJvv/2WnJycKDc3l3Jzc+nGjRu0ceNGmjhxYoUcgi4uLnT27Fl68OABubi40Lx589SWYiwnJ4fatl1Q7Qq+tQE4pVcfmqr0Enbu3ImwsDD8+eef+OyzzxTuL29BSqNG9+HtvRMvX15Dp06dMGHCBHTv3l1mFiAlJQX//PMPbty4gTt37qCoqAhNmjSBiYkNIiNtkJ3dAfn5NuDzzWFkxMhNlHH37l2sWrUKSUlJMDAwQGBgIKZNm4bNmzdj//79OHDgAJo1ayZtT0T466+/EBISAoZhMGPGDPj5+YFhGBw5cgQrVqyAjY0NiAhxcXHSkl4SWrZsiYkTJyIvLw8nTpxAXFwc2rdvjwYNGiAyMhJDhgzB7NmzVb472r1bhKAgqKXSjqpoitJzq+zUiKIKN0KhEF9//TUyMjLw888/o3379nL3L12QQjA1vYVmzXbAxOQuioudsW3beDg7O4NhGBQWFiI6Oho3btzAjRs3kJycDDMzM3Tt2hXu7u5YsWIFxo0bh7VrkyESPYWh4UswjBACgRny8ztAR8cGISEdYGdnJzOFJhAIcOTIEYSGhmLatGl48aId9u6dgKSkH2Fs3F/uIOOHDx+wefNmHDp0CDweD7Nnz8Z3332Hd+/eobi4GJ06dcKTJ08AAC1atEBAQAD8/f1hYWEhPQYR4fHjxzh58iTOnz+PDx8+AAC++OIL/Oc//6lRaS8JlpYiJCdX3L+6lXbUwSdj9JzSlyJP6fPy8qCjo4Ps7Gx89913eP78OT777DP4+/ujb9++aNKkibStlpYIZmaX0azZDhgbP0FWVg+8eTMeBQWd8fJlktTI7969KzUod3d3eHh4oGPHjtI4d8kqOz09HRABDFMIQ8MXMDJ6UvJ4igYNnkMoFOLq1asVpr9EIhF27y7Gb7+NRmGhBZ4/XwuAqVQhnzx5glmzVuDNm6dIT+8HhvkOU6fmQEfnII4ePYoNGzbAwcGhSuUuLCzE33//jZMnT+LOnTvYuXMnbG1ta/w3UaWCr7rRFKPnfHo1Ut6nj4mJIT8/PwoICKDr16+TSCSiR48e0apVq8jX15ecnZ3p66+/pjNnztDJkyfJ3n4QOTm5kLX1fDIy+pdMTa9Rq1YrycEhkFiWJV9fX5o/fz6dPXuWMjIyFPZDssqusjDfM2fOULdu3RT6zh07riV7e2/S1X2nVIiwOMmmkBo3Pkr29t5kaxtAZmYXaO3aOGJZlm7evFnt3zMnJ0dl375VK2GthDrXBHA+vfrQNKXX19fHkSNHsGrVKvj4+EBfXx+nT59Gr169MHPmTDRu3BgCgQA3b97E2bNn8d///hcAYGMTiLNneTA1PYZ69WLAMAIUFNiiZ083TJ3qjo4dOyp1qytR+gMHdOQG/2zdChQUbENERAQOHjyI9PR07N69GydOnMD69eshEAgwceJkvHy5FJmZsqG3ihSy7Fp5HZ1MtGixHo0bn0Ru7nz07v03GIbBunXravrT1hjOp5eDqlcNTulLCQgIoMePH9OSJUvI2dmZdu7cKVWq27dv08CBA6lHjx506NAhEgqF0pF6be1csrbOovBwoqFDfyB7+2HUsOF5srbOrNFIc9n19IpmA5YsWULjxo2jkJAQcnd3p969exPLsnTx4kUaMGAA2drOIkCktEKWXytvZPQvsSxL9erdo5s3bxLLsvTy5cvqfxkV0eTRewD1ARwG8ATAYwBuAIYAeAhABIBXrv0qAHcA9Ch5bw2AAHxbps0GAOOoEptV2eg/1VV2ioylMmJjY2n06NHk7e0t93a2sLCQNm/eTK6urvT55+OpQYO4CivhvL0/p127dqnUd2Xy3ktq7vn7+9Pqya/jAAAgAElEQVShQ4coPT2dWJalkSNHUs+ePWnr1vRq5cQv70q0aTOb2refSJaWIhKJRDR48GBasWKFSt+rJmjyKjsAfwKYVPJar+Qi0BFABwBXyho9AJsSozcCcJBKjT4VwHMAevSxjP5TVPqaFoHo168f9erViwYOHFipqsXHx5Od3SRycnIhC4sNxDAFBBBpa2cSy7J0+/ZtlfqvTOacc+fO0eHDh6moqIiIiN6/fy+dN7948SIRVe/CV/Y309dPIicnHtWvf4F27RIQEdGRI0fIw8ODsrKyVPpu1UVTI/IAmAJ4iZLB9PIPOUbfGcAvAAwBHKJSo48FsAXAZPpYRm9vb08ikYiKi4vr7Pn58+eUmZmptuNZWla8rQWILC0r3+/p06eUkJBAkydPJk9PT/r7778VtmcYATVqdKxk0CuQTExukomJ+DY4JydHpf4/fPiQ8vPzq7Xfy5cviWVZmjNnTo3PGx4uIktLEVlaLid7+8G0fPlLKioqIpFIRDk5OeTj40Pbt2//qP8fBQUFFBsbW6f/n5LnckbvACAKwE4AMQC2ATBWZPQl29YDiAbgU87oW5e4CNofxej19fXp7t27xOPx6uzZ39+fOnbsqLbjMYx8o2cYUaX7de/enWxtbSkqKkom4uzOnTsV2uvpvSaASEcnnaytxRFxtraBZGsboHL/nZycyN7evlr7sSxLs2bNIpZlVTo/y7Lk6upKLMtSu3btiGVZunLlCvF4PFqwYIG0KOXH+v9wdHQkW1vbOv3/lDyXM3oeAAEA15L36wAsrczo5dwNWAOILXm9C8BozqevITXNaPPs2TOZmnPnzp0jd3d3+uabbyrc1oaHE+nqlh7bxOQmde7cn6ytf1Z5oEnZWna1waZNm6h3795UVFREV69eld715Ofn07t378jFxYXOnz//0fqjqT49gGYAEsq89wRwpsz76hq9TYnqb6rK6LnMOXKoKtmiIoKDg3HhwgXp+z59+mDnzp1ITEysUKZp5EjA1LT0fW5uVzx8eAwJCXOVznCjCGVr2amb/Px8HDp0CMOHD0dUVBSmT5+OzMxMFBQUIDo6Gk2aNIGfnx/27dv3UfukiZlziOgtgGSGYSQrlnoCeKTC8Z6U7P95VW25bLhyqGlhRxsbG2zYsAG5ubnSbe3atYO5uTlatGhRoX1GRvktDAAtldNl1UWFGwA4efIkioqKkJycjOnTp8Pb2xu7du1Cp06dEBkZCQAYPnw4YmNj8eDBg4/SJw3PnPMtgD0MwzyA2MdfzjDMQIZhUiCevjvDMMxf1TjeMgAtq2xV2W2AMo9PcfS+pvTs2ZNYlqXly5dLtyUlJRHLshQZGVmhfW0lxlRH3ntFKBrR5/P51K9fP2JZlrp3704RERHS+vSbNm2iwYMHS/e3sxtP1taLPsqcuaaO3tflg/Pp1YhkwI5lWYqJiSEisY/7+eefk1AorNBe/tSgiLZsSVOpH7Xl01c2lRkREUEsy9Lo0aMpOTmZiEqz4UoG+DZvfkNGRsXUpUtfsrDYUO0svTVBU336unxwSq9GAgICaPjw4cSyLA0ePJgKCgrI39+fQkNDFe4jq5xCmjBhJfF4vBrFqkuoLaWv7M7k/v37tG3bNpksPBKl5/P55OnpSe3bH6OGDU+Ro2NX0tF5/1Hi4Dmlr/jgYu/VSFxcHC5fvowdO3ZAT08Ptra2iIyMxIkTJ+T69GURCoVYvnw5zp07Bzs7O7x48QJ79+6VWYWnLLVVn76qunHliY2NRefOncEwDL7//nucOKEHff0E5Od3RmLigir3Vwdc5pyKcKP3amTmzJn47LPPwOfz0adPH0RGRoLH4+HKlRaVVpsRCARYtGiRNCnF2rVr0aBBA8yfP1+mQoyy1NbovaLU3Iq2l61w07VrV9SvfxlGRs+RmjpKqf3VgaaO3tcl3Oi9Glm9ejXc3Nxgbm6Opk2bon///rCymlhp6So+n4+5c+fi2rVr+O2331BcXIzVq1cjOzsb0dHRePnyZbX7UdXofU3r51V3KlOS9x4A3NzcwDBC5OZ6orCwtVL7qwMNH72vG1T1DzifvhTJevoVK1ZIs8lW5gcXFhbSuHHjiGVZ8vPzI3d3d3J2dqaJEyfSjh076MWLFzXqR2U+fU3XFZTdX9l4fIlPTyTOm7ds2TIKCXn6UVe8cT59LQzkcaP3pUgi8q5du0Y8Ho+SkpIUlmfW1s6nkSNHSkN158yZQ2fOnFEp3bOE8qP3ZQ1VW1vxRUjdlK1lV1dwo/e1YPSc0pciUfqCggIaMmQIubq6UufOS0lPL1nGwLS0csnObqzU4NVh6GUpv56+vLLLX1eg1i4QkazS1xWc0nNKXyuEhYWRr68vxcbGSmPv+Xw+nT17lnr1GkxOTs5kbT2fDAyek7Z2FnXqNFpq8HFxcWrvT1mlV+RecEr/8dEUo+dG79VAfn6+tOrqX3+JoyZ1dHTQt29fnD9/AAEBK2FmFo/OnYfCzu5LGBqKQ6xXr15daUrsmlJ29F6ZkN7aGkwrX5++LuBG7+Wg6lWDU3oxV69elar39OnTK0TgiUQiunbtGg0cOJBYlqUdO3bUWl+UUXpt7dofTOOUXhZwSq8+6lrpAcDT01NaDOLatWtwcXFBSkqK9HOGYdCtWzfo6enB0dFRWr5ZWbKzs7Fo0SK8ffu2yrZllV7RNNuff0KmRnxtwCm9hqLqVYNT+lKePXtG+fn5tHr1aqnq79ixQ0btDh48SC4uLvT48WOlj/vhwwcaO1Y88Fe+bJY8Khu9/5iJITmllwUaovQqH4AbvS+lbN77R48eSQ2/R48elJ6eTkSldd4ksflVUVhYSFOnTiWWZcnLy0taLTY0NJSSkpLk7lObq+yqAzd6L4umGD0XkadGVq9eLY2x79ixI65evYpWrVrhw4cP8PPzw5UrV8AwDBYuXIiMjAxs3Lix0uMJBALMnTsXcXFx6Ny5M2xsbLBr1y4MHDgQYWFhuH//vrRt2Si7uLgLOHeuQW1+VaUoG5FXV3AReXJQ9arBKX0p8qrWSpRdovqLFi2i3NxcunjxYqWVX4RCIc2fP5+6d+9Ojx8/pv79+0vXqm/YsEGaa45I/ly8oaGozvK7S+CUXhZoiNKrfADOpy+lfI48CdHR0cSyLIWHh1Pfvn2pX79+dPv2bVq4cCH16dOnQv48kUhEy5cvJ3d3d7p79y6JRCIKDAykZcuWUVpaGr148YJYlqXExEQiqr1kHKrC+fSyaIrRc6P3amTmzJl49epVhe2Ojo5o27YtUlJSsH//fjg4OGDq1KnQ1taGUCjEL7/8ItN+48aNOHHiBFatWgVHR0cwDIMTJ07gxx9/RKNGjZCamgoAaNq0KQDFc/Hlt1//ag9SdKwhYrSQomON618pudKmhnCj9xqKqlcNTulLKav0knn5/Px8IiI6dOgQeXp6Svu5YEEEOTr6kJOTONPO/PnniIhox44d5OzsTBcuXFB4nmPHjpGPj4/0vTJKfy04nD5A1gf4ACO6Flx7PgCn9LJAQ5Re5QNwPn0pZX36rKwscnV1JT8/P9q3bx9lZmaSp6cnHTx4UOqD6+q+o88+E/v7Dg5eNHWq2Fc/fvx4pecJDQ2l4cOHS98r49Mna1vJvTIka1vVwi8hhvPpZflkjJ5T+lLK+/Q7duyQDuD5+/tTYGAgDRkyhKysyhbTEFHjxoel7fbs2VPleX766SeaPn26zLayc/HNmxdJy0lJEEL+cj8hamGlTQmc0suiKUbP+fRqpLxPP3r0aNjZ2aFFixbw9vbG+/fvER8fj/T0sumfGfD5jQAAGRl9MWLEiCrPk5qaCnNzc5ltI0eKo+tEIqBdu17o2zdT5vPX2vLT0yjarg44n14z4ebp1UjZeXoA0NbWxuLFi5Geno6GDRvi+PHjmDhxIpo0aSxtY2IShTZt5iI1dQREoiVKnUee0ZdFXuachKBlyIPs3yoPRkgIqr20Ndw8vYai6q0C59OXIm+enojowIED5OrqSk+fPiWiUh/c2Pg+OTh0IyurJWRkpNy8ukgkom7dutGZM2cUtlEUkXctOJySta1ICIaSta1qdRCPSOzTX7p0qVbPURWcT8/59LWKonl6oVBIwcHBNHToUGlp6JCQp+To6EVt2vyHrKwESgfS5OTkVFnOui5r2UnIyMigAQMG0IABA+q0H5xPz/n0tYqieXotLS0sXLgQb9++xZYtW5CUlISzZ79Bt25d8OTJUiQkaCu90k0yRy9Z0SePuqplJyE9PR1Tp05FcnIygoOD66wfAOfTy0XVqwan9KUoUnoJp06dImdnZ+rduzdNmjRJqQU35bl27RqxLCu9Y5BHXSr9+/fv6YsvviCWZWns2LFyK/t8TDil55S+VlGk9BL69euHnj17QigUYvXq1TAwMKj2Od69e4eGDRtCT09PYZu6Uvp3794hKCgICQkJ0NXVRUxMDES1VcVCSTilrwg3eq9Gyo/el4dhGCxfvhxEhM2bN9foHKmpqdLwW0XUVtVaIsKlS5cwYsQIHD9+XOazt2/fIigoCAUFBQCAsWPHcqP3Ggqn9GqkKqUHxIZvYWGBQ4cO4cyZM9U+R1XTdUDtKH1UVBTGjh2LuXPn4sWLF0hLS5N+9vr1awQFBcHExAQdO3aEiYkJunbtilGjRiEmJgZ3797F9evXER4ejocPH6q1X1XBKX1FVC52pilKX5UhfAyqUnoJ7dq1w6NHj7B8+XK0a9cO7du3V/oc7969g7W1daVt1Kn0Dx8+xMaNGxEVFQVvb28sXrwYwcHBaNBAvF4/JSUFU6ZMQZMmTbB27Vr4+voCACZNmgQdHR0EBQVVOOadO3fU0jdl4JS+IpzSqxFllB4QG33Dhg3h5OSEH374ATk5OUqf4+3bt1Xe3qtD6RMSEjB79myMHTsWQqEQO3bswKpVq9C6dWtkZWWhQYMGSExMxOTJk5GamoqMjAz069dP5hhEBG9vb0yePBnLly/H/v37cfPmTZX6VV04pa8Ip/RqRFmlb9++PTIyMjBz5kx8++23WLRoEVavXg0tLcXX4KKiIjx48ACpqamVTtcBqin927dvERYWhlOnTqF9+/bYsGEDXF1dwTAMACA3NxdCoRBZWVkICgpCeno6AKBv375o3bo1rKysYGlpCWNjY5mqtXUFp/RyUHX4n4vIK0VRRF55srOziWVZioyMpIcPH5Kbmxtt27ZNpg2fz6cHDx7Q9u3baerUqeTm5kYsy9KwYcMoJSWl0uPXJEdeZmYmhYSEkJubGw0cOJAiIiLkTrclJiYSy7LUtWtX6SKh06dPyz0mt8pOFmjIlB2n9GpEWaU3NTVFs2bN8OzZM4waNQqzZ8/GsmXL0KRJE3z48AG3b9/G3bt3kZeXhxYtWsDZ2RkDBgwAj8dDo0aNqjx+dZQ+Pz8fe/fuxe7du2FkZIQffvgBAQEBCmvbS9wGIyMjZGdnY+zYsRVu6yVwo/caiqpXDU7pS1FW6cPDiWxtp5O19UJpSuqlS5dKq9f++OOPdOzYsSoVXRHKKH1xcTHt37+fevXqRd7e3vTnn39WGSyUkZFBDx8+pN9++418fHxo+vTpJBAIFLbnlF4WaIjSM+K+1BxHR0eKiYlRzxWohsTHx8Pc3BzGxsZ12o+4uDi0atWq0qCbPXvE9enr198EM7PrePx4L4yMgNBQAby83qBly5Yq+8CPHz9Gu3btZNR6zx5g3jwgKUmIdu3+QosWoSguzsDw4cMxZswYmJiYyByDiPD69WvExMTg3r17iImJQWJiIgwMDNC2bVsUFhbijz/+qPQ31wSfXigU4unTp+jUqVOd9UECwzDRRMSr635wSq9GlFF6KysBaWtnUpMm+8jRsSsB/GolsUxKSiKWZSkhIUFhm/JKL17VJyIzs6vUseNQcnJyodatV1JoaGkboVBIz549owMHDtCcOXOob9++xLIseXh4UHBwMG3ZsoVmzJhBLMuSj4+PUnchnNLLAk7p1YcmK31aWhpmzZqFtLQ05ObmIi8vT/oZkTYePDgPgaABGEacAKMqeDyxUFy4cEE6V16e8kpvbQ3o6k6DqekNZGT0wevXU1Bc3BJWVsCTJ4VYsmQJbt68idzcXNSvXx8ODg5wcHCAo6MjOnToID3O119/jcjISCxatAgBAQFV9pVTelk4pVcjmqz0BQUF9Msvv5CXlxexLEuOjt5kYbGeDA2fkbZ2ZrXSVZ86dYpYlqVZs2ZV2q680jMMUadOX1DLlqsr1KRfsmQJ+fr60tGjR+nly5eVpreSjNYrO9bAKb0s0BClV/kA3Cq7UipbZVdYWEgRERH0xRffkpOTM9nZ+VH9+n8TIE6oUdV6+uLiYqnRVTZ4RlRxlZ2VFVGrViuofftJMkbfvv1p4vF4CgtulCUsLFN6fisr5RJ+cDnyZPlkjJ5T+lKU8en5fD4NG/YfcnJyofr1LytdUFLiT1eVKZdIvk9vbh5Bjo6uxDAFBBDVrx9Prq4etGnTpiqPFx5O1LhxJLEsK71wKHOh4pRelk/G6DmlL6Wq9fQfPnygb7/9ljw9PenGjRtKH1cyeMeyrFLt5a2n37o1nViWJVPTSLKyyic/vyE0efJkpYzSyoqoadPdxLIsNWv2h9IuCaf0smiK0XOx92qkstj79+/fY/LkyXj+/DkGDdqG4cPdoKUlHmTbU0mhGSLCwIEDAQDh4eFK9UNe7P3kyQ3Rpk0brFhxB+PH/wqiLCxfvlxhEE5ZkpIAU9MoAMCHD11ktlcGlw1XQ1H1qsEpfSmKlD43N5f8/f1p6NChtHnz2wqFKSq7VZYM3pUtblEV5ZU+Ly+P4uLi6JtvviGWZYnH49GtW7eUPp6VVekgnpZWHqf0NQSc0qsPTVd6PT09GBoawszMDCtXNkL5nyw/Xxw4U57c3FwsXrwYALBu3Tql+xEUFIT//Oc/GDt2LHr16oXu3btj2LBh0hVukyZNgqurq9LHW7KEL30tEonDro2MgGVVZM/mlF4z4WLv1Yii2Hs9PT2sWrUKY8aMgUCwCcB3FdrIu1XesGEDAKBx48aIjo5G165dlYqpd3Nzw6NHj9CnTx9YWFigRYsWaNSoEWbPno2mTZti0qRJ1fperq7xAIDiYhcwDGBpKTb4qpJ5crH3Goqqtwrc6H0pVY3eR0REEMuyVL/+pSrLSj948IB4PB5NmzaNvvnmG3JzcyMej0djxoyhf/75p9J+8Hg8+u2332S2LVq0iPz8/Kq9+o6I6Pjx48SyLJ04caJa+3Gj97JAQ27vOZ9ejVQ1ek9ENHnyanJw6E76+i8V+vTiab1hNH36dKlPXFBQQHPnziWWZenYsWMKjy9Ztnvt2jXpthMnThCPx6PIyMgafa/g4GBiWZZevnxZrf04n16WT8boOaUvRdl5+oCAiWRvP4S0tfPkztPv2rWLPDw86PXr19Jtu3eLp8zCq5gcl6TIlsTmx8XFkbu7O23ZsqVG34modBCvuumsOaWX5ZMxek7pS1FG6YnEueH9/Pxo7ty5FZTw9evX5OHhQbt27SIicRmrrVu3Eo/Ho8OHD1d57PXr19OgQYOIz+dTQUEBffHFFxQcHFxlFJ8iRCJRtWIEysIpvSyaYvTc6L0aUTZHXuPGjbFy5UpcunQJBw4ckG4nIvz6669o1aoVhg8fDiLC+vXrERYWhu+//x56enqYMWMGevTogSdPnsg99r179/Dq1StkZWXh9evXSEhIwJQpU2o8oPbu3TsAgIeHR7X35UbvNRNu9F6NKJs5BwAcHR3x3XffISQkBDY2NnBwcMDly5dx/fp1/PHHH9DS0sKqVatw8OBBGBgYYO3atdDV1YWzszPy8vKQm5tb4ZjFxcV49OgRgoKCUL9+fTRq1Ajm5ua4ffs27O3ta/SdoqOjAUAaIFQduNF7zYRTejWirNJLGDFiBHx8fDBnzhwkJSXht99+w6BBg9ClSxccPnwYBw8eRL169eDj44NffvkFFy9exMKFCwFAbqKOx48fo7i4GDt27EBWVhYYhoGPjw/+/vvvGn8nSVELlmWrvS+n9BqKqv4B59OXoqxPX5a8vDwaPHgwdevWjfz8/CgnJ4eIiFJTUykqKoqKi4tl2r9+/ZpYlqVnz55VONbOnTvp888/l4nIi4mJIZZlKTk5We75q/K5a+rPE3E+fXnA+fTq4/+r0gPi289Vq1ZJk1JK0lY1bdoUzs7O0NXVlWlfWFgIQL7S37t3D/b29jKx93Z2dmjUqBEuXboEIkJCQgIOHz4MDw8P8Hg8ODs71+SrKgWn9JoJ59Orker49GWxtrbGuXPnKs17L0GR0YtEIjx48ADBwcEYOXKkNHJPS0sLPj4+2L17NzZt2lTBCH/99VeF55Jk+fHy8qrO15HC+fSaCaf0aqQmSi9BGYMHFBt9YmIisrOz4eDgUGGVnaOjI7KysqQGP27cOJw5cwZ37tyBj4+PwnNJxgIGDRpUre8igVN6zYRTejVSU6UvT2ZmJrZu3Yr69eujY8eOsLGxQZMmTcAwjEKjv3fvHurVq4c2bdogNDQUJiYmuHLlCvbt24fo6Gi0b98ew4YNg5+fn9Ilso8cOQIA1VqcUxZO6TUTlY1eU5R+xowZcHJyqtN+zJw5EyEhIWjbtm2Nj5GRkYHg4GB8+PABxsbG2L59O0QiERo1aoQOHTpAT08P2traFdbBS/x5LS0tTJ06FTweD9euXYOXlxfCwsLg4OBQ7QSVksSbNTXckSNHIjo6Wqk1+7WFROlv3bpVZ33QOFQdCeRG70upyeh9WdLT02nIkCE0aNAg6cKY/Px8un//Pu3du5dYlqXevXvT2LFjK+wbGBhI27dvJyKi2NhY8vT0pCNHjtS4L0TitNhFRUU13p8bvZcF5UbvAdQHcBjAEwCPAbgBaAjgAoC4kucGJW21AOwCcANA55JtXgAIQECZY54G4EXc6P3HQRWfPj09HVOnTgURYcuWLWjcuDEAwNDQEHZ2dtKIuIEDB2Lnzp0y+6alpeHVq1dwcHAAIM6ck5+fDzs7u5p/GYjHGfT09Gq8P+fTV8k6AOeJyAaAPcSGPwfAJSJqB+BSyXsA8AMQCWAggJlljpECQE42BsWobPSa4tN36NChrrtRY58+LS0NU6dOBSCuQycx+LK8efMGAHD06FEIBAKZz+7duwcdHR106tQJ17/ag6DHqTAQCqFr44vrX1WSi6uW4Xx6xTAMYwqgO4DtAEBExUSUBaA/gD9Lmv0JYEDJa20AopJHWT/tPoBshmF6KXtulY0+JycHxcXFePPmTZ09L1iwANevX6/zfsyYMQMxMTHV2u/hw4eYMmUKhEIhfv/9dxQXF8tt9/jxYxgaGiIrKwsXLlzAmzdvUFhYiL1792L16tXo1KkToqYdguPmILwzLECHggJYCRPhuDkIV6fsrpPfY8SIEUhJSanTv0t8fDzGjx9fp/8XkudytAHwHsAOhmFiGIbZxjCMMQBzInoDACXPTUva/wWgB4CTANaUO9bPAOYrbbSV3fsr8zAxMaF///2X+vTpU2fPZ8+eJV9f3zrvx+nTp6lXr15Kt4+KiiIXFxfq378/+fn5Vdre09OTBg0aRO7u7jRixAjq2bMnDRkyhFiWpdmzZ5Ofnx8laVkSATShQwf6rWVLkizYT9KyrJPf4+jRo9S7d+86/bv07t2bjh8/Xqf/F5JnlPHpAfAACAC4lrxfB2ApgKxyfn+mItuD2Kc/XfL6vwA8oYRPr7LRc+vpS1G2aq2E9PR08vb2poULF1Y54LVgwQKaNWsWRUdHS0Njv/vuO4qPj5e2EYIhPkAeDg50tmFDqdELwdT4O6kCt55elnJG3wxAQpn3ngDOAHgKoHnJtuYAnpIi45U1ej8A55Uxes6nVyPV9ekbNmyIFStW4Pz589i7d2+lbd+8eYPmzZvD0dERQUFB2LBhA9atW4fWrVtL27zWtkSCgQEKtbXRsUzNvNfaltX/MmqA8+kVQ0RvASQzDCP5x+0J4BHEt+9jS7aNBXBCyeNFAGgA8YBglY05pVcT1VV6Cfv37ydnZ+dKc9/169eP9u7dW+lxTk0MpSntOpCPvT0JS1T+A4zoWrASJXRqAU7pZUHFKTsHAHcAPABwvMRoG0E8ah9X8tyQFNgeyih9yftAiKfwavf2npunL6Wm8/QikYh+/vln6t69u9w8dHw+n1xcXOjy5csK9z9z5gx5eXlRv2696LJxGxKCoWRtqzozeCJunr485Y2+rh6c0quRmio9kbhA5eTJk2ngwIGUnZ0t85lkOe2TJ08q7Jeenk6zZs0iHo9Ha9eupcLCwgq17OoKTull+WSMnlP6UlSNyMvMzKSAgAAKDg4mPp9PxcXFFBUVRYsXLyaWZStcDC5evEg9e/akAQMGUExMjHS7vFp2dQGn9LJ8MkbPKX0pqii9hLi4OPL09KQRI0ZQ9+7diWVZGjFiBO3cuZOIxJlzra2zqHXrecSyLE2c+Avl5+dL9w8PJ9LXf0MMI1JYETc8XJxnn2FI6aq5NYFTelk+GaPnlL4UVZVewj///ENz5syhY8eO0bt376Tbw8OJmjS5Rl269CZb235kYhIlkzM/PJyqrJOnTBt1wSm9LJ+M0XNKX4o6lF4Rubm51LnzUmJZlqyslpCWVm6F6jhWVkT6+okVKuiUrZ5jZSVr8ACRvv5LsrKqmSJXdtfAKb0sn4zRc0pfirqUvjwfPnygwMBAsrPzJVPTaxWMlimJvWEYIdnYjCZ7ex8CRBU+F7cp3U9XN5Vat55DLMtSvXr3qt2vqu4aOKWXRVOMnltlp0ZUWWVXGfr6+mjTpg10dHJhYJAI8VRsKZYlsTft2p2CsfEj6OhkQ1c3tcLnpa8FaNo0HJ07D0aDBheRlPQDGjWqforsefNQaQVebpWdhqLqVYNT+lJqS+mJxHPx33+/h5ycXKlt22mko5Mho7d1JW8AACAASURBVKw5OTnUrZsvtW69ipycXMjM7LJcf33lyrvUufOXxLIsOTq6U/36f9fYpy971yDvzoNTelnAKb36+NSVHgAYhsHq1SMwcuQOmJgkoGPHEfjsszvYulVcMnrLli0wNtbGggXBKCpqBWPjx7CygvTzjIwMLF68GIcOTYaBwQuIRGaIi9sMMzNvaZvqYqkguleynVN6DUXVqwan9KXUptKXJTc3l+bNm0c8Ho9CQ0PpyZMn5OLiQufOnSMiomnTptF3331HRGKlO3jwIHl5eZGLiwuxLEv9+/enxMRElfvB+fTVAxqi9CofgBu9L6U2R+/LIxKJ6MSJE+Th4UFubm7k4eFBK1asoPv375Obmxv5+flRbGwsjRo1irp27UrTp0+nHj160JgxYyg9PV1t/eBG75VHU4yeEfel5jg6OlJMTIxa7jpqSnx8PMzNzWFsbFyn/YiLi0OrVq2UzjarDl6+fIlNmzbhxYsXeP/+PQoKCmQ+d3Nzg4eHBzZu3Agej4fly5fD0NDwo/QtNjYWnTt3rnZCTnUiFArx9OlTdOrUqc76IIFhmGgi4tV1PzifXo2oy6dPS0tTum3r1q0xb948vH79GgsXLsTevXtlss/q6+sjJCQE/fr1w6pVqz6awQOcT6+xqHqrwPn0pajDp4+KiiJXV9cKNewq4+DBg9S9e3fpuR89ekQ5OTl0/PhxCg4Opt27d9eJb8359LJAQ27vOaVXI+pQ+qtXr8LAwKBCDbvKOHPmDHr16iV1K6ZOnYqioiL0798fmzZtwqhRo+rkFptTes2Ey5yjRtRR4ebmzZuwsLBQun1CQgJiY2PRr18/6bbQ0FCcO9cA1taAlhZgbQ3sqYOkuFzmHM2EU3o1oqrSv337FgkJCdILx5w5c7Bu3ToUFxcr3Ofs2bNo0aIF7O1LI+oGDjyEKVO0kJgonkhLTASCgj6+4XNKr5lwSq9GVFX6mzdvAgAsLCyQkJCAixcv4uDBgxg/fjwSEhIqtBeJRDh37hz8/f1lCmDm5MxBQYHs7XzZ8NiPBaf0mgmn9GqkJkp/7NgxSKY8JUbfvHlzXLx4Ec2aNcOhQ4egr6+PUaNG4cSJE+LgihJiYmLw5s0b+Pv7yxzzzRv5teOSkqrVNZXhlF5DUXUkkBu9L6W6o/e3b98mlmVpzJgxxOfzqUePHsSyLF29epWGDh1Ka9asISJxjrzQ0FBydnamOXPmUE5ODhER/fTTTzR+/PgKx23evEhuTHzZJbZE4pp3wcHBtTbCzo3eywJu9F59/H9U+vz8fCxZsgQdO3bEw4cPcfLkSXz48AEAUFxcjOfPn6NXL3GlIh0dHUyZMgWhoaF48OABRowYgcjISFy6dKmCygOAqelKGBrKBl0ZGQHLlpW+FwgEWLZsGeLi4mr4bcVjBJUNFnJKr6GoetXglL6U6ij98uXLyc/PjzIzM+mLL76QpsZiWZZ+//136tevn1yVzMrKolmzZhHLstS1a1fKysqq0ObRo0e0a5eg0pRY+/btI5Zl6auvvqrBN1UuAw+n9LJAQ5Re5QNwsfelKBt7f/PmTWJZlq5cuUJERH/++SexLEvu7u7k6+tLX375JYWEhCjcXyQS0fHjxyvkwZfEwQNCatlSoHC57Pv376UXmbVr1yr9/coiLwNPeReCi72X5ZMxek7pS1FG6XNzc8nf358WLlwo3fb+/Xvy8PCgMWPGSI3x33//rda5q5P7bv78+RQYGEguLi505syZap1HQlVr6Yk4pS+Pphg959OrEWV8+pCQEIhEIsycWVpivHHjxoiIiICFhQXy8vLQvHlzdO7cuVrnriqLjYS7d+/i3LlzGDZsGIRCIdq3b6/wmAKBAOfOncPEiRORmJgo81lVa+kBzqfXVOTP7VQDTZmnNzc3r+tuVDlP/88//+DEiRNYt24dTE1NZT4zMjLC69evAQC+vr5Khc0SEZKTk2FpaalwOq7sdoFAgJUrV8Lb2xuNGzeGrq4urK2tZY6XmJiIo0ePVqitl5aWBisrK+n7ZcvEAT9lLzTlBwu5eXoNRdVbBc6nL6Uynz47O5v69OlDS5YsUbi/r68vsSxLsbGxSp3v7NmzxLIsRUZGKuVj7969mzw8POjNmze0YcMGGjFiBKWlpdG5c+do8uTJ0oFEycPd3Z1OnTpFQqFQ7vmryp/P+fSyQENu7zmfXo1U5tNfvnyZWJalzMxMuZ/n5+cTy7IUEBCglB+cnZ1NvXr1IldXV5o1a1aVPn1qaip5enrSjh07KDycqEuXaRWMnGVZmjBhAt25c0ehoVcHzqeXRVOMnvPp1UhlPn3Hjh0BQG44LQDprX3Pnj2lt/aFhYXYs2cP/vnnnwrt169fD11dXcyfPx9Xr15Fr17vsHUrIL4DF6FlS6FM7ruQkBCYm5tDS2skpkwphJ7edemxMjMHYMiQMERFRWH79u1gWVYmrLemcD69hqLqVYNT+lKqGr0fMGAAhYWFyf3s6tWrxLIsPXz4kAQCAR0/fpz69u1LLMvSrFmzZNreu3ePWJaly5cvE5/Ppz59+lBoaKj08/K17G7dukUsy1JUVBRZWYnIymoR2dn1pHr1bhMgkButpw44pZcFGqL0Kg/kaYrSz5gxA05OTnXaj5kzZyIkJARt27aV+zmPx8OdO3cwadKkCp+ZmZnB09MTaWlpWLz4/9o78/Cmqq3/fzZlLiBcJpnLDIIDTUGKWubhlUEKakXggl6p5SrIKCggeAUULsJVXkVAQGTw91MGQUSUmSoFLqMFWgQKFBDbUtvSUmibZL1/JE2bjmlz0kQ4n+fJk+Tk5GQlOet891p777VnER0dzXPPPcfNmzdtI/XAkoybO3cuAQEBdOnSBYBBgwbx5ZdfsnfvXtLT07l69SoPPPAAGRkZpKamIiL06tWL9u3bk5q6kQYNtnP+/P+SkpJVuckV4/KHDh3KsWPH7Cr5lDSZSn/o0CG32eBp6LPsNKSw7L3BYODXX38lLS0t12vNmzcnNTWVCRMmkJCQwL/+9S9GjRrFgw8+aFf3bv369Vy/fp3JkyfbtgUFBdG/f3/u3LlDdHQ0IkJiYiI+Pj6ULVsWgPHjx3P69GkaNPg316//k+TkDnafn18XnDPo2XvPRI/pNaSwfno/Pz/S09MJDw/P8/VatWrRokUL7ty5w9tvv023bt1Ys2YNt27dAuDGjRssW7aMV199lTp16pCcnMz169epUqUKO3bs4M8//6Rr166ULVuW9evX8/777wMWhy9dujRTpkyhWbMnSU4eYfe5ObvatEKP6T0UZ+MDPabPwpEReYMHD7aLvy9dupQr7jWbzRITEyNLliwRg8EggYGBYjabZdy4cfLCCy9IQkKCfPbZZ9K5c2fp2bOnGI1GGTVqlMycOVNEsmL6CRMmSFBQkKSlpcno0aMlMDBQkpOTS2ypaj2mtwcPiel1pdcQR0bkZcb1ACkpKQQFBZGzhLhSilq1atmep6ens3fvXkJDQ6lZsyaBgYF88803DBo0iD///JOIiAh69erF3r17SUtLIyQkhB9//JH9+/czdepUli9fzq+//sr8+fOpVKkSQ4fC5ctgNlvui7O6jSPoSu+Z6DG9hjhSOcfPz4/w8HDu3r3LzZs3MZlMxMXFAbBv3z5iYrIWnoyKigIgPj6eBQsWAHD27FlGjhzJ1q1bGTt2LPXr1ycsLIzu3btz9+5dwsLC+Pjjj1m6dCn9+vUjMTGRVatWMX36dJo1a0Z8fLzt81yNHtN7JrrSa4gjSm8wGDAajZw6dYr4+HgAkpKSuHnzJlOmTGHVqlW2fS9fvkyTJk3IyMhAKcXrr7/O1q1bGTFihO1i6+/vz8GDB6lWrRrt27fnp59+IiQkhFu3bjFw4EBmzpzJkCFD6NOnDwCff/45r7/+Omaz2UW/Qha60nsmutJriCNKX61aNZo2bcrRo0dti1okJSWxZcsWTCYTu3btwmg0YjQauXLlim1llu+++46RI0fm+r2feOIJzpw5Q2JiIr169WL//v0AjBw5krlz59KsWTPeeOMN2/5Vq1bl4sWL7NmzR8uvnie60nsmutJriKOVc9q3b8+xY8dsSp+QkMDmzZvp378/SUlJHDlyhGvXrmEymWwj+fKriPvYY48BcPjwYbp27YrJZMJkMhEeHk5SUhLz5s2z6yd/8MEHAVi2bJnL1V5Xes9EV/pikF+ZKEer4fr5+XHmzBmuXr0KwO7du4mJiSE4OJh27drx448/EhUVhZeXF82bNwfItUYdQExMDOPHj6ds2bLUrFmTypUrM3v2bMaMGcP+/fvJyMhgzpw5LFmyhN27d3Pt2jWb00dFRbFr1y5Nfo/80JXeM9GVvoisW2eZUppXTXlHld7X1xez2czevXsBS6LuySefpE6dOvTq1Yt9+/YRGRlJw4YNqVy5MmAZh5+dn3/+mRdffJHk5GTWrl1rG43Yo0cPli9fzoIFC3jllVd44IEHCA0N5e2332bgwIFMmDABgNq1a7Ns2bJ8ldhkMrFhwwYmTpxoGydQVHSl90z0+fRFpKBiFTt3Oqb0VapUoUWLFnYXqsGDBwMWp/33v//Npk2baNeunW2pqkynNxqNfPLJJ6xZs4ZBgwYxYcKEXKvkLl26lObNm9ua9Tt37mTPnj3s37/fNhowJiaGmjVrcufOHSpVqgRAYmIiq1evZs2aNXbHu337dq75/46gK71noo+9LyIFFasobOx9dvz8/Dh37hx16tShXLly+Pv7A5ZE2+OPP87Bgwdp0qSJzaHv3LnD77//zttvv82lS5eYO3cuvXr1yvPYISEhbNy4kRo1agCwceNGLly4wAsvvEDDhg3x8fGhYcOGVK1alcjISL744otciT1vb29mzpxJ165di70Onj723kNxdnTP/TYir6BiFUWphps5q27Dhg25Ck1s27ZNDAaD/PDDD3Lr1i0xGAyyaNEi6dKliwwbNkyio6MLPHbOWXbbt2+Xjh07Snx8vCQnJ8vOnTtl2LBhuebSDxkyRE6ePKnZKDp9RJ49eMiIPKcPcL9VzimoWIWj1XBFLAUyO3XqJGFhYbleS0lJkdGjR8uNGzckIyPD5pQLFiyQtLS0Qo8dEBAgcXFxtudpaWnSs2dP6datWy5HnzhxokRERLjEOfXKOfbcM05/vym9SP5looq6wk1sbKxDzvb+++/L3r17HT5uTqUXyWo9GAwG+eCDD1zm6NnRld6ee8bp7zelL4iiKL0ryan0IiLLly8Xf39/iYiIKDE7dKW3555x+vtR6fOjqErvKnIqfVhYmPj5+cm3335bonboSm+Ppzi93k+vIc6uT68VISEhJCYmApY176dNm8aAAQN45plnStQOvZ/eM1GWC1DxadeuneScGlrSREVFUbt2bby9vd1qx/nz52nQoEGufvOSJiIigubNm2M2mxk1ahRGo5EVK1aUuF2nT5+mTZs2xe7y0wKTycS5c+dscxjciVLqmIj4Fb6na9GVXkM8TekXLlxIdHQ08+bNc8uFSFd6D8XZ+ECP6bPwpJh+69attoq5KSkpEhcXJ1evXi3R30mP6e3BQ2J6p5v3LVu2FHer7PDhwz2iGu6AAQMcHpHnSgICAjCZTHkW4HzqqadYtGhRidjx6KOPun1EXnJyMj179vSIEXme0rzXY3oN8ZSY/uzZs8TGxrJz50727dtHWloa9erVIzAwkGeeeYZq1aqViB16TG+Ppzi9000FvZ8+C0/op79x44Z07NhRevbsKR06dJBJkybJwYMHNVmmqqjo/fT2cK8073Wlz8JdSm8ymQgLC2Pjxo388ssvVK1alcGDBxMYGGhXYLOk0ZXeHk9Rej17ryHuyt6/8847jBs3DpPJxIIFC7h79y6DBw/W1OHzKxxSEHr23kNxtqmgZ++zcFf2PjQ0VDp27CjvvfeeGI3GPMfeO0NhK+Lmh569twcPad7rMb2GuDOmDwsLk06dOsn06dPlqaeeyjX2vjikp6dLeHi4tGy5Xho3niqtWg0TL68Eu+nEBaHH9PbkdHrgMhAOnMx8DXgUCLNu/w6okm3/fwNHgc7W5z6AAGOy7fO/wEgpwGf1yjka4miNPEe4cOEChw8fpnv37ra6dgXRsWNHFi9ezLhx42jTpk2x8hvx8fGEh4dz6tQpwsPDiYiIIC0tjUqVICOjOnFxz2IyZVXQKWzRS71yjkN0FZGb2Z5/DkwSkf1KqZeBycAMpVQr6+sBwBfAfuvzWOANpdRSEcm7emoO9JheQ7SM6S9evMiiRYvo378/o0ePZtu2bdy+fbvA9/j6+vLJJ5/w3//+l0mTJuXZT5+J0WgkMjKSr7/+mhkzZvDMM8/Qu3dvJk2axJo1azh58iRpaWk8/PDDJCfPJjx8GzdujCL7KVPYopd6TF8sWgIHrI93AoOtj70AMxZlz54ZjQN2A/YLFBaA004PcOvWLQ4fPuy2+0mTJnHr1i232/Huu+9y/fp1TY5XpUoVfHx8bFVu58yZQ69evQgJCWH37t2EhYXl+b7U1FTeeustTp8+zdixYwkNDc2136FDh+jduzfDhg3j008/JTExkaZNm/Lwww9TtmxZypQpw+OPP86SJUsICQlh8uQAypWzP1XKlzcxfXpqgd9j6dKlHD161K3/S3h4OB9//LFbz4vM+zwQ4Cel1DGlVLB122lggPXxc0ADABE5A1QEfgaW5DjOB8BEpZRDzSqnnT4mJobY2FhWrlzptvuZM2eyePFit9sxadIklixZosnxvvjiC55//nliY2Pp0aMH5csPJyZmEnv2wJQpU3jzzS35vn/evHk8+OCDnD17lhkzZnDjxg3b6x9++CHjx49HKcWiRYuoW7cuZrOZAwcOcP36dWrUqMEnn3yC0WikWrVqrFy5ki5dfsfffxV162YAQt26Gfj7r6JLl98L/B4vvfQSK1ascOv/snz5coKDg916XmTe58ETIuIL/A/wmlIqAHjZ+vgYUBmwNdlFZIyIGETErqChiFwCjgAvOuS0BQX8jtz07H0WWmfvzWazjBo1SgYOHC0VK5qtCTSj+Pp2kFq19sratSIJCQkSGhpqy5LfvXtXpk6dKn369BGDwSCDBw+WpKQkMZvNsnLlSjEYDDJ//nzZsGGDBAUFicFgkODgYNm1a5fTSbecFYU++CBaz95ngwKy98AsLLF89m0tgCMFvMcHOG193ApLK+FTCknk6dl7DdEye79582YZM2aMbNq0SQwGg1SpclBApHTpeDEYDOLtfUoaNRJ58803xWAwyKhRo+TChQu2QprPP/+8XLx4Ucxms6Snp8u7774rBoNBOnfuLF26dJEnnnhC5syZI+fPn9fE3ry69ZRKlS+/NGpy/OLiqdl7wBuonO3xQaAPUMu6rRTwJfCy5Oe82Zze+vxrILowp9ez9xqiZfb+p59+Ijw8nEOHDiHiRb16H3PrVgfKlLEshZWRUZ34+BPs3r2byZMns3btjzz//IvExr5IuXJTuHhxHnFxcdSoUYMePXrYlrB64IEHeO655xgwYECxatnnR17rAYhUYPp0YfhwzT6myHhw9r42sNk6WrE0sF5Ediil3lBKvWbdZxOwKr8D5MEcoNDhsfosOw3Rapad2WymW7dujBs3jkqVKjF58kd4ed0gKmouRmMVWrR4nRMnQmnbNphu3Wrg57eI4GAzFSpso379jzCby+PllUzlyhWoUcObaGvf2n/+8x86depEqVKa5G/tKFXKou85UUowm903DFefZZcbfS07DdFK6a9du0ZKSgoPPfQQPXr0YNy4b/jjjwncvduQMmVuYjJ5U7v2bsqV+41x48ZZVbYU8fEDOH16I0lJnfDySiU1NZ6UlBSmTJmCUopr1665xOEh/+67Bg1c8nEO48FK7zb0fnoN0aqf/uzZs5QrV44mTZoAMGJEeebNe5FatVpRtmw8UJHmzT8hKOh5GjVqZDdIxmSqSnT0NCIjV3H3rg/JyckkJSUxduxYPvzwQ5ctWjlnDuS8/it1h9mzXbsybmH8BfvpXU9BAb8jNz17n4VW2fsPP/xQRo4cmedrCxcuFIPBIN26dZOkpCQRKWjVnQz56quvJCAgQAIDA+XFF18Uf39/lyU99ex9weAhY+91pdcQrZQ+IiIi36mgmWvaBwcH2xJxeauscOVKaaZOfYEXX9xI69atOXfuHOnp6UycOJGLFy86bWdOhg6Fy5fBbLbcr1/fTx+R54HoMb2GaBHTm0wmIiMjad26dZ6v//nnn/j4+NhWuc2kQoXszwQRS/LsyhWYMKEGDz00h08//ZRGjRqRnJzM2LFjiYmJccrWwtDH3nsozjYV9H76LLTop79w4YIYDAa5ePFinq+HhobKuXPnbM/z6h/Pb4FNEcu6ditWrJBOnTrJK6+84pSthaHPsrMHD2ne6zG9hmgR03/33Xfy1FNPidHo2KCW/OL5nDel7N937dq1PBfP1BJH59PntzagFugxvR7TuxQtYvqzZ8/SqlUrh5vFhU1vzSRnl1q9evXo2LFjEa0rGo7Mslu3DoKDLWGIiOU+ONixyjyOoMf0eeDsVUNX+iy0UPoRI0bIwoULHd7fEaUvqMqNK1XWEaXPv+dBGxt0pXdB816P6bNwNqbPyMgQf39/2bFjh8PvKSymV0pk9GjH3+tIGSxHcSSmV8qxcKS46DG9C5xeV/osnFX6yMhIMRgMEh0dXaT3Zap1YUm8nLhaZXWlt8dTnF6P6TXE2Zj+8uXLeHl5FbmKbWb/eH6VpvOL+4u6vag4EtPnNcagYkXLdi3QY/o8cPaqoSt9Fs4qfXx8vHTq1Em2bt0qIkWPt4uqmp6g9CJ69r6kb04fQI/ps9Cin3727Nny/PPPy5o15iLH26NH546RC0viuTumdzV6TO8Cp9eVPgstsveXLl0Sg8EgzZodLJIK513EIv8kXvb3FUVli7K/XvfennvG6XWlz0Kryjnjxo2T5s1HFymr7eqmuohjLYPsF4UyZa7rlXOycc84va70WWg1y+7YsWNiMBikQoVzDjuxq7u+RAq/sOR9UTBrGqMXFV3pc9/07L2GaDXLrl27dtSu/RB16661215QVju/IhaF1aYvCoVl+/MqmZWaqpg2zfHPKM6aeQWhZ+/zwNmrhq70WWhZDXfHjh3Svn0Hadz4D4fiZ1cn5UQKV3pnWxuu+A660rugea/H9FloWQ03IyND+vbtKx999JHD78kapGOS+vWNmjerC3NKZ/MKrshLuDumv3btmuzatUtE7iGn15U+C63r3q9du1Y6d+5c5O+m9aq19jbln713NqZ3RV7C3Uq/fPlyMRgMEhYW5jFOr8f0GqL1+vQDBw4EYMuWLUV6X0hICImJiZrZkZ2c1XGGDrV/bdkyaNTIMjqwTJnf+ewzs90+BeGKvIS7Y/rM9Qffeecdt9mQE71yjoZoWfcewNvbm8DAQL766iuMRqPD7/vss8+oWrWqZnYUhewXhePH/2TYMMdPMVcMyXV35Zzbt2/j6+tL5cqV3WZDTnSl1xCtlR7ghRdeIDY2lj179hS+sxVXKn1RKOqqtTlbCo0aWZ472lLIC3crfWpqKtWrV2fBggVusyEn+go3GqK10gPUrl2b3r178/HHH1O3bl3atm1b6HvcqfTZKU6NvKFDnXPynHiC0levXp3GjRu7zYac6EqvIa5Q+szjtmjRgn/84x+sWrWqUPX8qyq9K3C30t++fRtvb2+3fX6eOJsJ1LP3WWidvc+O2WyWb775Rjp16iSvvvqqxMTE5LuvK7P3RUEfey8yZMgQWbp0qYh4TpedrvQa4iqlB1BK8eyzz7J69WoSEhIYMmQI+/bty3NfXemzcLfSp6am6krvCu4Hpc/OnTt3ZP78+WIwGGTu3Ll2n7l2rUidOmmilFnzuelFRVd6kR49esjmzZtFxHOU3ukD6CPystByRJ4jHDhwQLp37y7PPvusnDt3rkSG4haF+3E+fXJysuzfv18WLFggQ4YMEYPBIHv37hURz3F6p5eqbteunZw4UeiS2C4lKiqK2rVru70Zdf78eRo0aED58uVL7DNv3rzJzJkzOX78OAkJb3DuXBBgXzerUSNL33lJc/r0adq0aYPKr45XCWAymTh37ly+y4RpxY4dO1i/fj2RkZEAtG7dGj8/P9q3b0+HDh0oVaqUxyxVrSu9hpS00mdiMpnkyy+/lHbtHpdmzcZK6dLxmk+vLU5Jq/tF6Y8fPy4dOnSQWbNmyYEDByQ5OTnP/fAQpXf6AHpMn0VJxfT50aTJWWnTJlAeeaSXVKlyUJMJKyLFn/12P8T08fHx0qdPH5k6dWqh3/WecXpd6bNwl9JnsnatiLf3bWnU6F0xGAxSv/5C8fZOczqmL+7st3td6Y1Go4wePVoCAwMdEp17xul1pc/C3UovkpW9r1btR2nXrrP06TNELl265NQxizv77V5X+s8++0w6deok58+fd2h/T3F6vZ9eQ1zZT+8oQ4dC8+Y9+e03X77//ivq1q3AsGHD+Pbbby1X+WJQ3Nlv93I//aFDh/j888956623aNasmebHdynOXjV0pc/CE5RexH5EXkZGhixbtkzat28vkydPlsTExCIfT4/p7fnjjz+ke/fu8q9//atI78NDlN7pA+gxfRbujukzCQgIkLi4OLttJ06ckH79+snTTz8tR48eLfIx3Z29L+6CGFrH9BkZGfLSSy/JkCFDinyBv2ecXlf6LDxR6bNz69YtmTp1qvj5+cmnn37q8iSbVkrvzKAjrZX+ww8/lICAALly5UqR33vPOL2u9Fl4stJnYjabZevWrfLkk0/KyJEj5erVqw4d051K70ztPC2Vfvfu3WIwGGw174rKPeP0utJn4elKn50rV67I8OHDJSAgQH744YcC981U2nLloqV8+QsFKm1cXJzs379fRLRTemdq52ml9NHR0RIQECALPo3ONwAADElJREFUFiwo9jHuGafXlT6Lv4LSZyc9PV0++ugjMRgMMmPGjHxHkmUqrY/PNGnTJlDAlKfSfv/992IwGMRgMIjRaLxnlP7OnTsyZMgQGTlypKSnpxf7OPeM0+tKn8VfSemzc+jQIendu7cMGDBA1q1bJ1FRUXYKnam0LVu+LAaDQapUCbVT2uTkZBk1apTN4RcujLCGAmZp2ND5FW7cHdPPnj1bunXrJjdu3HDqOPeM0+tKn8VfTemzk5CQIO+995706NFDDAaD9O3bV2bPni27du0SH59bAiJt2/YVX98O0qzZazalDQ0NtTm7wWCQzz9PdMlMP0dzCjn3+/zzVKeUftu2beLn5ye//PJLsY+Riac4vT7LTkPcMcsuLyIiImjevDmlSxe9BKLZbOa3334jLCyMsLAwTp06hdkMt2+3oUKFM8TFDaJWrW+4fHklTz+9mbNnvwOgQ4cOLFq0iJYty3HlSu7jlsRMv3XrIDjYfmmtihWFGTOiiY8/xDffDCY6ujQNG1oq7BZWi+/ChQuMHDmSYcOGERIS4rR9+iw7DdGV3p7iKH1+pKSkyL59++Tllz+QRx75u5Qte0MeffQ5MRj8bOq+YMECMZlMIlIyC2nmR36xf9Wqu6wLgkY63PpISUmRQYMGyejRo8Vo1GblXXSl1w5d6e1xRukdISwsjDFjxgAwfvx4hlol886dO7RuXcFtSl+qlMWlc1Kv3n948MG1nDu3jJQU31w2/fLLL2zcuJH69evTsGFDGjRowLfffsuJEydYt24d1atX18Q+T1F6fey9hnjC2HtwbY28jIwMtm/fTpkyZXj//fdtDr9v3z769u3Lu++ma75ghaPkNxegSpXDAHh53bbbnrna7pYtW7h48SIREREsX76c1157jT179vD+++9r5vAehbNNBT17n8VfNXvvKMnJyTJ69Gjp0qVLrnBq+vTpYjAY5Pjx49mSadpk7x0lryx/lSqxtjCkWrUf8uzye/rpp2XNmjW246SkpEhCQoLm9uEhzXtd6TXkXlb6mJgYXnnlFaKjo1mxYgW+vlnNZKPRyMGDBwE4fvy4bWmr+vXfAmD4cG3Wmi+MvFbIefXVnxEphdH4AF5eKbZ9M1sfN2/eJCYmhjZt2the8/b29ojFQlyFvpadhrhihZvioNUKN+vWWZy1YsUL9OnzMklJpVi1ahVNmjSx2+/06dMkJSXh5+fH8ePHSUlJ4ZNPrpGSMoSkpAOULXuZK1csmfWScPzsC2yWLXuMRx9tS82aValR43au5bLOnDlDqVKlaNWqlWsN8yB0pdeQe0npM7u/4uOP0qLFK6SkNObnn5fz0081c+0bGhpKkyZN6NevH4cPH6ZLly6sWjWQJk3+QbNmE6lX71PA0pU2bZpTZhUJs9nM4cOHOX36NHXrVuKxx3awatVqvv32JM8+mwZYnL5p06ZUqFCh5AxzM/padhpyLyn9tGlgNN6gTZvXyciozaVLczGZvJk2LXf/dmhoKP7+/rl6LeLiAlHKSPXq2wET4GVLnpUEFy5cICEhgblz51KlShW2bdvGhg0bWLx4MWXKlKF169bExcXRsWPHkjPKA3Da6T1F6cePH28XZ7qDiRMnsmjRIpo2bepWO0JCQti4cSM1atQo9jGio0GkJjExI6hZ8//Ttm0gsbFBXL8eBDxg2+/3338nKiqKqKgou/ffuRNITMwwWrceyq1bHQDLQpbOrDVfVA4fPkyNGjWYMWMGhw8ftjl3bGwsp06d4tSpU5w9e5Zu3bqVnFEegN5PryH3Uj+9jw+2/vZSpVKoWXMDtWuvw8srjREjBjN06FBq1KjB119/zfz582nRogW9e/fG19eXlStX8scfZTl16gYVK57l4sX5JCZ2o3r1k7z5Jrz55mPafNFC+Oc//0nNmjUJCgpyed17R9D76TVEj+nt0SKmnzMHW3+72VyJmJiRXLz4Hd26/ZOffvqJAQMGMG/ePHbs2EGfPn1Yv349I0aM4OGHH6ZmzZpERe2jYsWzmM1/Izn5cR56aCE+Pq8g8iOpqamYzWYNvmluUlJS2LRpE3//+985cuQI/v7+bl3LzhPRY3oNuZdi+sy4fdo0S1PfMl69PEOHvkBGxmC2b9/O6tWriY6OJigoyO69lSpVshXEfOKJljRr9pKt+d+oUSO6du1K5cqV8fX1xc/PD4PBQJMmTYq9Eo6IEB4ezubNm9m5cydKKXr37s2bb75J69ataViSMcVfAKeb9y1bthR3q+zw4cM9IqYfMGCAR8T0nTt3djqmdwSTyUR4eDiPPPIIpUplNRqPHj3K4sWLOXPmDAAdO3bkxo0bVK1alcjISPr160fTpk05evQox44dIykpiWrVquHr64vBYMDPz4/GjRsXehFITEzk+++/Z8uWLURFRdG2bVsGDhxIz549baFecnIyPXv25NChQ677IRzEU5r3ekyvIfdSTO8s7733Ht999wNJSW9w/XobWrUaSZkyFena9Ulmz55tu0iYzWaioqJsF4Djx4/bLgIGg8F2EfDx8UEphdls5siRI2zZsoW9e/dSsWJF+vbtyzPPPJNnKeqSWsvOETzF6b1mzZrl1AFmz54966WXXuKXX36hWrVqbrmfP38+SUlJNGvWzK12vPPOO5QqVYqGDRu61Y4JEybwt7/9jTp16rjNju3bvdi+/WViY5+iQYMFlCt3jZSUh2nRYjjt21ew7Xfw4EGaNm1KUlISQUFBNG7cmKCgIO7evUtycjJbtmxh/fr1fPXVV5w8eZKFCxeyadMmlFIEBwfTpUsX+vfvT0RERJ527Nq1i3HjxtGkSRO3/R+Z92vWrLkxa9asZZp4rhM4rfRKqTggj3lVOvc3jzwMZcrm3p6RDr+Gl7w9HkEjEck9uqmEcdrpdXR0/lo43WWno6Pz10J3eh2d+4xCnV4pVV4pdUQpdUopdUYp9a51exulVJhSarVSqpR12xdKqUtKqZPW20EtjVVKeSmlTiiltrnLButnXFZKhVuPf9S6ra5Sao9SaotSqpJ12yyl1PVstpxUSmk2Z1MpVVUptUEpFamUilBK+Ze0HUqpljmOe0spNc4Ndoy3np+nlVJfWc9bd5yjb1htOKOUGmfdVuLnRoEUNuEeUEAl6+MywGGgI7ACqAmMAfpYX/8CeNZVk/+BCcB6YJv1eYnbYP2My0CNHNs+ANoA/YEQ67ZZwCQX2rEaeMX6uCxQ1R12ZLPHC/gDaFSSdgD1gEtABevzr4GRJX1+AG2B00BFLAPfdgHN3fmf5HUrVOnFQmb1gTLWm1j/YAHMWC4MLkUpVR/oC3yebXOJ2lAIXlY7Sur3qAIEYDmxEZF0EUksaTty0B24KCJX3GBHaaCCUqo0Fqf7nZI/P1oDh0QkVUSMwH4gEPf+J7kpwhX8JJACzLNua4dF9dcCXtmuopes+54E1ml4Fd0AGIAuZCl9idqQzZZLwHHgGBBs3dYIOAB8B1TOdjW/ns2WvRra8BhwxPp9T2C5GHqXtB05bFoJvO6m3+MN6/kZl/mfu+EcbQ38BlTHcuEJAxa78z/J084ifqmqwF6gbT6vf4ELmk5AP+BT62Ob05ekDTk+o671vhZwCgjIZ79ZuKgJB/gBRuBx6/OPgPdK2o5sn1EWuAnULmAfl9gBVAP2YGnKlwG+BYa54/wA/oFFEA4AnwGL3PWf5HcrUvZeLM3HfUCforxPA54ABiilLgP/D+imlFpbwjbYEJHfrfexwGaggxvMuAZcE5HD1ucbAHdOPvgf4LiIxLjhs3sAl0QkTkQygE1AJzfYgYisEBFfEQkA/gTOu8OOgnAke18zM6uolKqA5QeOdLVh2RGRt0Skvoj4AC8Ae0RkWEnakIlSylspVTnzMdALS/KmRBGRP4CrSqnM4oDdgbMlbUc2hgBfuemzo4GOSqmKyjJLpzsQ4Q5DlFK1rPcNgUG47zfJF0dmZNQBViulvLBcJL4WkW0F7P9vpdT0bM87iEi6M0YWA1faUBvYbDm3KA2sF5EdBew/XimV/QI1UEQua2TLGGCdUqosEAW85A47lFIVgZ7Aqw7srrkdInJYKbUBS7PaiCXHUdAYd1eeHxuVUtWBDOA1EUkoYF9Xnhv5og/D1dG5z9BH5Ono3GfoTq+jc5+hO72Ozn2G7vQ6OvcZutPr6Nxn6E6vo3OfoTu9js59hu70Ojr3Gf8H1aXc6Hw6v50AAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mnth = comb_all.loc[:,[\"Name\",\"County\", \"Longitude\",\"Latitude\",\"Municipality\", \"72acc\", \"EvenSumfrom24\", \"3DR5_SepOctNov\", \"3DR5_wholeYr\", \"20160928\", \"20160929\", \"20160930\" ]].copy()\n", "mnth = mnth[~mnth[\"EvenSumfrom24\"].isnull()]#, \"Year\", \"yr2016_sum\", \"\"]]\n", "mnth = mnth[~mnth[\"3DR5_wholeYr\"].isnull()]#, \"Year\", \"yr2016_sum\", \"\"]]\n", "all_lon,all_lat = mnth.Longitude.values, mnth.Latitude.values\n", "\n", "#mnth = mnth[(mnth[\"EvenSumfrom24\"]/mnth[\"3DR5_Sep\"] >1) | (mnth[\"72acc\"]/mnth[\"3DR5_Sep\"] >1)]\n", "#mnth = mnth[(mnth[\"EvenSumfrom24\"]/mnth[\"3DR5_SepOctNov\"] >1) | (mnth[\"72acc\"]/mnth[\"3DR5_SepOctNov\"] >1)]\n", "mnth = mnth[(mnth[\"EvenSumfrom24\"]/mnth[\"3DR5_wholeYr\"] >0.9) | (mnth[\"72acc\"]/mnth[\"3DR5_wholeYr\"] >0.9)]\n", "\n", "#mnth = mnth[(mnth[\"20160928\"]/mnth[\"3DR5_wholeYr\"] >1) | (mnth[\"20160929\"]/mnth[\"3DR5_wholeYr\"] >1)| (mnth[\"20160930\"]/mnth[\"1DR5_SepOctNov\"] >1)]\n", "print(\"tada\")\n", "display(mnth)\n", "\n", "import matplotlib.pyplot as plt \n", "from mpl_toolkits.basemap import Basemap\n", "fig, ax = plt.subplots(figsize=(5,5))\n", "\n", "map = Basemap( llcrnrlon = 3., llcrnrlat = 58., urcrnrlon = 9., urcrnrlat=62.,\\\n", " resolution = 'l', area_thresh = 10000., projection = 'merc' )\n", "map.drawcoastlines( linewidth = 1.3, linestyle = 'solid', color = \"0.2\", zorder=5)#[ 75./255., 75/255., 75/255. ] )\n", "parallels = np.arange(50,70,1.)\n", "# labels = [left,right,top,bottom]\n", "map.drawparallels(parallels,labels=[False,True,True,False])\n", "meridians = np.arange(-10.,30.,1.)\n", "map.drawmeridians(meridians,labels=[True,False,False,True])\n", "lon,lat = map(mnth.Longitude.values, mnth.Latitude.values)\n", "all_lon,all_lat = map(all_lon,all_lat)\n", "CS = map.plot(all_lon,all_lat, \"ob\")\n", "CS = map.plot(lon,lat, \"or\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### When was it most extreme" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda env:3point6]", "language": "python", "name": "conda-env-3point6-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 }