initial upload

GLB.Ts+dSST.csv

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ablauf.txt

@@ -10,5 +10,3 @@ __Verknüpfen der Klimadaten und Stationen___
 Stationsliste iterieren und Wetterdaten herunterladen: [StationsID]_[Startdatum]_[Enddatum].txt
 
 Mittelwert aller Stationen des Landes bilden
-
-

data/DwdAcquisition.py

@@ -23,7 +23,7 @@ def loadDWDGauges():
     stationList['lon'] = stationList['lon'].str.strip()
     stationList['lat'] = stationList['lat'].str.strip()
     
-    # rename countries to merge with world shape file
+    # rename countries to merge with geopandas world shape file
     stationList.loc[stationList['country']=="Korea, Dem. People's Rep.", 'country'] = 'South Korea'
     stationList.loc[stationList['country']=="Slovakia (Slovak. Rep.)", 'country'] = 'Slovakia'
     stationList.loc[stationList['country']=="Slowenia", 'country'] = 'Slovenia'
@@ -62,10 +62,10 @@ def loadDWDGauges():
     
     dwdFTP.quit()
     
-    # filter climate files list by longest timeseries (multiple timeseries per station)
+    # filter climate files list by longest timeseries 
+    # (because: there are multiple timeseries-files per station with same historical values)
     longestSeries = pd.DataFrame()
     for index, ftpFiles in fileList.groupby("id", axis=0):
-        #print(ftpFiles.iloc[-1])
         longestSeries = longestSeries.append(ftpFiles.iloc[-1])
     fileList.drop(fileList.index, inplace=True)
     
@@ -77,11 +77,11 @@ def loadDWDGauges():
     # with open("stationList.pickle","wb") as pf:
     #      pickle.dump(stationList, pf)
 
+
 def fillMissingData(annualData):
     months = ["Jan", "Feb", "Mrz","Apr","Mai","Jun","Jul","Aug","Sep","Okt","Nov","Dez"]
     
     for y in range(0,len(annualData)):
-        year = annualData.iloc[y]
         
         # check month for nan values
         for m in range(0,len(months)):
@@ -132,7 +132,7 @@ def loadTemperatureFromDWDGauges():
     global annualData
     global worldTemperature
         
-    with open("stationList.pickle", "rb") as pickleFile:
+    with open("./pickle/stationList.pickle", "rb") as pickleFile:
         stationList = pickle.load(pickleFile)
     #stationList  = pd.concat([stationList, pd.DataFrame(columns=list(range(1950,2020)))])
     
@@ -145,26 +145,55 @@ def loadTemperatureFromDWDGauges():
         for gid, gurl in zip(gaugeIds, gaugeURLs):
             annualData = pd.read_csv(gurl, delimiter=";")
             annualData = annualData.set_index("Jahr")
-            #annualData["mean"] = annualData.mean(axis=1)
-            annualData = fillMissingData(annualData)
-            # with open("annualData.pickle", "wb") as pickleFile:
-            #     pickle.dump(annualData, pickleFile)
+            annualData["mean"] = annualData.mean(axis=1)
+            #annualData = fillMissingData(annualData)
             
             for dataIndex, annualMean in annualData.iterrows():
                 try:
                     stationList.at[gid, dataIndex] = annualMean["mean"]
                 except:
                     continue
-    #for index, gaugeCountry in stationList.groupby("country", axis=0):
-    with open("stationList_temps.pickle", "wb") as pickleFile:
+                
+    with open("./pickle/stationList_temps_missingData.pickle", "wb") as pickleFile:
         pickle.dump(stationList, pickleFile)
             
-#
+        
+def buildAverageTimeseries(fromYear, toYear, name):
+    global stationList
+    
+    meanAverage = []
+    for stationID, station in stationList.iterrows():
+        temps = []
+        for i in range(fromYear,toYear):
+            if not np.isnan(station[i]):
+                temps.append(station[i])
+        
+        if len(temps) > 5:
+            meanAverage.append(np.mean(temps))
+        else:
+            meanAverage.append(np.NaN)
+    
+    stationList[name] = np.round(meanAverage,1)
+    
+def cleanAverageTimeseries():
+    # determine gauges that includes both timeseries. If not delete them.
+    global stationList
+    
+    for stationID, station in stationList.iterrows():
+        if np.isnan(station['m1961T1990']) or np.isnan(station['m1991T2018']):
+            #station['m1961T1990'] = None
+            #station['m2010T2018'] = None
+            stationList.at[stationID, "m1961T1990"] = None
+            stationList.at[stationID, "m1991T2018"] = None
+            
+    
+    
+    
 def buildStationListGDP():
     global stationList
     global stationGPD
     
-    with open("stationList_temps.pickle", "rb") as pickleFile:
+    with open("./pickle/stationList_temps_missingData.pickle", "rb") as pickleFile:
         stationList = pickle.load(pickleFile)
     
     del stationList["file"]
@@ -175,33 +204,42 @@ def buildStationListGDP():
     stationList["lat"] = stationList["lat"].astype(str).astype(float)
     stationList["lon"] = stationList["lon"].astype(str).astype(float)
     
+    buildAverageTimeseries(1961,1990,"m1961T1990")
+    buildAverageTimeseries(1991,2018,"m1991T2018")
+    cleanAverageTimeseries()
+    
     stationGPD = gpd.GeoDataFrame(stationList, geometry=gpd.points_from_xy(stationList.lat, stationList.lon)).reset_index()
     del stationGPD["lat"]
     del stationGPD["lon"]
+    
     stationGPD.columns = stationGPD.columns.astype(str)
     stationGPD = stationGPD.sort_index(axis=1, ascending=False)
-    stationGPD.to_file("stationList.shp", "ESRI Shapefile")
+    #stationGPD.to_file("stationList.shp", "ESRI Shapefile")
+    
     
 def buildAnnualCountryTemp():
     global stationList
     global countryTemp
     global countryAnnualTemp
-    global countryMeanGPD
-    global world
-    world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
     
+    world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
     
     countryMeanGPD = gpd.sjoin(world, stationGPD, how="inner", op='intersects')
-    print(countryMeanGPD.head())
     countryMeanGPD = countryMeanGPD.groupby("name", axis=0).mean().reset_index()
+    
+    countryMeanGPD["anomalie"] = countryMeanGPD["m1991T2018"] - countryMeanGPD["m1961T1990"]
+    
     del countryMeanGPD["pop_est"]
     del countryMeanGPD["gdp_md_est"]
     
+    for i in range(1873,1950):
+        del countryMeanGPD[str(i)]
+    
     worldGauge = world.set_index("name").join(countryMeanGPD.set_index("name"))
     #worldGauge = worldGauge.loc[(worldGauge.continent=="Europe")]
     #worldGauge.columns = countryAnnualTemp.columns.map(str)
     
-    worldGauge.to_file("globalAnnualTemp_country.shp", "ESRI Shapefile")
+    worldGauge.to_file("./output/countryAnnualTemperature.shp", "ESRI Shapefile")
     
     #countryAnnualTemp = stationList.groupby("country", axis=0).mean().reset_index()
     #countryAnnualTemp.columns = countryAnnualTemp.columns.map(str)

data/globalAnnualTemp_country.cpg

-ISO-8859-1
\ No newline at end of file

data/globalAnnualTemp_country.prj

-GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137,298.257223563]],PRIMEM["Greenwich",0],UNIT["Degree",0.017453292519943295]]
\ No newline at end of file

data/output/test_shape/Germany.cpg

+UTF-8
\ No newline at end of file

data/output/test_shape/Germany.prj

+PROJCS["ETRS_1989_UTM_Zone_33N",GEOGCS["GCS_ETRS_1989",DATUM["D_ETRS_1989",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Transverse_Mercator"],PARAMETER["False_Easting",500000.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",15.0],PARAMETER["Scale_Factor",0.9996],PARAMETER["Latitude_Of_Origin",0.0],UNIT["Meter",1.0]]
\ No newline at end of file