Merge remote-tracking branch 'origin/master'

api/GetAverageData.py

 import configparser
 import numpy as np
 from psycopg2 import sql
+import numpy as np
 
 cfg = configparser.ConfigParser()
 cfg.read('../config.ini')
@@ -127,3 +128,15 @@ def get_interpolation_data_for_point(lat, lon, columns, cursor):
 
 
 # get_average_data_for_point(52.5, 13.4)
+
+def calcAverageYear(stationList, fromYear, toYear):
+    
+    dateRange = np.arange(fromYear, toYear+1)
+    dateRangeRegex = "|".join(np.char.mod('%d', dateRange))
+    
+    stationListDate = stationList.filter(regex=dateRangeRegex)
+    
+    stationList["anomalie"] = stationListDate.mean(axis=1)
+    stationList = stationList.dropna(axis=0, subset=['anomalie'])
+    
+    return stationList

api/test_GetAverageData.py

+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Sep 27 20:06:22 2021
+
+@author: geopeter
+"""
+
+import SQLPandasTools as s2pTool
+import GetAverageData as cut
+import unittest
+import pickle
+
+class Testapi(unittest.TestCase):
+    def testcalcAverageYear(self):
+        with open("./pickle/fetchall.pickle", "rb") as pickleFile:
+            sqlResult = pickle.load(pickleFile)
+            
+            stationList = s2pTool.sqlToGeoPandas(sqlResult)
+            
+            stationList = cut.calcAverageYear(stationList, 1961, 1990)
+            
+            for id, item in stationList.iterrows():
+                self.assertTrue(type(item["anomalie"]) == float)
+            
+            
+if __name__ == '__main__':
+    unittest.main()
\ No newline at end of file

api/test_SQLPandasTools.py

@@ -5,7 +5,7 @@ import unittest
 import pickle
 
 class Testapi(unittest.TestCase):
-    def _testSqlToGeoPandas(self):
+    def testSqlToGeoPandas(self):
         
         with open("./pickle/fetchall.pickle", "rb") as pickleFile:
             sqlResult = pickle.load(pickleFile)
@@ -28,7 +28,7 @@ class Testapi(unittest.TestCase):
             self.assertTrue(annualMean[len(annualMean)-5]['date']>=2000)
             self.assertTrue(annualMean[len(annualMean)-5]['value']>=2)
     
-    def _testDetermineAnnualMeanWithList(self):
+    def testDetermineAnnualMeanWithList(self):
         with open("./pickle/fetchall.pickle", "rb") as pickleFile:
             stationList = pickle.load(pickleFile)
             

dataacquisition/sandbox_raster.py.orig

+import configparser
+import numpy as np
+import psycopg2
+import pandas as pd
+from osgeo import gdal, osr
+
+cfg = configparser.ConfigParser()
+cfg.read('../config.ini')
+assert "POSTGRES" in cfg, "missing POSTGRES in config.ini"
+param_postgres = cfg["POSTGRES"]
+param_interpol = cfg["INTERPOLATION"]
+
+db_name, user, pw, host, port = param_postgres['dbName'], param_postgres["user"], param_postgres["password"], param_postgres["host"], param_postgres["port"]
+
+with psycopg2.connect(database=db_name, user=user, password=pw, host=param_postgres["host"], port=port) as connection:
+    with connection.cursor() as cursor:
+        cursor.execute('SELECT station_id, "2018" FROM stations WHERE file IS NULL ORDER BY station_id ASC;')
+        pixel_array = []
+        pixel_array_alpha = []
+        results = cursor.fetchall()
+        for j in range(0, 36):
+            row_array = []
+            row_array_alpha = []
+            for i, station_id in enumerate(results):
+                if i % 36 == 0:
+<<<<<<< HEAD
+                    row_array.append(results[i + j][1])                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 
+=======
+                    value = results[i + j][1]
+                    row_array.append(value)
+                    row_array_alpha.append(value)
+                    np_row_array = np.array(row_array)
+                    np_row_array_alpha = np.array(row_array_alpha)
+                    np_row_array_alpha = np.where(np_row_array_alpha < 8, 0, 255)
+>>>>>>> 94001e7882d184c3101f8663bc43b41ad9cb4e98
+                    # print(row_array)
+            pixel_array.append(np_row_array)
+            pixel_array_alpha.append(np_row_array_alpha)
+        np_pixel_array = np.array(pixel_array)
+        np_pixel_array_alpha = np.array(pixel_array_alpha)
+
+        xmin, ymin, xmax, ymax = [5.01, 47.15, 14.81, 55.33]
+        nrows, ncols = np.shape(np_pixel_array)
+        xres = (xmax - xmin) / float(ncols)
+        yres = (ymax - ymin) / float(nrows)
+        geotransform = (xmin, xres, 0, ymax, 0, -yres)
+
+        output_raster = gdal.GetDriverByName('GTiff').Create('D:/Uni/Master/01_SS2021/Automatisierte_Geodatenprozessierung/temperaturverteilung/dataacquisition/output/myraster.tif', ncols, nrows, 4, gdal.GDT_Float32)  # Open the file
+        output_raster.SetGeoTransform(geotransform)
+        srs = osr.SpatialReference()
+        srs.ImportFromEPSG(4326)
+        output_raster.SetProjection(srs.ExportToWkt())
+        output_raster.GetRasterBand(1).WriteArray(np_pixel_array)
+        # output_raster.GetRasterBand(2).WriteArray(np_pixel_array)
+        # output_raster.GetRasterBand(3).WriteArray(np_pixel_array)
+        output_raster.GetRasterBand(4).WriteArray(np_pixel_array_alpha)
+
+        output_raster.FlushCache()