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Commit e843614e authored by Peter Morstein's avatar Peter Morstein
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build average range

parents ca5203c0 94001e78
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api/GetAverageData.py
+ 20
− 2
View file @ e843614e
import configparser
import psycopg2
import numpy as np
from psycopg2 import sql
import numpy as np
......@@ -12,6 +11,25 @@ param_postgres = cfg["POSTGRES"]
param_interpol = cfg["INTERPOLATION"]
def get_average_of_multiple_years(cursor, years):
avg_strings = " "
where_sql = " WHERE lat IS NOT NULL "
and_strings = ""
n = int(years[1]) - int(years[0])
for year in range(int(years[0]), int(years[1])+1):
avg_string = ' AVG ("{}") + '.format(str(year))
and_string = """ AND "{}" != 'NaN' """.format(str(year))
avg_strings += avg_string
and_strings += and_string
avg_strings = avg_strings[:-2]
query = """SELECT station_id, ROUND(({}) / {}, 1), transparent FROM stations WHERE file IS NULL GROUP BY station_id, transparent ORDER BY station_id ASC;""".format(avg_strings, n)
print(query)
cursor.execute(query)
return cursor.fetchall()
# Getting all available year columns from database
def get_year_columns(cursor):
columns = []
......
api/api.py
+ 27
− 10
View file @ e843614e
import psycopg2
from flask import Flask, jsonify, request
from flask_cors import cross_origin
from flask import Flask, jsonify, request, send_from_directory
# from flask_cors import cross_origin
from psycopg2 import sql
import configparser
from GetAverageData import get_interpolation_data_for_point
import SQLPandasTools as s2pTool
from GetAverageData import get_interpolation_data_for_point, get_year_columns, get_average_of_multiple_years
from dataacquisition import write_raster
# import SQLPandasTools as s2pTool
cfg = configparser.ConfigParser()
cfg.read('../config.ini')
......@@ -94,22 +95,38 @@ def getStandardQuery():
return query
@app.route('/raster', methods=['GET'])
def get_raster():
if 'years' in request.args:
years = request.args['years'].split('-')
if int(years[1]) < int(years[0]):
years = [years[1], years[0]]
with psycopg2.connect(database=param_postgres["dbName"], user=param_postgres["user"], password=param_postgres["password"], host=param_postgres["host"], port=param_postgres["port"]) as connection:
with connection.cursor() as cursor:
# all_years = get_year_columns(cursor)
average_data = get_average_of_multiple_years(cursor, years)
write_raster.write_raster(average_data)
return send_from_directory('D:/Uni/Master/01_SS2021/Automatisierte_Geodatenprozessierung/temperaturverteilung/dataacquisition/output', filename='myraster.tif', as_attachment=True)
# return 'Läuft, Brudi'
@app.route('/annualMean', methods=['GET'])
@cross_origin()
def annualMean():
query = getStandardQuery()
with psycopg2.connect(database=param_postgres["dbName"], user=param_postgres["user"], password=param_postgres["password"], host=param_postgres["host"], port=param_postgres["port"]) as connection:
with connection.cursor() as cursor:
cursor.execute(query)
results = cursor.fetchall()[0][0]
return jsonify(s2pTool.determineAnnualMean(results))
return "{}"
if __name__ == '__main__':
app.run(host='127.0.0.1', port=42000)
dataacquisition/sandbox_raster.py
+ 17
− 6
View file @ e843614e
......@@ -16,28 +16,39 @@ with psycopg2.connect(database=db_name, user=user, password=pw, host=param_postg
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:
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)
# print(row_array)
pixel_array.append(row_array)
pixel_array.append(np_row_array)
pixel_array_alpha.append(np_row_array_alpha)
np_pixel_array = np.array(pixel_array)
print(np_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('./output/myraster.tif', ncols, nrows, 1, gdal.GDT_Float32) # Open the file
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()
\ No newline at end of file
output_raster.FlushCache()
dataacquisition/sandbox_raster.py.orig 0 → 100644
+ 58
− 0
View file @ e843614e
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()
dataacquisition/write_raster.py 0 → 100644
+ 46
− 0
View file @ e843614e
import numpy as np
from osgeo import gdal, osr
def write_raster(data):
pixel_array = []
pixel_array_alpha = []
for j in range(0, 36):
row_array = []
row_array_alpha = []
for i, station_id in enumerate(data):
if i % 36 == 0:
value = data[i + j][1]
value = 0 if not value else value
value = 0 if str(value) == 'NaN' else value
alpha_value = data[i + j][2]
alpha_value = 0 if alpha_value else 255
# print(value)
row_array.append(value)
row_array_alpha.append(alpha_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)
# 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()
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