def RetrieveData(args):
"""
This function retrieves JRC data for a given date from the
http://storage.googleapis.com/global-surface-water/downloads/ server.
Keyword arguments:
args -- A list of parameters defined in the DownloadData function.
"""
# Argument
[output_folder, Names_to_download, lonlim, latlim] = args
# Collect the data from the JRC webpage and returns the data and lat and long in meters of those tiles
try:
Collect_data(Names_to_download, output_folder)
except:
print "Was not able to download the file"
# Clip the data to the users extend
if len(Names_to_download) == 1:
trash_folder = os.path.join(output_folder, "Trash")
data_in = os.path.join(trash_folder, Names_to_download[0])
data_end, geo_end = RC.clip_data(data_in, latlim, lonlim)
else:
data_end = np.zeros([int((latlim[1] - latlim[0])/0.00025), int((lonlim[1] - lonlim[0])/0.00025)])
for Name_to_merge in Names_to_download:
trash_folder = os.path.join(output_folder, "Trash")
data_in = os.path.join(trash_folder, Name_to_merge)
geo_out, proj, size_X, size_Y = RC.Open_array_info(data_in)
lat_min_merge = np.maximum(latlim[0], geo_out[3] + size_Y * geo_out[5])
lat_max_merge = np.minimum(latlim[1], geo_out[3])
lon_min_merge = np.maximum(lonlim[0], geo_out[0])
lon_max_merge = np.minimum(lonlim[1], geo_out[0] + size_X * geo_out[1])
lonmerge = [lon_min_merge, lon_max_merge]
latmerge = [lat_min_merge, lat_max_merge]
data_one, geo_one = RC.clip_data(data_in, latmerge, lonmerge)
Ystart = int((geo_one[3] - latlim[1])/geo_one[5])
Yend = int(Ystart + np.shape(data_one)[0])
Xstart = int((geo_one[0] - lonlim[0])/geo_one[1])
Xend = int(Xstart + np.shape(data_one)[1])
data_end[Ystart:Yend, Xstart:Xend] = data_one
geo_end = tuple([lonlim[0], geo_one[1], 0, latlim[1], 0, geo_one[5]])
# Save results as Gtiff
fileName_out = os.path.join(output_folder, 'JRC_Occurrence_percent.tif')
DC.Save_as_tiff(name=fileName_out, data=data_end, geo=geo_end, projection='WGS84')
shutil.rmtree(trash_folder)
return True
def RetrieveData(Date, args):
"""
This function retrieves MOD11 LST data for a given date from the
https://e4ftl01.cr.usgs.gov/ server.
Keyword arguments:
Date -- 'yyyy-mm-dd'
args -- A list of parameters defined in the DownloadData function.
"""
# Argument
[
output_folder, TilesVertical, TilesHorizontal, lonlim, latlim,
TimeStep, hdf_library
] = args
# Collect the data from the MODIS webpage and returns the data and lat and long in meters of those tiles
try:
Collect_data(TilesHorizontal, TilesVertical, Date, output_folder,
TimeStep, hdf_library)
except:
print "Was not able to download the file"
# Define the output name of the collect data function
name_collect = os.path.join(output_folder, 'Merged.tif')
# Reproject the MODIS product to epsg_to
epsg_to = '4326'
name_reprojected = RC.reproject_MODIS(name_collect, epsg_to)
# Clip the data to the users extend
data, geo = RC.clip_data(name_reprojected, latlim, lonlim)
# Save results as Gtiff
if TimeStep == 8:
LSTfileName = os.path.join(
output_folder, 'LST_MOD11A2_K_8-daily_' + Date.strftime('%Y') +
'.' + Date.strftime('%m') + '.' + Date.strftime('%d') + '.tif')
if TimeStep == 1:
LSTfileName = os.path.join(
output_folder, 'LST_MOD11A1_K_daily_' + Date.strftime('%Y') + '.' +
Date.strftime('%m') + '.' + Date.strftime('%d') + '.tif')
DC.Save_as_tiff(name=LSTfileName, data=data, geo=geo, projection='WGS84')
# remove the side products
os.remove(os.path.join(output_folder, name_collect))
os.remove(os.path.join(output_folder, name_reprojected))
return True
def RetrieveData(Date, args):
"""
This function retrieves MOD16 ET data for a given date from the
ftp://ftp.ntsg.umt.edu/ server.
Keyword arguments:
Date -- 'yyyy-mm-dd'
args -- A list of parameters defined in the DownloadData function.
"""
# Argument
[
output_folder, TilesVertical, TilesHorizontal, latlim, lonlim,
timestep, hdf_library
] = args
# Collect the data from the MODIS webpage and returns the data and lat and long in meters of those tiles
try:
Collect_data(TilesHorizontal, TilesVertical, Date, output_folder,
timestep, hdf_library)
except:
print "Was not able to download the file"
# Define the output name of the collect data function
name_collect = os.path.join(output_folder, 'Merged.tif')
# Reproject the MODIS product to epsg_to
epsg_to = '4326'
name_reprojected = RC.reproject_MODIS(name_collect, epsg_to)
# Clip the data to the users extend
data, geo = RC.clip_data(name_reprojected, latlim, lonlim)
if timestep == 'monthly':
ETfileName = os.path.join(
output_folder, 'ET_MOD16A2_mm-month-1_monthly_' +
Date.strftime('%Y') + '.' + Date.strftime('%m') + '.01.tif')
elif timestep == '8-daily':
ETfileName = os.path.join(
output_folder,
'ET_MOD16A2_mm-8days-1_8-daily_' + Date.strftime('%Y') + '.' +
Date.strftime('%m') + '.' + Date.strftime('%d') + '.tif')
DC.Save_as_tiff(name=ETfileName, data=data, geo=geo, projection='WGS84')
# remove the side products
os.remove(os.path.join(output_folder, name_collect))
os.remove(os.path.join(output_folder, name_reprojected))
return ()
def Download_GWF_from_WA_FTP(output_folder, filename_Out, lonlim, latlim):
"""
This function retrieves GWF data for a given date from the
ftp.wateraccounting.unesco-ihe.org server.
Keyword arguments:
output_folder -- name of the end file with the weekly ALEXI data
End_filename -- name of the end file
lonlim -- [ymin, ymax] (values must be between -60 and 70)
latlim -- [xmin, xmax] (values must be between -180 and 180)
"""
try:
# Collect account and FTP information
username, password = WebAccounts.Accounts(Type='FTP_WA')
ftpserver = "ftp.wateraccounting.unesco-ihe.org"
# Set the file names and directories
filename = "Gray_Water_Footprint.tif"
local_filename = os.path.join(output_folder, filename)
# Download data from FTP
ftp = FTP(ftpserver)
ftp.login(username, password)
directory = "/WaterAccounting_Guest/Static_WA_Datasets/"
ftp.cwd(directory)
lf = open(local_filename, "wb")
ftp.retrbinary("RETR " + filename, lf.write)
lf.close()
# Clip extend out of world data
dataset, Geo_out = RC.clip_data(local_filename, latlim, lonlim)
# make geotiff file
DC.Save_as_tiff(name=filename_Out,
data=dataset,
geo=Geo_out,
projection="WGS84")
# delete old tif file
os.remove(local_filename)
except:
print "file not exists"
return
def Download_GWF_from_WA_FTP(output_folder, filename_Out, lonlim, latlim):
"""
This function retrieves GWF data for a given date from the
ftp.wateraccounting.unesco-ihe.org server.
Keyword arguments:
output_folder -- name of the end file with the weekly ALEXI data
End_filename -- name of the end file
lonlim -- [ymin, ymax] (values must be between -60 and 70)
latlim -- [xmin, xmax] (values must be between -180 and 180)
"""
try:
# Collect account and FTP information
username, password = WebAccounts.Accounts(Type = 'FTP_WA')
ftpserver = "ftp.wateraccounting.unesco-ihe.org"
# Set the file names and directories
filename = "Gray_Water_Footprint.tif"
local_filename = os.path.join(output_folder, filename)
# Download data from FTP
ftp=FTP(ftpserver)
ftp.login(username,password)
directory="/WaterAccounting_Guest/Static_WA_Datasets/"
ftp.cwd(directory)
lf = open(local_filename, "wb")
ftp.retrbinary("RETR " + filename, lf.write)
lf.close()
# Clip extend out of world data
dataset, Geo_out = RC.clip_data(local_filename, latlim, lonlim)
# make geotiff file
DC.Save_as_tiff(name = filename_Out, data = dataset, geo = Geo_out, projection = "WGS84")
# delete old tif file
os.remove(local_filename)
except:
print "file not exists"
return
def RetrieveData(Date, args):
"""
This function retrieves MOD11 LST data for a given date from the
https://e4ftl01.cr.usgs.gov/ server.
Keyword arguments:
Date -- 'yyyy-mm-dd'
args -- A list of parameters defined in the DownloadData function.
"""
# Argument
[output_folder, TilesVertical, TilesHorizontal,lonlim, latlim, TimeStep, hdf_library] = args
# Collect the data from the MODIS webpage and returns the data and lat and long in meters of those tiles
try:
Collect_data(TilesHorizontal, TilesVertical, Date, output_folder, TimeStep, hdf_library)
except:
print "Was not able to download the file"
# Define the output name of the collect data function
name_collect = os.path.join(output_folder, 'Merged.tif')
# Reproject the MODIS product to epsg_to
epsg_to ='4326'
name_reprojected = RC.reproject_MODIS(name_collect, epsg_to)
# Clip the data to the users extend
data, geo = RC.clip_data(name_reprojected, latlim, lonlim)
# Save results as Gtiff
if TimeStep == 8:
LSTfileName = os.path.join(output_folder, 'LST_MOD11A2_K_8-daily_' + Date.strftime('%Y') + '.' + Date.strftime('%m') + '.' + Date.strftime('%d') + '.tif')
if TimeStep == 1:
LSTfileName = os.path.join(output_folder, 'LST_MOD11A1_K_daily_' + Date.strftime('%Y') + '.' + Date.strftime('%m') + '.' + Date.strftime('%d') + '.tif')
DC.Save_as_tiff(name=LSTfileName, data=data, geo=geo, projection='WGS84')
# remove the side products
os.remove(os.path.join(output_folder, name_collect))
os.remove(os.path.join(output_folder, name_reprojected))
return True
def RetrieveData(Date, args):
"""
This function retrieves MOD16 ET data for a given date from the
ftp://ftp.ntsg.umt.edu/ server.
Keyword arguments:
Date -- 'yyyy-mm-dd'
args -- A list of parameters defined in the DownloadData function.
"""
# Argument
[output_folder, TilesVertical, TilesHorizontal,latlim, lonlim, timestep, hdf_library] = args
# Collect the data from the MODIS webpage and returns the data and lat and long in meters of those tiles
try:
Collect_data(TilesHorizontal,TilesVertical,Date,output_folder, timestep, hdf_library)
except:
print "Was not able to download the file"
# Define the output name of the collect data function
name_collect = os.path.join(output_folder, 'Merged.tif')
# Reproject the MODIS product to epsg_to
epsg_to ='4326'
name_reprojected = RC.reproject_MODIS(name_collect, epsg_to)
# Clip the data to the users extend
data, geo = RC.clip_data(name_reprojected, latlim, lonlim)
if timestep == 'monthly':
ETfileName = os.path.join(output_folder, 'ET_MOD16A2_mm-month-1_monthly_'+Date.strftime('%Y')+'.' + Date.strftime('%m')+'.01.tif')
elif timestep == '8-daily':
ETfileName = os.path.join(output_folder, 'ET_MOD16A2_mm-8days-1_8-daily_'+Date.strftime('%Y') + '.' + Date.strftime('%m') + '.' + Date.strftime('%d') + '.tif')
DC.Save_as_tiff(name=ETfileName, data=data, geo=geo, projection='WGS84')
# remove the side products
os.remove(os.path.join(output_folder, name_collect))
os.remove(os.path.join(output_folder, name_reprojected))
return()
def Add_Reservoirs(Name_NC_Rivers, Name_NC_Acc_Pixels, Diff_Water_Volume,
River_dict, Discharge_dict, DEM_dict, Distance_dict,
Regions, Example_dataset):
import numpy as np
import wa.General.raster_conversions as RC
import wa.General.data_conversions as DC
# Extract Rivers data from NetCDF file
Rivers = RC.Open_nc_array(Name_NC_Rivers)
# Open data array info based on example data
geo_out, epsg, size_X, size_Y = RC.Open_array_info(Example_dataset)
# Extract flow direction data from NetCDF file
acc_pixels = RC.Open_nc_array(Name_NC_Acc_Pixels)
# Create ID Matrix
y, x = np.indices((size_Y, size_X))
ID_Matrix = np.int32(
np.ravel_multi_index(np.vstack((y.ravel(), x.ravel())),
(size_Y, size_X),
mode='clip').reshape(x.shape)) + 1
del x, y
Acc_Pixels_Rivers = Rivers * acc_pixels
ID_Rivers = Rivers * ID_Matrix
Amount_of_Reservoirs = len(Regions)
Reservoir_is_in_River = np.ones([len(Regions), 3]) * -9999
for reservoir in range(0, Amount_of_Reservoirs):
region = Regions[reservoir, :]
dest = DC.Save_as_MEM(Acc_Pixels_Rivers, geo_out, projection='WGS84')
Rivers_Acc_Pixels_reservoir, Geo_out = RC.clip_data(
dest, latlim=[region[2], region[3]], lonlim=[region[0], region[1]])
dest = DC.Save_as_MEM(ID_Rivers, geo_out, projection='WGS84')
Rivers_ID_reservoir, Geo_out = RC.clip_data(
dest, latlim=[region[2], region[3]], lonlim=[region[0], region[1]])
size_Y_reservoir, size_X_reservoir = np.shape(
Rivers_Acc_Pixels_reservoir)
IDs_Edges = []
IDs_Edges = np.append(IDs_Edges, Rivers_Acc_Pixels_reservoir[0, :])
IDs_Edges = np.append(IDs_Edges, Rivers_Acc_Pixels_reservoir[:, 0])
IDs_Edges = np.append(
IDs_Edges,
Rivers_Acc_Pixels_reservoir[int(size_Y_reservoir) - 1, :])
IDs_Edges = np.append(
IDs_Edges, Rivers_Acc_Pixels_reservoir[:,
int(size_X_reservoir) - 1])
Value_Reservoir = np.max(np.unique(IDs_Edges))
y_pix_res, x_pix_res = np.argwhere(
Rivers_Acc_Pixels_reservoir == Value_Reservoir)[0]
ID_reservoir = Rivers_ID_reservoir[y_pix_res, x_pix_res]
# Find exact reservoir area in river directory
for River_part in River_dict.iteritems():
if len(np.argwhere(River_part[1] == ID_reservoir)) > 0:
Reservoir_is_in_River[reservoir, 0] = np.argwhere(
River_part[1] == ID_reservoir) #River_part_good
Reservoir_is_in_River[reservoir,
1] = River_part[0] #River_Add_Reservoir
Reservoir_is_in_River[reservoir, 2] = 1 #Reservoir_is_in_River
numbers = abs(Reservoir_is_in_River[:, 1].argsort() -
len(Reservoir_is_in_River) + 1)
for number in range(0, len(Reservoir_is_in_River)):
row_reservoir = np.argwhere(numbers == number)[0][0]
if not Reservoir_is_in_River[row_reservoir, 2] == -9999:
# Get discharge into the reservoir:
Flow_in_res_m3 = Discharge_dict[int(Reservoir_is_in_River[
row_reservoir, 1])][:,
int(Reservoir_is_in_River[row_reservoir,
0])]
# Get difference reservoir
Change_Reservoir_m3 = Diff_Water_Volume[row_reservoir, :, 2]
# Total Change outflow
Change_outflow_m3 = np.minimum(Flow_in_res_m3, Change_Reservoir_m3)
Difference = Change_outflow_m3 - Change_Reservoir_m3
if abs(np.sum(Difference)) > 10000 and np.sum(
Change_Reservoir_m3[Change_outflow_m3 > 0]) > 0:
Change_outflow_m3[Change_outflow_m3 < 0] = Change_outflow_m3[
Change_outflow_m3 < 0] * np.sum(
Change_outflow_m3[Change_outflow_m3 > 0]) / np.sum(
Change_Reservoir_m3[Change_outflow_m3 > 0])
# Find key name (which is also the lenght of the river dictionary)
i = len(River_dict)
#River_with_reservoirs_dict[i]=list((River_dict[River_Add_Reservoir][River_part_good[0][0]:]).flat) < MAAK DIRECTORIES ARRAYS OP DEZE MANIER DAN IS DE ARRAY 1D
River_dict[i] = River_dict[int(Reservoir_is_in_River[
row_reservoir, 1])][int(Reservoir_is_in_River[row_reservoir,
0]):]
River_dict[int(
Reservoir_is_in_River[row_reservoir, 1])] = River_dict[int(
Reservoir_is_in_River[
row_reservoir,
1])][:int(Reservoir_is_in_River[row_reservoir, 0]) + 1]
DEM_dict[i] = DEM_dict[int(Reservoir_is_in_River[
row_reservoir, 1])][int(Reservoir_is_in_River[row_reservoir,
0]):]
DEM_dict[int(
Reservoir_is_in_River[row_reservoir, 1])] = DEM_dict[int(
Reservoir_is_in_River[
row_reservoir,
1])][:int(Reservoir_is_in_River[row_reservoir, 0]) + 1]
Distance_dict[i] = Distance_dict[int(Reservoir_is_in_River[
row_reservoir, 1])][int(Reservoir_is_in_River[row_reservoir,
0]):]
Distance_dict[int(
Reservoir_is_in_River[row_reservoir, 1])] = Distance_dict[int(
Reservoir_is_in_River[
row_reservoir,
1])][:int(Reservoir_is_in_River[row_reservoir, 0]) + 1]
Discharge_dict[i] = Discharge_dict[int(Reservoir_is_in_River[
row_reservoir, 1])][:,
int(Reservoir_is_in_River[row_reservoir,
0]):]
Discharge_dict[int(
Reservoir_is_in_River[row_reservoir, 1])] = Discharge_dict[int(
Reservoir_is_in_River[
row_reservoir,
1])][:, :int(Reservoir_is_in_River[row_reservoir, 0]) +
1]
Discharge_dict[int(Reservoir_is_in_River[
row_reservoir,
1])][:, 1:int(Reservoir_is_in_River[row_reservoir, 0]) +
1] = Discharge_dict[int(
Reservoir_is_in_River[row_reservoir, 1]
)][:, 1:int(Reservoir_is_in_River[row_reservoir, 0]) +
1] - Change_outflow_m3[:, None]
Next_ID = River_dict[int(Reservoir_is_in_River[row_reservoir,
1])][0]
times = 0
while len(River_dict) > times:
for River_part in River_dict.iteritems():
if River_part[-1][-1] == Next_ID:
Next_ID = River_part[-1][0]
item = River_part[0]
#Always 10 procent of the incoming discharge will pass the dam
Change_outflow_m3[:, None] = np.minimum(
0.9 * Discharge_dict[item][:, -1:],
Change_outflow_m3[:, None])
Discharge_dict[item][:, 1:] = Discharge_dict[
item][:, 1:] - Change_outflow_m3[:, None]
print(item)
times = 0
times += 1
return (Discharge_dict, River_dict, DEM_dict, Distance_dict)
def Find_Area_Volume_Relation(region, input_JRC, DEM_dataset):
# Find relation between V and A
import numpy as np
import wa.General.raster_conversions as RC
import wa.General.data_conversions as DC
from scipy.optimize import curve_fit
import matplotlib.pyplot as plt
def func(x, a, b):
"""
This function is used for finding relation area and volume
"""
return (a * x**b)
def func3(x, a, b, c, d):
"""
This function is used for finding relation area and volume
"""
return (a * (x - c)**b + d)
#Array, Geo_out = RC.clip_data(input_JRC,latlim=[14.528,14.985],lonlim =[35.810,36.005])
Array, Geo_out = RC.clip_data(
input_JRC,
latlim=[region[2], region[3]],
lonlim=[region[0], region[1]
]) # This reservoir was not filled when SRTM was taken
size_Y = int(np.shape([Array])[-2])
size_X = int(np.shape([Array])[-1])
Water_array = np.zeros(np.shape(Array))
buffer_zone = 4
Array[Array > 0] = 1
for i in range(0, size_Y):
for j in range(0, size_X):
Water_array[i, j] = np.max(Array[
np.maximum(0, i -
buffer_zone):np.minimum(size_Y, i + buffer_zone +
1),
np.maximum(0, j -
buffer_zone):np.minimum(size_X, j + buffer_zone +
1)])
del Array
# Open DEM and reproject
# Save Example as memory file
dest_example = DC.Save_as_MEM(Water_array, Geo_out, projection='WGS84')
# reproject DEM by using example
dest_out = RC.reproject_dataset_example(DEM_dataset,
dest_example,
method=2)
DEM = dest_out.GetRasterBand(1).ReadAsArray()
# find DEM water heights
DEM_water = np.zeros(np.shape(Water_array))
DEM_water[Water_array != 1] = np.nan
DEM_water[Water_array == 1.] = DEM[Water_array == 1.]
# Get array with areas
import wa.Functions.Start.Area_converter as Area
dlat, dlon = Area.Calc_dlat_dlon(Geo_out, size_X, size_Y)
area_in_m2 = dlat * dlon
# find volume and Area
min_DEM_water = int(np.round(np.nanmin(DEM_water)))
max_DEM_water = int(np.round(np.nanmax(DEM_water)))
Reservoir_characteristics = np.zeros([1, 5])
i = 0
for height in range(min_DEM_water + 1, max_DEM_water):
DEM_water_below_height = np.zeros(np.shape(DEM_water))
DEM_water[np.isnan(DEM_water)] = 1000000
DEM_water_below_height[DEM_water < height] = 1
pixels = np.sum(DEM_water_below_height)
area = np.sum(DEM_water_below_height * area_in_m2)
if height == min_DEM_water + 1:
volume = 0.5 * area
histogram = pixels
Reservoir_characteristics[:] = [
height, pixels, area, volume, histogram
]
else:
area_previous = Reservoir_characteristics[i, 2]
volume_previous = Reservoir_characteristics[i, 3]
volume = volume_previous + 0.5 * (
area - area_previous) + 1 * area_previous
histogram_previous = Reservoir_characteristics[i, 1]
histogram = pixels - histogram_previous
Reservoir_characteristics_one = [
height, pixels, area, volume, histogram
]
Reservoir_characteristics = np.append(
Reservoir_characteristics, Reservoir_characteristics_one)
i += 1
Reservoir_characteristics = np.resize(Reservoir_characteristics,
(i + 1, 5))
maxi = int(len(Reservoir_characteristics[:, 3]))
# find minimum value for reservoirs height (DEM is same value if reservoir was already filled whe SRTM was created)
Historgram = Reservoir_characteristics[:, 4]
hist_mean = np.mean(Historgram)
hist_std = np.std(Historgram)
mini_tresh = hist_std * 5 + hist_mean
Check_hist = np.zeros([len(Historgram)])
Check_hist[Historgram > mini_tresh] = Historgram[Historgram > mini_tresh]
if np.max(Check_hist) != 0.0:
col = np.argwhere(Historgram == np.max(Check_hist))[0][0]
mini = col + 1
else:
mini = 0
fitted = 0
# find starting point reservoirs
V0 = Reservoir_characteristics[mini, 3]
A0 = Reservoir_characteristics[mini, 2]
# Calculate the best maxi reservoir characteristics, based on the normal V = a*x**b relation
while fitted == 0:
try:
if mini == 0:
popt1, pcov1 = curve_fit(
func, Reservoir_characteristics[mini:maxi, 2],
Reservoir_characteristics[mini:maxi, 3])
else:
popt1, pcov1 = curve_fit(
func, Reservoir_characteristics[mini:maxi, 2] - A0,
Reservoir_characteristics[mini:maxi, 3] - V0)
fitted = 1
except:
maxi -= 1
if maxi < mini:
print 'ERROR: was not able to find optimal fit'
fitted = 1
# Remove last couple of pixels of maxi
maxi_end = int(np.round(maxi - 0.2 * (maxi - mini)))
done = 0
times = 0
while done == 0 and times > 20 and maxi_end < mini:
try:
if mini == 0:
popt, pcov = curve_fit(
func, Reservoir_characteristics[mini:maxi_end, 2],
Reservoir_characteristics[mini:maxi_end, 3])
else:
popt, pcov = curve_fit(
func3, Reservoir_characteristics[mini:maxi_end, 2],
Reservoir_characteristics[mini:maxi_end, 3])
except:
maxi_end = int(maxi)
if mini == 0:
popt, pcov = curve_fit(
func, Reservoir_characteristics[mini:maxi_end, 2],
Reservoir_characteristics[mini:maxi_end, 3])
else:
popt, pcov = curve_fit(
func3, Reservoir_characteristics[mini:maxi_end, 2],
Reservoir_characteristics[mini:maxi_end, 3])
if mini == 0:
plt.plot(Reservoir_characteristics[mini:maxi_end, 2],
Reservoir_characteristics[mini:maxi_end, 3], 'ro')
t = np.arange(0., np.max(Reservoir_characteristics[:, 2]), 1000)
plt.plot(t, popt[0] * (t)**popt[1], 'g--')
plt.axis([
0,
np.max(Reservoir_characteristics[mini:maxi_end, 2]), 0,
np.max(Reservoir_characteristics[mini:maxi_end, 3])
])
plt.show()
done = 1
else:
plt.plot(Reservoir_characteristics[mini:maxi_end, 2],
Reservoir_characteristics[mini:maxi_end, 3], 'ro')
t = np.arange(0., np.max(Reservoir_characteristics[:, 2]), 1000)
plt.plot(t, popt[0] * (t - popt[2])**popt[1] + popt[3], 'g--')
plt.axis([
0,
np.max(Reservoir_characteristics[mini:maxi_end, 2]), 0,
np.max(Reservoir_characteristics[mini:maxi_end, 3])
])
plt.show()
Volume_error = popt[3] / V0 * 100 - 100
print 'error Volume = %s percent' % Volume_error
print 'error Area = %s percent' % (A0 / popt[2] * 100 - 100)
if Volume_error < 30 and Volume_error > -30:
done = 1
else:
times += 1
maxi_end -= 1
print 'Another run is done in order to improve the result'
if done == 0:
popt = np.append(popt1, [A0, V0])
if len(popt) == 2:
popt = np.append(popt, [0, 0])
return (popt)
def DownloadData(output_folder, latlim, lonlim, parameter, resolution):
"""
This function downloads DEM data from HydroSHED
Keyword arguments:
output_folder -- directory of the result
latlim -- [ymin, ymax] (values must be between -50 and 50)
lonlim -- [xmin, xmax] (values must be between -180 and 180)
Resample -- 1 = The data will be resampled to 0.001 degree spatial
resolution
-- 0 = The data will have the same pixel size as the data obtained
from the internet
"""
# Define parameter depedent variables
if parameter == "dir_3s":
para_name = "DIR"
unit = "-"
resolution = '3s'
parameter = 'dir'
if parameter == "dem_3s":
para_name = "DEM"
unit = "m"
resolution = '3s'
parameter = 'dem'
if parameter == "dir_15s":
para_name = "DIR"
unit = "-"
resolution = '15s'
parameter = 'dir'
if parameter == "dem_15s":
para_name = "DEM"
unit = "m"
resolution = '15s'
parameter = 'dem'
# converts the latlim and lonlim into names of the tiles which must be
# downloaded
if resolution == '3s':
name, rangeLon, rangeLat = Find_Document_Names(latlim, lonlim, parameter)
# Memory for the map x and y shape (starts with zero)
size_X_tot = 0
size_Y_tot = 0
if resolution == '15s':
name = Find_Document_names_15s(latlim, lonlim, parameter, resolution)
nameResults = []
# Create a temporary folder for processing
output_folder_trash = os.path.join(output_folder, "Temp")
if not os.path.exists(output_folder_trash):
os.makedirs(output_folder_trash)
# Download, extract, and converts all the files to tiff files
for nameFile in name:
try:
# Download the data from
# http://earlywarning.usgs.gov/hydrodata/
output_file, file_name = Download_Data(nameFile,
output_folder_trash, parameter, para_name,resolution)
# extract zip data
DC.Extract_Data(output_file, output_folder_trash)
# Converts the data with a adf extention to a tiff extension.
# The input is the file name and in which directory the data must be stored
file_name_tiff = file_name.split('.')[0] + '_trans_temporary.tif'
file_name_extract = file_name.split('_')[0:3]
if resolution == '3s':
file_name_extract2 = file_name_extract[0]+'_'+file_name_extract[1]
if resolution == '15s':
file_name_extract2 = file_name_extract[0]+'_'+file_name_extract[1]+'_15s'
input_adf = os.path.join(output_folder_trash, file_name_extract2,
file_name_extract2, 'hdr.adf')
output_tiff = os.path.join(output_folder_trash, file_name_tiff)
# convert data from adf to a tiff file
output_tiff = DC.Convert_adf_to_tiff(input_adf, output_tiff)
geo_out, proj, size_X, size_Y = RC.Open_array_info(output_tiff)
if int(size_X) != int(6000) or int(size_Y) != int(6000):
data = np.ones((6000, 6000)) * -9999
# Create the latitude bound
Vfile = str(nameFile)[1:3]
SignV = str(nameFile)[0]
SignVer = 1
# If the sign before the filename is a south sign than latitude is negative
if SignV is "s":
SignVer = -1
Bound2 = int(SignVer)*int(Vfile)
# Create the longitude bound
Hfile = str(nameFile)[4:7]
SignH = str(nameFile)[3]
SignHor = 1
# If the sign before the filename is a west sign than longitude is negative
if SignH is "w":
SignHor = -1
Bound1 = int(SignHor) * int(Hfile)
Expected_X_min = Bound1
Expected_Y_max = Bound2 + 5
Xid_start = int(np.round((geo_out[0] - Expected_X_min)/geo_out[1]))
Xid_end = int(np.round(((geo_out[0] + size_X * geo_out[1]) - Expected_X_min)/geo_out[1]))
Yid_start = int(np.round((Expected_Y_max - geo_out[3])/(-geo_out[5])))
Yid_end = int(np.round((Expected_Y_max - (geo_out[3] + (size_Y * geo_out[5])))/(-geo_out[5])))
data[Yid_start:Yid_end,Xid_start:Xid_end] = RC.Open_tiff_array(output_tiff)
if np.max(data)==255:
data[data==255] = -9999
data[data<-9999] = -9999
geo_in = [Bound1, 0.00083333333333333, 0.0, int(Bound2 + 5),
0.0, -0.0008333333333333333333]
# save chunk as tiff file
DC.Save_as_tiff(name=output_tiff, data=data, geo=geo_in,
projection="WGS84")
except:
if resolution == '3s':
# If tile not exist create a replacing zero tile (sea tiles)
output = nameFile.split('.')[0] + "_trans_temporary.tif"
output_tiff = os.path.join(output_folder_trash, output)
file_name = nameFile
data = np.ones((6000, 6000)) * -9999
data = data.astype(np.float32)
# Create the latitude bound
Vfile = str(file_name)[1:3]
SignV = str(file_name)[0]
SignVer = 1
# If the sign before the filename is a south sign than latitude is negative
if SignV is "s":
SignVer = -1
Bound2 = int(SignVer)*int(Vfile)
# Create the longitude bound
Hfile = str(file_name)[4:7]
SignH = str(file_name)[3]
SignHor = 1
# If the sign before the filename is a west sign than longitude is negative
if SignH is "w":
SignHor = -1
Bound1 = int(SignHor) * int(Hfile)
# Geospatial data for the tile
geo_in = [Bound1, 0.00083333333333333, 0.0, int(Bound2 + 5),
0.0, -0.0008333333333333333333]
# save chunk as tiff file
DC.Save_as_tiff(name=output_tiff, data=data, geo=geo_in,
projection="WGS84")
if resolution == '15s':
print 'no 15s data is in dataset'
if resolution =='3s':
# clip data
Data, Geo_data = RC.clip_data(output_tiff, latlim, lonlim)
size_Y_out = int(np.shape(Data)[0])
size_X_out = int(np.shape(Data)[1])
# Total size of the product so far
size_Y_tot = int(size_Y_tot + size_Y_out)
size_X_tot = int(size_X_tot + size_X_out)
if nameFile is name[0]:
Geo_x_end = Geo_data[0]
Geo_y_end = Geo_data[3]
else:
Geo_x_end = np.min([Geo_x_end,Geo_data[0]])
Geo_y_end = np.max([Geo_y_end,Geo_data[3]])
# create name for chunk
FileNameEnd = "%s_temporary.tif" % (nameFile)
nameForEnd = os.path.join(output_folder_trash, FileNameEnd)
nameResults.append(str(nameForEnd))
# save chunk as tiff file
DC.Save_as_tiff(name=nameForEnd, data=Data, geo=Geo_data,
projection="WGS84")
if resolution =='3s':
#size_X_end = int(size_X_tot) #!
#size_Y_end = int(size_Y_tot) #!
size_X_end = int(size_X_tot/len(rangeLat)) + 1 #!
size_Y_end = int(size_Y_tot/len(rangeLon)) + 1 #!
# Define the georeference of the end matrix
geo_out = [Geo_x_end, Geo_data[1], 0, Geo_y_end, 0, Geo_data[5]]
latlim_out = [geo_out[3] + geo_out[5] * size_Y_end, geo_out[3]]
lonlim_out = [geo_out[0], geo_out[0] + geo_out[1] * size_X_end]
# merge chunk together resulting in 1 tiff map
datasetTot = Merge_DEM(latlim_out, lonlim_out, nameResults, size_Y_end,
size_X_end)
datasetTot[datasetTot<-9999] = -9999
if resolution =='15s':
output_file_merged = os.path.join(output_folder_trash,'merged.tif')
datasetTot, geo_out = Merge_DEM_15s(output_folder_trash, output_file_merged,latlim, lonlim)
# name of the end result
output_DEM_name = "%s_HydroShed_%s_%s.tif" %(para_name,unit,resolution)
Save_name = os.path.join(output_folder, output_DEM_name)
# Make geotiff file
DC.Save_as_tiff(name=Save_name, data=datasetTot, geo=geo_out, projection="WGS84")
os.chdir(output_folder)
# Delete the temporary folder
shutil.rmtree(output_folder_trash)
def RetrieveData(args):
"""
This function retrieves JRC data for a given date from the
http://storage.googleapis.com/global-surface-water/downloads/ server.
Keyword arguments:
args -- A list of parameters defined in the DownloadData function.
"""
# Argument
[output_folder, Names_to_download, lonlim, latlim] = args
# Collect the data from the JRC webpage and returns the data and lat and long in meters of those tiles
try:
Collect_data(Names_to_download, output_folder)
except:
print "Was not able to download the file"
# Clip the data to the users extend
if len(Names_to_download) == 1:
trash_folder = os.path.join(output_folder, "Trash")
data_in = os.path.join(trash_folder, Names_to_download[0])
data_end, geo_end = RC.clip_data(data_in, latlim, lonlim)
else:
data_end = np.zeros([
int((latlim[1] - latlim[0]) / 0.00025),
int((lonlim[1] - lonlim[0]) / 0.00025)
])
for Name_to_merge in Names_to_download:
trash_folder = os.path.join(output_folder, "Trash")
data_in = os.path.join(trash_folder, Name_to_merge)
geo_out, proj, size_X, size_Y = RC.Open_array_info(data_in)
lat_min_merge = np.maximum(latlim[0],
geo_out[3] + size_Y * geo_out[5])
lat_max_merge = np.minimum(latlim[1], geo_out[3])
lon_min_merge = np.maximum(lonlim[0], geo_out[0])
lon_max_merge = np.minimum(lonlim[1],
geo_out[0] + size_X * geo_out[1])
lonmerge = [lon_min_merge, lon_max_merge]
latmerge = [lat_min_merge, lat_max_merge]
data_one, geo_one = RC.clip_data(data_in, latmerge, lonmerge)
Ystart = int((geo_one[3] - latlim[1]) / geo_one[5])
Yend = int(Ystart + np.shape(data_one)[0])
Xstart = int((geo_one[0] - lonlim[0]) / geo_one[1])
Xend = int(Xstart + np.shape(data_one)[1])
data_end[Ystart:Yend, Xstart:Xend] = data_one
geo_end = tuple([lonlim[0], geo_one[1], 0, latlim[1], 0, geo_one[5]])
# Save results as Gtiff
fileName_out = os.path.join(output_folder, 'JRC_Occurrence_percent.tif')
DC.Save_as_tiff(name=fileName_out,
data=data_end,
geo=geo_end,
projection='WGS84')
shutil.rmtree(trash_folder)
return True
def Add_Reservoirs(output_nc, Diff_Water_Volume, Regions):
import numpy as np
import wa.General.raster_conversions as RC
import wa.General.data_conversions as DC
# Extract data from NetCDF file
Discharge_dict = RC.Open_nc_dict(output_nc, "dischargedict_dynamic")
River_dict = RC.Open_nc_dict(output_nc, "riverdict_static")
DEM_dict = RC.Open_nc_dict(output_nc, "demdict_static")
Distance_dict = RC.Open_nc_dict(output_nc, "distancedict_static")
Rivers = RC.Open_nc_array(output_nc, "rivers")
acc_pixels = RC.Open_nc_array(output_nc, "accpix")
# Open data array info based on example data
geo_out, epsg, size_X, size_Y, size_Z, time = RC.Open_nc_info(output_nc)
# Create ID Matrix
y,x = np.indices((size_Y, size_X))
ID_Matrix = np.int32(np.ravel_multi_index(np.vstack((y.ravel(),x.ravel())),(size_Y,size_X),mode='clip').reshape(x.shape))+1
del x, y
Acc_Pixels_Rivers = Rivers * acc_pixels
ID_Rivers = Rivers * ID_Matrix
Amount_of_Reservoirs = len(Regions)
Reservoir_is_in_River = np.ones([len(Regions),3]) * -9999
for reservoir in range(0,Amount_of_Reservoirs):
region = Regions[reservoir,:]
dest = DC.Save_as_MEM(Acc_Pixels_Rivers, geo_out, projection='WGS84')
Rivers_Acc_Pixels_reservoir, Geo_out = RC.clip_data(dest,latlim=[region[2],region[3]],lonlim =[region[0],region[1]])
dest = DC.Save_as_MEM(ID_Rivers, geo_out, projection='WGS84')
Rivers_ID_reservoir, Geo_out = RC.clip_data(dest,latlim=[region[2],region[3]],lonlim =[region[0],region[1]])
size_Y_reservoir, size_X_reservoir = np.shape(Rivers_Acc_Pixels_reservoir)
IDs_Edges = []
IDs_Edges = np.append(IDs_Edges,Rivers_Acc_Pixels_reservoir[0,:])
IDs_Edges = np.append(IDs_Edges,Rivers_Acc_Pixels_reservoir[:,0])
IDs_Edges = np.append(IDs_Edges,Rivers_Acc_Pixels_reservoir[int(size_Y_reservoir)-1,:])
IDs_Edges = np.append(IDs_Edges,Rivers_Acc_Pixels_reservoir[:,int(size_X_reservoir)-1])
Value_Reservoir = np.max(np.unique(IDs_Edges))
y_pix_res,x_pix_res = np.argwhere(Rivers_Acc_Pixels_reservoir==Value_Reservoir)[0]
ID_reservoir = Rivers_ID_reservoir[y_pix_res,x_pix_res]
# Find exact reservoir area in river directory
for River_part in River_dict.iteritems():
if len(np.argwhere(River_part[1] == ID_reservoir)) > 0:
Reservoir_is_in_River[reservoir, 0] = np.argwhere(River_part[1] == ID_reservoir) #River_part_good
Reservoir_is_in_River[reservoir, 1] = River_part[0] #River_Add_Reservoir
Reservoir_is_in_River[reservoir, 2] = 1 #Reservoir_is_in_River
numbers = abs(Reservoir_is_in_River[:,1].argsort() - len(Reservoir_is_in_River)+1)
for number in range(0,len(Reservoir_is_in_River)):
row_reservoir = np.argwhere(numbers==number)[0][0]
if not Reservoir_is_in_River[row_reservoir,2] == -9999:
# Get discharge into the reservoir:
Flow_in_res_m3 = Discharge_dict[int(Reservoir_is_in_River[row_reservoir,1])][:,int(Reservoir_is_in_River[row_reservoir,0])]
# Get difference reservoir
Change_Reservoir_m3 = Diff_Water_Volume[row_reservoir,:,2]
# Total Change outflow
Change_outflow_m3 = np.minimum(Flow_in_res_m3, Change_Reservoir_m3)
Difference = Change_outflow_m3 - Change_Reservoir_m3
if abs(np.sum(Difference))>10000 and np.sum(Change_Reservoir_m3[Change_outflow_m3>0])>0:
Change_outflow_m3[Change_outflow_m3<0] = Change_outflow_m3[Change_outflow_m3<0]*np.sum(Change_outflow_m3[Change_outflow_m3>0])/np.sum(Change_Reservoir_m3[Change_outflow_m3>0])
# Find key name (which is also the lenght of the river dictionary)
i = len(River_dict)
#River_with_reservoirs_dict[i]=list((River_dict[River_Add_Reservoir][River_part_good[0][0]:]).flat) < MAAK DIRECTORIES ARRAYS OP DEZE MANIER DAN IS DE ARRAY 1D
River_dict[i]=River_dict[int(Reservoir_is_in_River[row_reservoir,1])][int(Reservoir_is_in_River[row_reservoir,0]):]
River_dict[int(Reservoir_is_in_River[row_reservoir,1])] = River_dict[int(Reservoir_is_in_River[row_reservoir,1])][:int(Reservoir_is_in_River[row_reservoir,0])+1]
DEM_dict[i]=DEM_dict[int(Reservoir_is_in_River[row_reservoir,1])][int(Reservoir_is_in_River[row_reservoir,0]):]
DEM_dict[int(Reservoir_is_in_River[row_reservoir,1])] = DEM_dict[int(Reservoir_is_in_River[row_reservoir,1])][:int(Reservoir_is_in_River[row_reservoir,0])+1]
Distance_dict[i]=Distance_dict[int(Reservoir_is_in_River[row_reservoir,1])][int(Reservoir_is_in_River[row_reservoir,0]):]
Distance_dict[int(Reservoir_is_in_River[row_reservoir,1])] = Distance_dict[int(Reservoir_is_in_River[row_reservoir,1])][:int(Reservoir_is_in_River[row_reservoir,0])+1]
Discharge_dict[i]=Discharge_dict[int(Reservoir_is_in_River[row_reservoir,1])][:,int(Reservoir_is_in_River[row_reservoir,0]):]
Discharge_dict[int(Reservoir_is_in_River[row_reservoir,1])] = Discharge_dict[int(Reservoir_is_in_River[row_reservoir,1])][:,:int(Reservoir_is_in_River[row_reservoir,0])+1]
Discharge_dict[int(Reservoir_is_in_River[row_reservoir,1])][:,1:int(Reservoir_is_in_River[row_reservoir,0])+1] = Discharge_dict[int(Reservoir_is_in_River[row_reservoir,1])][:,1:int(Reservoir_is_in_River[row_reservoir,0])+1] - Change_outflow_m3[:,None]
Next_ID = River_dict[int(Reservoir_is_in_River[row_reservoir,1])][0]
times = 0
while len(River_dict) > times:
for River_part in River_dict.iteritems():
if River_part[-1][-1] == Next_ID:
Next_ID = River_part[-1][0]
item = River_part[0]
#Always 10 procent of the incoming discharge will pass the dam
Change_outflow_m3[:,None] = np.minimum(0.9 * Discharge_dict[item][:,-1:], Change_outflow_m3[:,None])
Discharge_dict[item][:,1:] = Discharge_dict[item][:,1:] - Change_outflow_m3[:,None]
print(item)
times = 0
times += 1
return(Discharge_dict, River_dict, DEM_dict, Distance_dict)
def Find_Area_Volume_Relation(region, input_JRC, input_nc):
# Find relation between V and A
import numpy as np
import wa.General.raster_conversions as RC
import wa.General.data_conversions as DC
from scipy.optimize import curve_fit
import matplotlib.pyplot as plt
def func(x,a,b):
"""
This function is used for finding relation area and volume
"""
return(a*x**b)
def func3(x,a,b,c,d):
"""
This function is used for finding relation area and volume
"""
return(a*(x-c)**b+d)
#Array, Geo_out = RC.clip_data(input_JRC,latlim=[14.528,14.985],lonlim =[35.810,36.005])
Array, Geo_out = RC.clip_data(input_JRC,latlim=[region[2],region[3]],lonlim =[region[0],region[1]]) # This reservoir was not filled when SRTM was taken
size_Y = int(np.shape([Array])[-2])
size_X = int(np.shape([Array])[-1])
Water_array = np.zeros(np.shape(Array))
buffer_zone = 4
Array[Array > 0] = 1
for i in range(0,size_Y):
for j in range(0,size_X):
Water_array[i,j]=np.max(Array[np.maximum(0,i-buffer_zone):np.minimum(size_Y,i+buffer_zone+1),np.maximum(0,j-buffer_zone):np.minimum(size_X,j+buffer_zone+1)])
del Array
# Open DEM and reproject
DEM_Array = RC.Open_nc_array(input_nc, "dem" )
Geo_out_dem, proj_dem, size_X_dem, size_Y_dem, size_Z_dem, time = RC.Open_nc_info(input_nc)
# Save Example as memory file
dest_example = DC.Save_as_MEM(Water_array, Geo_out, projection='WGS84')
dest_dem = DC.Save_as_MEM(DEM_Array, Geo_out_dem, projection='WGS84')
# reproject DEM by using example
dest_out=RC.reproject_dataset_example(dest_dem, dest_example, method=2)
DEM=dest_out.GetRasterBand(1).ReadAsArray()
# find DEM water heights
DEM_water = np.zeros(np.shape(Water_array))
DEM_water[Water_array != 1] = np.nan
DEM_water[Water_array == 1.] = DEM[Water_array == 1.]
# Get array with areas
import wa.Functions.Start.Area_converter as Area
dlat, dlon = Area.Calc_dlat_dlon(Geo_out, size_X, size_Y)
area_in_m2 = dlat * dlon
# find volume and Area
min_DEM_water = int(np.round(np.nanmin(DEM_water)))
max_DEM_water = int(np.round(np.nanmax(DEM_water)))
Reservoir_characteristics = np.zeros([1,5])
i = 0
for height in range(min_DEM_water+1, max_DEM_water):
DEM_water_below_height = np.zeros(np.shape(DEM_water))
DEM_water[np.isnan(DEM_water)] = 1000000
DEM_water_below_height[DEM_water < height] = 1
pixels = np.sum(DEM_water_below_height)
area = np.sum(DEM_water_below_height * area_in_m2)
if height == min_DEM_water + 1:
volume = 0.5 * area
histogram = pixels
Reservoir_characteristics[:] = [height, pixels, area, volume, histogram]
else:
area_previous = Reservoir_characteristics[i, 2]
volume_previous = Reservoir_characteristics[i, 3]
volume = volume_previous + 0.5 * (area - area_previous) + 1 * area_previous
histogram_previous = Reservoir_characteristics[i, 1]
histogram = pixels - histogram_previous
Reservoir_characteristics_one = [height, pixels, area, volume, histogram]
Reservoir_characteristics = np.append(Reservoir_characteristics,Reservoir_characteristics_one)
i += 1
Reservoir_characteristics = np.resize(Reservoir_characteristics, (i+1,5))
maxi = int(len(Reservoir_characteristics[:,3]))
# find minimum value for reservoirs height (DEM is same value if reservoir was already filled whe SRTM was created)
Historgram = Reservoir_characteristics[:,4]
hist_mean = np.mean(Historgram)
hist_std = np.std(Historgram)
mini_tresh = hist_std * 5 + hist_mean
Check_hist = np.zeros([len(Historgram)])
Check_hist[Historgram>mini_tresh] = Historgram[Historgram>mini_tresh]
if np.max(Check_hist) != 0.0:
col = np.argwhere(Historgram == np.max(Check_hist))[0][0]
mini = col + 1
else:
mini = 0
fitted = 0
# find starting point reservoirs
V0 = Reservoir_characteristics[mini,3]
A0 = Reservoir_characteristics[mini,2]
# Calculate the best maxi reservoir characteristics, based on the normal V = a*x**b relation
while fitted == 0:
try:
if mini == 0:
popt1, pcov1 = curve_fit(func, Reservoir_characteristics[mini:maxi,2], Reservoir_characteristics[mini:maxi,3])
else:
popt1, pcov1 = curve_fit(func, Reservoir_characteristics[mini:maxi,2] - A0, Reservoir_characteristics[mini:maxi,3]-V0)
fitted = 1
except:
maxi -= 1
if maxi < mini:
print 'ERROR: was not able to find optimal fit'
fitted = 1
# Remove last couple of pixels of maxi
maxi_end = int(np.round(maxi - 0.2 * (maxi - mini)))
done = 0
times = 0
while done == 0 and times > 20 and maxi_end < mini:
try:
if mini == 0:
popt, pcov = curve_fit(func, Reservoir_characteristics[mini:maxi_end,2], Reservoir_characteristics[mini:maxi_end,3])
else:
popt, pcov = curve_fit(func3, Reservoir_characteristics[mini:maxi_end,2], Reservoir_characteristics[mini:maxi_end,3])
except:
maxi_end = int(maxi)
if mini == 0:
popt, pcov = curve_fit(func, Reservoir_characteristics[mini:maxi_end,2], Reservoir_characteristics[mini:maxi_end,3])
else:
popt, pcov = curve_fit(func3, Reservoir_characteristics[mini:maxi_end,2], Reservoir_characteristics[mini:maxi_end,3])
if mini == 0:
plt.plot(Reservoir_characteristics[mini:maxi_end,2], Reservoir_characteristics[mini:maxi_end,3], 'ro')
t = np.arange(0., np.max(Reservoir_characteristics[:,2]), 1000)
plt.plot(t, popt[0]*(t)**popt[1], 'g--')
plt.axis([0, np.max(Reservoir_characteristics[mini:maxi_end,2]), 0, np.max(Reservoir_characteristics[mini:maxi_end,3])])
plt.show()
done = 1
else:
plt.plot(Reservoir_characteristics[mini:maxi_end,2], Reservoir_characteristics[mini:maxi_end,3], 'ro')
t = np.arange(0., np.max(Reservoir_characteristics[:,2]), 1000)
plt.plot(t, popt[0]*(t-popt[2])**popt[1] + popt[3], 'g--')
plt.axis([0, np.max(Reservoir_characteristics[mini:maxi_end,2]), 0, np.max(Reservoir_characteristics[mini:maxi_end,3])])
plt.show()
Volume_error = popt[3]/V0 * 100 - 100
print 'error Volume = %s percent' %Volume_error
print 'error Area = %s percent' %(A0/popt[2] * 100 - 100)
if Volume_error < 30 and Volume_error > -30:
done = 1
else:
times += 1
maxi_end -= 1
print 'Another run is done in order to improve the result'
if done == 0:
popt = np.append(popt1, [A0, V0])
if len(popt) == 2:
popt = np.append(popt, [0, 0])
return(popt)
