def extract_over_station(xls_in,
xls_out,
files,
band=1,
n=66,
sort=True,
verbose=False):
"""
it reads the gis file defined in the files
then extract the data at coordinates defined in the xls_in
and then write the data in the xls_out file
Input:
xls_in: the name of the input xls file containing the co-ordinates of the plot
xls_out: the xls file in which the output will be written
files: the data source file name in the gis format, these files must be in the
tiff format
band: band of the raster data to extract
n: number of data fields in the input xls file
sort: sort the files
"""
if type(files) is not list:
raise TypeError('input files should be of list type')
if sort:
files.sort()
xy = xlsread(xls_in).get_cells('B2:BO3', 'Sheet1')
book_out = xlwt.Workbook()
extracted_data = np.empty((len(files), n))
for gis_file, i in zip(files, range(len(files))):
dataset = gdal.Open(gis_file, GA_ReadOnly)
data = dataset.GetRasterBand(band).ReadAsArray()
GT = dataset.GetGeoTransform()
dataset = None
x, y = utm2image(GT, xy.T)
extracted_data[i, :] = data[y, x]
if verbose:
print('%s' % gis_file)
sheet = book_out.add_sheet('Sheet1')
sheet.write(0, 0, 'File \ Plot no.')
for i in range(extracted_data.shape[1]):
sheet.write(0, i + 1, i + 1)
for gis_file, i in zip(files, range(len(files))):
sheet.write(i + 1, 0, os.path.basename(gis_file).split('.')[0])
for i in range(extracted_data.shape[0]):
for j in range(extracted_data.shape[1]):
sheet.write(i + 1, j + 1, extracted_data[i, j])
book_out.save(xls_out)
def extract_over_station(xls_in, xls_out, files, band=1, n=66, sort=True, verbose=False):
"""
it reads the gis file defined in the files
then extract the data at coordinates defined in the xls_in
and then write the data in the xls_out file
Input:
xls_in: the name of the input xls file containing the co-ordinates of the plot
xls_out: the xls file in which the output will be written
files: the data source file name in the gis format, these files must be in the
tiff format
band: band of the raster data to extract
n: number of data fields in the input xls file
sort: sort the files
"""
if type(files) is not list:
raise TypeError('input files should be of list type')
if sort:
files.sort()
xy = xlsread(xls_in).get_cells('B2:BO3', 'Sheet1')
book_out = xlwt.Workbook()
extracted_data = np.empty((len(files),n))
for gis_file,i in zip(files,range(len(files))):
dataset = gdal.Open(gis_file, GA_ReadOnly)
data = dataset.GetRasterBand(band).ReadAsArray()
GT = dataset.GetGeoTransform()
dataset = None
x,y = utm2image(GT,xy.T)
extracted_data[i,:] = data[y,x]
if verbose:
print('%s'%gis_file)
sheet = book_out.add_sheet('Sheet1')
sheet.write(0,0,'File \ Plot no.')
for i in range(extracted_data.shape[1]):
sheet.write(0,i+1,i+1)
for gis_file,i in zip(files,range(len(files))):
sheet.write(i+1,0,os.path.basename(gis_file).split('.')[0])
for i in range(extracted_data.shape[0]):
for j in range(extracted_data.shape[1]):
sheet.write(i+1, j+1, extracted_data[i,j])
book_out.save(xls_out)
# -*- coding: utf-8 -*- """ Created on Fri Feb 03 18:12:26 2012 @author: K. Sreelash @website: www.ambhas.com @email: [email protected] """ # import required libraries from __future__ import division import numpy as np from ambhas.xls import xlsread #input infile_name = 'D:/svn/ambhas/examples/input_stics.xls' # read the parameters xls_file = xlsread(infile_name) # soil par argis = xls_file.get_cells('B2', 'soil_par') norgs = xls_file.get_cells('C2', 'soil_par') # plant par # tech par # climate
def corner_to_grid(xls_in, xls_out, in_sheet='Sheet1', res=5, rows=(2, 67)):
"""
it reads the xls file defined in the xls_in
take the corner of the monitoring plots
make a grid of res m resolution
and save the output in xls_out
this xls_out file can be used by ambhas.extract_gis_data.extract_gis function
to extract the data over monitoring plots
Note that there should be only and only 4 corner points defined for the plots
Input:
xls_in: name of xls input file
xls_out: name of xls output file
in_sheet: name of the sheet to be read from xls input file, default(Sheet1)
res: resolution of the output grid in meters, default(5)
rows: a tuple indicating the beginning and end of rows to read from
input xls file, e.g. (2,67)
"""
xls_file = xlsread(xls_in)
xy = xls_file.get_cells('B%i:I%i' % (rows[0], rows[1]), in_sheet)
# writting output to excel file
book = xlwt.Workbook()
for i in xrange(xy.shape[0]):
sheet = book.add_sheet(str(i + 1))
verts = xy[i, :].reshape(4, 2)
min_x = verts.min(axis=0)[0]
max_x = verts.max(axis=0)[0]
min_y = verts.min(axis=0)[1]
max_y = verts.max(axis=0)[1]
min_x = min_x - np.mod(min_x, res)
min_y = min_y - np.mod(min_y, res)
max_x = max_x - np.mod(max_x, res) + res
max_y = max_y - np.mod(max_y, res) + res
x = np.arange(min_x, max_x, res)
y = np.arange(min_y, max_y, res)
X, Y = np.meshgrid(x, y)
points = np.vstack([X.flatten(), Y.flatten()]).T
#foo = nx.points_inside_poly(points, verts)
foo = matplotlib.path.Path(verts).contains_points(points)
sheet.write(0, 0, 'x')
sheet.write(0, 1, 'y')
for j in xrange(foo.sum()):
sheet.write(j + 1, 0, points[foo, 0][j])
sheet.write(j + 1, 1, points[foo, 1][j])
print("%i/%i" % (i + 1, xy.shape[0]))
book.save(xls_out)
import matplotlib.pyplot as plt
from ambhas.xls import xlsread
params = {
'axes.labelsize': 10,
'text.fontsize': 10,
'legend.fontsize': 10,
'xtick.labelsize': 10,
'ytick.labelsize': 10,
'text.usetex': False
}
plt.rcParams.update(params)
in_file = 'maddur.xls'
xls_file = xlsread(in_file)
doy = xls_file.get_cells('B2:B366', 'forcing')
rain = xls_file.get_cells('C2:C366', 'forcing')
pet = xls_file.get_cells('D2:D366', 'forcing')
plt.clf()
plt.figure(figsize=(4, 3))
ax = plt.axes([0.15, 0.15, 0.7, 0.7])
plt.bar(doy, rain, color='m', edgecolor='m', label='Rainfall')
plt.plot(pet, label='PET')
plt.grid(True)
plt.legend()
plt.xlabel('DOY')
plt.ylabel('mm')
plt.savefig('../../ambhas-wiki/images/richards_forcing.png')
plt.close()
if __name__ == "__main__":
from scipy.interpolate import Rbf
from random import sample
import matplotlib.pyplot as plt
################## horizontal 2d grounwater model ##############
# read the watershed in tiff format
dataset = gdal.Open(
'/home/tomer/south_gundal/geospatial_data/watershed_500.tif',
GA_ReadOnly)
watershed = dataset.GetRasterBand(1).ReadAsArray()
watershed[-2, :] = 0
GT = dataset.GetGeoTransform()
# read the initial groundwater level data from xls file
fname = '/home/tomer/south_gundal/gw_level.xls'
xls_file = xlsread(fname)
sample_id = sample(xrange(23908), 500)
gw_x = xls_file.get_cells('A1:A23908', 'dec2009')[sample_id].flatten()
gw_y = xls_file.get_cells('B1:B23908', 'dec2009')[sample_id].flatten()
gw_ini = xls_file.get_cells('E1:E23908', 'dec2009')[sample_id].flatten()
# convert the point value of groundwater levels into a map having the same
#extent and size as of watershed
rbfi = Rbf(gw_x, gw_y, gw_ini, function='linear')
xi = np.linspace(GT[0] + GT[1] / 2,
GT[0] + GT[1] * (dataset.RasterXSize - 0.5),
dataset.RasterXSize)
yi = np.linspace(GT[3] + GT[5] / 2,
GT[3] + GT[5] * (dataset.RasterYSize - 0.5),
dataset.RasterYSize)
XI, YI = np.meshgrid(xi, yi)
hini = rbfi(XI, YI)
# -*- coding: utf-8 -*- """ Created on Fri Feb 03 18:12:26 2012 @author: K. Sreelash @website: www.ambhas.com @email: [email protected] """ # import required libraries from __future__ import division import numpy as np from ambhas.xls import xlsread #input infile_name = 'D:/svn/ambhas/examples/input_stics.xls' # read the parameters xls_file = xlsread(infile_name) # soil par argis = xls_file.get_cells('B2', 'soil_par') norgs = xls_file.get_cells('C2', 'soil_par') # plant par # tech par # climate
def corner_to_grid(xls_in, xls_out, in_sheet='Sheet1', res=5, rows=(2,67)):
"""
it reads the xls file defined in the xls_in
take the corner of the monitoring plots
make a grid of res m resolution
and save the output in xls_out
this xls_out file can be used by ambhas.extract_gis_data.extract_gis function
to extract the data over monitoring plots
Note that there should be only and only 4 corner points defined for the plots
Input:
xls_in: name of xls input file
xls_out: name of xls output file
in_sheet: name of the sheet to be read from xls input file, default(Sheet1)
res: resolution of the output grid in meters, default(5)
rows: a tuple indicating the beginning and end of rows to read from
input xls file, e.g. (2,67)
"""
xls_file = xlsread(xls_in)
xy = xls_file.get_cells('B%i:I%i'%(rows[0],rows[1]), in_sheet)
# writting output to excel file
book = xlwt.Workbook()
for i in xrange(xy.shape[0]):
sheet = book.add_sheet(str(i+1))
verts = xy[i,:].reshape(4,2)
min_x = verts.min(axis=0)[0]
max_x = verts.max(axis=0)[0]
min_y = verts.min(axis=0)[1]
max_y = verts.max(axis=0)[1]
min_x = min_x - np.mod(min_x,res)
min_y = min_y - np.mod(min_y,res)
max_x = max_x - np.mod(max_x,res)+res
max_y = max_y - np.mod(max_y,res)+res
x = np.arange(min_x, max_x, res)
y = np.arange(min_y, max_y, res)
X,Y = np.meshgrid(x,y)
points = np.vstack([X.flatten(), Y.flatten()]).T
#foo = nx.points_inside_poly(points, verts)
foo = matplotlib.path.Path(verts).contains_points(points)
sheet.write(0,0,'x')
sheet.write(0,1,'y')
for j in xrange(foo.sum()):
sheet.write(j+1,0,points[foo,0][j])
sheet.write(j+1,1,points[foo,1][j])
print("%i/%i"%(i+1,xy.shape[0]))
book.save(xls_out)
this scripts plots the forcing for the richards' input file
"""
import matplotlib.pyplot as plt
from ambhas.xls import xlsread
params = {'axes.labelsize': 10,
'text.fontsize': 10,
'legend.fontsize': 10,
'xtick.labelsize': 10,
'ytick.labelsize': 10,
'text.usetex': False}
plt.rcParams.update(params)
in_file = 'maddur.xls'
xls_file = xlsread(in_file)
doy = xls_file.get_cells('B2:B366','forcing')
rain = xls_file.get_cells('C2:C366','forcing')
pet = xls_file.get_cells('D2:D366','forcing')
plt.clf()
plt.figure(figsize=(4, 3))
ax = plt.axes([0.15, 0.15, 0.7, 0.7])
plt.bar(doy,rain, color='m', edgecolor='m', label='Rainfall')
plt.plot(pet, label='PET')
plt.grid(True)
plt.legend()
plt.xlabel('DOY')
plt.ylabel('mm')
plt.savefig('../../ambhas-wiki/images/richards_forcing.png')
plt.close()
self.hnew = hnew
if __name__ == "__main__":
from scipy.interpolate import Rbf
from random import sample
import matplotlib.pyplot as plt
################## horizontal 2d grounwater model ##############
# read the watershed in tiff format
dataset = gdal.Open('/home/tomer/south_gundal/geospatial_data/watershed_500.tif',GA_ReadOnly)
watershed = dataset.GetRasterBand(1).ReadAsArray()
watershed[-2,:] = 0
GT = dataset.GetGeoTransform()
# read the initial groundwater level data from xls file
fname = '/home/tomer/south_gundal/gw_level.xls'
xls_file = xlsread(fname)
sample_id = sample(xrange(23908), 500)
gw_x = xls_file.get_cells('A1:A23908', 'dec2009')[sample_id].flatten()
gw_y = xls_file.get_cells('B1:B23908', 'dec2009')[sample_id].flatten()
gw_ini = xls_file.get_cells('E1:E23908', 'dec2009')[sample_id].flatten()
# convert the point value of groundwater levels into a map having the same
#extent and size as of watershed
rbfi = Rbf(gw_x, gw_y, gw_ini, function='linear')
xi = np.linspace(GT[0]+GT[1]/2, GT[0]+GT[1]*(dataset.RasterXSize-0.5),
dataset.RasterXSize)
yi = np.linspace(GT[3]+GT[5]/2, GT[3]+GT[5]*(dataset.RasterYSize-0.5),
dataset.RasterYSize)
XI, YI = np.meshgrid(xi,yi)
hini = rbfi(XI, YI)
#run the model
