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)
def extract_gis(xls_in,
xls_out,
ds,
ds_short_name=None,
band=1,
n=66,
method='median',
alpha=0.1):
"""
Extract the data from the tiff files over selected field plots.
It reads the gis file defined in the ds then extract the data at
coordinates defined in each sheet of the xls_in file
and then write the data in the xls_out file
the header of the data in the xls_out are written as defined in the
ds_short_name
Parameters
----------
xls_in : str
the name of the input xls file containing the co-ordinates of the plot
xls_out : str
the xls file in which the output will be written
ds : tiff file
the data source file name in the gis format, these files must be in the tiff format
ds_short_name : list of str
the name that will appear as header in the output xls file If None, than it will use the file names
band : int
band of the raster data to extract
n : int
number of data fields in the input xls file
method : {median, mean, truncated}
'median' for the median, 'mean' for the simple arithmetic mean, 'truncated' for the truncated mean of the data
Return
-------
None
"""
if type(ds) is not list:
raise TypeError('input ds should be of list type')
book_out = xlwt.Workbook()
file_basename = []
final_data = np.empty((n, 2, len(ds)))
for k in range(len(ds)):
file_basename.append(os.path.basename(ds[k]))
dataset = gdal.Open(ds[k], GA_ReadOnly)
data = dataset.GetRasterBand(band).ReadAsArray()
GT = dataset.GetGeoTransform()
book = xlrd.open_workbook(xls_in)
for i in xrange(n):
sheet = book.sheet_by_name(str(i + 1))
xy = np.empty((sheet.nrows - 1, 2))
for j in range(xy.shape[0]):
xy[j, 0] = sheet.cell_value(j + 1, 0)
xy[j, 1] = sheet.cell_value(j + 1, 1)
x, y = utm2image(GT, xy)
extracted_data = data[y, x]
if method == 'median':
final_data[i, 0, k] = np.median(extracted_data)
final_data[i, 1, k] = nanstd(extracted_data)
elif method == 'mean':
final_data[i, 0, k] = nanmean(extracted_data)
final_data[i, 1, k] = nanstd(extracted_data)
elif method == 'truncated':
extracted_data = extracted_data[~np.isnan(extracted_data)]
isort = np.argsort(extracted_data)
n_extracted_data = len(extracted_data)
extracted_data = extracted_data[isort[n_extracted_data *
alpha:n_extracted_data *
(1 - alpha)]]
final_data[i, 0, k] = nanmean(extracted_data)
final_data[i, 1, k] = nanstd(extracted_data)
else:
raise TypeError(
"method should be either 'median', 'mean', or 'truncated' "
)
print('%i/%i' % (k + 1, len(ds)))
dataset = None
sheet_median = book_out.add_sheet('median')
sheet_std = book_out.add_sheet('std')
sheet_median.write(0, 0, 'Plot no.')
sheet_std.write(0, 0, 'Plot no.')
for i in range(final_data.shape[0]):
sheet_median.write(i + 1, 0, i + 1)
sheet_std.write(i + 1, 0, i + 1)
if ds_short_name is None:
ds_short_name = file_basename
for i in range(len(ds)):
sheet_median.write(0, i + 1, ds_short_name[i])
sheet_std.write(0, i + 1, ds_short_name[i])
for i in range(final_data.shape[0]):
for j in range(final_data.shape[2]):
sheet_median.write(i + 1, j + 1, final_data[i, 0, j])
sheet_std.write(i + 1, j + 1, final_data[i, 1, j])
book_out.save(xls_out)
def extract_gis(xls_in, xls_out, ds, ds_short_name=None, band=1, n=66, method='median', alpha=0.1):
"""
Extract the data from the tiff files over selected field plots.
It reads the gis file defined in the ds then extract the data at
coordinates defined in each sheet of the xls_in file
and then write the data in the xls_out file
the header of the data in the xls_out are written as defined in the
ds_short_name
Parameters
----------
xls_in : str
the name of the input xls file containing the co-ordinates of the plot
xls_out : str
the xls file in which the output will be written
ds : tiff file
the data source file name in the gis format, these files must be in the tiff format
ds_short_name : list of str
the name that will appear as header in the output xls file If None, than it will use the file names
band : int
band of the raster data to extract
n : int
number of data fields in the input xls file
method : {median, mean, truncated}
'median' for the median, 'mean' for the simple arithmetic mean, 'truncated' for the truncated mean of the data
Return
-------
None
"""
if type(ds) is not list:
raise TypeError('input ds should be of list type')
book_out = xlwt.Workbook()
file_basename = []
final_data = np.empty((n, 2, len(ds)))
for k in range(len(ds)):
file_basename.append(os.path.basename(ds[k]))
dataset = gdal.Open(ds[k], GA_ReadOnly)
data = dataset.GetRasterBand(band).ReadAsArray()
GT = dataset.GetGeoTransform()
book = xlrd.open_workbook(xls_in)
for i in xrange(n):
sheet = book.sheet_by_name(str(i+1))
xy = np.empty((sheet.nrows-1,2))
for j in range(xy.shape[0]):
xy[j,0] = sheet.cell_value(j+1,0)
xy[j,1] = sheet.cell_value(j+1,1)
x, y = utm2image(GT,xy)
extracted_data = data[y,x]
if method == 'median':
final_data[i,0,k] = np.median(extracted_data)
final_data[i,1,k] = nanstd(extracted_data)
elif method == 'mean':
final_data[i,0,k] = nanmean(extracted_data)
final_data[i,1,k] = nanstd(extracted_data)
elif method == 'truncated':
extracted_data = extracted_data[~np.isnan(extracted_data)]
isort = np.argsort(extracted_data)
n_extracted_data = len(extracted_data)
extracted_data = extracted_data[isort[n_extracted_data*alpha:n_extracted_data*(1-alpha)]]
final_data[i,0,k] = nanmean(extracted_data)
final_data[i,1,k] = nanstd(extracted_data)
else:
raise TypeError("method should be either 'median', 'mean', or 'truncated' ")
print('%i/%i'%(k+1,len(ds)))
dataset = None
sheet_median = book_out.add_sheet('median')
sheet_std = book_out.add_sheet('std')
sheet_median.write(0,0,'Plot no.')
sheet_std.write(0,0,'Plot no.')
for i in range(final_data.shape[0]):
sheet_median.write(i+1,0,i+1)
sheet_std.write(i+1,0,i+1)
if ds_short_name is None:
ds_short_name = file_basename
for i in range(len(ds)):
sheet_median.write(0,i+1,ds_short_name[i])
sheet_std.write(0,i+1,ds_short_name[i])
for i in range(final_data.shape[0]):
for j in range(final_data.shape[2]):
sheet_median.write(i+1, j+1, final_data[i,0,j])
sheet_std.write(i+1, j+1, final_data[i,1,j])
book_out.save(xls_out)
