def _pixels_of_interest_to_array(self, point_tif):
# =========== Jul 8, 2017
# print 'point tif', point_tif
point_arr = convert_raster_to_array(point_tif)
try:
if os.path.isfile(paths.mask):
point_arr = apply_mask(paths.mask, point_arr)
except TypeError:
pass
point_arr = apply_mask_pixel_tracker(paths.point_shape, point_arr)
# print 'new point arr', point_arr
# print 'new point arr shape', point_arr.shape
return point_arr
def _pixels_of_interest_to_array(self, point_tif):
# =========== Jul 8, 2017
# print 'point tif', point_tif
point_arr = convert_raster_to_array(point_tif)
try:
if os.path.isfile(paths.mask):
point_arr = apply_mask(paths.mask, point_arr)
except TypeError:
pass
point_arr = apply_mask_pixel_tracker(paths.point_shape, point_arr)
# print 'new point arr', point_arr
# print 'new point arr shape', point_arr.shape
return point_arr
def get_kcb(in_path, date_object):
"""
Find NDVI image and convert to Kcb.
:param in_path: NDVI input data path.
:type in_path: str
:param date_object: Datetime object of date.
:return: numpy array object
"""
# print date_object
doy = date_object.timetuple().tm_yday
year = date_object.year
# print('date object', date_object)
if year == 2000:
band = 1
raster = '{}_{}.tif'.format(year, doy)
elif year == 2001:
for num in NUMS:
diff = doy - num
if 0 <= diff <= 15:
start = num
if num == 353:
nd = num + 12
else:
nd = num + 15
band = diff + 1
raster = '{}_{}_{}.tif'.format(year, start, nd)
print('raster', raster)
break
else:
for i, num in enumerate(NUMS):
diff = doy - num
if 0 <= diff <= 15:
band = diff + 1
raster = '{}_{}.tif'.format(year, i + 1)
break
ndvi = convert_raster_to_array(in_path, raster, band=band)
return ndvi
# =================================IMPORTS=======================================
from osgeo import gdal
from numpy import array, where, count_nonzero, ones, zeros
import os
from recharge.raster_tools import convert_raster_to_array
from recharge.raster_tools import get_raster_geo_attributes as get_geo
input_path = 'E:\\'
sand_tif = 'combo_pct_sand.tif'
clay_tif = 'combo_pct_clay.tif'
out_name = 'rew_22SEPT16.tif'
geo = get_geo(input_path)
sand = convert_raster_to_array(input_path, sand_tif)
clay = convert_raster_to_array(input_path, clay_tif)
#From ASCE pg 195, equations from Ritchie et al., 1989
rew = 8 + 0.08 * clay
rew = where(sand > 80.0, 20.0 - 0.15 * sand, rew)
rew = where(clay > 50, 11 - 0.06 * clay, rew)
driver = gdal.GetDriverByName('GTiff')
out_data_set = driver.Create(os.path.join(input_path, out_name), geo['cols'], geo['rows'],
geo['bands'], geo['data_type'])
out_data_set.SetGeoTransform(geo['geotransform'])
out_data_set.SetProjection(geo['projection'])
output_band = out_data_set.GetRasterBand(1)
output_band.WriteArray(rew, 0, 0)
# you may not use this file except in compliance
# with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ===============================================================================
# =================================IMPORTS=======================================
from osgeo import gdal
from numpy import array, where, count_nonzero, ones, zeros
import os
from recharge.raster_tools import convert_raster_to_array
from recharge.raster_tools import get_raster_geo_attributes as get_geo
input_path = 'C:\Users\David\Documents\Recharge\Thesis\Figures\maps'
sand_tif = 'total_precip_2000_2013_11OCT16.tif'
geo = get_geo(input_path)
ppt = convert_raster_to_array(input_path, sand_tif)
print 'mean precip = {}'.format(ppt.mean())
# ========================== EOF ==============================================
EXu 13 SEP 2017 """ from recharge.raster_tools import convert_raster_to_array import numpy as np #Locations for Walnut Gulch input_path = 'G:\\Walnut\\WalnutSoilProperties\\' sand_tif = 'Merged_percSand.tif' clay_tif = 'Merged_percClay.tif' silt_tif = 'Merged_percSilt.tif' BD_w_tif = 'Merged_gcmBD3rdbar.tif' Ksat_tif = 'Merged_umsKsat.tif' OM_tif = 'Merged_OrgMatter.tif' WC_tif = 'Merged_WC3rdbar.tif' sand = convert_raster_to_array(input_path, sand_tif) clay = convert_raster_to_array(input_path, clay_tif) silt_e = convert_raster_to_array(input_path, silt_tif) #elev = convert_raster_to_array(input_path, elev_tif) BD_w = convert_raster_to_array(input_path, BD_w_tif) #wet bulk density Ksat = convert_raster_to_array(input_path, Ksat_tif) #extracted OM = convert_raster_to_array(input_path, OM_tif) WC = convert_raster_to_array(input_path, WC_tif) sand_1d = sand.flatten() clay_1d = clay.flatten() silt_e_1d = silt_e.flatten() silt_s_1d = np.subtract(100, clay_1d + sand_1d) #elev_1d = elev.flatten() BD_w_1d = BD_w.flatten() Ksat_1d = Ksat.flatten()
""" from recharge.raster_tools import convert_raster_to_array import numpy as np #Locations for Walnut Gulch input_path = 'G:\\Walnut\\WalnutSoilProperties\\' sand_tif = 'Merged_percSand.tif' clay_tif = 'Merged_percClay.tif' silt_tif = 'Merged_percSilt.tif' BD_w_tif = 'Merged_gcmBD3rdbar.tif' Ksat_tif = 'Merged_umsKsat.tif' OM_tif = 'Merged_OrgMatter.tif' WC_tif = 'Merged_WC3rdbar.tif' sand = convert_raster_to_array(input_path, sand_tif) clay = convert_raster_to_array(input_path, clay_tif) silt_e = convert_raster_to_array(input_path, silt_tif) #elev = convert_raster_to_array(input_path, elev_tif) BD_w = convert_raster_to_array(input_path, BD_w_tif)#wet bulk density Ksat = convert_raster_to_array(input_path, Ksat_tif)#extracted OM = convert_raster_to_array(input_path, OM_tif) WC = convert_raster_to_array(input_path, WC_tif) sand_1d = sand.flatten() clay_1d = clay.flatten() silt_e_1d = silt_e.flatten() silt_s_1d = np.subtract(100,clay_1d+sand_1d) #elev_1d = elev.flatten() BD_w_1d = BD_w.flatten() Ksat_1d = Ksat.flatten()
from recharge.raster_tools import get_raster_geo_attributes
input_folder = 'G:\\ETRM_inputs_Walnut\\NDVI\\NDVI_Real\\'
files = os.listdir(input_folder)
root = "G:\\ETRM_inputs_Walnut\\PM_RAD\\PM2000\\"
for it in files:
in_folder = input_folder+it+"\\"
os.chdir(in_folder)
for raster in find_format(in_folder, '*.tif'):
FOLDER,r_name = os.path.split(raster)
tiff = r_name
temp = "temp.tif"
cut = 'gdalwarp -overwrite -s_srs EPSG:{a} -t_srs EPSG:{a} -te {b} {c} {d} {e} -ts {f} {g}\n' \
' -r {h} -multi {j} {k}'.format(a=projection, b=x_min_n, c=y_min_n, d=x_max, e=y_max, f=cols_n, g=rows_n,
h=resample_method, j=tiff, k=temp)
call(cut)
ndvi = convert_raster_to_array(in_folder, temp)
arr = ndvi*0.0001
geo = get_raster_geo_attributes(root)
output_path = in_folder+tiff
convert_array_to_raster(output_path, arr, geo, output_band=1)
###=========================
#Prism
#=============================================
#Walnut
import os, fnmatch
from subprocess import call
# =================================IMPORTS=======================================
from osgeo import gdal
from numpy import array, where, count_nonzero, ones, zeros
import os
from recharge.raster_tools import convert_raster_to_array
from recharge.raster_tools import get_raster_geo_attributes as get_geo
input_path = 'E:\\'
sand_tif = 'combo_pct_sand.tif'
clay_tif = 'combo_pct_clay.tif'
out_name = 'rew_22SEPT16.tif'
geo = get_geo(input_path)
sand = convert_raster_to_array(input_path, sand_tif)
clay = convert_raster_to_array(input_path, clay_tif)
#From ASCE pg 195, equations from Ritchie et al., 1989
rew = 8 + 0.08 * clay
rew = where(sand > 80.0, 20.0 - 0.15 * sand, rew)
rew = where(clay > 50, 11 - 0.06 * clay, rew)
driver = gdal.GetDriverByName('GTiff')
out_data_set = driver.Create(os.path.join(input_path, out_name), geo['cols'],
geo['rows'], geo['bands'], geo['data_type'])
out_data_set.SetGeoTransform(geo['geotransform'])
out_data_set.SetProjection(geo['projection'])
output_band = out_data_set.GetRasterBand(1)
output_band.WriteArray(rew, 0, 0)