def run(input_root, simulation_period):
paths.build(input_root)
amf_dict = amf_obs_time_series(BASE_AMF_DICT,
complete_days_only=True,
return_low_err=True)
# get_etrm_time_series(paths.amf_extract, dict_=amf_dict)
for k, v in amf_dict.iteritems():
for kk, vv in v.iteritems():
if isinstance(vv, DataFrame):
p = os.path.join(paths.amf_output_root,
'{}_{}.csv'.format(k, kk))
print 'writing to {}'.format(p)
vv.to_csv(p)
val = amf_dict.values()[0]
cfg = Config()
for runspec in cfg.runspecs:
paths.build(runspec.input_root, runspec.output_root)
etrm = Processes(runspec)
etrm.configure_run(runspec)
etrm.run()
save_run(etrm, val)
def main():
paths.build('F:')
startday = datetime(2013, 12, 17, 0)
endday = datetime(2013, 12, 31, 0)
finalyear = 2013
base_dir = paths.ndvi_individ
output = paths.ndvi_spline
year = '2000'
ref_map = os.path.join(base_dir, year, 'NDVI2000_01_01.tif')
_, _, _, _, lon, lat, linke, prj, fill_val = read_map(ref_map, 'Gtiff')
srs = osr.SpatialReference(prj)
sr_wkt = srs.ExportToWkt()
for day in rrule.rrule(rrule.DAILY, dtstart=startday, until=endday):
nr = day.strftime('%j')
year = day.strftime('%Y')
# ndvi_daily = time_interpolation(day, lat, lon, finalyear)
ndvi_daily = time_interpolation(base_dir, day, finalyear)
# Write daily values to new daily rasters
daily_doy = 'ndvi{}_{}.tif'.format(year, nr)
outpath = os.path.join(output, year)
if not os.path.exists(outpath):
os.makedirs(outpath)
outname = os.path.join(outpath, daily_doy)
write_map(outname, 'Gtiff', lon, lat, ndvi_daily, sr_wkt, fill_val)
def main():
paths.build('F:')
startday = datetime(2013, 12, 17, 0)
endday = datetime(2013, 12, 31, 0)
finalyear = 2013
base_dir = paths.ndvi_individ
output = paths.ndvi_spline
year = '2000'
ref_map = os.path.join(base_dir, year, 'NDVI2000_01_01.tif')
_, _, _, _, lon, lat, linke, prj, fill_val = read_map(ref_map, 'Gtiff')
srs = osr.SpatialReference(prj)
sr_wkt = srs.ExportToWkt()
for day in rrule.rrule(rrule.DAILY, dtstart=startday, until=endday):
nr = day.strftime('%j')
year = day.strftime('%Y')
# ndvi_daily = time_interpolation(day, lat, lon, finalyear)
ndvi_daily = time_interpolation(base_dir, day, finalyear)
# Write daily values to new daily rasters
daily_doy = 'ndvi{}_{}.tif'.format(year, nr)
outpath = os.path.join(output, year)
if not os.path.exists(outpath):
os.makedirs(outpath)
outname = os.path.join(outpath, daily_doy)
write_map(outname, 'Gtiff', lon, lat, ndvi_daily, sr_wkt, fill_val)
def run_dataset():
print('Running Dataset')
cfg = Config()
for runspec in cfg.runspecs:
paths.build(runspec.input_root, runspec.output_root)
paths.set_mask_path(runspec.mask)
generate_dataset(runspec.date_range, runspec.output_root)
def run_model(cfg_path=None):
print('Running Model')
print('supplied cfg_path {}'.format(cfg_path))
cfg = Config(cfg_path)
print('Using configuration from {}'.format(cfg.path))
for i, runspec in enumerate(cfg.runspecs):
print('Using mask path {}'.format(runspec.mask))
runspec.output_root = '{}{:03n}'.format(runspec.output_root, i)
# TODO - make a modified make_results_dir to only create of the 'outer' folder.
make_results_dir(runspec.output_root)
paths.build(runspec.input_root, runspec.output_root)
etrm = Processes(runspec)
etrm.configure_run(runspec)
etrm.run()
def extract_etrm_point_inputs(input_root, output_root, simulation_period):
# ndvi_path = os.path.join(input_root, 'NDVI', 'NDVI_std_all')
# prism_path = os.path.join(input_root, 'PRISM')
# penman_path = os.path.join(input_root, 'PM_RAD')
paths.build(input_root)
# statics = os.path.join(input_root, 'statics')
# sa_path = os.path.join(input_root, 'shapefiles') # (input_root, 'shapefiles')
# sb_path is where the outputs are going. sa_path used to be for sensitivity analysis.
# sb_path = os.path.join(input_root, 'ameriflux_ex_sac') # ameriflux_sites
# save_path = sb_path # os.path.join(sa_path, 'AMF_extracts')
# save_path = sb_path # os.path.join(sa_path, 'AMF_extracts')
shapefile = os.path.join('/Users', 'Gabe', 'Desktop', 'QGIS_Ameriflux', 'coords_attempt3.shp')
# shape = os.path.join(sa_path, 'amf_sites_UTM.shp')
print(("Here is the save path {}".format(output_root)))
get_dynamic_inputs_from_shape(shapefile, simulation_period, output_root)
def extract_etrm_point_inputs(input_root, output_root, simulation_period):
# ndvi_path = os.path.join(input_root, 'NDVI', 'NDVI_std_all')
# prism_path = os.path.join(input_root, 'PRISM')
# penman_path = os.path.join(input_root, 'PM_RAD')
paths.build(input_root)
# statics = os.path.join(input_root, 'statics')
# sa_path = os.path.join(input_root, 'shapefiles') # (input_root, 'shapefiles')
# sb_path is where the outputs are going. sa_path used to be for sensitivity analysis.
# sb_path = os.path.join(input_root, 'ameriflux_ex_sac') # ameriflux_sites
# save_path = sb_path # os.path.join(sa_path, 'AMF_extracts')
# save_path = sb_path # os.path.join(sa_path, 'AMF_extracts')
shapefile = os.path.join('/Users', 'Gabe', 'Desktop', 'QGIS_Ameriflux', 'coords_attempt3.shp')
# shape = os.path.join(sa_path, 'amf_sites_UTM.shp')
print("Here is the save path {}".format(output_root))
get_dynamic_inputs_from_shape(shapefile, simulation_period, output_root)
def run():
paths.build('F:')
penman_example = 'PM_NM_2000_001.tif'
map_for_reference = os.path.join(paths.penman, '2000', penman_example)
_, _, _, _, x, y, _, prj, fill_val = read_map(map_for_reference, 'Gtiff')
start = datetime.datetime(2000, 1, 1, 0)
end = datetime.datetime(2001, 12, 31, 0)
fill_val = -999.
for day in rrule.rrule(rrule.DAILY, dtstart=start, until=end):
year = day.strftime('%Y')
month = day.strftime('%m')
day_mth = day.strftime('%d')
# yearstr = str(year)
# output = os.path.join(output_dir, new_dir, year)
# print(output_dir)
ndvi = get_kcb(paths.ndvi_std_all, day)
# kcb = remake_array(mask_path, kcb)
ndvi[ndvi == fill_val] = 0
# print(ndvi, ndvi.shape)
new_ndvi = 'NDVI{}_{}_{}.tif'.format(year, month, day_mth)
output_dir = os.path.join(paths.etrm_input_root, 'NDVI_individ',
str(year))
ndvi_out = os.path.join(output_dir, new_ndvi)
if not os.path.isdir(output_dir):
os.makedirs(output_dir)
print(('Saving New NDVI file as {}'.format(ndvi_out)))
write_map(ndvi_out, 'Gtiff', x, y, ndvi, prj, fill_val)
def run():
paths.build('F:')
penman_example = 'PM_NM_2000_001.tif'
map_for_reference = os.path.join(paths.penman, '2000', penman_example)
_, _, _, _, x, y, _, prj, fill_val = read_map(map_for_reference, 'Gtiff')
start = datetime.datetime(2000, 1, 1, 0)
end = datetime.datetime(2001, 12, 31, 0)
fill_val = -999.
for day in rrule.rrule(rrule.DAILY, dtstart=start, until=end):
year = day.strftime('%Y')
month = day.strftime('%m')
day_mth = day.strftime('%d')
# yearstr = str(year)
# output = os.path.join(output_dir, new_dir, year)
# print(output_dir)
ndvi = get_kcb(paths.ndvi_std_all, day)
# kcb = remake_array(mask_path, kcb)
ndvi[ndvi == fill_val] = 0
# print(ndvi, ndvi.shape)
new_ndvi = 'NDVI{}_{}_{}.tif'.format(year, month, day_mth)
output_dir = os.path.join(paths.etrm_input_root, 'NDVI_individ', str(year))
ndvi_out = os.path.join(output_dir, new_ndvi)
if not os.path.isdir(output_dir):
os.makedirs(output_dir)
print('Saving New NDVI file as {}'.format(ndvi_out))
write_map(ndvi_out, 'Gtiff', x, y, ndvi, prj, fill_val)
def run(input_root, coords_path):
paths.build(input_root)
# a function that takes uses GDAL to get the coordinates of points.
point_dict = get_point_dict(coords_path)
simulation_period = datetime(2007, 1, 1), datetime(2013, 12, 29)
get_etrm_time_series(paths.amf_ex_sac_extract, dict_=point_dict)
print 'amf dict w/ etrm input time series: \n{}'.format(point_dict) # fix this so it appends to all sites
output = os.path.join(paths.amf_ex_sac_output_root, 'trackers')
for key, val in point_dict.iteritems():
# TODO: use of point dicts no longer supported by Processes. need to update accordingly to use masks.
# =================================================================
# instantiate for each item to get a clean master dict
etrm = Processes(simulation_period,
paths.amf_trackers,
point_dict=point_dict)
print 'point dict, pre-etrm run {}'.format(point_dict) # for testing
print 'key : {}'.format(key)
# =================================================================
# TODO: `run` does not return anything. need different way to access tracker.
# probably not even necessary as tracker results are saved at the end of `run`
tracker = etrm.run(simulation_period, point_dict=point_dict, point_dict_key=key, modify_soils=True,
ro_reinf_frac=0.7, allen_ceff=0.8)
# print 'tracker after etrm run: \n {}'.format(tracker)
csv_path_filename = os.path.join(output, '{}.csv'.format(val['Name']))
print 'this should be your csv: {}'.format(csv_path_filename)
# saves the model results to the tracker. Keep this part.
tracker.to_csv(csv_path_filename, na_rep='nan', index_label='Date')
def slice_and_save(p, arr, geo, startc, endc, startr, endr):
if not os.path.isdir(os.path.dirname(p)):
os.makedirs(os.path.dirname(p))
raster = Raster.fromarray(arr)
marr = raster.unmasked()
marr = marr[slice(startr, endr), slice(startc, endc)]
# print 'saving {}'.format(p)
raster.save(p, marr, geo)
def make_reduced_path(out, tag, k):
p = os.path.join(out, tag)
# if not os.path.isdir(p):
# os.makedirs(p)
p = os.path.join(p, '{}_reduced.tif'.format(k))
return p
def get_transform(startc, startr):
transform = get_tiff_transform(paths.mask)
transform *= Affine.translation(startc, startr)
transform = transform.to_gdal()
return transform
if __name__ == '__main__':
paths.build('/Volumes/Seagate Expansion Drive/ETRM_inputs')
generate_dataset(['1/1/2000', '12/31/2013'], '/Volumes/Seagate Expansion Drive/gabe_aoi_inputs')
# ============= EOF =============================================
def slice_and_save(p, arr, geo, startc, endc, startr, endr):
if not os.path.isdir(os.path.dirname(p)):
os.makedirs(os.path.dirname(p))
raster = Raster.fromarray(arr)
marr = raster.unmasked()
marr = marr[slice(startr, endr), slice(startc, endc)]
# print 'saving {}'.format(p)
raster.save(p, marr, geo)
def make_reduced_path(out, tag, k):
p = os.path.join(out, tag)
# if not os.path.isdir(p):
# os.makedirs(p)
p = os.path.join(p, '{}_reduced.tif'.format(k))
return p
def get_transform(startc, startr):
transform = get_tiff_transform(paths.mask)
transform *= Affine.translation(startc, startr)
transform = transform.to_gdal()
return transform
if __name__ == '__main__':
paths.build('/Volumes/Seagate Expansion Drive/ETRM_inputs')
generate_dataset(['1/1/2000', '12/31/2013'], '/Volumes/Seagate Expansion Drive/gabe_aoi_inputs')
# ============= EOF =============================================
