def _load_standard_coordinates(self, etc_path, coord_setting):
"""
"""
print('Loading coordinates settings..')
paths = utils.generate_filepaths(etc_path,
pattern=coord_setting,
endswith='.txt')
settings = {}
for p in paths:
file_name = os.path.basename(p).replace('.txt', '')
file_name = file_name.replace(coord_setting, 'array')
settings[file_name] = np_txt_reader(p)
self.set_attributes(self, **settings)
def _load_settings(self, etc_path):
"""
:param etc_path: str, local path to settings
:return: Updates attributes of self
"""
print('Loading tiff settings..')
paths = utils.generate_filepaths(etc_path, pattern='.tiff')
settings = {}
for p in paths:
file_name = os.path.basename(p).replace('.tiff', '')
settings[file_name], settings[file_name + '_meta'] = raster_reader(
p, include_meta=True)
self.set_attributes(self, **settings)
# writer='raster')
if __name__ == "__main__":
# Set path to data directory
data_path = 'C:\\Temp\\baws_reanalys\\tiff_archive'
# Create the Session object
s = Session(data_path=data_path)
# If we want to save data to a specific location, we set the export path here.
# s.setting.set_export_directory(path=None)
for year in range(2002, 2010):
year = str(year)
# Generate filepaths
generator = generate_filepaths(s.data_path,
pattern='cyano_daymap_' + year,
endswith='.tiff',
only_from_dir=True)
# Loop through the file-generator and aggregate the data.
# aggregation is a numpy 2d-array
aggregation = raster_aggregation(generator)
# Export the aggragation in a tiff file.
# WARNING! tiff files only handles integer data with values <=100.
# The benefit of tiff-files are the super compressed format
s.export_data(data=aggregation,
file_name='aggregation_%s.tiff' % year,
writer='raster')
"""
from bawsvis.utils import generate_filepaths
from bawsvis.session import Session
from bawsvis.data_handler import aggregation_annuals
if __name__ == "__main__":
# Set path to data directory
data_path = 'C:\\Utveckling\\BAWS-vis\\bawsvis\\export'
# Create the Session object
s = Session(data_path=data_path)
# Generate filepaths
generator = generate_filepaths(s.data_path,
pattern='Cumu_',
endswith='.txt',
only_from_dir=True)
# Loop through the file-generator and aggregate the data.
# aggregation is a numpy 2d-array
aggregation = aggregation_annuals(generator, reader='text')
# Export the aggragation in a tiff file.
# WARNING! tiff files only handles integer data with values <=100.
# The benefit of tiff-files are the super compressed format
s.export_data(data=aggregation,
file_name='period_aggregation.txt',
writer='text')
from bawsvis.session import Session
from bawsvis.data_handler import get_daily_stats, get_weekly_stats
if __name__ == "__main__":
# Set path to data directory
data_path = '...\\Manuell_algtolkning'
# Create the Session object
s = Session(data_path=data_path)
# If we want to save data to a specific location, we set the export path here.
# s.setting.set_export_directory(path=None)
# Generate filepaths (daily)
generator = generate_filepaths(s.setting.export_directory,
pattern='cyano_daymap_',
endswith='.shp',
only_from_dir=True)
# Loop through the file-generator and aggregate the data.
stats_daily = get_daily_stats(generator)
# Generate filepaths (weekly)
generator = generate_filepaths(s.data_path,
pattern='cyano_weekmap_',
endswith='.shp',
only_from_dir=True)
# Loop through the file-generator and aggregate the data.
stats_weekly = get_weekly_stats(generator)
stats = recursive_dict_update(stats_daily, stats_weekly)
from bawsvis.utils import generate_filepaths
from bawsvis.session import Session
from bawsvis.data_handler import raster_aggregation
if __name__ == "__main__":
# Set path to data directory
data_path = 'E:\\Johannes_exjobb\\MODIS_data\\outdata\\attribute_data\\BAWS'
# Create the Session object
s = Session(data_path=data_path)
# If we want to save data to a specific location, we set the export path here.
# s.setting.set_export_directory(path=None)
for year in range(2019, 2021):
year = str(year)
# Generate filepaths
generator = generate_filepaths(s.data_path,
pattern='BAWS_' + year,
endswith='.txt',
only_from_dir=False)
# Loop through the file-generator and aggregate the data.
# aggregation is a numpy 2d-array
aggregation = raster_aggregation(generator, reader='text')
# Export the aggragation
s.export_data(data=aggregation,
file_name='aggregation_%s.txt' % year,
writer='text')