def classify(ctx, config, algo, job_number, total_jobs, resume):
cfg = parse_config_file(config)
df = csvfile_to_dataframe(cfg['dataset']['input_file'],
cfg['dataset']['date_format'])
nrow = get_image_attribute(df['filename'][0])[0]
classifier = joblib.load(algo)
# Split into lines and classify
job_lines = distribute_jobs(job_number, total_jobs, nrow)
logger.debug('Responsible for lines: {l}'.format(l=job_lines))
start_time = time.time()
logger.info('Starting to run lines')
for job_line in job_lines:
filename = get_output_name(cfg['dataset'], job_line)
if not os.path.exists(filename):
logger.warning('No model result found for line {l} '
'(file {f})'.format(l=job_line, f=filename))
pass
if resume and try_resume(filename):
logger.debug('Already processed line {l}'.format(l=job_line))
continue
logger.debug('Classifying line {l}'.format(l=job_line))
classify_line(filename, classifier)
logger.debug('Completed {n} lines in {m} minutes'.format(
n=len(job_lines),
m=round((time.time() - start_time) / 60.0, 2))
)
def main(args):
""" Classify dataset """
# Parse config and file data
dataset_config, yatsm_config = parse_config_file(args['config_file'])
# Get some attributes about the dataset
dates, sensors, images = csvfile_to_dataset(
dataset_config['input_file'],
date_format=dataset_config['date_format']
)
nrow, _, _, _ = get_image_attribute(images[0])
# Read in the saved classification result
try:
_ = open(args['algo'])
except:
logger.error('Could not open pickled classifier')
sys.exit(1)
classifier = joblib.load(args['algo'])
# Split into lines and classify
job_lines = calculate_lines(args['job_number'] - 1, args['total_jobs'],
nrow)
logger.debug('Responsible for lines: {l}'.format(l=job_lines))
start_time = time.time()
logger.info('Starting to run lines')
for job_line in job_lines:
filename = get_output_name(dataset_config, job_line)
if not os.path.exists(filename):
logger.warning('No model result found for line {l} '
'(file {f})'.format(l=job_line, f=filename))
pass
if args['resume'] and try_resume(filename):
logger.debug('Already processed line {l}'.format(l=job_line))
continue
logger.debug('Classifying line {l}'.format(l=job_line))
classify_line(filename, classifier)
logger.debug('Completed {n} lines in {m} minutes'.format(
n=len(job_lines),
m=round((time.time() - start_time) / 60.0, 2))
)
def line(ctx, config, job_number, total_jobs,
resume, check_cache, do_not_run, verbose_yatsm):
if verbose_yatsm:
logger_algo.setLevel(logging.DEBUG)
# Parse config
cfg = parse_config_file(config)
if ('phenology' in cfg and cfg['phenology'].get('enable')) and not pheno:
click.secho('Could not import yatsm.phenology but phenology metrics '
'are requested', fg='red')
click.secho('Error: %s' % pheno_exception, fg='red')
raise click.Abort()
# Make sure output directory exists and is writable
output_dir = cfg['dataset']['output']
try:
os.makedirs(output_dir)
except OSError as e:
# File exists
if e.errno == 17:
pass
elif e.errno == 13:
click.secho('Cannot create output directory %s' % output_dir,
fg='red')
raise click.Abort()
if not os.access(output_dir, os.W_OK):
click.secho('Cannot write to output directory %s' % output_dir,
fg='red')
raise click.Abort()
# Test existence of cache directory
read_cache, write_cache = test_cache(cfg['dataset'])
logger.info('Job {i} of {n} - using config file {f}'.format(i=job_number,
n=total_jobs,
f=config))
df = csvfile_to_dataframe(cfg['dataset']['input_file'],
cfg['dataset']['date_format'])
df['image_ID'] = get_image_IDs(df['filename'])
# Get attributes of one of the images
nrow, ncol, nband, dtype = get_image_attribute(df['filename'][0])
# Calculate the lines this job ID works on
job_lines = distribute_jobs(job_number, total_jobs, nrow)
logger.debug('Responsible for lines: {l}'.format(l=job_lines))
# Calculate X feature input
dates = np.asarray(df['date'])
kws = {'x': dates}
kws.update(df.to_dict())
X = patsy.dmatrix(cfg['YATSM']['design_matrix'], kws)
cfg['YATSM']['design'] = X.design_info.column_name_indexes
# Form YATSM class arguments
fit_indices = np.arange(cfg['dataset']['n_bands'])
if cfg['dataset']['mask_band'] is not None:
fit_indices = fit_indices[:-1]
if cfg['YATSM']['reverse']:
X = np.flipud(X)
# Create output metadata to save
md = {
'YATSM': cfg['YATSM'],
cfg['YATSM']['algorithm']: cfg[cfg['YATSM']['algorithm']]
}
if cfg['phenology']['enable']:
md.update({'phenology': cfg['phenology']})
# Begin process
start_time_all = time.time()
for line in job_lines:
out = get_output_name(cfg['dataset'], line)
if resume:
try:
np.load(out)
except:
pass
else:
logger.debug('Already processed line %s' % line)
continue
logger.debug('Running line %s' % line)
start_time = time.time()
Y = read_line(line, df['filename'], df['image_ID'], cfg['dataset'],
ncol, nband, dtype,
read_cache=read_cache, write_cache=write_cache,
validate_cache=False)
if do_not_run:
continue
if cfg['YATSM']['reverse']:
Y = np.fliplr(Y)
output = []
for col in np.arange(Y.shape[-1]):
_Y = Y.take(col, axis=2)
# Mask
idx_mask = cfg['dataset']['mask_band'] - 1
valid = cyprep.get_valid_mask(
_Y,
cfg['dataset']['min_values'],
cfg['dataset']['max_values']).astype(bool)
valid *= np.in1d(_Y.take(idx_mask, axis=0),
cfg['dataset']['mask_values'],
invert=True).astype(np.bool)
_Y = np.delete(_Y, idx_mask, axis=0)[:, valid]
_X = X[valid, :]
_dates = dates[valid]
# Run model
cls = cfg['YATSM']['algorithm_cls']
algo_cfg = cfg[cfg['YATSM']['algorithm']]
yatsm = cls(lm=cfg['YATSM']['prediction_object'],
**algo_cfg.get('init', {}))
yatsm.px = col
yatsm.py = line
try:
yatsm.fit(_X, _Y, _dates, **algo_cfg.get('fit', {}))
except TSLengthException:
continue
if yatsm.record is None or len(yatsm.record) == 0:
continue
# Postprocess
if cfg['YATSM'].get('commission_alpha'):
yatsm.record = postprocess.commission_test(
yatsm, cfg['YATSM']['commission_alpha'])
for prefix, lm in zip(cfg['YATSM']['refit']['prefix'],
cfg['YATSM']['refit']['prediction_object']):
yatsm.record = postprocess.refit_record(yatsm, prefix, lm,
keep_regularized=True)
if cfg['phenology']['enable']:
pcfg = cfg['phenology']
ltm = pheno.LongTermMeanPhenology(**pcfg.get('init', {}))
yatsm.record = ltm.fit(yatsm, **pcfg.get('fit', {}))
output.extend(yatsm.record)
logger.debug(' Saving YATSM output to %s' % out)
np.savez(out,
record=np.array(output),
version=__version__,
metadata=md)
run_time = time.time() - start_time
logger.debug('Line %s took %ss to run' % (line, run_time))
logger.info('Completed {n} lines in {m} minutes'.format(
n=len(job_lines),
m=round((time.time() - start_time_all) / 60.0, 2)))
def main(dataset_config, yatsm_config,
check=False, resume=False,
do_not_run=False,
read_cache=False, write_cache=False,
validate_cache=False):
""" Read in dataset and YATSM for a complete line
Args:
dataset_config (dict): dict of dataset configuration options
yatsm_config (dict): dict of YATSM algorithm options
check (bool, optional): check to make sure images are readible
resume (bool, optional): do not overwrite existing results, instead
continue from first non-existing result file
do_not_run (bool, optional): Don't run YATSM
read_cache (bool, optional): try to read from cache directory
(default: False)
write_cache (bool, optional): try to to write to cache directory
(default: False)
validate_cache (bool, optional): ensure data from cache file come from
images specified in configuration (default: False)
"""
# Read in dataset
dates, sensors, images = csvfile_to_dataset(
dataset_config['input_file'],
date_format=dataset_config['date_format']
)
image_IDs = get_image_IDs(images)
# Check for existence of files and remove missing
if check:
to_delete = []
for i, img in enumerate(images):
if not os.path.isfile(img):
logger.warning('Could not find file {f} -- removing'.
format(f=img))
to_delete.append(i)
if len(to_delete) == 0:
logger.debug('Checked and found all input images')
else:
logger.warning('Removing {n} images'.format(n=len(to_delete)))
dates = np.delete(dates, np.array(to_delete))
images = np.delete(images, np.array(to_delete))
# Get attributes of one of the images
nrow, ncol, nband, dtype = get_image_attribute(images[0])
# Calculate the lines this job ID works on
job_lines = calculate_lines(job_number, total_jobs, nrow)
logger.debug('Responsible for lines: {l}'.format(l=job_lines))
# Calculate X feature input
X = patsy.dmatrix(yatsm_config['design_matrix'],
{'x': dates, 'sensor': sensors})
# Start running YATSM
start_time_all = time.time()
logger.info('Starting to run lines')
for job_line in job_lines:
if resume:
try:
z = np.load(get_output_name(dataset_config, job_line))
except:
pass
else:
del z
logger.debug('Already processed line {l}'.format(l=job_line))
continue
logger.debug('Running line {l}'.format(l=job_line))
start_time = time.time()
try:
run_line(job_line, X, images, image_IDs,
dataset_config, yatsm_config,
nrow, ncol, nband, dtype,
do_not_run=do_not_run,
read_cache=read_cache, write_cache=write_cache,
validate_cache=validate_cache)
except Exception as e:
logger.error('Could not process line {l}'.format(l=job_line))
logger.error(type(e))
logger.error(str(e))
logger.debug('Took {s}s to run'.format(
s=round(time.time() - start_time, 2)))
logger.info('Completed {n} lines in {m} minutes'.format(
n=len(job_lines),
m=round((time.time() - start_time_all) / 60.0, 2)
))
def main(dataset_config,
yatsm_config,
check=False,
resume=False,
do_not_run=False,
read_cache=False,
write_cache=False,
validate_cache=False):
""" Read in dataset and YATSM for a complete line
Args:
dataset_config (dict): dict of dataset configuration options
yatsm_config (dict): dict of YATSM algorithm options
check (bool, optional): check to make sure images are readible
resume (bool, optional): do not overwrite existing results, instead
continue from first non-existing result file
do_not_run (bool, optional): Don't run YATSM
read_cache (bool, optional): try to read from cache directory
(default: False)
write_cache (bool, optional): try to to write to cache directory
(default: False)
validate_cache (bool, optional): ensure data from cache file come from
images specified in configuration (default: False)
"""
# Read in dataset
dates, sensors, images = csvfile_to_dataset(
dataset_config['input_file'],
date_format=dataset_config['date_format'])
image_IDs = get_image_IDs(images)
# Check for existence of files and remove missing
if check:
to_delete = []
for i, img in enumerate(images):
if not os.path.isfile(img):
logger.warning(
'Could not find file {f} -- removing'.format(f=img))
to_delete.append(i)
if len(to_delete) == 0:
logger.debug('Checked and found all input images')
else:
logger.warning('Removing {n} images'.format(n=len(to_delete)))
dates = np.delete(dates, np.array(to_delete))
images = np.delete(images, np.array(to_delete))
# Get attributes of one of the images
nrow, ncol, nband, dtype = get_image_attribute(images[0])
# Calculate the lines this job ID works on
job_lines = calculate_lines(job_number, total_jobs, nrow)
logger.debug('Responsible for lines: {l}'.format(l=job_lines))
# Calculate X feature input
X = patsy.dmatrix(yatsm_config['design_matrix'], {
'x': dates,
'sensor': sensors
})
# Start running YATSM
start_time_all = time.time()
logger.info('Starting to run lines')
for job_line in job_lines:
if resume:
try:
z = np.load(get_output_name(dataset_config, job_line))
except:
pass
else:
del z
logger.debug('Already processed line {l}'.format(l=job_line))
continue
logger.debug('Running line {l}'.format(l=job_line))
start_time = time.time()
try:
run_line(job_line,
X,
images,
image_IDs,
dataset_config,
yatsm_config,
nrow,
ncol,
nband,
dtype,
do_not_run=do_not_run,
read_cache=read_cache,
write_cache=write_cache,
validate_cache=validate_cache)
except Exception as e:
logger.error('Could not process line {l}'.format(l=job_line))
logger.error(type(e))
logger.error(str(e))
logger.debug(
'Took {s}s to run'.format(s=round(time.time() - start_time, 2)))
logger.info('Completed {n} lines in {m} minutes'.format(
n=len(job_lines), m=round((time.time() - start_time_all) / 60.0, 2)))
def main(args):
# Parse and validate configuration file
dataset_config, yatsm_config = config_parser.parse_config_file(
args['config_file'])
if not os.path.isdir(dataset_config['cache_line_dir']):
os.makedirs(dataset_config['cache_line_dir'])
dates, images = utils.csvfile_to_dataset(
dataset_config['input_file'],
date_format=dataset_config['date_format']
)
image_IDs = utils.get_image_IDs(images)
nrow, ncol, nband, dtype = reader.get_image_attribute(images[0])
# Determine lines to work on
job_lines = utils.calculate_lines(args['job_number'],
args['total_jobs'],
nrow,
interlaced=args['interlace'])
logger.debug('Responsible for lines: {l}'.format(l=job_lines))
# Determine file reader
if dataset_config['use_bip_reader']:
logger.debug('Reading in data from disk using BIP reader')
image_reader = reader.read_row_BIP
image_reader_kwargs = {'size': (ncol, nband),
'dtype': dtype}
else:
logger.debug('Reading in data from disk using GDAL')
image_reader = reader.read_row_GDAL
image_reader_kwargs = {}
# Attempt to update cache files
previous_cache = None
if args['update_pattern']:
previous_cache = fnmatch.filter(
os.listdir(dataset_config['cache_line_dir']),
args['update_pattern'])
if not previous_cache:
logger.warning('Could not find cache files to update with pattern'
'{p}'.format(p=args['update_pattern']))
else:
logger.debug('Found {n} previously cached files to update'.format(
n=len(previous_cache)))
for job_line in job_lines:
cache_filename = cache.get_line_cache_name(
dataset_config, len(images), job_line, nband)
logger.debug('Caching line {l} to {f}'.format(
l=job_line, f=cache_filename))
start_time = time.time()
# Find matching cache file
update = False
if previous_cache:
pattern = cache.get_line_cache_pattern(
job_line, nband, regex=False)
potential = fnmatch.filter(previous_cache, pattern)
if not potential:
logger.info('Found zero previous cache files for '
'line {l}'.format(l=job_line))
elif len(potential) > 1:
logger.info('Found more than one previous cache file for '
'line {l}. Keeping first'.format(l=job_line))
update = os.path.join(dataset_config['cache_line_dir'],
potential[0])
else:
update = os.path.join(dataset_config['cache_line_dir'],
potential[0])
logger.info('Updating from cache file {f}'.format(f=update))
if update:
cache.update_cache_file(
images, image_IDs,
update, cache_filename,
job_line, image_reader, image_reader_kwargs
)
else:
if dataset_config['use_bip_reader']:
# Use BIP reader
logger.debug('Reading in data from disk using BIP reader')
Y = reader.read_row_BIP(images, job_line, (ncol, nband), dtype)
else:
# Read in data just using GDAL
logger.debug('Reading in data from disk using GDAL')
Y = reader.read_row_GDAL(images, job_line)
cache.write_cache_file(cache_filename, Y, image_IDs)
logger.debug('Took {s}s to cache the data'.format(
s=round(time.time() - start_time, 2)))
def main(args):
# Parse and validate configuration file
dataset_config, yatsm_config = config_parser.parse_config_file(
args['config_file'])
if not os.path.isdir(dataset_config['cache_line_dir']):
os.makedirs(dataset_config['cache_line_dir'])
dates, images = utils.csvfile_to_dataset(
dataset_config['input_file'],
date_format=dataset_config['date_format'])
image_IDs = utils.get_image_IDs(images)
nrow, ncol, nband, dtype = reader.get_image_attribute(images[0])
# Determine lines to work on
job_lines = utils.calculate_lines(args['job_number'],
args['total_jobs'],
nrow,
interlaced=args['interlace'])
logger.debug('Responsible for lines: {l}'.format(l=job_lines))
# Determine file reader
if dataset_config['use_bip_reader']:
logger.debug('Reading in data from disk using BIP reader')
image_reader = reader.read_row_BIP
image_reader_kwargs = {'size': (ncol, nband), 'dtype': dtype}
else:
logger.debug('Reading in data from disk using GDAL')
image_reader = reader.read_row_GDAL
image_reader_kwargs = {}
# Attempt to update cache files
previous_cache = None
if args['update_pattern']:
previous_cache = fnmatch.filter(
os.listdir(dataset_config['cache_line_dir']),
args['update_pattern'])
if not previous_cache:
logger.warning('Could not find cache files to update with pattern'
'{p}'.format(p=args['update_pattern']))
else:
logger.debug('Found {n} previously cached files to update'.format(
n=len(previous_cache)))
for job_line in job_lines:
cache_filename = cache.get_line_cache_name(dataset_config, len(images),
job_line, nband)
logger.debug('Caching line {l} to {f}'.format(l=job_line,
f=cache_filename))
start_time = time.time()
# Find matching cache file
update = False
if previous_cache:
pattern = cache.get_line_cache_pattern(job_line,
nband,
regex=False)
potential = fnmatch.filter(previous_cache, pattern)
if not potential:
logger.info('Found zero previous cache files for '
'line {l}'.format(l=job_line))
elif len(potential) > 1:
logger.info('Found more than one previous cache file for '
'line {l}. Keeping first'.format(l=job_line))
update = os.path.join(dataset_config['cache_line_dir'],
potential[0])
else:
update = os.path.join(dataset_config['cache_line_dir'],
potential[0])
logger.info('Updating from cache file {f}'.format(f=update))
if update:
cache.update_cache_file(images, image_IDs, update, cache_filename,
job_line, image_reader,
image_reader_kwargs)
else:
if dataset_config['use_bip_reader']:
# Use BIP reader
logger.debug('Reading in data from disk using BIP reader')
Y = reader.read_row_BIP(images, job_line, (ncol, nband), dtype)
else:
# Read in data just using GDAL
logger.debug('Reading in data from disk using GDAL')
Y = reader.read_row_GDAL(images, job_line)
cache.write_cache_file(cache_filename, Y, image_IDs)
logger.debug('Took {s}s to cache the data'.format(
s=round(time.time() - start_time, 2)))
def cache(ctx, config, job_number, total_jobs, update_pattern, interlace):
cfg = parse_config_file(config)
if not os.path.isdir(cfg['dataset']['cache_line_dir']):
os.makedirs(cfg['dataset']['cache_line_dir'])
df = csvfile_to_dataframe(cfg['dataset']['input_file'],
cfg['dataset']['date_format'])
df['image_IDs'] = get_image_IDs(df['filename'])
nrow, ncol, nband, dtype = reader.get_image_attribute(df['filename'][0])
# Determine lines to work on
job_lines = distribute_jobs(job_number, total_jobs, nrow,
interlaced=interlace)
logger.debug('Responsible for lines: {l}'.format(l=job_lines))
# Determine file reader
if cfg['dataset']['use_bip_reader']:
logger.debug('Reading in data from disk using BIP reader')
image_reader = reader.read_row_BIP
image_reader_kwargs = {'size': (ncol, nband),
'dtype': dtype}
else:
logger.debug('Reading in data from disk using GDAL')
image_reader = reader.read_row_GDAL
image_reader_kwargs = {}
# Attempt to update cache files
previous_cache = None
if update_pattern:
previous_cache = fnmatch.filter(
os.listdir(cfg['dataset']['cache_line_dir']), update_pattern)
if not previous_cache:
logger.warning('Could not find cache files to update with pattern '
'%s' % update_pattern)
else:
logger.debug('Found %s previously cached files to update' %
len(previous_cache))
for job_line in job_lines:
cache_filename = get_line_cache_name(cfg['dataset'], len(df),
job_line, nband)
logger.debug('Caching line {l} to {f}'.format(
l=job_line, f=cache_filename))
start_time = time.time()
# Find matching cache file
update = False
if previous_cache:
pattern = get_line_cache_pattern(job_line, nband, regex=False)
potential = fnmatch.filter(previous_cache, pattern)
if not potential:
logger.info('Found zero previous cache files for '
'line {l}'.format(l=job_line))
elif len(potential) > 1:
logger.info('Found more than one previous cache file for '
'line {l}. Keeping first'.format(l=job_line))
update = os.path.join(cfg['dataset']['cache_line_dir'],
potential[0])
else:
update = os.path.join(cfg['dataset']['cache_line_dir'],
potential[0])
logger.info('Updating from cache file {f}'.format(f=update))
if update:
update_cache_file(df['filename'], df['image_IDs'],
update, cache_filename,
job_line, image_reader, image_reader_kwargs)
else:
if cfg['dataset']['use_bip_reader']:
# Use BIP reader
logger.debug('Reading in data from disk using BIP reader')
Y = reader.read_row_BIP(df['filename'], job_line,
(ncol, nband), dtype)
else:
# Read in data just using GDAL
logger.debug('Reading in data from disk using GDAL')
Y = reader.read_row_GDAL(df['filename'], job_line)
write_cache_file(cache_filename, Y, df['image_IDs'])
logger.debug('Took {s}s to cache the data'.format(
s=round(time.time() - start_time, 2)))
