def get_phenology(
date, result_location, image_ds, after=False, before=False, qa=False, ndv=-9999, pattern=_result_record
):
""" Output a raster containing phenology information
Phenology information includes spring_doy, autumn_doy, pheno_cor, peak_evi,
peak_doy, and pheno_nobs.
Args:
date (int): Ordinal date for prediction image
result_location (str): Location of the results
image_ds (gdal.Dataset): Example dataset
after (bool, optional): If date intersects a disturbed period, use next
available time segment
before (bool, optional): If date does not intersect a model, use previous
non-disturbed time segment
qa (bool, optional): Add QA flag specifying segment type (intersect,
after, or before)
ndv (int, optional): NoDataValue
pattern (str, optional): filename pattern of saved record results
Returns:
tuple (np.ndarray, list): A tuple (np.ndarray, list) containing the 3D
np.ndarray of the phenology metrics, and the band names for
the output dataset
"""
# Find results
records = find_results(result_location, pattern)
n_bands = 7
attributes = ["spring_doy", "autumn_doy", "pheno_cor", "peak_evi", "peak_doy", "pheno_nobs"]
band_names = ["SpringDOY", "AutumnDOY", "Pheno_R*10000", "Peak_EVI*10000", "Peak_DOY", "Pheno_NObs", "GrowingDOY"]
if qa:
n_bands += 1
band_names.append("SegmentQAQC")
logger.debug("Allocating memory")
raster = np.ones((image_ds.RasterYSize, image_ds.RasterXSize, n_bands), dtype=np.int32) * int(ndv)
logger.debug("Processing results")
for rec in iter_records(records, warn_on_empty=WARN_ON_EMPTY):
if not all([_attr in rec.dtype.names for _attr in attributes]):
raise ValueError("Results do not have phenology metrics")
for _qa, index in find_indices(rec, date, after=after, before=before):
if index.shape[0] == 0:
continue
# Apply scale factors for R and peak EVI
rec["pheno_cor"][index] *= 10000.0
rec["peak_evi"][index] *= 10000.0
for _b, _attr in enumerate(attributes):
raster[rec["py"][index], rec["px"][index], _b] = rec[_attr][index]
raster[rec["py"][index], rec["px"][index], 6] = rec["autumn_doy"][index] - rec["spring_doy"][index]
if qa:
raster[rec["py"][index], rec["px"][index], -1] = _qa
return raster, band_names
def get_numchangemap(start,
end,
result_location,
image_ds,
ndv=-9999,
pattern=_result_record):
""" Output raster with changemap
Args:
start (int): Ordinal date for start of map records
end (int): Ordinal date for end of map records
result_location (str): Location of results
image_ds (gdal.Dataset): Example dataset
ndv (int, optional): NoDataValue
pattern (str, optional): filename pattern of saved record results
Returns:
np.ndarray: 2D numpy array containing the number of changes
between the start and end date; list containing band names
"""
# Find results
records = find_results(result_location, pattern)
logger.debug('Allocating memory...')
raster = np.ones((image_ds.RasterYSize, image_ds.RasterXSize),
dtype=np.int32) * int(ndv)
logger.debug('Processing results')
for rec in iter_records(records):
# X location of each changed model
px_changed = rec['px'][(rec['break'] >= start) & (rec['break'] <= end)]
# Count occurrences of changed pixel locations
bincount = np.bincount(px_changed)
# How many changes for unique values of px_changed?
n_change = bincount[np.nonzero(bincount)[0]]
# Add in the values
px = np.unique(px_changed)
py = rec['py'][np.in1d(px, rec['px'])]
raster[py, px] = n_change
return raster
def get_numchangemap(start, end, result_location, image_ds,
ndv=-9999, pattern=_result_record):
""" Output raster with changemap
Args:
start (int): Ordinal date for start of map records
end (int): Ordinal date for end of map records
result_location (str): Location of results
image_ds (gdal.Dataset): Example dataset
ndv (int, optional): NoDataValue
pattern (str, optional): filename pattern of saved record results
Returns:
np.ndarray: 2D numpy array containing the number of changes
between the start and end date; list containing band names
"""
# Find results
records = find_results(result_location, pattern)
logger.debug('Allocating memory...')
raster = np.ones((image_ds.RasterYSize, image_ds.RasterXSize),
dtype=np.int32) * int(ndv)
logger.debug('Processing results')
for rec in iter_records(records):
# X location of each changed model
px_changed = rec['px'][(rec['break'] >= start) & (rec['break'] <= end)]
# Count occurrences of changed pixel locations
bincount = np.bincount(px_changed)
# How many changes for unique values of px_changed?
n_change = bincount[np.nonzero(bincount)[0]]
# Add in the values
px = np.unique(px_changed)
py = rec['py'][np.in1d(px, rec['px'])]
raster[py, px] = n_change
return raster
def get_prediction(
date,
result_location,
image_ds,
bands="all",
use_robust=False,
after=False,
before=False,
qa=False,
ndv=-9999,
pattern=_result_record,
):
""" Output a raster with the predictions from model fit for a given date
Args:
date (int): Ordinal date for prediction image
result_location (str): Location of the results
image_ds (gdal.Dataset): Example dataset
bands (str, list): Bands to predict - 'all' for every band, or specify a
list of bands
use_robust (bool, optional): Map robust coefficients and RMSE instead of
normal ones
after (bool, optional): If date intersects a disturbed period, use next
available time segment
before (bool, optional): If date does not intersect a model, use previous
non-disturbed time segment
qa (bool, optional): Add QA flag specifying segment type (intersect,
after, or before)
ndv (int, optional): NoDataValue
pattern (str, optional): filename pattern of saved record results
Returns:
np.ndarray: A 3D numpy.ndarray containing the prediction for each band,
for each pixel
"""
# Find results
records = find_results(result_location, pattern)
# Find result attributes to extract
i_bands, _, _, _, design, design_info = find_result_attributes(records, bands, None, use_robust=use_robust)
n_bands = len(i_bands)
band_names = ["Band_{0}".format(b) for b in range(n_bands)]
if qa:
n_bands += 1
band_names.append("SegmentQAQC")
n_i_bands = len(i_bands)
# Create X matrix from date -- ignoring categorical variables
if re.match(r".*C\(.*\).*", design):
logger.warning("Categorical variable found in design matrix not used" " in predicted image estimate")
design = re.sub(r"[\+\-][\ ]+C\(.*\)", "", design)
X = patsy.dmatrix(design, {"x": date}).squeeze()
i_coef = []
for k, v in design_info.iteritems():
if not re.match("C\(.*\)", k):
i_coef.append(v)
i_coef = np.asarray(i_coef)
logger.debug("Allocating memory")
raster = np.ones((image_ds.RasterYSize, image_ds.RasterXSize, n_bands), dtype=np.int16) * int(ndv)
logger.debug("Processing results")
for rec in iter_records(records, warn_on_empty=WARN_ON_EMPTY):
for _qa, index in find_indices(rec, date, after=after, before=before):
if index.shape[0] == 0:
continue
# Calculate prediction
_coef = rec["coef"].take(index, axis=0).take(i_coef, axis=1).take(i_bands, axis=2)
raster[rec["py"][index], rec["px"][index], :n_i_bands] = np.tensordot(_coef, X, axes=(1, 0))
if qa:
raster[rec["py"][index], rec["px"][index], -1] = _qa
return raster, band_names
def get_coefficients(
date,
result_location,
image_ds,
bands,
coefs,
use_robust=False,
amplitude=False,
after=False,
before=False,
qa=False,
ndv=-9999,
pattern=_result_record,
):
""" Output a raster with coefficients from CCDC
Args:
date (int): Ordinal date for prediction image
result_location (str): Location of the results
bands (list): Bands to predict
coefs (list): List of coefficients to output
image_ds (gdal.Dataset): Example dataset
use_robust (bool, optional): Map robust coefficients and RMSE instead of
normal ones
amplitude (bool, optional): Map amplitude of seasonality instead of
individual coefficient estimates for sin/cosine pair (default: False)
after (bool, optional): If date intersects a disturbed period, use next
available time segment (default: False)
before (bool, optional): If date does not intersect a model, use previous
non-disturbed time segment (default: False)
qa (bool, optional): Add QA flag specifying segment type (intersect,
after, or before) (default: False)
ndv (int, optional): NoDataValue (default: -9999)
pattern (str, optional): filename pattern of saved record results
Returns:
tuple: A tuple (np.ndarray, list) containing the 3D numpy.ndarray of the
coefficients (coefficient x band x pixel), and the band names for
the output dataset
"""
# Find results
records = find_results(result_location, pattern)
# Find result attributes to extract
i_bands, i_coefs, use_rmse, coef_names, _, _ = find_result_attributes(records, bands, coefs, use_robust=use_robust)
# Process amplitude transform for seasonality coefficients
if amplitude:
harm_coefs = []
for i, (c, n) in enumerate(zip(i_coefs, coef_names)):
if re.match(r"harm\(x, [0-9]+\)\[0]", n):
harm_coefs.append(c)
coef_names[i] = re.sub(r"harm(.*)\[.*", r"amplitude\1", n)
# Remove sin term from each harmonic pair
i_coefs = [c for c in i_coefs if c - 1 not in harm_coefs]
coef_names = [n for n in coef_names if "harm" not in n]
n_bands = len(i_bands)
n_coefs = len(i_coefs)
n_rmse = n_bands if use_rmse else 0
# Setup output band names
band_names = []
for _c in coef_names:
for _b in i_bands:
band_names.append("B" + str(_b + 1) + "_" + _c.replace(" ", ""))
if use_rmse is True:
for _b in i_bands:
band_names.append("B" + str(_b + 1) + "_RMSE")
n_qa = 0
if qa:
n_qa += 1
band_names.append("SegmentQAQC")
n_out_bands = n_bands * n_coefs + n_rmse + n_qa
_coef = "robust_coef" if use_robust else "coef"
_rmse = "robust_rmse" if use_robust else "rmse"
logger.debug("Allocating memory...")
raster = np.ones((image_ds.RasterYSize, image_ds.RasterXSize, n_out_bands), dtype=np.float32) * ndv
logger.debug("Processing results")
for rec in iter_records(records, warn_on_empty=WARN_ON_EMPTY):
for _qa, index in find_indices(rec, date, after=after, before=before):
if index.shape[0] == 0:
continue
if n_coefs > 0:
# Normalize intercept to mid-point in time segment
rec[_coef][index, 0, :] += ((rec["start"][index] + rec["end"][index]) / 2.0)[:, np.newaxis] * rec[
_coef
][index, 1, :]
# If we want amplitude, calculate it
if amplitude:
for harm_coef in harm_coefs:
rec[_coef][index, harm_coef, :] = np.linalg.norm(
rec[_coef][index, harm_coef : harm_coef + 2, :], axis=1
)
# Extract coefficients
raster[rec["py"][index], rec["px"][index], : n_coefs * n_bands] = np.reshape(
rec[_coef][index][:, i_coefs, :][:, :, i_bands], (index.size, n_coefs * n_bands)
)
if use_rmse:
raster[rec["py"][index], rec["px"][index], n_coefs * n_bands : n_out_bands - n_qa] = rec[_rmse][index][
:, i_bands
]
if qa:
raster[rec["py"][index], rec["px"][index], -1] = _qa
return raster, band_names
def get_classification(
date,
result_location,
image_ds,
after=False,
before=False,
qa=False,
pred_proba=False,
ndv=0,
pattern=_result_record,
):
""" Output raster with classification results
Args:
date (int): ordinal date for prediction image
result_location (str): Location of the results
image_ds (gdal.Dataset): Example dataset
after (bool, optional): If date intersects a disturbed period, use next
available time segment
before (bool, optional): If date does not intersect a model, use previous
non-disturbed time segment
qa (bool, optional): Add QA flag specifying segment type (intersect,
after, or before)
pred_proba (bool, optional): Include additional band with classification
value probabilities
ndv (int, optional): NoDataValue
pattern (str, optional): filename pattern of saved record results
Returns:
np.ndarray: 2D numpy array containing the classification map for the date
specified
"""
# Find results
records = find_results(result_location, pattern)
n_bands = 2 if pred_proba else 1
dtype = np.uint16 if pred_proba else np.uint8
band_names = ["Classification"]
if pred_proba:
band_names.append("Pred Proba (x10,000)")
if qa:
n_bands += 1
band_names.append("SegmentQAQC")
logger.debug("Allocating memory...")
raster = np.ones((image_ds.RasterYSize, image_ds.RasterXSize, n_bands), dtype=dtype) * int(ndv)
logger.debug("Processing results")
for rec, fname in iter_records(records, warn_on_empty=WARN_ON_EMPTY, yield_filename=True):
if "class" not in rec.dtype.names:
logger.warning("Results in {f} do not have classification labels".format(f=fname))
continue
if "class_proba" not in rec.dtype.names and pred_proba:
raise ValueError("Results do not have classification prediction" " probability values")
for _qa, index in find_indices(rec, date, after=after, before=before):
if index.shape[0] == 0:
continue
raster[rec["py"][index], rec["px"][index], 0] = rec["class"][index]
if pred_proba:
raster[rec["py"][index], rec["px"][index], 1] = rec["class_proba"][index].max(axis=1) * 10000
if qa:
raster[rec["py"][index], rec["px"][index], -1] = _qa
return raster, band_names
def get_datechangemap(start, end, result_location, image_ds,
first=False,
out_format='%Y%j',
magnitude=False,
ndv=-9999, pattern=_result_record):
""" Output raster with changemap
Args:
start (int): Ordinal date for start of map records
end (int): Ordinal date for end of map records
result_location (str): Location of results
image_ds (gdal.Dataset): Example dataset
first (bool): Use first change instead of last
out_format (str, optional): Output date format
magnitude (bool, optional): output magnitude of each change?
ndv (int, optional): NoDataValue
pattern (str, optional): filename pattern of saved record results
Returns:
tuple: A 2D np.ndarray array containing the changes between the
start and end date. Also includes, if specified, a 3D np.ndarray of
the magnitude of each change plus the indices of these magnitudes
"""
# Find results
records = find_results(result_location, pattern)
logger.debug('Allocating memory...')
datemap = np.ones((image_ds.RasterYSize, image_ds.RasterXSize),
dtype=np.int32) * int(ndv)
# Determine what magnitude information to output if requested
if magnitude:
magnitude_indices = get_magnitude_indices(records)
magnitudemap = np.ones((image_ds.RasterYSize, image_ds.RasterXSize,
magnitude_indices.size),
dtype=np.float32) * float(ndv)
logger.debug('Processing results')
for rec in iter_records(records):
index = np.where((rec['break'] >= start) &
(rec['break'] <= end))[0]
if first:
_, _index = np.unique(rec['px'][index], return_index=True)
index = index[_index]
if index.shape[0] != 0:
if out_format != 'ordinal':
dates = np.array([int(dt.fromordinal(_d).strftime(out_format))
for _d in rec['break'][index]])
datemap[rec['py'][index], rec['px'][index]] = dates
else:
datemap[rec['py'][index], rec['px'][index]] = \
rec['break'][index]
if magnitude:
magnitudemap[rec['py'][index], rec['px'][index], :] = \
rec[index]['magnitude'][:, magnitude_indices]
if magnitude:
return datemap, magnitudemap, magnitude_indices
else:
return datemap, None, None
def get_phenology(date,
result_location,
image_ds,
after=False,
before=False,
qa=False,
ndv=-9999,
pattern=_result_record):
""" Output a raster containing phenology information
Phenology information includes spring_doy, autumn_doy, pheno_cor, peak_evi,
peak_doy, and pheno_nobs.
Args:
date (int): Ordinal date for prediction image
result_location (str): Location of the results
image_ds (gdal.Dataset): Example dataset
after (bool, optional): If date intersects a disturbed period, use next
available time segment
before (bool, optional): If date does not intersect a model, use previous
non-disturbed time segment
qa (bool, optional): Add QA flag specifying segment type (intersect,
after, or before)
ndv (int, optional): NoDataValue
pattern (str, optional): filename pattern of saved record results
Returns:
tuple (np.ndarray, list): A tuple (np.ndarray, list) containing the 3D
np.ndarray of the phenology metrics, and the band names for
the output dataset
"""
# Find results
records = find_results(result_location, pattern)
n_bands = 7
attributes = [
'spring_doy', 'autumn_doy', 'pheno_cor', 'peak_evi', 'peak_doy',
'pheno_nobs'
]
band_names = [
'SpringDOY', 'AutumnDOY', 'Pheno_R*10000', 'Peak_EVI*10000',
'Peak_DOY', 'Pheno_NObs', 'GrowingDOY'
]
if qa:
n_bands += 1
band_names.append('SegmentQAQC')
logger.debug('Allocating memory')
raster = np.ones((image_ds.RasterYSize, image_ds.RasterXSize, n_bands),
dtype=np.int32) * int(ndv)
logger.debug('Processing results')
for rec in iter_records(records, warn_on_empty=WARN_ON_EMPTY):
if not all([_attr in rec.dtype.names for _attr in attributes]):
raise ValueError('Results do not have phenology metrics')
for _qa, index in find_indices(rec, date, after=after, before=before):
if index.shape[0] == 0:
continue
# Apply scale factors for R and peak EVI
rec['pheno_cor'][index] *= 10000.0
rec['peak_evi'][index] *= 10000.0
for _b, _attr in enumerate(attributes):
raster[rec['py'][index], rec['px'][index],
_b] = rec[_attr][index]
raster[rec['py'][index],
rec['px'][index], 6] = \
rec['autumn_doy'][index] - rec['spring_doy'][index]
if qa:
raster[rec['py'][index], rec['px'][index], -1] = _qa
return raster, band_names
def get_prediction(date,
result_location,
image_ds,
bands='all',
use_robust=False,
after=False,
before=False,
qa=False,
ndv=-9999,
pattern=_result_record):
""" Output a raster with the predictions from model fit for a given date
Args:
date (int): Ordinal date for prediction image
result_location (str): Location of the results
image_ds (gdal.Dataset): Example dataset
bands (str, list): Bands to predict - 'all' for every band, or specify a
list of bands
use_robust (bool, optional): Map robust coefficients and RMSE instead of
normal ones
after (bool, optional): If date intersects a disturbed period, use next
available time segment
before (bool, optional): If date does not intersect a model, use previous
non-disturbed time segment
qa (bool, optional): Add QA flag specifying segment type (intersect,
after, or before)
ndv (int, optional): NoDataValue
pattern (str, optional): filename pattern of saved record results
Returns:
np.ndarray: A 3D numpy.ndarray containing the prediction for each band,
for each pixel
"""
# Find results
records = find_results(result_location, pattern)
# Find result attributes to extract
i_bands, _, _, _, design, design_info = find_result_attributes(
records, bands, None, use_robust=use_robust)
n_bands = len(i_bands)
band_names = ['Band_{0}'.format(b) for b in range(n_bands)]
if qa:
n_bands += 1
band_names.append('SegmentQAQC')
n_i_bands = len(i_bands)
# Create X matrix from date -- ignoring categorical variables
if re.match(r'.*C\(.*\).*', design):
logger.warning('Categorical variable found in design matrix not used'
' in predicted image estimate')
design = re.sub(r'[\+\-][\ ]+C\(.*\)', '', design)
X = patsy.dmatrix(design, {'x': date}).squeeze()
i_coef = []
for k, v in design_info.iteritems():
if not re.match('C\(.*\)', k):
i_coef.append(v)
i_coef = np.asarray(i_coef)
logger.debug('Allocating memory')
raster = np.ones((image_ds.RasterYSize, image_ds.RasterXSize, n_bands),
dtype=np.int16) * int(ndv)
logger.debug('Processing results')
for rec in iter_records(records, warn_on_empty=WARN_ON_EMPTY):
for _qa, index in find_indices(rec, date, after=after, before=before):
if index.shape[0] == 0:
continue
# Calculate prediction
_coef = rec['coef'].take(index, axis=0).\
take(i_coef, axis=1).take(i_bands, axis=2)
raster[rec['py'][index], rec['px'][index], :n_i_bands] = \
np.tensordot(_coef, X, axes=(1, 0))
if qa:
raster[rec['py'][index], rec['px'][index], -1] = _qa
return raster, band_names
def get_coefficients(date,
result_location,
image_ds,
bands,
coefs,
use_robust=False,
amplitude=False,
after=False,
before=False,
qa=False,
ndv=-9999,
pattern=_result_record):
""" Output a raster with coefficients from CCDC
Args:
date (int): Ordinal date for prediction image
result_location (str): Location of the results
bands (list): Bands to predict
coefs (list): List of coefficients to output
image_ds (gdal.Dataset): Example dataset
use_robust (bool, optional): Map robust coefficients and RMSE instead of
normal ones
amplitude (bool, optional): Map amplitude of seasonality instead of
individual coefficient estimates for sin/cosine pair (default: False)
after (bool, optional): If date intersects a disturbed period, use next
available time segment (default: False)
before (bool, optional): If date does not intersect a model, use previous
non-disturbed time segment (default: False)
qa (bool, optional): Add QA flag specifying segment type (intersect,
after, or before) (default: False)
ndv (int, optional): NoDataValue (default: -9999)
pattern (str, optional): filename pattern of saved record results
Returns:
tuple: A tuple (np.ndarray, list) containing the 3D numpy.ndarray of the
coefficients (coefficient x band x pixel), and the band names for
the output dataset
"""
# Find results
records = find_results(result_location, pattern)
# Find result attributes to extract
i_bands, i_coefs, use_rmse, coef_names, _, _ = find_result_attributes(
records, bands, coefs, use_robust=use_robust)
# Process amplitude transform for seasonality coefficients
if amplitude:
harm_coefs = []
for i, (c, n) in enumerate(zip(i_coefs, coef_names)):
if re.match(r'harm\(x, [0-9]+\)\[0]', n):
harm_coefs.append(c)
coef_names[i] = re.sub(r'harm(.*)\[.*', r'amplitude\1', n)
# Remove sin term from each harmonic pair
i_coefs = [c for c in i_coefs if c - 1 not in harm_coefs]
coef_names = [n for n in coef_names if 'harm' not in n]
n_bands = len(i_bands)
n_coefs = len(i_coefs)
n_rmse = n_bands if use_rmse else 0
# Setup output band names
band_names = []
for _c in coef_names:
for _b in i_bands:
band_names.append('B' + str(_b + 1) + '_' + _c.replace(' ', ''))
if use_rmse is True:
for _b in i_bands:
band_names.append('B' + str(_b + 1) + '_RMSE')
n_qa = 0
if qa:
n_qa += 1
band_names.append('SegmentQAQC')
n_out_bands = n_bands * n_coefs + n_rmse + n_qa
_coef = 'robust_coef' if use_robust else 'coef'
_rmse = 'robust_rmse' if use_robust else 'rmse'
logger.debug('Allocating memory...')
raster = np.ones((image_ds.RasterYSize, image_ds.RasterXSize, n_out_bands),
dtype=np.float32) * ndv
logger.debug('Processing results')
for rec in iter_records(records, warn_on_empty=WARN_ON_EMPTY):
for _qa, index in find_indices(rec, date, after=after, before=before):
if index.shape[0] == 0:
continue
if n_coefs > 0:
# Normalize intercept to mid-point in time segment
rec[_coef][index, 0, :] += (
(rec['start'][index] + rec['end'][index])
/ 2.0)[:, np.newaxis] * \
rec[_coef][index, 1, :]
# If we want amplitude, calculate it
if amplitude:
for harm_coef in harm_coefs:
rec[_coef][index, harm_coef, :] = np.linalg.norm(
rec[_coef][index, harm_coef:harm_coef + 2, :],
axis=1)
# Extract coefficients
raster[rec['py'][index],
rec['px'][index], :n_coefs * n_bands] =\
np.reshape(rec[_coef][index][:, i_coefs, :][:, :, i_bands],
(index.size, n_coefs * n_bands))
if use_rmse:
raster[rec['py'][index], rec['px'][index],
n_coefs * n_bands:n_out_bands - n_qa] =\
rec[_rmse][index][:, i_bands]
if qa:
raster[rec['py'][index], rec['px'][index], -1] = _qa
return raster, band_names
def get_classification(date,
result_location,
image_ds,
after=False,
before=False,
qa=False,
pred_proba=False,
ndv=0,
pattern=_result_record):
""" Output raster with classification results
Args:
date (int): ordinal date for prediction image
result_location (str): Location of the results
image_ds (gdal.Dataset): Example dataset
after (bool, optional): If date intersects a disturbed period, use next
available time segment
before (bool, optional): If date does not intersect a model, use previous
non-disturbed time segment
qa (bool, optional): Add QA flag specifying segment type (intersect,
after, or before)
pred_proba (bool, optional): Include additional band with classification
value probabilities
ndv (int, optional): NoDataValue
pattern (str, optional): filename pattern of saved record results
Returns:
np.ndarray: 2D numpy array containing the classification map for the date
specified
"""
# Find results
records = find_results(result_location, pattern)
n_bands = 2 if pred_proba else 1
dtype = np.uint16 if pred_proba else np.uint8
band_names = ['Classification']
if pred_proba:
band_names.append('Pred Proba (x10,000)')
if qa:
n_bands += 1
band_names.append('SegmentQAQC')
logger.debug('Allocating memory...')
raster = np.ones((image_ds.RasterYSize, image_ds.RasterXSize, n_bands),
dtype=dtype) * int(ndv)
logger.debug('Processing results')
for rec, fname in iter_records(records,
warn_on_empty=WARN_ON_EMPTY,
yield_filename=True):
if 'class' not in rec.dtype.names:
logger.warning(
'Results in {f} do not have classification labels'.format(
f=fname))
continue
if 'class_proba' not in rec.dtype.names and pred_proba:
raise ValueError('Results do not have classification prediction'
' probability values')
for _qa, index in find_indices(rec, date, after=after, before=before):
if index.shape[0] == 0:
continue
raster[rec['py'][index], rec['px'][index], 0] = rec['class'][index]
if pred_proba:
raster[rec['py'][index],
rec['px'][index], 1] = \
rec['class_proba'][index].max(axis=1) * 10000
if qa:
raster[rec['py'][index], rec['px'][index], -1] = _qa
return raster, band_names
def get_coefficients(
date,
result_location,
image_ds,
bands,
coefs,
no_scale_intercept=False,
use_robust=False,
after=False,
before=False,
qa=False,
ndv=-9999,
pattern=_result_record,
):
""" Output a raster with coefficients from CCDC
Args:
date (int): Ordinal date for prediction image
result_location (str): Location of the results
bands (list): Bands to predict
coefs (list): List of coefficients to output
image_ds (gdal.Dataset): Example dataset
no_scale_intercept (bool, optional): Skip scaling of intercept
coefficient by slope (default: False)
use_robust (bool, optional): Map robust coefficients and RMSE instead of
normal ones
after (bool, optional): If date intersects a disturbed period, use next
available time segment
before (bool, optional): If date does not intersect a model, use previous
non-disturbed time segment
qa (bool, optional): Add QA flag specifying segment type (intersect,
after, or before)
ndv (int, optional): NoDataValue
pattern (str, optional): filename pattern of saved record results
Returns:
tuple: A tuple (np.ndarray, list) containing the 3D numpy.ndarray of the
coefficients (coefficient x band x pixel), and the band names for
the output dataset
"""
# Find results
records = find_results(result_location, pattern)
# Find result attributes to extract
i_bands, i_coefs, use_rmse, coef_names, _, _ = find_result_attributes(records, bands, coefs, use_robust=use_robust)
n_bands = len(i_bands)
n_coefs = len(i_coefs)
n_rmse = n_bands if use_rmse else 0
# Setup output band names
band_names = []
for _c in coef_names:
for _b in i_bands:
band_names.append("B" + str(_b + 1) + "_" + _c.replace(" ", ""))
if use_rmse is True:
for _b in i_bands:
band_names.append("B" + str(_b + 1) + "_RMSE")
n_qa = 0
if qa:
n_qa += 1
band_names.append("SegmentQAQC")
n_out_bands = n_bands * n_coefs + n_rmse + n_qa
logger.debug("Band names:")
logger.debug(band_names)
_coef = "robust_coef" if use_robust else "coef"
_rmse = "robust_rmse" if use_robust else "rmse"
logger.debug("Allocating memory...")
raster = np.ones((image_ds.RasterYSize, image_ds.RasterXSize, n_out_bands), dtype=np.float32) * ndv
logger.debug("Processing results")
for rec in iter_records(records, warn_on_empty=WARN_ON_EMPTY):
for _qa, index in find_indices(rec, date, after=after, before=before):
if index.shape[0] == 0:
continue
if n_coefs > 0:
# Normalize intercept to mid-point in time segment
if not no_scale_intercept:
rec[_coef][index, 0, :] += ((rec["start"][index] + rec["end"][index]) / 2.0)[:, None] * rec[_coef][
index, 1, :
]
# Extract coefficients
raster[rec["py"][index], rec["px"][index], : n_coefs * n_bands] = np.reshape(
rec[_coef][index][:, i_coefs, :][:, :, i_bands], (index.size, n_coefs * n_bands)
)
if use_rmse:
raster[rec["py"][index], rec["px"][index], n_coefs * n_bands : n_out_bands - n_qa] = rec[_rmse][index][
:, i_bands
]
if qa:
raster[rec["py"][index], rec["px"][index], -1] = _qa
return raster, band_names
def get_datechangemap(start,
end,
result_location,
image_ds,
first=False,
out_format='%Y%j',
magnitude=False,
ndv=-9999,
pattern=_result_record):
""" Output raster with changemap
Args:
start (int): Ordinal date for start of map records
end (int): Ordinal date for end of map records
result_location (str): Location of results
image_ds (gdal.Dataset): Example dataset
first (bool): Use first change instead of last
out_format (str, optional): Output date format
magnitude (bool, optional): output magnitude of each change?
ndv (int, optional): NoDataValue
pattern (str, optional): filename pattern of saved record results
Returns:
tuple: A 2D np.ndarray array containing the changes between the
start and end date. Also includes, if specified, a 3D np.ndarray of
the magnitude of each change plus the indices of these magnitudes
"""
# Find results
records = find_results(result_location, pattern)
logger.debug('Allocating memory...')
datemap = np.ones((image_ds.RasterYSize, image_ds.RasterXSize),
dtype=np.int32) * int(ndv)
# Determine what magnitude information to output if requested
if magnitude:
magnitude_indices = get_magnitude_indices(records)
magnitudemap = np.ones((image_ds.RasterYSize, image_ds.RasterXSize,
magnitude_indices.size),
dtype=np.float32) * float(ndv)
logger.debug('Processing results')
for rec in iter_records(records):
index = np.where((rec['break'] >= start) & (rec['break'] <= end))[0]
if first:
_, _index = np.unique(rec['px'][index], return_index=True)
index = index[_index]
if index.shape[0] != 0:
if out_format != 'ordinal':
dates = np.array([
int(dt.fromordinal(_d).strftime(out_format))
for _d in rec['break'][index]
])
datemap[rec['py'][index], rec['px'][index]] = dates
else:
datemap[rec['py'][index], rec['px'][index]] = \
rec['break'][index]
if magnitude:
magnitudemap[rec['py'][index], rec['px'][index], :] = \
rec[index]['magnitude'][:, magnitude_indices]
if magnitude:
return datemap, magnitudemap, magnitude_indices
else:
return datemap, None, None