def _significant_features(radar,
field,
gatefilter=None,
min_size=None,
size_bins=75,
size_limits=(0, 300),
structure=None,
remove_size_field=True,
fill_value=None,
size_field=None,
debug=False,
verbose=False):
"""
"""
# Parse fill value
if fill_value is None:
fill_value = get_fillvalue()
# Parse field names
if size_field is None:
size_field = '{}_feature_size'.format(field)
# Parse gate filter
if gatefilter is None:
gatefilter = GateFilter(radar, exclude_based=False)
# Parse binary structuring element
if structure is None:
structure = ndimage.generate_binary_structure(2, 1)
# Initialize echo feature size array
size_data = np.zeros_like(radar.fields[field]['data'],
subok=False,
dtype=np.int32)
# Loop over all sweeps
feature_sizes = []
for sweep in radar.iter_slice():
# Parse radar sweep data and define only valid gates
is_valid_gate = ~radar.fields[field]['data'][sweep].mask
# Label the connected features in radar sweep data and create index
# array which defines each unique label (feature)
labels, nlabels = ndimage.label(is_valid_gate,
structure=structure,
output=None)
index = np.arange(1, nlabels + 1, 1)
if debug:
print 'Number of unique features for {}: {}'.format(sweep, nlabels)
# Compute the size (in radar gates) of each echo feature
# Check for case where no echo features are found, e.g., no data in
# sweep
if nlabels > 0:
sweep_sizes = ndimage.labeled_comprehension(
is_valid_gate, labels, index, np.count_nonzero, np.int32, 0)
feature_sizes.append(sweep_sizes)
# Set each label (feature) to its total size (in radar gates)
for label, size in zip(index, sweep_sizes):
size_data[sweep][labels == label] = size
# Stack sweep echo feature sizes
feature_sizes = np.hstack(feature_sizes)
# Compute histogram of echo feature sizes, bin centers and bin
# width
counts, bin_edges = np.histogram(feature_sizes,
bins=size_bins,
range=size_limits,
normed=False,
weights=None,
density=False)
bin_centers = bin_edges[:-1] + np.diff(bin_edges) / 2.0
bin_width = np.diff(bin_edges).mean()
if debug:
print 'Bin width: {} gate(s)'.format(bin_width)
# Compute the peak of the echo feature size distribution
# We expect the peak of the echo feature size distribution to be close to 1
# radar gate
peak_size = bin_centers[counts.argmax()] - bin_width / 2.0
if debug:
print 'Feature size at peak: {} gate(s)'.format(peak_size)
# Determine the first instance when the count (sample size) for an echo
# feature size bin reaches 0 after the distribution peak
# This will define the minimum echo feature size
is_zero_size = np.logical_and(bin_centers > peak_size,
np.isclose(counts, 0, atol=1.0e-5))
min_size = bin_centers[is_zero_size].min() - bin_width / 2.0
if debug:
_range = [0.0, min_size]
print 'Insignificant feature size range: {} gates'.format(_range)
# Mask invalid feature sizes, e.g., zero-size features
size_data = np.ma.masked_equal(size_data, 0, copy=False)
size_data.set_fill_value(fill_value)
# Add echo feature size field to radar
size_dict = {
'data': size_data.astype(np.int32),
'standard_name': size_field,
'long_name': '',
'_FillValue': size_data.fill_value,
'units': 'unitless',
}
radar.add_field(size_field, size_dict, replace_existing=True)
# Update gate filter
gatefilter.include_above(size_field, min_size, op='and', inclusive=False)
# Remove eacho feature size field
if remove_size_field:
radar.fields.pop(size_field, None)
return gatefilter
def significant_detection(radar,
gatefilter=None,
remove_small_features=True,
size_bins=75,
size_limits=(0, 300),
fill_holes=False,
dilate=False,
structure=None,
iterations=1,
rays_wrap_around=False,
min_ncp=None,
ncp_field=None,
detect_field=None,
debug=False,
verbose=False):
"""
Determine the significant detection of a radar. Note that significant
detection can still include other non-meteorological echoes that the user
may still have to remove further down the processing chain.
Parameters
----------
radar : Radar
Radar object used to determine the appropriate GateFilter.
gatefilter : GateFilter, optional
If None, all radar gates will initially be assumed valid.
remove_small_features : bool, optional
True to remove insignificant echo features (e.g., salt and pepper
noise) from significant detection mask.
size_bins : int, optional
Number of bins used to bin echo feature sizes and thus define its
distribution.
size_limits : list or tuple, optional
Limits of the echo feature size distribution. The upper limit needs to
be large enough to include the minimum feature size.
fill_holes : bool, optional
Fill any holes in the significant detection mask. For most radar
volumes this should not be used since the default structuring element
will automatically fill any sized hole.
dilate : bool, optional
Use binary dilation to fill in edges of the significant detection mask.
structure : array_like, optional
The binary structuring element used for all morphology routines. See
SciPy's ndimage documentation for more information.
iterations : int, optional
The number of iterations to repeat binary dilation. If iterations is
less than 1, binary dilation is repeated until the result does not
change anymore.
rays_wrap_around : bool, optional
Whether the rays at the beginning and end of a sweep are connected
(e.g., PPI VCP).
min_ncp : float, optional
Minimum normalized coherent power (signal quality) value used to
indicate a significant echo.
ncp_field : str, optional
Minimum normalized coherent power (signal quality) field name. The
default uses the Py-ART configuation file.
detect_field : str, optional
Radar significant detection mask field name.
debug : bool, optional
True to print debugging information, False to suppress.
verbose : bool, optional
True to print progress information, False to suppress.
Returns
-------
gatefilter : GateFilter
Py-ART GateFilter object indicating which radar gates are valid and
invalid.
"""
# Parse field names
if ncp_field is None:
ncp_field = get_field_name('normalized_coherent_power')
if detect_field is None:
detect_field = 'significant_detection_mask'
# Parse gate filter
if gatefilter is None:
gatefilter = GateFilter(radar, exclude_based=False)
# Exclude gates with poor signal quality
if min_ncp is not None and ncp_field in radar.fields:
gatefilter.include_above(ncp_field, min_ncp, op='and', inclusive=True)
detect_dict = {
'data': gatefilter.gate_included.astype(np.int8),
'long_name': 'Radar significant detection mask',
'standard_name': 'significant_detection_mask',
'valid_min': 0,
'valid_max': 1,
'_FillValue': None,
'units': 'unitless',
'comment': '0 = no significant detection, 1 = significant detection',
}
radar.add_field(detect_field, detect_dict, replace_existing=True)
# Remove insignificant features from significant detection mask
if remove_small_features:
basic_fixes._binary_significant_features(radar,
detect_field,
size_bins=size_bins,
size_limits=size_limits,
structure=structure,
debug=debug,
verbose=verbose)
# Fill holes in significant detection mask
if fill_holes:
basic_fixes._binary_fill(radar, detect_field, structure=structure)
# Dilate significant detection mask
if dilate:
basic_fixes._binary_dilation(radar,
detect_field,
structure=structure,
iterations=iterations,
debug=debug,
verbose=verbose)
# Update gate filter
gatefilter.include_equal(detect_field, 1, op='new')
return gatefilter
def significant_detection(
radar, gatefilter=None, remove_small_features=True, size_bins=75,
size_limits=(0, 300), fill_holes=False, dilate=False, structure=None,
iterations=1, rays_wrap_around=False, min_ncp=None, ncp_field=None,
detect_field=None, debug=False, verbose=False):
"""
Determine the significant detection of a radar. Note that significant
detection can still include other non-meteorological echoes that the user
may still have to remove further down the processing chain.
Parameters
----------
radar : Radar
Radar object used to determine the appropriate GateFilter.
gatefilter : GateFilter, optional
If None, all radar gates will initially be assumed valid.
remove_small_features : bool, optional
True to remove insignificant echo features (e.g., salt and pepper
noise) from significant detection mask.
size_bins : int, optional
Number of bins used to bin echo feature sizes and thus define its
distribution.
size_limits : list or tuple, optional
Limits of the echo feature size distribution. The upper limit needs to
be large enough to include the minimum feature size.
fill_holes : bool, optional
Fill any holes in the significant detection mask. For most radar
volumes this should not be used since the default structuring element
will automatically fill any sized hole.
dilate : bool, optional
Use binary dilation to fill in edges of the significant detection mask.
structure : array_like, optional
The binary structuring element used for all morphology routines. See
SciPy's ndimage documentation for more information.
iterations : int, optional
The number of iterations to repeat binary dilation. If iterations is
less than 1, binary dilation is repeated until the result does not
change anymore.
rays_wrap_around : bool, optional
Whether the rays at the beginning and end of a sweep are connected
(e.g., PPI VCP).
min_ncp : float, optional
Minimum normalized coherent power (signal quality) value used to
indicate a significant echo.
ncp_field : str, optional
Minimum normalized coherent power (signal quality) field name. The
default uses the Py-ART configuation file.
detect_field : str, optional
Radar significant detection mask field name.
debug : bool, optional
True to print debugging information, False to suppress.
verbose : bool, optional
True to print progress information, False to suppress.
Returns
-------
gatefilter : GateFilter
Py-ART GateFilter object indicating which radar gates are valid and
invalid.
"""
# Parse field names
if ncp_field is None:
ncp_field = get_field_name('normalized_coherent_power')
if detect_field is None:
detect_field = 'significant_detection_mask'
# Parse gate filter
if gatefilter is None:
gatefilter = GateFilter(radar, exclude_based=False)
# Exclude gates with poor signal quality
if min_ncp is not None and ncp_field in radar.fields:
gatefilter.include_above(ncp_field, min_ncp, op='and', inclusive=True)
detect_dict = {
'data': gatefilter.gate_included.astype(np.int8),
'long_name': 'Radar significant detection mask',
'standard_name': 'significant_detection_mask',
'valid_min': 0,
'valid_max': 1,
'_FillValue': None,
'units': 'unitless',
'comment': '0 = no significant detection, 1 = significant detection',
}
radar.add_field(detect_field, detect_dict, replace_existing=True)
# Remove insignificant features from significant detection mask
if remove_small_features:
basic_fixes._binary_significant_features(
radar, detect_field, size_bins=size_bins, size_limits=size_limits,
structure=structure, debug=debug, verbose=verbose)
# Fill holes in significant detection mask
if fill_holes:
basic_fixes._binary_fill(radar, detect_field, structure=structure)
# Dilate significant detection mask
if dilate:
basic_fixes._binary_dilation(
radar, detect_field, structure=structure, iterations=iterations,
debug=debug, verbose=verbose)
# Update gate filter
gatefilter.include_equal(detect_field, 1, op='new')
return gatefilter
def _significant_features(
radar, field, gatefilter=None, min_size=None, size_bins=75,
size_limits=(0, 300), structure=None, remove_size_field=True,
fill_value=None, size_field=None, debug=False, verbose=False):
"""
"""
# Parse fill value
if fill_value is None:
fill_value = get_fillvalue()
# Parse field names
if size_field is None:
size_field = '{}_feature_size'.format(field)
# Parse gate filter
if gatefilter is None:
gatefilter = GateFilter(radar, exclude_based=False)
# Parse binary structuring element
if structure is None:
structure = ndimage.generate_binary_structure(2, 1)
# Initialize echo feature size array
size_data = np.zeros_like(
radar.fields[field]['data'], subok=False, dtype=np.int32)
# Loop over all sweeps
feature_sizes = []
for sweep in radar.iter_slice():
# Parse radar sweep data and define only valid gates
is_valid_gate = ~radar.fields[field]['data'][sweep].mask
# Label the connected features in radar sweep data and create index
# array which defines each unique label (feature)
labels, nlabels = ndimage.label(
is_valid_gate, structure=structure, output=None)
index = np.arange(1, nlabels + 1, 1)
if debug:
print 'Number of unique features for {}: {}'.format(sweep, nlabels)
# Compute the size (in radar gates) of each echo feature
# Check for case where no echo features are found, e.g., no data in
# sweep
if nlabels > 0:
sweep_sizes = ndimage.labeled_comprehension(
is_valid_gate, labels, index, np.count_nonzero, np.int32, 0)
feature_sizes.append(sweep_sizes)
# Set each label (feature) to its total size (in radar gates)
for label, size in zip(index, sweep_sizes):
size_data[sweep][labels == label] = size
# Stack sweep echo feature sizes
feature_sizes = np.hstack(feature_sizes)
# Compute histogram of echo feature sizes, bin centers and bin
# width
counts, bin_edges = np.histogram(
feature_sizes, bins=size_bins, range=size_limits, normed=False,
weights=None, density=False)
bin_centers = bin_edges[:-1] + np.diff(bin_edges) / 2.0
bin_width = np.diff(bin_edges).mean()
if debug:
print 'Bin width: {} gate(s)'.format(bin_width)
# Compute the peak of the echo feature size distribution
# We expect the peak of the echo feature size distribution to be close to 1
# radar gate
peak_size = bin_centers[counts.argmax()] - bin_width / 2.0
if debug:
print 'Feature size at peak: {} gate(s)'.format(peak_size)
# Determine the first instance when the count (sample size) for an echo
# feature size bin reaches 0 after the distribution peak
# This will define the minimum echo feature size
is_zero_size = np.logical_and(
bin_centers > peak_size, np.isclose(counts, 0, atol=1.0e-5))
min_size = bin_centers[is_zero_size].min() - bin_width / 2.0
if debug:
_range = [0.0, min_size]
print 'Insignificant feature size range: {} gates'.format(_range)
# Mask invalid feature sizes, e.g., zero-size features
size_data = np.ma.masked_equal(size_data, 0, copy=False)
size_data.set_fill_value(fill_value)
# Add echo feature size field to radar
size_dict = {
'data': size_data.astype(np.int32),
'standard_name': size_field,
'long_name': '',
'_FillValue': size_data.fill_value,
'units': 'unitless',
}
radar.add_field(size_field, size_dict, replace_existing=True)
# Update gate filter
gatefilter.include_above(size_field, min_size, op='and', inclusive=False)
# Remove eacho feature size field
if remove_size_field:
radar.fields.pop(size_field, None)
return gatefilter
def significant_features(
radar, fields, gatefilter=None, size_bins=100, size_limits=(0, 400),
structure=None, save_size_field=False, fill_value=None, debug=False,
verbose=False):
"""
Determine significant radar echo features on a sweep by sweep basis by
computing the size each echo feature. Here an echo feature is defined as
multiple connected radar gates with valid data, where the connection
structure is defined by the user.
Parameters
----------
radar : Radar
Py-ART Radar containing
fields : str or list or tuple
Radar fields to be used to identify significant echo featues.
gatefilter : GateFilter
Py-ART GateFilter instance.
size_bins : int, optional
Number of size bins used to bin feature size distribution.
size_limits : list or tuple, optional
Lower and upper limits of the feature size distribution. This together
with size_bins defines the bin width of the feature size distribution.
structure : array_like, optional
Binary structuring element used to define connected radar gates. The
default defines a structuring element in which diagonal radar gates are
not considered connected.
save_size_field : bool, optional
True to save size fields in the radar object, False to discard.
debug : bool, optional
True to print debugging information, False to suppress.
Returns
-------
gf : GateFilter
Py-ART GateFilter.
"""
# Parse fill value
if fill_value is None:
fill_value = get_fillvalue()
# Parse radar fields
if isinstance(fields, str):
fields = [fields]
# Parse gate filter
if gatefilter is None:
gf = GateFilter(radar, exclude_based=False)
# Parse binary structuring element
if structure is None:
structure = ndimage.generate_binary_structure(2, 1)
for field in fields:
if verbose:
print 'Processing echo features: {}'.format(field)
# Initialize echo feature size array
size_data = np.zeros_like(
radar.fields[field]['data'], subok=False, dtype=np.int32)
feature_sizes = []
for sweep, _slice in enumerate(radar.iter_slice()):
# Parse radar sweep data and define only valid gates
data = radar.get_field(sweep, field, copy=False)
is_valid_gate = ~np.ma.getmaskarray(data)
# Label the connected features in the radar sweep data and create
# index array which defines each unique label (feature)
labels, nlabels = ndimage.label(
is_valid_gate, structure=structure, output=None)
index = np.arange(1, nlabels + 1, 1)
if debug:
print 'Unique features in sweep {}: {}'.format(sweep, nlabels)
# Compute the size (in radar gates) of each echo feature
# Check for case where no echo features are found, e.g., no data in
# sweep
if nlabels > 0:
sweep_sizes = ndimage.labeled_comprehension(
is_valid_gate, labels, index, np.count_nonzero,
np.int32, 0)
feature_sizes.append(sweep_sizes)
# Set each label (feature) to its total size (in radar gates)
for label, size in zip(index, sweep_sizes):
size_data[_slice][labels == label] = size
# Stack sweep echo feature sizes
feature_sizes = np.hstack(feature_sizes)
# Bin and compute feature size occurrences
counts, bin_edges = np.histogram(
feature_sizes, bins=size_bins, range=size_limits, normed=False,
weights=None, density=False)
bin_centers = bin_edges[:-1] + np.diff(bin_edges) / 2.0
bin_width = np.diff(bin_edges).mean()
if debug:
print 'Bin width: {} gate(s)'.format(bin_width)
# Compute the peak of the echo feature size distribution. We expect the
# peak of the echo feature size distribution to be close to 1 radar
# gate
peak_size = bin_centers[counts.argmax()] - bin_width / 2.0
if debug:
print 'Feature size at peak: {} gate(s)'.format(peak_size)
# Determine the first instance when the count (sample size) for an echo
# feature size bin reaches 0 after the distribution peak. This will
# define the minimum echo feature size
is_zero_size = np.logical_and(
bin_centers > peak_size, np.isclose(counts, 0, atol=1.0e-1))
min_size = bin_centers[is_zero_size].min() - bin_width / 2.0
if debug:
_range = [0.0, min_size]
print 'Insignificant feature size range: {} gates'.format(_range)
# Mask invalid feature sizes, e.g., zero-size features
size_data = np.ma.masked_equal(size_data, 0, copy=False)
size_data.set_fill_value(fill_value)
# Parse echo feature size field name
size_field = '{}_feature_size'.format(field)
# Add echo feature size field to radar
size_dict = {
'data': size_data.astype(np.int32),
'long_name': 'Echo feature size in number of radar gates',
'_FillValue': size_data.fill_value,
'units': 'unitless',
'comment': None,
}
radar.add_field(size_field, size_dict, replace_existing=True)
# Update gate filter
gf.include_above(size_field, min_size, op='and', inclusive=False)
# Remove eacho feature size field if specified
if not save_size_field:
radar.fields.pop(size_field, None)
return gf
