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nvictus/priority-queue-dictionary
|
pqdict/__init__.py
|
pqdict.pushpopitem
|
def pushpopitem(self, key, value, node_factory=_Node):
"""
Equivalent to inserting a new item followed by removing the top
priority item, but faster. Raises ``KeyError`` if the new key is
already in the pqdict.
"""
heap = self._heap
position = self._position
precedes = self._precedes
prio = self._keyfn(value) if self._keyfn else value
node = node_factory(key, value, prio)
if key in self:
raise KeyError('%s is already in the queue' % repr(key))
if heap and precedes(heap[0].prio, node.prio):
node, heap[0] = heap[0], node
position[key] = 0
del position[node.key]
self._sink(0)
return node.key, node.value
|
python
|
def pushpopitem(self, key, value, node_factory=_Node):
"""
Equivalent to inserting a new item followed by removing the top
priority item, but faster. Raises ``KeyError`` if the new key is
already in the pqdict.
"""
heap = self._heap
position = self._position
precedes = self._precedes
prio = self._keyfn(value) if self._keyfn else value
node = node_factory(key, value, prio)
if key in self:
raise KeyError('%s is already in the queue' % repr(key))
if heap and precedes(heap[0].prio, node.prio):
node, heap[0] = heap[0], node
position[key] = 0
del position[node.key]
self._sink(0)
return node.key, node.value
|
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577f9d3086058bec0e49cc2050dd9454b788d93b
|
https://github.com/nvictus/priority-queue-dictionary/blob/577f9d3086058bec0e49cc2050dd9454b788d93b/pqdict/__init__.py#L305-L324
|
train
|
nvictus/priority-queue-dictionary
|
pqdict/__init__.py
|
pqdict.updateitem
|
def updateitem(self, key, new_val):
"""
Update the priority value of an existing item. Raises ``KeyError`` if
key is not in the pqdict.
"""
if key not in self._position:
raise KeyError(key)
self[key] = new_val
|
python
|
def updateitem(self, key, new_val):
"""
Update the priority value of an existing item. Raises ``KeyError`` if
key is not in the pqdict.
"""
if key not in self._position:
raise KeyError(key)
self[key] = new_val
|
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577f9d3086058bec0e49cc2050dd9454b788d93b
|
https://github.com/nvictus/priority-queue-dictionary/blob/577f9d3086058bec0e49cc2050dd9454b788d93b/pqdict/__init__.py#L326-L334
|
train
|
nvictus/priority-queue-dictionary
|
pqdict/__init__.py
|
pqdict.replace_key
|
def replace_key(self, key, new_key):
"""
Replace the key of an existing heap node in place. Raises ``KeyError``
if the key to replace does not exist or if the new key is already in
the pqdict.
"""
heap = self._heap
position = self._position
if new_key in self:
raise KeyError('%s is already in the queue' % repr(new_key))
pos = position.pop(key) # raises appropriate KeyError
position[new_key] = pos
heap[pos].key = new_key
|
python
|
def replace_key(self, key, new_key):
"""
Replace the key of an existing heap node in place. Raises ``KeyError``
if the key to replace does not exist or if the new key is already in
the pqdict.
"""
heap = self._heap
position = self._position
if new_key in self:
raise KeyError('%s is already in the queue' % repr(new_key))
pos = position.pop(key) # raises appropriate KeyError
position[new_key] = pos
heap[pos].key = new_key
|
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] |
577f9d3086058bec0e49cc2050dd9454b788d93b
|
https://github.com/nvictus/priority-queue-dictionary/blob/577f9d3086058bec0e49cc2050dd9454b788d93b/pqdict/__init__.py#L336-L349
|
train
|
nvictus/priority-queue-dictionary
|
pqdict/__init__.py
|
pqdict.swap_priority
|
def swap_priority(self, key1, key2):
"""
Fast way to swap the priority level of two items in the pqdict. Raises
``KeyError`` if either key does not exist.
"""
heap = self._heap
position = self._position
if key1 not in self or key2 not in self:
raise KeyError
pos1, pos2 = position[key1], position[key2]
heap[pos1].key, heap[pos2].key = key2, key1
position[key1], position[key2] = pos2, pos1
|
python
|
def swap_priority(self, key1, key2):
"""
Fast way to swap the priority level of two items in the pqdict. Raises
``KeyError`` if either key does not exist.
"""
heap = self._heap
position = self._position
if key1 not in self or key2 not in self:
raise KeyError
pos1, pos2 = position[key1], position[key2]
heap[pos1].key, heap[pos2].key = key2, key1
position[key1], position[key2] = pos2, pos1
|
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577f9d3086058bec0e49cc2050dd9454b788d93b
|
https://github.com/nvictus/priority-queue-dictionary/blob/577f9d3086058bec0e49cc2050dd9454b788d93b/pqdict/__init__.py#L351-L363
|
train
|
nvictus/priority-queue-dictionary
|
pqdict/__init__.py
|
pqdict.heapify
|
def heapify(self, key=__marker):
"""
Repair a broken heap. If the state of an item's priority value changes
you can re-sort the relevant item only by providing ``key``.
"""
if key is self.__marker:
n = len(self._heap)
for pos in reversed(range(n//2)):
self._sink(pos)
else:
try:
pos = self._position[key]
except KeyError:
raise KeyError(key)
self._reheapify(pos)
|
python
|
def heapify(self, key=__marker):
"""
Repair a broken heap. If the state of an item's priority value changes
you can re-sort the relevant item only by providing ``key``.
"""
if key is self.__marker:
n = len(self._heap)
for pos in reversed(range(n//2)):
self._sink(pos)
else:
try:
pos = self._position[key]
except KeyError:
raise KeyError(key)
self._reheapify(pos)
|
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577f9d3086058bec0e49cc2050dd9454b788d93b
|
https://github.com/nvictus/priority-queue-dictionary/blob/577f9d3086058bec0e49cc2050dd9454b788d93b/pqdict/__init__.py#L398-L413
|
train
|
ajk8/hatchery
|
hatchery/project.py
|
package_has_version_file
|
def package_has_version_file(package_name):
""" Check to make sure _version.py is contained in the package """
version_file_path = helpers.package_file_path('_version.py', package_name)
return os.path.isfile(version_file_path)
|
python
|
def package_has_version_file(package_name):
""" Check to make sure _version.py is contained in the package """
version_file_path = helpers.package_file_path('_version.py', package_name)
return os.path.isfile(version_file_path)
|
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Check to make sure _version.py is contained in the package
|
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] |
e068c9f5366d2c98225babb03d4cde36c710194f
|
https://github.com/ajk8/hatchery/blob/e068c9f5366d2c98225babb03d4cde36c710194f/hatchery/project.py#L45-L48
|
train
|
ajk8/hatchery
|
hatchery/project.py
|
get_project_name
|
def get_project_name():
""" Grab the project name out of setup.py """
setup_py_content = helpers.get_file_content('setup.py')
ret = helpers.value_of_named_argument_in_function(
'name', 'setup', setup_py_content, resolve_varname=True
)
if ret and ret[0] == ret[-1] in ('"', "'"):
ret = ret[1:-1]
return ret
|
python
|
def get_project_name():
""" Grab the project name out of setup.py """
setup_py_content = helpers.get_file_content('setup.py')
ret = helpers.value_of_named_argument_in_function(
'name', 'setup', setup_py_content, resolve_varname=True
)
if ret and ret[0] == ret[-1] in ('"', "'"):
ret = ret[1:-1]
return ret
|
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Grab the project name out of setup.py
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e068c9f5366d2c98225babb03d4cde36c710194f
|
https://github.com/ajk8/hatchery/blob/e068c9f5366d2c98225babb03d4cde36c710194f/hatchery/project.py#L78-L86
|
train
|
ajk8/hatchery
|
hatchery/project.py
|
get_version
|
def get_version(package_name, ignore_cache=False):
""" Get the version which is currently configured by the package """
if ignore_cache:
with microcache.temporarily_disabled():
found = helpers.regex_in_package_file(
VERSION_SET_REGEX, '_version.py', package_name, return_match=True
)
else:
found = helpers.regex_in_package_file(
VERSION_SET_REGEX, '_version.py', package_name, return_match=True
)
if found is None:
raise ProjectError('found {}, but __version__ is not defined')
current_version = found['version']
return current_version
|
python
|
def get_version(package_name, ignore_cache=False):
""" Get the version which is currently configured by the package """
if ignore_cache:
with microcache.temporarily_disabled():
found = helpers.regex_in_package_file(
VERSION_SET_REGEX, '_version.py', package_name, return_match=True
)
else:
found = helpers.regex_in_package_file(
VERSION_SET_REGEX, '_version.py', package_name, return_match=True
)
if found is None:
raise ProjectError('found {}, but __version__ is not defined')
current_version = found['version']
return current_version
|
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e068c9f5366d2c98225babb03d4cde36c710194f
|
https://github.com/ajk8/hatchery/blob/e068c9f5366d2c98225babb03d4cde36c710194f/hatchery/project.py#L89-L103
|
train
|
ajk8/hatchery
|
hatchery/project.py
|
set_version
|
def set_version(package_name, version_str):
""" Set the version in _version.py to version_str """
current_version = get_version(package_name)
version_file_path = helpers.package_file_path('_version.py', package_name)
version_file_content = helpers.get_file_content(version_file_path)
version_file_content = version_file_content.replace(current_version, version_str)
with open(version_file_path, 'w') as version_file:
version_file.write(version_file_content)
|
python
|
def set_version(package_name, version_str):
""" Set the version in _version.py to version_str """
current_version = get_version(package_name)
version_file_path = helpers.package_file_path('_version.py', package_name)
version_file_content = helpers.get_file_content(version_file_path)
version_file_content = version_file_content.replace(current_version, version_str)
with open(version_file_path, 'w') as version_file:
version_file.write(version_file_content)
|
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Set the version in _version.py to version_str
|
[
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e068c9f5366d2c98225babb03d4cde36c710194f
|
https://github.com/ajk8/hatchery/blob/e068c9f5366d2c98225babb03d4cde36c710194f/hatchery/project.py#L106-L113
|
train
|
ajk8/hatchery
|
hatchery/project.py
|
version_is_valid
|
def version_is_valid(version_str):
""" Check to see if the version specified is a valid as far as pkg_resources is concerned
>>> version_is_valid('blah')
False
>>> version_is_valid('1.2.3')
True
"""
try:
packaging.version.Version(version_str)
except packaging.version.InvalidVersion:
return False
return True
|
python
|
def version_is_valid(version_str):
""" Check to see if the version specified is a valid as far as pkg_resources is concerned
>>> version_is_valid('blah')
False
>>> version_is_valid('1.2.3')
True
"""
try:
packaging.version.Version(version_str)
except packaging.version.InvalidVersion:
return False
return True
|
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Check to see if the version specified is a valid as far as pkg_resources is concerned
>>> version_is_valid('blah')
False
>>> version_is_valid('1.2.3')
True
|
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e068c9f5366d2c98225babb03d4cde36c710194f
|
https://github.com/ajk8/hatchery/blob/e068c9f5366d2c98225babb03d4cde36c710194f/hatchery/project.py#L116-L128
|
train
|
ajk8/hatchery
|
hatchery/project.py
|
_get_uploaded_versions_warehouse
|
def _get_uploaded_versions_warehouse(project_name, index_url, requests_verify=True):
""" Query the pypi index at index_url using warehouse api to find all of the "releases" """
url = '/'.join((index_url, project_name, 'json'))
response = requests.get(url, verify=requests_verify)
if response.status_code == 200:
return response.json()['releases'].keys()
return None
|
python
|
def _get_uploaded_versions_warehouse(project_name, index_url, requests_verify=True):
""" Query the pypi index at index_url using warehouse api to find all of the "releases" """
url = '/'.join((index_url, project_name, 'json'))
response = requests.get(url, verify=requests_verify)
if response.status_code == 200:
return response.json()['releases'].keys()
return None
|
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e068c9f5366d2c98225babb03d4cde36c710194f
|
https://github.com/ajk8/hatchery/blob/e068c9f5366d2c98225babb03d4cde36c710194f/hatchery/project.py#L131-L137
|
train
|
ajk8/hatchery
|
hatchery/project.py
|
_get_uploaded_versions_pypicloud
|
def _get_uploaded_versions_pypicloud(project_name, index_url, requests_verify=True):
""" Query the pypi index at index_url using pypicloud api to find all versions """
api_url = index_url
for suffix in ('/pypi', '/pypi/', '/simple', '/simple/'):
if api_url.endswith(suffix):
api_url = api_url[:len(suffix) * -1] + '/api/package'
break
url = '/'.join((api_url, project_name))
response = requests.get(url, verify=requests_verify)
if response.status_code == 200:
return [p['version'] for p in response.json()['packages']]
return None
|
python
|
def _get_uploaded_versions_pypicloud(project_name, index_url, requests_verify=True):
""" Query the pypi index at index_url using pypicloud api to find all versions """
api_url = index_url
for suffix in ('/pypi', '/pypi/', '/simple', '/simple/'):
if api_url.endswith(suffix):
api_url = api_url[:len(suffix) * -1] + '/api/package'
break
url = '/'.join((api_url, project_name))
response = requests.get(url, verify=requests_verify)
if response.status_code == 200:
return [p['version'] for p in response.json()['packages']]
return None
|
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e068c9f5366d2c98225babb03d4cde36c710194f
|
https://github.com/ajk8/hatchery/blob/e068c9f5366d2c98225babb03d4cde36c710194f/hatchery/project.py#L140-L151
|
train
|
ajk8/hatchery
|
hatchery/project.py
|
version_already_uploaded
|
def version_already_uploaded(project_name, version_str, index_url, requests_verify=True):
""" Check to see if the version specified has already been uploaded to the configured index
"""
all_versions = _get_uploaded_versions(project_name, index_url, requests_verify)
return version_str in all_versions
|
python
|
def version_already_uploaded(project_name, version_str, index_url, requests_verify=True):
""" Check to see if the version specified has already been uploaded to the configured index
"""
all_versions = _get_uploaded_versions(project_name, index_url, requests_verify)
return version_str in all_versions
|
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e068c9f5366d2c98225babb03d4cde36c710194f
|
https://github.com/ajk8/hatchery/blob/e068c9f5366d2c98225babb03d4cde36c710194f/hatchery/project.py#L168-L172
|
train
|
ajk8/hatchery
|
hatchery/project.py
|
convert_readme_to_rst
|
def convert_readme_to_rst():
""" Attempt to convert a README.md file into README.rst """
project_files = os.listdir('.')
for filename in project_files:
if filename.lower() == 'readme':
raise ProjectError(
'found {} in project directory...'.format(filename) +
'not sure what to do with it, refusing to convert'
)
elif filename.lower() == 'readme.rst':
raise ProjectError(
'found {} in project directory...'.format(filename) +
'refusing to overwrite'
)
for filename in project_files:
if filename.lower() == 'readme.md':
rst_filename = 'README.rst'
logger.info('converting {} to {}'.format(filename, rst_filename))
try:
rst_content = pypandoc.convert(filename, 'rst')
with open('README.rst', 'w') as rst_file:
rst_file.write(rst_content)
return
except OSError as e:
raise ProjectError(
'could not convert readme to rst due to pypandoc error:' + os.linesep + str(e)
)
raise ProjectError('could not find any README.md file to convert')
|
python
|
def convert_readme_to_rst():
""" Attempt to convert a README.md file into README.rst """
project_files = os.listdir('.')
for filename in project_files:
if filename.lower() == 'readme':
raise ProjectError(
'found {} in project directory...'.format(filename) +
'not sure what to do with it, refusing to convert'
)
elif filename.lower() == 'readme.rst':
raise ProjectError(
'found {} in project directory...'.format(filename) +
'refusing to overwrite'
)
for filename in project_files:
if filename.lower() == 'readme.md':
rst_filename = 'README.rst'
logger.info('converting {} to {}'.format(filename, rst_filename))
try:
rst_content = pypandoc.convert(filename, 'rst')
with open('README.rst', 'w') as rst_file:
rst_file.write(rst_content)
return
except OSError as e:
raise ProjectError(
'could not convert readme to rst due to pypandoc error:' + os.linesep + str(e)
)
raise ProjectError('could not find any README.md file to convert')
|
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|
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] |
e068c9f5366d2c98225babb03d4cde36c710194f
|
https://github.com/ajk8/hatchery/blob/e068c9f5366d2c98225babb03d4cde36c710194f/hatchery/project.py#L208-L235
|
train
|
ajk8/hatchery
|
hatchery/project.py
|
get_packaged_files
|
def get_packaged_files(package_name):
""" Collect relative paths to all files which have already been packaged """
if not os.path.isdir('dist'):
return []
return [os.path.join('dist', filename) for filename in os.listdir('dist')]
|
python
|
def get_packaged_files(package_name):
""" Collect relative paths to all files which have already been packaged """
if not os.path.isdir('dist'):
return []
return [os.path.join('dist', filename) for filename in os.listdir('dist')]
|
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Collect relative paths to all files which have already been packaged
|
[
"Collect",
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"which",
"have",
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] |
e068c9f5366d2c98225babb03d4cde36c710194f
|
https://github.com/ajk8/hatchery/blob/e068c9f5366d2c98225babb03d4cde36c710194f/hatchery/project.py#L238-L242
|
train
|
ajk8/hatchery
|
hatchery/project.py
|
multiple_packaged_versions
|
def multiple_packaged_versions(package_name):
""" Look through built package directory and see if there are multiple versions there """
dist_files = os.listdir('dist')
versions = set()
for filename in dist_files:
version = funcy.re_find(r'{}-(.+).tar.gz'.format(package_name), filename)
if version:
versions.add(version)
return len(versions) > 1
|
python
|
def multiple_packaged_versions(package_name):
""" Look through built package directory and see if there are multiple versions there """
dist_files = os.listdir('dist')
versions = set()
for filename in dist_files:
version = funcy.re_find(r'{}-(.+).tar.gz'.format(package_name), filename)
if version:
versions.add(version)
return len(versions) > 1
|
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e068c9f5366d2c98225babb03d4cde36c710194f
|
https://github.com/ajk8/hatchery/blob/e068c9f5366d2c98225babb03d4cde36c710194f/hatchery/project.py#L245-L253
|
train
|
djgagne/hagelslag
|
hagelslag/data/HailForecastGrid.py
|
HailForecastGrid.period_neighborhood_probability
|
def period_neighborhood_probability(self, radius, smoothing, threshold, stride,start_time,end_time):
"""
Calculate the neighborhood probability over the full period of the forecast
Args:
radius: circular radius from each point in km
smoothing: width of Gaussian smoother in km
threshold: intensity of exceedance
stride: number of grid points to skip for reduced neighborhood grid
Returns:
(neighborhood probabilities)
"""
neighbor_x = self.x[::stride, ::stride]
neighbor_y = self.y[::stride, ::stride]
neighbor_kd_tree = cKDTree(np.vstack((neighbor_x.ravel(), neighbor_y.ravel())).T)
neighbor_prob = np.zeros((self.data.shape[0], neighbor_x.shape[0], neighbor_x.shape[1]))
print('Forecast Hours: {0}-{1}'.format(start_time, end_time))
for m in range(len(self.members)):
period_max = self.data[m,start_time:end_time,:,:].max(axis=0)
valid_i, valid_j = np.where(period_max >= threshold)
print(self.members[m], len(valid_i))
if len(valid_i) > 0:
var_kd_tree = cKDTree(np.vstack((self.x[valid_i, valid_j], self.y[valid_i, valid_j])).T)
exceed_points = np.unique(np.concatenate(var_kd_tree.query_ball_tree(neighbor_kd_tree, radius))).astype(int)
exceed_i, exceed_j = np.unravel_index(exceed_points, neighbor_x.shape)
neighbor_prob[m][exceed_i, exceed_j] = 1
if smoothing > 0:
neighbor_prob[m] = gaussian_filter(neighbor_prob[m], smoothing,mode='constant')
return neighbor_prob
|
python
|
def period_neighborhood_probability(self, radius, smoothing, threshold, stride,start_time,end_time):
"""
Calculate the neighborhood probability over the full period of the forecast
Args:
radius: circular radius from each point in km
smoothing: width of Gaussian smoother in km
threshold: intensity of exceedance
stride: number of grid points to skip for reduced neighborhood grid
Returns:
(neighborhood probabilities)
"""
neighbor_x = self.x[::stride, ::stride]
neighbor_y = self.y[::stride, ::stride]
neighbor_kd_tree = cKDTree(np.vstack((neighbor_x.ravel(), neighbor_y.ravel())).T)
neighbor_prob = np.zeros((self.data.shape[0], neighbor_x.shape[0], neighbor_x.shape[1]))
print('Forecast Hours: {0}-{1}'.format(start_time, end_time))
for m in range(len(self.members)):
period_max = self.data[m,start_time:end_time,:,:].max(axis=0)
valid_i, valid_j = np.where(period_max >= threshold)
print(self.members[m], len(valid_i))
if len(valid_i) > 0:
var_kd_tree = cKDTree(np.vstack((self.x[valid_i, valid_j], self.y[valid_i, valid_j])).T)
exceed_points = np.unique(np.concatenate(var_kd_tree.query_ball_tree(neighbor_kd_tree, radius))).astype(int)
exceed_i, exceed_j = np.unravel_index(exceed_points, neighbor_x.shape)
neighbor_prob[m][exceed_i, exceed_j] = 1
if smoothing > 0:
neighbor_prob[m] = gaussian_filter(neighbor_prob[m], smoothing,mode='constant')
return neighbor_prob
|
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",",
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")",
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Calculate the neighborhood probability over the full period of the forecast
Args:
radius: circular radius from each point in km
smoothing: width of Gaussian smoother in km
threshold: intensity of exceedance
stride: number of grid points to skip for reduced neighborhood grid
Returns:
(neighborhood probabilities)
|
[
"Calculate",
"the",
"neighborhood",
"probability",
"over",
"the",
"full",
"period",
"of",
"the",
"forecast"
] |
6fb6c3df90bf4867e13a97d3460b14471d107df1
|
https://github.com/djgagne/hagelslag/blob/6fb6c3df90bf4867e13a97d3460b14471d107df1/hagelslag/data/HailForecastGrid.py#L94-L123
|
train
|
base4sistemas/satcfe
|
satcfe/resposta/consultarnumerosessao.py
|
RespostaConsultarNumeroSessao.analisar
|
def analisar(retorno):
"""Constrói uma :class:`RespostaSAT` ou especialização dependendo da
função SAT encontrada na sessão consultada.
:param unicode retorno: Retorno da função ``ConsultarNumeroSessao``.
"""
if '|' not in retorno:
raise ErroRespostaSATInvalida('Resposta nao possui pipes '
'separando os campos: {!r}'.format(retorno))
resposta = _RespostaParcial(*(retorno.split('|')[:2]))
for faixa, construtor in _RESPOSTAS_POSSIVEIS:
if int(resposta.EEEEE) in xrange(faixa, faixa+1000):
return construtor(retorno)
return RespostaConsultarNumeroSessao._pos_analise(retorno)
|
python
|
def analisar(retorno):
"""Constrói uma :class:`RespostaSAT` ou especialização dependendo da
função SAT encontrada na sessão consultada.
:param unicode retorno: Retorno da função ``ConsultarNumeroSessao``.
"""
if '|' not in retorno:
raise ErroRespostaSATInvalida('Resposta nao possui pipes '
'separando os campos: {!r}'.format(retorno))
resposta = _RespostaParcial(*(retorno.split('|')[:2]))
for faixa, construtor in _RESPOSTAS_POSSIVEIS:
if int(resposta.EEEEE) in xrange(faixa, faixa+1000):
return construtor(retorno)
return RespostaConsultarNumeroSessao._pos_analise(retorno)
|
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"(",
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")"
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Constrói uma :class:`RespostaSAT` ou especialização dependendo da
função SAT encontrada na sessão consultada.
:param unicode retorno: Retorno da função ``ConsultarNumeroSessao``.
|
[
"Constrói",
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"função",
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"encontrada",
"na",
"sessão",
"consultada",
"."
] |
cb8e8815f4133d3e3d94cf526fa86767b4521ed9
|
https://github.com/base4sistemas/satcfe/blob/cb8e8815f4133d3e3d94cf526fa86767b4521ed9/satcfe/resposta/consultarnumerosessao.py#L65-L81
|
train
|
base4sistemas/satcfe
|
satcfe/resposta/padrao.py
|
analisar_retorno
|
def analisar_retorno(retorno,
classe_resposta=RespostaSAT, campos=RespostaSAT.CAMPOS,
campos_alternativos=[], funcao=None, manter_verbatim=True):
"""Analisa o retorno (supostamente um retorno de uma função do SAT) conforme
o padrão e campos esperados. O retorno deverá possuir dados separados entre
si através de pipes e o número de campos deverá coincidir com os campos
especificados.
O campos devem ser especificados como uma tupla onde cada elemento da tupla
deverá ser uma tupla contendo dois elementos: o nome do campo e uma função
de conversão a partir de uma string unicode. Por exemplo:
.. sourcecode:: python
>>> retorno = '123456|08000|SAT em operacao||'
>>> resposta = analisar_retorno(retorno, funcao='ConsultarSAT')
>>> resposta.numeroSessao
123456
>>> resposta.EEEEE
u'08000'
>>> resposta.mensagem
u'SAT em operacao'
>>> resposta.cod
u''
>>> resposta.mensagemSEFAZ
u''
>>> resposta.atributos.funcao
'ConsultarSAT'
>>> resposta.atributos.verbatim
'123456|08000|SAT em operacao||'
:param unicode retorno: O conteúdo **unicode** da resposta retornada pela
função da DLL SAT.
:param type classe_resposta: O tipo :class:`RespostaSAT` ou especialização
que irá representar o retorno, após sua decomposição em campos.
:param tuple campos: Especificação dos campos (nomes) e seus conversores a
a partir do tipo ``unicode``.
:param list campos_alternativos: Especifica conjuntos de campos alternativos
que serão considerados caso o número de campos encontrados na resposta
não coincida com o número de campos especificados em ``campos``.
Para que a relação alternativa de campos funcione, é importante que
cada relação de campos alternativos tenha um número diferente de campos.
:param str funcao: Nome da função da DLL SAT que gerou o retorno, que
estará disponível nos atributos adicionais à resposta.
:param bool manter_verbatim: Se uma cópia verbatim da resposta deverá ser
mantida nos atributos adicionais à resposta.
:raises ErroRespostaSATInvalida: Se o retorno não estiver em conformidade
com o padrão esperado ou se não possuir os campos especificados.
:return: Uma instância de :class:`RespostaSAT` ou especialização.
:rtype: satcfe.resposta.padrao.RespostaSAT
"""
if '|' not in retorno:
raise ErroRespostaSATInvalida('Resposta nao possui pipes separando os '
'campos: "%s"' % as_ascii(retorno))
partes = retorno.split('|')
if len(partes) != len(campos):
# procura por uma relação alternativa de campos do retorno
for relacao_alternativa in campos_alternativos:
if len(partes) == len(relacao_alternativa):
relacao_campos = relacao_alternativa
break
else:
raise ErroRespostaSATInvalida('Resposta nao possui o numero '
'esperado de campos. Esperados %d campos, mas '
'contem %d: "%s"' % (
len(campos), len(partes), as_ascii(retorno),))
else:
relacao_campos = campos
resultado = {}
def _enumerate(sequence):
for index, value in enumerate(sequence):
yield index, value[0], value[1]
for indice, campo, conversor in _enumerate(relacao_campos):
resultado[campo] = conversor(partes[indice])
resposta = classe_resposta(**resultado)
resposta.atributos.funcao = funcao
resposta.atributos.verbatim = retorno if manter_verbatim else None
return resposta
|
python
|
def analisar_retorno(retorno,
classe_resposta=RespostaSAT, campos=RespostaSAT.CAMPOS,
campos_alternativos=[], funcao=None, manter_verbatim=True):
"""Analisa o retorno (supostamente um retorno de uma função do SAT) conforme
o padrão e campos esperados. O retorno deverá possuir dados separados entre
si através de pipes e o número de campos deverá coincidir com os campos
especificados.
O campos devem ser especificados como uma tupla onde cada elemento da tupla
deverá ser uma tupla contendo dois elementos: o nome do campo e uma função
de conversão a partir de uma string unicode. Por exemplo:
.. sourcecode:: python
>>> retorno = '123456|08000|SAT em operacao||'
>>> resposta = analisar_retorno(retorno, funcao='ConsultarSAT')
>>> resposta.numeroSessao
123456
>>> resposta.EEEEE
u'08000'
>>> resposta.mensagem
u'SAT em operacao'
>>> resposta.cod
u''
>>> resposta.mensagemSEFAZ
u''
>>> resposta.atributos.funcao
'ConsultarSAT'
>>> resposta.atributos.verbatim
'123456|08000|SAT em operacao||'
:param unicode retorno: O conteúdo **unicode** da resposta retornada pela
função da DLL SAT.
:param type classe_resposta: O tipo :class:`RespostaSAT` ou especialização
que irá representar o retorno, após sua decomposição em campos.
:param tuple campos: Especificação dos campos (nomes) e seus conversores a
a partir do tipo ``unicode``.
:param list campos_alternativos: Especifica conjuntos de campos alternativos
que serão considerados caso o número de campos encontrados na resposta
não coincida com o número de campos especificados em ``campos``.
Para que a relação alternativa de campos funcione, é importante que
cada relação de campos alternativos tenha um número diferente de campos.
:param str funcao: Nome da função da DLL SAT que gerou o retorno, que
estará disponível nos atributos adicionais à resposta.
:param bool manter_verbatim: Se uma cópia verbatim da resposta deverá ser
mantida nos atributos adicionais à resposta.
:raises ErroRespostaSATInvalida: Se o retorno não estiver em conformidade
com o padrão esperado ou se não possuir os campos especificados.
:return: Uma instância de :class:`RespostaSAT` ou especialização.
:rtype: satcfe.resposta.padrao.RespostaSAT
"""
if '|' not in retorno:
raise ErroRespostaSATInvalida('Resposta nao possui pipes separando os '
'campos: "%s"' % as_ascii(retorno))
partes = retorno.split('|')
if len(partes) != len(campos):
# procura por uma relação alternativa de campos do retorno
for relacao_alternativa in campos_alternativos:
if len(partes) == len(relacao_alternativa):
relacao_campos = relacao_alternativa
break
else:
raise ErroRespostaSATInvalida('Resposta nao possui o numero '
'esperado de campos. Esperados %d campos, mas '
'contem %d: "%s"' % (
len(campos), len(partes), as_ascii(retorno),))
else:
relacao_campos = campos
resultado = {}
def _enumerate(sequence):
for index, value in enumerate(sequence):
yield index, value[0], value[1]
for indice, campo, conversor in _enumerate(relacao_campos):
resultado[campo] = conversor(partes[indice])
resposta = classe_resposta(**resultado)
resposta.atributos.funcao = funcao
resposta.atributos.verbatim = retorno if manter_verbatim else None
return resposta
|
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Analisa o retorno (supostamente um retorno de uma função do SAT) conforme
o padrão e campos esperados. O retorno deverá possuir dados separados entre
si através de pipes e o número de campos deverá coincidir com os campos
especificados.
O campos devem ser especificados como uma tupla onde cada elemento da tupla
deverá ser uma tupla contendo dois elementos: o nome do campo e uma função
de conversão a partir de uma string unicode. Por exemplo:
.. sourcecode:: python
>>> retorno = '123456|08000|SAT em operacao||'
>>> resposta = analisar_retorno(retorno, funcao='ConsultarSAT')
>>> resposta.numeroSessao
123456
>>> resposta.EEEEE
u'08000'
>>> resposta.mensagem
u'SAT em operacao'
>>> resposta.cod
u''
>>> resposta.mensagemSEFAZ
u''
>>> resposta.atributos.funcao
'ConsultarSAT'
>>> resposta.atributos.verbatim
'123456|08000|SAT em operacao||'
:param unicode retorno: O conteúdo **unicode** da resposta retornada pela
função da DLL SAT.
:param type classe_resposta: O tipo :class:`RespostaSAT` ou especialização
que irá representar o retorno, após sua decomposição em campos.
:param tuple campos: Especificação dos campos (nomes) e seus conversores a
a partir do tipo ``unicode``.
:param list campos_alternativos: Especifica conjuntos de campos alternativos
que serão considerados caso o número de campos encontrados na resposta
não coincida com o número de campos especificados em ``campos``.
Para que a relação alternativa de campos funcione, é importante que
cada relação de campos alternativos tenha um número diferente de campos.
:param str funcao: Nome da função da DLL SAT que gerou o retorno, que
estará disponível nos atributos adicionais à resposta.
:param bool manter_verbatim: Se uma cópia verbatim da resposta deverá ser
mantida nos atributos adicionais à resposta.
:raises ErroRespostaSATInvalida: Se o retorno não estiver em conformidade
com o padrão esperado ou se não possuir os campos especificados.
:return: Uma instância de :class:`RespostaSAT` ou especialização.
:rtype: satcfe.resposta.padrao.RespostaSAT
|
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"."
] |
cb8e8815f4133d3e3d94cf526fa86767b4521ed9
|
https://github.com/base4sistemas/satcfe/blob/cb8e8815f4133d3e3d94cf526fa86767b4521ed9/satcfe/resposta/padrao.py#L175-L268
|
train
|
base4sistemas/satcfe
|
satcfe/resposta/padrao.py
|
RespostaSAT.comunicar_certificado_icpbrasil
|
def comunicar_certificado_icpbrasil(retorno):
"""Constrói uma :class:`RespostaSAT` para o retorno (unicode) da função
:meth:`~satcfe.base.FuncoesSAT.comunicar_certificado_icpbrasil`.
"""
resposta = analisar_retorno(forcar_unicode(retorno),
funcao='ComunicarCertificadoICPBRASIL')
if resposta.EEEEE not in ('05000',):
raise ExcecaoRespostaSAT(resposta)
return resposta
|
python
|
def comunicar_certificado_icpbrasil(retorno):
"""Constrói uma :class:`RespostaSAT` para o retorno (unicode) da função
:meth:`~satcfe.base.FuncoesSAT.comunicar_certificado_icpbrasil`.
"""
resposta = analisar_retorno(forcar_unicode(retorno),
funcao='ComunicarCertificadoICPBRASIL')
if resposta.EEEEE not in ('05000',):
raise ExcecaoRespostaSAT(resposta)
return resposta
|
[
"def",
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")",
":",
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"ExcecaoRespostaSAT",
"(",
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")",
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] |
Constrói uma :class:`RespostaSAT` para o retorno (unicode) da função
:meth:`~satcfe.base.FuncoesSAT.comunicar_certificado_icpbrasil`.
|
[
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".",
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"FuncoesSAT",
".",
"comunicar_certificado_icpbrasil",
"."
] |
cb8e8815f4133d3e3d94cf526fa86767b4521ed9
|
https://github.com/base4sistemas/satcfe/blob/cb8e8815f4133d3e3d94cf526fa86767b4521ed9/satcfe/resposta/padrao.py#L80-L88
|
train
|
base4sistemas/satcfe
|
satcfe/resposta/padrao.py
|
RespostaSAT.consultar_sat
|
def consultar_sat(retorno):
"""Constrói uma :class:`RespostaSAT` para o retorno (unicode) da função
:meth:`~satcfe.base.FuncoesSAT.consultar_sat`.
"""
resposta = analisar_retorno(forcar_unicode(retorno),
funcao='ConsultarSAT')
if resposta.EEEEE not in ('08000',):
raise ExcecaoRespostaSAT(resposta)
return resposta
|
python
|
def consultar_sat(retorno):
"""Constrói uma :class:`RespostaSAT` para o retorno (unicode) da função
:meth:`~satcfe.base.FuncoesSAT.consultar_sat`.
"""
resposta = analisar_retorno(forcar_unicode(retorno),
funcao='ConsultarSAT')
if resposta.EEEEE not in ('08000',):
raise ExcecaoRespostaSAT(resposta)
return resposta
|
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Constrói uma :class:`RespostaSAT` para o retorno (unicode) da função
:meth:`~satcfe.base.FuncoesSAT.consultar_sat`.
|
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"meth",
":",
"~satcfe",
".",
"base",
".",
"FuncoesSAT",
".",
"consultar_sat",
"."
] |
cb8e8815f4133d3e3d94cf526fa86767b4521ed9
|
https://github.com/base4sistemas/satcfe/blob/cb8e8815f4133d3e3d94cf526fa86767b4521ed9/satcfe/resposta/padrao.py#L92-L100
|
train
|
base4sistemas/satcfe
|
satcfe/resposta/padrao.py
|
RespostaSAT.configurar_interface_de_rede
|
def configurar_interface_de_rede(retorno):
"""Constrói uma :class:`RespostaSAT` para o retorno (unicode) da função
:meth:`~satcfe.base.FuncoesSAT.configurar_interface_de_rede`.
"""
resposta = analisar_retorno(forcar_unicode(retorno),
funcao='ConfigurarInterfaceDeRede')
if resposta.EEEEE not in ('12000',):
raise ExcecaoRespostaSAT(resposta)
return resposta
|
python
|
def configurar_interface_de_rede(retorno):
"""Constrói uma :class:`RespostaSAT` para o retorno (unicode) da função
:meth:`~satcfe.base.FuncoesSAT.configurar_interface_de_rede`.
"""
resposta = analisar_retorno(forcar_unicode(retorno),
funcao='ConfigurarInterfaceDeRede')
if resposta.EEEEE not in ('12000',):
raise ExcecaoRespostaSAT(resposta)
return resposta
|
[
"def",
"configurar_interface_de_rede",
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":",
"resposta",
"=",
"analisar_retorno",
"(",
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"(",
"retorno",
")",
",",
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"=",
"'ConfigurarInterfaceDeRede'",
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"'12000'",
",",
")",
":",
"raise",
"ExcecaoRespostaSAT",
"(",
"resposta",
")",
"return",
"resposta"
] |
Constrói uma :class:`RespostaSAT` para o retorno (unicode) da função
:meth:`~satcfe.base.FuncoesSAT.configurar_interface_de_rede`.
|
[
"Constrói",
"uma",
":",
"class",
":",
"RespostaSAT",
"para",
"o",
"retorno",
"(",
"unicode",
")",
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":",
"meth",
":",
"~satcfe",
".",
"base",
".",
"FuncoesSAT",
".",
"configurar_interface_de_rede",
"."
] |
cb8e8815f4133d3e3d94cf526fa86767b4521ed9
|
https://github.com/base4sistemas/satcfe/blob/cb8e8815f4133d3e3d94cf526fa86767b4521ed9/satcfe/resposta/padrao.py#L104-L112
|
train
|
base4sistemas/satcfe
|
satcfe/resposta/padrao.py
|
RespostaSAT.associar_assinatura
|
def associar_assinatura(retorno):
"""Constrói uma :class:`RespostaSAT` para o retorno (unicode) da função
:meth:`~satcfe.base.FuncoesSAT.associar_assinatura`.
"""
resposta = analisar_retorno(forcar_unicode(retorno),
funcao='AssociarAssinatura')
if resposta.EEEEE not in ('13000',):
raise ExcecaoRespostaSAT(resposta)
return resposta
|
python
|
def associar_assinatura(retorno):
"""Constrói uma :class:`RespostaSAT` para o retorno (unicode) da função
:meth:`~satcfe.base.FuncoesSAT.associar_assinatura`.
"""
resposta = analisar_retorno(forcar_unicode(retorno),
funcao='AssociarAssinatura')
if resposta.EEEEE not in ('13000',):
raise ExcecaoRespostaSAT(resposta)
return resposta
|
[
"def",
"associar_assinatura",
"(",
"retorno",
")",
":",
"resposta",
"=",
"analisar_retorno",
"(",
"forcar_unicode",
"(",
"retorno",
")",
",",
"funcao",
"=",
"'AssociarAssinatura'",
")",
"if",
"resposta",
".",
"EEEEE",
"not",
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"(",
"'13000'",
",",
")",
":",
"raise",
"ExcecaoRespostaSAT",
"(",
"resposta",
")",
"return",
"resposta"
] |
Constrói uma :class:`RespostaSAT` para o retorno (unicode) da função
:meth:`~satcfe.base.FuncoesSAT.associar_assinatura`.
|
[
"Constrói",
"uma",
":",
"class",
":",
"RespostaSAT",
"para",
"o",
"retorno",
"(",
"unicode",
")",
"da",
"função",
":",
"meth",
":",
"~satcfe",
".",
"base",
".",
"FuncoesSAT",
".",
"associar_assinatura",
"."
] |
cb8e8815f4133d3e3d94cf526fa86767b4521ed9
|
https://github.com/base4sistemas/satcfe/blob/cb8e8815f4133d3e3d94cf526fa86767b4521ed9/satcfe/resposta/padrao.py#L116-L124
|
train
|
base4sistemas/satcfe
|
satcfe/resposta/padrao.py
|
RespostaSAT.atualizar_software_sat
|
def atualizar_software_sat(retorno):
"""Constrói uma :class:`RespostaSAT` para o retorno (unicode) da função
:meth:`~satcfe.base.FuncoesSAT.atualizar_software_sat`.
"""
resposta = analisar_retorno(forcar_unicode(retorno),
funcao='AtualizarSoftwareSAT')
if resposta.EEEEE not in ('14000',):
raise ExcecaoRespostaSAT(resposta)
return resposta
|
python
|
def atualizar_software_sat(retorno):
"""Constrói uma :class:`RespostaSAT` para o retorno (unicode) da função
:meth:`~satcfe.base.FuncoesSAT.atualizar_software_sat`.
"""
resposta = analisar_retorno(forcar_unicode(retorno),
funcao='AtualizarSoftwareSAT')
if resposta.EEEEE not in ('14000',):
raise ExcecaoRespostaSAT(resposta)
return resposta
|
[
"def",
"atualizar_software_sat",
"(",
"retorno",
")",
":",
"resposta",
"=",
"analisar_retorno",
"(",
"forcar_unicode",
"(",
"retorno",
")",
",",
"funcao",
"=",
"'AtualizarSoftwareSAT'",
")",
"if",
"resposta",
".",
"EEEEE",
"not",
"in",
"(",
"'14000'",
",",
")",
":",
"raise",
"ExcecaoRespostaSAT",
"(",
"resposta",
")",
"return",
"resposta"
] |
Constrói uma :class:`RespostaSAT` para o retorno (unicode) da função
:meth:`~satcfe.base.FuncoesSAT.atualizar_software_sat`.
|
[
"Constrói",
"uma",
":",
"class",
":",
"RespostaSAT",
"para",
"o",
"retorno",
"(",
"unicode",
")",
"da",
"função",
":",
"meth",
":",
"~satcfe",
".",
"base",
".",
"FuncoesSAT",
".",
"atualizar_software_sat",
"."
] |
cb8e8815f4133d3e3d94cf526fa86767b4521ed9
|
https://github.com/base4sistemas/satcfe/blob/cb8e8815f4133d3e3d94cf526fa86767b4521ed9/satcfe/resposta/padrao.py#L128-L136
|
train
|
base4sistemas/satcfe
|
satcfe/resposta/padrao.py
|
RespostaSAT.bloquear_sat
|
def bloquear_sat(retorno):
"""Constrói uma :class:`RespostaSAT` para o retorno (unicode) da função
:meth:`~satcfe.base.FuncoesSAT.bloquear_sat`.
"""
resposta = analisar_retorno(forcar_unicode(retorno),
funcao='BloquearSAT')
if resposta.EEEEE not in ('16000',):
raise ExcecaoRespostaSAT(resposta)
return resposta
|
python
|
def bloquear_sat(retorno):
"""Constrói uma :class:`RespostaSAT` para o retorno (unicode) da função
:meth:`~satcfe.base.FuncoesSAT.bloquear_sat`.
"""
resposta = analisar_retorno(forcar_unicode(retorno),
funcao='BloquearSAT')
if resposta.EEEEE not in ('16000',):
raise ExcecaoRespostaSAT(resposta)
return resposta
|
[
"def",
"bloquear_sat",
"(",
"retorno",
")",
":",
"resposta",
"=",
"analisar_retorno",
"(",
"forcar_unicode",
"(",
"retorno",
")",
",",
"funcao",
"=",
"'BloquearSAT'",
")",
"if",
"resposta",
".",
"EEEEE",
"not",
"in",
"(",
"'16000'",
",",
")",
":",
"raise",
"ExcecaoRespostaSAT",
"(",
"resposta",
")",
"return",
"resposta"
] |
Constrói uma :class:`RespostaSAT` para o retorno (unicode) da função
:meth:`~satcfe.base.FuncoesSAT.bloquear_sat`.
|
[
"Constrói",
"uma",
":",
"class",
":",
"RespostaSAT",
"para",
"o",
"retorno",
"(",
"unicode",
")",
"da",
"função",
":",
"meth",
":",
"~satcfe",
".",
"base",
".",
"FuncoesSAT",
".",
"bloquear_sat",
"."
] |
cb8e8815f4133d3e3d94cf526fa86767b4521ed9
|
https://github.com/base4sistemas/satcfe/blob/cb8e8815f4133d3e3d94cf526fa86767b4521ed9/satcfe/resposta/padrao.py#L140-L148
|
train
|
base4sistemas/satcfe
|
satcfe/resposta/padrao.py
|
RespostaSAT.desbloquear_sat
|
def desbloquear_sat(retorno):
"""Constrói uma :class:`RespostaSAT` para o retorno (unicode) da função
:meth:`~satcfe.base.FuncoesSAT.desbloquear_sat`.
"""
resposta = analisar_retorno(forcar_unicode(retorno),
funcao='DesbloquearSAT')
if resposta.EEEEE not in ('17000',):
raise ExcecaoRespostaSAT(resposta)
return resposta
|
python
|
def desbloquear_sat(retorno):
"""Constrói uma :class:`RespostaSAT` para o retorno (unicode) da função
:meth:`~satcfe.base.FuncoesSAT.desbloquear_sat`.
"""
resposta = analisar_retorno(forcar_unicode(retorno),
funcao='DesbloquearSAT')
if resposta.EEEEE not in ('17000',):
raise ExcecaoRespostaSAT(resposta)
return resposta
|
[
"def",
"desbloquear_sat",
"(",
"retorno",
")",
":",
"resposta",
"=",
"analisar_retorno",
"(",
"forcar_unicode",
"(",
"retorno",
")",
",",
"funcao",
"=",
"'DesbloquearSAT'",
")",
"if",
"resposta",
".",
"EEEEE",
"not",
"in",
"(",
"'17000'",
",",
")",
":",
"raise",
"ExcecaoRespostaSAT",
"(",
"resposta",
")",
"return",
"resposta"
] |
Constrói uma :class:`RespostaSAT` para o retorno (unicode) da função
:meth:`~satcfe.base.FuncoesSAT.desbloquear_sat`.
|
[
"Constrói",
"uma",
":",
"class",
":",
"RespostaSAT",
"para",
"o",
"retorno",
"(",
"unicode",
")",
"da",
"função",
":",
"meth",
":",
"~satcfe",
".",
"base",
".",
"FuncoesSAT",
".",
"desbloquear_sat",
"."
] |
cb8e8815f4133d3e3d94cf526fa86767b4521ed9
|
https://github.com/base4sistemas/satcfe/blob/cb8e8815f4133d3e3d94cf526fa86767b4521ed9/satcfe/resposta/padrao.py#L152-L160
|
train
|
base4sistemas/satcfe
|
satcfe/resposta/padrao.py
|
RespostaSAT.trocar_codigo_de_ativacao
|
def trocar_codigo_de_ativacao(retorno):
"""Constrói uma :class:`RespostaSAT` para o retorno (unicode) da função
:meth:`~satcfe.base.FuncoesSAT.trocar_codigo_de_ativacao`.
"""
resposta = analisar_retorno(forcar_unicode(retorno),
funcao='TrocarCodigoDeAtivacao')
if resposta.EEEEE not in ('18000',):
raise ExcecaoRespostaSAT(resposta)
return resposta
|
python
|
def trocar_codigo_de_ativacao(retorno):
"""Constrói uma :class:`RespostaSAT` para o retorno (unicode) da função
:meth:`~satcfe.base.FuncoesSAT.trocar_codigo_de_ativacao`.
"""
resposta = analisar_retorno(forcar_unicode(retorno),
funcao='TrocarCodigoDeAtivacao')
if resposta.EEEEE not in ('18000',):
raise ExcecaoRespostaSAT(resposta)
return resposta
|
[
"def",
"trocar_codigo_de_ativacao",
"(",
"retorno",
")",
":",
"resposta",
"=",
"analisar_retorno",
"(",
"forcar_unicode",
"(",
"retorno",
")",
",",
"funcao",
"=",
"'TrocarCodigoDeAtivacao'",
")",
"if",
"resposta",
".",
"EEEEE",
"not",
"in",
"(",
"'18000'",
",",
")",
":",
"raise",
"ExcecaoRespostaSAT",
"(",
"resposta",
")",
"return",
"resposta"
] |
Constrói uma :class:`RespostaSAT` para o retorno (unicode) da função
:meth:`~satcfe.base.FuncoesSAT.trocar_codigo_de_ativacao`.
|
[
"Constrói",
"uma",
":",
"class",
":",
"RespostaSAT",
"para",
"o",
"retorno",
"(",
"unicode",
")",
"da",
"função",
":",
"meth",
":",
"~satcfe",
".",
"base",
".",
"FuncoesSAT",
".",
"trocar_codigo_de_ativacao",
"."
] |
cb8e8815f4133d3e3d94cf526fa86767b4521ed9
|
https://github.com/base4sistemas/satcfe/blob/cb8e8815f4133d3e3d94cf526fa86767b4521ed9/satcfe/resposta/padrao.py#L164-L172
|
train
|
djgagne/hagelslag
|
hagelslag/data/ModelOutput.py
|
ModelOutput.load_data
|
def load_data(self):
"""
Load the specified variable from the ensemble files, then close the files.
"""
if self.ensemble_name.upper() == "SSEF":
if self.variable[0:2] == "rh":
pressure_level = self.variable[2:]
relh_vars = ["sph", "tmp"]
relh_vals = {}
for var in relh_vars:
mg = SSEFModelGrid(self.member_name,
self.run_date,
var + pressure_level,
self.start_date,
self.end_date,
self.path,
single_step=self.single_step)
relh_vals[var], units = mg.load_data()
mg.close()
self.data = relative_humidity_pressure_level(relh_vals["tmp"],
relh_vals["sph"],
float(pressure_level) * 100)
self.units = "%"
elif self.variable == "melth":
input_vars = ["hgtsfc", "hgt700", "hgt500", "tmp700", "tmp500"]
input_vals = {}
for var in input_vars:
mg = SSEFModelGrid(self.member_name,
self.run_date,
var,
self.start_date,
self.end_date,
self.path,
single_step=self.single_step)
input_vals[var], units = mg.load_data()
mg.close()
self.data = melting_layer_height(input_vals["hgtsfc"],
input_vals["hgt700"],
input_vals["hgt500"],
input_vals["tmp700"],
input_vals["tmp500"])
self.units = "m"
else:
mg = SSEFModelGrid(self.member_name,
self.run_date,
self.variable,
self.start_date,
self.end_date,
self.path,
single_step=self.single_step)
self.data, self.units = mg.load_data()
mg.close()
elif self.ensemble_name.upper() == "NCAR":
mg = NCARModelGrid(self.member_name,
self.run_date,
self.variable,
self.start_date,
self.end_date,
self.path,
single_step=self.single_step)
self.data, self.units = mg.load_data()
mg.close()
elif self.ensemble_name.upper() == "HREFV2":
proj_dict, grid_dict = read_ncar_map_file(self.map_file)
mapping_data = make_proj_grids(proj_dict, grid_dict)
mg = HREFv2ModelGrid(self.member_name,
self.run_date,
self.variable,
self.start_date,
self.end_date,
self.path,
mapping_data,
self.sector_ind_path,
single_step=self.single_step)
self.data, self.units = mg.load_data()
elif self.ensemble_name.upper() == "VSE":
mg = VSEModelGrid(self.member_name,
self.run_date,
self.variable,
self.start_date,
self.end_date,
self.path,
single_step=self.single_step)
self.data, self.units = mg.load_data()
mg.close()
elif self.ensemble_name.upper() == "HRRR":
mg = HRRRModelGrid(self.run_date,
self.variable,
self.start_date,
self.end_date,
self.path)
self.data, self.units = mg.load_data()
mg.close()
elif self.ensemble_name.upper() == "NCARSTORM":
mg = NCARStormEventModelGrid(self.run_date,
self.variable,
self.start_date,
self.end_date,
self.path)
self.data, self.units = mg.load_data()
mg.close()
else:
print(self.ensemble_name + " not supported.")
|
python
|
def load_data(self):
"""
Load the specified variable from the ensemble files, then close the files.
"""
if self.ensemble_name.upper() == "SSEF":
if self.variable[0:2] == "rh":
pressure_level = self.variable[2:]
relh_vars = ["sph", "tmp"]
relh_vals = {}
for var in relh_vars:
mg = SSEFModelGrid(self.member_name,
self.run_date,
var + pressure_level,
self.start_date,
self.end_date,
self.path,
single_step=self.single_step)
relh_vals[var], units = mg.load_data()
mg.close()
self.data = relative_humidity_pressure_level(relh_vals["tmp"],
relh_vals["sph"],
float(pressure_level) * 100)
self.units = "%"
elif self.variable == "melth":
input_vars = ["hgtsfc", "hgt700", "hgt500", "tmp700", "tmp500"]
input_vals = {}
for var in input_vars:
mg = SSEFModelGrid(self.member_name,
self.run_date,
var,
self.start_date,
self.end_date,
self.path,
single_step=self.single_step)
input_vals[var], units = mg.load_data()
mg.close()
self.data = melting_layer_height(input_vals["hgtsfc"],
input_vals["hgt700"],
input_vals["hgt500"],
input_vals["tmp700"],
input_vals["tmp500"])
self.units = "m"
else:
mg = SSEFModelGrid(self.member_name,
self.run_date,
self.variable,
self.start_date,
self.end_date,
self.path,
single_step=self.single_step)
self.data, self.units = mg.load_data()
mg.close()
elif self.ensemble_name.upper() == "NCAR":
mg = NCARModelGrid(self.member_name,
self.run_date,
self.variable,
self.start_date,
self.end_date,
self.path,
single_step=self.single_step)
self.data, self.units = mg.load_data()
mg.close()
elif self.ensemble_name.upper() == "HREFV2":
proj_dict, grid_dict = read_ncar_map_file(self.map_file)
mapping_data = make_proj_grids(proj_dict, grid_dict)
mg = HREFv2ModelGrid(self.member_name,
self.run_date,
self.variable,
self.start_date,
self.end_date,
self.path,
mapping_data,
self.sector_ind_path,
single_step=self.single_step)
self.data, self.units = mg.load_data()
elif self.ensemble_name.upper() == "VSE":
mg = VSEModelGrid(self.member_name,
self.run_date,
self.variable,
self.start_date,
self.end_date,
self.path,
single_step=self.single_step)
self.data, self.units = mg.load_data()
mg.close()
elif self.ensemble_name.upper() == "HRRR":
mg = HRRRModelGrid(self.run_date,
self.variable,
self.start_date,
self.end_date,
self.path)
self.data, self.units = mg.load_data()
mg.close()
elif self.ensemble_name.upper() == "NCARSTORM":
mg = NCARStormEventModelGrid(self.run_date,
self.variable,
self.start_date,
self.end_date,
self.path)
self.data, self.units = mg.load_data()
mg.close()
else:
print(self.ensemble_name + " not supported.")
|
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Load the specified variable from the ensemble files, then close the files.
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6fb6c3df90bf4867e13a97d3460b14471d107df1
|
https://github.com/djgagne/hagelslag/blob/6fb6c3df90bf4867e13a97d3460b14471d107df1/hagelslag/data/ModelOutput.py#L67-L172
|
train
|
djgagne/hagelslag
|
hagelslag/data/ModelOutput.py
|
ModelOutput.load_map_info
|
def load_map_info(self, map_file):
"""
Load map projection information and create latitude, longitude, x, y, i, and j grids for the projection.
Args:
map_file: File specifying the projection information.
"""
if self.ensemble_name.upper() == "SSEF":
proj_dict, grid_dict = read_arps_map_file(map_file)
self.dx = int(grid_dict["dx"])
mapping_data = make_proj_grids(proj_dict, grid_dict)
for m, v in mapping_data.items():
setattr(self, m, v)
self.i, self.j = np.indices(self.lon.shape)
self.proj = get_proj_obj(proj_dict)
elif self.ensemble_name.upper() in ["NCAR", "NCARSTORM", "HRRR", "VSE", "HREFV2"]:
proj_dict, grid_dict = read_ncar_map_file(map_file)
if self.member_name[0:7] == "1km_pbl": # Don't just look at the first 3 characters. You have to differentiate '1km_pbl1' and '1km_on_3km_pbl1'
grid_dict["dx"] = 1000
grid_dict["dy"] = 1000
grid_dict["sw_lon"] = 258.697
grid_dict["sw_lat"] = 23.999
grid_dict["ne_lon"] = 282.868269206236
grid_dict["ne_lat"] = 36.4822338520542
self.dx = int(grid_dict["dx"])
mapping_data = make_proj_grids(proj_dict, grid_dict)
for m, v in mapping_data.items():
setattr(self, m, v)
self.i, self.j = np.indices(self.lon.shape)
self.proj = get_proj_obj(proj_dict)
|
python
|
def load_map_info(self, map_file):
"""
Load map projection information and create latitude, longitude, x, y, i, and j grids for the projection.
Args:
map_file: File specifying the projection information.
"""
if self.ensemble_name.upper() == "SSEF":
proj_dict, grid_dict = read_arps_map_file(map_file)
self.dx = int(grid_dict["dx"])
mapping_data = make_proj_grids(proj_dict, grid_dict)
for m, v in mapping_data.items():
setattr(self, m, v)
self.i, self.j = np.indices(self.lon.shape)
self.proj = get_proj_obj(proj_dict)
elif self.ensemble_name.upper() in ["NCAR", "NCARSTORM", "HRRR", "VSE", "HREFV2"]:
proj_dict, grid_dict = read_ncar_map_file(map_file)
if self.member_name[0:7] == "1km_pbl": # Don't just look at the first 3 characters. You have to differentiate '1km_pbl1' and '1km_on_3km_pbl1'
grid_dict["dx"] = 1000
grid_dict["dy"] = 1000
grid_dict["sw_lon"] = 258.697
grid_dict["sw_lat"] = 23.999
grid_dict["ne_lon"] = 282.868269206236
grid_dict["ne_lat"] = 36.4822338520542
self.dx = int(grid_dict["dx"])
mapping_data = make_proj_grids(proj_dict, grid_dict)
for m, v in mapping_data.items():
setattr(self, m, v)
self.i, self.j = np.indices(self.lon.shape)
self.proj = get_proj_obj(proj_dict)
|
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Load map projection information and create latitude, longitude, x, y, i, and j grids for the projection.
Args:
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|
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6fb6c3df90bf4867e13a97d3460b14471d107df1
|
https://github.com/djgagne/hagelslag/blob/6fb6c3df90bf4867e13a97d3460b14471d107df1/hagelslag/data/ModelOutput.py#L174-L204
|
train
|
djgagne/hagelslag
|
hagelslag/processing/STObject.py
|
read_geojson
|
def read_geojson(filename):
"""
Reads a geojson file containing an STObject and initializes a new STObject from the information in the file.
Args:
filename: Name of the geojson file
Returns:
an STObject
"""
json_file = open(filename)
data = json.load(json_file)
json_file.close()
times = data["properties"]["times"]
main_data = dict(timesteps=[], masks=[], x=[], y=[], i=[], j=[])
attribute_data = dict()
for feature in data["features"]:
for main_name in main_data.keys():
main_data[main_name].append(np.array(feature["properties"][main_name]))
for k, v in feature["properties"]["attributes"].items():
if k not in attribute_data.keys():
attribute_data[k] = [np.array(v)]
else:
attribute_data[k].append(np.array(v))
kwargs = {}
for kw in ["dx", "step", "u", "v"]:
if kw in data["properties"].keys():
kwargs[kw] = data["properties"][kw]
sto = STObject(main_data["timesteps"], main_data["masks"], main_data["x"], main_data["y"],
main_data["i"], main_data["j"], times[0], times[-1], **kwargs)
for k, v in attribute_data.items():
sto.attributes[k] = v
return sto
|
python
|
def read_geojson(filename):
"""
Reads a geojson file containing an STObject and initializes a new STObject from the information in the file.
Args:
filename: Name of the geojson file
Returns:
an STObject
"""
json_file = open(filename)
data = json.load(json_file)
json_file.close()
times = data["properties"]["times"]
main_data = dict(timesteps=[], masks=[], x=[], y=[], i=[], j=[])
attribute_data = dict()
for feature in data["features"]:
for main_name in main_data.keys():
main_data[main_name].append(np.array(feature["properties"][main_name]))
for k, v in feature["properties"]["attributes"].items():
if k not in attribute_data.keys():
attribute_data[k] = [np.array(v)]
else:
attribute_data[k].append(np.array(v))
kwargs = {}
for kw in ["dx", "step", "u", "v"]:
if kw in data["properties"].keys():
kwargs[kw] = data["properties"][kw]
sto = STObject(main_data["timesteps"], main_data["masks"], main_data["x"], main_data["y"],
main_data["i"], main_data["j"], times[0], times[-1], **kwargs)
for k, v in attribute_data.items():
sto.attributes[k] = v
return sto
|
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Reads a geojson file containing an STObject and initializes a new STObject from the information in the file.
Args:
filename: Name of the geojson file
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|
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6fb6c3df90bf4867e13a97d3460b14471d107df1
|
https://github.com/djgagne/hagelslag/blob/6fb6c3df90bf4867e13a97d3460b14471d107df1/hagelslag/processing/STObject.py#L540-L572
|
train
|
djgagne/hagelslag
|
hagelslag/processing/STObject.py
|
STObject.center_of_mass
|
def center_of_mass(self, time):
"""
Calculate the center of mass at a given timestep.
Args:
time: Time at which the center of mass calculation is performed
Returns:
The x- and y-coordinates of the center of mass.
"""
if self.start_time <= time <= self.end_time:
diff = time - self.start_time
valid = np.flatnonzero(self.masks[diff] != 0)
if valid.size > 0:
com_x = 1.0 / self.timesteps[diff].ravel()[valid].sum() * np.sum(self.timesteps[diff].ravel()[valid] *
self.x[diff].ravel()[valid])
com_y = 1.0 / self.timesteps[diff].ravel()[valid].sum() * np.sum(self.timesteps[diff].ravel()[valid] *
self.y[diff].ravel()[valid])
else:
com_x = np.mean(self.x[diff])
com_y = np.mean(self.y[diff])
else:
com_x = None
com_y = None
return com_x, com_y
|
python
|
def center_of_mass(self, time):
"""
Calculate the center of mass at a given timestep.
Args:
time: Time at which the center of mass calculation is performed
Returns:
The x- and y-coordinates of the center of mass.
"""
if self.start_time <= time <= self.end_time:
diff = time - self.start_time
valid = np.flatnonzero(self.masks[diff] != 0)
if valid.size > 0:
com_x = 1.0 / self.timesteps[diff].ravel()[valid].sum() * np.sum(self.timesteps[diff].ravel()[valid] *
self.x[diff].ravel()[valid])
com_y = 1.0 / self.timesteps[diff].ravel()[valid].sum() * np.sum(self.timesteps[diff].ravel()[valid] *
self.y[diff].ravel()[valid])
else:
com_x = np.mean(self.x[diff])
com_y = np.mean(self.y[diff])
else:
com_x = None
com_y = None
return com_x, com_y
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The x- and y-coordinates of the center of mass.
|
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6fb6c3df90bf4867e13a97d3460b14471d107df1
|
https://github.com/djgagne/hagelslag/blob/6fb6c3df90bf4867e13a97d3460b14471d107df1/hagelslag/processing/STObject.py#L78-L102
|
train
|
djgagne/hagelslag
|
hagelslag/processing/STObject.py
|
STObject.trajectory
|
def trajectory(self):
"""
Calculates the center of mass for each time step and outputs an array
Returns:
"""
traj = np.zeros((2, self.times.size))
for t, time in enumerate(self.times):
traj[:, t] = self.center_of_mass(time)
return traj
|
python
|
def trajectory(self):
"""
Calculates the center of mass for each time step and outputs an array
Returns:
"""
traj = np.zeros((2, self.times.size))
for t, time in enumerate(self.times):
traj[:, t] = self.center_of_mass(time)
return traj
|
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6fb6c3df90bf4867e13a97d3460b14471d107df1
|
https://github.com/djgagne/hagelslag/blob/6fb6c3df90bf4867e13a97d3460b14471d107df1/hagelslag/processing/STObject.py#L143-L153
|
train
|
djgagne/hagelslag
|
hagelslag/processing/STObject.py
|
STObject.get_corner
|
def get_corner(self, time):
"""
Gets the corner array indices of the STObject at a given time that corresponds
to the upper left corner of the bounding box for the STObject.
Args:
time: time at which the corner is being extracted.
Returns:
corner index.
"""
if self.start_time <= time <= self.end_time:
diff = time - self.start_time
return self.i[diff][0, 0], self.j[diff][0, 0]
else:
return -1, -1
|
python
|
def get_corner(self, time):
"""
Gets the corner array indices of the STObject at a given time that corresponds
to the upper left corner of the bounding box for the STObject.
Args:
time: time at which the corner is being extracted.
Returns:
corner index.
"""
if self.start_time <= time <= self.end_time:
diff = time - self.start_time
return self.i[diff][0, 0], self.j[diff][0, 0]
else:
return -1, -1
|
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corner index.
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6fb6c3df90bf4867e13a97d3460b14471d107df1
|
https://github.com/djgagne/hagelslag/blob/6fb6c3df90bf4867e13a97d3460b14471d107df1/hagelslag/processing/STObject.py#L155-L170
|
train
|
djgagne/hagelslag
|
hagelslag/processing/STObject.py
|
STObject.size
|
def size(self, time):
"""
Gets the size of the object at a given time.
Args:
time: Time value being queried.
Returns:
size of the object in pixels
"""
if self.start_time <= time <= self.end_time:
return self.masks[time - self.start_time].sum()
else:
return 0
|
python
|
def size(self, time):
"""
Gets the size of the object at a given time.
Args:
time: Time value being queried.
Returns:
size of the object in pixels
"""
if self.start_time <= time <= self.end_time:
return self.masks[time - self.start_time].sum()
else:
return 0
|
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Gets the size of the object at a given time.
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size of the object in pixels
|
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6fb6c3df90bf4867e13a97d3460b14471d107df1
|
https://github.com/djgagne/hagelslag/blob/6fb6c3df90bf4867e13a97d3460b14471d107df1/hagelslag/processing/STObject.py#L172-L185
|
train
|
djgagne/hagelslag
|
hagelslag/processing/STObject.py
|
STObject.max_size
|
def max_size(self):
"""
Gets the largest size of the object over all timesteps.
Returns:
Maximum size of the object in pixels
"""
sizes = np.array([m.sum() for m in self.masks])
return sizes.max()
|
python
|
def max_size(self):
"""
Gets the largest size of the object over all timesteps.
Returns:
Maximum size of the object in pixels
"""
sizes = np.array([m.sum() for m in self.masks])
return sizes.max()
|
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Gets the largest size of the object over all timesteps.
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6fb6c3df90bf4867e13a97d3460b14471d107df1
|
https://github.com/djgagne/hagelslag/blob/6fb6c3df90bf4867e13a97d3460b14471d107df1/hagelslag/processing/STObject.py#L187-L195
|
train
|
djgagne/hagelslag
|
hagelslag/processing/STObject.py
|
STObject.max_intensity
|
def max_intensity(self, time):
"""
Calculate the maximum intensity found at a timestep.
"""
ti = np.where(time == self.times)[0][0]
return self.timesteps[ti].max()
|
python
|
def max_intensity(self, time):
"""
Calculate the maximum intensity found at a timestep.
"""
ti = np.where(time == self.times)[0][0]
return self.timesteps[ti].max()
|
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[
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6fb6c3df90bf4867e13a97d3460b14471d107df1
|
https://github.com/djgagne/hagelslag/blob/6fb6c3df90bf4867e13a97d3460b14471d107df1/hagelslag/processing/STObject.py#L197-L203
|
train
|
djgagne/hagelslag
|
hagelslag/processing/STObject.py
|
STObject.extend
|
def extend(self, step):
"""
Adds the data from another STObject to this object.
Args:
step: another STObject being added after the current one in time.
"""
self.timesteps.extend(step.timesteps)
self.masks.extend(step.masks)
self.x.extend(step.x)
self.y.extend(step.y)
self.i.extend(step.i)
self.j.extend(step.j)
self.end_time = step.end_time
self.times = np.arange(self.start_time, self.end_time + self.step, self.step)
self.u = np.concatenate((self.u, step.u))
self.v = np.concatenate((self.v, step.v))
for attr in self.attributes.keys():
if attr in step.attributes.keys():
self.attributes[attr].extend(step.attributes[attr])
|
python
|
def extend(self, step):
"""
Adds the data from another STObject to this object.
Args:
step: another STObject being added after the current one in time.
"""
self.timesteps.extend(step.timesteps)
self.masks.extend(step.masks)
self.x.extend(step.x)
self.y.extend(step.y)
self.i.extend(step.i)
self.j.extend(step.j)
self.end_time = step.end_time
self.times = np.arange(self.start_time, self.end_time + self.step, self.step)
self.u = np.concatenate((self.u, step.u))
self.v = np.concatenate((self.v, step.v))
for attr in self.attributes.keys():
if attr in step.attributes.keys():
self.attributes[attr].extend(step.attributes[attr])
|
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6fb6c3df90bf4867e13a97d3460b14471d107df1
|
https://github.com/djgagne/hagelslag/blob/6fb6c3df90bf4867e13a97d3460b14471d107df1/hagelslag/processing/STObject.py#L205-L224
|
train
|
djgagne/hagelslag
|
hagelslag/processing/STObject.py
|
STObject.boundary_polygon
|
def boundary_polygon(self, time):
"""
Get coordinates of object boundary in counter-clockwise order
"""
ti = np.where(time == self.times)[0][0]
com_x, com_y = self.center_of_mass(time)
# If at least one point along perimeter of the mask rectangle is unmasked, find_boundaries() works.
# But if all perimeter points are masked, find_boundaries() does not find the object.
# Therefore, pad the mask with zeroes first and run find_boundaries on the padded array.
padded_mask = np.pad(self.masks[ti], 1, 'constant', constant_values=0)
chull = convex_hull_image(padded_mask)
boundary_image = find_boundaries(chull, mode='inner', background=0)
# Now remove the padding.
boundary_image = boundary_image[1:-1,1:-1]
boundary_x = self.x[ti].ravel()[boundary_image.ravel()]
boundary_y = self.y[ti].ravel()[boundary_image.ravel()]
r = np.sqrt((boundary_x - com_x) ** 2 + (boundary_y - com_y) ** 2)
theta = np.arctan2((boundary_y - com_y), (boundary_x - com_x)) * 180.0 / np.pi + 360
polar_coords = np.array([(r[x], theta[x]) for x in range(r.size)], dtype=[('r', 'f4'), ('theta', 'f4')])
coord_order = np.argsort(polar_coords, order=['theta', 'r'])
ordered_coords = np.vstack([boundary_x[coord_order], boundary_y[coord_order]])
return ordered_coords
|
python
|
def boundary_polygon(self, time):
"""
Get coordinates of object boundary in counter-clockwise order
"""
ti = np.where(time == self.times)[0][0]
com_x, com_y = self.center_of_mass(time)
# If at least one point along perimeter of the mask rectangle is unmasked, find_boundaries() works.
# But if all perimeter points are masked, find_boundaries() does not find the object.
# Therefore, pad the mask with zeroes first and run find_boundaries on the padded array.
padded_mask = np.pad(self.masks[ti], 1, 'constant', constant_values=0)
chull = convex_hull_image(padded_mask)
boundary_image = find_boundaries(chull, mode='inner', background=0)
# Now remove the padding.
boundary_image = boundary_image[1:-1,1:-1]
boundary_x = self.x[ti].ravel()[boundary_image.ravel()]
boundary_y = self.y[ti].ravel()[boundary_image.ravel()]
r = np.sqrt((boundary_x - com_x) ** 2 + (boundary_y - com_y) ** 2)
theta = np.arctan2((boundary_y - com_y), (boundary_x - com_x)) * 180.0 / np.pi + 360
polar_coords = np.array([(r[x], theta[x]) for x in range(r.size)], dtype=[('r', 'f4'), ('theta', 'f4')])
coord_order = np.argsort(polar_coords, order=['theta', 'r'])
ordered_coords = np.vstack([boundary_x[coord_order], boundary_y[coord_order]])
return ordered_coords
|
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Get coordinates of object boundary in counter-clockwise order
|
[
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6fb6c3df90bf4867e13a97d3460b14471d107df1
|
https://github.com/djgagne/hagelslag/blob/6fb6c3df90bf4867e13a97d3460b14471d107df1/hagelslag/processing/STObject.py#L226-L247
|
train
|
djgagne/hagelslag
|
hagelslag/processing/STObject.py
|
STObject.estimate_motion
|
def estimate_motion(self, time, intensity_grid, max_u, max_v):
"""
Estimate the motion of the object with cross-correlation on the intensity values from the previous time step.
Args:
time: time being evaluated.
intensity_grid: 2D array of intensities used in cross correlation.
max_u: Maximum x-component of motion. Used to limit search area.
max_v: Maximum y-component of motion. Used to limit search area
Returns:
u, v, and the minimum error.
"""
ti = np.where(time == self.times)[0][0]
mask_vals = np.where(self.masks[ti].ravel() == 1)
i_vals = self.i[ti].ravel()[mask_vals]
j_vals = self.j[ti].ravel()[mask_vals]
obj_vals = self.timesteps[ti].ravel()[mask_vals]
u_shifts = np.arange(-max_u, max_u + 1)
v_shifts = np.arange(-max_v, max_v + 1)
min_error = 99999999999.0
best_u = 0
best_v = 0
for u in u_shifts:
j_shift = j_vals - u
for v in v_shifts:
i_shift = i_vals - v
if np.all((0 <= i_shift) & (i_shift < intensity_grid.shape[0]) &
(0 <= j_shift) & (j_shift < intensity_grid.shape[1])):
shift_vals = intensity_grid[i_shift, j_shift]
else:
shift_vals = np.zeros(i_shift.shape)
# This isn't correlation; it is mean absolute error.
error = np.abs(shift_vals - obj_vals).mean()
if error < min_error:
min_error = error
best_u = u * self.dx
best_v = v * self.dx
# 60 seems arbitrarily high
#if min_error > 60:
# best_u = 0
# best_v = 0
self.u[ti] = best_u
self.v[ti] = best_v
return best_u, best_v, min_error
|
python
|
def estimate_motion(self, time, intensity_grid, max_u, max_v):
"""
Estimate the motion of the object with cross-correlation on the intensity values from the previous time step.
Args:
time: time being evaluated.
intensity_grid: 2D array of intensities used in cross correlation.
max_u: Maximum x-component of motion. Used to limit search area.
max_v: Maximum y-component of motion. Used to limit search area
Returns:
u, v, and the minimum error.
"""
ti = np.where(time == self.times)[0][0]
mask_vals = np.where(self.masks[ti].ravel() == 1)
i_vals = self.i[ti].ravel()[mask_vals]
j_vals = self.j[ti].ravel()[mask_vals]
obj_vals = self.timesteps[ti].ravel()[mask_vals]
u_shifts = np.arange(-max_u, max_u + 1)
v_shifts = np.arange(-max_v, max_v + 1)
min_error = 99999999999.0
best_u = 0
best_v = 0
for u in u_shifts:
j_shift = j_vals - u
for v in v_shifts:
i_shift = i_vals - v
if np.all((0 <= i_shift) & (i_shift < intensity_grid.shape[0]) &
(0 <= j_shift) & (j_shift < intensity_grid.shape[1])):
shift_vals = intensity_grid[i_shift, j_shift]
else:
shift_vals = np.zeros(i_shift.shape)
# This isn't correlation; it is mean absolute error.
error = np.abs(shift_vals - obj_vals).mean()
if error < min_error:
min_error = error
best_u = u * self.dx
best_v = v * self.dx
# 60 seems arbitrarily high
#if min_error > 60:
# best_u = 0
# best_v = 0
self.u[ti] = best_u
self.v[ti] = best_v
return best_u, best_v, min_error
|
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Estimate the motion of the object with cross-correlation on the intensity values from the previous time step.
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time: time being evaluated.
intensity_grid: 2D array of intensities used in cross correlation.
max_u: Maximum x-component of motion. Used to limit search area.
max_v: Maximum y-component of motion. Used to limit search area
Returns:
u, v, and the minimum error.
|
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6fb6c3df90bf4867e13a97d3460b14471d107df1
|
https://github.com/djgagne/hagelslag/blob/6fb6c3df90bf4867e13a97d3460b14471d107df1/hagelslag/processing/STObject.py#L249-L293
|
train
|
djgagne/hagelslag
|
hagelslag/processing/STObject.py
|
STObject.count_overlap
|
def count_overlap(self, time, other_object, other_time):
"""
Counts the number of points that overlap between this STObject and another STObject. Used for tracking.
"""
ti = np.where(time == self.times)[0][0]
ma = np.where(self.masks[ti].ravel() == 1)
oti = np.where(other_time == other_object.times)[0]
obj_coords = np.zeros(self.masks[ti].sum(), dtype=[('x', int), ('y', int)])
other_obj_coords = np.zeros(other_object.masks[oti].sum(), dtype=[('x', int), ('y', int)])
obj_coords['x'] = self.i[ti].ravel()[ma]
obj_coords['y'] = self.j[ti].ravel()[ma]
other_obj_coords['x'] = other_object.i[oti][other_object.masks[oti] == 1]
other_obj_coords['y'] = other_object.j[oti][other_object.masks[oti] == 1]
return float(np.intersect1d(obj_coords,
other_obj_coords).size) / np.maximum(self.masks[ti].sum(),
other_object.masks[oti].sum())
|
python
|
def count_overlap(self, time, other_object, other_time):
"""
Counts the number of points that overlap between this STObject and another STObject. Used for tracking.
"""
ti = np.where(time == self.times)[0][0]
ma = np.where(self.masks[ti].ravel() == 1)
oti = np.where(other_time == other_object.times)[0]
obj_coords = np.zeros(self.masks[ti].sum(), dtype=[('x', int), ('y', int)])
other_obj_coords = np.zeros(other_object.masks[oti].sum(), dtype=[('x', int), ('y', int)])
obj_coords['x'] = self.i[ti].ravel()[ma]
obj_coords['y'] = self.j[ti].ravel()[ma]
other_obj_coords['x'] = other_object.i[oti][other_object.masks[oti] == 1]
other_obj_coords['y'] = other_object.j[oti][other_object.masks[oti] == 1]
return float(np.intersect1d(obj_coords,
other_obj_coords).size) / np.maximum(self.masks[ti].sum(),
other_object.masks[oti].sum())
|
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6fb6c3df90bf4867e13a97d3460b14471d107df1
|
https://github.com/djgagne/hagelslag/blob/6fb6c3df90bf4867e13a97d3460b14471d107df1/hagelslag/processing/STObject.py#L295-L310
|
train
|
djgagne/hagelslag
|
hagelslag/processing/STObject.py
|
STObject.extract_attribute_grid
|
def extract_attribute_grid(self, model_grid, potential=False, future=False):
"""
Extracts the data from a ModelOutput or ModelGrid object within the bounding box region of the STObject.
Args:
model_grid: A ModelGrid or ModelOutput Object
potential: Extracts from the time before instead of the same time as the object
"""
if potential:
var_name = model_grid.variable + "-potential"
timesteps = np.arange(self.start_time - 1, self.end_time)
elif future:
var_name = model_grid.variable + "-future"
timesteps = np.arange(self.start_time + 1, self.end_time + 2)
else:
var_name = model_grid.variable
timesteps = np.arange(self.start_time, self.end_time + 1)
self.attributes[var_name] = []
for ti, t in enumerate(timesteps):
self.attributes[var_name].append(
model_grid.data[t - model_grid.start_hour, self.i[ti], self.j[ti]])
|
python
|
def extract_attribute_grid(self, model_grid, potential=False, future=False):
"""
Extracts the data from a ModelOutput or ModelGrid object within the bounding box region of the STObject.
Args:
model_grid: A ModelGrid or ModelOutput Object
potential: Extracts from the time before instead of the same time as the object
"""
if potential:
var_name = model_grid.variable + "-potential"
timesteps = np.arange(self.start_time - 1, self.end_time)
elif future:
var_name = model_grid.variable + "-future"
timesteps = np.arange(self.start_time + 1, self.end_time + 2)
else:
var_name = model_grid.variable
timesteps = np.arange(self.start_time, self.end_time + 1)
self.attributes[var_name] = []
for ti, t in enumerate(timesteps):
self.attributes[var_name].append(
model_grid.data[t - model_grid.start_hour, self.i[ti], self.j[ti]])
|
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Extracts the data from a ModelOutput or ModelGrid object within the bounding box region of the STObject.
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potential: Extracts from the time before instead of the same time as the object
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6fb6c3df90bf4867e13a97d3460b14471d107df1
|
https://github.com/djgagne/hagelslag/blob/6fb6c3df90bf4867e13a97d3460b14471d107df1/hagelslag/processing/STObject.py#L312-L333
|
train
|
djgagne/hagelslag
|
hagelslag/processing/STObject.py
|
STObject.extract_attribute_array
|
def extract_attribute_array(self, data_array, var_name):
"""
Extracts data from a 2D array that has the same dimensions as the grid used to identify the object.
Args:
data_array: 2D numpy array
"""
if var_name not in self.attributes.keys():
self.attributes[var_name] = []
for t in range(self.times.size):
self.attributes[var_name].append(data_array[self.i[t], self.j[t]])
|
python
|
def extract_attribute_array(self, data_array, var_name):
"""
Extracts data from a 2D array that has the same dimensions as the grid used to identify the object.
Args:
data_array: 2D numpy array
"""
if var_name not in self.attributes.keys():
self.attributes[var_name] = []
for t in range(self.times.size):
self.attributes[var_name].append(data_array[self.i[t], self.j[t]])
|
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Extracts data from a 2D array that has the same dimensions as the grid used to identify the object.
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|
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6fb6c3df90bf4867e13a97d3460b14471d107df1
|
https://github.com/djgagne/hagelslag/blob/6fb6c3df90bf4867e13a97d3460b14471d107df1/hagelslag/processing/STObject.py#L335-L346
|
train
|
djgagne/hagelslag
|
hagelslag/processing/STObject.py
|
STObject.extract_tendency_grid
|
def extract_tendency_grid(self, model_grid):
"""
Extracts the difference in model outputs
Args:
model_grid: ModelOutput or ModelGrid object.
"""
var_name = model_grid.variable + "-tendency"
self.attributes[var_name] = []
timesteps = np.arange(self.start_time, self.end_time + 1)
for ti, t in enumerate(timesteps):
t_index = t - model_grid.start_hour
self.attributes[var_name].append(
model_grid.data[t_index, self.i[ti], self.j[ti]] - model_grid.data[t_index - 1, self.i[ti], self.j[ti]]
)
|
python
|
def extract_tendency_grid(self, model_grid):
"""
Extracts the difference in model outputs
Args:
model_grid: ModelOutput or ModelGrid object.
"""
var_name = model_grid.variable + "-tendency"
self.attributes[var_name] = []
timesteps = np.arange(self.start_time, self.end_time + 1)
for ti, t in enumerate(timesteps):
t_index = t - model_grid.start_hour
self.attributes[var_name].append(
model_grid.data[t_index, self.i[ti], self.j[ti]] - model_grid.data[t_index - 1, self.i[ti], self.j[ti]]
)
|
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6fb6c3df90bf4867e13a97d3460b14471d107df1
|
https://github.com/djgagne/hagelslag/blob/6fb6c3df90bf4867e13a97d3460b14471d107df1/hagelslag/processing/STObject.py#L349-L364
|
train
|
djgagne/hagelslag
|
hagelslag/processing/STObject.py
|
STObject.calc_attribute_statistics
|
def calc_attribute_statistics(self, statistic_name):
"""
Calculates summary statistics over the domains of each attribute.
Args:
statistic_name (string): numpy statistic, such as mean, std, max, min
Returns:
dict of statistics from each attribute grid.
"""
stats = {}
for var, grids in self.attributes.items():
if len(grids) > 1:
stats[var] = getattr(np.array([getattr(np.ma.array(x, mask=self.masks[t] == 0), statistic_name)()
for t, x in enumerate(grids)]), statistic_name)()
else:
stats[var] = getattr(np.ma.array(grids[0], mask=self.masks[0] == 0), statistic_name)()
return stats
|
python
|
def calc_attribute_statistics(self, statistic_name):
"""
Calculates summary statistics over the domains of each attribute.
Args:
statistic_name (string): numpy statistic, such as mean, std, max, min
Returns:
dict of statistics from each attribute grid.
"""
stats = {}
for var, grids in self.attributes.items():
if len(grids) > 1:
stats[var] = getattr(np.array([getattr(np.ma.array(x, mask=self.masks[t] == 0), statistic_name)()
for t, x in enumerate(grids)]), statistic_name)()
else:
stats[var] = getattr(np.ma.array(grids[0], mask=self.masks[0] == 0), statistic_name)()
return stats
|
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6fb6c3df90bf4867e13a97d3460b14471d107df1
|
https://github.com/djgagne/hagelslag/blob/6fb6c3df90bf4867e13a97d3460b14471d107df1/hagelslag/processing/STObject.py#L366-L383
|
train
|
djgagne/hagelslag
|
hagelslag/processing/STObject.py
|
STObject.calc_attribute_statistic
|
def calc_attribute_statistic(self, attribute, statistic, time):
"""
Calculate statistics based on the values of an attribute. The following statistics are supported:
mean, max, min, std, ptp (range), median, skew (mean - median), and percentile_(percentile value).
Args:
attribute: Attribute extracted from model grid
statistic: Name of statistic being used.
time: timestep of the object being investigated
Returns:
The value of the statistic
"""
ti = np.where(self.times == time)[0][0]
ma = np.where(self.masks[ti].ravel() == 1)
if statistic in ['mean', 'max', 'min', 'std', 'ptp']:
stat_val = getattr(self.attributes[attribute][ti].ravel()[ma], statistic)()
elif statistic == 'median':
stat_val = np.median(self.attributes[attribute][ti].ravel()[ma])
elif statistic == "skew":
stat_val = np.mean(self.attributes[attribute][ti].ravel()[ma]) - \
np.median(self.attributes[attribute][ti].ravel()[ma])
elif 'percentile' in statistic:
per = int(statistic.split("_")[1])
stat_val = np.percentile(self.attributes[attribute][ti].ravel()[ma], per)
elif 'dt' in statistic:
stat_name = statistic[:-3]
if ti == 0:
stat_val = 0
else:
stat_val = self.calc_attribute_statistic(attribute, stat_name, time) \
- self.calc_attribute_statistic(attribute, stat_name, time - 1)
else:
stat_val = np.nan
return stat_val
|
python
|
def calc_attribute_statistic(self, attribute, statistic, time):
"""
Calculate statistics based on the values of an attribute. The following statistics are supported:
mean, max, min, std, ptp (range), median, skew (mean - median), and percentile_(percentile value).
Args:
attribute: Attribute extracted from model grid
statistic: Name of statistic being used.
time: timestep of the object being investigated
Returns:
The value of the statistic
"""
ti = np.where(self.times == time)[0][0]
ma = np.where(self.masks[ti].ravel() == 1)
if statistic in ['mean', 'max', 'min', 'std', 'ptp']:
stat_val = getattr(self.attributes[attribute][ti].ravel()[ma], statistic)()
elif statistic == 'median':
stat_val = np.median(self.attributes[attribute][ti].ravel()[ma])
elif statistic == "skew":
stat_val = np.mean(self.attributes[attribute][ti].ravel()[ma]) - \
np.median(self.attributes[attribute][ti].ravel()[ma])
elif 'percentile' in statistic:
per = int(statistic.split("_")[1])
stat_val = np.percentile(self.attributes[attribute][ti].ravel()[ma], per)
elif 'dt' in statistic:
stat_name = statistic[:-3]
if ti == 0:
stat_val = 0
else:
stat_val = self.calc_attribute_statistic(attribute, stat_name, time) \
- self.calc_attribute_statistic(attribute, stat_name, time - 1)
else:
stat_val = np.nan
return stat_val
|
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time: timestep of the object being investigated
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6fb6c3df90bf4867e13a97d3460b14471d107df1
|
https://github.com/djgagne/hagelslag/blob/6fb6c3df90bf4867e13a97d3460b14471d107df1/hagelslag/processing/STObject.py#L385-L419
|
train
|
djgagne/hagelslag
|
hagelslag/processing/STObject.py
|
STObject.calc_timestep_statistic
|
def calc_timestep_statistic(self, statistic, time):
"""
Calculate statistics from the primary attribute of the StObject.
Args:
statistic: statistic being calculated
time: Timestep being investigated
Returns:
Value of the statistic
"""
ti = np.where(self.times == time)[0][0]
ma = np.where(self.masks[ti].ravel() == 1)
if statistic in ['mean', 'max', 'min', 'std', 'ptp']:
stat_val = getattr(self.timesteps[ti].ravel()[ma], statistic)()
elif statistic == 'median':
stat_val = np.median(self.timesteps[ti].ravel()[ma])
elif 'percentile' in statistic:
per = int(statistic.split("_")[1])
stat_val = np.percentile(self.timesteps[ti].ravel()[ma], per)
elif 'dt' in statistic:
stat_name = statistic[:-3]
if ti == 0:
stat_val = 0
else:
stat_val = self.calc_timestep_statistic(stat_name, time) -\
self.calc_timestep_statistic(stat_name, time - 1)
else:
stat_val = np.nan
return stat_val
|
python
|
def calc_timestep_statistic(self, statistic, time):
"""
Calculate statistics from the primary attribute of the StObject.
Args:
statistic: statistic being calculated
time: Timestep being investigated
Returns:
Value of the statistic
"""
ti = np.where(self.times == time)[0][0]
ma = np.where(self.masks[ti].ravel() == 1)
if statistic in ['mean', 'max', 'min', 'std', 'ptp']:
stat_val = getattr(self.timesteps[ti].ravel()[ma], statistic)()
elif statistic == 'median':
stat_val = np.median(self.timesteps[ti].ravel()[ma])
elif 'percentile' in statistic:
per = int(statistic.split("_")[1])
stat_val = np.percentile(self.timesteps[ti].ravel()[ma], per)
elif 'dt' in statistic:
stat_name = statistic[:-3]
if ti == 0:
stat_val = 0
else:
stat_val = self.calc_timestep_statistic(stat_name, time) -\
self.calc_timestep_statistic(stat_name, time - 1)
else:
stat_val = np.nan
return stat_val
|
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[
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6fb6c3df90bf4867e13a97d3460b14471d107df1
|
https://github.com/djgagne/hagelslag/blob/6fb6c3df90bf4867e13a97d3460b14471d107df1/hagelslag/processing/STObject.py#L421-L450
|
train
|
djgagne/hagelslag
|
hagelslag/processing/STObject.py
|
STObject.calc_shape_statistics
|
def calc_shape_statistics(self, stat_names):
"""
Calculate shape statistics using regionprops applied to the object mask.
Args:
stat_names: List of statistics to be extracted from those calculated by regionprops.
Returns:
Dictionary of shape statistics
"""
stats = {}
try:
all_props = [regionprops(m) for m in self.masks]
except TypeError:
print(self.masks)
exit()
for stat in stat_names:
stats[stat] = np.mean([p[0][stat] for p in all_props])
return stats
|
python
|
def calc_shape_statistics(self, stat_names):
"""
Calculate shape statistics using regionprops applied to the object mask.
Args:
stat_names: List of statistics to be extracted from those calculated by regionprops.
Returns:
Dictionary of shape statistics
"""
stats = {}
try:
all_props = [regionprops(m) for m in self.masks]
except TypeError:
print(self.masks)
exit()
for stat in stat_names:
stats[stat] = np.mean([p[0][stat] for p in all_props])
return stats
|
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Calculate shape statistics using regionprops applied to the object mask.
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Returns:
Dictionary of shape statistics
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6fb6c3df90bf4867e13a97d3460b14471d107df1
|
https://github.com/djgagne/hagelslag/blob/6fb6c3df90bf4867e13a97d3460b14471d107df1/hagelslag/processing/STObject.py#L452-L470
|
train
|
djgagne/hagelslag
|
hagelslag/processing/STObject.py
|
STObject.calc_shape_step
|
def calc_shape_step(self, stat_names, time):
"""
Calculate shape statistics for a single time step
Args:
stat_names: List of shape statistics calculated from region props
time: Time being investigated
Returns:
List of shape statistics
"""
ti = np.where(self.times == time)[0][0]
props = regionprops(self.masks[ti], self.timesteps[ti])[0]
shape_stats = []
for stat_name in stat_names:
if "moments_hu" in stat_name:
hu_index = int(stat_name.split("_")[-1])
hu_name = "_".join(stat_name.split("_")[:-1])
hu_val = np.log(props[hu_name][hu_index])
if np.isnan(hu_val):
shape_stats.append(0)
else:
shape_stats.append(hu_val)
else:
shape_stats.append(props[stat_name])
return shape_stats
|
python
|
def calc_shape_step(self, stat_names, time):
"""
Calculate shape statistics for a single time step
Args:
stat_names: List of shape statistics calculated from region props
time: Time being investigated
Returns:
List of shape statistics
"""
ti = np.where(self.times == time)[0][0]
props = regionprops(self.masks[ti], self.timesteps[ti])[0]
shape_stats = []
for stat_name in stat_names:
if "moments_hu" in stat_name:
hu_index = int(stat_name.split("_")[-1])
hu_name = "_".join(stat_name.split("_")[:-1])
hu_val = np.log(props[hu_name][hu_index])
if np.isnan(hu_val):
shape_stats.append(0)
else:
shape_stats.append(hu_val)
else:
shape_stats.append(props[stat_name])
return shape_stats
|
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Calculate shape statistics for a single time step
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time: Time being investigated
Returns:
List of shape statistics
|
[
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6fb6c3df90bf4867e13a97d3460b14471d107df1
|
https://github.com/djgagne/hagelslag/blob/6fb6c3df90bf4867e13a97d3460b14471d107df1/hagelslag/processing/STObject.py#L472-L498
|
train
|
djgagne/hagelslag
|
hagelslag/processing/STObject.py
|
STObject.to_geojson
|
def to_geojson(self, filename, proj, metadata=None):
"""
Output the data in the STObject to a geoJSON file.
Args:
filename: Name of the file
proj: PyProj object for converting the x and y coordinates back to latitude and longitue values.
metadata: Metadata describing the object to be included in the top-level properties.
"""
if metadata is None:
metadata = {}
json_obj = {"type": "FeatureCollection", "features": [], "properties": {}}
json_obj['properties']['times'] = self.times.tolist()
json_obj['properties']['dx'] = self.dx
json_obj['properties']['step'] = self.step
json_obj['properties']['u'] = self.u.tolist()
json_obj['properties']['v'] = self.v.tolist()
for k, v in metadata.items():
json_obj['properties'][k] = v
for t, time in enumerate(self.times):
feature = {"type": "Feature",
"geometry": {"type": "Polygon"},
"properties": {}}
boundary_coords = self.boundary_polygon(time)
lonlat = np.vstack(proj(boundary_coords[0], boundary_coords[1], inverse=True))
lonlat_list = lonlat.T.tolist()
if len(lonlat_list) > 0:
lonlat_list.append(lonlat_list[0])
feature["geometry"]["coordinates"] = [lonlat_list]
for attr in ["timesteps", "masks", "x", "y", "i", "j"]:
feature["properties"][attr] = getattr(self, attr)[t].tolist()
feature["properties"]["attributes"] = {}
for attr_name, steps in self.attributes.items():
feature["properties"]["attributes"][attr_name] = steps[t].tolist()
json_obj['features'].append(feature)
file_obj = open(filename, "w")
json.dump(json_obj, file_obj, indent=1, sort_keys=True)
file_obj.close()
return
|
python
|
def to_geojson(self, filename, proj, metadata=None):
"""
Output the data in the STObject to a geoJSON file.
Args:
filename: Name of the file
proj: PyProj object for converting the x and y coordinates back to latitude and longitue values.
metadata: Metadata describing the object to be included in the top-level properties.
"""
if metadata is None:
metadata = {}
json_obj = {"type": "FeatureCollection", "features": [], "properties": {}}
json_obj['properties']['times'] = self.times.tolist()
json_obj['properties']['dx'] = self.dx
json_obj['properties']['step'] = self.step
json_obj['properties']['u'] = self.u.tolist()
json_obj['properties']['v'] = self.v.tolist()
for k, v in metadata.items():
json_obj['properties'][k] = v
for t, time in enumerate(self.times):
feature = {"type": "Feature",
"geometry": {"type": "Polygon"},
"properties": {}}
boundary_coords = self.boundary_polygon(time)
lonlat = np.vstack(proj(boundary_coords[0], boundary_coords[1], inverse=True))
lonlat_list = lonlat.T.tolist()
if len(lonlat_list) > 0:
lonlat_list.append(lonlat_list[0])
feature["geometry"]["coordinates"] = [lonlat_list]
for attr in ["timesteps", "masks", "x", "y", "i", "j"]:
feature["properties"][attr] = getattr(self, attr)[t].tolist()
feature["properties"]["attributes"] = {}
for attr_name, steps in self.attributes.items():
feature["properties"]["attributes"][attr_name] = steps[t].tolist()
json_obj['features'].append(feature)
file_obj = open(filename, "w")
json.dump(json_obj, file_obj, indent=1, sort_keys=True)
file_obj.close()
return
|
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|
[
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] |
6fb6c3df90bf4867e13a97d3460b14471d107df1
|
https://github.com/djgagne/hagelslag/blob/6fb6c3df90bf4867e13a97d3460b14471d107df1/hagelslag/processing/STObject.py#L500-L538
|
train
|
nion-software/nionswift
|
nion/swift/model/MemoryStorageSystem.py
|
MemoryStorageSystem.rewrite_properties
|
def rewrite_properties(self, properties):
"""Set the properties and write to disk."""
with self.__library_storage_lock:
self.__library_storage = properties
self.__write_properties(None)
|
python
|
def rewrite_properties(self, properties):
"""Set the properties and write to disk."""
with self.__library_storage_lock:
self.__library_storage = properties
self.__write_properties(None)
|
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Set the properties and write to disk.
|
[
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"write",
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"."
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d43693eaf057b8683b9638e575000f055fede452
|
https://github.com/nion-software/nionswift/blob/d43693eaf057b8683b9638e575000f055fede452/nion/swift/model/MemoryStorageSystem.py#L113-L117
|
train
|
mgraffg/EvoDAG
|
EvoDAG/population.py
|
BasePopulation.model
|
def model(self, v=None):
"Returns the model of node v"
if v is None:
v = self.estopping
hist = self.hist
trace = self.trace(v)
ins = None
if self._base._probability_calibration is not None:
node = hist[-1]
node.normalize()
X = np.array([x.full_array() for x in node.hy]).T
y = np.array(self._base._y_klass.full_array())
mask = np.ones(X.shape[0], dtype=np.bool)
mask[np.array(self._base._mask_ts.index)] = False
ins = self._base._probability_calibration().fit(X[mask], y[mask])
if self._classifier:
nclasses = self._labels.shape[0]
else:
nclasses = None
m = Model(trace, hist, nvar=self._base._nvar,
classifier=self._classifier, labels=self._labels,
probability_calibration=ins, nclasses=nclasses)
return m
|
python
|
def model(self, v=None):
"Returns the model of node v"
if v is None:
v = self.estopping
hist = self.hist
trace = self.trace(v)
ins = None
if self._base._probability_calibration is not None:
node = hist[-1]
node.normalize()
X = np.array([x.full_array() for x in node.hy]).T
y = np.array(self._base._y_klass.full_array())
mask = np.ones(X.shape[0], dtype=np.bool)
mask[np.array(self._base._mask_ts.index)] = False
ins = self._base._probability_calibration().fit(X[mask], y[mask])
if self._classifier:
nclasses = self._labels.shape[0]
else:
nclasses = None
m = Model(trace, hist, nvar=self._base._nvar,
classifier=self._classifier, labels=self._labels,
probability_calibration=ins, nclasses=nclasses)
return m
|
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e11fa1fd1ca9e69cca92696c86661a3dc7b3a1d5
|
https://github.com/mgraffg/EvoDAG/blob/e11fa1fd1ca9e69cca92696c86661a3dc7b3a1d5/EvoDAG/population.py#L252-L274
|
train
|
mgraffg/EvoDAG
|
EvoDAG/population.py
|
BasePopulation.trace
|
def trace(self, n):
"Restore the position in the history of individual v's nodes"
trace_map = {}
self._trace(n, trace_map)
s = list(trace_map.keys())
s.sort()
return s
|
python
|
def trace(self, n):
"Restore the position in the history of individual v's nodes"
trace_map = {}
self._trace(n, trace_map)
s = list(trace_map.keys())
s.sort()
return s
|
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|
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e11fa1fd1ca9e69cca92696c86661a3dc7b3a1d5
|
https://github.com/mgraffg/EvoDAG/blob/e11fa1fd1ca9e69cca92696c86661a3dc7b3a1d5/EvoDAG/population.py#L276-L282
|
train
|
mgraffg/EvoDAG
|
EvoDAG/population.py
|
BasePopulation.tournament
|
def tournament(self, negative=False):
"""Tournament selection and when negative is True it performs negative
tournament selection"""
if self.generation <= self._random_generations and not negative:
return self.random_selection()
if not self._negative_selection and negative:
return self.random_selection(negative=negative)
vars = self.random()
fit = [(k, self.population[x].fitness) for k, x in enumerate(vars)]
if negative:
fit = min(fit, key=lambda x: x[1])
else:
fit = max(fit, key=lambda x: x[1])
index = fit[0]
return vars[index]
|
python
|
def tournament(self, negative=False):
"""Tournament selection and when negative is True it performs negative
tournament selection"""
if self.generation <= self._random_generations and not negative:
return self.random_selection()
if not self._negative_selection and negative:
return self.random_selection(negative=negative)
vars = self.random()
fit = [(k, self.population[x].fitness) for k, x in enumerate(vars)]
if negative:
fit = min(fit, key=lambda x: x[1])
else:
fit = max(fit, key=lambda x: x[1])
index = fit[0]
return vars[index]
|
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Tournament selection and when negative is True it performs negative
tournament selection
|
[
"Tournament",
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e11fa1fd1ca9e69cca92696c86661a3dc7b3a1d5
|
https://github.com/mgraffg/EvoDAG/blob/e11fa1fd1ca9e69cca92696c86661a3dc7b3a1d5/EvoDAG/population.py#L319-L333
|
train
|
mgraffg/EvoDAG
|
EvoDAG/population.py
|
BasePopulation.create_population
|
def create_population(self):
"Create the initial population"
base = self._base
if base._share_inputs:
used_inputs_var = SelectNumbers([x for x in range(base.nvar)])
used_inputs_naive = used_inputs_var
if base._pr_variable == 0:
used_inputs_var = SelectNumbers([])
used_inputs_naive = SelectNumbers([x for x in range(base.nvar)])
elif base._pr_variable == 1:
used_inputs_var = SelectNumbers([x for x in range(base.nvar)])
used_inputs_naive = SelectNumbers([])
else:
used_inputs_var = SelectNumbers([x for x in range(base.nvar)])
used_inputs_naive = SelectNumbers([x for x in range(base.nvar)])
nb_input = Inputs(base, used_inputs_naive, functions=base._input_functions)
while ((base._all_inputs and not base.stopping_criteria_tl()) or
(self.popsize < base.popsize and
not base.stopping_criteria())):
if base._all_inputs and used_inputs_var.empty() and used_inputs_naive.empty():
base._init_popsize = self.popsize
break
if nb_input.use_all_variables():
v = nb_input.all_variables()
if v is None:
continue
elif not used_inputs_var.empty() and np.random.random() < base._pr_variable:
v = self.variable_input(used_inputs_var)
if v is None:
used_inputs_var.pos = used_inputs_var.size
continue
elif not used_inputs_naive.empty():
v = nb_input.input()
if not used_inputs_var.empty() and used_inputs_naive.empty():
base._pr_variable = 1
if v is None:
used_inputs_naive.pos = used_inputs_naive.size
if not used_inputs_var.empty():
base._pr_variable = 1
continue
else:
gen = self.generation
self.generation = 0
v = base.random_offspring()
self.generation = gen
self.add(v)
|
python
|
def create_population(self):
"Create the initial population"
base = self._base
if base._share_inputs:
used_inputs_var = SelectNumbers([x for x in range(base.nvar)])
used_inputs_naive = used_inputs_var
if base._pr_variable == 0:
used_inputs_var = SelectNumbers([])
used_inputs_naive = SelectNumbers([x for x in range(base.nvar)])
elif base._pr_variable == 1:
used_inputs_var = SelectNumbers([x for x in range(base.nvar)])
used_inputs_naive = SelectNumbers([])
else:
used_inputs_var = SelectNumbers([x for x in range(base.nvar)])
used_inputs_naive = SelectNumbers([x for x in range(base.nvar)])
nb_input = Inputs(base, used_inputs_naive, functions=base._input_functions)
while ((base._all_inputs and not base.stopping_criteria_tl()) or
(self.popsize < base.popsize and
not base.stopping_criteria())):
if base._all_inputs and used_inputs_var.empty() and used_inputs_naive.empty():
base._init_popsize = self.popsize
break
if nb_input.use_all_variables():
v = nb_input.all_variables()
if v is None:
continue
elif not used_inputs_var.empty() and np.random.random() < base._pr_variable:
v = self.variable_input(used_inputs_var)
if v is None:
used_inputs_var.pos = used_inputs_var.size
continue
elif not used_inputs_naive.empty():
v = nb_input.input()
if not used_inputs_var.empty() and used_inputs_naive.empty():
base._pr_variable = 1
if v is None:
used_inputs_naive.pos = used_inputs_naive.size
if not used_inputs_var.empty():
base._pr_variable = 1
continue
else:
gen = self.generation
self.generation = 0
v = base.random_offspring()
self.generation = gen
self.add(v)
|
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] |
Create the initial population
|
[
"Create",
"the",
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"population"
] |
e11fa1fd1ca9e69cca92696c86661a3dc7b3a1d5
|
https://github.com/mgraffg/EvoDAG/blob/e11fa1fd1ca9e69cca92696c86661a3dc7b3a1d5/EvoDAG/population.py#L345-L390
|
train
|
mgraffg/EvoDAG
|
EvoDAG/population.py
|
BasePopulation.add
|
def add(self, v):
"Add an individual to the population"
self.population.append(v)
self._current_popsize += 1
v.position = len(self._hist)
self._hist.append(v)
self.bsf = v
self.estopping = v
self._density += self.get_density(v)
|
python
|
def add(self, v):
"Add an individual to the population"
self.population.append(v)
self._current_popsize += 1
v.position = len(self._hist)
self._hist.append(v)
self.bsf = v
self.estopping = v
self._density += self.get_density(v)
|
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Add an individual to the population
|
[
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] |
e11fa1fd1ca9e69cca92696c86661a3dc7b3a1d5
|
https://github.com/mgraffg/EvoDAG/blob/e11fa1fd1ca9e69cca92696c86661a3dc7b3a1d5/EvoDAG/population.py#L392-L400
|
train
|
mgraffg/EvoDAG
|
EvoDAG/population.py
|
BasePopulation.replace
|
def replace(self, v):
"""Replace an individual selected by negative tournament selection with
individual v"""
if self.popsize < self._popsize:
return self.add(v)
k = self.tournament(negative=True)
self.clean(self.population[k])
self.population[k] = v
v.position = len(self._hist)
self._hist.append(v)
self.bsf = v
self.estopping = v
self._inds_replace += 1
self._density += self.get_density(v)
if self._inds_replace == self._popsize:
self._inds_replace = 0
self.generation += 1
gc.collect()
|
python
|
def replace(self, v):
"""Replace an individual selected by negative tournament selection with
individual v"""
if self.popsize < self._popsize:
return self.add(v)
k = self.tournament(negative=True)
self.clean(self.population[k])
self.population[k] = v
v.position = len(self._hist)
self._hist.append(v)
self.bsf = v
self.estopping = v
self._inds_replace += 1
self._density += self.get_density(v)
if self._inds_replace == self._popsize:
self._inds_replace = 0
self.generation += 1
gc.collect()
|
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Replace an individual selected by negative tournament selection with
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|
[
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"an",
"individual",
"selected",
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"tournament",
"selection",
"with",
"individual",
"v"
] |
e11fa1fd1ca9e69cca92696c86661a3dc7b3a1d5
|
https://github.com/mgraffg/EvoDAG/blob/e11fa1fd1ca9e69cca92696c86661a3dc7b3a1d5/EvoDAG/population.py#L402-L419
|
train
|
nion-software/nionswift
|
nion/swift/model/HDF5Handler.py
|
make_directory_if_needed
|
def make_directory_if_needed(directory_path):
"""
Make the directory path, if needed.
"""
if os.path.exists(directory_path):
if not os.path.isdir(directory_path):
raise OSError("Path is not a directory:", directory_path)
else:
os.makedirs(directory_path)
|
python
|
def make_directory_if_needed(directory_path):
"""
Make the directory path, if needed.
"""
if os.path.exists(directory_path):
if not os.path.isdir(directory_path):
raise OSError("Path is not a directory:", directory_path)
else:
os.makedirs(directory_path)
|
[
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Make the directory path, if needed.
|
[
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"."
] |
d43693eaf057b8683b9638e575000f055fede452
|
https://github.com/nion-software/nionswift/blob/d43693eaf057b8683b9638e575000f055fede452/nion/swift/model/HDF5Handler.py#L18-L26
|
train
|
ajk8/hatchery
|
hatchery/main.py
|
hatchery
|
def hatchery():
""" Main entry point for the hatchery program """
args = docopt.docopt(__doc__)
task_list = args['<task>']
if not task_list or 'help' in task_list or args['--help']:
print(__doc__.format(version=_version.__version__, config_files=config.CONFIG_LOCATIONS))
return 0
level_str = args['--log-level']
try:
level_const = getattr(logging, level_str.upper())
logging.basicConfig(level=level_const)
if level_const == logging.DEBUG:
workdir.options.debug = True
except LookupError:
logging.basicConfig()
logger.error('received invalid log level: ' + level_str)
return 1
for task in task_list:
if task not in ORDERED_TASKS:
logger.info('starting task: check')
logger.error('received invalid task: ' + task)
return 1
for task in CHECK_TASKS:
if task in task_list:
task_check(args)
break
if 'package' in task_list and not args['--release-version']:
logger.error('--release-version is required for the package task')
return 1
config_dict = _get_config_or_die(
calling_task='hatchery',
required_params=['auto_push_tag']
)
if config_dict['auto_push_tag'] and 'upload' in task_list:
logger.info('adding task: tag (auto_push_tag==True)')
task_list.append('tag')
# all commands will raise a SystemExit if they fail
# check will have already been run
for task in ORDERED_TASKS:
if task in task_list and task != 'check':
logger.info('starting task: ' + task)
globals()['task_' + task](args)
logger.info("all's well that ends well...hatchery out")
return 0
|
python
|
def hatchery():
""" Main entry point for the hatchery program """
args = docopt.docopt(__doc__)
task_list = args['<task>']
if not task_list or 'help' in task_list or args['--help']:
print(__doc__.format(version=_version.__version__, config_files=config.CONFIG_LOCATIONS))
return 0
level_str = args['--log-level']
try:
level_const = getattr(logging, level_str.upper())
logging.basicConfig(level=level_const)
if level_const == logging.DEBUG:
workdir.options.debug = True
except LookupError:
logging.basicConfig()
logger.error('received invalid log level: ' + level_str)
return 1
for task in task_list:
if task not in ORDERED_TASKS:
logger.info('starting task: check')
logger.error('received invalid task: ' + task)
return 1
for task in CHECK_TASKS:
if task in task_list:
task_check(args)
break
if 'package' in task_list and not args['--release-version']:
logger.error('--release-version is required for the package task')
return 1
config_dict = _get_config_or_die(
calling_task='hatchery',
required_params=['auto_push_tag']
)
if config_dict['auto_push_tag'] and 'upload' in task_list:
logger.info('adding task: tag (auto_push_tag==True)')
task_list.append('tag')
# all commands will raise a SystemExit if they fail
# check will have already been run
for task in ORDERED_TASKS:
if task in task_list and task != 'check':
logger.info('starting task: ' + task)
globals()['task_' + task](args)
logger.info("all's well that ends well...hatchery out")
return 0
|
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Main entry point for the hatchery program
|
[
"Main",
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e068c9f5366d2c98225babb03d4cde36c710194f
|
https://github.com/ajk8/hatchery/blob/e068c9f5366d2c98225babb03d4cde36c710194f/hatchery/main.py#L379-L430
|
train
|
ajk8/hatchery
|
hatchery/executor.py
|
call
|
def call(cmd_args, suppress_output=False):
""" Call an arbitary command and return the exit value, stdout, and stderr as a tuple
Command can be passed in as either a string or iterable
>>> result = call('hatchery', suppress_output=True)
>>> result.exitval
0
>>> result = call(['hatchery', 'notreal'])
>>> result.exitval
1
"""
if not funcy.is_list(cmd_args) and not funcy.is_tuple(cmd_args):
cmd_args = shlex.split(cmd_args)
logger.info('executing `{}`'.format(' '.join(cmd_args)))
call_request = CallRequest(cmd_args, suppress_output=suppress_output)
call_result = call_request.run()
if call_result.exitval:
logger.error('`{}` returned error code {}'.format(' '.join(cmd_args), call_result.exitval))
return call_result
|
python
|
def call(cmd_args, suppress_output=False):
""" Call an arbitary command and return the exit value, stdout, and stderr as a tuple
Command can be passed in as either a string or iterable
>>> result = call('hatchery', suppress_output=True)
>>> result.exitval
0
>>> result = call(['hatchery', 'notreal'])
>>> result.exitval
1
"""
if not funcy.is_list(cmd_args) and not funcy.is_tuple(cmd_args):
cmd_args = shlex.split(cmd_args)
logger.info('executing `{}`'.format(' '.join(cmd_args)))
call_request = CallRequest(cmd_args, suppress_output=suppress_output)
call_result = call_request.run()
if call_result.exitval:
logger.error('`{}` returned error code {}'.format(' '.join(cmd_args), call_result.exitval))
return call_result
|
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Command can be passed in as either a string or iterable
>>> result = call('hatchery', suppress_output=True)
>>> result.exitval
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>>> result = call(['hatchery', 'notreal'])
>>> result.exitval
1
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e068c9f5366d2c98225babb03d4cde36c710194f
|
https://github.com/ajk8/hatchery/blob/e068c9f5366d2c98225babb03d4cde36c710194f/hatchery/executor.py#L66-L85
|
train
|
ajk8/hatchery
|
hatchery/executor.py
|
setup
|
def setup(cmd_args, suppress_output=False):
""" Call a setup.py command or list of commands
>>> result = setup('--name', suppress_output=True)
>>> result.exitval
0
>>> result = setup('notreal')
>>> result.exitval
1
"""
if not funcy.is_list(cmd_args) and not funcy.is_tuple(cmd_args):
cmd_args = shlex.split(cmd_args)
cmd_args = [sys.executable, 'setup.py'] + [x for x in cmd_args]
return call(cmd_args, suppress_output=suppress_output)
|
python
|
def setup(cmd_args, suppress_output=False):
""" Call a setup.py command or list of commands
>>> result = setup('--name', suppress_output=True)
>>> result.exitval
0
>>> result = setup('notreal')
>>> result.exitval
1
"""
if not funcy.is_list(cmd_args) and not funcy.is_tuple(cmd_args):
cmd_args = shlex.split(cmd_args)
cmd_args = [sys.executable, 'setup.py'] + [x for x in cmd_args]
return call(cmd_args, suppress_output=suppress_output)
|
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>>> result = setup('notreal')
>>> result.exitval
1
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[
"Call",
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] |
e068c9f5366d2c98225babb03d4cde36c710194f
|
https://github.com/ajk8/hatchery/blob/e068c9f5366d2c98225babb03d4cde36c710194f/hatchery/executor.py#L88-L101
|
train
|
djgagne/hagelslag
|
hagelslag/data/MRMSGrid.py
|
MRMSGrid.load_data
|
def load_data(self):
"""
Loads data files and stores the output in the data attribute.
"""
data = []
valid_dates = []
mrms_files = np.array(sorted(os.listdir(self.path + self.variable + "/")))
mrms_file_dates = np.array([m_file.split("_")[-2].split("-")[0]
for m_file in mrms_files])
old_mrms_file = None
file_obj = None
for t in range(self.all_dates.shape[0]):
file_index = np.where(mrms_file_dates == self.all_dates[t].strftime("%Y%m%d"))[0]
if len(file_index) > 0:
mrms_file = mrms_files[file_index][0]
if mrms_file is not None:
if file_obj is not None:
file_obj.close()
file_obj = Dataset(self.path + self.variable + "/" + mrms_file)
#old_mrms_file = mrms_file
if "time" in file_obj.variables.keys():
time_var = "time"
else:
time_var = "date"
file_valid_dates = pd.DatetimeIndex(num2date(file_obj.variables[time_var][:],
file_obj.variables[time_var].units))
else:
file_valid_dates = pd.DatetimeIndex([])
time_index = np.where(file_valid_dates.values == self.all_dates.values[t])[0]
if len(time_index) > 0:
data.append(file_obj.variables[self.variable][time_index[0]])
valid_dates.append(self.all_dates[t])
if file_obj is not None:
file_obj.close()
self.data = np.array(data)
self.data[self.data < 0] = 0
self.data[self.data > 150] = 150
self.valid_dates = pd.DatetimeIndex(valid_dates)
|
python
|
def load_data(self):
"""
Loads data files and stores the output in the data attribute.
"""
data = []
valid_dates = []
mrms_files = np.array(sorted(os.listdir(self.path + self.variable + "/")))
mrms_file_dates = np.array([m_file.split("_")[-2].split("-")[0]
for m_file in mrms_files])
old_mrms_file = None
file_obj = None
for t in range(self.all_dates.shape[0]):
file_index = np.where(mrms_file_dates == self.all_dates[t].strftime("%Y%m%d"))[0]
if len(file_index) > 0:
mrms_file = mrms_files[file_index][0]
if mrms_file is not None:
if file_obj is not None:
file_obj.close()
file_obj = Dataset(self.path + self.variable + "/" + mrms_file)
#old_mrms_file = mrms_file
if "time" in file_obj.variables.keys():
time_var = "time"
else:
time_var = "date"
file_valid_dates = pd.DatetimeIndex(num2date(file_obj.variables[time_var][:],
file_obj.variables[time_var].units))
else:
file_valid_dates = pd.DatetimeIndex([])
time_index = np.where(file_valid_dates.values == self.all_dates.values[t])[0]
if len(time_index) > 0:
data.append(file_obj.variables[self.variable][time_index[0]])
valid_dates.append(self.all_dates[t])
if file_obj is not None:
file_obj.close()
self.data = np.array(data)
self.data[self.data < 0] = 0
self.data[self.data > 150] = 150
self.valid_dates = pd.DatetimeIndex(valid_dates)
|
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Loads data files and stores the output in the data attribute.
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[
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] |
6fb6c3df90bf4867e13a97d3460b14471d107df1
|
https://github.com/djgagne/hagelslag/blob/6fb6c3df90bf4867e13a97d3460b14471d107df1/hagelslag/data/MRMSGrid.py#L44-L82
|
train
|
djgagne/hagelslag
|
hagelslag/processing/EnhancedWatershedSegmenter.py
|
rescale_data
|
def rescale_data(data, data_min, data_max, out_min=0.0, out_max=100.0):
"""
Rescale your input data so that is ranges over integer values, which will perform better in the watershed.
Args:
data: 2D or 3D ndarray being rescaled
data_min: minimum value of input data for scaling purposes
data_max: maximum value of input data for scaling purposes
out_min: minimum value of scaled data
out_max: maximum value of scaled data
Returns:
Linearly scaled ndarray
"""
return (out_max - out_min) / (data_max - data_min) * (data - data_min) + out_min
|
python
|
def rescale_data(data, data_min, data_max, out_min=0.0, out_max=100.0):
"""
Rescale your input data so that is ranges over integer values, which will perform better in the watershed.
Args:
data: 2D or 3D ndarray being rescaled
data_min: minimum value of input data for scaling purposes
data_max: maximum value of input data for scaling purposes
out_min: minimum value of scaled data
out_max: maximum value of scaled data
Returns:
Linearly scaled ndarray
"""
return (out_max - out_min) / (data_max - data_min) * (data - data_min) + out_min
|
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Rescale your input data so that is ranges over integer values, which will perform better in the watershed.
Args:
data: 2D or 3D ndarray being rescaled
data_min: minimum value of input data for scaling purposes
data_max: maximum value of input data for scaling purposes
out_min: minimum value of scaled data
out_max: maximum value of scaled data
Returns:
Linearly scaled ndarray
|
[
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"."
] |
6fb6c3df90bf4867e13a97d3460b14471d107df1
|
https://github.com/djgagne/hagelslag/blob/6fb6c3df90bf4867e13a97d3460b14471d107df1/hagelslag/processing/EnhancedWatershedSegmenter.py#L297-L311
|
train
|
djgagne/hagelslag
|
hagelslag/processing/EnhancedWatershedSegmenter.py
|
EnhancedWatershed.label
|
def label(self, input_grid):
"""
Labels input grid using enhanced watershed algorithm.
Args:
input_grid (numpy.ndarray): Grid to be labeled.
Returns:
Array of labeled pixels
"""
marked = self.find_local_maxima(input_grid)
marked = np.where(marked >= 0, 1, 0)
# splabel returns two things in a tuple: an array and an integer
# assign the first thing (array) to markers
markers = splabel(marked)[0]
return markers
|
python
|
def label(self, input_grid):
"""
Labels input grid using enhanced watershed algorithm.
Args:
input_grid (numpy.ndarray): Grid to be labeled.
Returns:
Array of labeled pixels
"""
marked = self.find_local_maxima(input_grid)
marked = np.where(marked >= 0, 1, 0)
# splabel returns two things in a tuple: an array and an integer
# assign the first thing (array) to markers
markers = splabel(marked)[0]
return markers
|
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Labels input grid using enhanced watershed algorithm.
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Returns:
Array of labeled pixels
|
[
"Labels",
"input",
"grid",
"using",
"enhanced",
"watershed",
"algorithm",
"."
] |
6fb6c3df90bf4867e13a97d3460b14471d107df1
|
https://github.com/djgagne/hagelslag/blob/6fb6c3df90bf4867e13a97d3460b14471d107df1/hagelslag/processing/EnhancedWatershedSegmenter.py#L42-L57
|
train
|
djgagne/hagelslag
|
hagelslag/processing/EnhancedWatershedSegmenter.py
|
EnhancedWatershed.find_local_maxima
|
def find_local_maxima(self, input_grid):
"""
Finds the local maxima in the inputGrid and perform region growing to identify objects.
Args:
input_grid: Raw input data.
Returns:
array with labeled objects.
"""
pixels, q_data = self.quantize(input_grid)
centers = OrderedDict()
for p in pixels.keys():
centers[p] = []
marked = np.ones(q_data.shape, dtype=int) * self.UNMARKED
MIN_INFL = int(np.round(1 + 0.5 * np.sqrt(self.max_size)))
MAX_INFL = 2 * MIN_INFL
marked_so_far = []
# Find the maxima. These are high-values with enough clearance
# around them.
# Work from high to low bins. The pixels in the highest bin mark their
# neighborhoods first. If you did it from low to high the lowest maxima
# would mark their neighborhoods first and interfere with the identification of higher maxima.
for b in sorted(pixels.keys(),reverse=True):
# Square starts large with high intensity bins and gets smaller with low intensity bins.
infl_dist = MIN_INFL + int(np.round(float(b) / self.max_bin * (MAX_INFL - MIN_INFL)))
for p in pixels[b]:
if marked[p] == self.UNMARKED:
ok = False
del marked_so_far[:]
# Temporarily mark unmarked points in square around point (keep track of them in list marked_so_far).
# If none of the points in square were marked already from a higher intensity center,
# this counts as a new center and ok=True and points will remain marked.
# Otherwise ok=False and marked points that were previously unmarked will be unmarked.
for (i, j), v in np.ndenumerate(marked[p[0] - infl_dist:p[0] + infl_dist + 1,
p[1] - infl_dist:p[1]+ infl_dist + 1]):
if v == self.UNMARKED:
ok = True
marked[i - infl_dist + p[0],j - infl_dist + p[1]] = b
marked_so_far.append((i - infl_dist + p[0],j - infl_dist + p[1]))
else:
# neighborhood already taken
ok = False
break
# ok if point and surrounding square were not marked already.
if ok:
# highest point in its neighborhood
centers[b].append(p)
else:
for m in marked_so_far:
marked[m] = self.UNMARKED
# Erase marks and start over. You have a list of centers now.
marked[:, :] = self.UNMARKED
deferred_from_last = []
deferred_to_next = []
# delta (int): maximum number of increments the cluster is allowed to range over. Larger d results in clusters over larger scales.
for delta in range(0, self.delta + 1):
# Work from high to low bins.
for b in sorted(centers.keys(), reverse=True):
bin_lower = b - delta
deferred_from_last[:] = deferred_to_next[:]
del deferred_to_next[:]
foothills = []
n_centers = len(centers[b])
tot_centers = n_centers + len(deferred_from_last)
for i in range(tot_centers):
# done this way to minimize memory overhead of maintaining two lists
if i < n_centers:
center = centers[b][i]
else:
center = deferred_from_last[i - n_centers]
if bin_lower < 0:
bin_lower = 0
if marked[center] == self.UNMARKED:
captured = self.set_maximum(q_data, marked, center, bin_lower, foothills)
if not captured:
# decrement to lower value to see if it'll get big enough
deferred_to_next.append(center)
else:
pass
# this is the last one for this bin
self.remove_foothills(q_data, marked, b, bin_lower, centers, foothills)
del deferred_from_last[:]
del deferred_to_next[:]
return marked
|
python
|
def find_local_maxima(self, input_grid):
"""
Finds the local maxima in the inputGrid and perform region growing to identify objects.
Args:
input_grid: Raw input data.
Returns:
array with labeled objects.
"""
pixels, q_data = self.quantize(input_grid)
centers = OrderedDict()
for p in pixels.keys():
centers[p] = []
marked = np.ones(q_data.shape, dtype=int) * self.UNMARKED
MIN_INFL = int(np.round(1 + 0.5 * np.sqrt(self.max_size)))
MAX_INFL = 2 * MIN_INFL
marked_so_far = []
# Find the maxima. These are high-values with enough clearance
# around them.
# Work from high to low bins. The pixels in the highest bin mark their
# neighborhoods first. If you did it from low to high the lowest maxima
# would mark their neighborhoods first and interfere with the identification of higher maxima.
for b in sorted(pixels.keys(),reverse=True):
# Square starts large with high intensity bins and gets smaller with low intensity bins.
infl_dist = MIN_INFL + int(np.round(float(b) / self.max_bin * (MAX_INFL - MIN_INFL)))
for p in pixels[b]:
if marked[p] == self.UNMARKED:
ok = False
del marked_so_far[:]
# Temporarily mark unmarked points in square around point (keep track of them in list marked_so_far).
# If none of the points in square were marked already from a higher intensity center,
# this counts as a new center and ok=True and points will remain marked.
# Otherwise ok=False and marked points that were previously unmarked will be unmarked.
for (i, j), v in np.ndenumerate(marked[p[0] - infl_dist:p[0] + infl_dist + 1,
p[1] - infl_dist:p[1]+ infl_dist + 1]):
if v == self.UNMARKED:
ok = True
marked[i - infl_dist + p[0],j - infl_dist + p[1]] = b
marked_so_far.append((i - infl_dist + p[0],j - infl_dist + p[1]))
else:
# neighborhood already taken
ok = False
break
# ok if point and surrounding square were not marked already.
if ok:
# highest point in its neighborhood
centers[b].append(p)
else:
for m in marked_so_far:
marked[m] = self.UNMARKED
# Erase marks and start over. You have a list of centers now.
marked[:, :] = self.UNMARKED
deferred_from_last = []
deferred_to_next = []
# delta (int): maximum number of increments the cluster is allowed to range over. Larger d results in clusters over larger scales.
for delta in range(0, self.delta + 1):
# Work from high to low bins.
for b in sorted(centers.keys(), reverse=True):
bin_lower = b - delta
deferred_from_last[:] = deferred_to_next[:]
del deferred_to_next[:]
foothills = []
n_centers = len(centers[b])
tot_centers = n_centers + len(deferred_from_last)
for i in range(tot_centers):
# done this way to minimize memory overhead of maintaining two lists
if i < n_centers:
center = centers[b][i]
else:
center = deferred_from_last[i - n_centers]
if bin_lower < 0:
bin_lower = 0
if marked[center] == self.UNMARKED:
captured = self.set_maximum(q_data, marked, center, bin_lower, foothills)
if not captured:
# decrement to lower value to see if it'll get big enough
deferred_to_next.append(center)
else:
pass
# this is the last one for this bin
self.remove_foothills(q_data, marked, b, bin_lower, centers, foothills)
del deferred_from_last[:]
del deferred_to_next[:]
return marked
|
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Finds the local maxima in the inputGrid and perform region growing to identify objects.
Args:
input_grid: Raw input data.
Returns:
array with labeled objects.
|
[
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] |
6fb6c3df90bf4867e13a97d3460b14471d107df1
|
https://github.com/djgagne/hagelslag/blob/6fb6c3df90bf4867e13a97d3460b14471d107df1/hagelslag/processing/EnhancedWatershedSegmenter.py#L81-L166
|
train
|
djgagne/hagelslag
|
hagelslag/processing/EnhancedWatershedSegmenter.py
|
EnhancedWatershed.set_maximum
|
def set_maximum(self, q_data, marked, center, bin_lower, foothills):
"""
Grow a region at a certain bin level and check if the region has reached the maximum size.
Args:
q_data: Quantized data array
marked: Array marking points that are objects
center: Coordinates of the center pixel of the region being grown
bin_lower: Intensity level of lower bin being evaluated
foothills: List of points that are associated with a center but fall outside the the size or
intensity criteria
Returns:
True if the object is finished growing and False if the object should be grown again at the next
threshold level.
"""
as_bin = [] # pixels to be included in peak
as_glob = [] # pixels to be globbed up as part of foothills
marked_so_far = [] # pixels that have already been marked
will_be_considered_again = False
as_bin.append(center)
center_data = q_data[center]
while len(as_bin) > 0:
p = as_bin.pop(-1) # remove and return last pixel in as_bin
if marked[p] != self.UNMARKED: # already processed
continue
marked[p] = q_data[center]
marked_so_far.append(p)
# check neighbors
for index,val in np.ndenumerate(marked[p[0] - 1:p[0] + 2, p[1] - 1:p[1] + 2]):
# is neighbor part of peak or part of mountain?
if val == self.UNMARKED:
pixel = (index[0] - 1 + p[0],index[1] - 1 + p[1])
p_data = q_data[pixel]
if (not will_be_considered_again) and (p_data >= 0) and (p_data < center_data):
will_be_considered_again = True
if p_data >= bin_lower and (np.abs(center_data - p_data) <= self.delta):
as_bin.append(pixel)
# Do not check that this is the closest: this way, a narrow channel of globbed pixels form
elif p_data >= 0:
as_glob.append(pixel)
if bin_lower == 0:
will_be_considered_again = False
big_enough = len(marked_so_far) >= self.max_size
if big_enough:
# remove lower values within region of influence
foothills.append((center, as_glob))
elif will_be_considered_again: # remove the check if you want to ignore regions smaller than max_size
for m in marked_so_far:
marked[m] = self.UNMARKED
del as_bin[:]
del as_glob[:]
del marked_so_far[:]
return big_enough or (not will_be_considered_again)
|
python
|
def set_maximum(self, q_data, marked, center, bin_lower, foothills):
"""
Grow a region at a certain bin level and check if the region has reached the maximum size.
Args:
q_data: Quantized data array
marked: Array marking points that are objects
center: Coordinates of the center pixel of the region being grown
bin_lower: Intensity level of lower bin being evaluated
foothills: List of points that are associated with a center but fall outside the the size or
intensity criteria
Returns:
True if the object is finished growing and False if the object should be grown again at the next
threshold level.
"""
as_bin = [] # pixels to be included in peak
as_glob = [] # pixels to be globbed up as part of foothills
marked_so_far = [] # pixels that have already been marked
will_be_considered_again = False
as_bin.append(center)
center_data = q_data[center]
while len(as_bin) > 0:
p = as_bin.pop(-1) # remove and return last pixel in as_bin
if marked[p] != self.UNMARKED: # already processed
continue
marked[p] = q_data[center]
marked_so_far.append(p)
# check neighbors
for index,val in np.ndenumerate(marked[p[0] - 1:p[0] + 2, p[1] - 1:p[1] + 2]):
# is neighbor part of peak or part of mountain?
if val == self.UNMARKED:
pixel = (index[0] - 1 + p[0],index[1] - 1 + p[1])
p_data = q_data[pixel]
if (not will_be_considered_again) and (p_data >= 0) and (p_data < center_data):
will_be_considered_again = True
if p_data >= bin_lower and (np.abs(center_data - p_data) <= self.delta):
as_bin.append(pixel)
# Do not check that this is the closest: this way, a narrow channel of globbed pixels form
elif p_data >= 0:
as_glob.append(pixel)
if bin_lower == 0:
will_be_considered_again = False
big_enough = len(marked_so_far) >= self.max_size
if big_enough:
# remove lower values within region of influence
foothills.append((center, as_glob))
elif will_be_considered_again: # remove the check if you want to ignore regions smaller than max_size
for m in marked_so_far:
marked[m] = self.UNMARKED
del as_bin[:]
del as_glob[:]
del marked_so_far[:]
return big_enough or (not will_be_considered_again)
|
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Grow a region at a certain bin level and check if the region has reached the maximum size.
Args:
q_data: Quantized data array
marked: Array marking points that are objects
center: Coordinates of the center pixel of the region being grown
bin_lower: Intensity level of lower bin being evaluated
foothills: List of points that are associated with a center but fall outside the the size or
intensity criteria
Returns:
True if the object is finished growing and False if the object should be grown again at the next
threshold level.
|
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] |
6fb6c3df90bf4867e13a97d3460b14471d107df1
|
https://github.com/djgagne/hagelslag/blob/6fb6c3df90bf4867e13a97d3460b14471d107df1/hagelslag/processing/EnhancedWatershedSegmenter.py#L168-L221
|
train
|
djgagne/hagelslag
|
hagelslag/processing/EnhancedWatershedSegmenter.py
|
EnhancedWatershed.remove_foothills
|
def remove_foothills(self, q_data, marked, bin_num, bin_lower, centers, foothills):
"""
Mark points determined to be foothills as globbed, so that they are not included in
future searches. Also searches neighboring points to foothill points to determine
if they should also be considered foothills.
Args:
q_data: Quantized data
marked: Marked
bin_num: Current bin being searched
bin_lower: Next bin being searched
centers: dictionary of local maxima considered to be object centers
foothills: List of foothill points being removed.
"""
hills = []
for foot in foothills:
center = foot[0]
hills[:] = foot[1][:]
# remove all foothills
while len(hills) > 0:
# mark this point
pt = hills.pop(-1)
marked[pt] = self.GLOBBED
for s_index, val in np.ndenumerate(marked[pt[0]-1:pt[0]+2,pt[1]-1:pt[1]+2]):
index = (s_index[0] - 1 + pt[0], s_index[1] - 1 + pt[1])
# is neighbor part of peak or part of mountain?
if val == self.UNMARKED:
# will let in even minor peaks
if (q_data[index] >= 0) and \
(q_data[index] < bin_lower) and \
((q_data[index] <= q_data[pt]) or self.is_closest(index, center, centers, bin_num)):
hills.append(index)
del foothills[:]
|
python
|
def remove_foothills(self, q_data, marked, bin_num, bin_lower, centers, foothills):
"""
Mark points determined to be foothills as globbed, so that they are not included in
future searches. Also searches neighboring points to foothill points to determine
if they should also be considered foothills.
Args:
q_data: Quantized data
marked: Marked
bin_num: Current bin being searched
bin_lower: Next bin being searched
centers: dictionary of local maxima considered to be object centers
foothills: List of foothill points being removed.
"""
hills = []
for foot in foothills:
center = foot[0]
hills[:] = foot[1][:]
# remove all foothills
while len(hills) > 0:
# mark this point
pt = hills.pop(-1)
marked[pt] = self.GLOBBED
for s_index, val in np.ndenumerate(marked[pt[0]-1:pt[0]+2,pt[1]-1:pt[1]+2]):
index = (s_index[0] - 1 + pt[0], s_index[1] - 1 + pt[1])
# is neighbor part of peak or part of mountain?
if val == self.UNMARKED:
# will let in even minor peaks
if (q_data[index] >= 0) and \
(q_data[index] < bin_lower) and \
((q_data[index] <= q_data[pt]) or self.is_closest(index, center, centers, bin_num)):
hills.append(index)
del foothills[:]
|
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bin_num: Current bin being searched
bin_lower: Next bin being searched
centers: dictionary of local maxima considered to be object centers
foothills: List of foothill points being removed.
|
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6fb6c3df90bf4867e13a97d3460b14471d107df1
|
https://github.com/djgagne/hagelslag/blob/6fb6c3df90bf4867e13a97d3460b14471d107df1/hagelslag/processing/EnhancedWatershedSegmenter.py#L223-L255
|
train
|
djgagne/hagelslag
|
hagelslag/processing/EnhancedWatershedSegmenter.py
|
EnhancedWatershed.quantize
|
def quantize(self, input_grid):
"""
Quantize a grid into discrete steps based on input parameters.
Args:
input_grid: 2-d array of values
Returns:
Dictionary of value pointing to pixel locations, and quantized 2-d array of data
"""
pixels = {}
for i in range(self.max_bin+1):
pixels[i] = []
data = (np.array(input_grid, dtype=int) - self.min_thresh) / self.data_increment
data[data < 0] = -1
data[data > self.max_bin] = self.max_bin
good_points = np.where(data >= 0)
for g in np.arange(good_points[0].shape[0]):
pixels[data[(good_points[0][g], good_points[1][g])]].append((good_points[0][g], good_points[1][g]))
return pixels, data
|
python
|
def quantize(self, input_grid):
"""
Quantize a grid into discrete steps based on input parameters.
Args:
input_grid: 2-d array of values
Returns:
Dictionary of value pointing to pixel locations, and quantized 2-d array of data
"""
pixels = {}
for i in range(self.max_bin+1):
pixels[i] = []
data = (np.array(input_grid, dtype=int) - self.min_thresh) / self.data_increment
data[data < 0] = -1
data[data > self.max_bin] = self.max_bin
good_points = np.where(data >= 0)
for g in np.arange(good_points[0].shape[0]):
pixels[data[(good_points[0][g], good_points[1][g])]].append((good_points[0][g], good_points[1][g]))
return pixels, data
|
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[
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6fb6c3df90bf4867e13a97d3460b14471d107df1
|
https://github.com/djgagne/hagelslag/blob/6fb6c3df90bf4867e13a97d3460b14471d107df1/hagelslag/processing/EnhancedWatershedSegmenter.py#L270-L290
|
train
|
softvar/simplegist
|
simplegist/mygist.py
|
Mygist.listall
|
def listall(self):
'''
will display all the filenames.
Result can be stored in an array for easy fetching of gistNames
for future purposes.
eg. a = Gist().mygists().listall()
print a[0] #to fetch first gistName
'''
file_name = []
r = requests.get(
'%s/users/%s/gists' % (BASE_URL, self.user),
headers=self.gist.header
)
r_text = json.loads(r.text)
limit = len(r.json())
if (r.status_code == 200 ):
for g,no in zip(r_text, range(0,limit)):
for key,value in r.json()[no]['files'].iteritems():
file_name.append(value['filename'])
return file_name
raise Exception('Username not found')
|
python
|
def listall(self):
'''
will display all the filenames.
Result can be stored in an array for easy fetching of gistNames
for future purposes.
eg. a = Gist().mygists().listall()
print a[0] #to fetch first gistName
'''
file_name = []
r = requests.get(
'%s/users/%s/gists' % (BASE_URL, self.user),
headers=self.gist.header
)
r_text = json.loads(r.text)
limit = len(r.json())
if (r.status_code == 200 ):
for g,no in zip(r_text, range(0,limit)):
for key,value in r.json()[no]['files'].iteritems():
file_name.append(value['filename'])
return file_name
raise Exception('Username not found')
|
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will display all the filenames.
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eg. a = Gist().mygists().listall()
print a[0] #to fetch first gistName
|
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8d53edd15d76c7b10fb963a659c1cf9f46f5345d
|
https://github.com/softvar/simplegist/blob/8d53edd15d76c7b10fb963a659c1cf9f46f5345d/simplegist/mygist.py#L13-L34
|
train
|
softvar/simplegist
|
simplegist/mygist.py
|
Mygist.content
|
def content(self, **args):
'''
Doesn't require manual fetching of gistID of a gist
passing gistName will return the content of gist. In case,
names are ambigious, provide GistID or it will return the contents
of recent ambigious gistname
'''
self.gist_name = ''
if 'name' in args:
self.gist_name = args['name']
self.gist_id = self.getMyID(self.gist_name)
elif 'id' in args:
self.gist_id = args['id']
else:
raise Exception('Either provide authenticated user\'s Unambigious Gistname or any unique Gistid')
if self.gist_id:
r = requests.get(
'%s'%BASE_URL+'/gists/%s' %self.gist_id,
headers=self.gist.header
)
if (r.status_code == 200):
r_text = json.loads(r.text)
if self.gist_name!='':
content = r.json()['files'][self.gist_name]['content']
else:
for key,value in r.json()['files'].iteritems():
content = r.json()['files'][value['filename']]['content']
return content
raise Exception('No such gist found')
|
python
|
def content(self, **args):
'''
Doesn't require manual fetching of gistID of a gist
passing gistName will return the content of gist. In case,
names are ambigious, provide GistID or it will return the contents
of recent ambigious gistname
'''
self.gist_name = ''
if 'name' in args:
self.gist_name = args['name']
self.gist_id = self.getMyID(self.gist_name)
elif 'id' in args:
self.gist_id = args['id']
else:
raise Exception('Either provide authenticated user\'s Unambigious Gistname or any unique Gistid')
if self.gist_id:
r = requests.get(
'%s'%BASE_URL+'/gists/%s' %self.gist_id,
headers=self.gist.header
)
if (r.status_code == 200):
r_text = json.loads(r.text)
if self.gist_name!='':
content = r.json()['files'][self.gist_name]['content']
else:
for key,value in r.json()['files'].iteritems():
content = r.json()['files'][value['filename']]['content']
return content
raise Exception('No such gist found')
|
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names are ambigious, provide GistID or it will return the contents
of recent ambigious gistname
|
[
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] |
8d53edd15d76c7b10fb963a659c1cf9f46f5345d
|
https://github.com/softvar/simplegist/blob/8d53edd15d76c7b10fb963a659c1cf9f46f5345d/simplegist/mygist.py#L78-L109
|
train
|
softvar/simplegist
|
simplegist/mygist.py
|
Mygist.edit
|
def edit(self, **args):
'''
Doesn't require manual fetching of gistID of a gist
passing gistName will return edit the gist
'''
self.gist_name = ''
if 'description' in args:
self.description = args['description']
else:
self.description = ''
if 'name' in args and 'id' in args:
self.gist_name = args['name']
self.gist_id = args['id']
elif 'name' in args:
self.gist_name = args['name']
self.gist_id = self.getMyID(self.gist_name)
elif 'id' in args:
self.gist_id = args['id']
else:
raise Exception('Gist Name/ID must be provided')
if 'content' in args:
self.content = args['content']
else:
raise Exception('Gist content can\'t be empty')
if (self.gist_name == ''):
self.gist_name = self.getgist(id=self.gist_id)
data = {"description": self.description,
"files": {
self.gist_name: {
"content": self.content
}
}
}
else:
data = {"description": self.description,
"files": {
self.gist_name: {
"content": self.content
}
}
}
if self.gist_id:
r = requests.patch(
'%s/gists/%s'%(BASE_URL,self.gist_id),
headers=self.gist.header,
data=json.dumps(data),
)
if (r.status_code == 200):
r_text = json.loads(r.text)
response = {
'updated_content': self.content,
'created_at': r.json()['created_at'],
'comments':r.json()['comments']
}
return response
raise Exception('No such gist found')
|
python
|
def edit(self, **args):
'''
Doesn't require manual fetching of gistID of a gist
passing gistName will return edit the gist
'''
self.gist_name = ''
if 'description' in args:
self.description = args['description']
else:
self.description = ''
if 'name' in args and 'id' in args:
self.gist_name = args['name']
self.gist_id = args['id']
elif 'name' in args:
self.gist_name = args['name']
self.gist_id = self.getMyID(self.gist_name)
elif 'id' in args:
self.gist_id = args['id']
else:
raise Exception('Gist Name/ID must be provided')
if 'content' in args:
self.content = args['content']
else:
raise Exception('Gist content can\'t be empty')
if (self.gist_name == ''):
self.gist_name = self.getgist(id=self.gist_id)
data = {"description": self.description,
"files": {
self.gist_name: {
"content": self.content
}
}
}
else:
data = {"description": self.description,
"files": {
self.gist_name: {
"content": self.content
}
}
}
if self.gist_id:
r = requests.patch(
'%s/gists/%s'%(BASE_URL,self.gist_id),
headers=self.gist.header,
data=json.dumps(data),
)
if (r.status_code == 200):
r_text = json.loads(r.text)
response = {
'updated_content': self.content,
'created_at': r.json()['created_at'],
'comments':r.json()['comments']
}
return response
raise Exception('No such gist found')
|
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")",
"[",
"'comments'",
"]",
"}",
"return",
"response",
"raise",
"Exception",
"(",
"'No such gist found'",
")"
] |
Doesn't require manual fetching of gistID of a gist
passing gistName will return edit the gist
|
[
"Doesn",
"t",
"require",
"manual",
"fetching",
"of",
"gistID",
"of",
"a",
"gist",
"passing",
"gistName",
"will",
"return",
"edit",
"the",
"gist"
] |
8d53edd15d76c7b10fb963a659c1cf9f46f5345d
|
https://github.com/softvar/simplegist/blob/8d53edd15d76c7b10fb963a659c1cf9f46f5345d/simplegist/mygist.py#L132-L195
|
train
|
softvar/simplegist
|
simplegist/mygist.py
|
Mygist.delete
|
def delete(self, **args):
'''
Delete a gist by gistname/gistID
'''
if 'name' in args:
self.gist_name = args['name']
self.gist_id = self.getMyID(self.gist_name)
elif 'id' in args:
self.gist_id = args['id']
else:
raise Exception('Provide GistName to delete')
url = 'gists'
if self.gist_id:
r = requests.delete(
'%s/%s/%s'%(BASE_URL,url,self.gist_id),
headers=self.gist.header
)
if (r.status_code == 204):
response = {
'id': self.gist_id,
}
return response
raise Exception('Can not delete gist')
|
python
|
def delete(self, **args):
'''
Delete a gist by gistname/gistID
'''
if 'name' in args:
self.gist_name = args['name']
self.gist_id = self.getMyID(self.gist_name)
elif 'id' in args:
self.gist_id = args['id']
else:
raise Exception('Provide GistName to delete')
url = 'gists'
if self.gist_id:
r = requests.delete(
'%s/%s/%s'%(BASE_URL,url,self.gist_id),
headers=self.gist.header
)
if (r.status_code == 204):
response = {
'id': self.gist_id,
}
return response
raise Exception('Can not delete gist')
|
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",",
"}",
"return",
"response",
"raise",
"Exception",
"(",
"'Can not delete gist'",
")"
] |
Delete a gist by gistname/gistID
|
[
"Delete",
"a",
"gist",
"by",
"gistname",
"/",
"gistID"
] |
8d53edd15d76c7b10fb963a659c1cf9f46f5345d
|
https://github.com/softvar/simplegist/blob/8d53edd15d76c7b10fb963a659c1cf9f46f5345d/simplegist/mygist.py#L198-L223
|
train
|
softvar/simplegist
|
simplegist/mygist.py
|
Mygist.starred
|
def starred(self, **args):
'''
List the authenticated user's starred gists
'''
ids =[]
r = requests.get(
'%s/gists/starred'%BASE_URL,
headers=self.gist.header
)
if 'limit' in args:
limit = args['limit']
else:
limit = len(r.json())
if (r.status_code == 200):
for g in range(0,limit ):
ids.append('%s/%s/%s' %(GIST_URL,r.json()[g]['user']['login'],r.json()[g]['id']))
return ids
raise Exception('Username not found')
|
python
|
def starred(self, **args):
'''
List the authenticated user's starred gists
'''
ids =[]
r = requests.get(
'%s/gists/starred'%BASE_URL,
headers=self.gist.header
)
if 'limit' in args:
limit = args['limit']
else:
limit = len(r.json())
if (r.status_code == 200):
for g in range(0,limit ):
ids.append('%s/%s/%s' %(GIST_URL,r.json()[g]['user']['login'],r.json()[g]['id']))
return ids
raise Exception('Username not found')
|
[
"def",
"starred",
"(",
"self",
",",
"*",
"*",
"args",
")",
":",
"ids",
"=",
"[",
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"=",
"requests",
".",
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"(",
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"'limit'",
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"return",
"ids",
"raise",
"Exception",
"(",
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")"
] |
List the authenticated user's starred gists
|
[
"List",
"the",
"authenticated",
"user",
"s",
"starred",
"gists"
] |
8d53edd15d76c7b10fb963a659c1cf9f46f5345d
|
https://github.com/softvar/simplegist/blob/8d53edd15d76c7b10fb963a659c1cf9f46f5345d/simplegist/mygist.py#L226-L246
|
train
|
softvar/simplegist
|
simplegist/mygist.py
|
Mygist.links
|
def links(self,**args):
'''
Return Gist URL-Link, Clone-Link and Script-Link to embed
'''
if 'name' in args:
self.gist_name = args['name']
self.gist_id = self.getMyID(self.gist_name)
elif 'id' in args:
self.gist_id = args['id']
else:
raise Exception('Gist Name/ID must be provided')
if self.gist_id:
r = requests.get(
'%s/gists/%s'%(BASE_URL,self.gist_id),
headers=self.gist.header,
)
if (r.status_code == 200):
content = {
'Github-User': r.json()['user']['login'],
'GistID': r.json()['id'],
'Gist-Link': '%s/%s/%s' %(GIST_URL,self.gist.username,r.json()['id']),
'Clone-Link': '%s/%s.git' %(GIST_URL,r.json()['id']),
'Embed-Script': '<script src="%s/%s/%s.js"</script>' %(GIST_URL,self.gist.username,r.json()['id'])
}
return content
raise Exception('No such gist found')
|
python
|
def links(self,**args):
'''
Return Gist URL-Link, Clone-Link and Script-Link to embed
'''
if 'name' in args:
self.gist_name = args['name']
self.gist_id = self.getMyID(self.gist_name)
elif 'id' in args:
self.gist_id = args['id']
else:
raise Exception('Gist Name/ID must be provided')
if self.gist_id:
r = requests.get(
'%s/gists/%s'%(BASE_URL,self.gist_id),
headers=self.gist.header,
)
if (r.status_code == 200):
content = {
'Github-User': r.json()['user']['login'],
'GistID': r.json()['id'],
'Gist-Link': '%s/%s/%s' %(GIST_URL,self.gist.username,r.json()['id']),
'Clone-Link': '%s/%s.git' %(GIST_URL,r.json()['id']),
'Embed-Script': '<script src="%s/%s/%s.js"</script>' %(GIST_URL,self.gist.username,r.json()['id'])
}
return content
raise Exception('No such gist found')
|
[
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")",
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"[",
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")",
",",
"'Clone-Link'",
":",
"'%s/%s.git'",
"%",
"(",
"GIST_URL",
",",
"r",
".",
"json",
"(",
")",
"[",
"'id'",
"]",
")",
",",
"'Embed-Script'",
":",
"'<script src=\"%s/%s/%s.js\"</script>'",
"%",
"(",
"GIST_URL",
",",
"self",
".",
"gist",
".",
"username",
",",
"r",
".",
"json",
"(",
")",
"[",
"'id'",
"]",
")",
"}",
"return",
"content",
"raise",
"Exception",
"(",
"'No such gist found'",
")"
] |
Return Gist URL-Link, Clone-Link and Script-Link to embed
|
[
"Return",
"Gist",
"URL",
"-",
"Link",
"Clone",
"-",
"Link",
"and",
"Script",
"-",
"Link",
"to",
"embed"
] |
8d53edd15d76c7b10fb963a659c1cf9f46f5345d
|
https://github.com/softvar/simplegist/blob/8d53edd15d76c7b10fb963a659c1cf9f46f5345d/simplegist/mygist.py#L248-L275
|
train
|
djgagne/hagelslag
|
hagelslag/evaluation/NeighborEvaluator.py
|
NeighborEvaluator.load_forecasts
|
def load_forecasts(self):
"""
Load neighborhood probability forecasts.
"""
run_date_str = self.run_date.strftime("%Y%m%d")
forecast_file = self.forecast_path + "{0}/{1}_{2}_{3}_consensus_{0}.nc".format(run_date_str,
self.ensemble_name,
self.model_name,
self.forecast_variable)
print("Forecast file: " + forecast_file)
forecast_data = Dataset(forecast_file)
for size_threshold in self.size_thresholds:
for smoothing_radius in self.smoothing_radii:
for neighbor_radius in self.neighbor_radii:
hour_var = "neighbor_prob_r_{0:d}_s_{1:d}_{2}_{3:0.2f}".format(neighbor_radius, smoothing_radius,
self.forecast_variable,
float(size_threshold))
period_var = "neighbor_prob_{0:d}-hour_r_{1:d}_s_{2:d}_{3}_{4:0.2f}".format(self.end_hour -
self.start_hour + 1,
neighbor_radius,
smoothing_radius,
self.forecast_variable,
float(size_threshold))
print("Loading forecasts {0} {1} {2} {3} {4}".format(self.run_date, self.model_name,
self.forecast_variable, size_threshold,
smoothing_radius))
if hour_var in forecast_data.variables.keys():
self.hourly_forecasts[hour_var] = forecast_data.variables[hour_var][:]
if period_var in forecast_data.variables.keys():
self.period_forecasts[period_var] = forecast_data.variables[period_var][:]
forecast_data.close()
|
python
|
def load_forecasts(self):
"""
Load neighborhood probability forecasts.
"""
run_date_str = self.run_date.strftime("%Y%m%d")
forecast_file = self.forecast_path + "{0}/{1}_{2}_{3}_consensus_{0}.nc".format(run_date_str,
self.ensemble_name,
self.model_name,
self.forecast_variable)
print("Forecast file: " + forecast_file)
forecast_data = Dataset(forecast_file)
for size_threshold in self.size_thresholds:
for smoothing_radius in self.smoothing_radii:
for neighbor_radius in self.neighbor_radii:
hour_var = "neighbor_prob_r_{0:d}_s_{1:d}_{2}_{3:0.2f}".format(neighbor_radius, smoothing_radius,
self.forecast_variable,
float(size_threshold))
period_var = "neighbor_prob_{0:d}-hour_r_{1:d}_s_{2:d}_{3}_{4:0.2f}".format(self.end_hour -
self.start_hour + 1,
neighbor_radius,
smoothing_radius,
self.forecast_variable,
float(size_threshold))
print("Loading forecasts {0} {1} {2} {3} {4}".format(self.run_date, self.model_name,
self.forecast_variable, size_threshold,
smoothing_radius))
if hour_var in forecast_data.variables.keys():
self.hourly_forecasts[hour_var] = forecast_data.variables[hour_var][:]
if period_var in forecast_data.variables.keys():
self.period_forecasts[period_var] = forecast_data.variables[period_var][:]
forecast_data.close()
|
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] |
Load neighborhood probability forecasts.
|
[
"Load",
"neighborhood",
"probability",
"forecasts",
"."
] |
6fb6c3df90bf4867e13a97d3460b14471d107df1
|
https://github.com/djgagne/hagelslag/blob/6fb6c3df90bf4867e13a97d3460b14471d107df1/hagelslag/evaluation/NeighborEvaluator.py#L62-L93
|
train
|
djgagne/hagelslag
|
hagelslag/evaluation/NeighborEvaluator.py
|
NeighborEvaluator.load_obs
|
def load_obs(self, mask_threshold=0.5):
"""
Loads observations and masking grid (if needed).
Args:
mask_threshold: Values greater than the threshold are kept, others are masked.
"""
print("Loading obs ", self.run_date, self.model_name, self.forecast_variable)
start_date = self.run_date + timedelta(hours=self.start_hour)
end_date = self.run_date + timedelta(hours=self.end_hour)
mrms_grid = MRMSGrid(start_date, end_date, self.mrms_variable, self.mrms_path)
mrms_grid.load_data()
if len(mrms_grid.data) > 0:
self.raw_obs[self.mrms_variable] = np.where(mrms_grid.data > 100, 100, mrms_grid.data)
self.period_obs[self.mrms_variable] = self.raw_obs[self.mrms_variable].max(axis=0)
if self.obs_mask:
mask_grid = MRMSGrid(start_date, end_date, self.mask_variable, self.mrms_path)
mask_grid.load_data()
self.raw_obs[self.mask_variable] = np.where(mask_grid.data >= mask_threshold, 1, 0)
self.period_obs[self.mask_variable] = self.raw_obs[self.mask_variable].max(axis=0)
|
python
|
def load_obs(self, mask_threshold=0.5):
"""
Loads observations and masking grid (if needed).
Args:
mask_threshold: Values greater than the threshold are kept, others are masked.
"""
print("Loading obs ", self.run_date, self.model_name, self.forecast_variable)
start_date = self.run_date + timedelta(hours=self.start_hour)
end_date = self.run_date + timedelta(hours=self.end_hour)
mrms_grid = MRMSGrid(start_date, end_date, self.mrms_variable, self.mrms_path)
mrms_grid.load_data()
if len(mrms_grid.data) > 0:
self.raw_obs[self.mrms_variable] = np.where(mrms_grid.data > 100, 100, mrms_grid.data)
self.period_obs[self.mrms_variable] = self.raw_obs[self.mrms_variable].max(axis=0)
if self.obs_mask:
mask_grid = MRMSGrid(start_date, end_date, self.mask_variable, self.mrms_path)
mask_grid.load_data()
self.raw_obs[self.mask_variable] = np.where(mask_grid.data >= mask_threshold, 1, 0)
self.period_obs[self.mask_variable] = self.raw_obs[self.mask_variable].max(axis=0)
|
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] |
Loads observations and masking grid (if needed).
Args:
mask_threshold: Values greater than the threshold are kept, others are masked.
|
[
"Loads",
"observations",
"and",
"masking",
"grid",
"(",
"if",
"needed",
")",
"."
] |
6fb6c3df90bf4867e13a97d3460b14471d107df1
|
https://github.com/djgagne/hagelslag/blob/6fb6c3df90bf4867e13a97d3460b14471d107df1/hagelslag/evaluation/NeighborEvaluator.py#L95-L114
|
train
|
djgagne/hagelslag
|
hagelslag/evaluation/NeighborEvaluator.py
|
NeighborEvaluator.load_coordinates
|
def load_coordinates(self):
"""
Loads lat-lon coordinates from a netCDF file.
"""
coord_file = Dataset(self.coordinate_file)
if "lon" in coord_file.variables.keys():
self.coordinates["lon"] = coord_file.variables["lon"][:]
self.coordinates["lat"] = coord_file.variables["lat"][:]
else:
self.coordinates["lon"] = coord_file.variables["XLONG"][0]
self.coordinates["lat"] = coord_file.variables["XLAT"][0]
coord_file.close()
|
python
|
def load_coordinates(self):
"""
Loads lat-lon coordinates from a netCDF file.
"""
coord_file = Dataset(self.coordinate_file)
if "lon" in coord_file.variables.keys():
self.coordinates["lon"] = coord_file.variables["lon"][:]
self.coordinates["lat"] = coord_file.variables["lat"][:]
else:
self.coordinates["lon"] = coord_file.variables["XLONG"][0]
self.coordinates["lat"] = coord_file.variables["XLAT"][0]
coord_file.close()
|
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"\"XLAT\"",
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"[",
"0",
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"close",
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] |
Loads lat-lon coordinates from a netCDF file.
|
[
"Loads",
"lat",
"-",
"lon",
"coordinates",
"from",
"a",
"netCDF",
"file",
"."
] |
6fb6c3df90bf4867e13a97d3460b14471d107df1
|
https://github.com/djgagne/hagelslag/blob/6fb6c3df90bf4867e13a97d3460b14471d107df1/hagelslag/evaluation/NeighborEvaluator.py#L116-L127
|
train
|
djgagne/hagelslag
|
hagelslag/evaluation/NeighborEvaluator.py
|
NeighborEvaluator.evaluate_hourly_forecasts
|
def evaluate_hourly_forecasts(self):
"""
Calculates ROC curves and Reliability scores for each forecast hour.
Returns:
A pandas DataFrame containing forecast metadata as well as DistributedROC and Reliability objects.
"""
score_columns = ["Run_Date", "Forecast_Hour", "Ensemble Name", "Model_Name", "Forecast_Variable",
"Neighbor_Radius", "Smoothing_Radius", "Size_Threshold", "ROC", "Reliability"]
all_scores = pd.DataFrame(columns=score_columns)
for h, hour in enumerate(range(self.start_hour, self.end_hour + 1)):
for neighbor_radius in self.neighbor_radii:
n_filter = disk(neighbor_radius)
for s, size_threshold in enumerate(self.size_thresholds):
print("Eval hourly forecast {0:02d} {1} {2} {3} {4:d} {5:d}".format(hour, self.model_name,
self.forecast_variable,
self.run_date, neighbor_radius,
size_threshold))
hour_obs = fftconvolve(self.raw_obs[self.mrms_variable][h] >= self.obs_thresholds[s],
n_filter, mode="same")
hour_obs[hour_obs > 1] = 1
hour_obs[hour_obs < 1] = 0
if self.obs_mask:
hour_obs = hour_obs[self.raw_obs[self.mask_variable][h] > 0]
for smoothing_radius in self.smoothing_radii:
hour_var = "neighbor_prob_r_{0:d}_s_{1:d}_{2}_{3:0.2f}".format(neighbor_radius,
smoothing_radius,
self.forecast_variable,
size_threshold)
if self.obs_mask:
hour_forecast = self.hourly_forecasts[hour_var][h][self.raw_obs[self.mask_variable][h] > 0]
else:
hour_forecast = self.hourly_forecasts[hour_var][h]
roc = DistributedROC(thresholds=self.probability_levels, obs_threshold=0.5)
roc.update(hour_forecast, hour_obs)
rel = DistributedReliability(thresholds=self.probability_levels, obs_threshold=0.5)
rel.update(hour_forecast, hour_obs)
row = [self.run_date, hour, self.ensemble_name, self.model_name, self.forecast_variable,
neighbor_radius,
smoothing_radius, size_threshold, roc, rel]
all_scores.loc[hour_var + "_{0:d}".format(hour)] = row
return all_scores
|
python
|
def evaluate_hourly_forecasts(self):
"""
Calculates ROC curves and Reliability scores for each forecast hour.
Returns:
A pandas DataFrame containing forecast metadata as well as DistributedROC and Reliability objects.
"""
score_columns = ["Run_Date", "Forecast_Hour", "Ensemble Name", "Model_Name", "Forecast_Variable",
"Neighbor_Radius", "Smoothing_Radius", "Size_Threshold", "ROC", "Reliability"]
all_scores = pd.DataFrame(columns=score_columns)
for h, hour in enumerate(range(self.start_hour, self.end_hour + 1)):
for neighbor_radius in self.neighbor_radii:
n_filter = disk(neighbor_radius)
for s, size_threshold in enumerate(self.size_thresholds):
print("Eval hourly forecast {0:02d} {1} {2} {3} {4:d} {5:d}".format(hour, self.model_name,
self.forecast_variable,
self.run_date, neighbor_radius,
size_threshold))
hour_obs = fftconvolve(self.raw_obs[self.mrms_variable][h] >= self.obs_thresholds[s],
n_filter, mode="same")
hour_obs[hour_obs > 1] = 1
hour_obs[hour_obs < 1] = 0
if self.obs_mask:
hour_obs = hour_obs[self.raw_obs[self.mask_variable][h] > 0]
for smoothing_radius in self.smoothing_radii:
hour_var = "neighbor_prob_r_{0:d}_s_{1:d}_{2}_{3:0.2f}".format(neighbor_radius,
smoothing_radius,
self.forecast_variable,
size_threshold)
if self.obs_mask:
hour_forecast = self.hourly_forecasts[hour_var][h][self.raw_obs[self.mask_variable][h] > 0]
else:
hour_forecast = self.hourly_forecasts[hour_var][h]
roc = DistributedROC(thresholds=self.probability_levels, obs_threshold=0.5)
roc.update(hour_forecast, hour_obs)
rel = DistributedReliability(thresholds=self.probability_levels, obs_threshold=0.5)
rel.update(hour_forecast, hour_obs)
row = [self.run_date, hour, self.ensemble_name, self.model_name, self.forecast_variable,
neighbor_radius,
smoothing_radius, size_threshold, roc, rel]
all_scores.loc[hour_var + "_{0:d}".format(hour)] = row
return all_scores
|
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Calculates ROC curves and Reliability scores for each forecast hour.
Returns:
A pandas DataFrame containing forecast metadata as well as DistributedROC and Reliability objects.
|
[
"Calculates",
"ROC",
"curves",
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"scores",
"for",
"each",
"forecast",
"hour",
"."
] |
6fb6c3df90bf4867e13a97d3460b14471d107df1
|
https://github.com/djgagne/hagelslag/blob/6fb6c3df90bf4867e13a97d3460b14471d107df1/hagelslag/evaluation/NeighborEvaluator.py#L129-L170
|
train
|
djgagne/hagelslag
|
hagelslag/evaluation/NeighborEvaluator.py
|
NeighborEvaluator.evaluate_period_forecasts
|
def evaluate_period_forecasts(self):
"""
Evaluates ROC and Reliability scores for forecasts over the full period from start hour to end hour
Returns:
A pandas DataFrame with full-period metadata and verification statistics
"""
score_columns = ["Run_Date", "Ensemble Name", "Model_Name", "Forecast_Variable", "Neighbor_Radius",
"Smoothing_Radius", "Size_Threshold", "ROC", "Reliability"]
all_scores = pd.DataFrame(columns=score_columns)
if self.coordinate_file is not None:
coord_mask = np.where((self.coordinates["lon"] >= self.lon_bounds[0]) &
(self.coordinates["lon"] <= self.lon_bounds[1]) &
(self.coordinates["lat"] >= self.lat_bounds[0]) &
(self.coordinates["lat"] <= self.lat_bounds[1]) &
(self.period_obs[self.mask_variable] > 0))
else:
coord_mask = None
for neighbor_radius in self.neighbor_radii:
n_filter = disk(neighbor_radius)
for s, size_threshold in enumerate(self.size_thresholds):
period_obs = fftconvolve(self.period_obs[self.mrms_variable] >= self.obs_thresholds[s],
n_filter, mode="same")
period_obs[period_obs > 1] = 1
if self.obs_mask and self.coordinate_file is None:
period_obs = period_obs[self.period_obs[self.mask_variable] > 0]
elif self.obs_mask and self.coordinate_file is not None:
period_obs = period_obs[coord_mask[0], coord_mask[1]]
else:
period_obs = period_obs.ravel()
for smoothing_radius in self.smoothing_radii:
print("Eval period forecast {0} {1} {2} {3} {4} {5}".format(self.model_name,
self.forecast_variable,
self.run_date,
neighbor_radius,
size_threshold, smoothing_radius))
period_var = "neighbor_prob_{0:d}-hour_r_{1:d}_s_{2:d}_{3}_{4:0.2f}".format(self.end_hour -
self.start_hour + 1,
neighbor_radius,
smoothing_radius,
self.forecast_variable,
size_threshold)
if self.obs_mask and self.coordinate_file is None:
period_forecast = self.period_forecasts[period_var][self.period_obs[self.mask_variable] > 0]
elif self.obs_mask and self.coordinate_file is not None:
period_forecast = self.period_forecasts[period_var][coord_mask[0], coord_mask[1]]
else:
period_forecast = self.period_forecasts[period_var].ravel()
roc = DistributedROC(thresholds=self.probability_levels, obs_threshold=0.5)
roc.update(period_forecast, period_obs)
rel = DistributedReliability(thresholds=self.probability_levels, obs_threshold=0.5)
rel.update(period_forecast, period_obs)
row = [self.run_date, self.ensemble_name, self.model_name, self.forecast_variable, neighbor_radius,
smoothing_radius, size_threshold, roc, rel]
all_scores.loc[period_var] = row
return all_scores
|
python
|
def evaluate_period_forecasts(self):
"""
Evaluates ROC and Reliability scores for forecasts over the full period from start hour to end hour
Returns:
A pandas DataFrame with full-period metadata and verification statistics
"""
score_columns = ["Run_Date", "Ensemble Name", "Model_Name", "Forecast_Variable", "Neighbor_Radius",
"Smoothing_Radius", "Size_Threshold", "ROC", "Reliability"]
all_scores = pd.DataFrame(columns=score_columns)
if self.coordinate_file is not None:
coord_mask = np.where((self.coordinates["lon"] >= self.lon_bounds[0]) &
(self.coordinates["lon"] <= self.lon_bounds[1]) &
(self.coordinates["lat"] >= self.lat_bounds[0]) &
(self.coordinates["lat"] <= self.lat_bounds[1]) &
(self.period_obs[self.mask_variable] > 0))
else:
coord_mask = None
for neighbor_radius in self.neighbor_radii:
n_filter = disk(neighbor_radius)
for s, size_threshold in enumerate(self.size_thresholds):
period_obs = fftconvolve(self.period_obs[self.mrms_variable] >= self.obs_thresholds[s],
n_filter, mode="same")
period_obs[period_obs > 1] = 1
if self.obs_mask and self.coordinate_file is None:
period_obs = period_obs[self.period_obs[self.mask_variable] > 0]
elif self.obs_mask and self.coordinate_file is not None:
period_obs = period_obs[coord_mask[0], coord_mask[1]]
else:
period_obs = period_obs.ravel()
for smoothing_radius in self.smoothing_radii:
print("Eval period forecast {0} {1} {2} {3} {4} {5}".format(self.model_name,
self.forecast_variable,
self.run_date,
neighbor_radius,
size_threshold, smoothing_radius))
period_var = "neighbor_prob_{0:d}-hour_r_{1:d}_s_{2:d}_{3}_{4:0.2f}".format(self.end_hour -
self.start_hour + 1,
neighbor_radius,
smoothing_radius,
self.forecast_variable,
size_threshold)
if self.obs_mask and self.coordinate_file is None:
period_forecast = self.period_forecasts[period_var][self.period_obs[self.mask_variable] > 0]
elif self.obs_mask and self.coordinate_file is not None:
period_forecast = self.period_forecasts[period_var][coord_mask[0], coord_mask[1]]
else:
period_forecast = self.period_forecasts[period_var].ravel()
roc = DistributedROC(thresholds=self.probability_levels, obs_threshold=0.5)
roc.update(period_forecast, period_obs)
rel = DistributedReliability(thresholds=self.probability_levels, obs_threshold=0.5)
rel.update(period_forecast, period_obs)
row = [self.run_date, self.ensemble_name, self.model_name, self.forecast_variable, neighbor_radius,
smoothing_radius, size_threshold, roc, rel]
all_scores.loc[period_var] = row
return all_scores
|
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] |
Evaluates ROC and Reliability scores for forecasts over the full period from start hour to end hour
Returns:
A pandas DataFrame with full-period metadata and verification statistics
|
[
"Evaluates",
"ROC",
"and",
"Reliability",
"scores",
"for",
"forecasts",
"over",
"the",
"full",
"period",
"from",
"start",
"hour",
"to",
"end",
"hour"
] |
6fb6c3df90bf4867e13a97d3460b14471d107df1
|
https://github.com/djgagne/hagelslag/blob/6fb6c3df90bf4867e13a97d3460b14471d107df1/hagelslag/evaluation/NeighborEvaluator.py#L172-L227
|
train
|
nion-software/nionswift
|
nion/swift/command.py
|
bootstrap_main
|
def bootstrap_main(args):
"""
Main function explicitly called from the C++ code.
Return the main application object.
"""
version_info = sys.version_info
if version_info.major != 3 or version_info.minor < 6:
return None, "python36"
main_fn = load_module_as_package("nionui_app.nionswift")
if main_fn:
return main_fn(["nionui_app.nionswift"] + args, {"pyqt": None}), None
return None, "main"
|
python
|
def bootstrap_main(args):
"""
Main function explicitly called from the C++ code.
Return the main application object.
"""
version_info = sys.version_info
if version_info.major != 3 or version_info.minor < 6:
return None, "python36"
main_fn = load_module_as_package("nionui_app.nionswift")
if main_fn:
return main_fn(["nionui_app.nionswift"] + args, {"pyqt": None}), None
return None, "main"
|
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Main function explicitly called from the C++ code.
Return the main application object.
|
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"object",
"."
] |
d43693eaf057b8683b9638e575000f055fede452
|
https://github.com/nion-software/nionswift/blob/d43693eaf057b8683b9638e575000f055fede452/nion/swift/command.py#L49-L60
|
train
|
nion-software/nionswift
|
nion/swift/model/Profile.py
|
_migrate_library
|
def _migrate_library(workspace_dir: pathlib.Path, do_logging: bool=True) -> pathlib.Path:
""" Migrate library to latest version. """
library_path_11 = workspace_dir / "Nion Swift Workspace.nslib"
library_path_12 = workspace_dir / "Nion Swift Library 12.nslib"
library_path_13 = workspace_dir / "Nion Swift Library 13.nslib"
library_paths = (library_path_11, library_path_12)
library_path_latest = library_path_13
if not os.path.exists(library_path_latest):
for library_path in reversed(library_paths):
if os.path.exists(library_path):
if do_logging:
logging.info("Migrating library: %s -> %s", library_path, library_path_latest)
shutil.copyfile(library_path, library_path_latest)
break
return library_path_latest
|
python
|
def _migrate_library(workspace_dir: pathlib.Path, do_logging: bool=True) -> pathlib.Path:
""" Migrate library to latest version. """
library_path_11 = workspace_dir / "Nion Swift Workspace.nslib"
library_path_12 = workspace_dir / "Nion Swift Library 12.nslib"
library_path_13 = workspace_dir / "Nion Swift Library 13.nslib"
library_paths = (library_path_11, library_path_12)
library_path_latest = library_path_13
if not os.path.exists(library_path_latest):
for library_path in reversed(library_paths):
if os.path.exists(library_path):
if do_logging:
logging.info("Migrating library: %s -> %s", library_path, library_path_latest)
shutil.copyfile(library_path, library_path_latest)
break
return library_path_latest
|
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Migrate library to latest version.
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"."
] |
d43693eaf057b8683b9638e575000f055fede452
|
https://github.com/nion-software/nionswift/blob/d43693eaf057b8683b9638e575000f055fede452/nion/swift/model/Profile.py#L99-L117
|
train
|
base4sistemas/satcfe
|
satcfe/resposta/consultarstatusoperacional.py
|
RespostaConsultarStatusOperacional.status
|
def status(self):
"""Nome amigável do campo ``ESTADO_OPERACAO``, conforme a "Tabela de
Informações do Status do SAT".
"""
for valor, rotulo in ESTADOS_OPERACAO:
if self.ESTADO_OPERACAO == valor:
return rotulo
return u'(desconhecido: {})'.format(self.ESTADO_OPERACAO)
|
python
|
def status(self):
"""Nome amigável do campo ``ESTADO_OPERACAO``, conforme a "Tabela de
Informações do Status do SAT".
"""
for valor, rotulo in ESTADOS_OPERACAO:
if self.ESTADO_OPERACAO == valor:
return rotulo
return u'(desconhecido: {})'.format(self.ESTADO_OPERACAO)
|
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] |
Nome amigável do campo ``ESTADO_OPERACAO``, conforme a "Tabela de
Informações do Status do SAT".
|
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"."
] |
cb8e8815f4133d3e3d94cf526fa86767b4521ed9
|
https://github.com/base4sistemas/satcfe/blob/cb8e8815f4133d3e3d94cf526fa86767b4521ed9/satcfe/resposta/consultarstatusoperacional.py#L123-L130
|
train
|
base4sistemas/satcfe
|
satcfe/resposta/consultarstatusoperacional.py
|
RespostaConsultarStatusOperacional.analisar
|
def analisar(retorno):
"""Constrói uma :class:`RespostaConsultarStatusOperacional` a partir do
retorno informado.
:param unicode retorno: Retorno da função ``ConsultarStatusOperacional``.
"""
resposta = analisar_retorno(forcar_unicode(retorno),
funcao='ConsultarStatusOperacional',
classe_resposta=RespostaConsultarStatusOperacional,
campos=RespostaSAT.CAMPOS + (
('NSERIE', as_clean_unicode),
('TIPO_LAN', as_clean_unicode),
('LAN_IP', normalizar_ip),
('LAN_MAC', unicode),
('LAN_MASK', normalizar_ip),
('LAN_GW', normalizar_ip),
('LAN_DNS_1', normalizar_ip),
('LAN_DNS_2', normalizar_ip),
('STATUS_LAN', as_clean_unicode),
('NIVEL_BATERIA', as_clean_unicode),
('MT_TOTAL', as_clean_unicode),
('MT_USADA', as_clean_unicode),
('DH_ATUAL', as_datetime),
('VER_SB', as_clean_unicode),
('VER_LAYOUT', as_clean_unicode),
('ULTIMO_CF_E_SAT', as_clean_unicode),
('LISTA_INICIAL', as_clean_unicode),
('LISTA_FINAL', as_clean_unicode),
('DH_CFE', as_datetime_or_none),
('DH_ULTIMA', as_datetime),
('CERT_EMISSAO', as_date),
('CERT_VENCIMENTO', as_date),
('ESTADO_OPERACAO', int),
),
campos_alternativos=[
# se falhar resultarão apenas os 5 campos padrão
RespostaSAT.CAMPOS,
]
)
if resposta.EEEEE not in ('10000',):
raise ExcecaoRespostaSAT(resposta)
return resposta
|
python
|
def analisar(retorno):
"""Constrói uma :class:`RespostaConsultarStatusOperacional` a partir do
retorno informado.
:param unicode retorno: Retorno da função ``ConsultarStatusOperacional``.
"""
resposta = analisar_retorno(forcar_unicode(retorno),
funcao='ConsultarStatusOperacional',
classe_resposta=RespostaConsultarStatusOperacional,
campos=RespostaSAT.CAMPOS + (
('NSERIE', as_clean_unicode),
('TIPO_LAN', as_clean_unicode),
('LAN_IP', normalizar_ip),
('LAN_MAC', unicode),
('LAN_MASK', normalizar_ip),
('LAN_GW', normalizar_ip),
('LAN_DNS_1', normalizar_ip),
('LAN_DNS_2', normalizar_ip),
('STATUS_LAN', as_clean_unicode),
('NIVEL_BATERIA', as_clean_unicode),
('MT_TOTAL', as_clean_unicode),
('MT_USADA', as_clean_unicode),
('DH_ATUAL', as_datetime),
('VER_SB', as_clean_unicode),
('VER_LAYOUT', as_clean_unicode),
('ULTIMO_CF_E_SAT', as_clean_unicode),
('LISTA_INICIAL', as_clean_unicode),
('LISTA_FINAL', as_clean_unicode),
('DH_CFE', as_datetime_or_none),
('DH_ULTIMA', as_datetime),
('CERT_EMISSAO', as_date),
('CERT_VENCIMENTO', as_date),
('ESTADO_OPERACAO', int),
),
campos_alternativos=[
# se falhar resultarão apenas os 5 campos padrão
RespostaSAT.CAMPOS,
]
)
if resposta.EEEEE not in ('10000',):
raise ExcecaoRespostaSAT(resposta)
return resposta
|
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":",
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"(",
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] |
Constrói uma :class:`RespostaConsultarStatusOperacional` a partir do
retorno informado.
:param unicode retorno: Retorno da função ``ConsultarStatusOperacional``.
|
[
"Constrói",
"uma",
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":",
"RespostaConsultarStatusOperacional",
"a",
"partir",
"do",
"retorno",
"informado",
"."
] |
cb8e8815f4133d3e3d94cf526fa86767b4521ed9
|
https://github.com/base4sistemas/satcfe/blob/cb8e8815f4133d3e3d94cf526fa86767b4521ed9/satcfe/resposta/consultarstatusoperacional.py#L134-L175
|
train
|
djgagne/hagelslag
|
hagelslag/util/merge_forecast_data.py
|
merge_input_csv_forecast_json
|
def merge_input_csv_forecast_json(input_csv_file, forecast_json_path, condition_models, dist_models):
"""
Reads forecasts from json files and merges them with the input data from the step csv files.
Args:
input_csv_file: Name of the input data csv file being processed
forecast_json_path: Path to the forecast json files toplevel directory
condition_models: List of models used to forecast hail or no hail
dist_models: List of models used to forecast the hail size distribution
Returns:
"""
try:
run_date = input_csv_file[:-4].split("_")[-1]
print(run_date)
ens_member = "_".join(input_csv_file.split("/")[-1][:-4].split("_")[3:-1])
ens_name = input_csv_file.split("/")[-1].split("_")[2]
input_data = pd.read_csv(input_csv_file, index_col="Step_ID")
full_json_path = forecast_json_path + "{0}/{1}/".format(run_date, ens_member)
track_ids = sorted(input_data["Track_ID"].unique())
model_pred_cols = []
condition_models_ns = []
dist_models_ns = []
gamma_params = ["Shape", "Location", "Scale"]
for condition_model in condition_models:
model_pred_cols.append(condition_model.replace(" ", "-") + "_Condition")
condition_models_ns.append(condition_model.replace(" ", "-"))
for dist_model in dist_models:
dist_models_ns.append(dist_model.replace(" ", "-"))
for param in gamma_params:
model_pred_cols.append(dist_model.replace(" ", "-") + "_" + param)
pred_data = pd.DataFrame(index=input_data.index, columns=model_pred_cols,
dtype=float)
for track_id in track_ids:
track_id_num = track_id.split("_")[-1]
json_filename = full_json_path + "{0}_{1}_{2}_model_track_{3}.json".format(ens_name,
run_date,
ens_member,
track_id_num)
json_file = open(json_filename)
json_data = json.load(json_file)
json_file.close()
for s, step in enumerate(json_data["features"]):
step_id = track_id + "_{0:02d}".format(s)
for cond_model in condition_models_ns:
pred_data.loc[step_id, cond_model + "_Condition"] = step["properties"]["condition_" + cond_model]
for dist_model in dist_models_ns:
pred_data.loc[step_id, [dist_model + "_" + p
for p in gamma_params]] = step["properties"]["dist_" + dist_model]
out_data = input_data.merge(pred_data, left_index=True, right_index=True)
return out_data, ens_name, ens_member
except Exception as e:
print(traceback.format_exc())
raise e
|
python
|
def merge_input_csv_forecast_json(input_csv_file, forecast_json_path, condition_models, dist_models):
"""
Reads forecasts from json files and merges them with the input data from the step csv files.
Args:
input_csv_file: Name of the input data csv file being processed
forecast_json_path: Path to the forecast json files toplevel directory
condition_models: List of models used to forecast hail or no hail
dist_models: List of models used to forecast the hail size distribution
Returns:
"""
try:
run_date = input_csv_file[:-4].split("_")[-1]
print(run_date)
ens_member = "_".join(input_csv_file.split("/")[-1][:-4].split("_")[3:-1])
ens_name = input_csv_file.split("/")[-1].split("_")[2]
input_data = pd.read_csv(input_csv_file, index_col="Step_ID")
full_json_path = forecast_json_path + "{0}/{1}/".format(run_date, ens_member)
track_ids = sorted(input_data["Track_ID"].unique())
model_pred_cols = []
condition_models_ns = []
dist_models_ns = []
gamma_params = ["Shape", "Location", "Scale"]
for condition_model in condition_models:
model_pred_cols.append(condition_model.replace(" ", "-") + "_Condition")
condition_models_ns.append(condition_model.replace(" ", "-"))
for dist_model in dist_models:
dist_models_ns.append(dist_model.replace(" ", "-"))
for param in gamma_params:
model_pred_cols.append(dist_model.replace(" ", "-") + "_" + param)
pred_data = pd.DataFrame(index=input_data.index, columns=model_pred_cols,
dtype=float)
for track_id in track_ids:
track_id_num = track_id.split("_")[-1]
json_filename = full_json_path + "{0}_{1}_{2}_model_track_{3}.json".format(ens_name,
run_date,
ens_member,
track_id_num)
json_file = open(json_filename)
json_data = json.load(json_file)
json_file.close()
for s, step in enumerate(json_data["features"]):
step_id = track_id + "_{0:02d}".format(s)
for cond_model in condition_models_ns:
pred_data.loc[step_id, cond_model + "_Condition"] = step["properties"]["condition_" + cond_model]
for dist_model in dist_models_ns:
pred_data.loc[step_id, [dist_model + "_" + p
for p in gamma_params]] = step["properties"]["dist_" + dist_model]
out_data = input_data.merge(pred_data, left_index=True, right_index=True)
return out_data, ens_name, ens_member
except Exception as e:
print(traceback.format_exc())
raise e
|
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Args:
input_csv_file: Name of the input data csv file being processed
forecast_json_path: Path to the forecast json files toplevel directory
condition_models: List of models used to forecast hail or no hail
dist_models: List of models used to forecast the hail size distribution
Returns:
|
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6fb6c3df90bf4867e13a97d3460b14471d107df1
|
https://github.com/djgagne/hagelslag/blob/6fb6c3df90bf4867e13a97d3460b14471d107df1/hagelslag/util/merge_forecast_data.py#L53-L107
|
train
|
nion-software/nionswift
|
nion/swift/Thumbnails.py
|
ThumbnailProcessor.mark_data_dirty
|
def mark_data_dirty(self):
""" Called from item to indicate its data or metadata has changed."""
self.__cache.set_cached_value_dirty(self.__display_item, self.__cache_property_name)
self.__initialize_cache()
self.__cached_value_dirty = True
|
python
|
def mark_data_dirty(self):
""" Called from item to indicate its data or metadata has changed."""
self.__cache.set_cached_value_dirty(self.__display_item, self.__cache_property_name)
self.__initialize_cache()
self.__cached_value_dirty = True
|
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"(",
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Called from item to indicate its data or metadata has changed.
|
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"to",
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"or",
"metadata",
"has",
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"."
] |
d43693eaf057b8683b9638e575000f055fede452
|
https://github.com/nion-software/nionswift/blob/d43693eaf057b8683b9638e575000f055fede452/nion/swift/Thumbnails.py#L56-L60
|
train
|
nion-software/nionswift
|
nion/swift/Thumbnails.py
|
ThumbnailProcessor.__initialize_cache
|
def __initialize_cache(self):
"""Initialize the cache values (cache values are used for optimization)."""
if self.__cached_value_dirty is None:
self.__cached_value_dirty = self.__cache.is_cached_value_dirty(self.__display_item, self.__cache_property_name)
self.__cached_value = self.__cache.get_cached_value(self.__display_item, self.__cache_property_name)
|
python
|
def __initialize_cache(self):
"""Initialize the cache values (cache values are used for optimization)."""
if self.__cached_value_dirty is None:
self.__cached_value_dirty = self.__cache.is_cached_value_dirty(self.__display_item, self.__cache_property_name)
self.__cached_value = self.__cache.get_cached_value(self.__display_item, self.__cache_property_name)
|
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d43693eaf057b8683b9638e575000f055fede452
|
https://github.com/nion-software/nionswift/blob/d43693eaf057b8683b9638e575000f055fede452/nion/swift/Thumbnails.py#L62-L66
|
train
|
nion-software/nionswift
|
nion/swift/Thumbnails.py
|
ThumbnailProcessor.recompute_if_necessary
|
def recompute_if_necessary(self, ui):
"""Recompute the data on a thread, if necessary.
If the data has recently been computed, this call will be rescheduled for the future.
If the data is currently being computed, it do nothing."""
self.__initialize_cache()
if self.__cached_value_dirty:
with self.__is_recomputing_lock:
is_recomputing = self.__is_recomputing
self.__is_recomputing = True
if is_recomputing:
pass
else:
# the only way to get here is if we're not currently computing
# this has the side effect of limiting the number of threads that
# are sleeping.
def recompute():
try:
if self.__recompute_thread_cancel.wait(0.01): # helps tests run faster
return
minimum_time = 0.5
current_time = time.time()
if current_time < self.__cached_value_time + minimum_time:
if self.__recompute_thread_cancel.wait(self.__cached_value_time + minimum_time - current_time):
return
self.recompute_data(ui)
finally:
self.__is_recomputing = False
self.__recompute_thread = None
with self.__is_recomputing_lock:
self.__recompute_thread = threading.Thread(target=recompute)
self.__recompute_thread.start()
|
python
|
def recompute_if_necessary(self, ui):
"""Recompute the data on a thread, if necessary.
If the data has recently been computed, this call will be rescheduled for the future.
If the data is currently being computed, it do nothing."""
self.__initialize_cache()
if self.__cached_value_dirty:
with self.__is_recomputing_lock:
is_recomputing = self.__is_recomputing
self.__is_recomputing = True
if is_recomputing:
pass
else:
# the only way to get here is if we're not currently computing
# this has the side effect of limiting the number of threads that
# are sleeping.
def recompute():
try:
if self.__recompute_thread_cancel.wait(0.01): # helps tests run faster
return
minimum_time = 0.5
current_time = time.time()
if current_time < self.__cached_value_time + minimum_time:
if self.__recompute_thread_cancel.wait(self.__cached_value_time + minimum_time - current_time):
return
self.recompute_data(ui)
finally:
self.__is_recomputing = False
self.__recompute_thread = None
with self.__is_recomputing_lock:
self.__recompute_thread = threading.Thread(target=recompute)
self.__recompute_thread.start()
|
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Recompute the data on a thread, if necessary.
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|
[
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d43693eaf057b8683b9638e575000f055fede452
|
https://github.com/nion-software/nionswift/blob/d43693eaf057b8683b9638e575000f055fede452/nion/swift/Thumbnails.py#L68-L100
|
train
|
nion-software/nionswift
|
nion/swift/Thumbnails.py
|
ThumbnailProcessor.recompute_data
|
def recompute_data(self, ui):
"""Compute the data associated with this processor.
This method is thread safe and may take a long time to return. It should not be called from
the UI thread. Upon return, the results will be calculated with the latest data available
and the cache will not be marked dirty.
"""
self.__initialize_cache()
with self.__recompute_lock:
if self.__cached_value_dirty:
try:
calculated_data = self.get_calculated_data(ui)
except Exception as e:
import traceback
traceback.print_exc()
traceback.print_stack()
raise
self.__cache.set_cached_value(self.__display_item, self.__cache_property_name, calculated_data)
self.__cached_value = calculated_data
self.__cached_value_dirty = False
self.__cached_value_time = time.time()
else:
calculated_data = None
if calculated_data is None:
calculated_data = self.get_default_data()
if calculated_data is not None:
# if the default is not None, treat is as valid cached data
self.__cache.set_cached_value(self.__display_item, self.__cache_property_name, calculated_data)
self.__cached_value = calculated_data
self.__cached_value_dirty = False
self.__cached_value_time = time.time()
else:
# otherwise remove everything from the cache
self.__cache.remove_cached_value(self.__display_item, self.__cache_property_name)
self.__cached_value = None
self.__cached_value_dirty = None
self.__cached_value_time = 0
self.__recompute_lock.release()
if callable(self.on_thumbnail_updated):
self.on_thumbnail_updated()
self.__recompute_lock.acquire()
|
python
|
def recompute_data(self, ui):
"""Compute the data associated with this processor.
This method is thread safe and may take a long time to return. It should not be called from
the UI thread. Upon return, the results will be calculated with the latest data available
and the cache will not be marked dirty.
"""
self.__initialize_cache()
with self.__recompute_lock:
if self.__cached_value_dirty:
try:
calculated_data = self.get_calculated_data(ui)
except Exception as e:
import traceback
traceback.print_exc()
traceback.print_stack()
raise
self.__cache.set_cached_value(self.__display_item, self.__cache_property_name, calculated_data)
self.__cached_value = calculated_data
self.__cached_value_dirty = False
self.__cached_value_time = time.time()
else:
calculated_data = None
if calculated_data is None:
calculated_data = self.get_default_data()
if calculated_data is not None:
# if the default is not None, treat is as valid cached data
self.__cache.set_cached_value(self.__display_item, self.__cache_property_name, calculated_data)
self.__cached_value = calculated_data
self.__cached_value_dirty = False
self.__cached_value_time = time.time()
else:
# otherwise remove everything from the cache
self.__cache.remove_cached_value(self.__display_item, self.__cache_property_name)
self.__cached_value = None
self.__cached_value_dirty = None
self.__cached_value_time = 0
self.__recompute_lock.release()
if callable(self.on_thumbnail_updated):
self.on_thumbnail_updated()
self.__recompute_lock.acquire()
|
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Compute the data associated with this processor.
This method is thread safe and may take a long time to return. It should not be called from
the UI thread. Upon return, the results will be calculated with the latest data available
and the cache will not be marked dirty.
|
[
"Compute",
"the",
"data",
"associated",
"with",
"this",
"processor",
"."
] |
d43693eaf057b8683b9638e575000f055fede452
|
https://github.com/nion-software/nionswift/blob/d43693eaf057b8683b9638e575000f055fede452/nion/swift/Thumbnails.py#L102-L142
|
train
|
nion-software/nionswift
|
nion/swift/Thumbnails.py
|
ThumbnailManager.thumbnail_source_for_display_item
|
def thumbnail_source_for_display_item(self, ui, display_item: DisplayItem.DisplayItem) -> ThumbnailSource:
"""Returned ThumbnailSource must be closed."""
with self.__lock:
thumbnail_source = self.__thumbnail_sources.get(display_item)
if not thumbnail_source:
thumbnail_source = ThumbnailSource(ui, display_item)
self.__thumbnail_sources[display_item] = thumbnail_source
def will_delete(thumbnail_source):
del self.__thumbnail_sources[thumbnail_source._display_item]
thumbnail_source._on_will_delete = will_delete
else:
assert thumbnail_source._ui == ui
return thumbnail_source.add_ref()
|
python
|
def thumbnail_source_for_display_item(self, ui, display_item: DisplayItem.DisplayItem) -> ThumbnailSource:
"""Returned ThumbnailSource must be closed."""
with self.__lock:
thumbnail_source = self.__thumbnail_sources.get(display_item)
if not thumbnail_source:
thumbnail_source = ThumbnailSource(ui, display_item)
self.__thumbnail_sources[display_item] = thumbnail_source
def will_delete(thumbnail_source):
del self.__thumbnail_sources[thumbnail_source._display_item]
thumbnail_source._on_will_delete = will_delete
else:
assert thumbnail_source._ui == ui
return thumbnail_source.add_ref()
|
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] |
Returned ThumbnailSource must be closed.
|
[
"Returned",
"ThumbnailSource",
"must",
"be",
"closed",
"."
] |
d43693eaf057b8683b9638e575000f055fede452
|
https://github.com/nion-software/nionswift/blob/d43693eaf057b8683b9638e575000f055fede452/nion/swift/Thumbnails.py#L242-L256
|
train
|
nion-software/nionswift
|
nion/swift/model/PlugInManager.py
|
load_plug_ins
|
def load_plug_ins(app, root_dir):
"""Load plug-ins."""
global extensions
ui = app.ui
# a list of directories in which sub-directories PlugIns will be searched.
subdirectories = []
# the default location is where the directory main packages are located.
if root_dir:
subdirectories.append(root_dir)
# also search the default data location; create directory there if it doesn't exist to make it easier for user.
# default data location will be application specific.
data_location = ui.get_data_location()
if data_location is not None:
subdirectories.append(data_location)
# create directories here if they don't exist
plugins_dir = os.path.abspath(os.path.join(data_location, "PlugIns"))
if not os.path.exists(plugins_dir):
logging.info("Creating plug-ins directory %s", plugins_dir)
os.makedirs(plugins_dir)
# search the Nion/Swift subdirectory of the default document location too,
# but don't create directories here - avoid polluting user visible directories.
document_location = ui.get_document_location()
if document_location is not None:
subdirectories.append(os.path.join(document_location, "Nion", "Swift"))
# do not create them in documents if they don't exist. this location is optional.
# build a list of directories that will be loaded as plug-ins.
PlugInDir = collections.namedtuple("PlugInDir", ["directory", "relative_path"])
plugin_dirs = list()
# track directories that have already been searched.
seen_plugin_dirs = list()
# for each subdirectory, look in PlugIns for sub-directories that represent the plug-ins.
for subdirectory in subdirectories:
plugins_dir = os.path.abspath(os.path.join(subdirectory, "PlugIns"))
if os.path.exists(plugins_dir) and not plugins_dir in seen_plugin_dirs:
logging.info("Loading plug-ins from %s", plugins_dir)
# add the PlugIns directory to the system import path.
sys.path.append(plugins_dir)
# now build a list of sub-directories representing plug-ins within plugins_dir.
sorted_relative_paths = sorted([d for d in os.listdir(plugins_dir) if os.path.isdir(os.path.join(plugins_dir, d))])
plugin_dirs.extend([PlugInDir(plugins_dir, sorted_relative_path) for sorted_relative_path in sorted_relative_paths])
# mark plugins_dir as 'seen' to avoid search it twice.
seen_plugin_dirs.append(plugins_dir)
else:
logging.info("NOT Loading plug-ins from %s (missing)", plugins_dir)
version_map = dict()
module_exists_map = dict()
plugin_adapters = list()
import nionswift_plugin
for module_info in pkgutil.iter_modules(nionswift_plugin.__path__):
plugin_adapters.append(ModuleAdapter(nionswift_plugin.__name__, module_info))
for directory, relative_path in plugin_dirs:
plugin_adapters.append(PlugInAdapter(directory, relative_path))
progress = True
while progress:
progress = False
plugin_adapters_copy = copy.deepcopy(plugin_adapters)
plugin_adapters = list()
for plugin_adapter in plugin_adapters_copy:
manifest_path = plugin_adapter.manifest_path
manifest = plugin_adapter.manifest
if manifest:
manifest_valid = True
if not "name" in manifest:
logging.info("Invalid manifest (missing 'name'): %s", manifest_path)
manifest_valid = False
if not "identifier" in manifest:
logging.info("Invalid manifest (missing 'identifier'): %s", manifest_path)
manifest_valid = False
if "identifier" in manifest and not re.match("[_\-a-zA-Z][_\-a-zA-Z0-9.]*$", manifest["identifier"]):
logging.info("Invalid manifest (invalid 'identifier': '%s'): %s", manifest["identifier"], manifest_path)
manifest_valid = False
if not "version" in manifest:
logging.info("Invalid manifest (missing 'version'): %s", manifest_path)
manifest_valid = False
if "requires" in manifest and not isinstance(manifest["requires"], list):
logging.info("Invalid manifest ('requires' not a list): %s", manifest_path)
manifest_valid = False
if not manifest_valid:
continue
for module in manifest.get("modules", list()):
if module in module_exists_map:
module_exists = module_exists_map.get(module)
else:
module_exists = importlib.util.find_spec(module) is not None
module_exists_map[module] = module_exists
if not module_exists:
logging.info("Plug-in '" + plugin_adapter.module_name + "' NOT loaded (" + plugin_adapter.module_path + ").")
logging.info("Cannot satisfy requirement (%s): %s", module, manifest_path)
manifest_valid = False
break
for requirement in manifest.get("requires", list()):
# TODO: see https://packaging.pypa.io/en/latest/
requirement_components = requirement.split()
if len(requirement_components) != 3 or requirement_components[1] != "~=":
logging.info("Invalid manifest (requirement '%s' invalid): %s", requirement, manifest_path)
manifest_valid = False
break
identifier, operator, version_specifier = requirement_components[0], requirement_components[1], requirement_components[2]
if identifier in version_map:
if Utility.compare_versions("~" + version_specifier, version_map[identifier]) != 0:
logging.info("Plug-in '" + plugin_adapter.module_name + "' NOT loaded (" + plugin_adapter.module_path + ").")
logging.info("Cannot satisfy requirement (%s): %s", requirement, manifest_path)
manifest_valid = False
break
else:
# requirements not loaded yet; add back to plugin_adapters, but don't mark progress since nothing was loaded.
logging.info("Plug-in '" + plugin_adapter.module_name + "' delayed (%s) (" + plugin_adapter.module_path + ").", requirement)
plugin_adapters.append(plugin_adapter)
manifest_valid = False
break
if not manifest_valid:
continue
version_map[manifest["identifier"]] = manifest["version"]
# read the manifests, if any
# repeat loop of plug-ins until no plug-ins left in the list
# if all dependencies satisfied for a plug-in, load it
# otherwise defer until next round
# stop if no plug-ins loaded in the round
# count on the user to have correct dependencies
module = plugin_adapter.load()
if module:
__modules.append(module)
progress = True
for plugin_adapter in plugin_adapters:
logging.info("Plug-in '" + plugin_adapter.module_name + "' NOT loaded (requirements) (" + plugin_adapter.module_path + ").")
notify_modules("run")
|
python
|
def load_plug_ins(app, root_dir):
"""Load plug-ins."""
global extensions
ui = app.ui
# a list of directories in which sub-directories PlugIns will be searched.
subdirectories = []
# the default location is where the directory main packages are located.
if root_dir:
subdirectories.append(root_dir)
# also search the default data location; create directory there if it doesn't exist to make it easier for user.
# default data location will be application specific.
data_location = ui.get_data_location()
if data_location is not None:
subdirectories.append(data_location)
# create directories here if they don't exist
plugins_dir = os.path.abspath(os.path.join(data_location, "PlugIns"))
if not os.path.exists(plugins_dir):
logging.info("Creating plug-ins directory %s", plugins_dir)
os.makedirs(plugins_dir)
# search the Nion/Swift subdirectory of the default document location too,
# but don't create directories here - avoid polluting user visible directories.
document_location = ui.get_document_location()
if document_location is not None:
subdirectories.append(os.path.join(document_location, "Nion", "Swift"))
# do not create them in documents if they don't exist. this location is optional.
# build a list of directories that will be loaded as plug-ins.
PlugInDir = collections.namedtuple("PlugInDir", ["directory", "relative_path"])
plugin_dirs = list()
# track directories that have already been searched.
seen_plugin_dirs = list()
# for each subdirectory, look in PlugIns for sub-directories that represent the plug-ins.
for subdirectory in subdirectories:
plugins_dir = os.path.abspath(os.path.join(subdirectory, "PlugIns"))
if os.path.exists(plugins_dir) and not plugins_dir in seen_plugin_dirs:
logging.info("Loading plug-ins from %s", plugins_dir)
# add the PlugIns directory to the system import path.
sys.path.append(plugins_dir)
# now build a list of sub-directories representing plug-ins within plugins_dir.
sorted_relative_paths = sorted([d for d in os.listdir(plugins_dir) if os.path.isdir(os.path.join(plugins_dir, d))])
plugin_dirs.extend([PlugInDir(plugins_dir, sorted_relative_path) for sorted_relative_path in sorted_relative_paths])
# mark plugins_dir as 'seen' to avoid search it twice.
seen_plugin_dirs.append(plugins_dir)
else:
logging.info("NOT Loading plug-ins from %s (missing)", plugins_dir)
version_map = dict()
module_exists_map = dict()
plugin_adapters = list()
import nionswift_plugin
for module_info in pkgutil.iter_modules(nionswift_plugin.__path__):
plugin_adapters.append(ModuleAdapter(nionswift_plugin.__name__, module_info))
for directory, relative_path in plugin_dirs:
plugin_adapters.append(PlugInAdapter(directory, relative_path))
progress = True
while progress:
progress = False
plugin_adapters_copy = copy.deepcopy(plugin_adapters)
plugin_adapters = list()
for plugin_adapter in plugin_adapters_copy:
manifest_path = plugin_adapter.manifest_path
manifest = plugin_adapter.manifest
if manifest:
manifest_valid = True
if not "name" in manifest:
logging.info("Invalid manifest (missing 'name'): %s", manifest_path)
manifest_valid = False
if not "identifier" in manifest:
logging.info("Invalid manifest (missing 'identifier'): %s", manifest_path)
manifest_valid = False
if "identifier" in manifest and not re.match("[_\-a-zA-Z][_\-a-zA-Z0-9.]*$", manifest["identifier"]):
logging.info("Invalid manifest (invalid 'identifier': '%s'): %s", manifest["identifier"], manifest_path)
manifest_valid = False
if not "version" in manifest:
logging.info("Invalid manifest (missing 'version'): %s", manifest_path)
manifest_valid = False
if "requires" in manifest and not isinstance(manifest["requires"], list):
logging.info("Invalid manifest ('requires' not a list): %s", manifest_path)
manifest_valid = False
if not manifest_valid:
continue
for module in manifest.get("modules", list()):
if module in module_exists_map:
module_exists = module_exists_map.get(module)
else:
module_exists = importlib.util.find_spec(module) is not None
module_exists_map[module] = module_exists
if not module_exists:
logging.info("Plug-in '" + plugin_adapter.module_name + "' NOT loaded (" + plugin_adapter.module_path + ").")
logging.info("Cannot satisfy requirement (%s): %s", module, manifest_path)
manifest_valid = False
break
for requirement in manifest.get("requires", list()):
# TODO: see https://packaging.pypa.io/en/latest/
requirement_components = requirement.split()
if len(requirement_components) != 3 or requirement_components[1] != "~=":
logging.info("Invalid manifest (requirement '%s' invalid): %s", requirement, manifest_path)
manifest_valid = False
break
identifier, operator, version_specifier = requirement_components[0], requirement_components[1], requirement_components[2]
if identifier in version_map:
if Utility.compare_versions("~" + version_specifier, version_map[identifier]) != 0:
logging.info("Plug-in '" + plugin_adapter.module_name + "' NOT loaded (" + plugin_adapter.module_path + ").")
logging.info("Cannot satisfy requirement (%s): %s", requirement, manifest_path)
manifest_valid = False
break
else:
# requirements not loaded yet; add back to plugin_adapters, but don't mark progress since nothing was loaded.
logging.info("Plug-in '" + plugin_adapter.module_name + "' delayed (%s) (" + plugin_adapter.module_path + ").", requirement)
plugin_adapters.append(plugin_adapter)
manifest_valid = False
break
if not manifest_valid:
continue
version_map[manifest["identifier"]] = manifest["version"]
# read the manifests, if any
# repeat loop of plug-ins until no plug-ins left in the list
# if all dependencies satisfied for a plug-in, load it
# otherwise defer until next round
# stop if no plug-ins loaded in the round
# count on the user to have correct dependencies
module = plugin_adapter.load()
if module:
__modules.append(module)
progress = True
for plugin_adapter in plugin_adapters:
logging.info("Plug-in '" + plugin_adapter.module_name + "' NOT loaded (requirements) (" + plugin_adapter.module_path + ").")
notify_modules("run")
|
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Load plug-ins.
|
[
"Load",
"plug",
"-",
"ins",
"."
] |
d43693eaf057b8683b9638e575000f055fede452
|
https://github.com/nion-software/nionswift/blob/d43693eaf057b8683b9638e575000f055fede452/nion/swift/model/PlugInManager.py#L140-L283
|
train
|
softvar/simplegist
|
simplegist/comments.py
|
Comments.getMyID
|
def getMyID(self,gist_name):
'''
Getting gistID of a gist in order to make the workflow
easy and uninterrupted.
'''
r = requests.get(
'%s'%BASE_URL+'/users/%s/gists' % self.user,
headers=self.gist.header
)
if (r.status_code == 200):
r_text = json.loads(r.text)
limit = len(r.json())
for g,no in zip(r_text, range(0,limit)):
for ka,va in r.json()[no]['files'].iteritems():
if str(va['filename']) == str(gist_name):
return r.json()[no]['id']
return 0
raise Exception('Username not found')
|
python
|
def getMyID(self,gist_name):
'''
Getting gistID of a gist in order to make the workflow
easy and uninterrupted.
'''
r = requests.get(
'%s'%BASE_URL+'/users/%s/gists' % self.user,
headers=self.gist.header
)
if (r.status_code == 200):
r_text = json.loads(r.text)
limit = len(r.json())
for g,no in zip(r_text, range(0,limit)):
for ka,va in r.json()[no]['files'].iteritems():
if str(va['filename']) == str(gist_name):
return r.json()[no]['id']
return 0
raise Exception('Username not found')
|
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Getting gistID of a gist in order to make the workflow
easy and uninterrupted.
|
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"Getting",
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"gist",
"in",
"order",
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"make",
"the",
"workflow",
"easy",
"and",
"uninterrupted",
"."
] |
8d53edd15d76c7b10fb963a659c1cf9f46f5345d
|
https://github.com/softvar/simplegist/blob/8d53edd15d76c7b10fb963a659c1cf9f46f5345d/simplegist/comments.py#L10-L29
|
train
|
nion-software/nionswift
|
nion/swift/DocumentController.py
|
DocumentController.close
|
def close(self):
"""Close the document controller.
This method must be called to shut down the document controller. There are several
paths by which it can be called, though.
* User quits application via menu item. The menu item will call back to Application.exit which will close each
document controller by calling this method.
* User quits application using dock menu item. The Qt application will call aboutToClose in the document windows
* User closes document window via menu item.
* User closes document window via close box.
The main concept of closing is that it is always triggered by the document window closing. This can be initiated
from within Python by calling request_close on the document window. When the window closes, either by explicit request
or by the user clicking a close box, it will invoke the about_to_close method on the document window. At this point,
the window would still be open, so the about_to_close message can be used to tell the document controller to save anything
it needs to save and prepare for closing.
"""
assert self.__closed == False
self.__closed = True
self.finish_periodic() # required to finish periodic operations during tests
# dialogs
for weak_dialog in self.__dialogs:
dialog = weak_dialog()
if dialog:
try:
dialog.request_close()
except Exception as e:
pass
# menus
self._file_menu = None
self._edit_menu = None
self._processing_menu = None
self._view_menu = None
self._window_menu = None
self._help_menu = None
self._library_menu = None
self._processing_arithmetic_menu = None
self._processing_reduce_menu = None
self._processing_transform_menu = None
self._processing_filter_menu = None
self._processing_fourier_menu = None
self._processing_graphics_menu = None
self._processing_sequence_menu = None
self._processing_redimension_menu = None
self._display_type_menu = None
if self.__workspace_controller:
self.__workspace_controller.close()
self.__workspace_controller = None
self.__call_soon_event_listener.close()
self.__call_soon_event_listener = None
self.__filtered_display_items_model.close()
self.__filtered_display_items_model = None
self.filter_controller.close()
self.filter_controller = None
self.__display_items_model.close()
self.__display_items_model = None
# document_model may be shared between several DocumentControllers, so use reference counting
# to determine when to close it.
self.document_model.remove_ref()
self.document_model = None
self.did_close_event.fire(self)
self.did_close_event = None
super().close()
|
python
|
def close(self):
"""Close the document controller.
This method must be called to shut down the document controller. There are several
paths by which it can be called, though.
* User quits application via menu item. The menu item will call back to Application.exit which will close each
document controller by calling this method.
* User quits application using dock menu item. The Qt application will call aboutToClose in the document windows
* User closes document window via menu item.
* User closes document window via close box.
The main concept of closing is that it is always triggered by the document window closing. This can be initiated
from within Python by calling request_close on the document window. When the window closes, either by explicit request
or by the user clicking a close box, it will invoke the about_to_close method on the document window. At this point,
the window would still be open, so the about_to_close message can be used to tell the document controller to save anything
it needs to save and prepare for closing.
"""
assert self.__closed == False
self.__closed = True
self.finish_periodic() # required to finish periodic operations during tests
# dialogs
for weak_dialog in self.__dialogs:
dialog = weak_dialog()
if dialog:
try:
dialog.request_close()
except Exception as e:
pass
# menus
self._file_menu = None
self._edit_menu = None
self._processing_menu = None
self._view_menu = None
self._window_menu = None
self._help_menu = None
self._library_menu = None
self._processing_arithmetic_menu = None
self._processing_reduce_menu = None
self._processing_transform_menu = None
self._processing_filter_menu = None
self._processing_fourier_menu = None
self._processing_graphics_menu = None
self._processing_sequence_menu = None
self._processing_redimension_menu = None
self._display_type_menu = None
if self.__workspace_controller:
self.__workspace_controller.close()
self.__workspace_controller = None
self.__call_soon_event_listener.close()
self.__call_soon_event_listener = None
self.__filtered_display_items_model.close()
self.__filtered_display_items_model = None
self.filter_controller.close()
self.filter_controller = None
self.__display_items_model.close()
self.__display_items_model = None
# document_model may be shared between several DocumentControllers, so use reference counting
# to determine when to close it.
self.document_model.remove_ref()
self.document_model = None
self.did_close_event.fire(self)
self.did_close_event = None
super().close()
|
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] |
Close the document controller.
This method must be called to shut down the document controller. There are several
paths by which it can be called, though.
* User quits application via menu item. The menu item will call back to Application.exit which will close each
document controller by calling this method.
* User quits application using dock menu item. The Qt application will call aboutToClose in the document windows
* User closes document window via menu item.
* User closes document window via close box.
The main concept of closing is that it is always triggered by the document window closing. This can be initiated
from within Python by calling request_close on the document window. When the window closes, either by explicit request
or by the user clicking a close box, it will invoke the about_to_close method on the document window. At this point,
the window would still be open, so the about_to_close message can be used to tell the document controller to save anything
it needs to save and prepare for closing.
|
[
"Close",
"the",
"document",
"controller",
"."
] |
d43693eaf057b8683b9638e575000f055fede452
|
https://github.com/nion-software/nionswift/blob/d43693eaf057b8683b9638e575000f055fede452/nion/swift/DocumentController.py#L130-L194
|
train
|
nion-software/nionswift
|
nion/swift/DocumentController.py
|
DocumentController.add_periodic
|
def add_periodic(self, interval: float, listener_fn):
"""Add a listener function and return listener token. Token can be closed or deleted to unlisten."""
class PeriodicListener:
def __init__(self, interval: float, listener_fn):
self.interval = interval
self.__listener_fn = listener_fn
# the call function is very performance critical; make it fast by using a property
# instead of a logic statement each time.
if callable(listener_fn):
self.call = self.__listener_fn
else:
def void(*args, **kwargs):
pass
self.call = void
self.next_scheduled_time = time.time() + interval
def close(self):
self.__listener_fn = None
def void(*args, **kwargs):
pass
self.call = void
listener = PeriodicListener(interval, listener_fn)
def remove_listener(weak_listener):
with self.__weak_periodic_listeners_mutex:
self.__weak_periodic_listeners.remove(weak_listener)
weak_listener = weakref.ref(listener, remove_listener)
with self.__weak_periodic_listeners_mutex:
self.__weak_periodic_listeners.append(weak_listener)
return listener
|
python
|
def add_periodic(self, interval: float, listener_fn):
"""Add a listener function and return listener token. Token can be closed or deleted to unlisten."""
class PeriodicListener:
def __init__(self, interval: float, listener_fn):
self.interval = interval
self.__listener_fn = listener_fn
# the call function is very performance critical; make it fast by using a property
# instead of a logic statement each time.
if callable(listener_fn):
self.call = self.__listener_fn
else:
def void(*args, **kwargs):
pass
self.call = void
self.next_scheduled_time = time.time() + interval
def close(self):
self.__listener_fn = None
def void(*args, **kwargs):
pass
self.call = void
listener = PeriodicListener(interval, listener_fn)
def remove_listener(weak_listener):
with self.__weak_periodic_listeners_mutex:
self.__weak_periodic_listeners.remove(weak_listener)
weak_listener = weakref.ref(listener, remove_listener)
with self.__weak_periodic_listeners_mutex:
self.__weak_periodic_listeners.append(weak_listener)
return listener
|
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d43693eaf057b8683b9638e575000f055fede452
|
https://github.com/nion-software/nionswift/blob/d43693eaf057b8683b9638e575000f055fede452/nion/swift/DocumentController.py#L562-L589
|
train
|
nion-software/nionswift
|
nion/swift/DocumentController.py
|
DocumentController.__update_display_items_model
|
def __update_display_items_model(self, display_items_model: ListModel.FilteredListModel, data_group: typing.Optional[DataGroup.DataGroup], filter_id: typing.Optional[str]) -> None:
"""Update the data item model with a new container, filter, and sorting.
This is called when the data item model is created or when the user changes
the data group or sorting settings.
"""
with display_items_model.changes(): # change filter and sort together
if data_group is not None:
display_items_model.container = data_group
display_items_model.filter = ListModel.Filter(True)
display_items_model.sort_key = None
display_items_model.filter_id = None
elif filter_id == "latest-session":
display_items_model.container = self.document_model
display_items_model.filter = ListModel.EqFilter("session_id", self.document_model.session_id)
display_items_model.sort_key = DataItem.sort_by_date_key
display_items_model.sort_reverse = True
display_items_model.filter_id = filter_id
elif filter_id == "temporary":
display_items_model.container = self.document_model
display_items_model.filter = ListModel.NotEqFilter("category", "persistent")
display_items_model.sort_key = DataItem.sort_by_date_key
display_items_model.sort_reverse = True
display_items_model.filter_id = filter_id
elif filter_id == "none": # not intended to be used directly
display_items_model.container = self.document_model
display_items_model.filter = ListModel.Filter(False)
display_items_model.sort_key = DataItem.sort_by_date_key
display_items_model.sort_reverse = True
display_items_model.filter_id = filter_id
else: # "all"
display_items_model.container = self.document_model
display_items_model.filter = ListModel.EqFilter("category", "persistent")
display_items_model.sort_key = DataItem.sort_by_date_key
display_items_model.sort_reverse = True
display_items_model.filter_id = None
|
python
|
def __update_display_items_model(self, display_items_model: ListModel.FilteredListModel, data_group: typing.Optional[DataGroup.DataGroup], filter_id: typing.Optional[str]) -> None:
"""Update the data item model with a new container, filter, and sorting.
This is called when the data item model is created or when the user changes
the data group or sorting settings.
"""
with display_items_model.changes(): # change filter and sort together
if data_group is not None:
display_items_model.container = data_group
display_items_model.filter = ListModel.Filter(True)
display_items_model.sort_key = None
display_items_model.filter_id = None
elif filter_id == "latest-session":
display_items_model.container = self.document_model
display_items_model.filter = ListModel.EqFilter("session_id", self.document_model.session_id)
display_items_model.sort_key = DataItem.sort_by_date_key
display_items_model.sort_reverse = True
display_items_model.filter_id = filter_id
elif filter_id == "temporary":
display_items_model.container = self.document_model
display_items_model.filter = ListModel.NotEqFilter("category", "persistent")
display_items_model.sort_key = DataItem.sort_by_date_key
display_items_model.sort_reverse = True
display_items_model.filter_id = filter_id
elif filter_id == "none": # not intended to be used directly
display_items_model.container = self.document_model
display_items_model.filter = ListModel.Filter(False)
display_items_model.sort_key = DataItem.sort_by_date_key
display_items_model.sort_reverse = True
display_items_model.filter_id = filter_id
else: # "all"
display_items_model.container = self.document_model
display_items_model.filter = ListModel.EqFilter("category", "persistent")
display_items_model.sort_key = DataItem.sort_by_date_key
display_items_model.sort_reverse = True
display_items_model.filter_id = None
|
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Update the data item model with a new container, filter, and sorting.
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|
[
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] |
d43693eaf057b8683b9638e575000f055fede452
|
https://github.com/nion-software/nionswift/blob/d43693eaf057b8683b9638e575000f055fede452/nion/swift/DocumentController.py#L658-L694
|
train
|
nion-software/nionswift
|
nion/swift/DocumentController.py
|
DocumentController.focused_data_item
|
def focused_data_item(self) -> typing.Optional[DataItem.DataItem]:
"""Return the data item with keyboard focus."""
return self.__focused_display_item.data_item if self.__focused_display_item else None
|
python
|
def focused_data_item(self) -> typing.Optional[DataItem.DataItem]:
"""Return the data item with keyboard focus."""
return self.__focused_display_item.data_item if self.__focused_display_item else None
|
[
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Return the data item with keyboard focus.
|
[
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"."
] |
d43693eaf057b8683b9638e575000f055fede452
|
https://github.com/nion-software/nionswift/blob/d43693eaf057b8683b9638e575000f055fede452/nion/swift/DocumentController.py#L772-L774
|
train
|
nion-software/nionswift
|
nion/swift/DocumentController.py
|
DocumentController.selected_display_item
|
def selected_display_item(self) -> typing.Optional[DisplayItem.DisplayItem]:
"""Return the selected display item.
The selected display is the display ite that has keyboard focus in the data panel or a display panel.
"""
# first check for the [focused] data browser
display_item = self.focused_display_item
if not display_item:
selected_display_panel = self.selected_display_panel
display_item = selected_display_panel.display_item if selected_display_panel else None
return display_item
|
python
|
def selected_display_item(self) -> typing.Optional[DisplayItem.DisplayItem]:
"""Return the selected display item.
The selected display is the display ite that has keyboard focus in the data panel or a display panel.
"""
# first check for the [focused] data browser
display_item = self.focused_display_item
if not display_item:
selected_display_panel = self.selected_display_panel
display_item = selected_display_panel.display_item if selected_display_panel else None
return display_item
|
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Return the selected display item.
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|
[
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] |
d43693eaf057b8683b9638e575000f055fede452
|
https://github.com/nion-software/nionswift/blob/d43693eaf057b8683b9638e575000f055fede452/nion/swift/DocumentController.py#L795-L805
|
train
|
nion-software/nionswift
|
nion/swift/DocumentController.py
|
DocumentController._get_two_data_sources
|
def _get_two_data_sources(self):
"""Get two sensible data sources, which may be the same."""
selected_display_items = self.selected_display_items
if len(selected_display_items) < 2:
selected_display_items = list()
display_item = self.selected_display_item
if display_item:
selected_display_items.append(display_item)
if len(selected_display_items) == 1:
display_item = selected_display_items[0]
data_item = display_item.data_item if display_item else None
if display_item and len(display_item.graphic_selection.indexes) == 2:
index1 = display_item.graphic_selection.anchor_index
index2 = list(display_item.graphic_selection.indexes.difference({index1}))[0]
graphic1 = display_item.graphics[index1]
graphic2 = display_item.graphics[index2]
if data_item:
if data_item.is_datum_1d and isinstance(graphic1, Graphics.IntervalGraphic) and isinstance(graphic2, Graphics.IntervalGraphic):
crop_graphic1 = graphic1
crop_graphic2 = graphic2
elif data_item.is_datum_2d and isinstance(graphic1, Graphics.RectangleTypeGraphic) and isinstance(graphic2, Graphics.RectangleTypeGraphic):
crop_graphic1 = graphic1
crop_graphic2 = graphic2
else:
crop_graphic1 = self.__get_crop_graphic(display_item)
crop_graphic2 = crop_graphic1
else:
crop_graphic1 = self.__get_crop_graphic(display_item)
crop_graphic2 = crop_graphic1
else:
crop_graphic1 = self.__get_crop_graphic(display_item)
crop_graphic2 = crop_graphic1
return (display_item, crop_graphic1), (display_item, crop_graphic2)
if len(selected_display_items) == 2:
display_item1 = selected_display_items[0]
crop_graphic1 = self.__get_crop_graphic(display_item1)
display_item2 = selected_display_items[1]
crop_graphic2 = self.__get_crop_graphic(display_item2)
return (display_item1, crop_graphic1), (display_item2, crop_graphic2)
return None
|
python
|
def _get_two_data_sources(self):
"""Get two sensible data sources, which may be the same."""
selected_display_items = self.selected_display_items
if len(selected_display_items) < 2:
selected_display_items = list()
display_item = self.selected_display_item
if display_item:
selected_display_items.append(display_item)
if len(selected_display_items) == 1:
display_item = selected_display_items[0]
data_item = display_item.data_item if display_item else None
if display_item and len(display_item.graphic_selection.indexes) == 2:
index1 = display_item.graphic_selection.anchor_index
index2 = list(display_item.graphic_selection.indexes.difference({index1}))[0]
graphic1 = display_item.graphics[index1]
graphic2 = display_item.graphics[index2]
if data_item:
if data_item.is_datum_1d and isinstance(graphic1, Graphics.IntervalGraphic) and isinstance(graphic2, Graphics.IntervalGraphic):
crop_graphic1 = graphic1
crop_graphic2 = graphic2
elif data_item.is_datum_2d and isinstance(graphic1, Graphics.RectangleTypeGraphic) and isinstance(graphic2, Graphics.RectangleTypeGraphic):
crop_graphic1 = graphic1
crop_graphic2 = graphic2
else:
crop_graphic1 = self.__get_crop_graphic(display_item)
crop_graphic2 = crop_graphic1
else:
crop_graphic1 = self.__get_crop_graphic(display_item)
crop_graphic2 = crop_graphic1
else:
crop_graphic1 = self.__get_crop_graphic(display_item)
crop_graphic2 = crop_graphic1
return (display_item, crop_graphic1), (display_item, crop_graphic2)
if len(selected_display_items) == 2:
display_item1 = selected_display_items[0]
crop_graphic1 = self.__get_crop_graphic(display_item1)
display_item2 = selected_display_items[1]
crop_graphic2 = self.__get_crop_graphic(display_item2)
return (display_item1, crop_graphic1), (display_item2, crop_graphic2)
return None
|
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"selected_display_items",
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"crop_graphic2",
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")",
",",
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"display_item2",
",",
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Get two sensible data sources, which may be the same.
|
[
"Get",
"two",
"sensible",
"data",
"sources",
"which",
"may",
"be",
"the",
"same",
"."
] |
d43693eaf057b8683b9638e575000f055fede452
|
https://github.com/nion-software/nionswift/blob/d43693eaf057b8683b9638e575000f055fede452/nion/swift/DocumentController.py#L2053-L2092
|
train
|
nion-software/nionswift
|
nion/swift/ImageCanvasItem.py
|
calculate_origin_and_size
|
def calculate_origin_and_size(canvas_size, data_shape, image_canvas_mode, image_zoom, image_position) -> typing.Tuple[typing.Any, typing.Any]:
"""Calculate origin and size for canvas size, data shape, and image display parameters."""
if data_shape is None:
return None, None
if image_canvas_mode == "fill":
data_shape = data_shape
scale_h = float(data_shape[1]) / canvas_size[1]
scale_v = float(data_shape[0]) / canvas_size[0]
if scale_v < scale_h:
image_canvas_size = (canvas_size[0], canvas_size[0] * data_shape[1] / data_shape[0])
else:
image_canvas_size = (canvas_size[1] * data_shape[0] / data_shape[1], canvas_size[1])
image_canvas_origin = (canvas_size[0] * 0.5 - image_canvas_size[0] * 0.5, canvas_size[1] * 0.5 - image_canvas_size[1] * 0.5)
elif image_canvas_mode == "fit":
image_canvas_size = canvas_size
image_canvas_origin = (0, 0)
elif image_canvas_mode == "1:1":
image_canvas_size = data_shape
image_canvas_origin = (canvas_size[0] * 0.5 - image_canvas_size[0] * 0.5, canvas_size[1] * 0.5 - image_canvas_size[1] * 0.5)
elif image_canvas_mode == "2:1":
image_canvas_size = (data_shape[0] * 0.5, data_shape[1] * 0.5)
image_canvas_origin = (canvas_size[0] * 0.5 - image_canvas_size[0] * 0.5, canvas_size[1] * 0.5 - image_canvas_size[1] * 0.5)
else:
image_canvas_size = (canvas_size[0] * image_zoom, canvas_size[1] * image_zoom)
canvas_rect = Geometry.fit_to_size(((0, 0), image_canvas_size), data_shape)
image_canvas_origin_y = (canvas_size[0] * 0.5) - image_position[0] * canvas_rect[1][0] - canvas_rect[0][0]
image_canvas_origin_x = (canvas_size[1] * 0.5) - image_position[1] * canvas_rect[1][1] - canvas_rect[0][1]
image_canvas_origin = (image_canvas_origin_y, image_canvas_origin_x)
return image_canvas_origin, image_canvas_size
|
python
|
def calculate_origin_and_size(canvas_size, data_shape, image_canvas_mode, image_zoom, image_position) -> typing.Tuple[typing.Any, typing.Any]:
"""Calculate origin and size for canvas size, data shape, and image display parameters."""
if data_shape is None:
return None, None
if image_canvas_mode == "fill":
data_shape = data_shape
scale_h = float(data_shape[1]) / canvas_size[1]
scale_v = float(data_shape[0]) / canvas_size[0]
if scale_v < scale_h:
image_canvas_size = (canvas_size[0], canvas_size[0] * data_shape[1] / data_shape[0])
else:
image_canvas_size = (canvas_size[1] * data_shape[0] / data_shape[1], canvas_size[1])
image_canvas_origin = (canvas_size[0] * 0.5 - image_canvas_size[0] * 0.5, canvas_size[1] * 0.5 - image_canvas_size[1] * 0.5)
elif image_canvas_mode == "fit":
image_canvas_size = canvas_size
image_canvas_origin = (0, 0)
elif image_canvas_mode == "1:1":
image_canvas_size = data_shape
image_canvas_origin = (canvas_size[0] * 0.5 - image_canvas_size[0] * 0.5, canvas_size[1] * 0.5 - image_canvas_size[1] * 0.5)
elif image_canvas_mode == "2:1":
image_canvas_size = (data_shape[0] * 0.5, data_shape[1] * 0.5)
image_canvas_origin = (canvas_size[0] * 0.5 - image_canvas_size[0] * 0.5, canvas_size[1] * 0.5 - image_canvas_size[1] * 0.5)
else:
image_canvas_size = (canvas_size[0] * image_zoom, canvas_size[1] * image_zoom)
canvas_rect = Geometry.fit_to_size(((0, 0), image_canvas_size), data_shape)
image_canvas_origin_y = (canvas_size[0] * 0.5) - image_position[0] * canvas_rect[1][0] - canvas_rect[0][0]
image_canvas_origin_x = (canvas_size[1] * 0.5) - image_position[1] * canvas_rect[1][1] - canvas_rect[0][1]
image_canvas_origin = (image_canvas_origin_y, image_canvas_origin_x)
return image_canvas_origin, image_canvas_size
|
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Calculate origin and size for canvas size, data shape, and image display parameters.
|
[
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"data",
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"display",
"parameters",
"."
] |
d43693eaf057b8683b9638e575000f055fede452
|
https://github.com/nion-software/nionswift/blob/d43693eaf057b8683b9638e575000f055fede452/nion/swift/ImageCanvasItem.py#L332-L360
|
train
|
nion-software/nionswift
|
nion/swift/model/FileStorageSystem.py
|
read_library
|
def read_library(persistent_storage_system, ignore_older_files) -> typing.Dict:
"""Read data items from the data reference handler and return as a list.
Data items will have persistent_object_context set upon return, but caller will need to call finish_reading
on each of the data items.
"""
data_item_uuids = set()
utilized_deletions = set() # the uuid's skipped due to being deleted
deletions = list()
reader_info_list, library_updates = auto_migrate_storage_system(persistent_storage_system=persistent_storage_system,
new_persistent_storage_system=persistent_storage_system,
data_item_uuids=data_item_uuids,
deletions=deletions,
utilized_deletions=utilized_deletions,
ignore_older_files=ignore_older_files)
# next, for each auto migration, create a temporary storage system and read items from that storage system
# using auto_migrate_storage_system. the data items returned will have been copied to the current storage
# system (persistent object context).
for auto_migration in reversed(persistent_storage_system.get_auto_migrations()):
old_persistent_storage_system = FileStorageSystem(auto_migration.library_path, auto_migration.paths) if auto_migration.paths else auto_migration.storage_system
new_reader_info_list, new_library_updates = auto_migrate_storage_system(persistent_storage_system=old_persistent_storage_system,
new_persistent_storage_system=persistent_storage_system,
data_item_uuids=data_item_uuids,
deletions=deletions,
utilized_deletions=utilized_deletions,
ignore_older_files=ignore_older_files)
reader_info_list.extend(new_reader_info_list)
library_updates.update(new_library_updates)
assert len(reader_info_list) == len(data_item_uuids)
library_storage_properties = persistent_storage_system.library_storage_properties
for reader_info in reader_info_list:
properties = reader_info.properties
properties = Utility.clean_dict(copy.deepcopy(properties) if properties else dict())
version = properties.get("version", 0)
if version == DataItem.DataItem.writer_version:
data_item_uuid = uuid.UUID(properties.get("uuid", uuid.uuid4()))
library_update = library_updates.get(data_item_uuid, dict())
library_storage_properties.setdefault("connections", list()).extend(library_update.get("connections", list()))
library_storage_properties.setdefault("computations", list()).extend(library_update.get("computations", list()))
library_storage_properties.setdefault("display_items", list()).extend(library_update.get("display_items", list()))
# mark deletions that need to be tracked because they've been deleted but are also present in older libraries
# and would be migrated during reading unless they explicitly are prevented from doing so (via data_item_deletions).
# utilized deletions are the ones that were attempted; if nothing was attempted, then no reason to track it anymore
# since there is nothing to migrate in the future.
library_storage_properties["data_item_deletions"] = [str(uuid_) for uuid_ in utilized_deletions]
connections_list = library_storage_properties.get("connections", list())
assert len(connections_list) == len({connection.get("uuid") for connection in connections_list})
computations_list = library_storage_properties.get("computations", list())
assert len(computations_list) == len({computation.get("uuid") for computation in computations_list})
# migrations
if library_storage_properties.get("version", 0) < 2:
for data_group_properties in library_storage_properties.get("data_groups", list()):
data_group_properties.pop("data_groups")
display_item_references = data_group_properties.setdefault("display_item_references", list())
data_item_uuid_strs = data_group_properties.pop("data_item_uuids", list())
for data_item_uuid_str in data_item_uuid_strs:
for display_item_properties in library_storage_properties.get("display_items", list()):
data_item_references = [d.get("data_item_reference", None) for d in display_item_properties.get("display_data_channels", list())]
if data_item_uuid_str in data_item_references:
display_item_references.append(display_item_properties["uuid"])
data_item_uuid_to_display_item_uuid_map = dict()
data_item_uuid_to_display_item_dict_map = dict()
display_to_display_item_map = dict()
display_to_display_data_channel_map = dict()
for display_item_properties in library_storage_properties.get("display_items", list()):
display_to_display_item_map[display_item_properties["display"]["uuid"]] = display_item_properties["uuid"]
display_to_display_data_channel_map[display_item_properties["display"]["uuid"]] = display_item_properties["display_data_channels"][0]["uuid"]
data_item_references = [d.get("data_item_reference", None) for d in display_item_properties.get("display_data_channels", list())]
for data_item_uuid_str in data_item_references:
data_item_uuid_to_display_item_uuid_map.setdefault(data_item_uuid_str, display_item_properties["uuid"])
data_item_uuid_to_display_item_dict_map.setdefault(data_item_uuid_str, display_item_properties)
display_item_properties.pop("display", None)
for workspace_properties in library_storage_properties.get("workspaces", list()):
def replace1(d):
if "children" in d:
for dd in d["children"]:
replace1(dd)
if "data_item_uuid" in d:
data_item_uuid_str = d.pop("data_item_uuid")
display_item_uuid_str = data_item_uuid_to_display_item_uuid_map.get(data_item_uuid_str)
if display_item_uuid_str:
d["display_item_uuid"] = display_item_uuid_str
replace1(workspace_properties["layout"])
for connection_dict in library_storage_properties.get("connections", list()):
source_uuid_str = connection_dict["source_uuid"]
if connection_dict["type"] == "interval-list-connection":
connection_dict["source_uuid"] = display_to_display_item_map.get(source_uuid_str, None)
if connection_dict["type"] == "property-connection" and connection_dict["source_property"] == "slice_interval":
connection_dict["source_uuid"] = display_to_display_data_channel_map.get(source_uuid_str, None)
def fix_specifier(specifier_dict):
if specifier_dict.get("type") in ("data_item", "display_xdata", "cropped_xdata", "cropped_display_xdata", "filter_xdata", "filtered_xdata"):
if specifier_dict.get("uuid") in data_item_uuid_to_display_item_dict_map:
specifier_dict["uuid"] = data_item_uuid_to_display_item_dict_map[specifier_dict["uuid"]]["display_data_channels"][0]["uuid"]
else:
specifier_dict.pop("uuid", None)
if specifier_dict.get("type") == "data_item":
specifier_dict["type"] = "data_source"
if specifier_dict.get("type") == "data_item_object":
specifier_dict["type"] = "data_item"
if specifier_dict.get("type") == "region":
specifier_dict["type"] = "graphic"
for computation_dict in library_storage_properties.get("computations", list()):
for variable_dict in computation_dict.get("variables", list()):
if "specifier" in variable_dict:
specifier_dict = variable_dict["specifier"]
if specifier_dict is not None:
fix_specifier(specifier_dict)
if "secondary_specifier" in variable_dict:
specifier_dict = variable_dict["secondary_specifier"]
if specifier_dict is not None:
fix_specifier(specifier_dict)
for result_dict in computation_dict.get("results", list()):
fix_specifier(result_dict["specifier"])
library_storage_properties["version"] = DocumentModel.DocumentModel.library_version
# TODO: add consistency checks: no duplicated items [by uuid] such as connections or computations or data items
assert library_storage_properties["version"] == DocumentModel.DocumentModel.library_version
persistent_storage_system.rewrite_properties(library_storage_properties)
properties = copy.deepcopy(library_storage_properties)
for reader_info in reader_info_list:
data_item_properties = Utility.clean_dict(reader_info.properties if reader_info.properties else dict())
if data_item_properties.get("version", 0) == DataItem.DataItem.writer_version:
data_item_properties["__large_format"] = reader_info.large_format
data_item_properties["__identifier"] = reader_info.identifier
properties.setdefault("data_items", list()).append(data_item_properties)
def data_item_created(data_item_properties: typing.Mapping) -> str:
return data_item_properties.get("created", "1900-01-01T00:00:00.000000")
properties["data_items"] = sorted(properties.get("data_items", list()), key=data_item_created)
return properties
|
python
|
def read_library(persistent_storage_system, ignore_older_files) -> typing.Dict:
"""Read data items from the data reference handler and return as a list.
Data items will have persistent_object_context set upon return, but caller will need to call finish_reading
on each of the data items.
"""
data_item_uuids = set()
utilized_deletions = set() # the uuid's skipped due to being deleted
deletions = list()
reader_info_list, library_updates = auto_migrate_storage_system(persistent_storage_system=persistent_storage_system,
new_persistent_storage_system=persistent_storage_system,
data_item_uuids=data_item_uuids,
deletions=deletions,
utilized_deletions=utilized_deletions,
ignore_older_files=ignore_older_files)
# next, for each auto migration, create a temporary storage system and read items from that storage system
# using auto_migrate_storage_system. the data items returned will have been copied to the current storage
# system (persistent object context).
for auto_migration in reversed(persistent_storage_system.get_auto_migrations()):
old_persistent_storage_system = FileStorageSystem(auto_migration.library_path, auto_migration.paths) if auto_migration.paths else auto_migration.storage_system
new_reader_info_list, new_library_updates = auto_migrate_storage_system(persistent_storage_system=old_persistent_storage_system,
new_persistent_storage_system=persistent_storage_system,
data_item_uuids=data_item_uuids,
deletions=deletions,
utilized_deletions=utilized_deletions,
ignore_older_files=ignore_older_files)
reader_info_list.extend(new_reader_info_list)
library_updates.update(new_library_updates)
assert len(reader_info_list) == len(data_item_uuids)
library_storage_properties = persistent_storage_system.library_storage_properties
for reader_info in reader_info_list:
properties = reader_info.properties
properties = Utility.clean_dict(copy.deepcopy(properties) if properties else dict())
version = properties.get("version", 0)
if version == DataItem.DataItem.writer_version:
data_item_uuid = uuid.UUID(properties.get("uuid", uuid.uuid4()))
library_update = library_updates.get(data_item_uuid, dict())
library_storage_properties.setdefault("connections", list()).extend(library_update.get("connections", list()))
library_storage_properties.setdefault("computations", list()).extend(library_update.get("computations", list()))
library_storage_properties.setdefault("display_items", list()).extend(library_update.get("display_items", list()))
# mark deletions that need to be tracked because they've been deleted but are also present in older libraries
# and would be migrated during reading unless they explicitly are prevented from doing so (via data_item_deletions).
# utilized deletions are the ones that were attempted; if nothing was attempted, then no reason to track it anymore
# since there is nothing to migrate in the future.
library_storage_properties["data_item_deletions"] = [str(uuid_) for uuid_ in utilized_deletions]
connections_list = library_storage_properties.get("connections", list())
assert len(connections_list) == len({connection.get("uuid") for connection in connections_list})
computations_list = library_storage_properties.get("computations", list())
assert len(computations_list) == len({computation.get("uuid") for computation in computations_list})
# migrations
if library_storage_properties.get("version", 0) < 2:
for data_group_properties in library_storage_properties.get("data_groups", list()):
data_group_properties.pop("data_groups")
display_item_references = data_group_properties.setdefault("display_item_references", list())
data_item_uuid_strs = data_group_properties.pop("data_item_uuids", list())
for data_item_uuid_str in data_item_uuid_strs:
for display_item_properties in library_storage_properties.get("display_items", list()):
data_item_references = [d.get("data_item_reference", None) for d in display_item_properties.get("display_data_channels", list())]
if data_item_uuid_str in data_item_references:
display_item_references.append(display_item_properties["uuid"])
data_item_uuid_to_display_item_uuid_map = dict()
data_item_uuid_to_display_item_dict_map = dict()
display_to_display_item_map = dict()
display_to_display_data_channel_map = dict()
for display_item_properties in library_storage_properties.get("display_items", list()):
display_to_display_item_map[display_item_properties["display"]["uuid"]] = display_item_properties["uuid"]
display_to_display_data_channel_map[display_item_properties["display"]["uuid"]] = display_item_properties["display_data_channels"][0]["uuid"]
data_item_references = [d.get("data_item_reference", None) for d in display_item_properties.get("display_data_channels", list())]
for data_item_uuid_str in data_item_references:
data_item_uuid_to_display_item_uuid_map.setdefault(data_item_uuid_str, display_item_properties["uuid"])
data_item_uuid_to_display_item_dict_map.setdefault(data_item_uuid_str, display_item_properties)
display_item_properties.pop("display", None)
for workspace_properties in library_storage_properties.get("workspaces", list()):
def replace1(d):
if "children" in d:
for dd in d["children"]:
replace1(dd)
if "data_item_uuid" in d:
data_item_uuid_str = d.pop("data_item_uuid")
display_item_uuid_str = data_item_uuid_to_display_item_uuid_map.get(data_item_uuid_str)
if display_item_uuid_str:
d["display_item_uuid"] = display_item_uuid_str
replace1(workspace_properties["layout"])
for connection_dict in library_storage_properties.get("connections", list()):
source_uuid_str = connection_dict["source_uuid"]
if connection_dict["type"] == "interval-list-connection":
connection_dict["source_uuid"] = display_to_display_item_map.get(source_uuid_str, None)
if connection_dict["type"] == "property-connection" and connection_dict["source_property"] == "slice_interval":
connection_dict["source_uuid"] = display_to_display_data_channel_map.get(source_uuid_str, None)
def fix_specifier(specifier_dict):
if specifier_dict.get("type") in ("data_item", "display_xdata", "cropped_xdata", "cropped_display_xdata", "filter_xdata", "filtered_xdata"):
if specifier_dict.get("uuid") in data_item_uuid_to_display_item_dict_map:
specifier_dict["uuid"] = data_item_uuid_to_display_item_dict_map[specifier_dict["uuid"]]["display_data_channels"][0]["uuid"]
else:
specifier_dict.pop("uuid", None)
if specifier_dict.get("type") == "data_item":
specifier_dict["type"] = "data_source"
if specifier_dict.get("type") == "data_item_object":
specifier_dict["type"] = "data_item"
if specifier_dict.get("type") == "region":
specifier_dict["type"] = "graphic"
for computation_dict in library_storage_properties.get("computations", list()):
for variable_dict in computation_dict.get("variables", list()):
if "specifier" in variable_dict:
specifier_dict = variable_dict["specifier"]
if specifier_dict is not None:
fix_specifier(specifier_dict)
if "secondary_specifier" in variable_dict:
specifier_dict = variable_dict["secondary_specifier"]
if specifier_dict is not None:
fix_specifier(specifier_dict)
for result_dict in computation_dict.get("results", list()):
fix_specifier(result_dict["specifier"])
library_storage_properties["version"] = DocumentModel.DocumentModel.library_version
# TODO: add consistency checks: no duplicated items [by uuid] such as connections or computations or data items
assert library_storage_properties["version"] == DocumentModel.DocumentModel.library_version
persistent_storage_system.rewrite_properties(library_storage_properties)
properties = copy.deepcopy(library_storage_properties)
for reader_info in reader_info_list:
data_item_properties = Utility.clean_dict(reader_info.properties if reader_info.properties else dict())
if data_item_properties.get("version", 0) == DataItem.DataItem.writer_version:
data_item_properties["__large_format"] = reader_info.large_format
data_item_properties["__identifier"] = reader_info.identifier
properties.setdefault("data_items", list()).append(data_item_properties)
def data_item_created(data_item_properties: typing.Mapping) -> str:
return data_item_properties.get("created", "1900-01-01T00:00:00.000000")
properties["data_items"] = sorted(properties.get("data_items", list()), key=data_item_created)
return properties
|
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] |
Read data items from the data reference handler and return as a list.
Data items will have persistent_object_context set upon return, but caller will need to call finish_reading
on each of the data items.
|
[
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] |
d43693eaf057b8683b9638e575000f055fede452
|
https://github.com/nion-software/nionswift/blob/d43693eaf057b8683b9638e575000f055fede452/nion/swift/model/FileStorageSystem.py#L419-L567
|
train
|
nion-software/nionswift
|
nion/swift/model/FileStorageSystem.py
|
auto_migrate_storage_system
|
def auto_migrate_storage_system(*, persistent_storage_system=None, new_persistent_storage_system=None, data_item_uuids=None, deletions: typing.List[uuid.UUID] = None, utilized_deletions: typing.Set[uuid.UUID] = None, ignore_older_files: bool = True):
"""Migrate items from the storage system to the object context.
Files in data_item_uuids have already been loaded and are ignored (not migrated).
Files in deletes have been deleted in object context and are ignored (not migrated) and then added
to the utilized deletions list.
Data items will have persistent_object_context set upon return, but caller will need to call finish_reading
on each of the data items.
"""
storage_handlers = persistent_storage_system.find_data_items()
ReaderInfo = collections.namedtuple("ReaderInfo", ["properties", "changed_ref", "large_format", "storage_handler", "identifier"])
reader_info_list = list()
for storage_handler in storage_handlers:
try:
large_format = isinstance(storage_handler, HDF5Handler.HDF5Handler)
properties = Migration.transform_to_latest(storage_handler.read_properties())
reader_info = ReaderInfo(properties, [False], large_format, storage_handler, storage_handler.reference)
reader_info_list.append(reader_info)
except Exception as e:
logging.debug("Error reading %s", storage_handler.reference)
import traceback
traceback.print_exc()
traceback.print_stack()
library_storage_properties = persistent_storage_system.library_storage_properties
for deletion in copy.deepcopy(library_storage_properties.get("data_item_deletions", list())):
if not deletion in deletions:
deletions.append(deletion)
preliminary_library_updates = dict()
library_updates = dict()
if not ignore_older_files:
Migration.migrate_to_latest(reader_info_list, preliminary_library_updates)
good_reader_info_list = list()
count = len(reader_info_list)
for index, reader_info in enumerate(reader_info_list):
storage_handler = reader_info.storage_handler
properties = reader_info.properties
try:
version = properties.get("version", 0)
if version == DataItem.DataItem.writer_version:
data_item_uuid = uuid.UUID(properties["uuid"])
if not data_item_uuid in data_item_uuids:
if str(data_item_uuid) in deletions:
utilized_deletions.add(data_item_uuid)
else:
auto_migrate_data_item(reader_info, persistent_storage_system, new_persistent_storage_system, index, count)
good_reader_info_list.append(reader_info)
data_item_uuids.add(data_item_uuid)
library_update = preliminary_library_updates.get(data_item_uuid)
if library_update:
library_updates[data_item_uuid] = library_update
except Exception as e:
logging.debug("Error reading %s", storage_handler.reference)
import traceback
traceback.print_exc()
traceback.print_stack()
return good_reader_info_list, library_updates
|
python
|
def auto_migrate_storage_system(*, persistent_storage_system=None, new_persistent_storage_system=None, data_item_uuids=None, deletions: typing.List[uuid.UUID] = None, utilized_deletions: typing.Set[uuid.UUID] = None, ignore_older_files: bool = True):
"""Migrate items from the storage system to the object context.
Files in data_item_uuids have already been loaded and are ignored (not migrated).
Files in deletes have been deleted in object context and are ignored (not migrated) and then added
to the utilized deletions list.
Data items will have persistent_object_context set upon return, but caller will need to call finish_reading
on each of the data items.
"""
storage_handlers = persistent_storage_system.find_data_items()
ReaderInfo = collections.namedtuple("ReaderInfo", ["properties", "changed_ref", "large_format", "storage_handler", "identifier"])
reader_info_list = list()
for storage_handler in storage_handlers:
try:
large_format = isinstance(storage_handler, HDF5Handler.HDF5Handler)
properties = Migration.transform_to_latest(storage_handler.read_properties())
reader_info = ReaderInfo(properties, [False], large_format, storage_handler, storage_handler.reference)
reader_info_list.append(reader_info)
except Exception as e:
logging.debug("Error reading %s", storage_handler.reference)
import traceback
traceback.print_exc()
traceback.print_stack()
library_storage_properties = persistent_storage_system.library_storage_properties
for deletion in copy.deepcopy(library_storage_properties.get("data_item_deletions", list())):
if not deletion in deletions:
deletions.append(deletion)
preliminary_library_updates = dict()
library_updates = dict()
if not ignore_older_files:
Migration.migrate_to_latest(reader_info_list, preliminary_library_updates)
good_reader_info_list = list()
count = len(reader_info_list)
for index, reader_info in enumerate(reader_info_list):
storage_handler = reader_info.storage_handler
properties = reader_info.properties
try:
version = properties.get("version", 0)
if version == DataItem.DataItem.writer_version:
data_item_uuid = uuid.UUID(properties["uuid"])
if not data_item_uuid in data_item_uuids:
if str(data_item_uuid) in deletions:
utilized_deletions.add(data_item_uuid)
else:
auto_migrate_data_item(reader_info, persistent_storage_system, new_persistent_storage_system, index, count)
good_reader_info_list.append(reader_info)
data_item_uuids.add(data_item_uuid)
library_update = preliminary_library_updates.get(data_item_uuid)
if library_update:
library_updates[data_item_uuid] = library_update
except Exception as e:
logging.debug("Error reading %s", storage_handler.reference)
import traceback
traceback.print_exc()
traceback.print_stack()
return good_reader_info_list, library_updates
|
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] |
Migrate items from the storage system to the object context.
Files in data_item_uuids have already been loaded and are ignored (not migrated).
Files in deletes have been deleted in object context and are ignored (not migrated) and then added
to the utilized deletions list.
Data items will have persistent_object_context set upon return, but caller will need to call finish_reading
on each of the data items.
|
[
"Migrate",
"items",
"from",
"the",
"storage",
"system",
"to",
"the",
"object",
"context",
"."
] |
d43693eaf057b8683b9638e575000f055fede452
|
https://github.com/nion-software/nionswift/blob/d43693eaf057b8683b9638e575000f055fede452/nion/swift/model/FileStorageSystem.py#L601-L658
|
train
|
nion-software/nionswift
|
nion/swift/model/FileStorageSystem.py
|
FileStorageSystem.rewrite_properties
|
def rewrite_properties(self, properties):
"""Set the properties and write to disk."""
with self.__properties_lock:
self.__properties = properties
self.__write_properties(None)
|
python
|
def rewrite_properties(self, properties):
"""Set the properties and write to disk."""
with self.__properties_lock:
self.__properties = properties
self.__write_properties(None)
|
[
"def",
"rewrite_properties",
"(",
"self",
",",
"properties",
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":",
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".",
"__properties_lock",
":",
"self",
".",
"__properties",
"=",
"properties",
"self",
".",
"__write_properties",
"(",
"None",
")"
] |
Set the properties and write to disk.
|
[
"Set",
"the",
"properties",
"and",
"write",
"to",
"disk",
"."
] |
d43693eaf057b8683b9638e575000f055fede452
|
https://github.com/nion-software/nionswift/blob/d43693eaf057b8683b9638e575000f055fede452/nion/swift/model/FileStorageSystem.py#L137-L141
|
train
|
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