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def load_data(self):
"""
Loads data from MRMS GRIB2 files and handles compression duties if files are compressed.
"""
data = []
loaded_dates = []
loaded_indices = []
for t, timestamp in enumerate(self.all_dates):
date_str = timestamp.date().strftime("%... |
def interpolate_grid(self, in_lon, in_lat):
"""
Interpolates MRMS data to a different grid using cubic bivariate splines
"""
out_data = np.zeros((self.data.shape[0], in_lon.shape[0], in_lon.shape[1]))
for d in range(self.data.shape[0]):
print("Loading ", d, self.varia... |
def max_neighbor(self, in_lon, in_lat, radius=0.05):
"""
Finds the largest value within a given radius of a point on the interpolated grid.
Args:
in_lon: 2D array of longitude values
in_lat: 2D array of latitude values
radius: radius of influence for largest ... |
def interpolate_to_netcdf(self, in_lon, in_lat, out_path, date_unit="seconds since 1970-01-01T00:00",
interp_type="spline"):
"""
Calls the interpolation function and then saves the MRMS data to a netCDF file. It will also create
separate directories for each variab... |
def get_data_generator_by_id(hardware_source_id, sync=True):
"""
Return a generator for data.
:param bool sync: whether to wait for current frame to finish then collect next frame
NOTE: a new ndarray is created for each call.
"""
hardware_source = HardwareSourceManager().get_hardwa... |
def parse_hardware_aliases_config_file(config_path):
"""
Parse config file for aliases and automatically register them.
Returns True if alias file was found and parsed (successfully or unsuccessfully).
Returns False if alias file was not found.
Config file is a standard .ini file ... |
def make_instrument_alias(self, instrument_id, alias_instrument_id, display_name):
""" Configure an alias.
Callers can use the alias to refer to the instrument or hardware source.
The alias should be lowercase, no spaces. The display name may be used to display alias to
the ... |
def update(self, data_and_metadata: DataAndMetadata.DataAndMetadata, state: str, sub_area, view_id) -> None:
"""Called from hardware source when new data arrives."""
self.__state = state
self.__sub_area = sub_area
hardware_source_id = self.__hardware_source.hardware_source_id
ch... |
def start(self):
"""Called from hardware source when data starts streaming."""
old_start_count = self.__start_count
self.__start_count += 1
if old_start_count == 0:
self.data_channel_start_event.fire() |
def connect_data_item_reference(self, data_item_reference):
"""Connect to the data item reference, creating a crop graphic if necessary.
If the data item reference does not yet have an associated data item, add a
listener and wait for the data item to be set, then connect.
"""
d... |
def grab_earliest(self, timeout: float=None) -> typing.List[DataAndMetadata.DataAndMetadata]:
"""Grab the earliest data from the buffer, blocking until one is available."""
timeout = timeout if timeout is not None else 10.0
with self.__buffer_lock:
if len(self.__buffer) == 0:
... |
def grab_next(self, timeout: float=None) -> typing.List[DataAndMetadata.DataAndMetadata]:
"""Grab the next data to finish from the buffer, blocking until one is available."""
with self.__buffer_lock:
self.__buffer = list()
return self.grab_latest(timeout) |
def grab_following(self, timeout: float=None) -> typing.List[DataAndMetadata.DataAndMetadata]:
"""Grab the next data to start from the buffer, blocking until one is available."""
self.grab_next(timeout)
return self.grab_next(timeout) |
def pause(self) -> None:
"""Pause recording.
Thread safe and UI safe."""
with self.__state_lock:
if self.__state == DataChannelBuffer.State.started:
self.__state = DataChannelBuffer.State.paused |
def resume(self) -> None:
"""Resume recording after pause.
Thread safe and UI safe."""
with self.__state_lock:
if self.__state == DataChannelBuffer.State.paused:
self.__state = DataChannelBuffer.State.started |
def nlargest(n, mapping):
"""
Takes a mapping and returns the n keys associated with the largest values
in descending order. If the mapping has fewer than n items, all its keys
are returned.
Equivalent to:
``next(zip(*heapq.nlargest(mapping.items(), key=lambda x: x[1])))``
Returns
... |
def fromkeys(cls, iterable, value, **kwargs):
"""
Return a new pqict mapping keys from an iterable to the same value.
"""
return cls(((k, value) for k in iterable), **kwargs) |
def copy(self):
"""
Return a shallow copy of a pqdict.
"""
return self.__class__(self, key=self._keyfn, precedes=self._precedes) |
def pop(self, key=__marker, default=__marker):
"""
If ``key`` is in the pqdict, remove it and return its priority value,
else return ``default``. If ``default`` is not provided and ``key`` is
not in the pqdict, raise a ``KeyError``.
If ``key`` is not provided, remove the top ite... |
def popitem(self):
"""
Remove and return the item with highest priority. Raises ``KeyError``
if pqdict is empty.
"""
heap = self._heap
position = self._position
try:
end = heap.pop(-1)
except IndexError:
raise KeyError('pqdict is ... |
def topitem(self):
"""
Return the item with highest priority. Raises ``KeyError`` if pqdict is
empty.
"""
try:
node = self._heap[0]
except IndexError:
raise KeyError('pqdict is empty')
return node.key, node.value |
def additem(self, key, value):
"""
Add a new item. Raises ``KeyError`` if key is already in the pqdict.
"""
if key in self._position:
raise KeyError('%s is already in the queue' % repr(key))
self[key] = value |
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
... |
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 |
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 ... |
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 K... |
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)):
... |
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) |
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 =... |
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... |
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_con... |
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.InvalidVers... |
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 ... |
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... |
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 |
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) +
... |
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')] |
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)
i... |
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 ... |
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('Resp... |
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 ent... |
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='ComunicarCertificadoICPB... |
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',):
... |
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')
... |
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 n... |
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... |
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',):
... |
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 ('1700... |
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 ... |
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"]
... |
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_d... |
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_fil... |
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 <=... |
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 |
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 inde... |
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_ti... |
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() |
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() |
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.... |
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_boundar... |
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 correl... |
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... |
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 ... |
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.attribut... |
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.st... |
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.
... |
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:
attr... |
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
"""
... |
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
... |
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
"""
... |
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 de... |
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) |
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... |
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 |
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:
... |
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 = Select... |
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) |
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... |
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) |
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
l... |
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', 'notr... |
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):... |
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]
... |
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
... |
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.wher... |
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_g... |
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: Coordi... |
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 ... |
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 = {}
... |
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),
... |
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 = arg... |
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 = ... |
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:
... |
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 ):
... |
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.gis... |
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,
... |
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)
... |
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_f... |
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",... |
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 N... |
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