sentence1 stringlengths 52 3.87M | sentence2 stringlengths 1 47.2k | label stringclasses 1
value |
|---|---|---|
def make_full_ivar():
""" take the scatters and skylines and make final ivars """
# skylines come as an ivar
# don't use them for now, because I don't really trust them...
# skylines = np.load("%s/skylines.npz" %DATA_DIR)['arr_0']
ref_flux = np.load("%s/ref_flux_all.npz" %DATA_DIR)['arr_0']
re... | take the scatters and skylines and make final ivars | entailment |
def _sinusoid(x, p, L, y):
""" Return the sinusoid cont func evaluated at input x for the continuum.
Parameters
----------
x: float or np.array
data, input to function
p: ndarray
coefficients of fitting function
L: float
width of x data
y: float or np.array
... | Return the sinusoid cont func evaluated at input x for the continuum.
Parameters
----------
x: float or np.array
data, input to function
p: ndarray
coefficients of fitting function
L: float
width of x data
y: float or np.array
output data corresponding to input ... | entailment |
def _weighted_median(values, weights, quantile):
""" Calculate a weighted median for values above a particular quantile cut
Used in pseudo continuum normalization
Parameters
----------
values: np ndarray of floats
the values to take the median of
weights: np ndarray of floats
t... | Calculate a weighted median for values above a particular quantile cut
Used in pseudo continuum normalization
Parameters
----------
values: np ndarray of floats
the values to take the median of
weights: np ndarray of floats
the weights associated with the values
quantile: float... | entailment |
def _find_cont_gaussian_smooth(wl, fluxes, ivars, w):
""" Returns the weighted mean block of spectra
Parameters
----------
wl: numpy ndarray
wavelength vector
flux: numpy ndarray
block of flux values
ivar: numpy ndarray
block of ivar values
L: float
width of... | Returns the weighted mean block of spectra
Parameters
----------
wl: numpy ndarray
wavelength vector
flux: numpy ndarray
block of flux values
ivar: numpy ndarray
block of ivar values
L: float
width of Gaussian used to assign weights
Returns
-------
... | entailment |
def _cont_norm_gaussian_smooth(dataset, L):
""" Continuum normalize by dividing by a Gaussian-weighted smoothed spectrum
Parameters
----------
dataset: Dataset
the dataset to continuum normalize
L: float
the width of the Gaussian used for weighting
Returns
-------
datas... | Continuum normalize by dividing by a Gaussian-weighted smoothed spectrum
Parameters
----------
dataset: Dataset
the dataset to continuum normalize
L: float
the width of the Gaussian used for weighting
Returns
-------
dataset: Dataset
updated dataset | entailment |
def _find_cont_fitfunc(fluxes, ivars, contmask, deg, ffunc, n_proc=1):
""" Fit a continuum to a continuum pixels in a segment of spectra
Functional form can be either sinusoid or chebyshev, with specified degree
Parameters
----------
fluxes: numpy ndarray of shape (nstars, npixels)
trainin... | Fit a continuum to a continuum pixels in a segment of spectra
Functional form can be either sinusoid or chebyshev, with specified degree
Parameters
----------
fluxes: numpy ndarray of shape (nstars, npixels)
training set or test set pixel intensities
ivars: numpy ndarray of shape (nstars,... | entailment |
def _find_cont_fitfunc_regions(fluxes, ivars, contmask, deg, ranges, ffunc,
n_proc=1):
""" Run fit_cont, dealing with spectrum in regions or chunks
This is useful if a spectrum has gaps.
Parameters
----------
fluxes: ndarray of shape (nstars, npixels)
trainin... | Run fit_cont, dealing with spectrum in regions or chunks
This is useful if a spectrum has gaps.
Parameters
----------
fluxes: ndarray of shape (nstars, npixels)
training set or test set pixel intensities
ivars: numpy ndarray of shape (nstars, npixels)
inverse variances, parallel t... | entailment |
def _find_cont_running_quantile(wl, fluxes, ivars, q, delta_lambda,
verbose=False):
""" Perform continuum normalization using a running quantile
Parameters
----------
wl: numpy ndarray
wavelength vector
fluxes: numpy ndarray of shape (nstars, npixels)
... | Perform continuum normalization using a running quantile
Parameters
----------
wl: numpy ndarray
wavelength vector
fluxes: numpy ndarray of shape (nstars, npixels)
pixel intensities
ivars: numpy ndarray of shape (nstars, npixels)
inverse variances, parallel to fluxes
q:... | entailment |
def _cont_norm_running_quantile_mp(wl, fluxes, ivars, q, delta_lambda,
n_proc=2, verbose=False):
"""
The same as _cont_norm_running_quantile() above,
but using multi-processing.
Bo Zhang (NAOC)
"""
nStar = fluxes.shape[0]
# start mp.Pool
mp_results = ... | The same as _cont_norm_running_quantile() above,
but using multi-processing.
Bo Zhang (NAOC) | entailment |
def _cont_norm_running_quantile_regions(wl, fluxes, ivars, q, delta_lambda,
ranges, verbose=True):
""" Perform continuum normalization using running quantile, for spectrum
that comes in chunks
"""
print("contnorm.py: continuum norm using running quantile")
pri... | Perform continuum normalization using running quantile, for spectrum
that comes in chunks | entailment |
def _cont_norm_running_quantile_regions_mp(wl, fluxes, ivars, q, delta_lambda,
ranges, n_proc=2, verbose=False):
"""
Perform continuum normalization using running quantile, for spectrum
that comes in chunks.
The same as _cont_norm_running_quantile_regions(),
... | Perform continuum normalization using running quantile, for spectrum
that comes in chunks.
The same as _cont_norm_running_quantile_regions(),
but using multi-processing.
Bo Zhang (NAOC) | entailment |
def _cont_norm(fluxes, ivars, cont):
""" Continuum-normalize a continuous segment of spectra.
Parameters
----------
fluxes: numpy ndarray
pixel intensities
ivars: numpy ndarray
inverse variances, parallel to fluxes
contmask: boolean mask
True indicates that pixel is co... | Continuum-normalize a continuous segment of spectra.
Parameters
----------
fluxes: numpy ndarray
pixel intensities
ivars: numpy ndarray
inverse variances, parallel to fluxes
contmask: boolean mask
True indicates that pixel is continuum
Returns
-------
norm_flu... | entailment |
def _cont_norm_regions(fluxes, ivars, cont, ranges):
""" Perform continuum normalization for spectra in chunks
Useful for spectra that have gaps
Parameters
---------
fluxes: numpy ndarray
pixel intensities
ivars: numpy ndarray
inverse variances, parallel to fluxes
cont: num... | Perform continuum normalization for spectra in chunks
Useful for spectra that have gaps
Parameters
---------
fluxes: numpy ndarray
pixel intensities
ivars: numpy ndarray
inverse variances, parallel to fluxes
cont: numpy ndarray
the continuum
ranges: list or np ndarr... | entailment |
def train(self, ds):
""" Run training step: solve for best-fit spectral model """
if self.useErrors:
self.coeffs, self.scatters, self.new_tr_labels, self.chisqs, self.pivots, self.scales = _train_model_new(ds)
else:
self.coeffs, self.scatters, self.chisqs, self.pivots, se... | Run training step: solve for best-fit spectral model | entailment |
def infer_spectra(self, ds):
"""
After inferring labels for the test spectra,
infer the model spectra and update the dataset
model_spectra attribute.
Parameters
----------
ds: Dataset object
"""
lvec_all = _get_lvec(ds.test_label_vals, se... | After inferring labels for the test spectra,
infer the model spectra and update the dataset
model_spectra attribute.
Parameters
----------
ds: Dataset object | entailment |
def plot_contpix(self, x, y, contpix_x, contpix_y, figname):
""" Plot baseline spec with continuum pix overlaid
Parameters
----------
"""
fig, axarr = plt.subplots(2, sharex=True)
plt.xlabel(r"Wavelength $\lambda (\AA)$")
plt.xlim(min(x), max(x))
ax = ax... | Plot baseline spec with continuum pix overlaid
Parameters
---------- | entailment |
def diagnostics_contpix(self, data, nchunks=10, fig = "baseline_spec_with_cont_pix"):
""" Call plot_contpix once for each nth of the spectrum """
if data.contmask is None:
print("No contmask set")
else:
coeffs_all = self.coeffs
wl = data.wl
baselin... | Call plot_contpix once for each nth of the spectrum | entailment |
def diagnostics_plot_chisq(self, ds, figname = "modelfit_chisqs.png"):
""" Produce a set of diagnostic plots for the model
Parameters
----------
(optional) chisq_dist_plot_name: str
Filename of output saved plot
"""
label_names = ds.get_plotting_labels()
... | Produce a set of diagnostic plots for the model
Parameters
----------
(optional) chisq_dist_plot_name: str
Filename of output saved plot | entailment |
def calc_mass(nu_max, delta_nu, teff):
""" asteroseismic scaling relations """
NU_MAX = 3140.0 # microHz
DELTA_NU = 135.03 # microHz
TEFF = 5777.0
return (nu_max/NU_MAX)**3 * (delta_nu/DELTA_NU)**(-4) * (teff/TEFF)**1.5 | asteroseismic scaling relations | entailment |
def calc_mass_2(mh,cm,nm,teff,logg):
""" Table A2 in Martig 2016 """
CplusN = calc_sum(mh,cm,nm)
t = teff/4000.
return (95.8689 - 10.4042*mh - 0.7266*mh**2
+ 41.3642*cm - 5.3242*cm*mh - 46.7792*cm**2
+ 15.0508*nm - 0.9342*nm*mh - 30.5159*nm*cm - 1.6083*nm**2
- 67.6093... | Table A2 in Martig 2016 | entailment |
def corner(xs, bins=20, range=None, weights=None, color="k",
smooth=None, smooth1d=None,
labels=None, label_kwargs=None,
show_titles=False, title_fmt=".2f", title_kwargs=None,
truths=None, truth_color="#4682b4",
scale_hist=False, quantiles=None, verbose=False, fig=... | Make a *sick* corner plot showing the projections of a data set in a
multi-dimensional space. kwargs are passed to hist2d() or used for
`matplotlib` styling.
Parameters
----------
xs : array_like (nsamples, ndim)
The samples. This should be a 1- or 2-dimensional array. For a 1-D
arr... | entailment |
def quantile(x, q, weights=None):
"""
Like numpy.percentile, but:
* Values of q are quantiles [0., 1.] rather than percentiles [0., 100.]
* scalar q not supported (q must be iterable)
* optional weights on x
"""
if weights is None:
return np.percentile(x, [100. * qi for qi in q])
... | Like numpy.percentile, but:
* Values of q are quantiles [0., 1.] rather than percentiles [0., 100.]
* scalar q not supported (q must be iterable)
* optional weights on x | entailment |
def hist2d(x, y, bins=20, range=None, weights=None, levels=None, smooth=None,
ax=None, color=None, plot_datapoints=True, plot_density=True,
plot_contours=True, no_fill_contours=False, fill_contours=False,
contour_kwargs=None, contourf_kwargs=None, data_kwargs=None,
**kwargs):... | Plot a 2-D histogram of samples.
Parameters
----------
x, y : array_like (nsamples,)
The samples.
levels : array_like
The contour levels to draw.
ax : matplotlib.Axes (optional)
A axes instance on which to add the 2-D histogram.
plot_datapoints : bool (optional)
... | entailment |
def calc_dist(lamost_point, training_points, coeffs):
""" avg dist from one lamost point to nearest 10 training points """
diff2 = (training_points - lamost_point)**2
dist = np.sqrt(np.sum(diff2*coeffs, axis=1))
return np.mean(dist[dist.argsort()][0:10]) | avg dist from one lamost point to nearest 10 training points | entailment |
def make_classifier(self, name, ids, labels):
"""Entrenar un clasificador SVM sobre los textos cargados.
Crea un clasificador que se guarda en el objeto bajo el nombre `name`.
Args:
name (str): Nombre para el clasidicador.
ids (list): Se espera una lista de N ids de tex... | Entrenar un clasificador SVM sobre los textos cargados.
Crea un clasificador que se guarda en el objeto bajo el nombre `name`.
Args:
name (str): Nombre para el clasidicador.
ids (list): Se espera una lista de N ids de textos ya almacenados
en el TextClassifier.
... | entailment |
def retrain(self, name, ids, labels):
"""Reentrenar parcialmente un clasificador SVM.
Args:
name (str): Nombre para el clasidicador.
ids (list): Se espera una lista de N ids de textos ya almacenados
en el TextClassifier.
labels (list): Se espera una l... | Reentrenar parcialmente un clasificador SVM.
Args:
name (str): Nombre para el clasidicador.
ids (list): Se espera una lista de N ids de textos ya almacenados
en el TextClassifier.
labels (list): Se espera una lista de N etiquetas. Una por cada id
... | entailment |
def classify(self, classifier_name, examples, max_labels=None,
goodness_of_fit=False):
"""Usar un clasificador SVM para etiquetar textos nuevos.
Args:
classifier_name (str): Nombre del clasidicador a usar.
examples (list or str): Se espera un ejemplo o una lista... | Usar un clasificador SVM para etiquetar textos nuevos.
Args:
classifier_name (str): Nombre del clasidicador a usar.
examples (list or str): Se espera un ejemplo o una lista de
ejemplos a clasificar en texto plano o en ids.
max_labels (int, optional): Cantidad... | entailment |
def _make_text_vectors(self, examples):
"""Funcion para generar los vectores tf-idf de una lista de textos.
Args:
examples (list or str): Se espera un ejemplo o una lista de:
o bien ids, o bien textos.
Returns:
textvec (sparse matrix): Devuelve una matriz... | Funcion para generar los vectores tf-idf de una lista de textos.
Args:
examples (list or str): Se espera un ejemplo o una lista de:
o bien ids, o bien textos.
Returns:
textvec (sparse matrix): Devuelve una matriz sparse que contiene
los vectores T... | entailment |
def get_similar(self, example, max_similars=3, similarity_cutoff=None,
term_diff_max_rank=10, filter_list=None,
term_diff_cutoff=None):
"""Devuelve textos similares al ejemplo dentro de los textos entrenados.
Nota:
Usa la distancia de coseno del vecto... | Devuelve textos similares al ejemplo dentro de los textos entrenados.
Nota:
Usa la distancia de coseno del vector de features TF-IDF
Args:
example (str): Se espera un id de texto o un texto a partir del
cual se buscaran otros textos similares.
max_si... | entailment |
def reload_texts(self, texts, ids, vocabulary=None):
"""Calcula los vectores de terminos de textos y los almacena.
A diferencia de :func:`~TextClassifier.TextClassifier.store_text` esta
funcion borra cualquier informacion almacenada y comienza el conteo
desde cero. Se usa para redefinir... | Calcula los vectores de terminos de textos y los almacena.
A diferencia de :func:`~TextClassifier.TextClassifier.store_text` esta
funcion borra cualquier informacion almacenada y comienza el conteo
desde cero. Se usa para redefinir el vocabulario sobre el que se
construyen los vectores.... | entailment |
def name_suggest(q=None, datasetKey=None, rank=None, limit=100, offset=None, **kwargs):
'''
A quick and simple autocomplete service that returns up to 20 name usages by
doing prefix matching against the scientific name. Results are ordered by relevance.
:param q: [str] Simple search parameter. The value for th... | A quick and simple autocomplete service that returns up to 20 name usages by
doing prefix matching against the scientific name. Results are ordered by relevance.
:param q: [str] Simple search parameter. The value for this parameter can be a
simple word or a phrase. Wildcards can be added to the simple word pa... | entailment |
def dataset_metrics(uuid, **kwargs):
'''
Get details on a GBIF dataset.
:param uuid: [str] One or more dataset UUIDs. See examples.
References: http://www.gbif.org/developer/registry#datasetMetrics
Usage::
from pygbif import registry
registry.dataset_metrics(uuid='3f8a1297-3259-4700-91fc-acc4170b27ce')
... | Get details on a GBIF dataset.
:param uuid: [str] One or more dataset UUIDs. See examples.
References: http://www.gbif.org/developer/registry#datasetMetrics
Usage::
from pygbif import registry
registry.dataset_metrics(uuid='3f8a1297-3259-4700-91fc-acc4170b27ce')
registry.dataset_metrics(uuid='66dd0960-2... | entailment |
def datasets(data = 'all', type = None, uuid = None, query = None, id = None,
limit = 100, offset = None, **kwargs):
'''
Search for datasets and dataset metadata.
:param data: [str] The type of data to get. Default: ``all``
:param type: [str] Type of dataset, options include ``OCCURRENCE``, etc.
:param uui... | Search for datasets and dataset metadata.
:param data: [str] The type of data to get. Default: ``all``
:param type: [str] Type of dataset, options include ``OCCURRENCE``, etc.
:param uuid: [str] UUID of the data node provider. This must be specified if data
is anything other than ``all``.
:param query: [str] Qu... | entailment |
def dataset_suggest(q=None, type=None, keyword=None, owningOrg=None,
publishingOrg=None, hostingOrg=None, publishingCountry=None, decade=None,
limit = 100, offset = None, **kwargs):
'''
Search that returns up to 20 matching datasets. Results are ordered by relevance.
:param q: [str] Query term(s) for full text s... | Search that returns up to 20 matching datasets. Results are ordered by relevance.
:param q: [str] Query term(s) for full text search. The value for this parameter can be a simple word or a phrase. Wildcards can be added to the simple word parameters only, e.g. ``q=*puma*``
:param type: [str] Type of dataset, optio... | entailment |
def dataset_search(q=None, type=None, keyword=None,
owningOrg=None, publishingOrg=None, hostingOrg=None, decade=None,
publishingCountry = None, facet = None, facetMincount=None,
facetMultiselect = None, hl = False, limit = 100, offset = None,
**kwargs):
'''
Full text search across all datasets. Results are ordere... | Full text search across all datasets. Results are ordered by relevance.
:param q: [str] Query term(s) for full text search. The value for this parameter
can be a simple word or a phrase. Wildcards can be added to the simple word
parameters only, e.g. ``q=*puma*``
:param type: [str] Type of dataset, options in... | entailment |
def wkt_rewind(x, digits = None):
'''
reverse WKT winding order
:param x: [str] WKT string
:param digits: [int] number of digits after decimal to use for the return string.
by default, we use the mean number of digits in your string.
:return: a string
Usage::
from py... | reverse WKT winding order
:param x: [str] WKT string
:param digits: [int] number of digits after decimal to use for the return string.
by default, we use the mean number of digits in your string.
:return: a string
Usage::
from pygbif import wkt_rewind
x = 'POLYGON((1... | entailment |
def occ_issues_lookup(issue=None, code=None):
'''
Lookup occurrence issue definitions and short codes
:param issue: Full name of issue, e.g, CONTINENT_COUNTRY_MISMATCH
:param code: an issue short code, e.g. ccm
Usage
pygbif.occ_issues_lookup(issue = 'CONTINENT_COUNTRY_MISMATCH')
pygbif.occ... | Lookup occurrence issue definitions and short codes
:param issue: Full name of issue, e.g, CONTINENT_COUNTRY_MISMATCH
:param code: an issue short code, e.g. ccm
Usage
pygbif.occ_issues_lookup(issue = 'CONTINENT_COUNTRY_MISMATCH')
pygbif.occ_issues_lookup(issue = 'MULTIMEDIA_DATE_INVALID')
pygb... | entailment |
def search(taxonKey=None, repatriated=None,
kingdomKey=None, phylumKey=None, classKey=None, orderKey=None,
familyKey=None, genusKey=None, subgenusKey=None, scientificName=None,
country=None, publishingCountry=None, hasCoordinate=None, typeStatus=None,
recordNumber=None, lastInterpreted=None, continent=N... | Search GBIF occurrences
:param taxonKey: [int] A GBIF occurrence identifier
:param q: [str] Simple search parameter. The value for this parameter can be a simple word or a phrase.
:param spellCheck: [bool] If ``True`` ask GBIF to check your spelling of the value passed to the ``search`` parameter.
... | entailment |
def networks(data = 'all', uuid = None, q = None, identifier = None,
identifierType = None, limit = 100, offset = None, **kwargs):
'''
Networks metadata.
Note: there's only 1 network now, so there's not a lot you can do with this method.
:param data: [str] The type of data to get. Default: ``all``
:param ... | Networks metadata.
Note: there's only 1 network now, so there's not a lot you can do with this method.
:param data: [str] The type of data to get. Default: ``all``
:param uuid: [str] UUID of the data network provider. This must be specified if data
is anything other than ``all``.
:param q: [str] Query ne... | entailment |
def map(source = 'density', z = 0, x = 0, y = 0, format = '@1x.png',
srs='EPSG:4326', bin=None, hexPerTile=None, style='classic.point',
taxonKey=None, country=None, publishingCountry=None, publisher=None,
datasetKey=None, year=None, basisOfRecord=None, **kwargs):
'''
GBIF maps API
:param sou... | GBIF maps API
:param source: [str] Either ``density`` for fast, precalculated tiles,
or ``adhoc`` for any search
:param z: [str] zoom level
:param x: [str] longitude
:param y: [str] latitude
:param format: [str] format of returned data. One of:
- ``.mvt`` - vector tile
- ``@Hx.... | entailment |
def name_usage(key = None, name = None, data = 'all', language = None,
datasetKey = None, uuid = None, sourceId = None, rank = None, shortname = None,
limit = 100, offset = None, **kwargs):
'''
Lookup details for specific names in all taxonomies in GBIF.
:param key: [fixnum] A GBIF key for a taxon
:param name: [... | Lookup details for specific names in all taxonomies in GBIF.
:param key: [fixnum] A GBIF key for a taxon
:param name: [str] Filters by a case insensitive, canonical namestring,
e.g. 'Puma concolor'
:param data: [str] The type of data to get. Default: ``all``. Options: ``all``,
``verbatim``, ``name``, ``parents... | entailment |
def _check_environ(variable, value):
"""check if a variable is present in the environmental variables"""
if is_not_none(value):
return value
else:
value = os.environ.get(variable)
if is_none(value):
stop(''.join([variable,
""" not supplied and no... | check if a variable is present in the environmental variables | entailment |
def download(queries, user=None, pwd=None,
email=None, pred_type='and'):
"""
Spin up a download request for GBIF occurrence data.
:param queries: One or more of query arguments to kick of a download job.
See Details.
:type queries: str or list
:param pred_type: (character) One ... | Spin up a download request for GBIF occurrence data.
:param queries: One or more of query arguments to kick of a download job.
See Details.
:type queries: str or list
:param pred_type: (character) One of ``equals`` (``=``), ``and`` (``&``),
`or`` (``|``), ``lessThan`` (``<``), ``lessThanOrE... | entailment |
def download_list(user=None, pwd=None, limit=20, offset=0):
"""
Lists the downloads created by a user.
:param user: [str] A user name, look at env var ``GBIF_USER`` first
:param pwd: [str] Your password, look at env var ``GBIF_PWD`` first
:param limit: [int] Number of records to return. Default: ``... | Lists the downloads created by a user.
:param user: [str] A user name, look at env var ``GBIF_USER`` first
:param pwd: [str] Your password, look at env var ``GBIF_PWD`` first
:param limit: [int] Number of records to return. Default: ``20``
:param offset: [int] Record number to start at. Default: ``0``
... | entailment |
def download_get(key, path=".", **kwargs):
"""
Get a download from GBIF.
:param key: [str] A key generated from a request, like that from ``download``
:param path: [str] Path to write zip file to. Default: ``"."``, with a ``.zip`` appended to the end.
:param **kwargs**: Further named arguments pass... | Get a download from GBIF.
:param key: [str] A key generated from a request, like that from ``download``
:param path: [str] Path to write zip file to. Default: ``"."``, with a ``.zip`` appended to the end.
:param **kwargs**: Further named arguments passed on to ``requests.get``
Downloads the zip file t... | entailment |
def main_pred_type(self, value):
"""set main predicate combination type
:param value: (character) One of ``equals`` (``=``), ``and`` (``&``), ``or`` (``|``),
``lessThan`` (``<``), ``lessThanOrEquals`` (``<=``), ``greaterThan`` (``>``),
``greaterThanOrEquals`` (``>=``), ``in``, ``within`... | set main predicate combination type
:param value: (character) One of ``equals`` (``=``), ``and`` (``&``), ``or`` (``|``),
``lessThan`` (``<``), ``lessThanOrEquals`` (``<=``), ``greaterThan`` (``>``),
``greaterThanOrEquals`` (``>=``), ``in``, ``within``, ``not`` (``!``), ``like`` | entailment |
def add_predicate(self, key, value, predicate_type='equals'):
"""
add key, value, type combination of a predicate
:param key: query KEY parameter
:param value: the value used in the predicate
:param predicate_type: the type of predicate (e.g. ``equals``)
"""
if p... | add key, value, type combination of a predicate
:param key: query KEY parameter
:param value: the value used in the predicate
:param predicate_type: the type of predicate (e.g. ``equals``) | entailment |
def _extract_values(values_list):
"""extract values from either file or list
:param values_list: list or file name (str) with list of values
"""
values = []
# check if file or list of values to iterate
if isinstance(values_list, str):
with open(values_list) a... | extract values from either file or list
:param values_list: list or file name (str) with list of values | entailment |
def add_iterative_predicate(self, key, values_list):
"""add an iterative predicate with a key and set of values
which it can be equal to in and or function.
The individual predicates are specified with the type ``equals`` and
combined with a type ``or``.
The main reason for this... | add an iterative predicate with a key and set of values
which it can be equal to in and or function.
The individual predicates are specified with the type ``equals`` and
combined with a type ``or``.
The main reason for this addition is the inability of using ``in`` as
predicate ... | entailment |
def get(key, **kwargs):
'''
Gets details for a single, interpreted occurrence
:param key: [int] A GBIF occurrence key
:return: A dictionary, of results
Usage::
from pygbif import occurrences
occurrences.get(key = 1258202889)
occurrences.get(key = 1227768771)
occur... | Gets details for a single, interpreted occurrence
:param key: [int] A GBIF occurrence key
:return: A dictionary, of results
Usage::
from pygbif import occurrences
occurrences.get(key = 1258202889)
occurrences.get(key = 1227768771)
occurrences.get(key = 1227769518) | entailment |
def get_verbatim(key, **kwargs):
'''
Gets a verbatim occurrence record without any interpretation
:param key: [int] A GBIF occurrence key
:return: A dictionary, of results
Usage::
from pygbif import occurrences
occurrences.get_verbatim(key = 1258202889)
occurrences.get_ve... | Gets a verbatim occurrence record without any interpretation
:param key: [int] A GBIF occurrence key
:return: A dictionary, of results
Usage::
from pygbif import occurrences
occurrences.get_verbatim(key = 1258202889)
occurrences.get_verbatim(key = 1227768771)
occurrences.... | entailment |
def get_fragment(key, **kwargs):
'''
Get a single occurrence fragment in its raw form (xml or json)
:param key: [int] A GBIF occurrence key
:return: A dictionary, of results
Usage::
from pygbif import occurrences
occurrences.get_fragment(key = 1052909293)
occurrences.get_... | Get a single occurrence fragment in its raw form (xml or json)
:param key: [int] A GBIF occurrence key
:return: A dictionary, of results
Usage::
from pygbif import occurrences
occurrences.get_fragment(key = 1052909293)
occurrences.get_fragment(key = 1227768771)
occurrence... | entailment |
def name_backbone(name, rank=None, kingdom=None, phylum=None, clazz=None,
order=None, family=None, genus=None, strict=False, verbose=False,
offset=None, limit=100, **kwargs):
'''
Lookup names in the GBIF backbone taxonomy.
:param name: [str] Full scientific name potentially with authorship (required)
:para... | Lookup names in the GBIF backbone taxonomy.
:param name: [str] Full scientific name potentially with authorship (required)
:param rank: [str] The rank given as our rank enum. (optional)
:param kingdom: [str] If provided default matching will also try to match against this
if no direct match is found for the... | entailment |
def name_parser(name, **kwargs):
'''
Parse taxon names using the GBIF name parser
:param name: [str] A character vector of scientific names. (required)
reference: http://www.gbif.org/developer/species#parser
Usage::
from pygbif import species
species.name_parser('x Agropogon littoralis')
... | Parse taxon names using the GBIF name parser
:param name: [str] A character vector of scientific names. (required)
reference: http://www.gbif.org/developer/species#parser
Usage::
from pygbif import species
species.name_parser('x Agropogon littoralis')
species.name_parser(['Arrhenatherum elat... | entailment |
def name_lookup(q=None, rank=None, higherTaxonKey=None, status=None, isExtinct=None,
habitat=None, nameType=None, datasetKey=None, nomenclaturalStatus=None,
limit=100, offset=None, facet=False, facetMincount=None, facetMultiselect=None,
type=None, hl=False, verbose=False, **kwargs):
'''
Lookup names in all taxonom... | Lookup names in all taxonomies in GBIF.
This service uses fuzzy lookup so that you can put in partial names and
you should get back those things that match. See examples below.
:param q: [str] Query term(s) for full text search (optional)
:param rank: [str] ``CLASS``, ``CULTIVAR``, ``CULTIVAR_GROUP``, ``DOMAIN``,... | entailment |
def count(taxonKey=None, basisOfRecord=None, country=None, isGeoreferenced=None,
datasetKey=None, publishingCountry=None, typeStatus=None,
issue=None, year=None, **kwargs):
'''
Returns occurrence counts for a predefined set of dimensions
:param taxonKey: [int] A GBIF occurrence identifier
:para... | Returns occurrence counts for a predefined set of dimensions
:param taxonKey: [int] A GBIF occurrence identifier
:param basisOfRecord: [str] A GBIF occurrence identifier
:param country: [str] A GBIF occurrence identifier
:param isGeoreferenced: [bool] A GBIF occurrence identifier
:param datasetKey:... | entailment |
def count_year(year, **kwargs):
'''
Lists occurrence counts by year
:param year: [int] year range, e.g., ``1990,2000``. Does not support ranges like ``asterisk,2010``
:return: dict
Usage::
from pygbif import occurrences
occurrences.count_year(year = '1990,2000')
'''
... | Lists occurrence counts by year
:param year: [int] year range, e.g., ``1990,2000``. Does not support ranges like ``asterisk,2010``
:return: dict
Usage::
from pygbif import occurrences
occurrences.count_year(year = '1990,2000') | entailment |
def count_datasets(taxonKey = None, country = None, **kwargs):
'''
Lists occurrence counts for datasets that cover a given taxon or country
:param taxonKey: [int] Taxon key
:param country: [str] A country, two letter code
:return: dict
Usage::
from pygbif import occurrences
... | Lists occurrence counts for datasets that cover a given taxon or country
:param taxonKey: [int] Taxon key
:param country: [str] A country, two letter code
:return: dict
Usage::
from pygbif import occurrences
occurrences.count_datasets(country = "DE") | entailment |
def count_countries(publishingCountry, **kwargs):
'''
Lists occurrence counts for all countries covered by the data published by the given country
:param publishingCountry: [str] A two letter country code
:return: dict
Usage::
from pygbif import occurrences
occurrences.co... | Lists occurrence counts for all countries covered by the data published by the given country
:param publishingCountry: [str] A two letter country code
:return: dict
Usage::
from pygbif import occurrences
occurrences.count_countries(publishingCountry = "DE") | entailment |
def count_publishingcountries(country, **kwargs):
'''
Lists occurrence counts for all countries that publish data about the given country
:param country: [str] A country, two letter code
:return: dict
Usage::
from pygbif import occurrences
occurrences.count_publishingcoun... | Lists occurrence counts for all countries that publish data about the given country
:param country: [str] A country, two letter code
:return: dict
Usage::
from pygbif import occurrences
occurrences.count_publishingcountries(country = "DE") | entailment |
def _detect_notebook() -> bool:
"""Detect if code is running in a Jupyter Notebook.
This isn't 100% correct but seems good enough
Returns
-------
bool
True if it detects this is a notebook, otherwise False.
"""
try:
from IPython import get_ipython
from ipykernel im... | Detect if code is running in a Jupyter Notebook.
This isn't 100% correct but seems good enough
Returns
-------
bool
True if it detects this is a notebook, otherwise False. | entailment |
def _merge_layout(x: go.Layout, y: go.Layout) -> go.Layout:
"""Merge attributes from two layouts."""
xjson = x.to_plotly_json()
yjson = y.to_plotly_json()
if 'shapes' in yjson and 'shapes' in xjson:
xjson['shapes'] += yjson['shapes']
yjson.update(xjson)
return go.Layout(yjson) | Merge attributes from two layouts. | entailment |
def _try_pydatetime(x):
"""Try to convert to pandas objects to datetimes.
Plotly doesn't know how to handle them.
"""
try:
# for datetimeindex
x = [y.isoformat() for y in x.to_pydatetime()]
except AttributeError:
pass
try:
# for generic series
x = [y.isof... | Try to convert to pandas objects to datetimes.
Plotly doesn't know how to handle them. | entailment |
def spark_shape(points, shapes, fill=None, color='blue', width=5, yindex=0, heights=None):
"""TODO: Docstring for spark.
Parameters
----------
points : array-like
shapes : array-like
fill : array-like, optional
Returns
-------
Chart
"""
assert len(points) == len(shapes) + ... | TODO: Docstring for spark.
Parameters
----------
points : array-like
shapes : array-like
fill : array-like, optional
Returns
-------
Chart | entailment |
def vertical(x, ymin=0, ymax=1, color=None, width=None, dash=None, opacity=None):
"""Draws a vertical line from `ymin` to `ymax`.
Parameters
----------
xmin : int, optional
xmax : int, optional
color : str, optional
width : number, optional
Returns
-------
Chart
"""
li... | Draws a vertical line from `ymin` to `ymax`.
Parameters
----------
xmin : int, optional
xmax : int, optional
color : str, optional
width : number, optional
Returns
-------
Chart | entailment |
def horizontal(y, xmin=0, xmax=1, color=None, width=None, dash=None, opacity=None):
"""Draws a horizontal line from `xmin` to `xmax`.
Parameters
----------
xmin : int, optional
xmax : int, optional
color : str, optional
width : number, optional
Returns
-------
Chart
"""
... | Draws a horizontal line from `xmin` to `xmax`.
Parameters
----------
xmin : int, optional
xmax : int, optional
color : str, optional
width : number, optional
Returns
-------
Chart | entailment |
def line(
x=None,
y=None,
label=None,
color=None,
width=None,
dash=None,
opacity=None,
mode='lines+markers',
yaxis=1,
fill=None,
text="",
markersize=6,
):
"""Draws connected dots.
Parameters
----------
x : array-like, optional
y : array-like, optional... | Draws connected dots.
Parameters
----------
x : array-like, optional
y : array-like, optional
label : array-like, optional
Returns
-------
Chart | entailment |
def line3d(
x, y, z, label=None, color=None, width=None, dash=None, opacity=None, mode='lines+markers'
):
"""Create a 3d line chart."""
x = np.atleast_1d(x)
y = np.atleast_1d(y)
z = np.atleast_1d(z)
assert x.shape == y.shape
assert y.shape == z.shape
lineattr = {}
if color:
l... | Create a 3d line chart. | entailment |
def scatter(
x=None,
y=None,
label=None,
color=None,
width=None,
dash=None,
opacity=None,
markersize=6,
yaxis=1,
fill=None,
text="",
mode='markers',
):
"""Draws dots.
Parameters
----------
x : array-like, optional
y : array-like, optional
label : ... | Draws dots.
Parameters
----------
x : array-like, optional
y : array-like, optional
label : array-like, optional
Returns
-------
Chart | entailment |
def bar(x=None, y=None, label=None, mode='group', yaxis=1, opacity=None):
"""Create a bar chart.
Parameters
----------
x : array-like, optional
y : TODO, optional
label : TODO, optional
mode : 'group' or 'stack', default 'group'
opacity : TODO, optional
Returns
-------
Char... | Create a bar chart.
Parameters
----------
x : array-like, optional
y : TODO, optional
label : TODO, optional
mode : 'group' or 'stack', default 'group'
opacity : TODO, optional
Returns
-------
Chart
A Chart with bar graph data. | entailment |
def heatmap(z, x=None, y=None, colorscale='Viridis'):
"""Create a heatmap.
Parameters
----------
z : TODO
x : TODO, optional
y : TODO, optional
colorscale : TODO, optional
Returns
-------
Chart
"""
z = np.atleast_1d(z)
data = [go.Heatmap(z=z, x=x, y=y, colorscale=... | Create a heatmap.
Parameters
----------
z : TODO
x : TODO, optional
y : TODO, optional
colorscale : TODO, optional
Returns
-------
Chart | entailment |
def fill_zero(
x=None,
y=None,
label=None,
color=None,
width=None,
dash=None,
opacity=None,
mode='lines+markers',
**kargs
):
"""Fill to zero.
Parameters
----------
x : array-like, optional
y : TODO, optional
label : TODO, optional
Returns
-------
... | Fill to zero.
Parameters
----------
x : array-like, optional
y : TODO, optional
label : TODO, optional
Returns
-------
Chart | entailment |
def fill_between(
x=None,
ylow=None,
yhigh=None,
label=None,
color=None,
width=None,
dash=None,
opacity=None,
mode='lines+markers',
**kargs
):
"""Fill between `ylow` and `yhigh`.
Parameters
----------
x : array-like, optional
ylow : TODO, optional
yhigh :... | Fill between `ylow` and `yhigh`.
Parameters
----------
x : array-like, optional
ylow : TODO, optional
yhigh : TODO, optional
Returns
-------
Chart | entailment |
def rug(x, label=None, opacity=None):
"""Rug chart.
Parameters
----------
x : array-like, optional
label : TODO, optional
opacity : TODO, optional
Returns
-------
Chart
"""
x = _try_pydatetime(x)
x = np.atleast_1d(x)
data = [
go.Scatter(
x=x,
... | Rug chart.
Parameters
----------
x : array-like, optional
label : TODO, optional
opacity : TODO, optional
Returns
-------
Chart | entailment |
def surface(x, y, z):
"""Surface plot.
Parameters
----------
x : array-like, optional
y : array-like, optional
z : array-like, optional
Returns
-------
Chart
"""
data = [go.Surface(x=x, y=y, z=z)]
return Chart(data=data) | Surface plot.
Parameters
----------
x : array-like, optional
y : array-like, optional
z : array-like, optional
Returns
-------
Chart | entailment |
def hist(x, mode='overlay', label=None, opacity=None, horz=False, histnorm=None):
"""Histogram.
Parameters
----------
x : array-like
mode : str, optional
label : TODO, optional
opacity : float, optional
horz : bool, optional
histnorm : None, "percent", "probability", "density", "pro... | Histogram.
Parameters
----------
x : array-like
mode : str, optional
label : TODO, optional
opacity : float, optional
horz : bool, optional
histnorm : None, "percent", "probability", "density", "probability density", optional
Specifies the type of normalization used for this his... | entailment |
def hist2d(x, y, label=None, opacity=None):
"""2D Histogram.
Parameters
----------
x : array-like, optional
y : array-like, optional
label : TODO, optional
opacity : float, optional
Returns
-------
Chart
"""
x = np.atleast_1d(x)
y = np.atleast_1d(y)
data = [go.... | 2D Histogram.
Parameters
----------
x : array-like, optional
y : array-like, optional
label : TODO, optional
opacity : float, optional
Returns
-------
Chart | entailment |
def ytickangle(self, angle, index=1):
"""Set the angle of the y-axis tick labels.
Parameters
----------
value : int
Angle in degrees
index : int, optional
Y-axis index
Returns
-------
Chart
"""
self.layout['yaxis'... | Set the angle of the y-axis tick labels.
Parameters
----------
value : int
Angle in degrees
index : int, optional
Y-axis index
Returns
-------
Chart | entailment |
def ylabelsize(self, size, index=1):
"""Set the size of the label.
Parameters
----------
size : int
Returns
-------
Chart
"""
self.layout['yaxis' + str(index)]['titlefont']['size'] = size
return self | Set the size of the label.
Parameters
----------
size : int
Returns
-------
Chart | entailment |
def yticksize(self, size, index=1):
"""Set the tick font size.
Parameters
----------
size : int
Returns
-------
Chart
"""
self.layout['yaxis' + str(index)]['tickfont']['size'] = size
return self | Set the tick font size.
Parameters
----------
size : int
Returns
-------
Chart | entailment |
def ytickvals(self, values, index=1):
"""Set the tick values.
Parameters
----------
values : array-like
Returns
-------
Chart
"""
self.layout['yaxis' + str(index)]['tickvals'] = values
return self | Set the tick values.
Parameters
----------
values : array-like
Returns
-------
Chart | entailment |
def yticktext(self, labels, index=1):
"""Set the tick labels.
Parameters
----------
labels : array-like
Returns
-------
Chart
"""
self.layout['yaxis' + str(index)]['ticktext'] = labels
return self | Set the tick labels.
Parameters
----------
labels : array-like
Returns
-------
Chart | entailment |
def ylim(self, low, high, index=1):
"""Set yaxis limits.
Parameters
----------
low : number
high : number
index : int, optional
Returns
-------
Chart
"""
self.layout['yaxis' + str(index)]['range'] = [low, high]
return sel... | Set yaxis limits.
Parameters
----------
low : number
high : number
index : int, optional
Returns
-------
Chart | entailment |
def ydtick(self, dtick, index=1):
"""Set the tick distance."""
self.layout['yaxis' + str(index)]['dtick'] = dtick
return self | Set the tick distance. | entailment |
def ynticks(self, nticks, index=1):
"""Set the number of ticks."""
self.layout['yaxis' + str(index)]['nticks'] = nticks
return self | Set the number of ticks. | entailment |
def show(
self,
filename: Optional[str] = None,
show_link: bool = True,
auto_open: bool = True,
detect_notebook: bool = True,
) -> None:
"""Display the chart.
Parameters
----------
filename : str, optional
Save plot to this filenam... | Display the chart.
Parameters
----------
filename : str, optional
Save plot to this filename, otherwise it's saved to a temporary file.
show_link : bool, optional
Show link to plotly.
auto_open : bool, optional
Automatically open the plot (in ... | entailment |
def save(
self,
filename: Optional[str] = None,
show_link: bool = True,
auto_open: bool = False,
output: str = 'file',
plotlyjs: bool = True,
) -> str:
"""Save the chart to an html file."""
if filename is None:
filename = NamedTemporaryFile... | Save the chart to an html file. | entailment |
def RegisterMethod(cls, *args, **kwargs):
"""
**RegisterMethod**
RegisterMethod(f, library_path, alias=None, original_name=None, doc=None, wrapped=None, explanation="", method_type=utils.identity, explain=True)
`classmethod` for registering functions as methods of this class.
**Arguments**
* **f** : the... | **RegisterMethod**
RegisterMethod(f, library_path, alias=None, original_name=None, doc=None, wrapped=None, explanation="", method_type=utils.identity, explain=True)
`classmethod` for registering functions as methods of this class.
**Arguments**
* **f** : the particular function being registered as a method
* **... | entailment |
def RegisterAt(cls, *args, **kwargs):
"""
**RegisterAt**
RegisterAt(n, f, library_path, alias=None, original_name=None, doc=None, wrapped=None, explanation="", method_type=utils.identity, explain=True, _return_type=None)
Most of the time you don't want to register an method as such, that is, you don't car... | **RegisterAt**
RegisterAt(n, f, library_path, alias=None, original_name=None, doc=None, wrapped=None, explanation="", method_type=utils.identity, explain=True, _return_type=None)
Most of the time you don't want to register an method as such, that is, you don't care about the `self` builder object, instead you wan... | entailment |
def PatchAt(cls, n, module, method_wrapper=None, module_alias=None, method_name_modifier=utils.identity, blacklist_predicate=_False, whitelist_predicate=_True, return_type_predicate=_None, getmembers_predicate=inspect.isfunction, admit_private=False, explanation=""):
"""
This classmethod lets you easily patch a... | This classmethod lets you easily patch all of functions/callables from a module or class as methods a Builder class.
**Arguments**
* **n** : the position the the object being piped will take in the arguments when the function being patched is applied. See `RegisterMethod` and `ThenAt`.
* **module** : a module or clas... | entailment |
def get_method_sig(method):
""" Given a function, it returns a string that pretty much looks how the
function signature_ would be written in python.
:param method: a python method
:return: A string similar describing the pythong method signature_.
eg: "my_method(first_argArg, second_arg=42, third_a... | Given a function, it returns a string that pretty much looks how the
function signature_ would be written in python.
:param method: a python method
:return: A string similar describing the pythong method signature_.
eg: "my_method(first_argArg, second_arg=42, third_arg='something')" | entailment |
def Pipe(self, *sequence, **kwargs):
"""
`Pipe` runs any `phi.dsl.Expression`. Its highly inspired by Elixir's [|> (pipe)](https://hexdocs.pm/elixir/Kernel.html#%7C%3E/2) operator.
**Arguments**
* ***sequence**: any variable amount of expressions. All expressions inside of `sequence` will be composed together... | `Pipe` runs any `phi.dsl.Expression`. Its highly inspired by Elixir's [|> (pipe)](https://hexdocs.pm/elixir/Kernel.html#%7C%3E/2) operator.
**Arguments**
* ***sequence**: any variable amount of expressions. All expressions inside of `sequence` will be composed together using `phi.dsl.Expression.Seq`.
* ****kwargs**: ... | entailment |
def ThenAt(self, n, f, *_args, **kwargs):
"""
`ThenAt` enables you to create a partially apply many arguments to a function, the returned partial expects a single arguments which will be applied at the `n`th position of the original function.
**Arguments**
* **n**: position at which the created partial will a... | `ThenAt` enables you to create a partially apply many arguments to a function, the returned partial expects a single arguments which will be applied at the `n`th position of the original function.
**Arguments**
* **n**: position at which the created partial will apply its awaited argument on the original function.
* ... | entailment |
def Then0(self, f, *args, **kwargs):
"""
`Then0(f, ...)` is equivalent to `ThenAt(0, f, ...)`. Checkout `phi.builder.Builder.ThenAt` for more information.
"""
return self.ThenAt(0, f, *args, **kwargs) | `Then0(f, ...)` is equivalent to `ThenAt(0, f, ...)`. Checkout `phi.builder.Builder.ThenAt` for more information. | entailment |
def Then(self, f, *args, **kwargs):
"""
`Then(f, ...)` is equivalent to `ThenAt(1, f, ...)`. Checkout `phi.builder.Builder.ThenAt` for more information.
"""
return self.ThenAt(1, f, *args, **kwargs) | `Then(f, ...)` is equivalent to `ThenAt(1, f, ...)`. Checkout `phi.builder.Builder.ThenAt` for more information. | entailment |
def Then2(self, f, arg1, *args, **kwargs):
"""
`Then2(f, ...)` is equivalent to `ThenAt(2, f, ...)`. Checkout `phi.builder.Builder.ThenAt` for more information.
"""
args = (arg1,) + args
return self.ThenAt(2, f, *args, **kwargs) | `Then2(f, ...)` is equivalent to `ThenAt(2, f, ...)`. Checkout `phi.builder.Builder.ThenAt` for more information. | entailment |
def Then3(self, f, arg1, arg2, *args, **kwargs):
"""
`Then3(f, ...)` is equivalent to `ThenAt(3, f, ...)`. Checkout `phi.builder.Builder.ThenAt` for more information.
"""
args = (arg1, arg2) + args
return self.ThenAt(3, f, *args, **kwargs) | `Then3(f, ...)` is equivalent to `ThenAt(3, f, ...)`. Checkout `phi.builder.Builder.ThenAt` for more information. | entailment |
def Then4(self, f, arg1, arg2, arg3, *args, **kwargs):
"""
`Then4(f, ...)` is equivalent to `ThenAt(4, f, ...)`. Checkout `phi.builder.Builder.ThenAt` for more information.
"""
args = (arg1, arg2, arg3) + args
return self.ThenAt(4, f, *args, **kwargs) | `Then4(f, ...)` is equivalent to `ThenAt(4, f, ...)`. Checkout `phi.builder.Builder.ThenAt` for more information. | entailment |
def Then5(self, f, arg1, arg2, arg3, arg4, *args, **kwargs):
"""
`Then5(f, ...)` is equivalent to `ThenAt(5, f, ...)`. Checkout `phi.builder.Builder.ThenAt` for more information.
"""
args = (arg1, arg2, arg3, arg4) + args
return self.ThenAt(5, f, *args, **kwargs) | `Then5(f, ...)` is equivalent to `ThenAt(5, f, ...)`. Checkout `phi.builder.Builder.ThenAt` for more information. | entailment |
def List(self, *branches, **kwargs):
"""
While `Seq` is sequential, `phi.dsl.Expression.List` allows you to split the computation and get back a list with the result of each path. While the list literal should be the most incarnation of this expresion, it can actually be any iterable (implements `__iter__`) tha... | While `Seq` is sequential, `phi.dsl.Expression.List` allows you to split the computation and get back a list with the result of each path. While the list literal should be the most incarnation of this expresion, it can actually be any iterable (implements `__iter__`) that is not a tuple and yields a valid expresion.
T... | entailment |
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