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annayqho/TheCannon | code/aaomega/aaomega_munge_data.py | make_full_ivar | 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... | python | 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']
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annayqho/TheCannon | TheCannon/normalization.py | _sinusoid | 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
... | python | def _sinusoid(x, p, L, y):
""" Return the sinusoid cont func evaluated at input x for the continuum.
Parameters
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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
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annayqho/TheCannon | TheCannon/normalization.py | _weighted_median | 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... | python | 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
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annayqho/TheCannon | TheCannon/normalization.py | _find_cont_gaussian_smooth | 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... | python | 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... | [
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block of flux values
ivar: numpy ndarray
block of ivar values
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width of Gaussian used to assign weights
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annayqho/TheCannon | TheCannon/normalization.py | _cont_norm_gaussian_smooth | 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... | python | 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
-------
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annayqho/TheCannon | TheCannon/normalization.py | _find_cont_fitfunc | 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... | python | 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)
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annayqho/TheCannon | TheCannon/normalization.py | _find_cont_fitfunc_regions | 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... | python | 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
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fluxes: ndarray of shape (nstars, npixels)
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annayqho/TheCannon | TheCannon/normalization.py | _find_cont_running_quantile | 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)
... | python | 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)
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annayqho/TheCannon | TheCannon/normalization.py | _cont_norm_running_quantile_mp | 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 = ... | python | 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 = ... | [
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annayqho/TheCannon | TheCannon/normalization.py | _cont_norm_running_quantile_regions | 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... | python | def _cont_norm_running_quantile_regions(wl, fluxes, ivars, q, delta_lambda,
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""" Perform continuum normalization using running quantile, for spectrum
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"""
print("contnorm.py: continuum norm using running quantile")
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annayqho/TheCannon | TheCannon/normalization.py | _cont_norm_running_quantile_regions_mp | 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(),
... | python | def _cont_norm_running_quantile_regions_mp(wl, fluxes, ivars, q, delta_lambda,
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annayqho/TheCannon | TheCannon/normalization.py | _cont_norm | 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
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""" Continuum-normalize a continuous segment of spectra.
Parameters
----------
fluxes: numpy ndarray
pixel intensities
ivars: numpy ndarray
inverse variances, parallel to fluxes
contmask: boolean mask
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annayqho/TheCannon | TheCannon/normalization.py | _cont_norm_regions | 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... | python | def _cont_norm_regions(fluxes, ivars, cont, ranges):
""" Perform continuum normalization for spectra in chunks
Useful for spectra that have gaps
Parameters
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fluxes: numpy ndarray
pixel intensities
ivars: numpy ndarray
inverse variances, parallel to fluxes
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inverse variances, parallel to fluxes
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annayqho/TheCannon | TheCannon/model.py | CannonModel.train | 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... | python | 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)
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annayqho/TheCannon | TheCannon/model.py | CannonModel.infer_spectra | 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... | python | def infer_spectra(self, ds):
"""
After inferring labels for the test spectra,
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----------
ds: Dataset object
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annayqho/TheCannon | TheCannon/model.py | CannonModel.plot_contpix | 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... | python | 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)
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plt.xlim(min(x), max(x))
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annayqho/TheCannon | TheCannon/model.py | CannonModel.diagnostics_contpix | 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... | python | 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")
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coeffs_all = self.coeffs
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annayqho/TheCannon | TheCannon/model.py | CannonModel.diagnostics_plot_chisq | 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()
... | python | 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
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annayqho/TheCannon | code/lamost/mass_age/cn/calc_astroseismic_mass.py | calc_mass | 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 | python | 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 | [
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annayqho/TheCannon | code/lamost/mass_age/mass_age_functions.py | calc_mass_2 | 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
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+ 15.0508*nm - 0.9342*nm*mh - 30.5159*nm*cm - 1.6083*nm**2
- 67.6093... | python | 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
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annayqho/TheCannon | TheCannon/helpers/corner/corner.py | corner | 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=... | python | 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,
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annayqho/TheCannon | TheCannon/helpers/corner/corner.py | quantile | 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
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return np.percentile(x, [100. * qi for qi in q])
... | python | 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
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annayqho/TheCannon | TheCannon/helpers/corner/corner.py | hist2d | def hist2d(x, y, bins=20, range=None, weights=None, levels=None, smooth=None,
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annayqho/TheCannon | code/lamost/xcalib_5labels/paper_plots/distance_cut.py | calc_dist | 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]) | python | 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))
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datosgobar/textar | textar/text_classifier.py | TextClassifier.make_classifier | 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.
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datosgobar/textar | textar/text_classifier.py | TextClassifier.retrain | def retrain(self, name, ids, labels):
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Args:
name (str): Nombre para el clasidicador.
ids (list): Se espera una lista de N ids de textos ya almacenados
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labels (list): Se espera una l... | python | def retrain(self, name, ids, labels):
"""Reentrenar parcialmente un clasificador SVM.
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name (str): Nombre para el clasidicador.
ids (list): Se espera una lista de N ids de textos ya almacenados
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datosgobar/textar | textar/text_classifier.py | TextClassifier.classify | def classify(self, classifier_name, examples, max_labels=None,
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Args:
classifier_name (str): Nombre del clasidicador a usar.
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classifier_name (str): Nombre del clasidicador a usar.
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datosgobar/textar | textar/text_classifier.py | TextClassifier._make_text_vectors | def _make_text_vectors(self, examples):
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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... | python | 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:
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datosgobar/textar | textar/text_classifier.py | TextClassifier.get_similar | 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... | python | 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.
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datosgobar/textar | textar/text_classifier.py | TextClassifier.reload_texts | 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
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"""Calcula los vectores de terminos de textos y los almacena.
A diferencia de :func:`~TextClassifier.TextClassifier.store_text` esta
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sckott/pygbif | pygbif/species/name_suggest.py | name_suggest | 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... | python | 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.
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sckott/pygbif | pygbif/registry/datasets.py | dataset_metrics | 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')
... | python | 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')
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sckott/pygbif | pygbif/registry/datasets.py | datasets | 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... | python | 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.
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sckott/pygbif | pygbif/registry/datasets.py | dataset_suggest | 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... | python | 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.
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sckott/pygbif | pygbif/registry/datasets.py | dataset_search | 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... | python | 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,
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sckott/pygbif | pygbif/utils/wkt_rewind.py | wkt_rewind | 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... | python | 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
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from py... | [
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sckott/pygbif | pygbif/gbifissues.py | occ_issues_lookup | 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... | python | 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')
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sckott/pygbif | pygbif/occurrences/search.py | search | 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... | python | def search(taxonKey=None, repatriated=None,
kingdomKey=None, phylumKey=None, classKey=None, orderKey=None,
familyKey=None, genusKey=None, subgenusKey=None, scientificName=None,
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sckott/pygbif | pygbif/registry/networks.py | networks | 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 ... | python | 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``
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sckott/pygbif | pygbif/maps/map.py | map | 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... | python | 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,
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sckott/pygbif | pygbif/species/name_usage.py | name_usage | 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: [... | python | 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
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sckott/pygbif | pygbif/occurrences/download.py | _check_environ | 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... | python | def _check_environ(variable, value):
"""check if a variable is present in the environmental variables"""
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sckott/pygbif | pygbif/occurrences/download.py | download | 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 ... | python | def download(queries, user=None, pwd=None,
email=None, pred_type='and'):
"""
Spin up a download request for GBIF occurrence data.
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See Details.
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sckott/pygbif | pygbif/occurrences/download.py | download_list | def download_list(user=None, pwd=None, limit=20, offset=0):
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Lists the downloads created by a user.
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:param pwd: [str] Your password, look at env var ``GBIF_PWD`` first
:param limit: [int] Number of records to return. Default: ``... | python | 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
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sckott/pygbif | pygbif/occurrences/download.py | download_get | def download_get(key, path=".", **kwargs):
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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... | python | def download_get(key, path=".", **kwargs):
"""
Get a download from GBIF.
:param key: [str] A key generated from a request, like that from ``download``
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sckott/pygbif | pygbif/occurrences/download.py | GbifDownload.main_pred_type | def main_pred_type(self, value):
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"""set main predicate combination type
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sckott/pygbif | pygbif/occurrences/download.py | GbifDownload.add_predicate | def add_predicate(self, key, value, predicate_type='equals'):
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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``)
"""
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"""
add key, value, type combination of a predicate
:param key: query KEY parameter
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sckott/pygbif | pygbif/occurrences/download.py | GbifDownload._extract_values | 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... | python | 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):
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sckott/pygbif | pygbif/occurrences/download.py | GbifDownload.add_iterative_predicate | def add_iterative_predicate(self, key, values_list):
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sckott/pygbif | pygbif/occurrences/get.py | get | 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... | python | 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... | [
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from pygbif import occurrences
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occurrences.get(key = 1227768771)
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sckott/pygbif | pygbif/occurrences/get.py | get_verbatim | 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... | python | 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)
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sckott/pygbif | pygbif/occurrences/get.py | get_fragment | 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_... | python | 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)
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sckott/pygbif | pygbif/species/name_backbone.py | name_backbone | 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... | python | 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.
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sckott/pygbif | pygbif/species/name_parser.py | name_parser | 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')
... | python | 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')
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sckott/pygbif | pygbif/species/name_lookup.py | name_lookup | 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,
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'''
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limit=100, offset=None, facet=False, facetMincount=None, facetMultiselect=None,
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sckott/pygbif | pygbif/occurrences/count.py | count | 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... | python | def count(taxonKey=None, basisOfRecord=None, country=None, isGeoreferenced=None,
datasetKey=None, publishingCountry=None, typeStatus=None,
issue=None, year=None, **kwargs):
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sckott/pygbif | pygbif/occurrences/count.py | count_year | 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')
'''
... | python | 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')
'''
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sckott/pygbif | pygbif/occurrences/count.py | count_datasets | 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
... | python | 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
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sckott/pygbif | pygbif/occurrences/count.py | count_countries | 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... | python | 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::
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occurrences.co... | [
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sckott/pygbif | pygbif/occurrences/count.py | count_publishingcountries | 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... | python | 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
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jwkvam/plotlywrapper | plotlywrapper.py | _detect_notebook | 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... | python | 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
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jwkvam/plotlywrapper | plotlywrapper.py | _merge_layout | 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) | python | 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) | [
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jwkvam/plotlywrapper | plotlywrapper.py | _try_pydatetime | 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... | python | 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
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jwkvam/plotlywrapper | plotlywrapper.py | spark_shape | 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) + ... | python | 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
"""
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jwkvam/plotlywrapper | plotlywrapper.py | vertical | 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... | python | 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
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jwkvam/plotlywrapper | plotlywrapper.py | horizontal | 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
"""
... | python | 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
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jwkvam/plotlywrapper | plotlywrapper.py | line | 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... | python | def line(
x=None,
y=None,
label=None,
color=None,
width=None,
dash=None,
opacity=None,
mode='lines+markers',
yaxis=1,
fill=None,
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markersize=6,
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"""Draws connected dots.
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x : array-like, optional
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jwkvam/plotlywrapper | plotlywrapper.py | line3d | 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... | python | 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:
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jwkvam/plotlywrapper | plotlywrapper.py | scatter | def scatter(
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width=None,
dash=None,
opacity=None,
markersize=6,
yaxis=1,
fill=None,
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"""Draws dots.
Parameters
----------
x : array-like, optional
y : array-like, optional
label : ... | python | 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',
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"""Draws dots.
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----------
x : array-like, optional
y : array-like, optional
label : ... | [
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jwkvam/plotlywrapper | plotlywrapper.py | bar | 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... | python | 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
-------
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jwkvam/plotlywrapper | plotlywrapper.py | heatmap | 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=... | python | 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=... | [
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jwkvam/plotlywrapper | plotlywrapper.py | fill_zero | 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
-------
... | python | 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
-------
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jwkvam/plotlywrapper | plotlywrapper.py | fill_between | 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 :... | python | 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 :... | [
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jwkvam/plotlywrapper | plotlywrapper.py | rug | 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,
... | python | 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,
... | [
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x : array-like, optional
label : TODO, optional
opacity : TODO, optional
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jwkvam/plotlywrapper | plotlywrapper.py | surface | 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) | python | 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) | [
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jwkvam/plotlywrapper | plotlywrapper.py | hist | 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... | python | 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
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jwkvam/plotlywrapper | plotlywrapper.py | hist2d | 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.... | python | 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.... | [
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jwkvam/plotlywrapper | plotlywrapper.py | Chart.ytickangle | 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
"""
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"""Set the angle of the y-axis tick labels.
Parameters
----------
value : int
Angle in degrees
index : int, optional
Y-axis index
Returns
-------
Chart
"""
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jwkvam/plotlywrapper | plotlywrapper.py | Chart.ylabelsize | 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 | python | 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
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jwkvam/plotlywrapper | plotlywrapper.py | Chart.yticksize | 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 | python | def yticksize(self, size, index=1):
"""Set the tick font size.
Parameters
----------
size : int
Returns
-------
Chart
"""
self.layout['yaxis' + str(index)]['tickfont']['size'] = size
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jwkvam/plotlywrapper | plotlywrapper.py | Chart.ytickvals | 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 | python | def ytickvals(self, values, index=1):
"""Set the tick values.
Parameters
----------
values : array-like
Returns
-------
Chart
"""
self.layout['yaxis' + str(index)]['tickvals'] = values
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jwkvam/plotlywrapper | plotlywrapper.py | Chart.yticktext | 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 | python | def yticktext(self, labels, index=1):
"""Set the tick labels.
Parameters
----------
labels : array-like
Returns
-------
Chart
"""
self.layout['yaxis' + str(index)]['ticktext'] = labels
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jwkvam/plotlywrapper | plotlywrapper.py | Chart.ylim | 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... | python | 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]
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jwkvam/plotlywrapper | plotlywrapper.py | Chart.ydtick | def ydtick(self, dtick, index=1):
"""Set the tick distance."""
self.layout['yaxis' + str(index)]['dtick'] = dtick
return self | python | def ydtick(self, dtick, index=1):
"""Set the tick distance."""
self.layout['yaxis' + str(index)]['dtick'] = dtick
return self | [
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jwkvam/plotlywrapper | plotlywrapper.py | Chart.ynticks | def ynticks(self, nticks, index=1):
"""Set the number of ticks."""
self.layout['yaxis' + str(index)]['nticks'] = nticks
return self | python | def ynticks(self, nticks, index=1):
"""Set the number of ticks."""
self.layout['yaxis' + str(index)]['nticks'] = nticks
return self | [
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jwkvam/plotlywrapper | plotlywrapper.py | Chart.show | 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... | python | 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
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Save plot to this filename, otherwise it's saved to a temporary file.
show_link : bool, optional
Show link to plotly.
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jwkvam/plotlywrapper | plotlywrapper.py | Chart.save | 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... | python | 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... | [
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cgarciae/phi | phi/builder.py | Builder.RegisterMethod | 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... | python | 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**
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`classmethod` for registering functions as methods of this class.
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cgarciae/phi | phi/builder.py | Builder.RegisterAt | 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... | python | 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)
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cgarciae/phi | phi/builder.py | Builder.PatchAt | 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... | python | 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=""):
"""
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cgarciae/phi | phi/utils.py | get_method_sig | 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... | python | 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_.
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cgarciae/phi | phi/dsl.py | Expression.Pipe | 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... | python | 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**
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cgarciae/phi | phi/dsl.py | Expression.ThenAt | 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... | python | 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.
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cgarciae/phi | phi/dsl.py | Expression.Then0 | 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) | python | def Then0(self, f, *args, **kwargs):
"""
`Then0(f, ...)` is equivalent to `ThenAt(0, f, ...)`. Checkout `phi.builder.Builder.ThenAt` for more information.
"""
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cgarciae/phi | phi/dsl.py | Expression.Then | 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) | python | 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) | [
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cgarciae/phi | phi/dsl.py | Expression.Then2 | 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) | python | 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) | [
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cgarciae/phi | phi/dsl.py | Expression.Then3 | 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) | python | 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) | [
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cgarciae/phi | phi/dsl.py | Expression.Then4 | 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) | python | 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) | [
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cgarciae/phi | phi/dsl.py | Expression.Then5 | 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) | python | 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) | [
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cgarciae/phi | phi/dsl.py | Expression.List | 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... | python | 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... | [
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