body stringlengths 26 98.2k | body_hash int64 -9,222,864,604,528,158,000 9,221,803,474B | docstring stringlengths 1 16.8k | path stringlengths 5 230 | name stringlengths 1 96 | repository_name stringlengths 7 89 | lang stringclasses 1
value | body_without_docstring stringlengths 20 98.2k |
|---|---|---|---|---|---|---|---|
def abort_iocb(self, addr, iocbid, err):
'Called when the client or server receives an abort request.'
if _debug:
IOProxyServer._debug('abort_iocb %r %r %r', addr, iocbid, err)
if (not self.localIOCB.has_key(iocbid)):
raise RuntimeError(('no reference to aborting iocb: %r' % (iocbid,)))
... | 7,061,142,439,102,379,000 | Called when the client or server receives an abort request. | sandbox/io.py | abort_iocb | DB-CL/bacpypes | python | def abort_iocb(self, addr, iocbid, err):
if _debug:
IOProxyServer._debug('abort_iocb %r %r %r', addr, iocbid, err)
if (not self.localIOCB.has_key(iocbid)):
raise RuntimeError(('no reference to aborting iocb: %r' % (iocbid,)))
iocb = self.localIOCB[iocbid]
del self.localIOCB[iocbid]
... |
@classmethod
def title(cls):
'Display title'
CLIOutput.empty_line(1)
CLIOutput.center(cls._day.get_title()) | -9,188,701,884,550,395,000 | Display title | cli/day.py | title | sunarch/woyo | python | @classmethod
def title(cls):
CLIOutput.empty_line(1)
CLIOutput.center(cls._day.get_title()) |
@classmethod
def new_words(cls, display_in_full=True):
'Display new words section'
regular = list()
phonetic = list()
for unit in cls._day.get_new_words():
regular.append(unit['regular'])
phonetic.append(unit['phonetic'])
if display_in_full:
CLIOutput.section_title('NEW WORDS... | 1,520,179,378,384,501,800 | Display new words section | cli/day.py | new_words | sunarch/woyo | python | @classmethod
def new_words(cls, display_in_full=True):
regular = list()
phonetic = list()
for unit in cls._day.get_new_words():
regular.append(unit['regular'])
phonetic.append(unit['phonetic'])
if display_in_full:
CLIOutput.section_title('NEW WORDS')
CLIOutput.empty_... |
@classmethod
def intro_text(cls):
'Display intro text'
parts = cls._day.get_intro_text()
CLIOutput.empty_line(2)
CLIOutput.framed(parts, cls.INTRO_TEXT_WIDTH) | -2,856,950,738,659,759,000 | Display intro text | cli/day.py | intro_text | sunarch/woyo | python | @classmethod
def intro_text(cls):
parts = cls._day.get_intro_text()
CLIOutput.empty_line(2)
CLIOutput.framed(parts, cls.INTRO_TEXT_WIDTH) |
@classmethod
def _answer_cycle(cls, prompt, l_pr_question, answers, l_pr_answer, prev_action, l_prev_msg, l_next_msg):
'Answer cycle'
while True:
CLIOutput.empty_line(1)
(a_type, a_content) = CLIUserInput.get_answer(prompt)
if (a_type == CLIUserInput.TYPE_ANSWER):
if (a_conte... | -3,586,564,492,552,391,000 | Answer cycle | cli/day.py | _answer_cycle | sunarch/woyo | python | @classmethod
def _answer_cycle(cls, prompt, l_pr_question, answers, l_pr_answer, prev_action, l_prev_msg, l_next_msg):
while True:
CLIOutput.empty_line(1)
(a_type, a_content) = CLIUserInput.get_answer(prompt)
if (a_type == CLIUserInput.TYPE_ANSWER):
if (a_content in answers)... |
@classmethod
def sample_sentences(cls):
"Display 'sample sentences' task"
data = cls._day.get_sample_sentences()
CLIOutput.section_title('SAMPLE SENTENCES')
CLIOutput.empty_line(1)
CLIOutput.simple(data['prompt'])
CLIOutput.empty_line(1)
for sentence in data['sentences']:
CLIOutput.n... | -4,594,590,147,745,163,000 | Display 'sample sentences' task | cli/day.py | sample_sentences | sunarch/woyo | python | @classmethod
def sample_sentences(cls):
data = cls._day.get_sample_sentences()
CLIOutput.section_title('SAMPLE SENTENCES')
CLIOutput.empty_line(1)
CLIOutput.simple(data['prompt'])
CLIOutput.empty_line(1)
for sentence in data['sentences']:
CLIOutput.numbered_sentence(sentence['id'], ... |
@classmethod
def definitions(cls):
"Display 'definitions' task"
return
data = cls._day.get_definitions()
CLIOutput.section_title('DEFINITIONS')
CLIOutput.empty_line(1)
CLIOutput.simple(data['prompt'])
CLIOutput.empty_line(1)
for definition in data['definitions']:
CLIOutput.number... | -6,911,846,803,249,598,000 | Display 'definitions' task | cli/day.py | definitions | sunarch/woyo | python | @classmethod
def definitions(cls):
return
data = cls._day.get_definitions()
CLIOutput.section_title('DEFINITIONS')
CLIOutput.empty_line(1)
CLIOutput.simple(data['prompt'])
CLIOutput.empty_line(1)
for definition in data['definitions']:
CLIOutput.numbered_sentence(definition['id']... |
@classmethod
def matching(cls):
"Display 'matching' task"
return
data = cls._day.get_matching()
CLIOutput.section_title(data['name'])
CLIOutput.empty_line(1)
CLIOutput.simple(data['prompt'])
CLIOutput.empty_line(1)
for sentence in data['sentences']:
CLIOutput.numbered_sentence(se... | 4,291,799,636,644,809,000 | Display 'matching' task | cli/day.py | matching | sunarch/woyo | python | @classmethod
def matching(cls):
return
data = cls._day.get_matching()
CLIOutput.section_title(data['name'])
CLIOutput.empty_line(1)
CLIOutput.simple(data['prompt'])
CLIOutput.empty_line(1)
for sentence in data['sentences']:
CLIOutput.numbered_sentence(sentence['id'], sentence['t... |
@classmethod
def other_new_words(cls):
'Display other new words section'
data = cls._day.get_other_new_words()
CLIOutput.section_title('OTHER NEW WORDS:')
CLIOutput.empty_line(1)
CLIOutput.simple(data['prompt'])
CLIOutput.empty_line(1)
(a_type, a_content) = CLIUserInput.get_answer('')
CL... | -1,445,370,580,328,307,200 | Display other new words section | cli/day.py | other_new_words | sunarch/woyo | python | @classmethod
def other_new_words(cls):
data = cls._day.get_other_new_words()
CLIOutput.section_title('OTHER NEW WORDS:')
CLIOutput.empty_line(1)
CLIOutput.simple(data['prompt'])
CLIOutput.empty_line(1)
(a_type, a_content) = CLIUserInput.get_answer()
CLIOutput.empty_line(1) |
def read(handle):
'Get output from primersearch into a PrimerSearchOutputRecord.'
record = OutputRecord()
for line in handle:
if (not line.strip()):
continue
elif line.startswith('Primer name'):
name = line.split()[(- 1)]
record.amplifiers[name] = []
... | -1,677,030,615,956,668,700 | Get output from primersearch into a PrimerSearchOutputRecord. | Bio/Emboss/PrimerSearch.py | read | EnjoyLifeFund/macHighSierra-py36-pkgs | python | def read(handle):
record = OutputRecord()
for line in handle:
if (not line.strip()):
continue
elif line.startswith('Primer name'):
name = line.split()[(- 1)]
record.amplifiers[name] = []
elif line.startswith('Amplimer'):
amplifier = Am... |
def add_primer_set(self, primer_name, first_primer_seq, second_primer_seq):
'Add primer information to the record.'
self.primer_info.append((primer_name, first_primer_seq, second_primer_seq)) | -2,234,440,617,279,405,600 | Add primer information to the record. | Bio/Emboss/PrimerSearch.py | add_primer_set | EnjoyLifeFund/macHighSierra-py36-pkgs | python | def add_primer_set(self, primer_name, first_primer_seq, second_primer_seq):
self.primer_info.append((primer_name, first_primer_seq, second_primer_seq)) |
def _decompose_entangle(cmd):
' Decompose the entangle gate. '
qr = cmd.qubits[0]
eng = cmd.engine
with Control(eng, cmd.control_qubits):
(H | qr[0])
with Control(eng, qr[0]):
(All(X) | qr[1:]) | -1,206,310,081,312,765,700 | Decompose the entangle gate. | projectq/setups/decompositions/entangle.py | _decompose_entangle | VirtueQuantumCloud/Ex | python | def _decompose_entangle(cmd):
' '
qr = cmd.qubits[0]
eng = cmd.engine
with Control(eng, cmd.control_qubits):
(H | qr[0])
with Control(eng, qr[0]):
(All(X) | qr[1:]) |
def predict(self, params, exog=None, exog_precision=None, which='mean'):
'Predict values for mean or precision\n\n Parameters\n ----------\n params : array_like\n The model parameters.\n exog : array_like\n Array of predictor variables for mean.\n exog_precis... | -7,767,475,233,672,883,000 | Predict values for mean or precision
Parameters
----------
params : array_like
The model parameters.
exog : array_like
Array of predictor variables for mean.
exog_precision : array_like
Array of predictor variables for precision parameter.
which : str
- "mean" : mean, conditional expectation E(endog |... | statsmodels/othermod/betareg.py | predict | EC-AI/statsmodels | python | def predict(self, params, exog=None, exog_precision=None, which='mean'):
'Predict values for mean or precision\n\n Parameters\n ----------\n params : array_like\n The model parameters.\n exog : array_like\n Array of predictor variables for mean.\n exog_precis... |
def _predict_precision(self, params, exog_precision=None):
'Predict values for precision function for given exog_precision.\n\n Parameters\n ----------\n params : array_like\n The model parameters.\n exog_precision : array_like\n Array of predictor variables for pre... | 5,514,060,033,690,942,000 | Predict values for precision function for given exog_precision.
Parameters
----------
params : array_like
The model parameters.
exog_precision : array_like
Array of predictor variables for precision.
Returns
-------
Predicted precision. | statsmodels/othermod/betareg.py | _predict_precision | EC-AI/statsmodels | python | def _predict_precision(self, params, exog_precision=None):
'Predict values for precision function for given exog_precision.\n\n Parameters\n ----------\n params : array_like\n The model parameters.\n exog_precision : array_like\n Array of predictor variables for pre... |
def _predict_var(self, params, exog=None, exog_precision=None):
'predict values for conditional variance V(endog | exog)\n\n Parameters\n ----------\n params : array_like\n The model parameters.\n exog : array_like\n Array of predictor variables for mean.\n e... | 755,736,759,623,645,600 | predict values for conditional variance V(endog | exog)
Parameters
----------
params : array_like
The model parameters.
exog : array_like
Array of predictor variables for mean.
exog_precision : array_like
Array of predictor variables for precision.
Returns
-------
Predicted conditional variance. | statsmodels/othermod/betareg.py | _predict_var | EC-AI/statsmodels | python | def _predict_var(self, params, exog=None, exog_precision=None):
'predict values for conditional variance V(endog | exog)\n\n Parameters\n ----------\n params : array_like\n The model parameters.\n exog : array_like\n Array of predictor variables for mean.\n e... |
def loglikeobs(self, params):
'\n Loglikelihood for observations of the Beta regressionmodel.\n\n Parameters\n ----------\n params : ndarray\n The parameters of the model, coefficients for linear predictors\n of the mean and of the precision function.\n\n Ret... | 2,338,312,586,881,387,000 | Loglikelihood for observations of the Beta regressionmodel.
Parameters
----------
params : ndarray
The parameters of the model, coefficients for linear predictors
of the mean and of the precision function.
Returns
-------
loglike : ndarray
The log likelihood for each observation of the model evaluated
... | statsmodels/othermod/betareg.py | loglikeobs | EC-AI/statsmodels | python | def loglikeobs(self, params):
'\n Loglikelihood for observations of the Beta regressionmodel.\n\n Parameters\n ----------\n params : ndarray\n The parameters of the model, coefficients for linear predictors\n of the mean and of the precision function.\n\n Ret... |
def _llobs(self, endog, exog, exog_precision, params):
'\n Loglikelihood for observations with data arguments.\n\n Parameters\n ----------\n endog : ndarray\n 1d array of endogenous variable.\n exog : ndarray\n 2d array of explanatory variables.\n exog... | -8,845,710,265,055,055,000 | Loglikelihood for observations with data arguments.
Parameters
----------
endog : ndarray
1d array of endogenous variable.
exog : ndarray
2d array of explanatory variables.
exog_precision : ndarray
2d array of explanatory variables for precision.
params : ndarray
The parameters of the model, coefficien... | statsmodels/othermod/betareg.py | _llobs | EC-AI/statsmodels | python | def _llobs(self, endog, exog, exog_precision, params):
'\n Loglikelihood for observations with data arguments.\n\n Parameters\n ----------\n endog : ndarray\n 1d array of endogenous variable.\n exog : ndarray\n 2d array of explanatory variables.\n exog... |
def score(self, params):
'\n Returns the score vector of the log-likelihood.\n\n http://www.tandfonline.com/doi/pdf/10.1080/00949650903389993\n\n Parameters\n ----------\n params : ndarray\n Parameter at which score is evaluated.\n\n Returns\n -------\n ... | 6,804,556,562,903,432,000 | Returns the score vector of the log-likelihood.
http://www.tandfonline.com/doi/pdf/10.1080/00949650903389993
Parameters
----------
params : ndarray
Parameter at which score is evaluated.
Returns
-------
score : ndarray
First derivative of loglikelihood function. | statsmodels/othermod/betareg.py | score | EC-AI/statsmodels | python | def score(self, params):
'\n Returns the score vector of the log-likelihood.\n\n http://www.tandfonline.com/doi/pdf/10.1080/00949650903389993\n\n Parameters\n ----------\n params : ndarray\n Parameter at which score is evaluated.\n\n Returns\n -------\n ... |
def _score_check(self, params):
'Inherited score with finite differences\n\n Parameters\n ----------\n params : ndarray\n Parameter at which score is evaluated.\n\n Returns\n -------\n score based on numerical derivatives\n '
return super(BetaModel, se... | -8,920,612,915,008,268,000 | Inherited score with finite differences
Parameters
----------
params : ndarray
Parameter at which score is evaluated.
Returns
-------
score based on numerical derivatives | statsmodels/othermod/betareg.py | _score_check | EC-AI/statsmodels | python | def _score_check(self, params):
'Inherited score with finite differences\n\n Parameters\n ----------\n params : ndarray\n Parameter at which score is evaluated.\n\n Returns\n -------\n score based on numerical derivatives\n '
return super(BetaModel, se... |
def score_factor(self, params, endog=None):
'Derivative of loglikelihood function w.r.t. linear predictors.\n\n This needs to be multiplied with the exog to obtain the score_obs.\n\n Parameters\n ----------\n params : ndarray\n Parameter at which score is evaluated.\n\n ... | -6,567,238,699,126,569,000 | Derivative of loglikelihood function w.r.t. linear predictors.
This needs to be multiplied with the exog to obtain the score_obs.
Parameters
----------
params : ndarray
Parameter at which score is evaluated.
Returns
-------
score_factor : ndarray, 2-D
A 2d weight vector used in the calculation of the score_o... | statsmodels/othermod/betareg.py | score_factor | EC-AI/statsmodels | python | def score_factor(self, params, endog=None):
'Derivative of loglikelihood function w.r.t. linear predictors.\n\n This needs to be multiplied with the exog to obtain the score_obs.\n\n Parameters\n ----------\n params : ndarray\n Parameter at which score is evaluated.\n\n ... |
def score_hessian_factor(self, params, return_hessian=False, observed=True):
'Derivatives of loglikelihood function w.r.t. linear predictors.\n\n This calculates score and hessian factors at the same time, because\n there is a large overlap in calculations.\n\n Parameters\n ----------\n ... | 5,840,432,985,829,507,000 | Derivatives of loglikelihood function w.r.t. linear predictors.
This calculates score and hessian factors at the same time, because
there is a large overlap in calculations.
Parameters
----------
params : ndarray
Parameter at which score is evaluated.
return_hessian : bool
If False, then only score_factors ar... | statsmodels/othermod/betareg.py | score_hessian_factor | EC-AI/statsmodels | python | def score_hessian_factor(self, params, return_hessian=False, observed=True):
'Derivatives of loglikelihood function w.r.t. linear predictors.\n\n This calculates score and hessian factors at the same time, because\n there is a large overlap in calculations.\n\n Parameters\n ----------\n ... |
def score_obs(self, params):
'\n Score, first derivative of the loglikelihood for each observation.\n\n Parameters\n ----------\n params : ndarray\n Parameter at which score is evaluated.\n\n Returns\n -------\n score_obs : ndarray, 2d\n The fir... | 3,554,911,380,494,363,600 | Score, first derivative of the loglikelihood for each observation.
Parameters
----------
params : ndarray
Parameter at which score is evaluated.
Returns
-------
score_obs : ndarray, 2d
The first derivative of the loglikelihood function evaluated at
params for each observation. | statsmodels/othermod/betareg.py | score_obs | EC-AI/statsmodels | python | def score_obs(self, params):
'\n Score, first derivative of the loglikelihood for each observation.\n\n Parameters\n ----------\n params : ndarray\n Parameter at which score is evaluated.\n\n Returns\n -------\n score_obs : ndarray, 2d\n The fir... |
def hessian(self, params, observed=None):
'Hessian, second derivative of loglikelihood function\n\n Parameters\n ----------\n params : ndarray\n Parameter at which Hessian is evaluated.\n observed : bool\n If True, then the observed Hessian is returned (default).\n ... | -7,365,549,943,060,540,000 | Hessian, second derivative of loglikelihood function
Parameters
----------
params : ndarray
Parameter at which Hessian is evaluated.
observed : bool
If True, then the observed Hessian is returned (default).
If False, then the expected information matrix is returned.
Returns
-------
hessian : ndarray
H... | statsmodels/othermod/betareg.py | hessian | EC-AI/statsmodels | python | def hessian(self, params, observed=None):
'Hessian, second derivative of loglikelihood function\n\n Parameters\n ----------\n params : ndarray\n Parameter at which Hessian is evaluated.\n observed : bool\n If True, then the observed Hessian is returned (default).\n ... |
def hessian_factor(self, params, observed=True):
'Derivatives of loglikelihood function w.r.t. linear predictors.\n '
(_, hf) = self.score_hessian_factor(params, return_hessian=True, observed=observed)
return hf | 1,568,852,675,676,506,000 | Derivatives of loglikelihood function w.r.t. linear predictors. | statsmodels/othermod/betareg.py | hessian_factor | EC-AI/statsmodels | python | def hessian_factor(self, params, observed=True):
'\n '
(_, hf) = self.score_hessian_factor(params, return_hessian=True, observed=observed)
return hf |
def _start_params(self, niter=2, return_intermediate=False):
'find starting values\n\n Parameters\n ----------\n niter : int\n Number of iterations of WLS approximation\n return_intermediate : bool\n If False (default), then only the preliminary parameter estimate\n... | 1,173,509,138,294,590,200 | find starting values
Parameters
----------
niter : int
Number of iterations of WLS approximation
return_intermediate : bool
If False (default), then only the preliminary parameter estimate
will be returned.
If True, then also the two results instances of the WLS estimate
for mean parameters and for... | statsmodels/othermod/betareg.py | _start_params | EC-AI/statsmodels | python | def _start_params(self, niter=2, return_intermediate=False):
'find starting values\n\n Parameters\n ----------\n niter : int\n Number of iterations of WLS approximation\n return_intermediate : bool\n If False (default), then only the preliminary parameter estimate\n... |
def fit(self, start_params=None, maxiter=1000, disp=False, method='bfgs', **kwds):
'\n Fit the model by maximum likelihood.\n\n Parameters\n ----------\n start_params : array-like\n A vector of starting values for the regression\n coefficients. If None, a default i... | -1,019,944,970,131,715,300 | Fit the model by maximum likelihood.
Parameters
----------
start_params : array-like
A vector of starting values for the regression
coefficients. If None, a default is chosen.
maxiter : integer
The maximum number of iterations
disp : bool
Show convergence stats.
method : str
The optimization metho... | statsmodels/othermod/betareg.py | fit | EC-AI/statsmodels | python | def fit(self, start_params=None, maxiter=1000, disp=False, method='bfgs', **kwds):
'\n Fit the model by maximum likelihood.\n\n Parameters\n ----------\n start_params : array-like\n A vector of starting values for the regression\n coefficients. If None, a default i... |
def _deriv_mean_dparams(self, params):
'\n Derivative of the expected endog with respect to the parameters.\n\n not verified yet\n\n Parameters\n ----------\n params : ndarray\n parameter at which score is evaluated\n\n Returns\n -------\n The value... | 2,393,203,179,363,964,000 | Derivative of the expected endog with respect to the parameters.
not verified yet
Parameters
----------
params : ndarray
parameter at which score is evaluated
Returns
-------
The value of the derivative of the expected endog with respect
to the parameter vector. | statsmodels/othermod/betareg.py | _deriv_mean_dparams | EC-AI/statsmodels | python | def _deriv_mean_dparams(self, params):
'\n Derivative of the expected endog with respect to the parameters.\n\n not verified yet\n\n Parameters\n ----------\n params : ndarray\n parameter at which score is evaluated\n\n Returns\n -------\n The value... |
def _deriv_score_obs_dendog(self, params):
'derivative of score_obs w.r.t. endog\n\n Parameters\n ----------\n params : ndarray\n parameter at which score is evaluated\n\n Returns\n -------\n derivative : ndarray_2d\n The derivative of the score_obs wi... | 2,784,673,522,723,553,000 | derivative of score_obs w.r.t. endog
Parameters
----------
params : ndarray
parameter at which score is evaluated
Returns
-------
derivative : ndarray_2d
The derivative of the score_obs with respect to endog. | statsmodels/othermod/betareg.py | _deriv_score_obs_dendog | EC-AI/statsmodels | python | def _deriv_score_obs_dendog(self, params):
'derivative of score_obs w.r.t. endog\n\n Parameters\n ----------\n params : ndarray\n parameter at which score is evaluated\n\n Returns\n -------\n derivative : ndarray_2d\n The derivative of the score_obs wi... |
def get_distribution_params(self, params, exog=None, exog_precision=None):
'\n Return distribution parameters converted from model prediction.\n\n Parameters\n ----------\n params : array_like\n The model parameters.\n exog : array_like\n Array of predictor v... | -3,883,005,072,907,994,000 | Return distribution parameters converted from model prediction.
Parameters
----------
params : array_like
The model parameters.
exog : array_like
Array of predictor variables for mean.
exog_precision : array_like
Array of predictor variables for mean.
Returns
-------
(alpha, beta) : tuple of ndarrays
... | statsmodels/othermod/betareg.py | get_distribution_params | EC-AI/statsmodels | python | def get_distribution_params(self, params, exog=None, exog_precision=None):
'\n Return distribution parameters converted from model prediction.\n\n Parameters\n ----------\n params : array_like\n The model parameters.\n exog : array_like\n Array of predictor v... |
def get_distribution(self, params, exog=None, exog_precision=None):
'\n Return a instance of the predictive distribution.\n\n Parameters\n ----------\n params : array_like\n The model parameters.\n exog : array_like\n Array of predictor variables for mean.\n ... | -5,312,023,450,071,919,000 | Return a instance of the predictive distribution.
Parameters
----------
params : array_like
The model parameters.
exog : array_like
Array of predictor variables for mean.
exog_precision : array_like
Array of predictor variables for mean.
Returns
-------
Instance of a scipy frozen distribution based on est... | statsmodels/othermod/betareg.py | get_distribution | EC-AI/statsmodels | python | def get_distribution(self, params, exog=None, exog_precision=None):
'\n Return a instance of the predictive distribution.\n\n Parameters\n ----------\n params : array_like\n The model parameters.\n exog : array_like\n Array of predictor variables for mean.\n ... |
@cache_readonly
def fittedvalues(self):
'In-sample predicted mean, conditional expectation.'
return self.model.predict(self.params) | -2,146,296,562,088,598,000 | In-sample predicted mean, conditional expectation. | statsmodels/othermod/betareg.py | fittedvalues | EC-AI/statsmodels | python | @cache_readonly
def fittedvalues(self):
return self.model.predict(self.params) |
@cache_readonly
def fitted_precision(self):
'In-sample predicted precision'
return self.model.predict(self.params, which='precision') | -4,571,141,172,921,569,300 | In-sample predicted precision | statsmodels/othermod/betareg.py | fitted_precision | EC-AI/statsmodels | python | @cache_readonly
def fitted_precision(self):
return self.model.predict(self.params, which='precision') |
@cache_readonly
def resid(self):
'Response residual'
return (self.model.endog - self.fittedvalues) | -2,164,090,418,230,139,400 | Response residual | statsmodels/othermod/betareg.py | resid | EC-AI/statsmodels | python | @cache_readonly
def resid(self):
return (self.model.endog - self.fittedvalues) |
@cache_readonly
def resid_pearson(self):
'Pearson standardize residual'
std = np.sqrt(self.model.predict(self.params, which='var'))
return (self.resid / std) | -5,317,540,306,562,735,000 | Pearson standardize residual | statsmodels/othermod/betareg.py | resid_pearson | EC-AI/statsmodels | python | @cache_readonly
def resid_pearson(self):
std = np.sqrt(self.model.predict(self.params, which='var'))
return (self.resid / std) |
@cache_readonly
def prsquared(self):
'Cox-Snell Likelihood-Ratio pseudo-R-squared.\n\n 1 - exp((llnull - .llf) * (2 / nobs))\n '
return self.pseudo_rsquared(kind='lr') | 5,066,028,713,186,439,000 | Cox-Snell Likelihood-Ratio pseudo-R-squared.
1 - exp((llnull - .llf) * (2 / nobs)) | statsmodels/othermod/betareg.py | prsquared | EC-AI/statsmodels | python | @cache_readonly
def prsquared(self):
'Cox-Snell Likelihood-Ratio pseudo-R-squared.\n\n 1 - exp((llnull - .llf) * (2 / nobs))\n '
return self.pseudo_rsquared(kind='lr') |
def get_distribution_params(self, exog=None, exog_precision=None, transform=True):
'\n Return distribution parameters converted from model prediction.\n\n Parameters\n ----------\n params : array_like\n The model parameters.\n exog : array_like\n Array of pre... | 6,766,482,164,122,011,000 | Return distribution parameters converted from model prediction.
Parameters
----------
params : array_like
The model parameters.
exog : array_like
Array of predictor variables for mean.
transform : bool
If transform is True and formulas have been used, then predictor
``exog`` is passed through the formu... | statsmodels/othermod/betareg.py | get_distribution_params | EC-AI/statsmodels | python | def get_distribution_params(self, exog=None, exog_precision=None, transform=True):
'\n Return distribution parameters converted from model prediction.\n\n Parameters\n ----------\n params : array_like\n The model parameters.\n exog : array_like\n Array of pre... |
def get_distribution(self, exog=None, exog_precision=None, transform=True):
'\n Return a instance of the predictive distribution.\n\n Parameters\n ----------\n exog : array_like\n Array of predictor variables for mean.\n exog_precision : array_like\n Array of... | -2,352,558,057,159,520,000 | Return a instance of the predictive distribution.
Parameters
----------
exog : array_like
Array of predictor variables for mean.
exog_precision : array_like
Array of predictor variables for mean.
transform : bool
If transform is True and formulas have been used, then predictor
``exog`` is passed throug... | statsmodels/othermod/betareg.py | get_distribution | EC-AI/statsmodels | python | def get_distribution(self, exog=None, exog_precision=None, transform=True):
'\n Return a instance of the predictive distribution.\n\n Parameters\n ----------\n exog : array_like\n Array of predictor variables for mean.\n exog_precision : array_like\n Array of... |
@glyph_method(glyphs.Annulus)
def annulus(self, **kwargs):
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.annulus(x=[1, 2, 3], y=[1, 2, 3], color="#7FC97F",\n inner_radius=0.... | 3,938,251,180,688,287,000 | Examples:
.. code-block:: python
from bokeh.plotting import figure, output_file, show
plot = figure(width=300, height=300)
plot.annulus(x=[1, 2, 3], y=[1, 2, 3], color="#7FC97F",
inner_radius=0.2, outer_radius=0.5)
show(plot) | bokeh/plotting/glyph_api.py | annulus | AzureTech/bokeh | python | @glyph_method(glyphs.Annulus)
def annulus(self, **kwargs):
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.annulus(x=[1, 2, 3], y=[1, 2, 3], color="#7FC97F",\n inner_radius=0.... |
@marker_method()
def asterisk(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.asterisk(x=[1,2,3], y=[1,2,3], size=20, color="#F0027F")\n\n ... | 1,979,245,211,455,648,800 | Examples:
.. code-block:: python
from bokeh.plotting import figure, output_file, show
plot = figure(width=300, height=300)
plot.asterisk(x=[1,2,3], y=[1,2,3], size=20, color="#F0027F")
show(plot) | bokeh/plotting/glyph_api.py | asterisk | AzureTech/bokeh | python | @marker_method()
def asterisk(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.asterisk(x=[1,2,3], y=[1,2,3], size=20, color="#F0027F")\n\n ... |
@glyph_method(glyphs.Circle)
def circle(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\n.. note::\n Only one of ``size`` or ``radius`` should be provided. Note that ``radius``\n defaults to |data units|.\n\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file... | 8,348,713,886,639,397,000 | .. note::
Only one of ``size`` or ``radius`` should be provided. Note that ``radius``
defaults to |data units|.
Examples:
.. code-block:: python
from bokeh.plotting import figure, output_file, show
plot = figure(width=300, height=300)
plot.circle(x=[1, 2, 3], y=[1, 2, 3], size=20... | bokeh/plotting/glyph_api.py | circle | AzureTech/bokeh | python | @glyph_method(glyphs.Circle)
def circle(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\n.. note::\n Only one of ``size`` or ``radius`` should be provided. Note that ``radius``\n defaults to |data units|.\n\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file... |
@marker_method()
def circle_cross(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.circle_cross(x=[1,2,3], y=[4,5,6], size=20,\n ... | -3,575,792,139,181,636,600 | Examples:
.. code-block:: python
from bokeh.plotting import figure, output_file, show
plot = figure(width=300, height=300)
plot.circle_cross(x=[1,2,3], y=[4,5,6], size=20,
color="#FB8072", fill_alpha=0.2, line_width=2)
show(plot) | bokeh/plotting/glyph_api.py | circle_cross | AzureTech/bokeh | python | @marker_method()
def circle_cross(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.circle_cross(x=[1,2,3], y=[4,5,6], size=20,\n ... |
@marker_method()
def circle_dot(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.circle_dot(x=[1,2,3], y=[4,5,6], size=20,\n ... | 8,496,416,513,568,618,000 | Examples:
.. code-block:: python
from bokeh.plotting import figure, output_file, show
plot = figure(width=300, height=300)
plot.circle_dot(x=[1,2,3], y=[4,5,6], size=20,
color="#FB8072", fill_color=None)
show(plot) | bokeh/plotting/glyph_api.py | circle_dot | AzureTech/bokeh | python | @marker_method()
def circle_dot(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.circle_dot(x=[1,2,3], y=[4,5,6], size=20,\n ... |
@marker_method()
def circle_x(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.circle_x(x=[1, 2, 3], y=[1, 2, 3], size=20,\n ... | 4,937,916,615,365,577,000 | Examples:
.. code-block:: python
from bokeh.plotting import figure, output_file, show
plot = figure(width=300, height=300)
plot.circle_x(x=[1, 2, 3], y=[1, 2, 3], size=20,
color="#DD1C77", fill_alpha=0.2)
show(plot) | bokeh/plotting/glyph_api.py | circle_x | AzureTech/bokeh | python | @marker_method()
def circle_x(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.circle_x(x=[1, 2, 3], y=[1, 2, 3], size=20,\n ... |
@marker_method()
def circle_y(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.circle_y(x=[1, 2, 3], y=[1, 2, 3], size=20,\n ... | -8,742,850,000,730,177,000 | Examples:
.. code-block:: python
from bokeh.plotting import figure, output_file, show
plot = figure(width=300, height=300)
plot.circle_y(x=[1, 2, 3], y=[1, 2, 3], size=20,
color="#DD1C77", fill_alpha=0.2)
show(plot) | bokeh/plotting/glyph_api.py | circle_y | AzureTech/bokeh | python | @marker_method()
def circle_y(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.circle_y(x=[1, 2, 3], y=[1, 2, 3], size=20,\n ... |
@marker_method()
def cross(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.cross(x=[1, 2, 3], y=[1, 2, 3], size=20,\n color="#... | 2,837,880,521,095,167,500 | Examples:
.. code-block:: python
from bokeh.plotting import figure, output_file, show
plot = figure(width=300, height=300)
plot.cross(x=[1, 2, 3], y=[1, 2, 3], size=20,
color="#E6550D", line_width=2)
show(plot) | bokeh/plotting/glyph_api.py | cross | AzureTech/bokeh | python | @marker_method()
def cross(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.cross(x=[1, 2, 3], y=[1, 2, 3], size=20,\n color="#... |
@marker_method()
def dash(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.dash(x=[1, 2, 3], y=[1, 2, 3], size=[10,20,25],\n col... | -6,676,188,663,389,309,000 | Examples:
.. code-block:: python
from bokeh.plotting import figure, output_file, show
plot = figure(width=300, height=300)
plot.dash(x=[1, 2, 3], y=[1, 2, 3], size=[10,20,25],
color="#99D594", line_width=2)
show(plot) | bokeh/plotting/glyph_api.py | dash | AzureTech/bokeh | python | @marker_method()
def dash(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.dash(x=[1, 2, 3], y=[1, 2, 3], size=[10,20,25],\n col... |
@marker_method()
def diamond(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.diamond(x=[1, 2, 3], y=[1, 2, 3], size=20,\n co... | 1,792,993,144,400,715,300 | Examples:
.. code-block:: python
from bokeh.plotting import figure, output_file, show
plot = figure(width=300, height=300)
plot.diamond(x=[1, 2, 3], y=[1, 2, 3], size=20,
color="#1C9099", line_width=2)
show(plot) | bokeh/plotting/glyph_api.py | diamond | AzureTech/bokeh | python | @marker_method()
def diamond(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.diamond(x=[1, 2, 3], y=[1, 2, 3], size=20,\n co... |
@marker_method()
def diamond_cross(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.diamond_cross(x=[1, 2, 3], y=[1, 2, 3], size=20,\n ... | 2,351,377,948,024,546,300 | Examples:
.. code-block:: python
from bokeh.plotting import figure, output_file, show
plot = figure(width=300, height=300)
plot.diamond_cross(x=[1, 2, 3], y=[1, 2, 3], size=20,
color="#386CB0", fill_color=None, line_width=2)
show(plot) | bokeh/plotting/glyph_api.py | diamond_cross | AzureTech/bokeh | python | @marker_method()
def diamond_cross(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.diamond_cross(x=[1, 2, 3], y=[1, 2, 3], size=20,\n ... |
@marker_method()
def diamond_dot(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.diamond_dot(x=[1, 2, 3], y=[1, 2, 3], size=20,\n ... | -2,226,707,390,867,855,600 | Examples:
.. code-block:: python
from bokeh.plotting import figure, output_file, show
plot = figure(width=300, height=300)
plot.diamond_dot(x=[1, 2, 3], y=[1, 2, 3], size=20,
color="#386CB0", fill_color=None)
show(plot) | bokeh/plotting/glyph_api.py | diamond_dot | AzureTech/bokeh | python | @marker_method()
def diamond_dot(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.diamond_dot(x=[1, 2, 3], y=[1, 2, 3], size=20,\n ... |
@marker_method()
def dot(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.dot(x=[1, 2, 3], y=[1, 2, 3], size=20, color="#386CB0")\n\n show... | 92,301,846,826,578,940 | Examples:
.. code-block:: python
from bokeh.plotting import figure, output_file, show
plot = figure(width=300, height=300)
plot.dot(x=[1, 2, 3], y=[1, 2, 3], size=20, color="#386CB0")
show(plot) | bokeh/plotting/glyph_api.py | dot | AzureTech/bokeh | python | @marker_method()
def dot(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.dot(x=[1, 2, 3], y=[1, 2, 3], size=20, color="#386CB0")\n\n show... |
@glyph_method(glyphs.HArea)
def harea(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.harea(x1=[0, 0, 0], x2=[1, 4, 2], y=[1, 2, 3],\n ... | -5,182,362,240,292,432,000 | Examples:
.. code-block:: python
from bokeh.plotting import figure, output_file, show
plot = figure(width=300, height=300)
plot.harea(x1=[0, 0, 0], x2=[1, 4, 2], y=[1, 2, 3],
fill_color="#99D594")
show(plot) | bokeh/plotting/glyph_api.py | harea | AzureTech/bokeh | python | @glyph_method(glyphs.HArea)
def harea(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.harea(x1=[0, 0, 0], x2=[1, 4, 2], y=[1, 2, 3],\n ... |
@glyph_method(glyphs.HBar)
def hbar(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.hbar(y=[1, 2, 3], height=0.5, left=0, right=[1,2,3], color="... | -7,922,850,661,979,236,000 | Examples:
.. code-block:: python
from bokeh.plotting import figure, output_file, show
plot = figure(width=300, height=300)
plot.hbar(y=[1, 2, 3], height=0.5, left=0, right=[1,2,3], color="#CAB2D6")
show(plot) | bokeh/plotting/glyph_api.py | hbar | AzureTech/bokeh | python | @glyph_method(glyphs.HBar)
def hbar(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.hbar(y=[1, 2, 3], height=0.5, left=0, right=[1,2,3], color="... |
@glyph_method(glyphs.Ellipse)
def ellipse(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.ellipse(x=[1, 2, 3], y=[1, 2, 3], width=30, height=20,... | 6,402,112,770,406,569,000 | Examples:
.. code-block:: python
from bokeh.plotting import figure, output_file, show
plot = figure(width=300, height=300)
plot.ellipse(x=[1, 2, 3], y=[1, 2, 3], width=30, height=20,
color="#386CB0", fill_color=None, line_width=2)
show(plot) | bokeh/plotting/glyph_api.py | ellipse | AzureTech/bokeh | python | @glyph_method(glyphs.Ellipse)
def ellipse(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.ellipse(x=[1, 2, 3], y=[1, 2, 3], width=30, height=20,... |
@marker_method()
def hex(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.hex(x=[1, 2, 3], y=[1, 2, 3], size=[10,20,30], color="#74ADD1")\n\n ... | 8,798,436,814,191,611,000 | Examples:
.. code-block:: python
from bokeh.plotting import figure, output_file, show
plot = figure(width=300, height=300)
plot.hex(x=[1, 2, 3], y=[1, 2, 3], size=[10,20,30], color="#74ADD1")
show(plot) | bokeh/plotting/glyph_api.py | hex | AzureTech/bokeh | python | @marker_method()
def hex(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.hex(x=[1, 2, 3], y=[1, 2, 3], size=[10,20,30], color="#74ADD1")\n\n ... |
@marker_method()
def hex_dot(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.hex_dot(x=[1, 2, 3], y=[1, 2, 3], size=[10,20,30],\n ... | -2,901,453,211,579,782,000 | Examples:
.. code-block:: python
from bokeh.plotting import figure, output_file, show
plot = figure(width=300, height=300)
plot.hex_dot(x=[1, 2, 3], y=[1, 2, 3], size=[10,20,30],
color="#74ADD1", fill_color=None)
show(plot) | bokeh/plotting/glyph_api.py | hex_dot | AzureTech/bokeh | python | @marker_method()
def hex_dot(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.hex_dot(x=[1, 2, 3], y=[1, 2, 3], size=[10,20,30],\n ... |
@glyph_method(glyphs.HexTile)
def hex_tile(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300, match_aspect=True)\n plot.hex_tile(r=[0, 0, 1], q=[1, 2, 2],... | 3,511,653,394,743,668,000 | Examples:
.. code-block:: python
from bokeh.plotting import figure, output_file, show
plot = figure(width=300, height=300, match_aspect=True)
plot.hex_tile(r=[0, 0, 1], q=[1, 2, 2], fill_color="#74ADD1")
show(plot) | bokeh/plotting/glyph_api.py | hex_tile | AzureTech/bokeh | python | @glyph_method(glyphs.HexTile)
def hex_tile(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300, match_aspect=True)\n plot.hex_tile(r=[0, 0, 1], q=[1, 2, 2],... |
@glyph_method(glyphs.Image)
def image(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\n.. note::\n If both ``palette`` and ``color_mapper`` are passed, a ``ValueError``\n exception will be raised. If neither is passed, then the ``Greys9``\n palette will be used as a default.\n\n' | 5,084,553,159,512,576,000 | .. note::
If both ``palette`` and ``color_mapper`` are passed, a ``ValueError``
exception will be raised. If neither is passed, then the ``Greys9``
palette will be used as a default. | bokeh/plotting/glyph_api.py | image | AzureTech/bokeh | python | @glyph_method(glyphs.Image)
def image(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\n.. note::\n If both ``palette`` and ``color_mapper`` are passed, a ``ValueError``\n exception will be raised. If neither is passed, then the ``Greys9``\n palette will be used as a default.\n\n' |
@glyph_method(glyphs.ImageRGBA)
def image_rgba(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\n.. note::\n The ``image_rgba`` method accepts images as a two-dimensional array of RGBA\n values (encoded as 32-bit integers).\n\n' | 3,012,462,801,939,874,300 | .. note::
The ``image_rgba`` method accepts images as a two-dimensional array of RGBA
values (encoded as 32-bit integers). | bokeh/plotting/glyph_api.py | image_rgba | AzureTech/bokeh | python | @glyph_method(glyphs.ImageRGBA)
def image_rgba(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\n.. note::\n The ``image_rgba`` method accepts images as a two-dimensional array of RGBA\n values (encoded as 32-bit integers).\n\n' |
@marker_method()
def inverted_triangle(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.inverted_triangle(x=[1, 2, 3], y=[1, 2, 3], size=20, colo... | 7,029,303,627,944,421,000 | Examples:
.. code-block:: python
from bokeh.plotting import figure, output_file, show
plot = figure(width=300, height=300)
plot.inverted_triangle(x=[1, 2, 3], y=[1, 2, 3], size=20, color="#DE2D26")
show(plot) | bokeh/plotting/glyph_api.py | inverted_triangle | AzureTech/bokeh | python | @marker_method()
def inverted_triangle(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.inverted_triangle(x=[1, 2, 3], y=[1, 2, 3], size=20, colo... |
@glyph_method(glyphs.Line)
def line(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n p = figure(title="line", width=300, height=300)\n p.line(x=[1, 2, 3, 4, 5], y=[6, 7, 2, 4, 5])\n\n ... | -53,520,465,852,709,820 | Examples:
.. code-block:: python
from bokeh.plotting import figure, output_file, show
p = figure(title="line", width=300, height=300)
p.line(x=[1, 2, 3, 4, 5], y=[6, 7, 2, 4, 5])
show(p) | bokeh/plotting/glyph_api.py | line | AzureTech/bokeh | python | @glyph_method(glyphs.Line)
def line(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n p = figure(title="line", width=300, height=300)\n p.line(x=[1, 2, 3, 4, 5], y=[6, 7, 2, 4, 5])\n\n ... |
@glyph_method(glyphs.MultiLine)
def multi_line(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\n.. note::\n For this glyph, the data is not simply an array of scalars, it is an\n "array of arrays".\n\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\... | -8,230,569,948,412,799,000 | .. note::
For this glyph, the data is not simply an array of scalars, it is an
"array of arrays".
Examples:
.. code-block:: python
from bokeh.plotting import figure, output_file, show
p = figure(width=300, height=300)
p.multi_line(xs=[[1, 2, 3], [2, 3, 4]], ys=[[6, 7, 2], [4, 5, ... | bokeh/plotting/glyph_api.py | multi_line | AzureTech/bokeh | python | @glyph_method(glyphs.MultiLine)
def multi_line(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\n.. note::\n For this glyph, the data is not simply an array of scalars, it is an\n "array of arrays".\n\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\... |
@glyph_method(glyphs.MultiPolygons)
def multi_polygons(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
"\n.. note::\n For this glyph, the data is not simply an array of scalars, it is a\n nested array.\n\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, sho... | 5,069,927,588,036,277,000 | .. note::
For this glyph, the data is not simply an array of scalars, it is a
nested array.
Examples:
.. code-block:: python
from bokeh.plotting import figure, output_file, show
p = figure(width=300, height=300)
p.multi_polygons(xs=[[[[1, 1, 2, 2]]], [[[1, 1, 3], [1.5, 1.5, 2]]]]... | bokeh/plotting/glyph_api.py | multi_polygons | AzureTech/bokeh | python | @glyph_method(glyphs.MultiPolygons)
def multi_polygons(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
"\n.. note::\n For this glyph, the data is not simply an array of scalars, it is a\n nested array.\n\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, sho... |
@glyph_method(glyphs.Oval)
def oval(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.oval(x=[1, 2, 3], y=[1, 2, 3], width=0.2, height=0.4,\n ... | 9,096,103,592,002,765,000 | Examples:
.. code-block:: python
from bokeh.plotting import figure, output_file, show
plot = figure(width=300, height=300)
plot.oval(x=[1, 2, 3], y=[1, 2, 3], width=0.2, height=0.4,
angle=-0.7, color="#1D91C0")
show(plot) | bokeh/plotting/glyph_api.py | oval | AzureTech/bokeh | python | @glyph_method(glyphs.Oval)
def oval(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.oval(x=[1, 2, 3], y=[1, 2, 3], width=0.2, height=0.4,\n ... |
@glyph_method(glyphs.Patch)
def patch(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n p = figure(width=300, height=300)\n p.patch(x=[1, 2, 3, 2], y=[6, 7, 2, 2], color="#99d8c9")\n\n ... | -417,730,788,079,325,100 | Examples:
.. code-block:: python
from bokeh.plotting import figure, output_file, show
p = figure(width=300, height=300)
p.patch(x=[1, 2, 3, 2], y=[6, 7, 2, 2], color="#99d8c9")
show(p) | bokeh/plotting/glyph_api.py | patch | AzureTech/bokeh | python | @glyph_method(glyphs.Patch)
def patch(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n p = figure(width=300, height=300)\n p.patch(x=[1, 2, 3, 2], y=[6, 7, 2, 2], color="#99d8c9")\n\n ... |
@glyph_method(glyphs.Patches)
def patches(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\n.. note::\n For this glyph, the data is not simply an array of scalars, it is an\n "array of arrays".\n\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n ... | 8,282,966,439,202,146,000 | .. note::
For this glyph, the data is not simply an array of scalars, it is an
"array of arrays".
Examples:
.. code-block:: python
from bokeh.plotting import figure, output_file, show
p = figure(width=300, height=300)
p.patches(xs=[[1,2,3],[4,5,6,5]], ys=[[1,2,1],[4,5,5,4]],
... | bokeh/plotting/glyph_api.py | patches | AzureTech/bokeh | python | @glyph_method(glyphs.Patches)
def patches(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\n.. note::\n For this glyph, the data is not simply an array of scalars, it is an\n "array of arrays".\n\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n ... |
@marker_method()
def plus(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.plus(x=[1, 2, 3], y=[1, 2, 3], size=20, color="#DE2D26")\n\n sh... | 1,416,541,568,013,438,700 | Examples:
.. code-block:: python
from bokeh.plotting import figure, output_file, show
plot = figure(width=300, height=300)
plot.plus(x=[1, 2, 3], y=[1, 2, 3], size=20, color="#DE2D26")
show(plot) | bokeh/plotting/glyph_api.py | plus | AzureTech/bokeh | python | @marker_method()
def plus(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.plus(x=[1, 2, 3], y=[1, 2, 3], size=20, color="#DE2D26")\n\n sh... |
@glyph_method(glyphs.Quad)
def quad(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.quad(top=[2, 3, 4], bottom=[1, 2, 3], left=[1, 2, 3],\n ... | -4,907,198,913,454,473,000 | Examples:
.. code-block:: python
from bokeh.plotting import figure, output_file, show
plot = figure(width=300, height=300)
plot.quad(top=[2, 3, 4], bottom=[1, 2, 3], left=[1, 2, 3],
right=[1.2, 2.5, 3.7], color="#B3DE69")
show(plot) | bokeh/plotting/glyph_api.py | quad | AzureTech/bokeh | python | @glyph_method(glyphs.Quad)
def quad(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.quad(top=[2, 3, 4], bottom=[1, 2, 3], left=[1, 2, 3],\n ... |
@glyph_method(glyphs.Ray)
def ray(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.ray(x=[1, 2, 3], y=[1, 2, 3], length=45, angle=-0.7, color="#F... | -6,554,476,667,526,731,000 | Examples:
.. code-block:: python
from bokeh.plotting import figure, output_file, show
plot = figure(width=300, height=300)
plot.ray(x=[1, 2, 3], y=[1, 2, 3], length=45, angle=-0.7, color="#FB8072",
line_width=2)
show(plot) | bokeh/plotting/glyph_api.py | ray | AzureTech/bokeh | python | @glyph_method(glyphs.Ray)
def ray(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.ray(x=[1, 2, 3], y=[1, 2, 3], length=45, angle=-0.7, color="#F... |
@glyph_method(glyphs.Rect)
def rect(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.rect(x=[1, 2, 3], y=[1, 2, 3], width=10, height=20, color="#... | 705,059,381,429,693,000 | Examples:
.. code-block:: python
from bokeh.plotting import figure, output_file, show
plot = figure(width=300, height=300)
plot.rect(x=[1, 2, 3], y=[1, 2, 3], width=10, height=20, color="#CAB2D6",
width_units="screen", height_units="screen")
show(plot) | bokeh/plotting/glyph_api.py | rect | AzureTech/bokeh | python | @glyph_method(glyphs.Rect)
def rect(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.rect(x=[1, 2, 3], y=[1, 2, 3], width=10, height=20, color="#... |
@glyph_method(glyphs.Step)
def step(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.step(x=[1, 2, 3, 4, 5], y=[1, 2, 3, 2, 5], color="#FB8072")\... | -3,492,657,471,929,209,300 | Examples:
.. code-block:: python
from bokeh.plotting import figure, output_file, show
plot = figure(width=300, height=300)
plot.step(x=[1, 2, 3, 4, 5], y=[1, 2, 3, 2, 5], color="#FB8072")
show(plot) | bokeh/plotting/glyph_api.py | step | AzureTech/bokeh | python | @glyph_method(glyphs.Step)
def step(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.step(x=[1, 2, 3, 4, 5], y=[1, 2, 3, 2, 5], color="#FB8072")\... |
@glyph_method(glyphs.Segment)
def segment(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.segment(x0=[1, 2, 3], y0=[1, 2, 3],\n ... | -5,143,638,344,136,174,000 | Examples:
.. code-block:: python
from bokeh.plotting import figure, output_file, show
plot = figure(width=300, height=300)
plot.segment(x0=[1, 2, 3], y0=[1, 2, 3],
x1=[1, 2, 3], y1=[1.2, 2.5, 3.7],
color="#F4A582", line_width=3)
show(plot... | bokeh/plotting/glyph_api.py | segment | AzureTech/bokeh | python | @glyph_method(glyphs.Segment)
def segment(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.segment(x0=[1, 2, 3], y0=[1, 2, 3],\n ... |
@marker_method()
def square(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.square(x=[1, 2, 3], y=[1, 2, 3], size=[10,20,30], color="#74ADD1")\n... | 2,493,520,840,458,622,000 | Examples:
.. code-block:: python
from bokeh.plotting import figure, output_file, show
plot = figure(width=300, height=300)
plot.square(x=[1, 2, 3], y=[1, 2, 3], size=[10,20,30], color="#74ADD1")
show(plot) | bokeh/plotting/glyph_api.py | square | AzureTech/bokeh | python | @marker_method()
def square(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.square(x=[1, 2, 3], y=[1, 2, 3], size=[10,20,30], color="#74ADD1")\n... |
@marker_method()
def square_cross(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.square_cross(x=[1, 2, 3], y=[1, 2, 3], size=[10,20,25],\n ... | 7,120,505,516,620,560,000 | Examples:
.. code-block:: python
from bokeh.plotting import figure, output_file, show
plot = figure(width=300, height=300)
plot.square_cross(x=[1, 2, 3], y=[1, 2, 3], size=[10,20,25],
color="#7FC97F",fill_color=None, line_width=2)
show(plot) | bokeh/plotting/glyph_api.py | square_cross | AzureTech/bokeh | python | @marker_method()
def square_cross(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.square_cross(x=[1, 2, 3], y=[1, 2, 3], size=[10,20,25],\n ... |
@marker_method()
def square_dot(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.square_dot(x=[1, 2, 3], y=[1, 2, 3], size=[10,20,25],\n ... | -5,974,181,200,857,512,000 | Examples:
.. code-block:: python
from bokeh.plotting import figure, output_file, show
plot = figure(width=300, height=300)
plot.square_dot(x=[1, 2, 3], y=[1, 2, 3], size=[10,20,25],
color="#7FC97F", fill_color=None)
show(plot) | bokeh/plotting/glyph_api.py | square_dot | AzureTech/bokeh | python | @marker_method()
def square_dot(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.square_dot(x=[1, 2, 3], y=[1, 2, 3], size=[10,20,25],\n ... |
@marker_method()
def square_pin(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.square_pin(x=[1, 2, 3], y=[1, 2, 3], size=[10,20,25],\n ... | 6,103,200,413,580,941,000 | Examples:
.. code-block:: python
from bokeh.plotting import figure, output_file, show
plot = figure(width=300, height=300)
plot.square_pin(x=[1, 2, 3], y=[1, 2, 3], size=[10,20,25],
color="#7FC97F",fill_color=None, line_width=2)
show(plot) | bokeh/plotting/glyph_api.py | square_pin | AzureTech/bokeh | python | @marker_method()
def square_pin(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.square_pin(x=[1, 2, 3], y=[1, 2, 3], size=[10,20,25],\n ... |
@marker_method()
def square_x(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.square_x(x=[1, 2, 3], y=[1, 2, 3], size=[10,20,25],\n ... | 6,320,477,579,178,854,000 | Examples:
.. code-block:: python
from bokeh.plotting import figure, output_file, show
plot = figure(width=300, height=300)
plot.square_x(x=[1, 2, 3], y=[1, 2, 3], size=[10,20,25],
color="#FDAE6B",fill_color=None, line_width=2)
show(plot) | bokeh/plotting/glyph_api.py | square_x | AzureTech/bokeh | python | @marker_method()
def square_x(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.square_x(x=[1, 2, 3], y=[1, 2, 3], size=[10,20,25],\n ... |
@marker_method()
def star(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.star(x=[1, 2, 3], y=[1, 2, 3], size=20,\n color="#1C9... | -1,999,413,690,404,477,700 | Examples:
.. code-block:: python
from bokeh.plotting import figure, output_file, show
plot = figure(width=300, height=300)
plot.star(x=[1, 2, 3], y=[1, 2, 3], size=20,
color="#1C9099", line_width=2)
show(plot) | bokeh/plotting/glyph_api.py | star | AzureTech/bokeh | python | @marker_method()
def star(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.star(x=[1, 2, 3], y=[1, 2, 3], size=20,\n color="#1C9... |
@marker_method()
def star_dot(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.star_dot(x=[1, 2, 3], y=[1, 2, 3], size=20,\n ... | 344,974,706,167,149,630 | Examples:
.. code-block:: python
from bokeh.plotting import figure, output_file, show
plot = figure(width=300, height=300)
plot.star_dot(x=[1, 2, 3], y=[1, 2, 3], size=20,
color="#386CB0", fill_color=None, line_width=2)
show(plot) | bokeh/plotting/glyph_api.py | star_dot | AzureTech/bokeh | python | @marker_method()
def star_dot(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.star_dot(x=[1, 2, 3], y=[1, 2, 3], size=20,\n ... |
@glyph_method(glyphs.Text)
def text(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\n.. note::\n The location and angle of the text relative to the ``x``, ``y`` coordinates\n is indicated by the alignment and baseline text properties.\n\n' | -893,788,023,180,619,900 | .. note::
The location and angle of the text relative to the ``x``, ``y`` coordinates
is indicated by the alignment and baseline text properties. | bokeh/plotting/glyph_api.py | text | AzureTech/bokeh | python | @glyph_method(glyphs.Text)
def text(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\n.. note::\n The location and angle of the text relative to the ``x``, ``y`` coordinates\n is indicated by the alignment and baseline text properties.\n\n' |
@marker_method()
def triangle(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.triangle(x=[1, 2, 3], y=[1, 2, 3], size=[10,20,25],\n ... | 6,747,221,224,420,255,000 | Examples:
.. code-block:: python
from bokeh.plotting import figure, output_file, show
plot = figure(width=300, height=300)
plot.triangle(x=[1, 2, 3], y=[1, 2, 3], size=[10,20,25],
color="#99D594", line_width=2)
show(plot) | bokeh/plotting/glyph_api.py | triangle | AzureTech/bokeh | python | @marker_method()
def triangle(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.triangle(x=[1, 2, 3], y=[1, 2, 3], size=[10,20,25],\n ... |
@marker_method()
def triangle_dot(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.triangle_dot(x=[1, 2, 3], y=[1, 2, 3], size=[10,20,25],\n ... | 7,526,180,058,643,305,000 | Examples:
.. code-block:: python
from bokeh.plotting import figure, output_file, show
plot = figure(width=300, height=300)
plot.triangle_dot(x=[1, 2, 3], y=[1, 2, 3], size=[10,20,25],
color="#99D594", fill_color=None)
show(plot) | bokeh/plotting/glyph_api.py | triangle_dot | AzureTech/bokeh | python | @marker_method()
def triangle_dot(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.triangle_dot(x=[1, 2, 3], y=[1, 2, 3], size=[10,20,25],\n ... |
@marker_method()
def triangle_pin(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.triangle_pin(x=[1, 2, 3], y=[1, 2, 3], size=[10,20,25],\n ... | -4,137,067,794,809,148,000 | Examples:
.. code-block:: python
from bokeh.plotting import figure, output_file, show
plot = figure(width=300, height=300)
plot.triangle_pin(x=[1, 2, 3], y=[1, 2, 3], size=[10,20,25],
color="#99D594", line_width=2)
show(plot) | bokeh/plotting/glyph_api.py | triangle_pin | AzureTech/bokeh | python | @marker_method()
def triangle_pin(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.triangle_pin(x=[1, 2, 3], y=[1, 2, 3], size=[10,20,25],\n ... |
@glyph_method(glyphs.VArea)
def varea(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.varea(x=[1, 2, 3], y1=[0, 0, 0], y2=[1, 4, 2],\n ... | 8,477,394,530,676,108,000 | Examples:
.. code-block:: python
from bokeh.plotting import figure, output_file, show
plot = figure(width=300, height=300)
plot.varea(x=[1, 2, 3], y1=[0, 0, 0], y2=[1, 4, 2],
fill_color="#99D594")
show(plot) | bokeh/plotting/glyph_api.py | varea | AzureTech/bokeh | python | @glyph_method(glyphs.VArea)
def varea(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.varea(x=[1, 2, 3], y1=[0, 0, 0], y2=[1, 4, 2],\n ... |
@glyph_method(glyphs.VBar)
def vbar(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.vbar(x=[1, 2, 3], width=0.5, bottom=0, top=[1,2,3], color="#... | -3,424,809,155,875,691,500 | Examples:
.. code-block:: python
from bokeh.plotting import figure, output_file, show
plot = figure(width=300, height=300)
plot.vbar(x=[1, 2, 3], width=0.5, bottom=0, top=[1,2,3], color="#CAB2D6")
show(plot) | bokeh/plotting/glyph_api.py | vbar | AzureTech/bokeh | python | @glyph_method(glyphs.VBar)
def vbar(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.vbar(x=[1, 2, 3], width=0.5, bottom=0, top=[1,2,3], color="#... |
@glyph_method(glyphs.Wedge)
def wedge(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.wedge(x=[1, 2, 3], y=[1, 2, 3], radius=15, start_angle=0.6... | 188,312,062,759,754,080 | Examples:
.. code-block:: python
from bokeh.plotting import figure, output_file, show
plot = figure(width=300, height=300)
plot.wedge(x=[1, 2, 3], y=[1, 2, 3], radius=15, start_angle=0.6,
end_angle=4.1, radius_units="screen", color="#2b8cbe")
show(plot) | bokeh/plotting/glyph_api.py | wedge | AzureTech/bokeh | python | @glyph_method(glyphs.Wedge)
def wedge(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.wedge(x=[1, 2, 3], y=[1, 2, 3], radius=15, start_angle=0.6... |
@marker_method()
def x(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.x(x=[1, 2, 3], y=[1, 2, 3], size=[10, 20, 25], color="#fa9fb5")\n\n ... | -2,744,288,825,578,744,300 | Examples:
.. code-block:: python
from bokeh.plotting import figure, output_file, show
plot = figure(width=300, height=300)
plot.x(x=[1, 2, 3], y=[1, 2, 3], size=[10, 20, 25], color="#fa9fb5")
show(plot) | bokeh/plotting/glyph_api.py | x | AzureTech/bokeh | python | @marker_method()
def x(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.x(x=[1, 2, 3], y=[1, 2, 3], size=[10, 20, 25], color="#fa9fb5")\n\n ... |
@marker_method()
def y(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.y(x=[1, 2, 3], y=[1, 2, 3], size=20, color="#DE2D26")\n\n show(plo... | 5,345,449,241,740,763,000 | Examples:
.. code-block:: python
from bokeh.plotting import figure, output_file, show
plot = figure(width=300, height=300)
plot.y(x=[1, 2, 3], y=[1, 2, 3], size=20, color="#DE2D26")
show(plot) | bokeh/plotting/glyph_api.py | y | AzureTech/bokeh | python | @marker_method()
def y(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
'\nExamples:\n\n .. code-block:: python\n\n from bokeh.plotting import figure, output_file, show\n\n plot = figure(width=300, height=300)\n plot.y(x=[1, 2, 3], y=[1, 2, 3], size=20, color="#DE2D26")\n\n show(plo... |
def scatter(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
' Creates a scatter plot of the given x and y items.\n\n Args:\n x (str or seq[float]) : values or field names of center x coordinates\n\n y (str or seq[float]) : values or field names of center y coordinates\n\n ... | 4,243,868,176,108,034,000 | Creates a scatter plot of the given x and y items.
Args:
x (str or seq[float]) : values or field names of center x coordinates
y (str or seq[float]) : values or field names of center y coordinates
size (str or list[float]) : values or field names of sizes in |screen units|
marker (str, or list[str])... | bokeh/plotting/glyph_api.py | scatter | AzureTech/bokeh | python | def scatter(self, *args: Any, **kwargs: Any) -> GlyphRenderer:
' Creates a scatter plot of the given x and y items.\n\n Args:\n x (str or seq[float]) : values or field names of center x coordinates\n\n y (str or seq[float]) : values or field names of center y coordinates\n\n ... |
def docutilize(obj):
"Convert Numpy or Google style docstring into reStructuredText format.\n\n Args:\n obj (str or object):\n Takes an object and changes it's docstrings to a reStructuredText\n format.\n Returns:\n str or object:\n A converted string or an objec... | -4,612,370,075,998,829,000 | Convert Numpy or Google style docstring into reStructuredText format.
Args:
obj (str or object):
Takes an object and changes it's docstrings to a reStructuredText
format.
Returns:
str or object:
A converted string or an object with replaced docstring depending
on the type of the... | improver/cli/__init__.py | docutilize | anja-bom/improver | python | def docutilize(obj):
"Convert Numpy or Google style docstring into reStructuredText format.\n\n Args:\n obj (str or object):\n Takes an object and changes it's docstrings to a reStructuredText\n format.\n Returns:\n str or object:\n A converted string or an objec... |
def maybe_coerce_with(converter, obj, **kwargs):
'Apply converter if str, pass through otherwise.'
obj = getattr(obj, 'original_object', obj)
return (converter(obj, **kwargs) if isinstance(obj, str) else obj) | -8,289,763,171,914,728,000 | Apply converter if str, pass through otherwise. | improver/cli/__init__.py | maybe_coerce_with | anja-bom/improver | python | def maybe_coerce_with(converter, obj, **kwargs):
obj = getattr(obj, 'original_object', obj)
return (converter(obj, **kwargs) if isinstance(obj, str) else obj) |
@value_converter
def inputcube(to_convert):
'Loads cube from file or returns passed object.\n\n Args:\n to_convert (string or iris.cube.Cube):\n File name or Cube object.\n\n Returns:\n Loaded cube or passed object.\n\n '
from improver.utilities.load import load_cube
return... | 1,064,669,169,553,711,200 | Loads cube from file or returns passed object.
Args:
to_convert (string or iris.cube.Cube):
File name or Cube object.
Returns:
Loaded cube or passed object. | improver/cli/__init__.py | inputcube | anja-bom/improver | python | @value_converter
def inputcube(to_convert):
'Loads cube from file or returns passed object.\n\n Args:\n to_convert (string or iris.cube.Cube):\n File name or Cube object.\n\n Returns:\n Loaded cube or passed object.\n\n '
from improver.utilities.load import load_cube
return... |
@value_converter
def inputcube_nolazy(to_convert):
'Loads cube from file or returns passed object.\n Where a load is performed, it will not have lazy data.\n Args:\n to_convert (string or iris.cube.Cube):\n File name or Cube object.\n Returns:\n Loaded cube or passed object.\n '... | -8,033,249,565,026,459,000 | Loads cube from file or returns passed object.
Where a load is performed, it will not have lazy data.
Args:
to_convert (string or iris.cube.Cube):
File name or Cube object.
Returns:
Loaded cube or passed object. | improver/cli/__init__.py | inputcube_nolazy | anja-bom/improver | python | @value_converter
def inputcube_nolazy(to_convert):
'Loads cube from file or returns passed object.\n Where a load is performed, it will not have lazy data.\n Args:\n to_convert (string or iris.cube.Cube):\n File name or Cube object.\n Returns:\n Loaded cube or passed object.\n '... |
@value_converter
def inputcubelist(to_convert):
'Loads a cubelist from file or returns passed object.\n Args:\n to_convert (string or iris.cube.CubeList):\n File name or CubeList object.\n Returns:\n Loaded cubelist or passed object.\n '
from improver.utilities.load import load... | -6,123,243,750,426,282,000 | Loads a cubelist from file or returns passed object.
Args:
to_convert (string or iris.cube.CubeList):
File name or CubeList object.
Returns:
Loaded cubelist or passed object. | improver/cli/__init__.py | inputcubelist | anja-bom/improver | python | @value_converter
def inputcubelist(to_convert):
'Loads a cubelist from file or returns passed object.\n Args:\n to_convert (string or iris.cube.CubeList):\n File name or CubeList object.\n Returns:\n Loaded cubelist or passed object.\n '
from improver.utilities.load import load... |
@value_converter
def inputjson(to_convert):
'Loads json from file or returns passed object.\n\n Args:\n to_convert (string or dict):\n File name or json dictionary.\n\n Returns:\n Loaded json dictionary or passed object.\n\n '
from improver.utilities.cli_utilities import load_j... | -3,255,302,438,592,015,400 | Loads json from file or returns passed object.
Args:
to_convert (string or dict):
File name or json dictionary.
Returns:
Loaded json dictionary or passed object. | improver/cli/__init__.py | inputjson | anja-bom/improver | python | @value_converter
def inputjson(to_convert):
'Loads json from file or returns passed object.\n\n Args:\n to_convert (string or dict):\n File name or json dictionary.\n\n Returns:\n Loaded json dictionary or passed object.\n\n '
from improver.utilities.cli_utilities import load_j... |
@value_converter
def comma_separated_list(to_convert):
'Converts comma separated string to list or returns passed object.\n\n Args:\n to_convert (string or list)\n comma separated string or list\n\n Returns:\n list\n '
return maybe_coerce_with((lambda s: s.split(',')), to_conver... | 3,608,620,111,620,679,000 | Converts comma separated string to list or returns passed object.
Args:
to_convert (string or list)
comma separated string or list
Returns:
list | improver/cli/__init__.py | comma_separated_list | anja-bom/improver | python | @value_converter
def comma_separated_list(to_convert):
'Converts comma separated string to list or returns passed object.\n\n Args:\n to_convert (string or list)\n comma separated string or list\n\n Returns:\n list\n '
return maybe_coerce_with((lambda s: s.split(',')), to_conver... |
@value_converter
def comma_separated_list_of_float(to_convert):
'Converts comma separated string to list of floats or returns passed object.\n\n Args:\n to_convert (string or list)\n comma separated string or list\n\n Returns:\n list\n '
return maybe_coerce_with((lambda string: ... | 4,034,757,157,358,138,400 | Converts comma separated string to list of floats or returns passed object.
Args:
to_convert (string or list)
comma separated string or list
Returns:
list | improver/cli/__init__.py | comma_separated_list_of_float | anja-bom/improver | python | @value_converter
def comma_separated_list_of_float(to_convert):
'Converts comma separated string to list of floats or returns passed object.\n\n Args:\n to_convert (string or list)\n comma separated string or list\n\n Returns:\n list\n '
return maybe_coerce_with((lambda string: ... |
@value_converter
def inputpath(to_convert):
'Converts string paths to pathlib Path objects\n\n Args:\n to_convert (string or pathlib.Path):\n path represented as string\n\n Returns:\n (pathlib.Path): Path object\n\n '
return maybe_coerce_with(pathlib.Path, to_convert) | 6,136,849,895,679,115,000 | Converts string paths to pathlib Path objects
Args:
to_convert (string or pathlib.Path):
path represented as string
Returns:
(pathlib.Path): Path object | improver/cli/__init__.py | inputpath | anja-bom/improver | python | @value_converter
def inputpath(to_convert):
'Converts string paths to pathlib Path objects\n\n Args:\n to_convert (string or pathlib.Path):\n path represented as string\n\n Returns:\n (pathlib.Path): Path object\n\n '
return maybe_coerce_with(pathlib.Path, to_convert) |
@value_converter
def inputdatetime(to_convert):
'Converts string to datetime or returns passed object.\n\n Args:\n to_convert (string or datetime):\n datetime represented as string of the format YYYYMMDDTHHMMZ\n\n Returns:\n (datetime): datetime object\n\n '
from improver.utili... | 1,230,513,173,964,127,500 | Converts string to datetime or returns passed object.
Args:
to_convert (string or datetime):
datetime represented as string of the format YYYYMMDDTHHMMZ
Returns:
(datetime): datetime object | improver/cli/__init__.py | inputdatetime | anja-bom/improver | python | @value_converter
def inputdatetime(to_convert):
'Converts string to datetime or returns passed object.\n\n Args:\n to_convert (string or datetime):\n datetime represented as string of the format YYYYMMDDTHHMMZ\n\n Returns:\n (datetime): datetime object\n\n '
from improver.utili... |
def create_constrained_inputcubelist_converter(*constraints):
"Makes function that the input constraints are used in a loop.\n\n The function is a @value_converter, this means it is used by clize to convert\n strings into objects.\n This is a way of not using the IMPROVER load_cube which will try to merge\... | -340,979,575,987,960,260 | Makes function that the input constraints are used in a loop.
The function is a @value_converter, this means it is used by clize to convert
strings into objects.
This is a way of not using the IMPROVER load_cube which will try to merge
cubes. Iris load on the other hand won't deal with meta data properly.
So an exampl... | improver/cli/__init__.py | create_constrained_inputcubelist_converter | anja-bom/improver | python | def create_constrained_inputcubelist_converter(*constraints):
"Makes function that the input constraints are used in a loop.\n\n The function is a @value_converter, this means it is used by clize to convert\n strings into objects.\n This is a way of not using the IMPROVER load_cube which will try to merge\... |
@decorator
def with_output(wrapped, *args, output=None, compression_level=1, least_significant_digit: int=None, **kwargs):
'Add `output` keyword only argument.\n Add `compression_level` option.\n Add `least_significant_digit` option.\n\n This is used to add extra `output`, `compression_level` and `least_si... | 1,128,665,921,701,529,000 | Add `output` keyword only argument.
Add `compression_level` option.
Add `least_significant_digit` option.
This is used to add extra `output`, `compression_level` and `least_significant_digit` CLI
options. If `output` is provided, it saves the result of calling `wrapped` to file and returns
None, otherwise it returns t... | improver/cli/__init__.py | with_output | anja-bom/improver | python | @decorator
def with_output(wrapped, *args, output=None, compression_level=1, least_significant_digit: int=None, **kwargs):
'Add `output` keyword only argument.\n Add `compression_level` option.\n Add `least_significant_digit` option.\n\n This is used to add extra `output`, `compression_level` and `least_si... |
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