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train | save_load_result | Saves and/or loads func output (must be picklable). | nestcheck/io_utils.py | def save_load_result(func):
"""Saves and/or loads func output (must be picklable)."""
@functools.wraps(func)
def wrapper(*args, **kwargs):
"""
Default behavior is no saving and loading. Specify save_name to save
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save_name: str,... | def save_load_result(func):
"""Saves and/or loads func output (must be picklable)."""
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Default behavior is no saving and loading. Specify save_name to save
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train | pickle_save | Saves object with pickle.
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train | pickle_load | Load data with pickle.
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train | bootstrap_resample_run | Bootstrap resamples threads of nested sampling run, returning a new
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Get the individual threads for a nested sampling run.
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ns_run: dict
Nested sampling run dictionary.
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"""Bootstrap resamples threads of nested sampling run, returning a new
(resampled) nested sampling run.
Get the individual threads for a nested sampling run.
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For more details about bootstrap resampling for estimating sampling
errors see 'Sampling errors in nested sampling p... | nestcheck/error_analysis.py | def run_bootstrap_values(ns_run, estimator_list, **kwargs):
"""Uses bootstrap resampling to calculate an estimate of the
standard deviation of the distribution of sampling errors (the
uncertainty on the calculation) for a single nested sampling run.
For more details about bootstrap resampling for estim... | def run_bootstrap_values(ns_run, estimator_list, **kwargs):
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train | run_ci_bootstrap | Uses bootstrap resampling to calculate credible intervals on the
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train | run_std_simulate | Uses the 'simulated weights' method to calculate an estimate of the
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standard deviation of the distribution of sampling errors (the
uncertainty on the calculation) for a single nested sampling run.
Note that the simulated weights method i... | def run_std_simulate(ns_run, estimator_list, n_simulate=None):
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train | implementation_std | r"""Estimates varaition of results due to implementation-specific
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train | run_thread_values | Helper function for parallelising thread_values_df.
Parameters
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ns_run: dict
Nested sampling run dictionary.
estimator_list: list of functions
Returns
-------
vals_array: numpy array
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Parameters
----------
ns_run: dict
Nested sampling run dictionary.
estimator_list: list of functions
Returns
-------
vals_array: numpy array
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Nested sampling run dictionary.
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train | pairwise_distances | Applies statistical_distances to each unique pair of distribution
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Parameters
----------
dist_list: list of 1d arrays
earth_mover_dist: bool, optional
Passed to statistical_distances.
energy_dist: bool, optional
Passed to statistical_distances.
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"""Applies statistical_distances to each unique pair of distribution
samples in dist_list.
Parameters
----------
dist_list: list of 1d arrays
earth_mover_dist: bool, optional
Passed to statistical_distances.
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train | statistical_distances | Compute measures of the statistical distance between samples.
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samples2: 1d array
earth_mover_dist: bool, optional
Whether or not to compute the Earth mover's distance between the
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energy_dist: bool, optional
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"""Compute measures of the statistical distance between samples.
Parameters
----------
samples1: 1d array
samples2: 1d array
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train | get_dummy_thread | Generate dummy data for a single nested sampling thread.
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train | get_dummy_run | Generate dummy data for a nested sampling run.
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train | get_dummy_dynamic_run | Generate dummy data for a dynamic nested sampling run.
Loglikelihood values of points are generated from a uniform distribution
in (0, 1), sorted, scaled by logl_range and shifted by logl_start (if it is
not -np.inf). Theta values of each point are each generated from a uniform
distribution in (0, 1).
... | nestcheck/dummy_data.py | def get_dummy_dynamic_run(nsamples, **kwargs):
"""Generate dummy data for a dynamic nested sampling run.
Loglikelihood values of points are generated from a uniform distribution
in (0, 1), sorted, scaled by logl_range and shifted by logl_start (if it is
not -np.inf). Theta values of each point are each... | def get_dummy_dynamic_run(nsamples, **kwargs):
"""Generate dummy data for a dynamic nested sampling run.
Loglikelihood values of points are generated from a uniform distribution
in (0, 1), sorted, scaled by logl_range and shifted by logl_start (if it is
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train | get_long_description | Get PyPI long description from the .rst file. | setup.py | def get_long_description():
"""Get PyPI long description from the .rst file."""
pkg_dir = get_package_dir()
with open(os.path.join(pkg_dir, '.pypi_long_desc.rst')) as readme_file:
long_description = readme_file.read()
return long_description | def get_long_description():
"""Get PyPI long description from the .rst file."""
pkg_dir = get_package_dir()
with open(os.path.join(pkg_dir, '.pypi_long_desc.rst')) as readme_file:
long_description = readme_file.read()
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train | get_version | Get single-source __version__. | setup.py | def get_version():
"""Get single-source __version__."""
pkg_dir = get_package_dir()
with open(os.path.join(pkg_dir, 'nestcheck/_version.py')) as ver_file:
string = ver_file.read()
return string.strip().replace('__version__ = ', '').replace('\'', '') | def get_version():
"""Get single-source __version__."""
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with open(os.path.join(pkg_dir, 'nestcheck/_version.py')) as ver_file:
string = ver_file.read()
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train | plot_run_nlive | Plot the allocations of live points as a function of logX for the input
sets of nested sampling runs of the type used in the dynamic nested
sampling paper (Higson et al. 2019).
Plots also include analytically calculated distributions of relative
posterior mass and relative posterior mass remaining.
... | nestcheck/plots.py | def plot_run_nlive(method_names, run_dict, **kwargs):
"""Plot the allocations of live points as a function of logX for the input
sets of nested sampling runs of the type used in the dynamic nested
sampling paper (Higson et al. 2019).
Plots also include analytically calculated distributions of relative
... | def plot_run_nlive(method_names, run_dict, **kwargs):
"""Plot the allocations of live points as a function of logX for the input
sets of nested sampling runs of the type used in the dynamic nested
sampling paper (Higson et al. 2019).
Plots also include analytically calculated distributions of relative
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train | kde_plot_df | Plots kde estimates of distributions of samples in each cell of the
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There is one subplot for each dataframe column, and on each subplot there
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Parameters
----------
df: pandas data frame
Each cell must contain a 1d numpy array of samples.
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"""Plots kde estimates of distributions of samples in each cell of the
input pandas DataFrame.
There is one subplot for each dataframe column, and on each subplot there
is one kde line.
Parameters
----------
df: pandas data frame
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"""Plots kde estimates of distributions of samples in each cell of the
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There is one subplot for each dataframe column, and on each subplot there
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df: pandas data frame
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train | bs_param_dists | Creates posterior distributions and their bootstrap error functions for
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For a more detailed description and some example use cases, see 'nestcheck:
diagnostic tests for nested sampling calculations' (Higson et al. 2019).
Parameters
----------
run_list: dict or list o... | nestcheck/plots.py | def bs_param_dists(run_list, **kwargs):
"""Creates posterior distributions and their bootstrap error functions for
input runs and estimators.
For a more detailed description and some example use cases, see 'nestcheck:
diagnostic tests for nested sampling calculations' (Higson et al. 2019).
Paramet... | def bs_param_dists(run_list, **kwargs):
"""Creates posterior distributions and their bootstrap error functions for
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For a more detailed description and some example use cases, see 'nestcheck:
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train | param_logx_diagram | Creates diagrams of a nested sampling run's evolution as it iterates
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For a more detailed description and some example use cases, see 'nestcheck:
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"""Creates diagrams of a nested sampling run's evolution as it iterates
towards higher likelihoods, expressed as a function of log X, where X(L) is
the fraction of the prior volume with likelihood greater than some value L.
For a more detailed description and... | def param_logx_diagram(run_list, **kwargs):
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train | plot_bs_dists | Helper function for plotting uncertainties on posterior distributions
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Parameters
----------
run: dict
Nested sampling run to plot.
fthetas: list of functions
Quantities to plot. Each... | nestcheck/plots.py | def plot_bs_dists(run, fthetas, axes, **kwargs):
"""Helper function for plotting uncertainties on posterior distributions
using bootstrap resamples and the fgivenx module. Used by bs_param_dists
and param_logx_diagram.
Parameters
----------
run: dict
Nested sampling run to plot.
fth... | def plot_bs_dists(run, fthetas, axes, **kwargs):
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Nested sampling run to plot.
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train | alternate_helper | Helper function for making fgivenx plots of functions with 2 array
arguments of variable lengths. | nestcheck/plots.py | def alternate_helper(x, alt_samps, func=None):
"""Helper function for making fgivenx plots of functions with 2 array
arguments of variable lengths."""
alt_samps = alt_samps[~np.isnan(alt_samps)]
arg1 = alt_samps[::2]
arg2 = alt_samps[1::2]
return func(x, arg1, arg2) | def alternate_helper(x, alt_samps, func=None):
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alt_samps = alt_samps[~np.isnan(alt_samps)]
arg1 = alt_samps[::2]
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train | weighted_1d_gaussian_kde | Gaussian kde with weighted samples (1d only). Uses Scott bandwidth
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When all the sample weights are equal, this is equivalent to
kde = scipy.stats.gaussian_kde(theta)
return kde(x)
When the weights are not all equal, we compute the effective number
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"""Gaussian kde with weighted samples (1d only). Uses Scott bandwidth
factor.
When all the sample weights are equal, this is equivalent to
kde = scipy.stats.gaussian_kde(theta)
return kde(x)
When the weights are not all equal, we compute the ... | def weighted_1d_gaussian_kde(x, samples, weights):
"""Gaussian kde with weighted samples (1d only). Uses Scott bandwidth
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kde = scipy.stats.gaussian_kde(theta)
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train | rel_posterior_mass | Calculate the relative posterior mass for some array of logx values
given the likelihood, prior and number of dimensions.
The posterior mass at each logX value is proportional to L(X)X, where L(X)
is the likelihood.
The weight is returned normalized so that the integral of the weight with
respect to... | nestcheck/plots.py | def rel_posterior_mass(logx, logl):
"""Calculate the relative posterior mass for some array of logx values
given the likelihood, prior and number of dimensions.
The posterior mass at each logX value is proportional to L(X)X, where L(X)
is the likelihood.
The weight is returned normalized so that the... | def rel_posterior_mass(logx, logl):
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train | average_by_key | Helper function for plot_run_nlive.
Try returning the average of dict_in[key] and, if this does not work or if
key is None, return average of whole dict.
Parameters
----------
dict_in: dict
Values should be arrays.
key: str
Returns
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average: float | nestcheck/plots.py | def average_by_key(dict_in, key):
"""Helper function for plot_run_nlive.
Try returning the average of dict_in[key] and, if this does not work or if
key is None, return average of whole dict.
Parameters
----------
dict_in: dict
Values should be arrays.
key: str
Returns
----... | def average_by_key(dict_in, key):
"""Helper function for plot_run_nlive.
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dict_in: dict
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key: str
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train | batch_process_data | Process output from many nested sampling runs in parallel with optional
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The result can be cached using the 'save_name', 'save' and 'load' kwargs
(by default this is not done). See save_load_result docstring for more
details.
Remaining kwargs passed to parallel_utils.par... | nestcheck/data_processing.py | def batch_process_data(file_roots, **kwargs):
"""Process output from many nested sampling runs in parallel with optional
error handling and caching.
The result can be cached using the 'save_name', 'save' and 'load' kwargs
(by default this is not done). See save_load_result docstring for more
detail... | def batch_process_data(file_roots, **kwargs):
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train | process_error_helper | Wrapper which applies process_func and handles some common errors so one
bad run does not spoil the whole batch.
Useful errors to handle include:
OSError: if you are not sure if all the files exist
AssertionError: if some of the many assertions fail for known reasons;
for example is there are occa... | nestcheck/data_processing.py | def process_error_helper(root, base_dir, process_func, errors_to_handle=(),
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"""Wrapper which applies process_func and handles some common errors so one
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Useful errors to handle include:
OSError: if you are not sure if all ... | def process_error_helper(root, base_dir, process_func, errors_to_handle=(),
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"""Wrapper which applies process_func and handles some common errors so one
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train | process_polychord_run | Loads data from a PolyChord run into the nestcheck dictionary format for
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iso-likelihood contours within which points were sampled (where they were
"born") requies PolyChord version v1.13 or later and the setting
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**kwargs):
"""Loads data from a PolyChord run into the nestcheck dictionary format for
analysis.
N.B. producing required output file containing information about the
iso-likelihood contours within which po... | def process_polychord_run(file_root, base_dir, process_stats_file=True,
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train | process_multinest_run | Loads data from a MultiNest run into the nestcheck dictionary format for
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N.B. producing required output file containing information about the
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train | process_dynesty_run | Transforms results from a dynesty run into the nestcheck dictionary
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Note that the nestcheck point weights and evidence will not be exactly
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"""Transforms results from a dynesty run into the nestcheck dictionary
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Note that the nestcheck point weights and evidence will not be exactly
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train | process_polychord_stats | Reads a PolyChord <root>.stats output file and returns the information
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file_root: str
Root for run output file names (PolyChord file_root setting).
base_dir: str
Directory containing data (PolyChord base_dir setting).
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... | nestcheck/data_processing.py | def process_polychord_stats(file_root, base_dir):
"""Reads a PolyChord <root>.stats output file and returns the information
contained in a dictionary.
Parameters
----------
file_root: str
Root for run output file names (PolyChord file_root setting).
base_dir: str
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train | process_samples_array | Convert an array of nested sampling dead and live points of the type
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Parameters
----------
samples: 2d numpy array
Array of dead points and any remaining live points at termination.
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"""Convert an array of nested sampling dead and live points of the type
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dictionary.
Parameters
----------
samples: 2d numpy array
Array of dead points and any remaining... | def process_samples_array(samples, **kwargs):
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train | birth_inds_given_contours | Maps the iso-likelihood contours on which points were born to the
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MultiNest and PolyChord use different values to identify the inital live
points which were sampled from the whole prior (PolyChord uses -1e+30
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"""Maps the iso-likelihood contours on which points were born to the
index of the dead point on this contour.
MultiNest and PolyChord use different values to identify the inital live
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train | sample_less_than_condition | Creates a random sample from choices without replacement, subject to the
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element of the condition array.
condition should be in ascending order. | nestcheck/data_processing.py | def sample_less_than_condition(choices_in, condition):
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condition should be in ascending order.
"""
output = np.... | def sample_less_than_condition(choices_in, condition):
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train | threads_given_birth_inds | Divides a nested sampling run into threads, using info on the indexes
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parameter estimation" (Higson et al. 2018) for more information.
Parameters
----------
birth_inds: 1d numpy array
Indexes of the iso-likelihood contour... | nestcheck/data_processing.py | def threads_given_birth_inds(birth_inds):
"""Divides a nested sampling run into threads, using info on the indexes
at which points were sampled. See "Sampling errors in nested sampling
parameter estimation" (Higson et al. 2018) for more information.
Parameters
----------
birth_inds: 1d numpy ar... | def threads_given_birth_inds(birth_inds):
"""Divides a nested sampling run into threads, using info on the indexes
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train | parallel_map | Apply function to iterable with parallel map, and hence returns
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func_kwargs, meaning that the iterable argument must be the last positional
argument.
Roughly equivalent to
>>> [func(*func_pre_args, x, **func_kwargs) for x in a... | nestcheck/parallel_utils.py | def parallel_map(func, *arg_iterable, **kwargs):
"""Apply function to iterable with parallel map, and hence returns
results in order. functools.partial is used to freeze func_pre_args and
func_kwargs, meaning that the iterable argument must be the last positional
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Roughly equivalent to
... | def parallel_map(func, *arg_iterable, **kwargs):
"""Apply function to iterable with parallel map, and hence returns
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train | parallel_apply | Apply function to iterable with parallelisation and a tqdm progress bar.
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>>> [func(*func_pre_args, x, *func_args, **func_kwargs) for x in
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>>> [func(*func_pre_args, x, *func_args, **func_kwargs) for x in
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... | def parallel_apply(func, arg_iterable, **kwargs):
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train | select_tqdm | If running in a jupyter notebook, then returns tqdm_notebook.
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Returns
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progress: function | nestcheck/parallel_utils.py | def select_tqdm():
"""If running in a jupyter notebook, then returns tqdm_notebook.
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Returns
-------
progress: function
"""
try:
progress = tqdm.tqdm_notebook
assert get_ipython().has_trait('kernel')
except (NameError, Assert... | def select_tqdm():
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progress: function
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train | summary_df_from_array | Make a panda data frame of the mean and std devs of an array of results,
including the uncertainties on the values.
This function converts the array to a DataFrame and calls summary_df on it.
Parameters
----------
results_array: 2d numpy array
names: list of str
Names for the output df... | nestcheck/pandas_functions.py | def summary_df_from_array(results_array, names, axis=0, **kwargs):
"""Make a panda data frame of the mean and std devs of an array of results,
including the uncertainties on the values.
This function converts the array to a DataFrame and calls summary_df on it.
Parameters
----------
results_ar... | def summary_df_from_array(results_array, names, axis=0, **kwargs):
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train | summary_df_from_list | Make a panda data frame of the mean and std devs of each element of a
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Parameters
----------
results_list: list of 1d numpy arrays
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list of 1d arrays, including the uncertainties on the values.
This just converts the array to a DataFrame and calls summary_df on it.
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----------
results_... | def summary_df_from_list(results_list, names, **kwargs):
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Index levels to preserve.
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train | efficiency_gain_df | r"""Calculated data frame showing
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eff_gain_df: pandas DataFrame
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eff_gain_df: pandas DataFrame
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Given the variation in repeated calculations' results using the two
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.. math::
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train | array_ratio_std | r"""Gives error on the ratio of 2 floats or 2 1-dimensional arrays given
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train | run_estimators | Calculates values of list of quantities (such as the Bayesian evidence
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ns_run: dict
Nested sampling run dict (see data_processing module docstring for more
details).
estimator_list: list of functions for ... | nestcheck/ns_run_utils.py | def run_estimators(ns_run, estimator_list, simulate=False):
"""Calculates values of list of quantities (such as the Bayesian evidence
or mean of parameters) for a single nested sampling run.
Parameters
----------
ns_run: dict
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ns_run: dict
Nested sampling run dict (see data_processing module docstring for more
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Parameters
----------
ns_run: dict
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train | dict_given_run_array | Converts an array of information about samples back into a nested sampling
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N.B. the output dict only contains the following keys: 'logl',
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train | get_run_threads | Get the individual threads from a nested sampling run.
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ns_run: dict
Nested sampling run dict (see data_processing module docstring for more
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Returns
-------
threads: list of numpy array
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Get the individual threads from a nested sampling run.
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----------
ns_run: dict
Nested sampling run dict (see data_processing module docstring for more
details).
Returns
-------
threads: list of numpy array
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"""
Get the individual threads from a nested sampling run.
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ns_run: dict
Nested sampling run dict (see data_processing module docstring for more
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train | combine_ns_runs | Combine a list of complete nested sampling run dictionaries into a single
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Input runs must contain any repeated threads.
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run_list_in: list of dicts
List of nested sampling runs in dict format (see data_processing module
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Combine a list of complete nested sampling run dictionaries into a single
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Parameters
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run_list_in: list of dicts
List of nested sampling runs in dict format (see data_pro... | def combine_ns_runs(run_list_in, **kwargs):
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Combine a list of complete nested sampling run dictionaries into a single
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"""
Combine list of threads into a single ns run.
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Combine list of threads into a single ns run.
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train | get_logw | r"""Calculates the log posterior weights of the samples (using logarithms
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Uses the trapezium rule such that the weight of point i is
.. math:: w_i = \mathcal{L}_i (X_{i-1} - X_{i+1}) / 2
Parameters
----------
ns_run: dict
Nest... | nestcheck/ns_run_utils.py | def get_logw(ns_run, simulate=False):
r"""Calculates the log posterior weights of the samples (using logarithms
to avoid overflow errors with very large or small values).
Uses the trapezium rule such that the weight of point i is
.. math:: w_i = \mathcal{L}_i (X_{i-1} - X_{i+1}) / 2
Parameters
... | def get_logw(ns_run, simulate=False):
r"""Calculates the log posterior weights of the samples (using logarithms
to avoid overflow errors with very large or small values).
Uses the trapezium rule such that the weight of point i is
.. math:: w_i = \mathcal{L}_i (X_{i-1} - X_{i+1}) / 2
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train | get_w_rel | Get the relative posterior weights of the samples, normalised so
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Parameters
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ns_run: dict
Nested sampling run dict (see data_processing module docstring for more
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Parameters
----------
ns_run: dict
Nested sampling run dict (see data_... | def get_w_rel(ns_run, simulate=False):
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train | get_logx | r"""Returns a logx vector showing the expected or simulated logx positions
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The shrinkage factor between two points
.. math:: t_i = X_{i-1} / X_{i}
is distributed as the largest of :math:`n_i` uniform random variables
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r"""Returns a logx vector showing the expected or simulated logx positions
of points.
The shrinkage factor between two points
.. math:: t_i = X_{i-1} / X_{i}
is distributed as the largest of :math:`n_i` uniform random variables
between 1 and 0, where :math... | def get_logx(nlive, simulate=False):
r"""Returns a logx vector showing the expected or simulated logx positions
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.. math:: t_i = X_{i-1} / X_{i}
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train | log_subtract | r"""Numerically stable method for avoiding overflow errors when calculating
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:math:`a > b`.
See https://hips.seas.harvard.edu/blog/2013/01/09/computing-log-sum-exp/
for more details.
Parameters
----------
loga: float
... | nestcheck/ns_run_utils.py | def log_subtract(loga, logb):
r"""Numerically stable method for avoiding overflow errors when calculating
:math:`\log (a-b)`, given :math:`\log (a)`, :math:`\log (a)` and that
:math:`a > b`.
See https://hips.seas.harvard.edu/blog/2013/01/09/computing-log-sum-exp/
for more details.
Parameters
... | def log_subtract(loga, logb):
r"""Numerically stable method for avoiding overflow errors when calculating
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:math:`a > b`.
See https://hips.seas.harvard.edu/blog/2013/01/09/computing-log-sum-exp/
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train | check_ns_run | Checks a nestcheck format nested sampling run dictionary has the
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Parameters
----------
run: dict
nested sampling run to check.
dup_assert: bool, optional
See check_ns_run_logls docstring.
dup_wa... | nestcheck/ns_run_utils.py | def check_ns_run(run, dup_assert=False, dup_warn=False):
"""Checks a nestcheck format nested sampling run dictionary has the
expected properties (see the data_processing module docstring for more
details).
Parameters
----------
run: dict
nested sampling run to check.
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"""Checks a nestcheck format nested sampling run dictionary has the
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Parameters
----------
run: dict
nested sampling run to check.
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train | check_ns_run_members | Check nested sampling run member keys and values.
Parameters
----------
run: dict
nested sampling run to check.
Raises
------
AssertionError
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Parameters
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run: dict
nested sampling run to check.
Raises
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AssertionError
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run_keys = list(run.keys())
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nested sampling run to check.
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AssertionError
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train | check_ns_run_logls | Check run logls are unique and in the correct order.
Parameters
----------
run: dict
nested sampling run to check.
dup_assert: bool, optional
Whether to raise and AssertionError if there are duplicate logl values.
dup_warn: bool, optional
Whether to give a UserWarning if the... | nestcheck/ns_run_utils.py | def check_ns_run_logls(run, dup_assert=False, dup_warn=False):
"""Check run logls are unique and in the correct order.
Parameters
----------
run: dict
nested sampling run to check.
dup_assert: bool, optional
Whether to raise and AssertionError if there are duplicate logl values.
... | def check_ns_run_logls(run, dup_assert=False, dup_warn=False):
"""Check run logls are unique and in the correct order.
Parameters
----------
run: dict
nested sampling run to check.
dup_assert: bool, optional
Whether to raise and AssertionError if there are duplicate logl values.
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train | check_ns_run_threads | Check thread labels and thread_min_max have expected properties.
Parameters
----------
run: dict
Nested sampling run to check.
Raises
------
AssertionError
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"""Check thread labels and thread_min_max have expected properties.
Parameters
----------
run: dict
Nested sampling run to check.
Raises
------
AssertionError
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"""
assert run['thread_labels'].dt... | def check_ns_run_threads(run):
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Parameters
----------
run: dict
Nested sampling run to check.
Raises
------
AssertionError
If run does not have expected properties.
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train | count_samples | r"""Number of samples in run.
Unlike most estimators this does not require log weights, but for
convenience will not throw an error if they are specified.
Parameters
----------
ns_run: dict
Nested sampling run dict (see the data_processing module
docstring for more details).
R... | nestcheck/estimators.py | def count_samples(ns_run, **kwargs):
r"""Number of samples in run.
Unlike most estimators this does not require log weights, but for
convenience will not throw an error if they are specified.
Parameters
----------
ns_run: dict
Nested sampling run dict (see the data_processing module
... | def count_samples(ns_run, **kwargs):
r"""Number of samples in run.
Unlike most estimators this does not require log weights, but for
convenience will not throw an error if they are specified.
Parameters
----------
ns_run: dict
Nested sampling run dict (see the data_processing module
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train | logz | r"""Natural log of Bayesian evidence :math:`\log \mathcal{Z}`.
Parameters
----------
ns_run: dict
Nested sampling run dict (see the data_processing module
docstring for more details).
logw: None or 1d numpy array, optional
Log weights of samples.
simulate: bool, optional
... | nestcheck/estimators.py | def logz(ns_run, logw=None, simulate=False):
r"""Natural log of Bayesian evidence :math:`\log \mathcal{Z}`.
Parameters
----------
ns_run: dict
Nested sampling run dict (see the data_processing module
docstring for more details).
logw: None or 1d numpy array, optional
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Parameters
----------
ns_run: dict
Nested sampling run dict (see the data_processing module
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logw: None or 1d numpy array, optional
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train | evidence | r"""Bayesian evidence :math:`\log \mathcal{Z}`.
Parameters
----------
ns_run: dict
Nested sampling run dict (see the data_processing module
docstring for more details).
logw: None or 1d numpy array, optional
Log weights of samples.
simulate: bool, optional
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r"""Bayesian evidence :math:`\log \mathcal{Z}`.
Parameters
----------
ns_run: dict
Nested sampling run dict (see the data_processing module
docstring for more details).
logw: None or 1d numpy array, optional
Log weights of sam... | def evidence(ns_run, logw=None, simulate=False):
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ns_run: dict
Nested sampling run dict (see the data_processing module
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logw: None or 1d numpy array, optional
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train | param_mean | Mean of a single parameter (single component of theta).
Parameters
----------
ns_run: dict
Nested sampling run dict (see the data_processing module
docstring for more details).
logw: None or 1d numpy array, optional
Log weights of samples.
simulate: bool, optional
Pa... | nestcheck/estimators.py | def param_mean(ns_run, logw=None, simulate=False, param_ind=0,
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"""Mean of a single parameter (single component of theta).
Parameters
----------
ns_run: dict
Nested sampling run dict (see the data_processing module
docstring for more details).
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"""Mean of a single parameter (single component of theta).
Parameters
----------
ns_run: dict
Nested sampling run dict (see the data_processing module
docstring for more details).
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train | param_cred | One-tailed credible interval on the value of a single parameter
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----------
ns_run: dict
Nested sampling run dict (see the data_processing module
docstring for more details).
logw: None or 1d numpy array, optional
Log weights of samples.
s... | nestcheck/estimators.py | def param_cred(ns_run, logw=None, simulate=False, probability=0.5,
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"""One-tailed credible interval on the value of a single parameter
(component of theta).
Parameters
----------
ns_run: dict
Nested sampling run dict (see the data_processing module
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ns_run: dict
Nested sampling run dict (see the data_processing module
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train | param_squared_mean | Mean of the square of single parameter (second moment of its
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Parameters
----------
ns_run: dict
Nested sampling run dict (see the data_processing module
docstring for more details).
logw: None or 1d numpy array, optional
Log weights of samples.
s... | nestcheck/estimators.py | def param_squared_mean(ns_run, logw=None, simulate=False, param_ind=0):
"""Mean of the square of single parameter (second moment of its
posterior distribution).
Parameters
----------
ns_run: dict
Nested sampling run dict (see the data_processing module
docstring for more details).
... | def param_squared_mean(ns_run, logw=None, simulate=False, param_ind=0):
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Parameters
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ns_run: dict
Nested sampling run dict (see the data_processing module
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train | r_mean | Mean of the radial coordinate (magnitude of theta vector).
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Log weights of samples.
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Nested sampling run dict (see the data_processing module
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logw: None or 1d numpy array, optional
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Nested sampling run dict (see the data_processing module
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train | r_cred | One-tailed credible interval on the value of the radial coordinate
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ns_run: dict
Nested sampling run dict (see the data_processing module
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Parameters
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kwargs: dict, optional
Kwargs for function.
Returns
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latex_name: str
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probability: float
Quantile to estimate - must be in open interval (0, 1).
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Get quantile estimate for input probability given weighted samples using
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Parameters
----------
probability: float
Quantile to estimate - must be in open interval (0, 1).
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Get quantile estimate for input probability given weighted samples using
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Quantile to estimate - must be in open interval (0, 1).
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root = os.path.join(run['output']['base_dir'],
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Output files which can be made with this function (see the PolyChord
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.. code-block:: python
root = os.path.join(run['output']['base_dir'],
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ns_run: dict
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run_output_dict: dict
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NB when parallelised the results will not be produced in order (so results
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NB when parallelised the results will not be produced in order (so results
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run_list: list of dicts
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train | error_values_summary | Get summary statistics about calculation errors, including estimated
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error_values: pandas DataFrame
Of format output by run_list_error_values (look at it for more
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summary_df_kwargs: dict, optional
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Parameters
----------
error_values: pandas DataFrame
Of format output by run_list_error_values (look at it for more
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error_values: pandas DataFrame
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train | bs_values_df | Computes a data frame of bootstrap resampled values.
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run_list: list of dicts
List of nested sampling run dicts.
estimator_list: list of functions
Estimators to apply to runs.
estimator_names: list of strs
Name of each func in estimator_list.
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"""Computes a data frame of bootstrap resampled values.
Parameters
----------
run_list: list of dicts
List of nested sampling run dicts.
estimator_list: list of functions
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run_list: list of dicts
List of nested sampling run dicts.
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train | thread_values_df | Calculates estimator values for the constituent threads of the input
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run_list: list of dicts
List of nested sampling run dicts.
estimator_list: list of functions
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"""Calculates estimator values for the constituent threads of the input
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Parameters
----------
run_list: list of dicts
List of nested sampling run dicts.
estimator_list: list of functions
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run_list: list of dicts
List of nested sampling run dicts.
estimator_list: list of functions
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train | pairwise_dists_on_cols | Computes pairwise statistical distance measures.
parameters
----------
df_in: pandas data frame
Columns represent estimators and rows represent runs.
Each data frane element is an array of values which are used as samples
in the distance measures.
earth_mover_dist: bool, optiona... | nestcheck/diagnostics_tables.py | def pairwise_dists_on_cols(df_in, earth_mover_dist=True, energy_dist=True):
"""Computes pairwise statistical distance measures.
parameters
----------
df_in: pandas data frame
Columns represent estimators and rows represent runs.
Each data frane element is an array of values which are us... | def pairwise_dists_on_cols(df_in, earth_mover_dist=True, energy_dist=True):
"""Computes pairwise statistical distance measures.
parameters
----------
df_in: pandas data frame
Columns represent estimators and rows represent runs.
Each data frane element is an array of values which are us... | [
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] | ejhigson/nestcheck | python | https://github.com/ejhigson/nestcheck/blob/29151c314deb89746fd674f27f6ce54b77603189/nestcheck/diagnostics_tables.py#L334-L357 | [
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train | DictMySQL._backtick_columns | Quote the column names | dictmysql.py | def _backtick_columns(cols):
"""
Quote the column names
"""
def bt(s):
b = '' if s == '*' or not s else '`'
return [_ for _ in [b + (s or '') + b] if _]
formatted = []
for c in cols:
if c[0] == '#':
formatted.append(c[1... | def _backtick_columns(cols):
"""
Quote the column names
"""
def bt(s):
b = '' if s == '*' or not s else '`'
return [_ for _ in [b + (s or '') + b] if _]
formatted = []
for c in cols:
if c[0] == '#':
formatted.append(c[1... | [
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train | DictMySQL._value_parser | Input: {'c1': 'v', 'c2': None, '#c3': 'uuid()'}
Output:
('%s, %s, uuid()', [None, 'v']) # insert; columnname=False
('`c2` = %s, `c1` = %s, `c3` = uuid()', [None, 'v']) # update; columnname=True
No need to transform NULL value since it's supported in exe... | dictmysql.py | def _value_parser(self, value, columnname=False, placeholder='%s'):
"""
Input: {'c1': 'v', 'c2': None, '#c3': 'uuid()'}
Output:
('%s, %s, uuid()', [None, 'v']) # insert; columnname=False
('`c2` = %s, `c1` = %s, `c3` = uuid()', [None, 'v']) # upd... | def _value_parser(self, value, columnname=False, placeholder='%s'):
"""
Input: {'c1': 'v', 'c2': None, '#c3': 'uuid()'}
Output:
('%s, %s, uuid()', [None, 'v']) # insert; columnname=False
('`c2` = %s, `c1` = %s, `c3` = uuid()', [None, 'v']) # upd... | [
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train | DictMySQL._by_columns | Allow select.group and select.order accepting string and list | dictmysql.py | def _by_columns(self, columns):
"""
Allow select.group and select.order accepting string and list
"""
return columns if self.isstr(columns) else self._backtick_columns(columns) | def _by_columns(self, columns):
"""
Allow select.group and select.order accepting string and list
"""
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train | DictMySQL.select | :type table: string
:type columns: list
:type join: dict
:param join: {'[>]table1(t1)': {'user.id': 't1.user_id'}} -> "LEFT JOIN table AS t1 ON user.id = t1.user_id"
:type where: dict
:type group: string|list
:type having: string
:type order: string|list
:... | dictmysql.py | def select(self, table, columns=None, join=None, where=None, group=None, having=None, order=None, limit=None,
iterator=False, fetch=True):
"""
:type table: string
:type columns: list
:type join: dict
:param join: {'[>]table1(t1)': {'user.id': 't1.user_id'}} -> "LEF... | def select(self, table, columns=None, join=None, where=None, group=None, having=None, order=None, limit=None,
iterator=False, fetch=True):
"""
:type table: string
:type columns: list
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train | DictMySQL.select_page | :type limit: int
:param limit: The max row number for each page
:type offset: int
:param offset: The starting position of the page
:return: | dictmysql.py | def select_page(self, limit, offset=0, **kwargs):
"""
:type limit: int
:param limit: The max row number for each page
:type offset: int
:param offset: The starting position of the page
:return:
"""
start = offset
while True:
result = se... | def select_page(self, limit, offset=0, **kwargs):
"""
:type limit: int
:param limit: The max row number for each page
:type offset: int
:param offset: The starting position of the page
:return:
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train | DictMySQL.get | A simplified method of select, for getting the first result in one column only. A common case of using this
method is getting id.
:type table: string
:type column: str
:type join: dict
:type where: dict
:type insert: bool
:param insert: If insert==True, insert the... | dictmysql.py | def get(self, table, column, join=None, where=None, insert=False, ifnone=None):
"""
A simplified method of select, for getting the first result in one column only. A common case of using this
method is getting id.
:type table: string
:type column: str
:type join: dict
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"""
A simplified method of select, for getting the first result in one column only. A common case of using this
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:type table: string
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train | DictMySQL.insert | Insert a dict into db.
:type table: string
:type value: dict
:type ignore: bool
:type commit: bool
:return: int. The row id of the insert. | dictmysql.py | def insert(self, table, value, ignore=False, commit=True):
"""
Insert a dict into db.
:type table: string
:type value: dict
:type ignore: bool
:type commit: bool
:return: int. The row id of the insert.
"""
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Insert a dict into db.
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:type value: dict
:type ignore: bool
:type commit: bool
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train | DictMySQL.upsert | :type table: string
:type value: dict
:type update_columns: list
:param update_columns: specify the columns which will be updated if record exists
:type commit: bool | dictmysql.py | def upsert(self, table, value, update_columns=None, commit=True):
"""
:type table: string
:type value: dict
:type update_columns: list
:param update_columns: specify the columns which will be updated if record exists
:type commit: bool
"""
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"""
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:type value: dict
:type update_columns: list
:param update_columns: specify the columns which will be updated if record exists
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train | DictMySQL.insertmany | Insert multiple records within one query.
:type table: string
:type columns: list
:type value: list|tuple
:param value: Doesn't support MySQL functions
:param value: Example: [(value1_column1, value1_column2,), ]
:type ignore: bool
:type commit: bool
:retu... | dictmysql.py | def insertmany(self, table, columns, value, ignore=False, commit=True):
"""
Insert multiple records within one query.
:type table: string
:type columns: list
:type value: list|tuple
:param value: Doesn't support MySQL functions
:param value: Example: [(value1_colu... | def insertmany(self, table, columns, value, ignore=False, commit=True):
"""
Insert multiple records within one query.
:type table: string
:type columns: list
:type value: list|tuple
:param value: Doesn't support MySQL functions
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train | DictMySQL.update | :type table: string
:type value: dict
:type where: dict
:type join: dict
:type commit: bool | dictmysql.py | def update(self, table, value, where, join=None, commit=True):
"""
:type table: string
:type value: dict
:type where: dict
:type join: dict
:type commit: bool
"""
value_q, _value_args = self._value_parser(value, columnname=True)
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:type join: dict
:type commit: bool
"""
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train | DictMySQL.delete | :type table: string
:type where: dict
:type commit: bool | dictmysql.py | def delete(self, table, where=None, commit=True):
"""
:type table: string
:type where: dict
:type commit: bool
"""
where_q, _args = self._where_parser(where)
alias = self._tablename_parser(table)['alias']
_sql = ''.join(['DELETE ',
... | def delete(self, table, where=None, commit=True):
"""
:type table: string
:type where: dict
:type commit: bool
"""
where_q, _args = self._where_parser(where)
alias = self._tablename_parser(table)['alias']
_sql = ''.join(['DELETE ',
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train | get_whitespace | Returns a list containing the whitespace to the left and
right of a string as its two elements | mwtemplates/templateeditor2.py | def get_whitespace(txt):
"""
Returns a list containing the whitespace to the left and
right of a string as its two elements
"""
# if the entire parameter is whitespace
rall = re.search(r'^([\s])+$', txt)
if rall:
tmp = txt.split('\n', 1)
if len(tmp) == 2:
return ... | def get_whitespace(txt):
"""
Returns a list containing the whitespace to the left and
right of a string as its two elements
"""
# if the entire parameter is whitespace
rall = re.search(r'^([\s])+$', txt)
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tmp = txt.split('\n', 1)
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] | danmichaelo/mwtemplates | python | https://github.com/danmichaelo/mwtemplates/blob/4a57831ee32152eb6dea9d099cec478962782276/mwtemplates/templateeditor2.py#L89-L113 | [
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train | Parameters.find_whitespace_pattern | Try to find a whitespace pattern in the existing parameters
to be applied to a newly added parameter | mwtemplates/templateeditor2.py | def find_whitespace_pattern(self):
"""
Try to find a whitespace pattern in the existing parameters
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"""
name_ws = []
value_ws = []
for entry in self._entries:
name_ws.append(get_whitespace(entry.name))
... | def find_whitespace_pattern(self):
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Try to find a whitespace pattern in the existing parameters
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"""
name_ws = []
value_ws = []
for entry in self._entries:
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train | DiskDataCache._path_for_file | Generate the path on disk for a specified project and date.
:param project_name: the PyPI project name for the data
:type project: str
:param date: the date for the data
:type date: datetime.datetime
:return: path for where to store this data on disk
:rtype: str | pypi_download_stats/diskdatacache.py | def _path_for_file(self, project_name, date):
"""
Generate the path on disk for a specified project and date.
:param project_name: the PyPI project name for the data
:type project: str
:param date: the date for the data
:type date: datetime.datetime
:return: path... | def _path_for_file(self, project_name, date):
"""
Generate the path on disk for a specified project and date.
:param project_name: the PyPI project name for the data
:type project: str
:param date: the date for the data
:type date: datetime.datetime
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] | jantman/pypi-download-stats | python | https://github.com/jantman/pypi-download-stats/blob/44a7a6bbcd61a9e7f02bd02c52584a98183f80c5/pypi_download_stats/diskdatacache.py#L66-L80 | [
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train | DiskDataCache.get | Get the cache data for a specified project for the specified date.
Returns None if the data cannot be found in the cache.
:param project: PyPi project name to get data for
:type project: str
:param date: date to get data for
:type date: datetime.datetime
:return: dict of... | pypi_download_stats/diskdatacache.py | def get(self, project, date):
"""
Get the cache data for a specified project for the specified date.
Returns None if the data cannot be found in the cache.
:param project: PyPi project name to get data for
:type project: str
:param date: date to get data for
:typ... | def get(self, project, date):
"""
Get the cache data for a specified project for the specified date.
Returns None if the data cannot be found in the cache.
:param project: PyPi project name to get data for
:type project: str
:param date: date to get data for
:typ... | [
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train | DiskDataCache.set | Set the cache data for a specified project for the specified date.
:param project: project name to set data for
:type project: str
:param date: date to set data for
:type date: datetime.datetime
:param data: data to cache
:type data: dict
:param data_ts: maximum ... | pypi_download_stats/diskdatacache.py | def set(self, project, date, data, data_ts):
"""
Set the cache data for a specified project for the specified date.
:param project: project name to set data for
:type project: str
:param date: date to set data for
:type date: datetime.datetime
:param data: data t... | def set(self, project, date, data, data_ts):
"""
Set the cache data for a specified project for the specified date.
:param project: project name to set data for
:type project: str
:param date: date to set data for
:type date: datetime.datetime
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train | DiskDataCache.get_dates_for_project | Return a list of the dates we have in cache for the specified project,
sorted in ascending date order.
:param project: project name
:type project: str
:return: list of datetime.datetime objects
:rtype: datetime.datetime | pypi_download_stats/diskdatacache.py | def get_dates_for_project(self, project):
"""
Return a list of the dates we have in cache for the specified project,
sorted in ascending date order.
:param project: project name
:type project: str
:return: list of datetime.datetime objects
:rtype: datetime.dateti... | def get_dates_for_project(self, project):
"""
Return a list of the dates we have in cache for the specified project,
sorted in ascending date order.
:param project: project name
:type project: str
:return: list of datetime.datetime objects
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] | jantman/pypi-download-stats | python | https://github.com/jantman/pypi-download-stats/blob/44a7a6bbcd61a9e7f02bd02c52584a98183f80c5/pypi_download_stats/diskdatacache.py#L139-L158 | [
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train | parse_args | Use Argparse to parse command-line arguments.
:param argv: list of arguments to parse (``sys.argv[1:]``)
:type argv: ``list``
:return: parsed arguments
:rtype: :py:class:`argparse.Namespace` | pypi_download_stats/runner.py | def parse_args(argv):
"""
Use Argparse to parse command-line arguments.
:param argv: list of arguments to parse (``sys.argv[1:]``)
:type argv: ``list``
:return: parsed arguments
:rtype: :py:class:`argparse.Namespace`
"""
p = argparse.ArgumentParser(
description='pypi-download-st... | def parse_args(argv):
"""
Use Argparse to parse command-line arguments.
:param argv: list of arguments to parse (``sys.argv[1:]``)
:type argv: ``list``
:return: parsed arguments
:rtype: :py:class:`argparse.Namespace`
"""
p = argparse.ArgumentParser(
description='pypi-download-st... | [
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... | 44a7a6bbcd61a9e7f02bd02c52584a98183f80c5 |
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