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cbclab/MOT | mot/mcmc_diagnostics.py | get_auto_correlation_time | def get_auto_correlation_time(chain, max_lag=None):
r"""Compute the auto correlation time up to the given lag for the given chain (1d vector).
This will halt when the maximum lag :math:`m` is reached or when the sum of two consecutive lags for any
odd lag is lower or equal to zero.
The auto correlation sum is estimated as:
.. math::
\tau = 1 + 2 * \sum_{k=1}^{m}{\rho_{k}}
Where :math:`\rho_{k}` is estimated as:
.. math::
\hat{\rho}_{k} = \frac{E[(X_{t} - \mu)(X_{t + k} - \mu)]}{\sigma^{2}}
Args:
chain (ndarray): the vector with the samples
max_lag (int): the maximum lag to use in the autocorrelation computation. If not given we use:
:math:`min(n/3, 1000)`.
"""
max_lag = max_lag or min(len(chain) // 3, 1000)
normalized_chain = chain - np.mean(chain, dtype=np.float64)
previous_accoeff = 0
auto_corr_sum = 0
for lag in range(1, max_lag):
auto_correlation_coeff = np.mean(normalized_chain[:len(chain) - lag] * normalized_chain[lag:], dtype=np.float64)
if lag % 2 == 0:
if previous_accoeff + auto_correlation_coeff <= 0:
break
auto_corr_sum += auto_correlation_coeff
previous_accoeff = auto_correlation_coeff
return auto_corr_sum / np.var(chain, dtype=np.float64) | python | def get_auto_correlation_time(chain, max_lag=None):
r"""Compute the auto correlation time up to the given lag for the given chain (1d vector).
This will halt when the maximum lag :math:`m` is reached or when the sum of two consecutive lags for any
odd lag is lower or equal to zero.
The auto correlation sum is estimated as:
.. math::
\tau = 1 + 2 * \sum_{k=1}^{m}{\rho_{k}}
Where :math:`\rho_{k}` is estimated as:
.. math::
\hat{\rho}_{k} = \frac{E[(X_{t} - \mu)(X_{t + k} - \mu)]}{\sigma^{2}}
Args:
chain (ndarray): the vector with the samples
max_lag (int): the maximum lag to use in the autocorrelation computation. If not given we use:
:math:`min(n/3, 1000)`.
"""
max_lag = max_lag or min(len(chain) // 3, 1000)
normalized_chain = chain - np.mean(chain, dtype=np.float64)
previous_accoeff = 0
auto_corr_sum = 0
for lag in range(1, max_lag):
auto_correlation_coeff = np.mean(normalized_chain[:len(chain) - lag] * normalized_chain[lag:], dtype=np.float64)
if lag % 2 == 0:
if previous_accoeff + auto_correlation_coeff <= 0:
break
auto_corr_sum += auto_correlation_coeff
previous_accoeff = auto_correlation_coeff
return auto_corr_sum / np.var(chain, dtype=np.float64) | r"""Compute the auto correlation time up to the given lag for the given chain (1d vector).
This will halt when the maximum lag :math:`m` is reached or when the sum of two consecutive lags for any
odd lag is lower or equal to zero.
The auto correlation sum is estimated as:
.. math::
\tau = 1 + 2 * \sum_{k=1}^{m}{\rho_{k}}
Where :math:`\rho_{k}` is estimated as:
.. math::
\hat{\rho}_{k} = \frac{E[(X_{t} - \mu)(X_{t + k} - \mu)]}{\sigma^{2}}
Args:
chain (ndarray): the vector with the samples
max_lag (int): the maximum lag to use in the autocorrelation computation. If not given we use:
:math:`min(n/3, 1000)`. | https://github.com/cbclab/MOT/blob/fb3243b65025705842e82704705c00902f9a35af/mot/mcmc_diagnostics.py#L153-L194 |
cbclab/MOT | mot/mcmc_diagnostics.py | estimate_univariate_ess_standard_error | def estimate_univariate_ess_standard_error(chain, batch_size_generator=None, compute_method=None):
r"""Compute the univariate ESS using the standard error method.
This computes the ESS using:
.. math::
ESS(X) = n * \frac{\lambda^{2}}{\sigma^{2}}
Where :math:`\lambda` is the standard deviation of the chain and :math:`\sigma` is estimated using the monte carlo
standard error (which in turn is, by default, estimated using a batch means estimator).
Args:
chain (ndarray): the Markov chain
batch_size_generator (UniVariateESSBatchSizeGenerator): the method that generates that batch sizes
we will use. Per default it uses the :class:`SquareRootSingleBatch` method.
compute_method (ComputeMonteCarloStandardError): the method used to compute the standard error.
By default we will use the :class:`BatchMeansMCSE` method
Returns:
float: the estimated ESS
"""
sigma = (monte_carlo_standard_error(chain, batch_size_generator=batch_size_generator,
compute_method=compute_method) ** 2 * len(chain))
lambda_ = np.var(chain, dtype=np.float64)
return len(chain) * (lambda_ / sigma) | python | def estimate_univariate_ess_standard_error(chain, batch_size_generator=None, compute_method=None):
r"""Compute the univariate ESS using the standard error method.
This computes the ESS using:
.. math::
ESS(X) = n * \frac{\lambda^{2}}{\sigma^{2}}
Where :math:`\lambda` is the standard deviation of the chain and :math:`\sigma` is estimated using the monte carlo
standard error (which in turn is, by default, estimated using a batch means estimator).
Args:
chain (ndarray): the Markov chain
batch_size_generator (UniVariateESSBatchSizeGenerator): the method that generates that batch sizes
we will use. Per default it uses the :class:`SquareRootSingleBatch` method.
compute_method (ComputeMonteCarloStandardError): the method used to compute the standard error.
By default we will use the :class:`BatchMeansMCSE` method
Returns:
float: the estimated ESS
"""
sigma = (monte_carlo_standard_error(chain, batch_size_generator=batch_size_generator,
compute_method=compute_method) ** 2 * len(chain))
lambda_ = np.var(chain, dtype=np.float64)
return len(chain) * (lambda_ / sigma) | r"""Compute the univariate ESS using the standard error method.
This computes the ESS using:
.. math::
ESS(X) = n * \frac{\lambda^{2}}{\sigma^{2}}
Where :math:`\lambda` is the standard deviation of the chain and :math:`\sigma` is estimated using the monte carlo
standard error (which in turn is, by default, estimated using a batch means estimator).
Args:
chain (ndarray): the Markov chain
batch_size_generator (UniVariateESSBatchSizeGenerator): the method that generates that batch sizes
we will use. Per default it uses the :class:`SquareRootSingleBatch` method.
compute_method (ComputeMonteCarloStandardError): the method used to compute the standard error.
By default we will use the :class:`BatchMeansMCSE` method
Returns:
float: the estimated ESS | https://github.com/cbclab/MOT/blob/fb3243b65025705842e82704705c00902f9a35af/mot/mcmc_diagnostics.py#L230-L255 |
cbclab/MOT | mot/mcmc_diagnostics.py | minimum_multivariate_ess | def minimum_multivariate_ess(nmr_params, alpha=0.05, epsilon=0.05):
r"""Calculate the minimum multivariate Effective Sample Size you will need to obtain the desired precision.
This implements the inequality from Vats et al. (2016):
.. math::
\widehat{ESS} \geq \frac{2^{2/p}\pi}{(p\Gamma(p/2))^{2/p}} \frac{\chi^{2}_{1-\alpha,p}}{\epsilon^{2}}
Where :math:`p` is the number of free parameters.
Args:
nmr_params (int): the number of free parameters in the model
alpha (float): the level of confidence of the confidence region. For example, an alpha of 0.05 means
that we want to be in a 95% confidence region.
epsilon (float): the level of precision in our multivariate ESS estimate.
An epsilon of 0.05 means that we expect that the Monte Carlo error is 5% of the uncertainty in
the target distribution.
Returns:
float: the minimum multivariate Effective Sample Size that one should aim for in MCMC sample to
obtain the desired confidence region with the desired precision.
References:
Vats D, Flegal J, Jones G (2016). Multivariate Output Analysis for Markov Chain Monte Carlo.
arXiv:1512.07713v2 [math.ST]
"""
tmp = 2.0 / nmr_params
log_min_ess = tmp * np.log(2) + np.log(np.pi) - tmp * (np.log(nmr_params) + gammaln(nmr_params / 2)) \
+ np.log(chi2.ppf(1 - alpha, nmr_params)) - 2 * np.log(epsilon)
return int(round(np.exp(log_min_ess))) | python | def minimum_multivariate_ess(nmr_params, alpha=0.05, epsilon=0.05):
r"""Calculate the minimum multivariate Effective Sample Size you will need to obtain the desired precision.
This implements the inequality from Vats et al. (2016):
.. math::
\widehat{ESS} \geq \frac{2^{2/p}\pi}{(p\Gamma(p/2))^{2/p}} \frac{\chi^{2}_{1-\alpha,p}}{\epsilon^{2}}
Where :math:`p` is the number of free parameters.
Args:
nmr_params (int): the number of free parameters in the model
alpha (float): the level of confidence of the confidence region. For example, an alpha of 0.05 means
that we want to be in a 95% confidence region.
epsilon (float): the level of precision in our multivariate ESS estimate.
An epsilon of 0.05 means that we expect that the Monte Carlo error is 5% of the uncertainty in
the target distribution.
Returns:
float: the minimum multivariate Effective Sample Size that one should aim for in MCMC sample to
obtain the desired confidence region with the desired precision.
References:
Vats D, Flegal J, Jones G (2016). Multivariate Output Analysis for Markov Chain Monte Carlo.
arXiv:1512.07713v2 [math.ST]
"""
tmp = 2.0 / nmr_params
log_min_ess = tmp * np.log(2) + np.log(np.pi) - tmp * (np.log(nmr_params) + gammaln(nmr_params / 2)) \
+ np.log(chi2.ppf(1 - alpha, nmr_params)) - 2 * np.log(epsilon)
return int(round(np.exp(log_min_ess))) | r"""Calculate the minimum multivariate Effective Sample Size you will need to obtain the desired precision.
This implements the inequality from Vats et al. (2016):
.. math::
\widehat{ESS} \geq \frac{2^{2/p}\pi}{(p\Gamma(p/2))^{2/p}} \frac{\chi^{2}_{1-\alpha,p}}{\epsilon^{2}}
Where :math:`p` is the number of free parameters.
Args:
nmr_params (int): the number of free parameters in the model
alpha (float): the level of confidence of the confidence region. For example, an alpha of 0.05 means
that we want to be in a 95% confidence region.
epsilon (float): the level of precision in our multivariate ESS estimate.
An epsilon of 0.05 means that we expect that the Monte Carlo error is 5% of the uncertainty in
the target distribution.
Returns:
float: the minimum multivariate Effective Sample Size that one should aim for in MCMC sample to
obtain the desired confidence region with the desired precision.
References:
Vats D, Flegal J, Jones G (2016). Multivariate Output Analysis for Markov Chain Monte Carlo.
arXiv:1512.07713v2 [math.ST] | https://github.com/cbclab/MOT/blob/fb3243b65025705842e82704705c00902f9a35af/mot/mcmc_diagnostics.py#L258-L288 |
cbclab/MOT | mot/mcmc_diagnostics.py | multivariate_ess_precision | def multivariate_ess_precision(nmr_params, multi_variate_ess, alpha=0.05):
r"""Calculate the precision given your multivariate Effective Sample Size.
Given that you obtained :math:`ESS` multivariate effective samples in your estimate you can calculate the
precision with which you approximated your desired confidence region.
This implements the inequality from Vats et al. (2016), slightly restructured to give :math:`\epsilon` back instead
of the minimum ESS.
.. math::
\epsilon = \sqrt{\frac{2^{2/p}\pi}{(p\Gamma(p/2))^{2/p}} \frac{\chi^{2}_{1-\alpha,p}}{\widehat{ESS}}}
Where :math:`p` is the number of free parameters and ESS is the multivariate ESS from your samples.
Args:
nmr_params (int): the number of free parameters in the model
multi_variate_ess (int): the number of iid samples you obtained in your sample results.
alpha (float): the level of confidence of the confidence region. For example, an alpha of 0.05 means
that we want to be in a 95% confidence region.
Returns:
float: the minimum multivariate Effective Sample Size that one should aim for in MCMC sample to
obtain the desired confidence region with the desired precision.
References:
Vats D, Flegal J, Jones G (2016). Multivariate Output Analysis for Markov Chain Monte Carlo.
arXiv:1512.07713v2 [math.ST]
"""
tmp = 2.0 / nmr_params
log_min_ess = tmp * np.log(2) + np.log(np.pi) - tmp * (np.log(nmr_params) + gammaln(nmr_params / 2)) \
+ np.log(chi2.ppf(1 - alpha, nmr_params)) - np.log(multi_variate_ess)
return np.sqrt(np.exp(log_min_ess)) | python | def multivariate_ess_precision(nmr_params, multi_variate_ess, alpha=0.05):
r"""Calculate the precision given your multivariate Effective Sample Size.
Given that you obtained :math:`ESS` multivariate effective samples in your estimate you can calculate the
precision with which you approximated your desired confidence region.
This implements the inequality from Vats et al. (2016), slightly restructured to give :math:`\epsilon` back instead
of the minimum ESS.
.. math::
\epsilon = \sqrt{\frac{2^{2/p}\pi}{(p\Gamma(p/2))^{2/p}} \frac{\chi^{2}_{1-\alpha,p}}{\widehat{ESS}}}
Where :math:`p` is the number of free parameters and ESS is the multivariate ESS from your samples.
Args:
nmr_params (int): the number of free parameters in the model
multi_variate_ess (int): the number of iid samples you obtained in your sample results.
alpha (float): the level of confidence of the confidence region. For example, an alpha of 0.05 means
that we want to be in a 95% confidence region.
Returns:
float: the minimum multivariate Effective Sample Size that one should aim for in MCMC sample to
obtain the desired confidence region with the desired precision.
References:
Vats D, Flegal J, Jones G (2016). Multivariate Output Analysis for Markov Chain Monte Carlo.
arXiv:1512.07713v2 [math.ST]
"""
tmp = 2.0 / nmr_params
log_min_ess = tmp * np.log(2) + np.log(np.pi) - tmp * (np.log(nmr_params) + gammaln(nmr_params / 2)) \
+ np.log(chi2.ppf(1 - alpha, nmr_params)) - np.log(multi_variate_ess)
return np.sqrt(np.exp(log_min_ess)) | r"""Calculate the precision given your multivariate Effective Sample Size.
Given that you obtained :math:`ESS` multivariate effective samples in your estimate you can calculate the
precision with which you approximated your desired confidence region.
This implements the inequality from Vats et al. (2016), slightly restructured to give :math:`\epsilon` back instead
of the minimum ESS.
.. math::
\epsilon = \sqrt{\frac{2^{2/p}\pi}{(p\Gamma(p/2))^{2/p}} \frac{\chi^{2}_{1-\alpha,p}}{\widehat{ESS}}}
Where :math:`p` is the number of free parameters and ESS is the multivariate ESS from your samples.
Args:
nmr_params (int): the number of free parameters in the model
multi_variate_ess (int): the number of iid samples you obtained in your sample results.
alpha (float): the level of confidence of the confidence region. For example, an alpha of 0.05 means
that we want to be in a 95% confidence region.
Returns:
float: the minimum multivariate Effective Sample Size that one should aim for in MCMC sample to
obtain the desired confidence region with the desired precision.
References:
Vats D, Flegal J, Jones G (2016). Multivariate Output Analysis for Markov Chain Monte Carlo.
arXiv:1512.07713v2 [math.ST] | https://github.com/cbclab/MOT/blob/fb3243b65025705842e82704705c00902f9a35af/mot/mcmc_diagnostics.py#L291-L323 |
cbclab/MOT | mot/mcmc_diagnostics.py | estimate_multivariate_ess_sigma | def estimate_multivariate_ess_sigma(samples, batch_size):
r"""Calculates the Sigma matrix which is part of the multivariate ESS calculation.
This implementation is based on the Matlab implementation found at: https://github.com/lacerbi/multiESS
The Sigma matrix is defined as:
.. math::
\Sigma = \Lambda + 2 * \sum_{k=1}^{\infty}{Cov(Y_{1}, Y_{1+k})}
Where :math:`Y` are our samples and :math:`\Lambda` is the covariance matrix of the samples.
This implementation computes the :math:`\Sigma` matrix using a Batch Mean estimator using the given batch size.
The batch size has to be :math:`1 \le b_n \le n` and a typical value is either :math:`\lfloor n^{1/2} \rfloor`
for slow mixing chains or :math:`\lfloor n^{1/3} \rfloor` for reasonable mixing chains.
If the length of the chain is longer than the sum of the length of all the batches, this implementation
calculates :math:`\Sigma` for every offset and returns the average of those offsets.
Args:
samples (ndarray): the samples for which we compute the sigma matrix. Expects an (p, n) array with
p the number of parameters and n the sample size
batch_size (int): the batch size used in the approximation of the correlation covariance
Returns:
ndarray: an pxp array with p the number of parameters in the samples.
References:
Vats D, Flegal J, Jones G (2016). Multivariate Output Analysis for Markov Chain Monte Carlo.
arXiv:1512.07713v2 [math.ST]
"""
sample_means = np.mean(samples, axis=1, dtype=np.float64)
nmr_params, chain_length = samples.shape
nmr_batches = int(np.floor(chain_length / batch_size))
sigma = np.zeros((nmr_params, nmr_params))
nmr_offsets = chain_length - nmr_batches * batch_size + 1
for offset in range(nmr_offsets):
batches = np.reshape(samples[:, np.array(offset + np.arange(0, nmr_batches * batch_size), dtype=np.int)].T,
[batch_size, nmr_batches, nmr_params], order='F')
batch_means = np.squeeze(np.mean(batches, axis=0, dtype=np.float64))
Z = batch_means - sample_means
for x, y in itertools.product(range(nmr_params), range(nmr_params)):
sigma[x, y] += np.sum(Z[:, x] * Z[:, y])
return sigma * batch_size / (nmr_batches - 1) / nmr_offsets | python | def estimate_multivariate_ess_sigma(samples, batch_size):
r"""Calculates the Sigma matrix which is part of the multivariate ESS calculation.
This implementation is based on the Matlab implementation found at: https://github.com/lacerbi/multiESS
The Sigma matrix is defined as:
.. math::
\Sigma = \Lambda + 2 * \sum_{k=1}^{\infty}{Cov(Y_{1}, Y_{1+k})}
Where :math:`Y` are our samples and :math:`\Lambda` is the covariance matrix of the samples.
This implementation computes the :math:`\Sigma` matrix using a Batch Mean estimator using the given batch size.
The batch size has to be :math:`1 \le b_n \le n` and a typical value is either :math:`\lfloor n^{1/2} \rfloor`
for slow mixing chains or :math:`\lfloor n^{1/3} \rfloor` for reasonable mixing chains.
If the length of the chain is longer than the sum of the length of all the batches, this implementation
calculates :math:`\Sigma` for every offset and returns the average of those offsets.
Args:
samples (ndarray): the samples for which we compute the sigma matrix. Expects an (p, n) array with
p the number of parameters and n the sample size
batch_size (int): the batch size used in the approximation of the correlation covariance
Returns:
ndarray: an pxp array with p the number of parameters in the samples.
References:
Vats D, Flegal J, Jones G (2016). Multivariate Output Analysis for Markov Chain Monte Carlo.
arXiv:1512.07713v2 [math.ST]
"""
sample_means = np.mean(samples, axis=1, dtype=np.float64)
nmr_params, chain_length = samples.shape
nmr_batches = int(np.floor(chain_length / batch_size))
sigma = np.zeros((nmr_params, nmr_params))
nmr_offsets = chain_length - nmr_batches * batch_size + 1
for offset in range(nmr_offsets):
batches = np.reshape(samples[:, np.array(offset + np.arange(0, nmr_batches * batch_size), dtype=np.int)].T,
[batch_size, nmr_batches, nmr_params], order='F')
batch_means = np.squeeze(np.mean(batches, axis=0, dtype=np.float64))
Z = batch_means - sample_means
for x, y in itertools.product(range(nmr_params), range(nmr_params)):
sigma[x, y] += np.sum(Z[:, x] * Z[:, y])
return sigma * batch_size / (nmr_batches - 1) / nmr_offsets | r"""Calculates the Sigma matrix which is part of the multivariate ESS calculation.
This implementation is based on the Matlab implementation found at: https://github.com/lacerbi/multiESS
The Sigma matrix is defined as:
.. math::
\Sigma = \Lambda + 2 * \sum_{k=1}^{\infty}{Cov(Y_{1}, Y_{1+k})}
Where :math:`Y` are our samples and :math:`\Lambda` is the covariance matrix of the samples.
This implementation computes the :math:`\Sigma` matrix using a Batch Mean estimator using the given batch size.
The batch size has to be :math:`1 \le b_n \le n` and a typical value is either :math:`\lfloor n^{1/2} \rfloor`
for slow mixing chains or :math:`\lfloor n^{1/3} \rfloor` for reasonable mixing chains.
If the length of the chain is longer than the sum of the length of all the batches, this implementation
calculates :math:`\Sigma` for every offset and returns the average of those offsets.
Args:
samples (ndarray): the samples for which we compute the sigma matrix. Expects an (p, n) array with
p the number of parameters and n the sample size
batch_size (int): the batch size used in the approximation of the correlation covariance
Returns:
ndarray: an pxp array with p the number of parameters in the samples.
References:
Vats D, Flegal J, Jones G (2016). Multivariate Output Analysis for Markov Chain Monte Carlo.
arXiv:1512.07713v2 [math.ST] | https://github.com/cbclab/MOT/blob/fb3243b65025705842e82704705c00902f9a35af/mot/mcmc_diagnostics.py#L326-L377 |
cbclab/MOT | mot/mcmc_diagnostics.py | estimate_multivariate_ess | def estimate_multivariate_ess(samples, batch_size_generator=None, full_output=False):
r"""Compute the multivariate Effective Sample Size of your (single instance set of) samples.
This multivariate ESS is defined in Vats et al. (2016) and is given by:
.. math::
ESS = n \bigg(\frac{|\Lambda|}{|\Sigma|}\bigg)^{1/p}
Where :math:`n` is the number of samples, :math:`p` the number of parameters, :math:`\Lambda` is the covariance
matrix of the parameters and :math:`\Sigma` captures the covariance structure in the target together with
the covariance due to correlated samples. :math:`\Sigma` is estimated using
:func:`estimate_multivariate_ess_sigma`.
In the case of NaN in any part of the computation the ESS is set to 0.
To compute the multivariate ESS for multiple problems, please use :func:`multivariate_ess`.
Args:
samples (ndarray): an pxn matrix with for p parameters and n samples.
batch_size_generator (MultiVariateESSBatchSizeGenerator): the batch size generator, tells us how many
batches and of which size we use for estimating the minimum ESS. Defaults to :class:`SquareRootSingleBatch`
full_output (boolean): set to True to return the estimated :math:`\Sigma` and the optimal batch size.
Returns:
float or tuple: when full_output is set to True we return a tuple with the estimated multivariate ESS,
the estimated :math:`\Sigma` matrix and the optimal batch size. When full_output is False (the default)
we only return the ESS.
References:
Vats D, Flegal J, Jones G (2016). Multivariate Output Analysis for Markov Chain Monte Carlo.
arXiv:1512.07713v2 [math.ST]
"""
batch_size_generator = batch_size_generator or SquareRootSingleBatch()
batch_sizes = batch_size_generator.get_multivariate_ess_batch_sizes(*samples.shape)
nmr_params, chain_length = samples.shape
nmr_batches = len(batch_sizes)
det_lambda = det(np.cov(samples))
ess_estimates = np.zeros(nmr_batches)
sigma_estimates = np.zeros((nmr_params, nmr_params, nmr_batches))
for i in range(0, nmr_batches):
sigma = estimate_multivariate_ess_sigma(samples, int(batch_sizes[i]))
ess = chain_length * (det_lambda**(1.0 / nmr_params) / det(sigma)**(1.0 / nmr_params))
ess_estimates[i] = ess
sigma_estimates[..., i] = sigma
ess_estimates = np.nan_to_num(ess_estimates)
if nmr_batches > 1:
idx = np.argmin(ess_estimates)
else:
idx = 0
if full_output:
return ess_estimates[idx], sigma_estimates[..., idx], batch_sizes[idx]
return ess_estimates[idx] | python | def estimate_multivariate_ess(samples, batch_size_generator=None, full_output=False):
r"""Compute the multivariate Effective Sample Size of your (single instance set of) samples.
This multivariate ESS is defined in Vats et al. (2016) and is given by:
.. math::
ESS = n \bigg(\frac{|\Lambda|}{|\Sigma|}\bigg)^{1/p}
Where :math:`n` is the number of samples, :math:`p` the number of parameters, :math:`\Lambda` is the covariance
matrix of the parameters and :math:`\Sigma` captures the covariance structure in the target together with
the covariance due to correlated samples. :math:`\Sigma` is estimated using
:func:`estimate_multivariate_ess_sigma`.
In the case of NaN in any part of the computation the ESS is set to 0.
To compute the multivariate ESS for multiple problems, please use :func:`multivariate_ess`.
Args:
samples (ndarray): an pxn matrix with for p parameters and n samples.
batch_size_generator (MultiVariateESSBatchSizeGenerator): the batch size generator, tells us how many
batches and of which size we use for estimating the minimum ESS. Defaults to :class:`SquareRootSingleBatch`
full_output (boolean): set to True to return the estimated :math:`\Sigma` and the optimal batch size.
Returns:
float or tuple: when full_output is set to True we return a tuple with the estimated multivariate ESS,
the estimated :math:`\Sigma` matrix and the optimal batch size. When full_output is False (the default)
we only return the ESS.
References:
Vats D, Flegal J, Jones G (2016). Multivariate Output Analysis for Markov Chain Monte Carlo.
arXiv:1512.07713v2 [math.ST]
"""
batch_size_generator = batch_size_generator or SquareRootSingleBatch()
batch_sizes = batch_size_generator.get_multivariate_ess_batch_sizes(*samples.shape)
nmr_params, chain_length = samples.shape
nmr_batches = len(batch_sizes)
det_lambda = det(np.cov(samples))
ess_estimates = np.zeros(nmr_batches)
sigma_estimates = np.zeros((nmr_params, nmr_params, nmr_batches))
for i in range(0, nmr_batches):
sigma = estimate_multivariate_ess_sigma(samples, int(batch_sizes[i]))
ess = chain_length * (det_lambda**(1.0 / nmr_params) / det(sigma)**(1.0 / nmr_params))
ess_estimates[i] = ess
sigma_estimates[..., i] = sigma
ess_estimates = np.nan_to_num(ess_estimates)
if nmr_batches > 1:
idx = np.argmin(ess_estimates)
else:
idx = 0
if full_output:
return ess_estimates[idx], sigma_estimates[..., idx], batch_sizes[idx]
return ess_estimates[idx] | r"""Compute the multivariate Effective Sample Size of your (single instance set of) samples.
This multivariate ESS is defined in Vats et al. (2016) and is given by:
.. math::
ESS = n \bigg(\frac{|\Lambda|}{|\Sigma|}\bigg)^{1/p}
Where :math:`n` is the number of samples, :math:`p` the number of parameters, :math:`\Lambda` is the covariance
matrix of the parameters and :math:`\Sigma` captures the covariance structure in the target together with
the covariance due to correlated samples. :math:`\Sigma` is estimated using
:func:`estimate_multivariate_ess_sigma`.
In the case of NaN in any part of the computation the ESS is set to 0.
To compute the multivariate ESS for multiple problems, please use :func:`multivariate_ess`.
Args:
samples (ndarray): an pxn matrix with for p parameters and n samples.
batch_size_generator (MultiVariateESSBatchSizeGenerator): the batch size generator, tells us how many
batches and of which size we use for estimating the minimum ESS. Defaults to :class:`SquareRootSingleBatch`
full_output (boolean): set to True to return the estimated :math:`\Sigma` and the optimal batch size.
Returns:
float or tuple: when full_output is set to True we return a tuple with the estimated multivariate ESS,
the estimated :math:`\Sigma` matrix and the optimal batch size. When full_output is False (the default)
we only return the ESS.
References:
Vats D, Flegal J, Jones G (2016). Multivariate Output Analysis for Markov Chain Monte Carlo.
arXiv:1512.07713v2 [math.ST] | https://github.com/cbclab/MOT/blob/fb3243b65025705842e82704705c00902f9a35af/mot/mcmc_diagnostics.py#L380-L441 |
cbclab/MOT | mot/mcmc_diagnostics.py | monte_carlo_standard_error | def monte_carlo_standard_error(chain, batch_size_generator=None, compute_method=None):
"""Compute Monte Carlo standard errors for the expectations
This is a convenience function that calls the compute method for each batch size and returns the lowest ESS
over the used batch sizes.
Args:
chain (ndarray): the Markov chain
batch_size_generator (UniVariateESSBatchSizeGenerator): the method that generates that batch sizes
we will use. Per default it uses the :class:`SquareRootSingleBatch` method.
compute_method (ComputeMonteCarloStandardError): the method used to compute the standard error.
By default we will use the :class:`BatchMeansMCSE` method
"""
batch_size_generator = batch_size_generator or SquareRootSingleBatch()
compute_method = compute_method or BatchMeansMCSE()
batch_sizes = batch_size_generator.get_univariate_ess_batch_sizes(len(chain))
return np.min(list(compute_method.compute_standard_error(chain, b) for b in batch_sizes)) | python | def monte_carlo_standard_error(chain, batch_size_generator=None, compute_method=None):
"""Compute Monte Carlo standard errors for the expectations
This is a convenience function that calls the compute method for each batch size and returns the lowest ESS
over the used batch sizes.
Args:
chain (ndarray): the Markov chain
batch_size_generator (UniVariateESSBatchSizeGenerator): the method that generates that batch sizes
we will use. Per default it uses the :class:`SquareRootSingleBatch` method.
compute_method (ComputeMonteCarloStandardError): the method used to compute the standard error.
By default we will use the :class:`BatchMeansMCSE` method
"""
batch_size_generator = batch_size_generator or SquareRootSingleBatch()
compute_method = compute_method or BatchMeansMCSE()
batch_sizes = batch_size_generator.get_univariate_ess_batch_sizes(len(chain))
return np.min(list(compute_method.compute_standard_error(chain, b) for b in batch_sizes)) | Compute Monte Carlo standard errors for the expectations
This is a convenience function that calls the compute method for each batch size and returns the lowest ESS
over the used batch sizes.
Args:
chain (ndarray): the Markov chain
batch_size_generator (UniVariateESSBatchSizeGenerator): the method that generates that batch sizes
we will use. Per default it uses the :class:`SquareRootSingleBatch` method.
compute_method (ComputeMonteCarloStandardError): the method used to compute the standard error.
By default we will use the :class:`BatchMeansMCSE` method | https://github.com/cbclab/MOT/blob/fb3243b65025705842e82704705c00902f9a35af/mot/mcmc_diagnostics.py#L444-L462 |
cbclab/MOT | mot/stats.py | fit_gaussian | def fit_gaussian(samples, ddof=0):
"""Calculates the mean and the standard deviation of the given samples.
Args:
samples (ndarray): a one or two dimensional array. If one dimensional we calculate the fit using all
values. If two dimensional, we fit the Gaussian for every set of samples over the first dimension.
ddof (int): the difference degrees of freedom in the std calculation. See numpy.
"""
if len(samples.shape) == 1:
return np.mean(samples), np.std(samples, ddof=ddof)
return np.mean(samples, axis=1), np.std(samples, axis=1, ddof=ddof) | python | def fit_gaussian(samples, ddof=0):
"""Calculates the mean and the standard deviation of the given samples.
Args:
samples (ndarray): a one or two dimensional array. If one dimensional we calculate the fit using all
values. If two dimensional, we fit the Gaussian for every set of samples over the first dimension.
ddof (int): the difference degrees of freedom in the std calculation. See numpy.
"""
if len(samples.shape) == 1:
return np.mean(samples), np.std(samples, ddof=ddof)
return np.mean(samples, axis=1), np.std(samples, axis=1, ddof=ddof) | Calculates the mean and the standard deviation of the given samples.
Args:
samples (ndarray): a one or two dimensional array. If one dimensional we calculate the fit using all
values. If two dimensional, we fit the Gaussian for every set of samples over the first dimension.
ddof (int): the difference degrees of freedom in the std calculation. See numpy. | https://github.com/cbclab/MOT/blob/fb3243b65025705842e82704705c00902f9a35af/mot/stats.py#L18-L28 |
cbclab/MOT | mot/stats.py | fit_circular_gaussian | def fit_circular_gaussian(samples, high=np.pi, low=0):
"""Compute the circular mean for samples in a range
Args:
samples (ndarray): a one or two dimensional array. If one dimensional we calculate the fit using all
values. If two dimensional, we fit the Gaussian for every set of samples over the first dimension.
high (float): The maximum wrap point
low (float): The minimum wrap point
"""
cl_func = SimpleCLFunction.from_string('''
void compute(global mot_float_type* samples,
global mot_float_type* means,
global mot_float_type* stds,
int nmr_samples,
int low,
int high){
double cos_mean = 0;
double sin_mean = 0;
double ang;
for(uint i = 0; i < nmr_samples; i++){
ang = (samples[i] - low)*2*M_PI / (high - low);
cos_mean += (cos(ang) - cos_mean) / (i + 1);
sin_mean += (sin(ang) - sin_mean) / (i + 1);
}
double R = hypot(cos_mean, sin_mean);
if(R > 1){
R = 1;
}
double stds = 1/2. * sqrt(-2 * log(R));
double res = atan2(sin_mean, cos_mean);
if(res < 0){
res += 2 * M_PI;
}
*(means) = res*(high - low)/2.0/M_PI + low;
*(stds) = ((high - low)/2.0/M_PI) * sqrt(-2*log(R));
}
''')
def run_cl(samples):
data = {'samples': Array(samples, 'mot_float_type'),
'means': Zeros(samples.shape[0], 'mot_float_type'),
'stds': Zeros(samples.shape[0], 'mot_float_type'),
'nmr_samples': Scalar(samples.shape[1]),
'low': Scalar(low),
'high': Scalar(high),
}
cl_func.evaluate(data, samples.shape[0])
return data['means'].get_data(), data['stds'].get_data()
if len(samples.shape) == 1:
mean, std = run_cl(samples[None, :])
return mean[0], std[0]
return run_cl(samples) | python | def fit_circular_gaussian(samples, high=np.pi, low=0):
"""Compute the circular mean for samples in a range
Args:
samples (ndarray): a one or two dimensional array. If one dimensional we calculate the fit using all
values. If two dimensional, we fit the Gaussian for every set of samples over the first dimension.
high (float): The maximum wrap point
low (float): The minimum wrap point
"""
cl_func = SimpleCLFunction.from_string('''
void compute(global mot_float_type* samples,
global mot_float_type* means,
global mot_float_type* stds,
int nmr_samples,
int low,
int high){
double cos_mean = 0;
double sin_mean = 0;
double ang;
for(uint i = 0; i < nmr_samples; i++){
ang = (samples[i] - low)*2*M_PI / (high - low);
cos_mean += (cos(ang) - cos_mean) / (i + 1);
sin_mean += (sin(ang) - sin_mean) / (i + 1);
}
double R = hypot(cos_mean, sin_mean);
if(R > 1){
R = 1;
}
double stds = 1/2. * sqrt(-2 * log(R));
double res = atan2(sin_mean, cos_mean);
if(res < 0){
res += 2 * M_PI;
}
*(means) = res*(high - low)/2.0/M_PI + low;
*(stds) = ((high - low)/2.0/M_PI) * sqrt(-2*log(R));
}
''')
def run_cl(samples):
data = {'samples': Array(samples, 'mot_float_type'),
'means': Zeros(samples.shape[0], 'mot_float_type'),
'stds': Zeros(samples.shape[0], 'mot_float_type'),
'nmr_samples': Scalar(samples.shape[1]),
'low': Scalar(low),
'high': Scalar(high),
}
cl_func.evaluate(data, samples.shape[0])
return data['means'].get_data(), data['stds'].get_data()
if len(samples.shape) == 1:
mean, std = run_cl(samples[None, :])
return mean[0], std[0]
return run_cl(samples) | Compute the circular mean for samples in a range
Args:
samples (ndarray): a one or two dimensional array. If one dimensional we calculate the fit using all
values. If two dimensional, we fit the Gaussian for every set of samples over the first dimension.
high (float): The maximum wrap point
low (float): The minimum wrap point | https://github.com/cbclab/MOT/blob/fb3243b65025705842e82704705c00902f9a35af/mot/stats.py#L31-L91 |
cbclab/MOT | mot/stats.py | fit_truncated_gaussian | def fit_truncated_gaussian(samples, lower_bounds, upper_bounds):
"""Fits a truncated gaussian distribution on the given samples.
This will do a maximum likelihood estimation of a truncated Gaussian on the provided samples, with the
truncation points given by the lower and upper bounds.
Args:
samples (ndarray): a one or two dimensional array. If one dimensional we fit the truncated Gaussian on all
values. If two dimensional, we calculate the truncated Gaussian for every set of samples over the
first dimension.
lower_bounds (ndarray or float): the lower bound, either a scalar or a lower bound per problem (first index of
samples)
upper_bounds (ndarray or float): the upper bound, either a scalar or an upper bound per problem (first index of
samples)
Returns:
mean, std: the mean and std of the fitted truncated Gaussian
"""
if len(samples.shape) == 1:
return _TruncatedNormalFitter()((samples, lower_bounds, upper_bounds))
def item_generator():
for ind in range(samples.shape[0]):
if is_scalar(lower_bounds):
lower_bound = lower_bounds
else:
lower_bound = lower_bounds[ind]
if is_scalar(upper_bounds):
upper_bound = upper_bounds
else:
upper_bound = upper_bounds[ind]
yield (samples[ind], lower_bound, upper_bound)
results = np.array(multiprocess_mapping(_TruncatedNormalFitter(), item_generator()))
return results[:, 0], results[:, 1] | python | def fit_truncated_gaussian(samples, lower_bounds, upper_bounds):
"""Fits a truncated gaussian distribution on the given samples.
This will do a maximum likelihood estimation of a truncated Gaussian on the provided samples, with the
truncation points given by the lower and upper bounds.
Args:
samples (ndarray): a one or two dimensional array. If one dimensional we fit the truncated Gaussian on all
values. If two dimensional, we calculate the truncated Gaussian for every set of samples over the
first dimension.
lower_bounds (ndarray or float): the lower bound, either a scalar or a lower bound per problem (first index of
samples)
upper_bounds (ndarray or float): the upper bound, either a scalar or an upper bound per problem (first index of
samples)
Returns:
mean, std: the mean and std of the fitted truncated Gaussian
"""
if len(samples.shape) == 1:
return _TruncatedNormalFitter()((samples, lower_bounds, upper_bounds))
def item_generator():
for ind in range(samples.shape[0]):
if is_scalar(lower_bounds):
lower_bound = lower_bounds
else:
lower_bound = lower_bounds[ind]
if is_scalar(upper_bounds):
upper_bound = upper_bounds
else:
upper_bound = upper_bounds[ind]
yield (samples[ind], lower_bound, upper_bound)
results = np.array(multiprocess_mapping(_TruncatedNormalFitter(), item_generator()))
return results[:, 0], results[:, 1] | Fits a truncated gaussian distribution on the given samples.
This will do a maximum likelihood estimation of a truncated Gaussian on the provided samples, with the
truncation points given by the lower and upper bounds.
Args:
samples (ndarray): a one or two dimensional array. If one dimensional we fit the truncated Gaussian on all
values. If two dimensional, we calculate the truncated Gaussian for every set of samples over the
first dimension.
lower_bounds (ndarray or float): the lower bound, either a scalar or a lower bound per problem (first index of
samples)
upper_bounds (ndarray or float): the upper bound, either a scalar or an upper bound per problem (first index of
samples)
Returns:
mean, std: the mean and std of the fitted truncated Gaussian | https://github.com/cbclab/MOT/blob/fb3243b65025705842e82704705c00902f9a35af/mot/stats.py#L94-L130 |
cbclab/MOT | mot/stats.py | gaussian_overlapping_coefficient | def gaussian_overlapping_coefficient(means_0, stds_0, means_1, stds_1, lower=None, upper=None):
"""Compute the overlapping coefficient of two Gaussian continuous_distributions.
This computes the :math:`\int_{-\infty}^{\infty}{\min(f(x), g(x))\partial x}` where
:math:`f \sim \mathcal{N}(\mu_0, \sigma_0^{2})` and :math:`f \sim \mathcal{N}(\mu_1, \sigma_1^{2})` are normally
distributed variables.
This will compute the overlap for each element in the first dimension.
Args:
means_0 (ndarray): the set of means of the first distribution
stds_0 (ndarray): the set of stds of the fist distribution
means_1 (ndarray): the set of means of the second distribution
stds_1 (ndarray): the set of stds of the second distribution
lower (float): the lower limit of the integration. If not set we set it to -inf.
upper (float): the upper limit of the integration. If not set we set it to +inf.
"""
if lower is None:
lower = -np.inf
if upper is None:
upper = np.inf
def point_iterator():
for ind in range(means_0.shape[0]):
yield np.squeeze(means_0[ind]), np.squeeze(stds_0[ind]), np.squeeze(means_1[ind]), np.squeeze(stds_1[ind])
return np.array(list(multiprocess_mapping(_ComputeGaussianOverlap(lower, upper), point_iterator()))) | python | def gaussian_overlapping_coefficient(means_0, stds_0, means_1, stds_1, lower=None, upper=None):
"""Compute the overlapping coefficient of two Gaussian continuous_distributions.
This computes the :math:`\int_{-\infty}^{\infty}{\min(f(x), g(x))\partial x}` where
:math:`f \sim \mathcal{N}(\mu_0, \sigma_0^{2})` and :math:`f \sim \mathcal{N}(\mu_1, \sigma_1^{2})` are normally
distributed variables.
This will compute the overlap for each element in the first dimension.
Args:
means_0 (ndarray): the set of means of the first distribution
stds_0 (ndarray): the set of stds of the fist distribution
means_1 (ndarray): the set of means of the second distribution
stds_1 (ndarray): the set of stds of the second distribution
lower (float): the lower limit of the integration. If not set we set it to -inf.
upper (float): the upper limit of the integration. If not set we set it to +inf.
"""
if lower is None:
lower = -np.inf
if upper is None:
upper = np.inf
def point_iterator():
for ind in range(means_0.shape[0]):
yield np.squeeze(means_0[ind]), np.squeeze(stds_0[ind]), np.squeeze(means_1[ind]), np.squeeze(stds_1[ind])
return np.array(list(multiprocess_mapping(_ComputeGaussianOverlap(lower, upper), point_iterator()))) | Compute the overlapping coefficient of two Gaussian continuous_distributions.
This computes the :math:`\int_{-\infty}^{\infty}{\min(f(x), g(x))\partial x}` where
:math:`f \sim \mathcal{N}(\mu_0, \sigma_0^{2})` and :math:`f \sim \mathcal{N}(\mu_1, \sigma_1^{2})` are normally
distributed variables.
This will compute the overlap for each element in the first dimension.
Args:
means_0 (ndarray): the set of means of the first distribution
stds_0 (ndarray): the set of stds of the fist distribution
means_1 (ndarray): the set of means of the second distribution
stds_1 (ndarray): the set of stds of the second distribution
lower (float): the lower limit of the integration. If not set we set it to -inf.
upper (float): the upper limit of the integration. If not set we set it to +inf. | https://github.com/cbclab/MOT/blob/fb3243b65025705842e82704705c00902f9a35af/mot/stats.py#L133-L159 |
cbclab/MOT | mot/stats.py | deviance_information_criterions | def deviance_information_criterions(mean_posterior_lls, ll_per_sample):
r"""Calculates the Deviance Information Criteria (DIC) using three methods.
This returns a dictionary returning the ``DIC_2002``, the ``DIC_2004`` and the ``DIC_Ando_2011`` method.
The first is based on Spiegelhalter et al (2002), the second based on Gelman et al. (2004) and the last on
Ando (2011). All cases differ in how they calculate model complexity, i.e. the effective number of parameters
in the model. In all cases the model with the smallest DIC is preferred.
All these DIC methods measure fitness using the deviance, which is, for a likelihood :math:`p(y | \theta)`
defined as:
.. math::
D(\theta) = -2\log p(y|\theta)
From this, the posterior mean deviance,
.. math::
\bar{D} = \mathbb{E}_{\theta}[D(\theta)]
is then used as a measure of how well the model fits the data.
The complexity, or measure of effective number of parameters, can be measured in see ways, see
Spiegelhalter et al. (2002), Gelman et al (2004) and Ando (2011). The first method calculated the parameter
deviance as:
.. math::
:nowrap:
\begin{align}
p_{D} &= \mathbb{E}_{\theta}[D(\theta)] - D(\mathbb{E}[\theta)]) \\
&= \bar{D} - D(\bar{\theta})
\end{align}
i.e. posterior mean deviance minus the deviance evaluated at the posterior mean of the parameters.
The second method calculated :math:`p_{D}` as:
.. math::
p_{D} = p_{V} = \frac{1}{2}\hat{var}(D(\theta))
i.e. half the variance of the deviance is used as an estimate of the number of free parameters in the model.
The third method calculates the parameter deviance as:
.. math::
p_{D} = 2 \cdot (\bar{D} - D(\bar{\theta}))
That is, twice the complexity of that of the first method.
Finally, the DIC is (for all cases) defined as:
.. math::
DIC = \bar{D} + p_{D}
Args:
mean_posterior_lls (ndarray): a 1d matrix containing the log likelihood for the average posterior
point estimate. That is, the single log likelihood of the average parameters.
ll_per_sample (ndarray): a (d, n) array with for d problems the n log likelihoods.
This is the log likelihood per sample.
Returns:
dict: a dictionary containing the ``DIC_2002``, the ``DIC_2004`` and the ``DIC_Ando_2011`` information
criterion maps.
"""
mean_deviance = -2 * np.mean(ll_per_sample, axis=1)
deviance_at_mean = -2 * mean_posterior_lls
pd_2002 = mean_deviance - deviance_at_mean
pd_2004 = np.var(ll_per_sample, axis=1) / 2.0
return {'DIC_2002': np.nan_to_num(mean_deviance + pd_2002),
'DIC_2004': np.nan_to_num(mean_deviance + pd_2004),
'DIC_Ando_2011': np.nan_to_num(mean_deviance + 2 * pd_2002)} | python | def deviance_information_criterions(mean_posterior_lls, ll_per_sample):
r"""Calculates the Deviance Information Criteria (DIC) using three methods.
This returns a dictionary returning the ``DIC_2002``, the ``DIC_2004`` and the ``DIC_Ando_2011`` method.
The first is based on Spiegelhalter et al (2002), the second based on Gelman et al. (2004) and the last on
Ando (2011). All cases differ in how they calculate model complexity, i.e. the effective number of parameters
in the model. In all cases the model with the smallest DIC is preferred.
All these DIC methods measure fitness using the deviance, which is, for a likelihood :math:`p(y | \theta)`
defined as:
.. math::
D(\theta) = -2\log p(y|\theta)
From this, the posterior mean deviance,
.. math::
\bar{D} = \mathbb{E}_{\theta}[D(\theta)]
is then used as a measure of how well the model fits the data.
The complexity, or measure of effective number of parameters, can be measured in see ways, see
Spiegelhalter et al. (2002), Gelman et al (2004) and Ando (2011). The first method calculated the parameter
deviance as:
.. math::
:nowrap:
\begin{align}
p_{D} &= \mathbb{E}_{\theta}[D(\theta)] - D(\mathbb{E}[\theta)]) \\
&= \bar{D} - D(\bar{\theta})
\end{align}
i.e. posterior mean deviance minus the deviance evaluated at the posterior mean of the parameters.
The second method calculated :math:`p_{D}` as:
.. math::
p_{D} = p_{V} = \frac{1}{2}\hat{var}(D(\theta))
i.e. half the variance of the deviance is used as an estimate of the number of free parameters in the model.
The third method calculates the parameter deviance as:
.. math::
p_{D} = 2 \cdot (\bar{D} - D(\bar{\theta}))
That is, twice the complexity of that of the first method.
Finally, the DIC is (for all cases) defined as:
.. math::
DIC = \bar{D} + p_{D}
Args:
mean_posterior_lls (ndarray): a 1d matrix containing the log likelihood for the average posterior
point estimate. That is, the single log likelihood of the average parameters.
ll_per_sample (ndarray): a (d, n) array with for d problems the n log likelihoods.
This is the log likelihood per sample.
Returns:
dict: a dictionary containing the ``DIC_2002``, the ``DIC_2004`` and the ``DIC_Ando_2011`` information
criterion maps.
"""
mean_deviance = -2 * np.mean(ll_per_sample, axis=1)
deviance_at_mean = -2 * mean_posterior_lls
pd_2002 = mean_deviance - deviance_at_mean
pd_2004 = np.var(ll_per_sample, axis=1) / 2.0
return {'DIC_2002': np.nan_to_num(mean_deviance + pd_2002),
'DIC_2004': np.nan_to_num(mean_deviance + pd_2004),
'DIC_Ando_2011': np.nan_to_num(mean_deviance + 2 * pd_2002)} | r"""Calculates the Deviance Information Criteria (DIC) using three methods.
This returns a dictionary returning the ``DIC_2002``, the ``DIC_2004`` and the ``DIC_Ando_2011`` method.
The first is based on Spiegelhalter et al (2002), the second based on Gelman et al. (2004) and the last on
Ando (2011). All cases differ in how they calculate model complexity, i.e. the effective number of parameters
in the model. In all cases the model with the smallest DIC is preferred.
All these DIC methods measure fitness using the deviance, which is, for a likelihood :math:`p(y | \theta)`
defined as:
.. math::
D(\theta) = -2\log p(y|\theta)
From this, the posterior mean deviance,
.. math::
\bar{D} = \mathbb{E}_{\theta}[D(\theta)]
is then used as a measure of how well the model fits the data.
The complexity, or measure of effective number of parameters, can be measured in see ways, see
Spiegelhalter et al. (2002), Gelman et al (2004) and Ando (2011). The first method calculated the parameter
deviance as:
.. math::
:nowrap:
\begin{align}
p_{D} &= \mathbb{E}_{\theta}[D(\theta)] - D(\mathbb{E}[\theta)]) \\
&= \bar{D} - D(\bar{\theta})
\end{align}
i.e. posterior mean deviance minus the deviance evaluated at the posterior mean of the parameters.
The second method calculated :math:`p_{D}` as:
.. math::
p_{D} = p_{V} = \frac{1}{2}\hat{var}(D(\theta))
i.e. half the variance of the deviance is used as an estimate of the number of free parameters in the model.
The third method calculates the parameter deviance as:
.. math::
p_{D} = 2 \cdot (\bar{D} - D(\bar{\theta}))
That is, twice the complexity of that of the first method.
Finally, the DIC is (for all cases) defined as:
.. math::
DIC = \bar{D} + p_{D}
Args:
mean_posterior_lls (ndarray): a 1d matrix containing the log likelihood for the average posterior
point estimate. That is, the single log likelihood of the average parameters.
ll_per_sample (ndarray): a (d, n) array with for d problems the n log likelihoods.
This is the log likelihood per sample.
Returns:
dict: a dictionary containing the ``DIC_2002``, the ``DIC_2004`` and the ``DIC_Ando_2011`` information
criterion maps. | https://github.com/cbclab/MOT/blob/fb3243b65025705842e82704705c00902f9a35af/mot/stats.py#L162-L239 |
cbclab/MOT | mot/stats.py | _TruncatedNormalFitter.truncated_normal_log_likelihood | def truncated_normal_log_likelihood(params, low, high, data):
"""Calculate the log likelihood of the truncated normal distribution.
Args:
params: tuple with (mean, std), the parameters under which we evaluate the model
low (float): the lower truncation bound
high (float): the upper truncation bound
data (ndarray): the one dimension list of data points for which we want to calculate the likelihood
Returns:
float: the negative log likelihood of observing the given data under the given parameters.
This is meant to be used in minimization routines.
"""
mu = params[0]
sigma = params[1]
if sigma == 0:
return np.inf
ll = np.sum(norm.logpdf(data, mu, sigma))
ll -= len(data) * np.log((norm.cdf(high, mu, sigma) - norm.cdf(low, mu, sigma)))
return -ll | python | def truncated_normal_log_likelihood(params, low, high, data):
"""Calculate the log likelihood of the truncated normal distribution.
Args:
params: tuple with (mean, std), the parameters under which we evaluate the model
low (float): the lower truncation bound
high (float): the upper truncation bound
data (ndarray): the one dimension list of data points for which we want to calculate the likelihood
Returns:
float: the negative log likelihood of observing the given data under the given parameters.
This is meant to be used in minimization routines.
"""
mu = params[0]
sigma = params[1]
if sigma == 0:
return np.inf
ll = np.sum(norm.logpdf(data, mu, sigma))
ll -= len(data) * np.log((norm.cdf(high, mu, sigma) - norm.cdf(low, mu, sigma)))
return -ll | Calculate the log likelihood of the truncated normal distribution.
Args:
params: tuple with (mean, std), the parameters under which we evaluate the model
low (float): the lower truncation bound
high (float): the upper truncation bound
data (ndarray): the one dimension list of data points for which we want to calculate the likelihood
Returns:
float: the negative log likelihood of observing the given data under the given parameters.
This is meant to be used in minimization routines. | https://github.com/cbclab/MOT/blob/fb3243b65025705842e82704705c00902f9a35af/mot/stats.py#L291-L310 |
cbclab/MOT | mot/stats.py | _TruncatedNormalFitter.truncated_normal_ll_gradient | def truncated_normal_ll_gradient(params, low, high, data):
"""Return the gradient of the log likelihood of the truncated normal at the given position.
Args:
params: tuple with (mean, std), the parameters under which we evaluate the model
low (float): the lower truncation bound
high (float): the upper truncation bound
data (ndarray): the one dimension list of data points for which we want to calculate the likelihood
Returns:
tuple: the gradient of the log likelihood given as a tuple with (mean, std)
"""
if params[1] == 0:
return np.array([np.inf, np.inf])
return np.array([_TruncatedNormalFitter.partial_derivative_mu(params[0], params[1], low, high, data),
_TruncatedNormalFitter.partial_derivative_sigma(params[0], params[1], low, high, data)]) | python | def truncated_normal_ll_gradient(params, low, high, data):
"""Return the gradient of the log likelihood of the truncated normal at the given position.
Args:
params: tuple with (mean, std), the parameters under which we evaluate the model
low (float): the lower truncation bound
high (float): the upper truncation bound
data (ndarray): the one dimension list of data points for which we want to calculate the likelihood
Returns:
tuple: the gradient of the log likelihood given as a tuple with (mean, std)
"""
if params[1] == 0:
return np.array([np.inf, np.inf])
return np.array([_TruncatedNormalFitter.partial_derivative_mu(params[0], params[1], low, high, data),
_TruncatedNormalFitter.partial_derivative_sigma(params[0], params[1], low, high, data)]) | Return the gradient of the log likelihood of the truncated normal at the given position.
Args:
params: tuple with (mean, std), the parameters under which we evaluate the model
low (float): the lower truncation bound
high (float): the upper truncation bound
data (ndarray): the one dimension list of data points for which we want to calculate the likelihood
Returns:
tuple: the gradient of the log likelihood given as a tuple with (mean, std) | https://github.com/cbclab/MOT/blob/fb3243b65025705842e82704705c00902f9a35af/mot/stats.py#L313-L329 |
cbclab/MOT | mot/stats.py | _TruncatedNormalFitter.partial_derivative_mu | def partial_derivative_mu(mu, sigma, low, high, data):
"""The partial derivative with respect to the mean.
Args:
mu (float): the mean of the truncated normal
sigma (float): the std of the truncated normal
low (float): the lower truncation bound
high (float): the upper truncation bound
data (ndarray): the one dimension list of data points for which we want to calculate the likelihood
Returns:
float: the partial derivative evaluated at the given point
"""
pd_mu = np.sum(data - mu) / sigma ** 2
pd_mu -= len(data) * ((norm.pdf(low, mu, sigma) - norm.pdf(high, mu, sigma))
/ (norm.cdf(high, mu, sigma) - norm.cdf(low, mu, sigma)))
return -pd_mu | python | def partial_derivative_mu(mu, sigma, low, high, data):
"""The partial derivative with respect to the mean.
Args:
mu (float): the mean of the truncated normal
sigma (float): the std of the truncated normal
low (float): the lower truncation bound
high (float): the upper truncation bound
data (ndarray): the one dimension list of data points for which we want to calculate the likelihood
Returns:
float: the partial derivative evaluated at the given point
"""
pd_mu = np.sum(data - mu) / sigma ** 2
pd_mu -= len(data) * ((norm.pdf(low, mu, sigma) - norm.pdf(high, mu, sigma))
/ (norm.cdf(high, mu, sigma) - norm.cdf(low, mu, sigma)))
return -pd_mu | The partial derivative with respect to the mean.
Args:
mu (float): the mean of the truncated normal
sigma (float): the std of the truncated normal
low (float): the lower truncation bound
high (float): the upper truncation bound
data (ndarray): the one dimension list of data points for which we want to calculate the likelihood
Returns:
float: the partial derivative evaluated at the given point | https://github.com/cbclab/MOT/blob/fb3243b65025705842e82704705c00902f9a35af/mot/stats.py#L332-L348 |
cbclab/MOT | mot/stats.py | _TruncatedNormalFitter.partial_derivative_sigma | def partial_derivative_sigma(mu, sigma, low, high, data):
"""The partial derivative with respect to the standard deviation.
Args:
mu (float): the mean of the truncated normal
sigma (float): the std of the truncated normal
low (float): the lower truncation bound
high (float): the upper truncation bound
data (ndarray): the one dimension list of data points for which we want to calculate the likelihood
Returns:
float: the partial derivative evaluated at the given point
"""
pd_sigma = np.sum(-(1 / sigma) + ((data - mu) ** 2 / (sigma ** 3)))
pd_sigma -= len(data) * (((low - mu) * norm.pdf(low, mu, sigma) - (high - mu) * norm.pdf(high, mu, sigma))
/ (sigma * (norm.cdf(high, mu, sigma) - norm.cdf(low, mu, sigma))))
return -pd_sigma | python | def partial_derivative_sigma(mu, sigma, low, high, data):
"""The partial derivative with respect to the standard deviation.
Args:
mu (float): the mean of the truncated normal
sigma (float): the std of the truncated normal
low (float): the lower truncation bound
high (float): the upper truncation bound
data (ndarray): the one dimension list of data points for which we want to calculate the likelihood
Returns:
float: the partial derivative evaluated at the given point
"""
pd_sigma = np.sum(-(1 / sigma) + ((data - mu) ** 2 / (sigma ** 3)))
pd_sigma -= len(data) * (((low - mu) * norm.pdf(low, mu, sigma) - (high - mu) * norm.pdf(high, mu, sigma))
/ (sigma * (norm.cdf(high, mu, sigma) - norm.cdf(low, mu, sigma))))
return -pd_sigma | The partial derivative with respect to the standard deviation.
Args:
mu (float): the mean of the truncated normal
sigma (float): the std of the truncated normal
low (float): the lower truncation bound
high (float): the upper truncation bound
data (ndarray): the one dimension list of data points for which we want to calculate the likelihood
Returns:
float: the partial derivative evaluated at the given point | https://github.com/cbclab/MOT/blob/fb3243b65025705842e82704705c00902f9a35af/mot/stats.py#L351-L367 |
cbclab/MOT | mot/library_functions/__init__.py | NMSimplex.get_kernel_data | def get_kernel_data(self):
"""Get the kernel data needed for this optimization routine to work."""
return {
'nmsimplex_scratch': LocalMemory(
'mot_float_type', self._nmr_parameters * 2 + (self._nmr_parameters + 1) ** 2 + 1),
'initial_simplex_scale': LocalMemory('mot_float_type', self._nmr_parameters)
} | python | def get_kernel_data(self):
"""Get the kernel data needed for this optimization routine to work."""
return {
'nmsimplex_scratch': LocalMemory(
'mot_float_type', self._nmr_parameters * 2 + (self._nmr_parameters + 1) ** 2 + 1),
'initial_simplex_scale': LocalMemory('mot_float_type', self._nmr_parameters)
} | Get the kernel data needed for this optimization routine to work. | https://github.com/cbclab/MOT/blob/fb3243b65025705842e82704705c00902f9a35af/mot/library_functions/__init__.py#L498-L504 |
cbclab/MOT | mot/library_functions/__init__.py | Subplex.get_kernel_data | def get_kernel_data(self):
"""Get the kernel data needed for this optimization routine to work."""
return {
'subplex_scratch_float': LocalMemory(
'mot_float_type', 4 + self._var_replace_dict['NMR_PARAMS'] * 2
+ self._var_replace_dict['MAX_SUBSPACE_LENGTH'] * 2
+ (self._var_replace_dict['MAX_SUBSPACE_LENGTH'] * 2
+ self._var_replace_dict['MAX_SUBSPACE_LENGTH']+1)**2 + 1),
'subplex_scratch_int': LocalMemory(
'int', 2 + self._var_replace_dict['NMR_PARAMS']
+ (self._var_replace_dict['NMR_PARAMS'] // self._var_replace_dict['MIN_SUBSPACE_LENGTH'])),
'initial_simplex_scale': LocalMemory('mot_float_type', self._var_replace_dict['NMR_PARAMS'])
} | python | def get_kernel_data(self):
"""Get the kernel data needed for this optimization routine to work."""
return {
'subplex_scratch_float': LocalMemory(
'mot_float_type', 4 + self._var_replace_dict['NMR_PARAMS'] * 2
+ self._var_replace_dict['MAX_SUBSPACE_LENGTH'] * 2
+ (self._var_replace_dict['MAX_SUBSPACE_LENGTH'] * 2
+ self._var_replace_dict['MAX_SUBSPACE_LENGTH']+1)**2 + 1),
'subplex_scratch_int': LocalMemory(
'int', 2 + self._var_replace_dict['NMR_PARAMS']
+ (self._var_replace_dict['NMR_PARAMS'] // self._var_replace_dict['MIN_SUBSPACE_LENGTH'])),
'initial_simplex_scale': LocalMemory('mot_float_type', self._var_replace_dict['NMR_PARAMS'])
} | Get the kernel data needed for this optimization routine to work. | https://github.com/cbclab/MOT/blob/fb3243b65025705842e82704705c00902f9a35af/mot/library_functions/__init__.py#L549-L561 |
cbclab/MOT | mot/library_functions/__init__.py | LevenbergMarquardt.get_kernel_data | def get_kernel_data(self):
"""Get the kernel data needed for this optimization routine to work."""
return {
'scratch_mot_float_type': LocalMemory(
'mot_float_type', 8 +
2 * self._var_replace_dict['NMR_OBSERVATIONS'] +
5 * self._var_replace_dict['NMR_PARAMS'] +
self._var_replace_dict['NMR_PARAMS'] * self._var_replace_dict['NMR_OBSERVATIONS']),
'scratch_int': LocalMemory('int', self._var_replace_dict['NMR_PARAMS'])
} | python | def get_kernel_data(self):
"""Get the kernel data needed for this optimization routine to work."""
return {
'scratch_mot_float_type': LocalMemory(
'mot_float_type', 8 +
2 * self._var_replace_dict['NMR_OBSERVATIONS'] +
5 * self._var_replace_dict['NMR_PARAMS'] +
self._var_replace_dict['NMR_PARAMS'] * self._var_replace_dict['NMR_OBSERVATIONS']),
'scratch_int': LocalMemory('int', self._var_replace_dict['NMR_PARAMS'])
} | Get the kernel data needed for this optimization routine to work. | https://github.com/cbclab/MOT/blob/fb3243b65025705842e82704705c00902f9a35af/mot/library_functions/__init__.py#L605-L614 |
cbclab/MOT | mot/optimize/__init__.py | minimize | def minimize(func, x0, data=None, method=None, lower_bounds=None, upper_bounds=None, constraints_func=None,
nmr_observations=None, cl_runtime_info=None, options=None):
"""Minimization of one or more variables.
For an easy wrapper of function maximization, see :func:`maximize`.
All boundary conditions are enforced using the penalty method. That is, we optimize the objective function:
.. math::
F(x) = f(x) \mu \sum \max(0, g_i(x))^2
where :math:`F(x)` is the new objective function, :math:`f(x)` is the old objective function, :math:`g_i` are
the boundary functions defined as :math:`g_i(x) \leq 0` and :math:`\mu` is the penalty weight.
The penalty weight is by default :math:`\mu = 1e20` and can be set
using the ``options`` dictionary as ``penalty_weight``.
Args:
func (mot.lib.cl_function.CLFunction): A CL function with the signature:
.. code-block:: c
double <func_name>(local const mot_float_type* const x,
void* data,
local mot_float_type* objective_list);
The objective list needs to be filled when the provided pointer is not null. It should contain
the function values for each observation. This list is used by non-linear least-squares routines,
and will be squared by the least-square optimizer. This is only used by the ``Levenberg-Marquardt`` routine.
x0 (ndarray): Initial guess. Array of real elements of size (n, p), for 'n' problems and 'p'
independent variables.
data (mot.lib.kernel_data.KernelData): the kernel data we will load. This is returned to the likelihood function
as the ``void* data`` pointer.
method (str): Type of solver. Should be one of:
- 'Levenberg-Marquardt'
- 'Nelder-Mead'
- 'Powell'
- 'Subplex'
If not given, defaults to 'Powell'.
lower_bounds (tuple): per parameter a lower bound, if given, the optimizer ensures ``a <= x`` with
a the lower bound and x the parameter. If not given, -infinity is assumed for all parameters.
Each tuple element can either be a scalar or a vector. If a vector is given the first dimension length
should match that of the parameters.
upper_bounds (tuple): per parameter an upper bound, if given, the optimizer ensures ``x >= b`` with
b the upper bound and x the parameter. If not given, +infinity is assumed for all parameters.
Each tuple element can either be a scalar or a vector. If a vector is given the first dimension length
should match that of the parameters.
constraints_func (mot.optimize.base.ConstraintFunction): function to compute (inequality) constraints.
Should hold a CL function with the signature:
.. code-block:: c
void <func_name>(local const mot_float_type* const x,
void* data,
local mot_float_type* constraints);
Where ``constraints_values`` is filled as:
.. code-block:: c
constraints[i] = g_i(x)
That is, for each constraint function :math:`g_i`, formulated as :math:`g_i(x) <= 0`, we should return
the function value of :math:`g_i`.
nmr_observations (int): the number of observations returned by the optimization function.
This is only needed for the ``Levenberg-Marquardt`` method.
cl_runtime_info (mot.configuration.CLRuntimeInfo): the CL runtime information
options (dict): A dictionary of solver options. All methods accept the following generic options:
- patience (int): Maximum number of iterations to perform.
- penalty_weight (float): the weight of the penalty term for the boundary conditions
Returns:
mot.optimize.base.OptimizeResults:
The optimization result represented as a ``OptimizeResult`` object.
Important attributes are: ``x`` the solution array.
"""
if not method:
method = 'Powell'
cl_runtime_info = cl_runtime_info or CLRuntimeInfo()
if len(x0.shape) < 2:
x0 = x0[..., None]
lower_bounds = _bounds_to_array(lower_bounds or np.ones(x0.shape[1]) * -np.inf)
upper_bounds = _bounds_to_array(upper_bounds or np.ones(x0.shape[1]) * np.inf)
if method == 'Powell':
return _minimize_powell(func, x0, cl_runtime_info, lower_bounds, upper_bounds,
constraints_func=constraints_func, data=data, options=options)
elif method == 'Nelder-Mead':
return _minimize_nmsimplex(func, x0, cl_runtime_info, lower_bounds, upper_bounds,
constraints_func=constraints_func, data=data, options=options)
elif method == 'Levenberg-Marquardt':
return _minimize_levenberg_marquardt(func, x0, nmr_observations, cl_runtime_info, lower_bounds, upper_bounds,
constraints_func=constraints_func, data=data, options=options)
elif method == 'Subplex':
return _minimize_subplex(func, x0, cl_runtime_info, lower_bounds, upper_bounds,
constraints_func=constraints_func, data=data, options=options)
raise ValueError('Could not find the specified method "{}".'.format(method)) | python | def minimize(func, x0, data=None, method=None, lower_bounds=None, upper_bounds=None, constraints_func=None,
nmr_observations=None, cl_runtime_info=None, options=None):
"""Minimization of one or more variables.
For an easy wrapper of function maximization, see :func:`maximize`.
All boundary conditions are enforced using the penalty method. That is, we optimize the objective function:
.. math::
F(x) = f(x) \mu \sum \max(0, g_i(x))^2
where :math:`F(x)` is the new objective function, :math:`f(x)` is the old objective function, :math:`g_i` are
the boundary functions defined as :math:`g_i(x) \leq 0` and :math:`\mu` is the penalty weight.
The penalty weight is by default :math:`\mu = 1e20` and can be set
using the ``options`` dictionary as ``penalty_weight``.
Args:
func (mot.lib.cl_function.CLFunction): A CL function with the signature:
.. code-block:: c
double <func_name>(local const mot_float_type* const x,
void* data,
local mot_float_type* objective_list);
The objective list needs to be filled when the provided pointer is not null. It should contain
the function values for each observation. This list is used by non-linear least-squares routines,
and will be squared by the least-square optimizer. This is only used by the ``Levenberg-Marquardt`` routine.
x0 (ndarray): Initial guess. Array of real elements of size (n, p), for 'n' problems and 'p'
independent variables.
data (mot.lib.kernel_data.KernelData): the kernel data we will load. This is returned to the likelihood function
as the ``void* data`` pointer.
method (str): Type of solver. Should be one of:
- 'Levenberg-Marquardt'
- 'Nelder-Mead'
- 'Powell'
- 'Subplex'
If not given, defaults to 'Powell'.
lower_bounds (tuple): per parameter a lower bound, if given, the optimizer ensures ``a <= x`` with
a the lower bound and x the parameter. If not given, -infinity is assumed for all parameters.
Each tuple element can either be a scalar or a vector. If a vector is given the first dimension length
should match that of the parameters.
upper_bounds (tuple): per parameter an upper bound, if given, the optimizer ensures ``x >= b`` with
b the upper bound and x the parameter. If not given, +infinity is assumed for all parameters.
Each tuple element can either be a scalar or a vector. If a vector is given the first dimension length
should match that of the parameters.
constraints_func (mot.optimize.base.ConstraintFunction): function to compute (inequality) constraints.
Should hold a CL function with the signature:
.. code-block:: c
void <func_name>(local const mot_float_type* const x,
void* data,
local mot_float_type* constraints);
Where ``constraints_values`` is filled as:
.. code-block:: c
constraints[i] = g_i(x)
That is, for each constraint function :math:`g_i`, formulated as :math:`g_i(x) <= 0`, we should return
the function value of :math:`g_i`.
nmr_observations (int): the number of observations returned by the optimization function.
This is only needed for the ``Levenberg-Marquardt`` method.
cl_runtime_info (mot.configuration.CLRuntimeInfo): the CL runtime information
options (dict): A dictionary of solver options. All methods accept the following generic options:
- patience (int): Maximum number of iterations to perform.
- penalty_weight (float): the weight of the penalty term for the boundary conditions
Returns:
mot.optimize.base.OptimizeResults:
The optimization result represented as a ``OptimizeResult`` object.
Important attributes are: ``x`` the solution array.
"""
if not method:
method = 'Powell'
cl_runtime_info = cl_runtime_info or CLRuntimeInfo()
if len(x0.shape) < 2:
x0 = x0[..., None]
lower_bounds = _bounds_to_array(lower_bounds or np.ones(x0.shape[1]) * -np.inf)
upper_bounds = _bounds_to_array(upper_bounds or np.ones(x0.shape[1]) * np.inf)
if method == 'Powell':
return _minimize_powell(func, x0, cl_runtime_info, lower_bounds, upper_bounds,
constraints_func=constraints_func, data=data, options=options)
elif method == 'Nelder-Mead':
return _minimize_nmsimplex(func, x0, cl_runtime_info, lower_bounds, upper_bounds,
constraints_func=constraints_func, data=data, options=options)
elif method == 'Levenberg-Marquardt':
return _minimize_levenberg_marquardt(func, x0, nmr_observations, cl_runtime_info, lower_bounds, upper_bounds,
constraints_func=constraints_func, data=data, options=options)
elif method == 'Subplex':
return _minimize_subplex(func, x0, cl_runtime_info, lower_bounds, upper_bounds,
constraints_func=constraints_func, data=data, options=options)
raise ValueError('Could not find the specified method "{}".'.format(method)) | Minimization of one or more variables.
For an easy wrapper of function maximization, see :func:`maximize`.
All boundary conditions are enforced using the penalty method. That is, we optimize the objective function:
.. math::
F(x) = f(x) \mu \sum \max(0, g_i(x))^2
where :math:`F(x)` is the new objective function, :math:`f(x)` is the old objective function, :math:`g_i` are
the boundary functions defined as :math:`g_i(x) \leq 0` and :math:`\mu` is the penalty weight.
The penalty weight is by default :math:`\mu = 1e20` and can be set
using the ``options`` dictionary as ``penalty_weight``.
Args:
func (mot.lib.cl_function.CLFunction): A CL function with the signature:
.. code-block:: c
double <func_name>(local const mot_float_type* const x,
void* data,
local mot_float_type* objective_list);
The objective list needs to be filled when the provided pointer is not null. It should contain
the function values for each observation. This list is used by non-linear least-squares routines,
and will be squared by the least-square optimizer. This is only used by the ``Levenberg-Marquardt`` routine.
x0 (ndarray): Initial guess. Array of real elements of size (n, p), for 'n' problems and 'p'
independent variables.
data (mot.lib.kernel_data.KernelData): the kernel data we will load. This is returned to the likelihood function
as the ``void* data`` pointer.
method (str): Type of solver. Should be one of:
- 'Levenberg-Marquardt'
- 'Nelder-Mead'
- 'Powell'
- 'Subplex'
If not given, defaults to 'Powell'.
lower_bounds (tuple): per parameter a lower bound, if given, the optimizer ensures ``a <= x`` with
a the lower bound and x the parameter. If not given, -infinity is assumed for all parameters.
Each tuple element can either be a scalar or a vector. If a vector is given the first dimension length
should match that of the parameters.
upper_bounds (tuple): per parameter an upper bound, if given, the optimizer ensures ``x >= b`` with
b the upper bound and x the parameter. If not given, +infinity is assumed for all parameters.
Each tuple element can either be a scalar or a vector. If a vector is given the first dimension length
should match that of the parameters.
constraints_func (mot.optimize.base.ConstraintFunction): function to compute (inequality) constraints.
Should hold a CL function with the signature:
.. code-block:: c
void <func_name>(local const mot_float_type* const x,
void* data,
local mot_float_type* constraints);
Where ``constraints_values`` is filled as:
.. code-block:: c
constraints[i] = g_i(x)
That is, for each constraint function :math:`g_i`, formulated as :math:`g_i(x) <= 0`, we should return
the function value of :math:`g_i`.
nmr_observations (int): the number of observations returned by the optimization function.
This is only needed for the ``Levenberg-Marquardt`` method.
cl_runtime_info (mot.configuration.CLRuntimeInfo): the CL runtime information
options (dict): A dictionary of solver options. All methods accept the following generic options:
- patience (int): Maximum number of iterations to perform.
- penalty_weight (float): the weight of the penalty term for the boundary conditions
Returns:
mot.optimize.base.OptimizeResults:
The optimization result represented as a ``OptimizeResult`` object.
Important attributes are: ``x`` the solution array. | https://github.com/cbclab/MOT/blob/fb3243b65025705842e82704705c00902f9a35af/mot/optimize/__init__.py#L16-L119 |
cbclab/MOT | mot/optimize/__init__.py | _bounds_to_array | def _bounds_to_array(bounds):
"""Create a CompositeArray to hold the bounds."""
elements = []
for value in bounds:
if all_elements_equal(value):
elements.append(Scalar(get_single_value(value), ctype='mot_float_type'))
else:
elements.append(Array(value, ctype='mot_float_type', as_scalar=True))
return CompositeArray(elements, 'mot_float_type', address_space='local') | python | def _bounds_to_array(bounds):
"""Create a CompositeArray to hold the bounds."""
elements = []
for value in bounds:
if all_elements_equal(value):
elements.append(Scalar(get_single_value(value), ctype='mot_float_type'))
else:
elements.append(Array(value, ctype='mot_float_type', as_scalar=True))
return CompositeArray(elements, 'mot_float_type', address_space='local') | Create a CompositeArray to hold the bounds. | https://github.com/cbclab/MOT/blob/fb3243b65025705842e82704705c00902f9a35af/mot/optimize/__init__.py#L122-L130 |
cbclab/MOT | mot/optimize/__init__.py | maximize | def maximize(func, x0, nmr_observations, **kwargs):
"""Maximization of a function.
This wraps the objective function to take the negative of the computed values and passes it then on to one
of the minimization routines.
Args:
func (mot.lib.cl_function.CLFunction): A CL function with the signature:
.. code-block:: c
double <func_name>(local const mot_float_type* const x,
void* data,
local mot_float_type* objective_list);
The objective list needs to be filled when the provided pointer is not null. It should contain
the function values for each observation. This list is used by non-linear least-squares routines,
and will be squared by the least-square optimizer. This is only used by the ``Levenberg-Marquardt`` routine.
x0 (ndarray): Initial guess. Array of real elements of size (n, p), for 'n' problems and 'p'
independent variables.
nmr_observations (int): the number of observations returned by the optimization function.
**kwargs: see :func:`minimize`.
"""
wrapped_func = SimpleCLFunction.from_string('''
double _negate_''' + func.get_cl_function_name() + '''(
local mot_float_type* x,
void* data,
local mot_float_type* objective_list){
double return_val = ''' + func.get_cl_function_name() + '''(x, data, objective_list);
if(objective_list){
const uint nmr_observations = ''' + str(nmr_observations) + ''';
uint local_id = get_local_id(0);
uint workgroup_size = get_local_size(0);
uint observation_ind;
for(uint i = 0; i < (nmr_observations + workgroup_size - 1) / workgroup_size; i++){
observation_ind = i * workgroup_size + local_id;
if(observation_ind < nmr_observations){
objective_list[observation_ind] *= -1;
}
}
}
return -return_val;
}
''', dependencies=[func])
kwargs['nmr_observations'] = nmr_observations
return minimize(wrapped_func, x0, **kwargs) | python | def maximize(func, x0, nmr_observations, **kwargs):
"""Maximization of a function.
This wraps the objective function to take the negative of the computed values and passes it then on to one
of the minimization routines.
Args:
func (mot.lib.cl_function.CLFunction): A CL function with the signature:
.. code-block:: c
double <func_name>(local const mot_float_type* const x,
void* data,
local mot_float_type* objective_list);
The objective list needs to be filled when the provided pointer is not null. It should contain
the function values for each observation. This list is used by non-linear least-squares routines,
and will be squared by the least-square optimizer. This is only used by the ``Levenberg-Marquardt`` routine.
x0 (ndarray): Initial guess. Array of real elements of size (n, p), for 'n' problems and 'p'
independent variables.
nmr_observations (int): the number of observations returned by the optimization function.
**kwargs: see :func:`minimize`.
"""
wrapped_func = SimpleCLFunction.from_string('''
double _negate_''' + func.get_cl_function_name() + '''(
local mot_float_type* x,
void* data,
local mot_float_type* objective_list){
double return_val = ''' + func.get_cl_function_name() + '''(x, data, objective_list);
if(objective_list){
const uint nmr_observations = ''' + str(nmr_observations) + ''';
uint local_id = get_local_id(0);
uint workgroup_size = get_local_size(0);
uint observation_ind;
for(uint i = 0; i < (nmr_observations + workgroup_size - 1) / workgroup_size; i++){
observation_ind = i * workgroup_size + local_id;
if(observation_ind < nmr_observations){
objective_list[observation_ind] *= -1;
}
}
}
return -return_val;
}
''', dependencies=[func])
kwargs['nmr_observations'] = nmr_observations
return minimize(wrapped_func, x0, **kwargs) | Maximization of a function.
This wraps the objective function to take the negative of the computed values and passes it then on to one
of the minimization routines.
Args:
func (mot.lib.cl_function.CLFunction): A CL function with the signature:
.. code-block:: c
double <func_name>(local const mot_float_type* const x,
void* data,
local mot_float_type* objective_list);
The objective list needs to be filled when the provided pointer is not null. It should contain
the function values for each observation. This list is used by non-linear least-squares routines,
and will be squared by the least-square optimizer. This is only used by the ``Levenberg-Marquardt`` routine.
x0 (ndarray): Initial guess. Array of real elements of size (n, p), for 'n' problems and 'p'
independent variables.
nmr_observations (int): the number of observations returned by the optimization function.
**kwargs: see :func:`minimize`. | https://github.com/cbclab/MOT/blob/fb3243b65025705842e82704705c00902f9a35af/mot/optimize/__init__.py#L133-L183 |
cbclab/MOT | mot/optimize/__init__.py | get_minimizer_options | def get_minimizer_options(method):
"""Return a dictionary with the default options for the given minimization method.
Args:
method (str): the name of the method we want the options off
Returns:
dict: a dictionary with the default options
"""
if method == 'Powell':
return {'patience': 2,
'patience_line_search': None,
'reset_method': 'EXTRAPOLATED_POINT'}
elif method == 'Nelder-Mead':
return {'patience': 200,
'alpha': 1.0, 'beta': 0.5, 'gamma': 2.0, 'delta': 0.5, 'scale': 0.1,
'adaptive_scales': True}
elif method == 'Levenberg-Marquardt':
return {'patience': 250, 'step_bound': 100.0, 'scale_diag': 1, 'usertol_mult': 30}
elif method == 'Subplex':
return {'patience': 10,
'patience_nmsimplex': 100,
'alpha': 1.0, 'beta': 0.5, 'gamma': 2.0, 'delta': 0.5, 'scale': 1.0, 'psi': 0.0001, 'omega': 0.01,
'adaptive_scales': True,
'min_subspace_length': 'auto',
'max_subspace_length': 'auto'}
raise ValueError('Could not find the specified method "{}".'.format(method)) | python | def get_minimizer_options(method):
"""Return a dictionary with the default options for the given minimization method.
Args:
method (str): the name of the method we want the options off
Returns:
dict: a dictionary with the default options
"""
if method == 'Powell':
return {'patience': 2,
'patience_line_search': None,
'reset_method': 'EXTRAPOLATED_POINT'}
elif method == 'Nelder-Mead':
return {'patience': 200,
'alpha': 1.0, 'beta': 0.5, 'gamma': 2.0, 'delta': 0.5, 'scale': 0.1,
'adaptive_scales': True}
elif method == 'Levenberg-Marquardt':
return {'patience': 250, 'step_bound': 100.0, 'scale_diag': 1, 'usertol_mult': 30}
elif method == 'Subplex':
return {'patience': 10,
'patience_nmsimplex': 100,
'alpha': 1.0, 'beta': 0.5, 'gamma': 2.0, 'delta': 0.5, 'scale': 1.0, 'psi': 0.0001, 'omega': 0.01,
'adaptive_scales': True,
'min_subspace_length': 'auto',
'max_subspace_length': 'auto'}
raise ValueError('Could not find the specified method "{}".'.format(method)) | Return a dictionary with the default options for the given minimization method.
Args:
method (str): the name of the method we want the options off
Returns:
dict: a dictionary with the default options | https://github.com/cbclab/MOT/blob/fb3243b65025705842e82704705c00902f9a35af/mot/optimize/__init__.py#L186-L216 |
cbclab/MOT | mot/optimize/__init__.py | _clean_options | def _clean_options(method, provided_options):
"""Clean the given input options.
This will make sure that all options are present, either with their default values or with the given values,
and that no other options are present then those supported.
Args:
method (str): the method name
provided_options (dict): the given options
Returns:
dict: the resulting options dictionary
"""
provided_options = provided_options or {}
default_options = get_minimizer_options(method)
result = {}
for name, default in default_options.items():
if name in provided_options:
result[name] = provided_options[name]
else:
result[name] = default_options[name]
return result | python | def _clean_options(method, provided_options):
"""Clean the given input options.
This will make sure that all options are present, either with their default values or with the given values,
and that no other options are present then those supported.
Args:
method (str): the method name
provided_options (dict): the given options
Returns:
dict: the resulting options dictionary
"""
provided_options = provided_options or {}
default_options = get_minimizer_options(method)
result = {}
for name, default in default_options.items():
if name in provided_options:
result[name] = provided_options[name]
else:
result[name] = default_options[name]
return result | Clean the given input options.
This will make sure that all options are present, either with their default values or with the given values,
and that no other options are present then those supported.
Args:
method (str): the method name
provided_options (dict): the given options
Returns:
dict: the resulting options dictionary | https://github.com/cbclab/MOT/blob/fb3243b65025705842e82704705c00902f9a35af/mot/optimize/__init__.py#L219-L242 |
cbclab/MOT | mot/optimize/__init__.py | _minimize_powell | def _minimize_powell(func, x0, cl_runtime_info, lower_bounds, upper_bounds,
constraints_func=None, data=None, options=None):
"""
Options:
patience (int): Used to set the maximum number of iterations to patience*(number_of_parameters+1)
reset_method (str): one of 'EXTRAPOLATED_POINT' or 'RESET_TO_IDENTITY' lower case or upper case.
patience_line_search (int): the patience of the searching algorithm. Defaults to the
same patience as for the Powell algorithm itself.
"""
options = options or {}
nmr_problems = x0.shape[0]
nmr_parameters = x0.shape[1]
penalty_data, penalty_func = _get_penalty_function(nmr_parameters, constraints_func)
eval_func = SimpleCLFunction.from_string('''
double evaluate(local mot_float_type* x, void* data){
double penalty = _mle_penalty(
x,
((_powell_eval_func_data*)data)->data,
((_powell_eval_func_data*)data)->lower_bounds,
((_powell_eval_func_data*)data)->upper_bounds,
''' + str(options.get('penalty_weight', 1e30)) + ''',
((_powell_eval_func_data*)data)->penalty_data
);
double func_val = ''' + func.get_cl_function_name() + '''(x, ((_powell_eval_func_data*)data)->data, 0);
if(isnan(func_val)){
return INFINITY;
}
return func_val + penalty;
}
''', dependencies=[func, penalty_func])
optimizer_func = Powell(eval_func, nmr_parameters, **_clean_options('Powell', options))
kernel_data = {'model_parameters': Array(x0, ctype='mot_float_type', mode='rw'),
'data': Struct({'data': data,
'lower_bounds': lower_bounds,
'upper_bounds': upper_bounds,
'penalty_data': penalty_data}, '_powell_eval_func_data')}
kernel_data.update(optimizer_func.get_kernel_data())
return_code = optimizer_func.evaluate(
kernel_data, nmr_problems,
use_local_reduction=all(env.is_gpu for env in cl_runtime_info.cl_environments),
cl_runtime_info=cl_runtime_info)
return OptimizeResults({'x': kernel_data['model_parameters'].get_data(),
'status': return_code}) | python | def _minimize_powell(func, x0, cl_runtime_info, lower_bounds, upper_bounds,
constraints_func=None, data=None, options=None):
"""
Options:
patience (int): Used to set the maximum number of iterations to patience*(number_of_parameters+1)
reset_method (str): one of 'EXTRAPOLATED_POINT' or 'RESET_TO_IDENTITY' lower case or upper case.
patience_line_search (int): the patience of the searching algorithm. Defaults to the
same patience as for the Powell algorithm itself.
"""
options = options or {}
nmr_problems = x0.shape[0]
nmr_parameters = x0.shape[1]
penalty_data, penalty_func = _get_penalty_function(nmr_parameters, constraints_func)
eval_func = SimpleCLFunction.from_string('''
double evaluate(local mot_float_type* x, void* data){
double penalty = _mle_penalty(
x,
((_powell_eval_func_data*)data)->data,
((_powell_eval_func_data*)data)->lower_bounds,
((_powell_eval_func_data*)data)->upper_bounds,
''' + str(options.get('penalty_weight', 1e30)) + ''',
((_powell_eval_func_data*)data)->penalty_data
);
double func_val = ''' + func.get_cl_function_name() + '''(x, ((_powell_eval_func_data*)data)->data, 0);
if(isnan(func_val)){
return INFINITY;
}
return func_val + penalty;
}
''', dependencies=[func, penalty_func])
optimizer_func = Powell(eval_func, nmr_parameters, **_clean_options('Powell', options))
kernel_data = {'model_parameters': Array(x0, ctype='mot_float_type', mode='rw'),
'data': Struct({'data': data,
'lower_bounds': lower_bounds,
'upper_bounds': upper_bounds,
'penalty_data': penalty_data}, '_powell_eval_func_data')}
kernel_data.update(optimizer_func.get_kernel_data())
return_code = optimizer_func.evaluate(
kernel_data, nmr_problems,
use_local_reduction=all(env.is_gpu for env in cl_runtime_info.cl_environments),
cl_runtime_info=cl_runtime_info)
return OptimizeResults({'x': kernel_data['model_parameters'].get_data(),
'status': return_code}) | Options:
patience (int): Used to set the maximum number of iterations to patience*(number_of_parameters+1)
reset_method (str): one of 'EXTRAPOLATED_POINT' or 'RESET_TO_IDENTITY' lower case or upper case.
patience_line_search (int): the patience of the searching algorithm. Defaults to the
same patience as for the Powell algorithm itself. | https://github.com/cbclab/MOT/blob/fb3243b65025705842e82704705c00902f9a35af/mot/optimize/__init__.py#L245-L296 |
cbclab/MOT | mot/optimize/__init__.py | _lm_numdiff_jacobian | def _lm_numdiff_jacobian(eval_func, nmr_params, nmr_observations):
"""Get a numerical differentiated Jacobian function.
This computes the Jacobian of the observations (function vector) with respect to the parameters.
Args:
eval_func (mot.lib.cl_function.CLFunction): the evaluation function
nmr_params (int): the number of parameters
nmr_observations (int): the number of observations (the length of the function vector).
Returns:
mot.lib.cl_function.CLFunction: CL function for numerically estimating the Jacobian.
"""
return SimpleCLFunction.from_string(r'''
/**
* Compute the Jacobian for use in the Levenberg-Marquardt optimization routine.
*
* This should place the output in the ``fjac`` matrix.
*
* Parameters:
*
* model_parameters: (nmr_params,) the current point around which we want to know the Jacobian
* data: the current modeling data, used by the objective function
* fvec: (nmr_observations,), the function values corresponding to the current model parameters
* fjac: (nmr_parameters, nmr_observations), the memory location for the Jacobian
*/
void _lm_numdiff_jacobian(local mot_float_type* model_parameters,
void* data,
local mot_float_type* fvec,
local mot_float_type* const fjac){
const uint nmr_params = ''' + str(nmr_params) + ''';
const uint nmr_observations = ''' + str(nmr_observations) + ''';
local mot_float_type* lower_bounds = ((_lm_eval_func_data*)data)->lower_bounds;
local mot_float_type* upper_bounds = ((_lm_eval_func_data*)data)->upper_bounds;
local mot_float_type* jacobian_x_tmp = ((_lm_eval_func_data*)data)->jacobian_x_tmp;
mot_float_type step_size = 30 * MOT_EPSILON;
for (int i = 0; i < nmr_params; i++) {
if(model_parameters[i] + step_size > upper_bounds[i]){
_lm_numdiff_jacobian_backwards(model_parameters, i, step_size, data, fvec,
fjac + i*nmr_observations, jacobian_x_tmp);
}
else if(model_parameters[i] - step_size < lower_bounds[i]){
_lm_numdiff_jacobian_forwards(model_parameters, i, step_size, data, fvec,
fjac + i*nmr_observations, jacobian_x_tmp);
}
else{
_lm_numdiff_jacobian_centered(model_parameters, i, step_size, data, fvec,
fjac + i*nmr_observations, jacobian_x_tmp);
}
}
}
''', dependencies=[eval_func, SimpleCLFunction.from_string('''
void _lm_numdiff_jacobian_centered(
local mot_float_type* model_parameters,
uint px,
float step_size,
void* data,
local mot_float_type* fvec,
local mot_float_type* const fjac,
local mot_float_type* fjac_tmp){
const uint nmr_observations = ''' + str(nmr_observations) + ''';
uint observation_ind;
mot_float_type temp;
uint local_id = get_local_id(0);
uint workgroup_size = get_local_size(0);
// F(x + h)
if(get_local_id(0) == 0){
temp = model_parameters[px];
model_parameters[px] = temp + step_size;
}
barrier(CLK_LOCAL_MEM_FENCE);
''' + eval_func.get_cl_function_name() + '''(model_parameters, data, fjac);
// F(x - h)
if(get_local_id(0) == 0){
model_parameters[px] = temp - step_size;
}
barrier(CLK_LOCAL_MEM_FENCE);
''' + eval_func.get_cl_function_name() + '''(model_parameters, data, fjac_tmp);
// combine
for(int i = 0; i < (nmr_observations + workgroup_size - 1) / workgroup_size; i++){
observation_ind = i * workgroup_size + local_id;
if(observation_ind < nmr_observations){
fjac[observation_ind] = (fjac[observation_ind] - fjac_tmp[observation_ind]) / (2 * step_size);
}
}
// restore parameter vector
if(get_local_id(0) == 0){
model_parameters[px] = temp;
}
barrier(CLK_LOCAL_MEM_FENCE);
}
'''), SimpleCLFunction.from_string('''
void _lm_numdiff_jacobian_backwards(
local mot_float_type* model_parameters,
uint px,
float step_size,
void* data,
local mot_float_type* fvec,
local mot_float_type* const fjac,
local mot_float_type* fjac_tmp){
const uint nmr_observations = ''' + str(nmr_observations) + ''';
uint observation_ind;
mot_float_type temp;
uint local_id = get_local_id(0);
uint workgroup_size = get_local_size(0);
// F(x - h)
if(get_local_id(0) == 0){
temp = model_parameters[px];
model_parameters[px] = temp - step_size;
}
barrier(CLK_LOCAL_MEM_FENCE);
''' + eval_func.get_cl_function_name() + '''(model_parameters, data, fjac);
// F(x - 2*h)
if(get_local_id(0) == 0){
model_parameters[px] = temp - 2 * step_size;
}
barrier(CLK_LOCAL_MEM_FENCE);
''' + eval_func.get_cl_function_name() + '''(model_parameters, data, fjac_tmp);
// combine
for(int i = 0; i < (nmr_observations + workgroup_size - 1) / workgroup_size; i++){
observation_ind = i * workgroup_size + local_id;
if(observation_ind < nmr_observations){
fjac[observation_ind] = ( 3 * fvec[observation_ind]
- 4 * fjac[observation_ind]
+ fjac_tmp[observation_ind]) / (2 * step_size);
}
}
// restore parameter vector
if(get_local_id(0) == 0){
model_parameters[px] = temp;
}
barrier(CLK_LOCAL_MEM_FENCE);
}
'''), SimpleCLFunction.from_string('''
void _lm_numdiff_jacobian_forwards(
local mot_float_type* model_parameters,
uint px,
float step_size,
void* data,
local mot_float_type* fvec,
local mot_float_type* const fjac,
local mot_float_type* fjac_tmp){
const uint nmr_observations = ''' + str(nmr_observations) + ''';
uint observation_ind;
mot_float_type temp;
uint local_id = get_local_id(0);
uint workgroup_size = get_local_size(0);
// F(x + h)
if(get_local_id(0) == 0){
temp = model_parameters[px];
model_parameters[px] = temp + step_size;
}
barrier(CLK_LOCAL_MEM_FENCE);
''' + eval_func.get_cl_function_name() + '''(model_parameters, data, fjac);
// F(x + 2*h)
if(get_local_id(0) == 0){
model_parameters[px] = temp + 2 * step_size;
}
barrier(CLK_LOCAL_MEM_FENCE);
''' + eval_func.get_cl_function_name() + '''(model_parameters, data, fjac);
// combine
for(int i = 0; i < (nmr_observations + workgroup_size - 1) / workgroup_size; i++){
observation_ind = i * workgroup_size + local_id;
if(observation_ind < nmr_observations){
fjac[observation_ind] = (- 3 * fvec[observation_ind]
+ 4 * fjac[observation_ind]
- fjac_tmp[observation_ind]) / (2 * step_size);
}
}
// restore parameter vector
if(get_local_id(0) == 0){
model_parameters[px] = temp;
}
barrier(CLK_LOCAL_MEM_FENCE);
}
''')]) | python | def _lm_numdiff_jacobian(eval_func, nmr_params, nmr_observations):
"""Get a numerical differentiated Jacobian function.
This computes the Jacobian of the observations (function vector) with respect to the parameters.
Args:
eval_func (mot.lib.cl_function.CLFunction): the evaluation function
nmr_params (int): the number of parameters
nmr_observations (int): the number of observations (the length of the function vector).
Returns:
mot.lib.cl_function.CLFunction: CL function for numerically estimating the Jacobian.
"""
return SimpleCLFunction.from_string(r'''
/**
* Compute the Jacobian for use in the Levenberg-Marquardt optimization routine.
*
* This should place the output in the ``fjac`` matrix.
*
* Parameters:
*
* model_parameters: (nmr_params,) the current point around which we want to know the Jacobian
* data: the current modeling data, used by the objective function
* fvec: (nmr_observations,), the function values corresponding to the current model parameters
* fjac: (nmr_parameters, nmr_observations), the memory location for the Jacobian
*/
void _lm_numdiff_jacobian(local mot_float_type* model_parameters,
void* data,
local mot_float_type* fvec,
local mot_float_type* const fjac){
const uint nmr_params = ''' + str(nmr_params) + ''';
const uint nmr_observations = ''' + str(nmr_observations) + ''';
local mot_float_type* lower_bounds = ((_lm_eval_func_data*)data)->lower_bounds;
local mot_float_type* upper_bounds = ((_lm_eval_func_data*)data)->upper_bounds;
local mot_float_type* jacobian_x_tmp = ((_lm_eval_func_data*)data)->jacobian_x_tmp;
mot_float_type step_size = 30 * MOT_EPSILON;
for (int i = 0; i < nmr_params; i++) {
if(model_parameters[i] + step_size > upper_bounds[i]){
_lm_numdiff_jacobian_backwards(model_parameters, i, step_size, data, fvec,
fjac + i*nmr_observations, jacobian_x_tmp);
}
else if(model_parameters[i] - step_size < lower_bounds[i]){
_lm_numdiff_jacobian_forwards(model_parameters, i, step_size, data, fvec,
fjac + i*nmr_observations, jacobian_x_tmp);
}
else{
_lm_numdiff_jacobian_centered(model_parameters, i, step_size, data, fvec,
fjac + i*nmr_observations, jacobian_x_tmp);
}
}
}
''', dependencies=[eval_func, SimpleCLFunction.from_string('''
void _lm_numdiff_jacobian_centered(
local mot_float_type* model_parameters,
uint px,
float step_size,
void* data,
local mot_float_type* fvec,
local mot_float_type* const fjac,
local mot_float_type* fjac_tmp){
const uint nmr_observations = ''' + str(nmr_observations) + ''';
uint observation_ind;
mot_float_type temp;
uint local_id = get_local_id(0);
uint workgroup_size = get_local_size(0);
// F(x + h)
if(get_local_id(0) == 0){
temp = model_parameters[px];
model_parameters[px] = temp + step_size;
}
barrier(CLK_LOCAL_MEM_FENCE);
''' + eval_func.get_cl_function_name() + '''(model_parameters, data, fjac);
// F(x - h)
if(get_local_id(0) == 0){
model_parameters[px] = temp - step_size;
}
barrier(CLK_LOCAL_MEM_FENCE);
''' + eval_func.get_cl_function_name() + '''(model_parameters, data, fjac_tmp);
// combine
for(int i = 0; i < (nmr_observations + workgroup_size - 1) / workgroup_size; i++){
observation_ind = i * workgroup_size + local_id;
if(observation_ind < nmr_observations){
fjac[observation_ind] = (fjac[observation_ind] - fjac_tmp[observation_ind]) / (2 * step_size);
}
}
// restore parameter vector
if(get_local_id(0) == 0){
model_parameters[px] = temp;
}
barrier(CLK_LOCAL_MEM_FENCE);
}
'''), SimpleCLFunction.from_string('''
void _lm_numdiff_jacobian_backwards(
local mot_float_type* model_parameters,
uint px,
float step_size,
void* data,
local mot_float_type* fvec,
local mot_float_type* const fjac,
local mot_float_type* fjac_tmp){
const uint nmr_observations = ''' + str(nmr_observations) + ''';
uint observation_ind;
mot_float_type temp;
uint local_id = get_local_id(0);
uint workgroup_size = get_local_size(0);
// F(x - h)
if(get_local_id(0) == 0){
temp = model_parameters[px];
model_parameters[px] = temp - step_size;
}
barrier(CLK_LOCAL_MEM_FENCE);
''' + eval_func.get_cl_function_name() + '''(model_parameters, data, fjac);
// F(x - 2*h)
if(get_local_id(0) == 0){
model_parameters[px] = temp - 2 * step_size;
}
barrier(CLK_LOCAL_MEM_FENCE);
''' + eval_func.get_cl_function_name() + '''(model_parameters, data, fjac_tmp);
// combine
for(int i = 0; i < (nmr_observations + workgroup_size - 1) / workgroup_size; i++){
observation_ind = i * workgroup_size + local_id;
if(observation_ind < nmr_observations){
fjac[observation_ind] = ( 3 * fvec[observation_ind]
- 4 * fjac[observation_ind]
+ fjac_tmp[observation_ind]) / (2 * step_size);
}
}
// restore parameter vector
if(get_local_id(0) == 0){
model_parameters[px] = temp;
}
barrier(CLK_LOCAL_MEM_FENCE);
}
'''), SimpleCLFunction.from_string('''
void _lm_numdiff_jacobian_forwards(
local mot_float_type* model_parameters,
uint px,
float step_size,
void* data,
local mot_float_type* fvec,
local mot_float_type* const fjac,
local mot_float_type* fjac_tmp){
const uint nmr_observations = ''' + str(nmr_observations) + ''';
uint observation_ind;
mot_float_type temp;
uint local_id = get_local_id(0);
uint workgroup_size = get_local_size(0);
// F(x + h)
if(get_local_id(0) == 0){
temp = model_parameters[px];
model_parameters[px] = temp + step_size;
}
barrier(CLK_LOCAL_MEM_FENCE);
''' + eval_func.get_cl_function_name() + '''(model_parameters, data, fjac);
// F(x + 2*h)
if(get_local_id(0) == 0){
model_parameters[px] = temp + 2 * step_size;
}
barrier(CLK_LOCAL_MEM_FENCE);
''' + eval_func.get_cl_function_name() + '''(model_parameters, data, fjac);
// combine
for(int i = 0; i < (nmr_observations + workgroup_size - 1) / workgroup_size; i++){
observation_ind = i * workgroup_size + local_id;
if(observation_ind < nmr_observations){
fjac[observation_ind] = (- 3 * fvec[observation_ind]
+ 4 * fjac[observation_ind]
- fjac_tmp[observation_ind]) / (2 * step_size);
}
}
// restore parameter vector
if(get_local_id(0) == 0){
model_parameters[px] = temp;
}
barrier(CLK_LOCAL_MEM_FENCE);
}
''')]) | Get a numerical differentiated Jacobian function.
This computes the Jacobian of the observations (function vector) with respect to the parameters.
Args:
eval_func (mot.lib.cl_function.CLFunction): the evaluation function
nmr_params (int): the number of parameters
nmr_observations (int): the number of observations (the length of the function vector).
Returns:
mot.lib.cl_function.CLFunction: CL function for numerically estimating the Jacobian. | https://github.com/cbclab/MOT/blob/fb3243b65025705842e82704705c00902f9a35af/mot/optimize/__init__.py#L535-L739 |
cbclab/MOT | mot/optimize/__init__.py | _get_penalty_function | def _get_penalty_function(nmr_parameters, constraints_func=None):
"""Get a function to compute the penalty term for the boundary conditions.
This is meant to be used in the evaluation function of the optimization routines.
Args:
nmr_parameters (int): the number of parameters in the model
constraints_func (mot.optimize.base.ConstraintFunction): function to compute (inequality) constraints.
Should hold a CL function with the signature:
.. code-block:: c
void <func_name>(local const mot_float_type* const x,
void* data,
local mot_float_type* constraint_values);
Where ``constraints_values`` is filled as:
.. code-block:: c
constraint_values[i] = g_i(x)
That is, for each constraint function :math:`g_i`, formulated as :math:`g_i(x) <= 0`, we should return
the function value of :math:`g_i`.
Returns:
tuple: Struct and SimpleCLFunction, the required data for the penalty function and the penalty function itself.
"""
dependencies = []
data_requirements = {'scratch': LocalMemory('double', 1)}
constraints_code = ''
if constraints_func and constraints_func.get_nmr_constraints() > 0:
nmr_constraints = constraints_func.get_nmr_constraints()
dependencies.append(constraints_func)
data_requirements['constraints'] = LocalMemory('mot_float_type', nmr_constraints)
constraints_code = '''
local mot_float_type* constraints = ((_mle_penalty_data*)scratch_data)->constraints;
''' + constraints_func.get_cl_function_name() + '''(x, data, constraints);
for(int i = 0; i < ''' + str(nmr_constraints) + '''; i++){
*penalty_sum += pown(max((mot_float_type)0, constraints[i]), 2);
}
'''
data = Struct(data_requirements, '_mle_penalty_data')
func = SimpleCLFunction.from_string('''
double _mle_penalty(
local mot_float_type* x,
void* data,
local mot_float_type* lower_bounds,
local mot_float_type* upper_bounds,
float penalty_weight,
void* scratch_data){
local double* penalty_sum = ((_mle_penalty_data*)scratch_data)->scratch;
if(get_local_id(0) == 0){
*penalty_sum = 0;
// boundary conditions
for(int i = 0; i < ''' + str(nmr_parameters) + '''; i++){
if(isfinite(upper_bounds[i])){
*penalty_sum += pown(max((mot_float_type)0, x[i] - upper_bounds[i]), 2);
}
if(isfinite(lower_bounds[i])){
*penalty_sum += pown(max((mot_float_type)0, lower_bounds[i] - x[i]), 2);
}
}
}
barrier(CLK_LOCAL_MEM_FENCE);
// constraints
''' + constraints_code + '''
return penalty_weight * *penalty_sum;
}
''', dependencies=dependencies)
return data, func | python | def _get_penalty_function(nmr_parameters, constraints_func=None):
"""Get a function to compute the penalty term for the boundary conditions.
This is meant to be used in the evaluation function of the optimization routines.
Args:
nmr_parameters (int): the number of parameters in the model
constraints_func (mot.optimize.base.ConstraintFunction): function to compute (inequality) constraints.
Should hold a CL function with the signature:
.. code-block:: c
void <func_name>(local const mot_float_type* const x,
void* data,
local mot_float_type* constraint_values);
Where ``constraints_values`` is filled as:
.. code-block:: c
constraint_values[i] = g_i(x)
That is, for each constraint function :math:`g_i`, formulated as :math:`g_i(x) <= 0`, we should return
the function value of :math:`g_i`.
Returns:
tuple: Struct and SimpleCLFunction, the required data for the penalty function and the penalty function itself.
"""
dependencies = []
data_requirements = {'scratch': LocalMemory('double', 1)}
constraints_code = ''
if constraints_func and constraints_func.get_nmr_constraints() > 0:
nmr_constraints = constraints_func.get_nmr_constraints()
dependencies.append(constraints_func)
data_requirements['constraints'] = LocalMemory('mot_float_type', nmr_constraints)
constraints_code = '''
local mot_float_type* constraints = ((_mle_penalty_data*)scratch_data)->constraints;
''' + constraints_func.get_cl_function_name() + '''(x, data, constraints);
for(int i = 0; i < ''' + str(nmr_constraints) + '''; i++){
*penalty_sum += pown(max((mot_float_type)0, constraints[i]), 2);
}
'''
data = Struct(data_requirements, '_mle_penalty_data')
func = SimpleCLFunction.from_string('''
double _mle_penalty(
local mot_float_type* x,
void* data,
local mot_float_type* lower_bounds,
local mot_float_type* upper_bounds,
float penalty_weight,
void* scratch_data){
local double* penalty_sum = ((_mle_penalty_data*)scratch_data)->scratch;
if(get_local_id(0) == 0){
*penalty_sum = 0;
// boundary conditions
for(int i = 0; i < ''' + str(nmr_parameters) + '''; i++){
if(isfinite(upper_bounds[i])){
*penalty_sum += pown(max((mot_float_type)0, x[i] - upper_bounds[i]), 2);
}
if(isfinite(lower_bounds[i])){
*penalty_sum += pown(max((mot_float_type)0, lower_bounds[i] - x[i]), 2);
}
}
}
barrier(CLK_LOCAL_MEM_FENCE);
// constraints
''' + constraints_code + '''
return penalty_weight * *penalty_sum;
}
''', dependencies=dependencies)
return data, func | Get a function to compute the penalty term for the boundary conditions.
This is meant to be used in the evaluation function of the optimization routines.
Args:
nmr_parameters (int): the number of parameters in the model
constraints_func (mot.optimize.base.ConstraintFunction): function to compute (inequality) constraints.
Should hold a CL function with the signature:
.. code-block:: c
void <func_name>(local const mot_float_type* const x,
void* data,
local mot_float_type* constraint_values);
Where ``constraints_values`` is filled as:
.. code-block:: c
constraint_values[i] = g_i(x)
That is, for each constraint function :math:`g_i`, formulated as :math:`g_i(x) <= 0`, we should return
the function value of :math:`g_i`.
Returns:
tuple: Struct and SimpleCLFunction, the required data for the penalty function and the penalty function itself. | https://github.com/cbclab/MOT/blob/fb3243b65025705842e82704705c00902f9a35af/mot/optimize/__init__.py#L742-L822 |
cbclab/MOT | mot/optimize/base.py | SimpleConstraintFunction.from_string | def from_string(cls, cl_function, dependencies=(), nmr_constraints=None):
"""Parse the given CL function into a SimpleCLFunction object.
Args:
cl_function (str): the function we wish to turn into an object
dependencies (list or tuple of CLLibrary): The list of CL libraries this function depends on
Returns:
SimpleCLFunction: the CL data type for this parameter declaration
"""
return_type, function_name, parameter_list, body = split_cl_function(cl_function)
return SimpleConstraintFunction(return_type, function_name, parameter_list, body, dependencies=dependencies,
nmr_constraints=nmr_constraints) | python | def from_string(cls, cl_function, dependencies=(), nmr_constraints=None):
"""Parse the given CL function into a SimpleCLFunction object.
Args:
cl_function (str): the function we wish to turn into an object
dependencies (list or tuple of CLLibrary): The list of CL libraries this function depends on
Returns:
SimpleCLFunction: the CL data type for this parameter declaration
"""
return_type, function_name, parameter_list, body = split_cl_function(cl_function)
return SimpleConstraintFunction(return_type, function_name, parameter_list, body, dependencies=dependencies,
nmr_constraints=nmr_constraints) | Parse the given CL function into a SimpleCLFunction object.
Args:
cl_function (str): the function we wish to turn into an object
dependencies (list or tuple of CLLibrary): The list of CL libraries this function depends on
Returns:
SimpleCLFunction: the CL data type for this parameter declaration | https://github.com/cbclab/MOT/blob/fb3243b65025705842e82704705c00902f9a35af/mot/optimize/base.py#L90-L102 |
ksbg/sparklanes | sparklanes/_framework/validation.py | validate_schema | def validate_schema(yaml_def, branch=False):
"""Validates the schema of a dict
Parameters
----------
yaml_def : dict
dict whose schema shall be validated
branch : bool
Indicates whether `yaml_def` is a dict of a top-level lane, or of a branch
inside a lane (needed for recursion)
Returns
-------
bool
True if validation was successful
"""
schema = Schema({
'lane' if not branch else 'branch': {
Optional('name'): str,
Optional('run_parallel'): bool,
'tasks': list
}
})
schema.validate(yaml_def)
from schema import And, Use
task_schema = Schema({
'class': str,
Optional('kwargs'): Or({str: object}),
Optional('args'): Or([object], And(Use(lambda a: isinstance(a, dict)), False))
})
def validate_tasks(tasks): # pylint: disable=missing-docstring
for task in tasks:
try:
Schema({'branch': dict}).validate(task)
validate_schema(task, True)
except SchemaError:
task_schema.validate(task)
return True
return validate_tasks(yaml_def['lane']['tasks'] if not branch else yaml_def['branch']['tasks']) | python | def validate_schema(yaml_def, branch=False):
"""Validates the schema of a dict
Parameters
----------
yaml_def : dict
dict whose schema shall be validated
branch : bool
Indicates whether `yaml_def` is a dict of a top-level lane, or of a branch
inside a lane (needed for recursion)
Returns
-------
bool
True if validation was successful
"""
schema = Schema({
'lane' if not branch else 'branch': {
Optional('name'): str,
Optional('run_parallel'): bool,
'tasks': list
}
})
schema.validate(yaml_def)
from schema import And, Use
task_schema = Schema({
'class': str,
Optional('kwargs'): Or({str: object}),
Optional('args'): Or([object], And(Use(lambda a: isinstance(a, dict)), False))
})
def validate_tasks(tasks): # pylint: disable=missing-docstring
for task in tasks:
try:
Schema({'branch': dict}).validate(task)
validate_schema(task, True)
except SchemaError:
task_schema.validate(task)
return True
return validate_tasks(yaml_def['lane']['tasks'] if not branch else yaml_def['branch']['tasks']) | Validates the schema of a dict
Parameters
----------
yaml_def : dict
dict whose schema shall be validated
branch : bool
Indicates whether `yaml_def` is a dict of a top-level lane, or of a branch
inside a lane (needed for recursion)
Returns
-------
bool
True if validation was successful | https://github.com/ksbg/sparklanes/blob/62e70892e6ae025be2f4c419f4afc34714d6884c/sparklanes/_framework/validation.py#L10-L52 |
ksbg/sparklanes | sparklanes/_framework/validation.py | validate_params | def validate_params(cls, mtd_name, *args, **kwargs):
"""Validates if the given args/kwargs match the method signature. Checks if:
- at least all required args/kwargs are given
- no redundant args/kwargs are given
Parameters
----------
cls : Class
mtd_name : str
Name of the method whose parameters shall be validated
args: list
Positional arguments
kwargs : dict
Dict of keyword arguments
"""
mtd = getattr(cls, mtd_name)
py3_mtd_condition = (not (inspect.isfunction(mtd) or inspect.ismethod(mtd))
and hasattr(cls, mtd_name))
py2_mtd_condition = (not inspect.ismethod(mtd)
and not isinstance(cls.__dict__[mtd_name], staticmethod))
if (PY3 and py3_mtd_condition) or (PY2 and py2_mtd_condition):
raise TypeError('Attribute `%s` of class `%s` must be a method. Got type `%s` instead.'
% (mtd_name, cls.__name__, type(mtd)))
req_params, opt_params = arg_spec(cls, mtd_name)
n_params = len(req_params) + len(opt_params)
n_args_kwargs = len(args) + len(kwargs)
for k in kwargs:
if k not in req_params and k not in opt_params:
raise TaskInitializationError('kwarg `%s` is not a parameter of callable `%s`.'
% (k, mtd.__name__))
if n_args_kwargs < len(req_params):
raise TaskInitializationError('Not enough args/kwargs supplied for callable `%s`. '
'Required args: %s' % (mtd.__name__, str(req_params)))
if len(args) > n_params or n_args_kwargs > n_params or len(kwargs) > n_params:
raise TaskInitializationError('Too many args/kwargs supplied for callable `%s`. '
'Required args: %s' % (mtd.__name__, str(req_params)))
redundant_p = [p for p in kwargs if p not in req_params[len(args):] + opt_params]
if redundant_p:
raise TaskInitializationError('Supplied one or more kwargs that in the signature of '
'callable `%s`. Redundant kwargs: %s'
% (mtd.__name__, str(redundant_p)))
needed_kwargs = req_params[len(args):]
if not all([True if p in kwargs else False for p in needed_kwargs]):
raise TaskInitializationError('Not enough args/kwargs supplied for callable `%s`. '
'Required args: %s' % (mtd.__name__, str(req_params))) | python | def validate_params(cls, mtd_name, *args, **kwargs):
"""Validates if the given args/kwargs match the method signature. Checks if:
- at least all required args/kwargs are given
- no redundant args/kwargs are given
Parameters
----------
cls : Class
mtd_name : str
Name of the method whose parameters shall be validated
args: list
Positional arguments
kwargs : dict
Dict of keyword arguments
"""
mtd = getattr(cls, mtd_name)
py3_mtd_condition = (not (inspect.isfunction(mtd) or inspect.ismethod(mtd))
and hasattr(cls, mtd_name))
py2_mtd_condition = (not inspect.ismethod(mtd)
and not isinstance(cls.__dict__[mtd_name], staticmethod))
if (PY3 and py3_mtd_condition) or (PY2 and py2_mtd_condition):
raise TypeError('Attribute `%s` of class `%s` must be a method. Got type `%s` instead.'
% (mtd_name, cls.__name__, type(mtd)))
req_params, opt_params = arg_spec(cls, mtd_name)
n_params = len(req_params) + len(opt_params)
n_args_kwargs = len(args) + len(kwargs)
for k in kwargs:
if k not in req_params and k not in opt_params:
raise TaskInitializationError('kwarg `%s` is not a parameter of callable `%s`.'
% (k, mtd.__name__))
if n_args_kwargs < len(req_params):
raise TaskInitializationError('Not enough args/kwargs supplied for callable `%s`. '
'Required args: %s' % (mtd.__name__, str(req_params)))
if len(args) > n_params or n_args_kwargs > n_params or len(kwargs) > n_params:
raise TaskInitializationError('Too many args/kwargs supplied for callable `%s`. '
'Required args: %s' % (mtd.__name__, str(req_params)))
redundant_p = [p for p in kwargs if p not in req_params[len(args):] + opt_params]
if redundant_p:
raise TaskInitializationError('Supplied one or more kwargs that in the signature of '
'callable `%s`. Redundant kwargs: %s'
% (mtd.__name__, str(redundant_p)))
needed_kwargs = req_params[len(args):]
if not all([True if p in kwargs else False for p in needed_kwargs]):
raise TaskInitializationError('Not enough args/kwargs supplied for callable `%s`. '
'Required args: %s' % (mtd.__name__, str(req_params))) | Validates if the given args/kwargs match the method signature. Checks if:
- at least all required args/kwargs are given
- no redundant args/kwargs are given
Parameters
----------
cls : Class
mtd_name : str
Name of the method whose parameters shall be validated
args: list
Positional arguments
kwargs : dict
Dict of keyword arguments | https://github.com/ksbg/sparklanes/blob/62e70892e6ae025be2f4c419f4afc34714d6884c/sparklanes/_framework/validation.py#L55-L105 |
ksbg/sparklanes | sparklanes/_framework/validation.py | arg_spec | def arg_spec(cls, mtd_name):
"""Cross-version argument signature inspection
Parameters
----------
cls : class
mtd_name : str
Name of the method to be inspected
Returns
-------
required_params : list of str
List of required, positional parameters
optional_params : list of str
List of optional parameters, i.e. parameters with a default value
"""
mtd = getattr(cls, mtd_name)
required_params = []
optional_params = []
if hasattr(inspect, 'signature'): # Python 3
params = inspect.signature(mtd).parameters # pylint: disable=no-member
for k in params.keys():
if params[k].default == inspect.Parameter.empty: # pylint: disable=no-member
# Python 3 does not make a difference between unbound methods and functions, so the
# only way to distinguish if the first argument is of a regular method, or a class
# method, is to look for the conventional argument name. Yikes.
if not (params[k].name == 'self' or params[k].name == 'cls'):
required_params.append(k)
else:
optional_params.append(k)
else: # Python 2
params = inspect.getargspec(mtd) # pylint: disable=deprecated-method
num = len(params[0]) if params[0] else 0
n_opt = len(params[3]) if params[3] else 0
n_req = (num - n_opt) if n_opt <= num else 0
for i in range(0, n_req):
required_params.append(params[0][i])
for i in range(n_req, num):
optional_params.append(params[0][i])
if inspect.isroutine(getattr(cls, mtd_name)):
bound_mtd = cls.__dict__[mtd_name]
if not isinstance(bound_mtd, staticmethod):
del required_params[0]
return required_params, optional_params | python | def arg_spec(cls, mtd_name):
"""Cross-version argument signature inspection
Parameters
----------
cls : class
mtd_name : str
Name of the method to be inspected
Returns
-------
required_params : list of str
List of required, positional parameters
optional_params : list of str
List of optional parameters, i.e. parameters with a default value
"""
mtd = getattr(cls, mtd_name)
required_params = []
optional_params = []
if hasattr(inspect, 'signature'): # Python 3
params = inspect.signature(mtd).parameters # pylint: disable=no-member
for k in params.keys():
if params[k].default == inspect.Parameter.empty: # pylint: disable=no-member
# Python 3 does not make a difference between unbound methods and functions, so the
# only way to distinguish if the first argument is of a regular method, or a class
# method, is to look for the conventional argument name. Yikes.
if not (params[k].name == 'self' or params[k].name == 'cls'):
required_params.append(k)
else:
optional_params.append(k)
else: # Python 2
params = inspect.getargspec(mtd) # pylint: disable=deprecated-method
num = len(params[0]) if params[0] else 0
n_opt = len(params[3]) if params[3] else 0
n_req = (num - n_opt) if n_opt <= num else 0
for i in range(0, n_req):
required_params.append(params[0][i])
for i in range(n_req, num):
optional_params.append(params[0][i])
if inspect.isroutine(getattr(cls, mtd_name)):
bound_mtd = cls.__dict__[mtd_name]
if not isinstance(bound_mtd, staticmethod):
del required_params[0]
return required_params, optional_params | Cross-version argument signature inspection
Parameters
----------
cls : class
mtd_name : str
Name of the method to be inspected
Returns
-------
required_params : list of str
List of required, positional parameters
optional_params : list of str
List of optional parameters, i.e. parameters with a default value | https://github.com/ksbg/sparklanes/blob/62e70892e6ae025be2f4c419f4afc34714d6884c/sparklanes/_framework/validation.py#L108-L156 |
cbclab/MOT | mot/random.py | uniform | def uniform(nmr_distributions, nmr_samples, low=0, high=1, ctype='float', seed=None):
"""Draw random samples from the Uniform distribution.
Args:
nmr_distributions (int): the number of unique continuous_distributions to create
nmr_samples (int): The number of samples to draw
low (double): The minimum value of the random numbers
high (double): The minimum value of the random numbers
ctype (str): the C type of the output samples
seed (float): the seed for the RNG
Returns:
ndarray: A two dimensional numpy array as (nmr_distributions, nmr_samples).
"""
if is_scalar(low):
low = np.ones((nmr_distributions, 1)) * low
if is_scalar(high):
high = np.ones((nmr_distributions, 1)) * high
kernel_data = {'low': Array(low, as_scalar=True),
'high': Array(high, as_scalar=True)}
kernel = SimpleCLFunction.from_string('''
void compute(double low, double high, global uint* rng_state, global ''' + ctype + '''* samples){
rand123_data rand123_rng_data = rand123_initialize_data((uint[]){
rng_state[0], rng_state[1], rng_state[2], rng_state[3],
rng_state[4], rng_state[5], 0});
void* rng_data = (void*)&rand123_rng_data;
for(uint i = 0; i < ''' + str(nmr_samples) + '''; i++){
double4 randomnr = rand4(rng_data);
samples[i] = (''' + ctype + ''')(low + randomnr.x * (high - low));
}
}
''', dependencies=[Rand123()])
return _generate_samples(kernel, nmr_distributions, nmr_samples, ctype, kernel_data, seed=seed) | python | def uniform(nmr_distributions, nmr_samples, low=0, high=1, ctype='float', seed=None):
"""Draw random samples from the Uniform distribution.
Args:
nmr_distributions (int): the number of unique continuous_distributions to create
nmr_samples (int): The number of samples to draw
low (double): The minimum value of the random numbers
high (double): The minimum value of the random numbers
ctype (str): the C type of the output samples
seed (float): the seed for the RNG
Returns:
ndarray: A two dimensional numpy array as (nmr_distributions, nmr_samples).
"""
if is_scalar(low):
low = np.ones((nmr_distributions, 1)) * low
if is_scalar(high):
high = np.ones((nmr_distributions, 1)) * high
kernel_data = {'low': Array(low, as_scalar=True),
'high': Array(high, as_scalar=True)}
kernel = SimpleCLFunction.from_string('''
void compute(double low, double high, global uint* rng_state, global ''' + ctype + '''* samples){
rand123_data rand123_rng_data = rand123_initialize_data((uint[]){
rng_state[0], rng_state[1], rng_state[2], rng_state[3],
rng_state[4], rng_state[5], 0});
void* rng_data = (void*)&rand123_rng_data;
for(uint i = 0; i < ''' + str(nmr_samples) + '''; i++){
double4 randomnr = rand4(rng_data);
samples[i] = (''' + ctype + ''')(low + randomnr.x * (high - low));
}
}
''', dependencies=[Rand123()])
return _generate_samples(kernel, nmr_distributions, nmr_samples, ctype, kernel_data, seed=seed) | Draw random samples from the Uniform distribution.
Args:
nmr_distributions (int): the number of unique continuous_distributions to create
nmr_samples (int): The number of samples to draw
low (double): The minimum value of the random numbers
high (double): The minimum value of the random numbers
ctype (str): the C type of the output samples
seed (float): the seed for the RNG
Returns:
ndarray: A two dimensional numpy array as (nmr_distributions, nmr_samples). | https://github.com/cbclab/MOT/blob/fb3243b65025705842e82704705c00902f9a35af/mot/random.py#L36-L73 |
cbclab/MOT | mot/random.py | normal | def normal(nmr_distributions, nmr_samples, mean=0, std=1, ctype='float', seed=None):
"""Draw random samples from the Gaussian distribution.
Args:
nmr_distributions (int): the number of unique continuous_distributions to create
nmr_samples (int): The number of samples to draw
mean (float or ndarray): The mean of the distribution
std (float or ndarray): The standard deviation or the distribution
ctype (str): the C type of the output samples
seed (float): the seed for the RNG
Returns:
ndarray: A two dimensional numpy array as (nmr_distributions, nmr_samples).
"""
if is_scalar(mean):
mean = np.ones((nmr_distributions, 1)) * mean
if is_scalar(std):
std = np.ones((nmr_distributions, 1)) * std
kernel_data = {'mean': Array(mean, as_scalar=True),
'std': Array(std, as_scalar=True)}
kernel = SimpleCLFunction.from_string('''
void compute(double mean, double std, global uint* rng_state, global ''' + ctype + '''* samples){
rand123_data rand123_rng_data = rand123_initialize_data((uint[]){
rng_state[0], rng_state[1], rng_state[2], rng_state[3],
rng_state[4], rng_state[5], 0});
void* rng_data = (void*)&rand123_rng_data;
for(uint i = 0; i < ''' + str(nmr_samples) + '''; i++){
double4 randomnr = randn4(rng_data);
samples[i] = (''' + ctype + ''')(mean + randomnr.x * std);
}
}
''', dependencies=[Rand123()])
return _generate_samples(kernel, nmr_distributions, nmr_samples, ctype, kernel_data, seed=seed) | python | def normal(nmr_distributions, nmr_samples, mean=0, std=1, ctype='float', seed=None):
"""Draw random samples from the Gaussian distribution.
Args:
nmr_distributions (int): the number of unique continuous_distributions to create
nmr_samples (int): The number of samples to draw
mean (float or ndarray): The mean of the distribution
std (float or ndarray): The standard deviation or the distribution
ctype (str): the C type of the output samples
seed (float): the seed for the RNG
Returns:
ndarray: A two dimensional numpy array as (nmr_distributions, nmr_samples).
"""
if is_scalar(mean):
mean = np.ones((nmr_distributions, 1)) * mean
if is_scalar(std):
std = np.ones((nmr_distributions, 1)) * std
kernel_data = {'mean': Array(mean, as_scalar=True),
'std': Array(std, as_scalar=True)}
kernel = SimpleCLFunction.from_string('''
void compute(double mean, double std, global uint* rng_state, global ''' + ctype + '''* samples){
rand123_data rand123_rng_data = rand123_initialize_data((uint[]){
rng_state[0], rng_state[1], rng_state[2], rng_state[3],
rng_state[4], rng_state[5], 0});
void* rng_data = (void*)&rand123_rng_data;
for(uint i = 0; i < ''' + str(nmr_samples) + '''; i++){
double4 randomnr = randn4(rng_data);
samples[i] = (''' + ctype + ''')(mean + randomnr.x * std);
}
}
''', dependencies=[Rand123()])
return _generate_samples(kernel, nmr_distributions, nmr_samples, ctype, kernel_data, seed=seed) | Draw random samples from the Gaussian distribution.
Args:
nmr_distributions (int): the number of unique continuous_distributions to create
nmr_samples (int): The number of samples to draw
mean (float or ndarray): The mean of the distribution
std (float or ndarray): The standard deviation or the distribution
ctype (str): the C type of the output samples
seed (float): the seed for the RNG
Returns:
ndarray: A two dimensional numpy array as (nmr_distributions, nmr_samples). | https://github.com/cbclab/MOT/blob/fb3243b65025705842e82704705c00902f9a35af/mot/random.py#L76-L112 |
cbclab/MOT | mot/sample/base.py | AbstractSampler.sample | def sample(self, nmr_samples, burnin=0, thinning=1):
"""Take additional samples from the given likelihood and prior, using this sampler.
This method can be called multiple times in which the sample state is stored in between.
Args:
nmr_samples (int): the number of samples to return
burnin (int): the number of samples to discard before returning samples
thinning (int): how many sample we wait before storing a new one. This will draw extra samples such that
the total number of samples generated is ``nmr_samples * (thinning)`` and the number of samples
stored is ``nmr_samples``. If set to one or lower we store every sample after the burn in.
Returns:
SamplingOutput: the sample output object
"""
if not thinning or thinning < 1:
thinning = 1
if not burnin or burnin < 0:
burnin = 0
max_samples_per_batch = max(1000 // thinning, 100)
with self._logging(nmr_samples, burnin, thinning):
if burnin > 0:
for batch_start, batch_end in split_in_batches(burnin, max_samples_per_batch):
self._sample(batch_end - batch_start, return_output=False)
if nmr_samples > 0:
outputs = []
for batch_start, batch_end in split_in_batches(nmr_samples, max_samples_per_batch):
outputs.append(self._sample(batch_end - batch_start, thinning=thinning))
return SimpleSampleOutput(*[np.concatenate([o[ind] for o in outputs], axis=-1) for ind in range(3)]) | python | def sample(self, nmr_samples, burnin=0, thinning=1):
"""Take additional samples from the given likelihood and prior, using this sampler.
This method can be called multiple times in which the sample state is stored in between.
Args:
nmr_samples (int): the number of samples to return
burnin (int): the number of samples to discard before returning samples
thinning (int): how many sample we wait before storing a new one. This will draw extra samples such that
the total number of samples generated is ``nmr_samples * (thinning)`` and the number of samples
stored is ``nmr_samples``. If set to one or lower we store every sample after the burn in.
Returns:
SamplingOutput: the sample output object
"""
if not thinning or thinning < 1:
thinning = 1
if not burnin or burnin < 0:
burnin = 0
max_samples_per_batch = max(1000 // thinning, 100)
with self._logging(nmr_samples, burnin, thinning):
if burnin > 0:
for batch_start, batch_end in split_in_batches(burnin, max_samples_per_batch):
self._sample(batch_end - batch_start, return_output=False)
if nmr_samples > 0:
outputs = []
for batch_start, batch_end in split_in_batches(nmr_samples, max_samples_per_batch):
outputs.append(self._sample(batch_end - batch_start, thinning=thinning))
return SimpleSampleOutput(*[np.concatenate([o[ind] for o in outputs], axis=-1) for ind in range(3)]) | Take additional samples from the given likelihood and prior, using this sampler.
This method can be called multiple times in which the sample state is stored in between.
Args:
nmr_samples (int): the number of samples to return
burnin (int): the number of samples to discard before returning samples
thinning (int): how many sample we wait before storing a new one. This will draw extra samples such that
the total number of samples generated is ``nmr_samples * (thinning)`` and the number of samples
stored is ``nmr_samples``. If set to one or lower we store every sample after the burn in.
Returns:
SamplingOutput: the sample output object | https://github.com/cbclab/MOT/blob/fb3243b65025705842e82704705c00902f9a35af/mot/sample/base.py#L108-L138 |
cbclab/MOT | mot/sample/base.py | AbstractSampler._sample | def _sample(self, nmr_samples, thinning=1, return_output=True):
"""Sample the given number of samples with the given thinning.
If ``return_output`` we will return the samples, log likelihoods and log priors. If not, we will advance the
state of the sampler without returning storing the samples.
Args:
nmr_samples (int): the number of iterations to advance the sampler
thinning (int): the thinning to apply
return_output (boolean): if we should return the output
Returns:
None or tuple: if ``return_output`` is True three ndarrays as (samples, log_likelihoods, log_priors)
"""
kernel_data = self._get_kernel_data(nmr_samples, thinning, return_output)
sample_func = self._get_compute_func(nmr_samples, thinning, return_output)
sample_func.evaluate(kernel_data, self._nmr_problems,
use_local_reduction=all(env.is_gpu for env in self._cl_runtime_info.cl_environments),
cl_runtime_info=self._cl_runtime_info)
self._sampling_index += nmr_samples * thinning
if return_output:
return (kernel_data['samples'].get_data(),
kernel_data['log_likelihoods'].get_data(),
kernel_data['log_priors'].get_data()) | python | def _sample(self, nmr_samples, thinning=1, return_output=True):
"""Sample the given number of samples with the given thinning.
If ``return_output`` we will return the samples, log likelihoods and log priors. If not, we will advance the
state of the sampler without returning storing the samples.
Args:
nmr_samples (int): the number of iterations to advance the sampler
thinning (int): the thinning to apply
return_output (boolean): if we should return the output
Returns:
None or tuple: if ``return_output`` is True three ndarrays as (samples, log_likelihoods, log_priors)
"""
kernel_data = self._get_kernel_data(nmr_samples, thinning, return_output)
sample_func = self._get_compute_func(nmr_samples, thinning, return_output)
sample_func.evaluate(kernel_data, self._nmr_problems,
use_local_reduction=all(env.is_gpu for env in self._cl_runtime_info.cl_environments),
cl_runtime_info=self._cl_runtime_info)
self._sampling_index += nmr_samples * thinning
if return_output:
return (kernel_data['samples'].get_data(),
kernel_data['log_likelihoods'].get_data(),
kernel_data['log_priors'].get_data()) | Sample the given number of samples with the given thinning.
If ``return_output`` we will return the samples, log likelihoods and log priors. If not, we will advance the
state of the sampler without returning storing the samples.
Args:
nmr_samples (int): the number of iterations to advance the sampler
thinning (int): the thinning to apply
return_output (boolean): if we should return the output
Returns:
None or tuple: if ``return_output`` is True three ndarrays as (samples, log_likelihoods, log_priors) | https://github.com/cbclab/MOT/blob/fb3243b65025705842e82704705c00902f9a35af/mot/sample/base.py#L140-L163 |
cbclab/MOT | mot/sample/base.py | AbstractSampler._initialize_likelihood_prior | def _initialize_likelihood_prior(self, positions, log_likelihoods, log_priors):
"""Initialize the likelihood and the prior using the given positions.
This is a general method for computing the log likelihoods and log priors for given positions.
Subclasses can use this to instantiate secondary chains as well.
"""
func = SimpleCLFunction.from_string('''
void compute(global mot_float_type* chain_position,
global mot_float_type* log_likelihood,
global mot_float_type* log_prior,
local mot_float_type* x_tmp,
void* data){
bool is_first_work_item = get_local_id(0) == 0;
if(is_first_work_item){
for(uint i = 0; i < ''' + str(self._nmr_params) + '''; i++){
x_tmp[i] = chain_position[i];
}
*log_prior = _computeLogPrior(x_tmp, data);
}
barrier(CLK_LOCAL_MEM_FENCE);
*log_likelihood = _computeLogLikelihood(x_tmp, data);
}
''', dependencies=[self._get_log_prior_cl_func(), self._get_log_likelihood_cl_func()])
kernel_data = {
'chain_position': Array(positions, 'mot_float_type', mode='rw', ensure_zero_copy=True),
'log_likelihood': Array(log_likelihoods, 'mot_float_type', mode='rw', ensure_zero_copy=True),
'log_prior': Array(log_priors, 'mot_float_type', mode='rw', ensure_zero_copy=True),
'x_tmp': LocalMemory('mot_float_type', self._nmr_params),
'data': self._data
}
func.evaluate(kernel_data, self._nmr_problems,
use_local_reduction=all(env.is_gpu for env in self._cl_runtime_info.cl_environments),
cl_runtime_info=self._cl_runtime_info) | python | def _initialize_likelihood_prior(self, positions, log_likelihoods, log_priors):
"""Initialize the likelihood and the prior using the given positions.
This is a general method for computing the log likelihoods and log priors for given positions.
Subclasses can use this to instantiate secondary chains as well.
"""
func = SimpleCLFunction.from_string('''
void compute(global mot_float_type* chain_position,
global mot_float_type* log_likelihood,
global mot_float_type* log_prior,
local mot_float_type* x_tmp,
void* data){
bool is_first_work_item = get_local_id(0) == 0;
if(is_first_work_item){
for(uint i = 0; i < ''' + str(self._nmr_params) + '''; i++){
x_tmp[i] = chain_position[i];
}
*log_prior = _computeLogPrior(x_tmp, data);
}
barrier(CLK_LOCAL_MEM_FENCE);
*log_likelihood = _computeLogLikelihood(x_tmp, data);
}
''', dependencies=[self._get_log_prior_cl_func(), self._get_log_likelihood_cl_func()])
kernel_data = {
'chain_position': Array(positions, 'mot_float_type', mode='rw', ensure_zero_copy=True),
'log_likelihood': Array(log_likelihoods, 'mot_float_type', mode='rw', ensure_zero_copy=True),
'log_prior': Array(log_priors, 'mot_float_type', mode='rw', ensure_zero_copy=True),
'x_tmp': LocalMemory('mot_float_type', self._nmr_params),
'data': self._data
}
func.evaluate(kernel_data, self._nmr_problems,
use_local_reduction=all(env.is_gpu for env in self._cl_runtime_info.cl_environments),
cl_runtime_info=self._cl_runtime_info) | Initialize the likelihood and the prior using the given positions.
This is a general method for computing the log likelihoods and log priors for given positions.
Subclasses can use this to instantiate secondary chains as well. | https://github.com/cbclab/MOT/blob/fb3243b65025705842e82704705c00902f9a35af/mot/sample/base.py#L165-L203 |
cbclab/MOT | mot/sample/base.py | AbstractSampler._get_kernel_data | def _get_kernel_data(self, nmr_samples, thinning, return_output):
"""Get the kernel data we will input to the MCMC sampler.
This sets the items:
* data: the pointer to the user provided data
* method_data: the data specific to the MCMC method
* nmr_iterations: the number of iterations to sample
* iteration_offset: the current sample index, that is, the offset to the given number of iterations
* rng_state: the random number generator state
* current_chain_position: the current position of the sampled chain
* current_log_likelihood: the log likelihood of the current position on the chain
* current_log_prior: the log prior of the current position on the chain
Additionally, if ``return_output`` is True, we add to that the arrays:
* samples: for the samples
* log_likelihoods: for storing the log likelihoods
* log_priors: for storing the priors
Args:
nmr_samples (int): the number of samples we will draw
thinning (int): the thinning factor we want to use
return_output (boolean): if the kernel should return output
Returns:
dict[str: mot.lib.utils.KernelData]: the kernel input data
"""
kernel_data = {
'data': self._data,
'method_data': self._get_mcmc_method_kernel_data(),
'nmr_iterations': Scalar(nmr_samples * thinning, ctype='ulong'),
'iteration_offset': Scalar(self._sampling_index, ctype='ulong'),
'rng_state': Array(self._rng_state, 'uint', mode='rw', ensure_zero_copy=True),
'current_chain_position': Array(self._current_chain_position, 'mot_float_type',
mode='rw', ensure_zero_copy=True),
'current_log_likelihood': Array(self._current_log_likelihood, 'mot_float_type',
mode='rw', ensure_zero_copy=True),
'current_log_prior': Array(self._current_log_prior, 'mot_float_type',
mode='rw', ensure_zero_copy=True),
}
if return_output:
kernel_data.update({
'samples': Zeros((self._nmr_problems, self._nmr_params, nmr_samples), ctype='mot_float_type'),
'log_likelihoods': Zeros((self._nmr_problems, nmr_samples), ctype='mot_float_type'),
'log_priors': Zeros((self._nmr_problems, nmr_samples), ctype='mot_float_type'),
})
return kernel_data | python | def _get_kernel_data(self, nmr_samples, thinning, return_output):
"""Get the kernel data we will input to the MCMC sampler.
This sets the items:
* data: the pointer to the user provided data
* method_data: the data specific to the MCMC method
* nmr_iterations: the number of iterations to sample
* iteration_offset: the current sample index, that is, the offset to the given number of iterations
* rng_state: the random number generator state
* current_chain_position: the current position of the sampled chain
* current_log_likelihood: the log likelihood of the current position on the chain
* current_log_prior: the log prior of the current position on the chain
Additionally, if ``return_output`` is True, we add to that the arrays:
* samples: for the samples
* log_likelihoods: for storing the log likelihoods
* log_priors: for storing the priors
Args:
nmr_samples (int): the number of samples we will draw
thinning (int): the thinning factor we want to use
return_output (boolean): if the kernel should return output
Returns:
dict[str: mot.lib.utils.KernelData]: the kernel input data
"""
kernel_data = {
'data': self._data,
'method_data': self._get_mcmc_method_kernel_data(),
'nmr_iterations': Scalar(nmr_samples * thinning, ctype='ulong'),
'iteration_offset': Scalar(self._sampling_index, ctype='ulong'),
'rng_state': Array(self._rng_state, 'uint', mode='rw', ensure_zero_copy=True),
'current_chain_position': Array(self._current_chain_position, 'mot_float_type',
mode='rw', ensure_zero_copy=True),
'current_log_likelihood': Array(self._current_log_likelihood, 'mot_float_type',
mode='rw', ensure_zero_copy=True),
'current_log_prior': Array(self._current_log_prior, 'mot_float_type',
mode='rw', ensure_zero_copy=True),
}
if return_output:
kernel_data.update({
'samples': Zeros((self._nmr_problems, self._nmr_params, nmr_samples), ctype='mot_float_type'),
'log_likelihoods': Zeros((self._nmr_problems, nmr_samples), ctype='mot_float_type'),
'log_priors': Zeros((self._nmr_problems, nmr_samples), ctype='mot_float_type'),
})
return kernel_data | Get the kernel data we will input to the MCMC sampler.
This sets the items:
* data: the pointer to the user provided data
* method_data: the data specific to the MCMC method
* nmr_iterations: the number of iterations to sample
* iteration_offset: the current sample index, that is, the offset to the given number of iterations
* rng_state: the random number generator state
* current_chain_position: the current position of the sampled chain
* current_log_likelihood: the log likelihood of the current position on the chain
* current_log_prior: the log prior of the current position on the chain
Additionally, if ``return_output`` is True, we add to that the arrays:
* samples: for the samples
* log_likelihoods: for storing the log likelihoods
* log_priors: for storing the priors
Args:
nmr_samples (int): the number of samples we will draw
thinning (int): the thinning factor we want to use
return_output (boolean): if the kernel should return output
Returns:
dict[str: mot.lib.utils.KernelData]: the kernel input data | https://github.com/cbclab/MOT/blob/fb3243b65025705842e82704705c00902f9a35af/mot/sample/base.py#L205-L253 |
cbclab/MOT | mot/sample/base.py | AbstractSampler._get_compute_func | def _get_compute_func(self, nmr_samples, thinning, return_output):
"""Get the MCMC algorithm as a computable function.
Args:
nmr_samples (int): the number of samples we will draw
thinning (int): the thinning factor we want to use
return_output (boolean): if the kernel should return output
Returns:
mot.lib.cl_function.CLFunction: the compute function
"""
cl_func = '''
void compute(global uint* rng_state,
global mot_float_type* current_chain_position,
global mot_float_type* current_log_likelihood,
global mot_float_type* current_log_prior,
ulong iteration_offset,
ulong nmr_iterations,
''' + ('''global mot_float_type* samples,
global mot_float_type* log_likelihoods,
global mot_float_type* log_priors,''' if return_output else '') + '''
void* method_data,
void* data){
bool is_first_work_item = get_local_id(0) == 0;
rand123_data rand123_rng_data = rand123_initialize_data((uint[]){
rng_state[0], rng_state[1], rng_state[2], rng_state[3],
rng_state[4], rng_state[5], 0, 0});
void* rng_data = (void*)&rand123_rng_data;
for(ulong i = 0; i < nmr_iterations; i++){
'''
if return_output:
cl_func += '''
if(is_first_work_item){
if(i % ''' + str(thinning) + ''' == 0){
log_likelihoods[i / ''' + str(thinning) + '''] = *current_log_likelihood;
log_priors[i / ''' + str(thinning) + '''] = *current_log_prior;
for(uint j = 0; j < ''' + str(self._nmr_params) + '''; j++){
samples[(ulong)(i / ''' + str(thinning) + ''') // remove the interval
+ j * ''' + str(nmr_samples) + ''' // parameter index
] = current_chain_position[j];
}
}
}
'''
cl_func += '''
_advanceSampler(method_data, data, i + iteration_offset, rng_data,
current_chain_position, current_log_likelihood, current_log_prior);
}
if(is_first_work_item){
uint state[8];
rand123_data_to_array(rand123_rng_data, state);
for(uint i = 0; i < 6; i++){
rng_state[i] = state[i];
}
}
}
'''
return SimpleCLFunction.from_string(
cl_func,
dependencies=[Rand123(), self._get_log_prior_cl_func(),
self._get_log_likelihood_cl_func(),
SimpleCLCodeObject(self._get_state_update_cl_func(nmr_samples, thinning, return_output))]) | python | def _get_compute_func(self, nmr_samples, thinning, return_output):
"""Get the MCMC algorithm as a computable function.
Args:
nmr_samples (int): the number of samples we will draw
thinning (int): the thinning factor we want to use
return_output (boolean): if the kernel should return output
Returns:
mot.lib.cl_function.CLFunction: the compute function
"""
cl_func = '''
void compute(global uint* rng_state,
global mot_float_type* current_chain_position,
global mot_float_type* current_log_likelihood,
global mot_float_type* current_log_prior,
ulong iteration_offset,
ulong nmr_iterations,
''' + ('''global mot_float_type* samples,
global mot_float_type* log_likelihoods,
global mot_float_type* log_priors,''' if return_output else '') + '''
void* method_data,
void* data){
bool is_first_work_item = get_local_id(0) == 0;
rand123_data rand123_rng_data = rand123_initialize_data((uint[]){
rng_state[0], rng_state[1], rng_state[2], rng_state[3],
rng_state[4], rng_state[5], 0, 0});
void* rng_data = (void*)&rand123_rng_data;
for(ulong i = 0; i < nmr_iterations; i++){
'''
if return_output:
cl_func += '''
if(is_first_work_item){
if(i % ''' + str(thinning) + ''' == 0){
log_likelihoods[i / ''' + str(thinning) + '''] = *current_log_likelihood;
log_priors[i / ''' + str(thinning) + '''] = *current_log_prior;
for(uint j = 0; j < ''' + str(self._nmr_params) + '''; j++){
samples[(ulong)(i / ''' + str(thinning) + ''') // remove the interval
+ j * ''' + str(nmr_samples) + ''' // parameter index
] = current_chain_position[j];
}
}
}
'''
cl_func += '''
_advanceSampler(method_data, data, i + iteration_offset, rng_data,
current_chain_position, current_log_likelihood, current_log_prior);
}
if(is_first_work_item){
uint state[8];
rand123_data_to_array(rand123_rng_data, state);
for(uint i = 0; i < 6; i++){
rng_state[i] = state[i];
}
}
}
'''
return SimpleCLFunction.from_string(
cl_func,
dependencies=[Rand123(), self._get_log_prior_cl_func(),
self._get_log_likelihood_cl_func(),
SimpleCLCodeObject(self._get_state_update_cl_func(nmr_samples, thinning, return_output))]) | Get the MCMC algorithm as a computable function.
Args:
nmr_samples (int): the number of samples we will draw
thinning (int): the thinning factor we want to use
return_output (boolean): if the kernel should return output
Returns:
mot.lib.cl_function.CLFunction: the compute function | https://github.com/cbclab/MOT/blob/fb3243b65025705842e82704705c00902f9a35af/mot/sample/base.py#L255-L321 |
cbclab/MOT | mot/sample/base.py | AbstractSampler._get_log_prior_cl_func | def _get_log_prior_cl_func(self):
"""Get the CL log prior compute function.
Returns:
str: the compute function for computing the log prior.
"""
return SimpleCLFunction.from_string('''
mot_float_type _computeLogPrior(local const mot_float_type* x, void* data){
return ''' + self._log_prior_func.get_cl_function_name() + '''(x, data);
}
''', dependencies=[self._log_prior_func]) | python | def _get_log_prior_cl_func(self):
"""Get the CL log prior compute function.
Returns:
str: the compute function for computing the log prior.
"""
return SimpleCLFunction.from_string('''
mot_float_type _computeLogPrior(local const mot_float_type* x, void* data){
return ''' + self._log_prior_func.get_cl_function_name() + '''(x, data);
}
''', dependencies=[self._log_prior_func]) | Get the CL log prior compute function.
Returns:
str: the compute function for computing the log prior. | https://github.com/cbclab/MOT/blob/fb3243b65025705842e82704705c00902f9a35af/mot/sample/base.py#L323-L333 |
cbclab/MOT | mot/sample/base.py | AbstractSampler._get_log_likelihood_cl_func | def _get_log_likelihood_cl_func(self):
"""Get the CL log likelihood compute function.
This uses local reduction to compute the log likelihood for every observation in CL local space.
The results are then summed in the first work item and returned using a pointer.
Returns:
str: the CL code for the log likelihood compute func.
"""
return SimpleCLFunction.from_string('''
double _computeLogLikelihood(local const mot_float_type* current_position, void* data){
return ''' + self._ll_func.get_cl_function_name() + '''(current_position, data);
}
''', dependencies=[self._ll_func]) | python | def _get_log_likelihood_cl_func(self):
"""Get the CL log likelihood compute function.
This uses local reduction to compute the log likelihood for every observation in CL local space.
The results are then summed in the first work item and returned using a pointer.
Returns:
str: the CL code for the log likelihood compute func.
"""
return SimpleCLFunction.from_string('''
double _computeLogLikelihood(local const mot_float_type* current_position, void* data){
return ''' + self._ll_func.get_cl_function_name() + '''(current_position, data);
}
''', dependencies=[self._ll_func]) | Get the CL log likelihood compute function.
This uses local reduction to compute the log likelihood for every observation in CL local space.
The results are then summed in the first work item and returned using a pointer.
Returns:
str: the CL code for the log likelihood compute func. | https://github.com/cbclab/MOT/blob/fb3243b65025705842e82704705c00902f9a35af/mot/sample/base.py#L335-L348 |
cbclab/MOT | mot/sample/base.py | AbstractRWMSampler._get_mcmc_method_kernel_data_elements | def _get_mcmc_method_kernel_data_elements(self):
"""Get the mcmc method kernel data elements. Used by :meth:`_get_mcmc_method_kernel_data`."""
return {'proposal_stds': Array(self._proposal_stds, 'mot_float_type', mode='rw', ensure_zero_copy=True),
'x_tmp': LocalMemory('mot_float_type', nmr_items=1 + self._nmr_params)} | python | def _get_mcmc_method_kernel_data_elements(self):
"""Get the mcmc method kernel data elements. Used by :meth:`_get_mcmc_method_kernel_data`."""
return {'proposal_stds': Array(self._proposal_stds, 'mot_float_type', mode='rw', ensure_zero_copy=True),
'x_tmp': LocalMemory('mot_float_type', nmr_items=1 + self._nmr_params)} | Get the mcmc method kernel data elements. Used by :meth:`_get_mcmc_method_kernel_data`. | https://github.com/cbclab/MOT/blob/fb3243b65025705842e82704705c00902f9a35af/mot/sample/base.py#L423-L426 |
cbclab/MOT | mot/cl_routines/__init__.py | compute_log_likelihood | def compute_log_likelihood(ll_func, parameters, data=None, cl_runtime_info=None):
"""Calculate and return the log likelihood of the given model for the given parameters.
This calculates the log likelihoods for every problem in the model (typically after optimization),
or a log likelihood for every sample of every model (typically after sample). In the case of the first (after
optimization), the parameters must be an (d, p) array for d problems and p parameters. In the case of the
second (after sample), you must provide this function with a matrix of shape (d, p, n) with d problems,
p parameters and n samples.
Args:
ll_func (mot.lib.cl_function.CLFunction): The log-likelihood function. A CL function with the signature:
.. code-block:: c
double <func_name>(local const mot_float_type* const x, void* data);
parameters (ndarray): The parameters to use in the evaluation of the model. This is either an (d, p) matrix
or (d, p, n) matrix with d problems, p parameters and n samples.
data (mot.lib.kernel_data.KernelData): the user provided data for the ``void* data`` pointer.
cl_runtime_info (mot.configuration.CLRuntimeInfo): the runtime information
Returns:
ndarray: per problem the log likelihood, or, per problem and per sample the log likelihood.
"""
def get_cl_function():
nmr_params = parameters.shape[1]
if len(parameters.shape) > 2:
return SimpleCLFunction.from_string('''
void compute(global mot_float_type* parameters,
global mot_float_type* log_likelihoods,
void* data){
local mot_float_type x[''' + str(nmr_params) + '''];
for(uint sample_ind = 0; sample_ind < ''' + str(parameters.shape[2]) + '''; sample_ind++){
for(uint i = 0; i < ''' + str(nmr_params) + '''; i++){
x[i] = parameters[i *''' + str(parameters.shape[2]) + ''' + sample_ind];
}
double ll = ''' + ll_func.get_cl_function_name() + '''(x, data);
if(get_local_id(0) == 0){
log_likelihoods[sample_ind] = ll;
}
}
}
''', dependencies=[ll_func])
return SimpleCLFunction.from_string('''
void compute(local mot_float_type* parameters,
global mot_float_type* log_likelihoods,
void* data){
double ll = ''' + ll_func.get_cl_function_name() + '''(parameters, data);
if(get_local_id(0) == 0){
*(log_likelihoods) = ll;
}
}
''', dependencies=[ll_func])
kernel_data = {'data': data,
'parameters': Array(parameters, 'mot_float_type', mode='r')}
shape = parameters.shape
if len(shape) > 2:
kernel_data.update({
'log_likelihoods': Zeros((shape[0], shape[2]), 'mot_float_type'),
})
else:
kernel_data.update({
'log_likelihoods': Zeros((shape[0],), 'mot_float_type'),
})
get_cl_function().evaluate(kernel_data, parameters.shape[0], use_local_reduction=True,
cl_runtime_info=cl_runtime_info)
return kernel_data['log_likelihoods'].get_data() | python | def compute_log_likelihood(ll_func, parameters, data=None, cl_runtime_info=None):
"""Calculate and return the log likelihood of the given model for the given parameters.
This calculates the log likelihoods for every problem in the model (typically after optimization),
or a log likelihood for every sample of every model (typically after sample). In the case of the first (after
optimization), the parameters must be an (d, p) array for d problems and p parameters. In the case of the
second (after sample), you must provide this function with a matrix of shape (d, p, n) with d problems,
p parameters and n samples.
Args:
ll_func (mot.lib.cl_function.CLFunction): The log-likelihood function. A CL function with the signature:
.. code-block:: c
double <func_name>(local const mot_float_type* const x, void* data);
parameters (ndarray): The parameters to use in the evaluation of the model. This is either an (d, p) matrix
or (d, p, n) matrix with d problems, p parameters and n samples.
data (mot.lib.kernel_data.KernelData): the user provided data for the ``void* data`` pointer.
cl_runtime_info (mot.configuration.CLRuntimeInfo): the runtime information
Returns:
ndarray: per problem the log likelihood, or, per problem and per sample the log likelihood.
"""
def get_cl_function():
nmr_params = parameters.shape[1]
if len(parameters.shape) > 2:
return SimpleCLFunction.from_string('''
void compute(global mot_float_type* parameters,
global mot_float_type* log_likelihoods,
void* data){
local mot_float_type x[''' + str(nmr_params) + '''];
for(uint sample_ind = 0; sample_ind < ''' + str(parameters.shape[2]) + '''; sample_ind++){
for(uint i = 0; i < ''' + str(nmr_params) + '''; i++){
x[i] = parameters[i *''' + str(parameters.shape[2]) + ''' + sample_ind];
}
double ll = ''' + ll_func.get_cl_function_name() + '''(x, data);
if(get_local_id(0) == 0){
log_likelihoods[sample_ind] = ll;
}
}
}
''', dependencies=[ll_func])
return SimpleCLFunction.from_string('''
void compute(local mot_float_type* parameters,
global mot_float_type* log_likelihoods,
void* data){
double ll = ''' + ll_func.get_cl_function_name() + '''(parameters, data);
if(get_local_id(0) == 0){
*(log_likelihoods) = ll;
}
}
''', dependencies=[ll_func])
kernel_data = {'data': data,
'parameters': Array(parameters, 'mot_float_type', mode='r')}
shape = parameters.shape
if len(shape) > 2:
kernel_data.update({
'log_likelihoods': Zeros((shape[0], shape[2]), 'mot_float_type'),
})
else:
kernel_data.update({
'log_likelihoods': Zeros((shape[0],), 'mot_float_type'),
})
get_cl_function().evaluate(kernel_data, parameters.shape[0], use_local_reduction=True,
cl_runtime_info=cl_runtime_info)
return kernel_data['log_likelihoods'].get_data() | Calculate and return the log likelihood of the given model for the given parameters.
This calculates the log likelihoods for every problem in the model (typically after optimization),
or a log likelihood for every sample of every model (typically after sample). In the case of the first (after
optimization), the parameters must be an (d, p) array for d problems and p parameters. In the case of the
second (after sample), you must provide this function with a matrix of shape (d, p, n) with d problems,
p parameters and n samples.
Args:
ll_func (mot.lib.cl_function.CLFunction): The log-likelihood function. A CL function with the signature:
.. code-block:: c
double <func_name>(local const mot_float_type* const x, void* data);
parameters (ndarray): The parameters to use in the evaluation of the model. This is either an (d, p) matrix
or (d, p, n) matrix with d problems, p parameters and n samples.
data (mot.lib.kernel_data.KernelData): the user provided data for the ``void* data`` pointer.
cl_runtime_info (mot.configuration.CLRuntimeInfo): the runtime information
Returns:
ndarray: per problem the log likelihood, or, per problem and per sample the log likelihood. | https://github.com/cbclab/MOT/blob/fb3243b65025705842e82704705c00902f9a35af/mot/cl_routines/__init__.py#L12-L89 |
cbclab/MOT | mot/cl_routines/__init__.py | compute_objective_value | def compute_objective_value(objective_func, parameters, data=None, cl_runtime_info=None):
"""Calculate and return the objective function value of the given model for the given parameters.
Args:
objective_func (mot.lib.cl_function.CLFunction): A CL function with the signature:
.. code-block:: c
double <func_name>(local const mot_float_type* const x,
void* data,
local mot_float_type* objective_list);
parameters (ndarray): The parameters to use in the evaluation of the model, an (d, p) matrix
with d problems and p parameters.
data (mot.lib.kernel_data.KernelData): the user provided data for the ``void* data`` pointer.
cl_runtime_info (mot.configuration.CLRuntimeInfo): the runtime information
Returns:
ndarray: vector matrix with per problem the objective function value
"""
return objective_func.evaluate({'data': data, 'parameters': Array(parameters, 'mot_float_type', mode='r')},
parameters.shape[0], use_local_reduction=True, cl_runtime_info=cl_runtime_info) | python | def compute_objective_value(objective_func, parameters, data=None, cl_runtime_info=None):
"""Calculate and return the objective function value of the given model for the given parameters.
Args:
objective_func (mot.lib.cl_function.CLFunction): A CL function with the signature:
.. code-block:: c
double <func_name>(local const mot_float_type* const x,
void* data,
local mot_float_type* objective_list);
parameters (ndarray): The parameters to use in the evaluation of the model, an (d, p) matrix
with d problems and p parameters.
data (mot.lib.kernel_data.KernelData): the user provided data for the ``void* data`` pointer.
cl_runtime_info (mot.configuration.CLRuntimeInfo): the runtime information
Returns:
ndarray: vector matrix with per problem the objective function value
"""
return objective_func.evaluate({'data': data, 'parameters': Array(parameters, 'mot_float_type', mode='r')},
parameters.shape[0], use_local_reduction=True, cl_runtime_info=cl_runtime_info) | Calculate and return the objective function value of the given model for the given parameters.
Args:
objective_func (mot.lib.cl_function.CLFunction): A CL function with the signature:
.. code-block:: c
double <func_name>(local const mot_float_type* const x,
void* data,
local mot_float_type* objective_list);
parameters (ndarray): The parameters to use in the evaluation of the model, an (d, p) matrix
with d problems and p parameters.
data (mot.lib.kernel_data.KernelData): the user provided data for the ``void* data`` pointer.
cl_runtime_info (mot.configuration.CLRuntimeInfo): the runtime information
Returns:
ndarray: vector matrix with per problem the objective function value | https://github.com/cbclab/MOT/blob/fb3243b65025705842e82704705c00902f9a35af/mot/cl_routines/__init__.py#L92-L113 |
aaugustin/django-userlog | userlog/util.py | get_log | def get_log(username):
"""
Return a list of page views.
Each item is a dict with `datetime`, `method`, `path` and `code` keys.
"""
redis = get_redis_client()
log_key = 'log:{}'.format(username)
raw_log = redis.lrange(log_key, 0, -1)
log = []
for raw_item in raw_log:
item = json.loads(raw_item.decode())
item['datetime'] = convert_timestamp(item.pop('time'))
log.append(item)
return log | python | def get_log(username):
"""
Return a list of page views.
Each item is a dict with `datetime`, `method`, `path` and `code` keys.
"""
redis = get_redis_client()
log_key = 'log:{}'.format(username)
raw_log = redis.lrange(log_key, 0, -1)
log = []
for raw_item in raw_log:
item = json.loads(raw_item.decode())
item['datetime'] = convert_timestamp(item.pop('time'))
log.append(item)
return log | Return a list of page views.
Each item is a dict with `datetime`, `method`, `path` and `code` keys. | https://github.com/aaugustin/django-userlog/blob/6cd34d0a319f6a954fec74420d0d391c32c46060/userlog/util.py#L58-L72 |
aaugustin/django-userlog | userlog/util.py | get_token | def get_token(username, length=20, timeout=20):
"""
Obtain an access token that can be passed to a websocket client.
"""
redis = get_redis_client()
token = get_random_string(length)
token_key = 'token:{}'.format(token)
redis.set(token_key, username)
redis.expire(token_key, timeout)
return token | python | def get_token(username, length=20, timeout=20):
"""
Obtain an access token that can be passed to a websocket client.
"""
redis = get_redis_client()
token = get_random_string(length)
token_key = 'token:{}'.format(token)
redis.set(token_key, username)
redis.expire(token_key, timeout)
return token | Obtain an access token that can be passed to a websocket client. | https://github.com/aaugustin/django-userlog/blob/6cd34d0a319f6a954fec74420d0d391c32c46060/userlog/util.py#L75-L84 |
aaugustin/django-userlog | userlog/middleware.py | UserLogMiddleware.get_log | def get_log(self, request, response):
"""
Return a dict of data to log for a given request and response.
Override this method if you need to log a different set of values.
"""
return {
'method': request.method,
'path': request.get_full_path(),
'code': response.status_code,
'time': time.time(),
} | python | def get_log(self, request, response):
"""
Return a dict of data to log for a given request and response.
Override this method if you need to log a different set of values.
"""
return {
'method': request.method,
'path': request.get_full_path(),
'code': response.status_code,
'time': time.time(),
} | Return a dict of data to log for a given request and response.
Override this method if you need to log a different set of values. | https://github.com/aaugustin/django-userlog/blob/6cd34d0a319f6a954fec74420d0d391c32c46060/userlog/middleware.py#L42-L53 |
thebigmunch/google-music | src/google_music/clients/musicmanager.py | MusicManager.download | def download(self, song):
"""Download a song from a Google Music library.
Parameters:
song (dict): A song dict.
Returns:
tuple: Song content as bytestring, suggested filename.
"""
song_id = song['id']
response = self._call(
mm_calls.Export,
self.uploader_id,
song_id)
audio = response.body
suggested_filename = unquote(
response.headers['Content-Disposition'].split("filename*=UTF-8''")[-1]
)
return (audio, suggested_filename) | python | def download(self, song):
"""Download a song from a Google Music library.
Parameters:
song (dict): A song dict.
Returns:
tuple: Song content as bytestring, suggested filename.
"""
song_id = song['id']
response = self._call(
mm_calls.Export,
self.uploader_id,
song_id)
audio = response.body
suggested_filename = unquote(
response.headers['Content-Disposition'].split("filename*=UTF-8''")[-1]
)
return (audio, suggested_filename) | Download a song from a Google Music library.
Parameters:
song (dict): A song dict.
Returns:
tuple: Song content as bytestring, suggested filename. | https://github.com/thebigmunch/google-music/blob/d8a94dab462a1f063fbc1152187a73dc2f0e2a85/src/google_music/clients/musicmanager.py#L84-L105 |
thebigmunch/google-music | src/google_music/clients/musicmanager.py | MusicManager.quota | def quota(self):
"""Get the uploaded track count and allowance.
Returns:
tuple: Number of uploaded tracks, number of tracks allowed.
"""
response = self._call(
mm_calls.ClientState,
self.uploader_id
)
client_state = response.body.clientstate_response
return (client_state.total_track_count, client_state.locker_track_limit) | python | def quota(self):
"""Get the uploaded track count and allowance.
Returns:
tuple: Number of uploaded tracks, number of tracks allowed.
"""
response = self._call(
mm_calls.ClientState,
self.uploader_id
)
client_state = response.body.clientstate_response
return (client_state.total_track_count, client_state.locker_track_limit) | Get the uploaded track count and allowance.
Returns:
tuple: Number of uploaded tracks, number of tracks allowed. | https://github.com/thebigmunch/google-music/blob/d8a94dab462a1f063fbc1152187a73dc2f0e2a85/src/google_music/clients/musicmanager.py#L107-L120 |
thebigmunch/google-music | src/google_music/clients/musicmanager.py | MusicManager.songs | def songs(self, *, uploaded=True, purchased=True):
"""Get a listing of Music Library songs.
Returns:
list: Song dicts.
"""
if not uploaded and not purchased:
raise ValueError("'uploaded' and 'purchased' cannot both be False.")
if purchased and uploaded:
song_list = []
for chunk in self.songs_iter(export_type=1):
song_list.extend(chunk)
elif purchased:
song_list = []
for chunk in self.songs_iter(export_type=2):
song_list.extend(chunk)
elif uploaded:
purchased_songs = []
for chunk in self.songs_iter(export_type=2):
purchased_songs.extend(chunk)
song_list = [
song
for chunk in self.songs_iter(export_type=1)
for song in chunk
if song not in purchased_songs
]
return song_list | python | def songs(self, *, uploaded=True, purchased=True):
"""Get a listing of Music Library songs.
Returns:
list: Song dicts.
"""
if not uploaded and not purchased:
raise ValueError("'uploaded' and 'purchased' cannot both be False.")
if purchased and uploaded:
song_list = []
for chunk in self.songs_iter(export_type=1):
song_list.extend(chunk)
elif purchased:
song_list = []
for chunk in self.songs_iter(export_type=2):
song_list.extend(chunk)
elif uploaded:
purchased_songs = []
for chunk in self.songs_iter(export_type=2):
purchased_songs.extend(chunk)
song_list = [
song
for chunk in self.songs_iter(export_type=1)
for song in chunk
if song not in purchased_songs
]
return song_list | Get a listing of Music Library songs.
Returns:
list: Song dicts. | https://github.com/thebigmunch/google-music/blob/d8a94dab462a1f063fbc1152187a73dc2f0e2a85/src/google_music/clients/musicmanager.py#L122-L152 |
thebigmunch/google-music | src/google_music/clients/musicmanager.py | MusicManager.songs_iter | def songs_iter(self, *, continuation_token=None, export_type=1):
"""Get a paged iterator of Music Library songs.
Parameters:
continuation_token (str, Optional): The token of the page to return.
Default: Not sent to get first page.
export_type (int, Optional): The type of tracks to return. 1 for all tracks, 2 for promotional and purchased.
Default: ``1``
Yields:
list: Song dicts.
"""
def track_info_to_dict(track_info):
return dict(
(field.name, value)
for field, value in track_info.ListFields()
)
while True:
response = self._call(
mm_calls.ExportIDs,
self.uploader_id,
continuation_token=continuation_token,
export_type=export_type
)
items = [
track_info_to_dict(track_info)
for track_info in response.body.download_track_info
]
if items:
yield items
continuation_token = response.body.continuation_token
if not continuation_token:
break | python | def songs_iter(self, *, continuation_token=None, export_type=1):
"""Get a paged iterator of Music Library songs.
Parameters:
continuation_token (str, Optional): The token of the page to return.
Default: Not sent to get first page.
export_type (int, Optional): The type of tracks to return. 1 for all tracks, 2 for promotional and purchased.
Default: ``1``
Yields:
list: Song dicts.
"""
def track_info_to_dict(track_info):
return dict(
(field.name, value)
for field, value in track_info.ListFields()
)
while True:
response = self._call(
mm_calls.ExportIDs,
self.uploader_id,
continuation_token=continuation_token,
export_type=export_type
)
items = [
track_info_to_dict(track_info)
for track_info in response.body.download_track_info
]
if items:
yield items
continuation_token = response.body.continuation_token
if not continuation_token:
break | Get a paged iterator of Music Library songs.
Parameters:
continuation_token (str, Optional): The token of the page to return.
Default: Not sent to get first page.
export_type (int, Optional): The type of tracks to return. 1 for all tracks, 2 for promotional and purchased.
Default: ``1``
Yields:
list: Song dicts. | https://github.com/thebigmunch/google-music/blob/d8a94dab462a1f063fbc1152187a73dc2f0e2a85/src/google_music/clients/musicmanager.py#L154-L192 |
thebigmunch/google-music | src/google_music/clients/musicmanager.py | MusicManager.upload | def upload(
self,
song,
*,
album_art_path=None,
no_sample=False
):
"""Upload a song to a Google Music library.
Parameters:
song (os.PathLike or str or audio_metadata.Format):
The path to an audio file or an instance of :class:`audio_metadata.Format`.
album_art_path (os.PathLike or str, Optional):
The relative filename or absolute filepath to external album art.
no_sample(bool, Optional):
Don't generate an audio sample from song;
send empty audio sample.
Default: Create an audio sample using ffmpeg/avconv.
Returns:
dict: A result dict with keys: ``'filepath'``, ``'success'``, ``'reason'``, and ``'song_id'`` (if successful).
"""
if not isinstance(song, audio_metadata.Format):
try:
song = audio_metadata.load(song)
except audio_metadata.UnsupportedFormat:
raise ValueError("'song' must be FLAC, MP3, or WAV.")
if album_art_path:
album_art_path = Path(album_art_path).resolve()
if album_art_path.is_file():
with album_art_path.open('rb') as image_file:
external_art = image_file.read()
else:
external_art = None
else:
external_art = None
result = {'filepath': Path(song.filepath)}
track_info = mm_calls.Metadata.get_track_info(song)
response = self._call(
mm_calls.Metadata,
self.uploader_id,
[track_info]
)
metadata_response = response.body.metadata_response
if metadata_response.signed_challenge_info: # Sample requested.
sample_request = metadata_response.signed_challenge_info[0]
try:
track_sample = mm_calls.Sample.generate_sample(
song,
track_info,
sample_request,
external_art=external_art,
no_sample=no_sample
)
response = self._call(
mm_calls.Sample,
self.uploader_id,
[track_sample]
)
track_sample_response = response.body.sample_response.track_sample_response[0]
except (OSError, ValueError, subprocess.CalledProcessError):
raise # TODO
else:
track_sample_response = metadata_response.track_sample_response[0]
response_code = track_sample_response.response_code
if response_code == upload_pb2.TrackSampleResponse.MATCHED:
result.update(
{
'success': True,
'reason': 'Matched',
'song_id': track_sample_response.server_track_id
}
)
elif response_code == upload_pb2.TrackSampleResponse.UPLOAD_REQUESTED:
server_track_id = track_sample_response.server_track_id
self._call(
mm_calls.UploadState,
self.uploader_id,
'START'
)
attempts = 0
should_retry = True
while should_retry and attempts <= 10:
# Call with tenacity.retry_with to disable automatic retries.
response = self._call.retry_with(stop=stop_after_attempt(1))(
self,
mm_calls.ScottyAgentPost,
self.uploader_id,
server_track_id,
track_info,
song,
external_art=external_art,
total_song_count=1,
total_uploaded_count=0
)
session_response = response.body
if 'sessionStatus' in session_response:
break
try:
# WHY, GOOGLE?! WHY???????????
status_code = session_response['errorMessage']['additionalInfo'][
'uploader_service.GoogleRupioAdditionalInfo'
]['completionInfo']['customerSpecificInfo'][
'ResponseCode'
]
except KeyError:
status_code = None
if status_code == 503: # Upload server still syncing.
should_retry = True
reason = "Server syncing"
elif status_code == 200: # Song is already uploaded.
should_retry = False
reason = "Already uploaded"
elif status_code == 404: # Rejected.
should_retry = False
reason = "Rejected"
else:
should_retry = True
reason = "Unkown error"
attempts += 1
time.sleep(2) # Give the server time to sync.
else:
result.update(
{
'success': False,
'reason': f'Could not get upload session: {reason}'
}
)
if 'success' not in result:
transfer = session_response['sessionStatus']['externalFieldTransfers'][0]
upload_url = transfer['putInfo']['url']
content_type = transfer.get('content_type', 'audio/mpeg')
original_content_type = track_info.original_content_type
transcode = (
isinstance(song, audio_metadata.WAV)
or original_content_type != locker_pb2.Track.MP3
)
if (
transcode
or original_content_type == locker_pb2.Track.MP3
):
if transcode:
audio_file = transcode_to_mp3(song, quality='320k')
else:
with open(song.filepath, 'rb') as f:
audio_file = f.read()
# Google Music allows a maximum file size of 300 MiB.
if len(audio_file) >= 300 * 1024 * 1024:
result.update(
{
'success': False,
'reason': 'Maximum allowed file size is 300 MiB.'
}
)
else:
upload_response = self._call(
mm_calls.ScottyAgentPut,
upload_url,
audio_file,
content_type=content_type
).body
if upload_response.get('sessionStatus', {}).get('state'):
result.update(
{
'success': True,
'reason': 'Uploaded',
'song_id': track_sample_response.server_track_id
}
)
else:
result.update(
{
'success': False,
'reason': upload_response # TODO: Better error details.
}
)
else:
# Do not upload files if transcode option set to False.
result.update(
{
'success': False,
'reason': 'Transcoding disabled for file type.'
}
)
self._call(mm_calls.UploadState, self.uploader_id, 'STOPPED')
else:
response_codes = upload_pb2._TRACKSAMPLERESPONSE.enum_types[0]
response_type = response_codes.values_by_number[
track_sample_response.response_code
].name
reason = response_type
result.update(
{
'success': False,
'reason': f'{reason}'
}
)
if response_type == 'ALREADY_EXISTS':
result['song_id'] = track_sample_response.server_track_id
return result | python | def upload(
self,
song,
*,
album_art_path=None,
no_sample=False
):
"""Upload a song to a Google Music library.
Parameters:
song (os.PathLike or str or audio_metadata.Format):
The path to an audio file or an instance of :class:`audio_metadata.Format`.
album_art_path (os.PathLike or str, Optional):
The relative filename or absolute filepath to external album art.
no_sample(bool, Optional):
Don't generate an audio sample from song;
send empty audio sample.
Default: Create an audio sample using ffmpeg/avconv.
Returns:
dict: A result dict with keys: ``'filepath'``, ``'success'``, ``'reason'``, and ``'song_id'`` (if successful).
"""
if not isinstance(song, audio_metadata.Format):
try:
song = audio_metadata.load(song)
except audio_metadata.UnsupportedFormat:
raise ValueError("'song' must be FLAC, MP3, or WAV.")
if album_art_path:
album_art_path = Path(album_art_path).resolve()
if album_art_path.is_file():
with album_art_path.open('rb') as image_file:
external_art = image_file.read()
else:
external_art = None
else:
external_art = None
result = {'filepath': Path(song.filepath)}
track_info = mm_calls.Metadata.get_track_info(song)
response = self._call(
mm_calls.Metadata,
self.uploader_id,
[track_info]
)
metadata_response = response.body.metadata_response
if metadata_response.signed_challenge_info: # Sample requested.
sample_request = metadata_response.signed_challenge_info[0]
try:
track_sample = mm_calls.Sample.generate_sample(
song,
track_info,
sample_request,
external_art=external_art,
no_sample=no_sample
)
response = self._call(
mm_calls.Sample,
self.uploader_id,
[track_sample]
)
track_sample_response = response.body.sample_response.track_sample_response[0]
except (OSError, ValueError, subprocess.CalledProcessError):
raise # TODO
else:
track_sample_response = metadata_response.track_sample_response[0]
response_code = track_sample_response.response_code
if response_code == upload_pb2.TrackSampleResponse.MATCHED:
result.update(
{
'success': True,
'reason': 'Matched',
'song_id': track_sample_response.server_track_id
}
)
elif response_code == upload_pb2.TrackSampleResponse.UPLOAD_REQUESTED:
server_track_id = track_sample_response.server_track_id
self._call(
mm_calls.UploadState,
self.uploader_id,
'START'
)
attempts = 0
should_retry = True
while should_retry and attempts <= 10:
# Call with tenacity.retry_with to disable automatic retries.
response = self._call.retry_with(stop=stop_after_attempt(1))(
self,
mm_calls.ScottyAgentPost,
self.uploader_id,
server_track_id,
track_info,
song,
external_art=external_art,
total_song_count=1,
total_uploaded_count=0
)
session_response = response.body
if 'sessionStatus' in session_response:
break
try:
# WHY, GOOGLE?! WHY???????????
status_code = session_response['errorMessage']['additionalInfo'][
'uploader_service.GoogleRupioAdditionalInfo'
]['completionInfo']['customerSpecificInfo'][
'ResponseCode'
]
except KeyError:
status_code = None
if status_code == 503: # Upload server still syncing.
should_retry = True
reason = "Server syncing"
elif status_code == 200: # Song is already uploaded.
should_retry = False
reason = "Already uploaded"
elif status_code == 404: # Rejected.
should_retry = False
reason = "Rejected"
else:
should_retry = True
reason = "Unkown error"
attempts += 1
time.sleep(2) # Give the server time to sync.
else:
result.update(
{
'success': False,
'reason': f'Could not get upload session: {reason}'
}
)
if 'success' not in result:
transfer = session_response['sessionStatus']['externalFieldTransfers'][0]
upload_url = transfer['putInfo']['url']
content_type = transfer.get('content_type', 'audio/mpeg')
original_content_type = track_info.original_content_type
transcode = (
isinstance(song, audio_metadata.WAV)
or original_content_type != locker_pb2.Track.MP3
)
if (
transcode
or original_content_type == locker_pb2.Track.MP3
):
if transcode:
audio_file = transcode_to_mp3(song, quality='320k')
else:
with open(song.filepath, 'rb') as f:
audio_file = f.read()
# Google Music allows a maximum file size of 300 MiB.
if len(audio_file) >= 300 * 1024 * 1024:
result.update(
{
'success': False,
'reason': 'Maximum allowed file size is 300 MiB.'
}
)
else:
upload_response = self._call(
mm_calls.ScottyAgentPut,
upload_url,
audio_file,
content_type=content_type
).body
if upload_response.get('sessionStatus', {}).get('state'):
result.update(
{
'success': True,
'reason': 'Uploaded',
'song_id': track_sample_response.server_track_id
}
)
else:
result.update(
{
'success': False,
'reason': upload_response # TODO: Better error details.
}
)
else:
# Do not upload files if transcode option set to False.
result.update(
{
'success': False,
'reason': 'Transcoding disabled for file type.'
}
)
self._call(mm_calls.UploadState, self.uploader_id, 'STOPPED')
else:
response_codes = upload_pb2._TRACKSAMPLERESPONSE.enum_types[0]
response_type = response_codes.values_by_number[
track_sample_response.response_code
].name
reason = response_type
result.update(
{
'success': False,
'reason': f'{reason}'
}
)
if response_type == 'ALREADY_EXISTS':
result['song_id'] = track_sample_response.server_track_id
return result | Upload a song to a Google Music library.
Parameters:
song (os.PathLike or str or audio_metadata.Format):
The path to an audio file or an instance of :class:`audio_metadata.Format`.
album_art_path (os.PathLike or str, Optional):
The relative filename or absolute filepath to external album art.
no_sample(bool, Optional):
Don't generate an audio sample from song;
send empty audio sample.
Default: Create an audio sample using ffmpeg/avconv.
Returns:
dict: A result dict with keys: ``'filepath'``, ``'success'``, ``'reason'``, and ``'song_id'`` (if successful). | https://github.com/thebigmunch/google-music/blob/d8a94dab462a1f063fbc1152187a73dc2f0e2a85/src/google_music/clients/musicmanager.py#L196-L425 |
thebigmunch/google-music | src/google_music/clients/base.py | GoogleMusicClient.login | def login(self, username, *, token=None):
"""Log in to Google Music.
Parameters:
username (str, Optional): Your Google Music username.
Used for keeping stored OAuth tokens for multiple accounts separate.
device_id (str, Optional): A mobile device ID or music manager uploader ID.
Default: MAC address is used.
token (dict, Optional): An OAuth token compatible with ``requests-oauthlib``.
Returns:
bool: ``True`` if successfully authenticated, ``False`` if not.
"""
self._username = username
self._oauth(username, token=token)
return self.is_authenticated | python | def login(self, username, *, token=None):
"""Log in to Google Music.
Parameters:
username (str, Optional): Your Google Music username.
Used for keeping stored OAuth tokens for multiple accounts separate.
device_id (str, Optional): A mobile device ID or music manager uploader ID.
Default: MAC address is used.
token (dict, Optional): An OAuth token compatible with ``requests-oauthlib``.
Returns:
bool: ``True`` if successfully authenticated, ``False`` if not.
"""
self._username = username
self._oauth(username, token=token)
return self.is_authenticated | Log in to Google Music.
Parameters:
username (str, Optional): Your Google Music username.
Used for keeping stored OAuth tokens for multiple accounts separate.
device_id (str, Optional): A mobile device ID or music manager uploader ID.
Default: MAC address is used.
token (dict, Optional): An OAuth token compatible with ``requests-oauthlib``.
Returns:
bool: ``True`` if successfully authenticated, ``False`` if not. | https://github.com/thebigmunch/google-music/blob/d8a94dab462a1f063fbc1152187a73dc2f0e2a85/src/google_music/clients/base.py#L107-L124 |
thebigmunch/google-music | src/google_music/clients/base.py | GoogleMusicClient.switch_user | def switch_user(self, username='', *, token=None):
"""Log in to Google Music with a different user.
Parameters:
username (str, Optional): Your Google Music username.
Used for keeping stored OAuth tokens for multiple accounts separate.
token (dict, Optional): An OAuth token compatible with ``requests-oauthlib``.
Returns:
bool: ``True`` if successfully authenticated, ``False`` if not.
"""
if self.logout():
return self.login(username, token=token)
return False | python | def switch_user(self, username='', *, token=None):
"""Log in to Google Music with a different user.
Parameters:
username (str, Optional): Your Google Music username.
Used for keeping stored OAuth tokens for multiple accounts separate.
token (dict, Optional): An OAuth token compatible with ``requests-oauthlib``.
Returns:
bool: ``True`` if successfully authenticated, ``False`` if not.
"""
if self.logout():
return self.login(username, token=token)
return False | Log in to Google Music with a different user.
Parameters:
username (str, Optional): Your Google Music username.
Used for keeping stored OAuth tokens for multiple accounts separate.
token (dict, Optional): An OAuth token compatible with ``requests-oauthlib``.
Returns:
bool: ``True`` if successfully authenticated, ``False`` if not. | https://github.com/thebigmunch/google-music/blob/d8a94dab462a1f063fbc1152187a73dc2f0e2a85/src/google_music/clients/base.py#L136-L151 |
anrosent/LT-code | lt/sampler.py | gen_tau | def gen_tau(S, K, delta):
"""The Robust part of the RSD, we precompute an
array for speed
"""
pivot = floor(K/S)
return [S/K * 1/d for d in range(1, pivot)] \
+ [S/K * log(S/delta)] \
+ [0 for d in range(pivot, K)] | python | def gen_tau(S, K, delta):
"""The Robust part of the RSD, we precompute an
array for speed
"""
pivot = floor(K/S)
return [S/K * 1/d for d in range(1, pivot)] \
+ [S/K * log(S/delta)] \
+ [0 for d in range(pivot, K)] | The Robust part of the RSD, we precompute an
array for speed | https://github.com/anrosent/LT-code/blob/e13a4c927effc90f9d41ab3884f9fcbd95b9450d/lt/sampler.py#L25-L32 |
anrosent/LT-code | lt/sampler.py | gen_mu | def gen_mu(K, delta, c):
"""The Robust Soliton Distribution on the degree of
transmitted blocks
"""
S = c * log(K/delta) * sqrt(K)
tau = gen_tau(S, K, delta)
rho = gen_rho(K)
normalizer = sum(rho) + sum(tau)
return [(rho[d] + tau[d])/normalizer for d in range(K)] | python | def gen_mu(K, delta, c):
"""The Robust Soliton Distribution on the degree of
transmitted blocks
"""
S = c * log(K/delta) * sqrt(K)
tau = gen_tau(S, K, delta)
rho = gen_rho(K)
normalizer = sum(rho) + sum(tau)
return [(rho[d] + tau[d])/normalizer for d in range(K)] | The Robust Soliton Distribution on the degree of
transmitted blocks | https://github.com/anrosent/LT-code/blob/e13a4c927effc90f9d41ab3884f9fcbd95b9450d/lt/sampler.py#L40-L49 |
anrosent/LT-code | lt/sampler.py | gen_rsd_cdf | def gen_rsd_cdf(K, delta, c):
"""The CDF of the RSD on block degree, precomputed for
sampling speed"""
mu = gen_mu(K, delta, c)
return [sum(mu[:d+1]) for d in range(K)] | python | def gen_rsd_cdf(K, delta, c):
"""The CDF of the RSD on block degree, precomputed for
sampling speed"""
mu = gen_mu(K, delta, c)
return [sum(mu[:d+1]) for d in range(K)] | The CDF of the RSD on block degree, precomputed for
sampling speed | https://github.com/anrosent/LT-code/blob/e13a4c927effc90f9d41ab3884f9fcbd95b9450d/lt/sampler.py#L51-L56 |
anrosent/LT-code | lt/sampler.py | PRNG._get_next | def _get_next(self):
"""Executes the next iteration of the PRNG
evolution process, and returns the result
"""
self.state = PRNG_A * self.state % PRNG_M
return self.state | python | def _get_next(self):
"""Executes the next iteration of the PRNG
evolution process, and returns the result
"""
self.state = PRNG_A * self.state % PRNG_M
return self.state | Executes the next iteration of the PRNG
evolution process, and returns the result | https://github.com/anrosent/LT-code/blob/e13a4c927effc90f9d41ab3884f9fcbd95b9450d/lt/sampler.py#L74-L80 |
anrosent/LT-code | lt/sampler.py | PRNG._sample_d | def _sample_d(self):
"""Samples degree given the precomputed
distributions above and the linear PRNG output
"""
p = self._get_next() / PRNG_MAX_RAND
for ix, v in enumerate(self.cdf):
if v > p:
return ix + 1
return ix + 1 | python | def _sample_d(self):
"""Samples degree given the precomputed
distributions above and the linear PRNG output
"""
p = self._get_next() / PRNG_MAX_RAND
for ix, v in enumerate(self.cdf):
if v > p:
return ix + 1
return ix + 1 | Samples degree given the precomputed
distributions above and the linear PRNG output | https://github.com/anrosent/LT-code/blob/e13a4c927effc90f9d41ab3884f9fcbd95b9450d/lt/sampler.py#L82-L91 |
anrosent/LT-code | lt/sampler.py | PRNG.get_src_blocks | def get_src_blocks(self, seed=None):
"""Returns the indices of a set of `d` source blocks
sampled from indices i = 1, ..., K-1 uniformly, where
`d` is sampled from the RSD described above.
"""
if seed:
self.state = seed
blockseed = self.state
d = self._sample_d()
have = 0
nums = set()
while have < d:
num = self._get_next() % self.K
if num not in nums:
nums.add(num)
have += 1
return blockseed, d, nums | python | def get_src_blocks(self, seed=None):
"""Returns the indices of a set of `d` source blocks
sampled from indices i = 1, ..., K-1 uniformly, where
`d` is sampled from the RSD described above.
"""
if seed:
self.state = seed
blockseed = self.state
d = self._sample_d()
have = 0
nums = set()
while have < d:
num = self._get_next() % self.K
if num not in nums:
nums.add(num)
have += 1
return blockseed, d, nums | Returns the indices of a set of `d` source blocks
sampled from indices i = 1, ..., K-1 uniformly, where
`d` is sampled from the RSD described above. | https://github.com/anrosent/LT-code/blob/e13a4c927effc90f9d41ab3884f9fcbd95b9450d/lt/sampler.py#L101-L119 |
anrosent/LT-code | lt/encode/__main__.py | run | def run(fn, blocksize, seed, c, delta):
"""Run the encoder until the channel is broken, signalling that the
receiver has successfully reconstructed the file
"""
with open(fn, 'rb') as f:
for block in encode.encoder(f, blocksize, seed, c, delta):
sys.stdout.buffer.write(block) | python | def run(fn, blocksize, seed, c, delta):
"""Run the encoder until the channel is broken, signalling that the
receiver has successfully reconstructed the file
"""
with open(fn, 'rb') as f:
for block in encode.encoder(f, blocksize, seed, c, delta):
sys.stdout.buffer.write(block) | Run the encoder until the channel is broken, signalling that the
receiver has successfully reconstructed the file | https://github.com/anrosent/LT-code/blob/e13a4c927effc90f9d41ab3884f9fcbd95b9450d/lt/encode/__main__.py#L26-L33 |
thebigmunch/google-music | src/google_music/clients/mobileclient.py | MobileClient.is_subscribed | def is_subscribed(self):
"""The subscription status of the account linked to the :class:`MobileClient` instance."""
subscribed = next(
(
config_item['value'] == 'true'
for config_item in self.config()
if config_item['key'] == 'isNautilusUser'
),
None
)
if subscribed:
self.tier = 'aa'
else:
self.tier = 'fr'
return subscribed | python | def is_subscribed(self):
"""The subscription status of the account linked to the :class:`MobileClient` instance."""
subscribed = next(
(
config_item['value'] == 'true'
for config_item in self.config()
if config_item['key'] == 'isNautilusUser'
),
None
)
if subscribed:
self.tier = 'aa'
else:
self.tier = 'fr'
return subscribed | The subscription status of the account linked to the :class:`MobileClient` instance. | https://github.com/thebigmunch/google-music/blob/d8a94dab462a1f063fbc1152187a73dc2f0e2a85/src/google_music/clients/mobileclient.py#L87-L104 |
thebigmunch/google-music | src/google_music/clients/mobileclient.py | MobileClient.album | def album(self, album_id, *, include_description=True, include_songs=True):
"""Get information about an album.
Parameters:
album_id (str): An album ID. Album IDs start with a 'B'.
include_description (bool, Optional): Include description of the album in the returned dict.
include_songs (bool, Optional): Include songs from the album in the returned dict.
Default: ``True``.
Returns:
dict: Album information.
"""
response = self._call(
mc_calls.FetchAlbum,
album_id,
include_description=include_description,
include_tracks=include_songs
)
album_info = response.body
return album_info | python | def album(self, album_id, *, include_description=True, include_songs=True):
"""Get information about an album.
Parameters:
album_id (str): An album ID. Album IDs start with a 'B'.
include_description (bool, Optional): Include description of the album in the returned dict.
include_songs (bool, Optional): Include songs from the album in the returned dict.
Default: ``True``.
Returns:
dict: Album information.
"""
response = self._call(
mc_calls.FetchAlbum,
album_id,
include_description=include_description,
include_tracks=include_songs
)
album_info = response.body
return album_info | Get information about an album.
Parameters:
album_id (str): An album ID. Album IDs start with a 'B'.
include_description (bool, Optional): Include description of the album in the returned dict.
include_songs (bool, Optional): Include songs from the album in the returned dict.
Default: ``True``.
Returns:
dict: Album information. | https://github.com/thebigmunch/google-music/blob/d8a94dab462a1f063fbc1152187a73dc2f0e2a85/src/google_music/clients/mobileclient.py#L137-L158 |
thebigmunch/google-music | src/google_music/clients/mobileclient.py | MobileClient.artist | def artist(
self, artist_id, *, include_albums=True, num_related_artists=5, num_top_tracks=5
):
"""Get information about an artist.
Parameters:
artist_id (str): An artist ID. Artist IDs start with an 'A'.
include_albums (bool, Optional): Include albums by the artist in returned dict.
Default: ``True``.
num_related_artists (int, Optional): Include up to given number of related artists in returned dict.
Default: ``5``.
num_top_tracks (int, Optional): Include up to given number of top tracks in returned dict.
Default: ``5``.
Returns:
dict: Artist information.
"""
response = self._call(
mc_calls.FetchArtist,
artist_id,
include_albums=include_albums,
num_related_artists=num_related_artists,
num_top_tracks=num_top_tracks
)
artist_info = response.body
return artist_info | python | def artist(
self, artist_id, *, include_albums=True, num_related_artists=5, num_top_tracks=5
):
"""Get information about an artist.
Parameters:
artist_id (str): An artist ID. Artist IDs start with an 'A'.
include_albums (bool, Optional): Include albums by the artist in returned dict.
Default: ``True``.
num_related_artists (int, Optional): Include up to given number of related artists in returned dict.
Default: ``5``.
num_top_tracks (int, Optional): Include up to given number of top tracks in returned dict.
Default: ``5``.
Returns:
dict: Artist information.
"""
response = self._call(
mc_calls.FetchArtist,
artist_id,
include_albums=include_albums,
num_related_artists=num_related_artists,
num_top_tracks=num_top_tracks
)
artist_info = response.body
return artist_info | Get information about an artist.
Parameters:
artist_id (str): An artist ID. Artist IDs start with an 'A'.
include_albums (bool, Optional): Include albums by the artist in returned dict.
Default: ``True``.
num_related_artists (int, Optional): Include up to given number of related artists in returned dict.
Default: ``5``.
num_top_tracks (int, Optional): Include up to given number of top tracks in returned dict.
Default: ``5``.
Returns:
dict: Artist information. | https://github.com/thebigmunch/google-music/blob/d8a94dab462a1f063fbc1152187a73dc2f0e2a85/src/google_music/clients/mobileclient.py#L160-L187 |
thebigmunch/google-music | src/google_music/clients/mobileclient.py | MobileClient.browse_podcasts | def browse_podcasts(self, podcast_genre_id='JZCpodcasttopchartall'):
"""Get the podcasts for a genre from the Podcasts browse tab.
Parameters:
podcast_genre_id (str, Optional): A podcast genre ID as found
in :meth:`browse_podcasts_genres`.
Default: ``'JZCpodcasttopchartall'``.
Returns:
list: Podcast dicts.
"""
response = self._call(
mc_calls.PodcastBrowse,
podcast_genre_id=podcast_genre_id
)
podcast_series_list = response.body.get('series', [])
return podcast_series_list | python | def browse_podcasts(self, podcast_genre_id='JZCpodcasttopchartall'):
"""Get the podcasts for a genre from the Podcasts browse tab.
Parameters:
podcast_genre_id (str, Optional): A podcast genre ID as found
in :meth:`browse_podcasts_genres`.
Default: ``'JZCpodcasttopchartall'``.
Returns:
list: Podcast dicts.
"""
response = self._call(
mc_calls.PodcastBrowse,
podcast_genre_id=podcast_genre_id
)
podcast_series_list = response.body.get('series', [])
return podcast_series_list | Get the podcasts for a genre from the Podcasts browse tab.
Parameters:
podcast_genre_id (str, Optional): A podcast genre ID as found
in :meth:`browse_podcasts_genres`.
Default: ``'JZCpodcasttopchartall'``.
Returns:
list: Podcast dicts. | https://github.com/thebigmunch/google-music/blob/d8a94dab462a1f063fbc1152187a73dc2f0e2a85/src/google_music/clients/mobileclient.py#L189-L207 |
thebigmunch/google-music | src/google_music/clients/mobileclient.py | MobileClient.browse_podcasts_genres | def browse_podcasts_genres(self):
"""Get the genres from the Podcasts browse tab dropdown.
Returns:
list: Genre groups that contain sub groups.
"""
response = self._call(
mc_calls.PodcastBrowseHierarchy
)
genres = response.body.get('groups', [])
return genres | python | def browse_podcasts_genres(self):
"""Get the genres from the Podcasts browse tab dropdown.
Returns:
list: Genre groups that contain sub groups.
"""
response = self._call(
mc_calls.PodcastBrowseHierarchy
)
genres = response.body.get('groups', [])
return genres | Get the genres from the Podcasts browse tab dropdown.
Returns:
list: Genre groups that contain sub groups. | https://github.com/thebigmunch/google-music/blob/d8a94dab462a1f063fbc1152187a73dc2f0e2a85/src/google_music/clients/mobileclient.py#L209-L221 |
thebigmunch/google-music | src/google_music/clients/mobileclient.py | MobileClient.browse_stations | def browse_stations(self, station_category_id):
"""Get the stations for a category from Browse Stations.
Parameters:
station_category_id (str): A station category ID as
found with :meth:`browse_stations_categories`.
Returns:
list: Station dicts.
"""
response = self._call(
mc_calls.BrowseStations,
station_category_id
)
stations = response.body.get('stations', [])
return stations | python | def browse_stations(self, station_category_id):
"""Get the stations for a category from Browse Stations.
Parameters:
station_category_id (str): A station category ID as
found with :meth:`browse_stations_categories`.
Returns:
list: Station dicts.
"""
response = self._call(
mc_calls.BrowseStations,
station_category_id
)
stations = response.body.get('stations', [])
return stations | Get the stations for a category from Browse Stations.
Parameters:
station_category_id (str): A station category ID as
found with :meth:`browse_stations_categories`.
Returns:
list: Station dicts. | https://github.com/thebigmunch/google-music/blob/d8a94dab462a1f063fbc1152187a73dc2f0e2a85/src/google_music/clients/mobileclient.py#L223-L240 |
thebigmunch/google-music | src/google_music/clients/mobileclient.py | MobileClient.browse_stations_categories | def browse_stations_categories(self):
"""Get the categories from Browse Stations.
Returns:
list: Station categories that can contain subcategories.
"""
response = self._call(
mc_calls.BrowseStationCategories
)
station_categories = response.body.get('root', {}).get('subcategories', [])
return station_categories | python | def browse_stations_categories(self):
"""Get the categories from Browse Stations.
Returns:
list: Station categories that can contain subcategories.
"""
response = self._call(
mc_calls.BrowseStationCategories
)
station_categories = response.body.get('root', {}).get('subcategories', [])
return station_categories | Get the categories from Browse Stations.
Returns:
list: Station categories that can contain subcategories. | https://github.com/thebigmunch/google-music/blob/d8a94dab462a1f063fbc1152187a73dc2f0e2a85/src/google_music/clients/mobileclient.py#L242-L254 |
thebigmunch/google-music | src/google_music/clients/mobileclient.py | MobileClient.config | def config(self):
"""Get a listing of mobile client configuration settings."""
response = self._call(
mc_calls.Config
)
config_list = response.body.get('data', {}).get('entries', [])
return config_list | python | def config(self):
"""Get a listing of mobile client configuration settings."""
response = self._call(
mc_calls.Config
)
config_list = response.body.get('data', {}).get('entries', [])
return config_list | Get a listing of mobile client configuration settings. | https://github.com/thebigmunch/google-music/blob/d8a94dab462a1f063fbc1152187a73dc2f0e2a85/src/google_music/clients/mobileclient.py#L256-L264 |
thebigmunch/google-music | src/google_music/clients/mobileclient.py | MobileClient.device_set | def device_set(self, device):
"""Set device used by :class:`MobileClient` instance.
Parameters:
device (dict): A device dict as returned by :meth:`devices`.
"""
if device['id'].startswith('0x'):
self.device_id = device['id'][2:]
elif device['id'].startswith('ios:'):
self.device_id = device['id'].replace(':', '')
else:
self.device_id = device['id'] | python | def device_set(self, device):
"""Set device used by :class:`MobileClient` instance.
Parameters:
device (dict): A device dict as returned by :meth:`devices`.
"""
if device['id'].startswith('0x'):
self.device_id = device['id'][2:]
elif device['id'].startswith('ios:'):
self.device_id = device['id'].replace(':', '')
else:
self.device_id = device['id'] | Set device used by :class:`MobileClient` instance.
Parameters:
device (dict): A device dict as returned by :meth:`devices`. | https://github.com/thebigmunch/google-music/blob/d8a94dab462a1f063fbc1152187a73dc2f0e2a85/src/google_music/clients/mobileclient.py#L280-L292 |
thebigmunch/google-music | src/google_music/clients/mobileclient.py | MobileClient.devices | def devices(self):
"""Get a listing of devices registered to the Google Music account."""
response = self._call(
mc_calls.DeviceManagementInfo
)
registered_devices = response.body.get('data', {}).get('items', [])
return registered_devices | python | def devices(self):
"""Get a listing of devices registered to the Google Music account."""
response = self._call(
mc_calls.DeviceManagementInfo
)
registered_devices = response.body.get('data', {}).get('items', [])
return registered_devices | Get a listing of devices registered to the Google Music account. | https://github.com/thebigmunch/google-music/blob/d8a94dab462a1f063fbc1152187a73dc2f0e2a85/src/google_music/clients/mobileclient.py#L294-L302 |
thebigmunch/google-music | src/google_music/clients/mobileclient.py | MobileClient.explore_genres | def explore_genres(self, parent_genre_id=None):
"""Get a listing of song genres.
Parameters:
parent_genre_id (str, Optional): A genre ID.
If given, a listing of this genre's sub-genres is returned.
Returns:
list: Genre dicts.
"""
response = self._call(
mc_calls.ExploreGenres,
parent_genre_id
)
genre_list = response.body.get('genres', [])
return genre_list | python | def explore_genres(self, parent_genre_id=None):
"""Get a listing of song genres.
Parameters:
parent_genre_id (str, Optional): A genre ID.
If given, a listing of this genre's sub-genres is returned.
Returns:
list: Genre dicts.
"""
response = self._call(
mc_calls.ExploreGenres,
parent_genre_id
)
genre_list = response.body.get('genres', [])
return genre_list | Get a listing of song genres.
Parameters:
parent_genre_id (str, Optional): A genre ID.
If given, a listing of this genre's sub-genres is returned.
Returns:
list: Genre dicts. | https://github.com/thebigmunch/google-music/blob/d8a94dab462a1f063fbc1152187a73dc2f0e2a85/src/google_music/clients/mobileclient.py#L304-L321 |
thebigmunch/google-music | src/google_music/clients/mobileclient.py | MobileClient.explore_tabs | def explore_tabs(self, *, num_items=100, genre_id=None):
"""Get a listing of explore tabs.
Parameters:
num_items (int, Optional): Number of items per tab to return.
Default: ``100``
genre_id (genre_id, Optional): Genre ID from :meth:`explore_genres` to explore.
Default: ``None``.
Returns:
dict: Explore tabs content.
"""
response = self._call(
mc_calls.ExploreTabs,
num_items=num_items,
genre_id=genre_id
)
tab_list = response.body.get('tabs', [])
explore_tabs = {}
for tab in tab_list:
explore_tabs[tab['tab_type'].lower()] = tab
return explore_tabs | python | def explore_tabs(self, *, num_items=100, genre_id=None):
"""Get a listing of explore tabs.
Parameters:
num_items (int, Optional): Number of items per tab to return.
Default: ``100``
genre_id (genre_id, Optional): Genre ID from :meth:`explore_genres` to explore.
Default: ``None``.
Returns:
dict: Explore tabs content.
"""
response = self._call(
mc_calls.ExploreTabs,
num_items=num_items,
genre_id=genre_id
)
tab_list = response.body.get('tabs', [])
explore_tabs = {}
for tab in tab_list:
explore_tabs[tab['tab_type'].lower()] = tab
return explore_tabs | Get a listing of explore tabs.
Parameters:
num_items (int, Optional): Number of items per tab to return.
Default: ``100``
genre_id (genre_id, Optional): Genre ID from :meth:`explore_genres` to explore.
Default: ``None``.
Returns:
dict: Explore tabs content. | https://github.com/thebigmunch/google-music/blob/d8a94dab462a1f063fbc1152187a73dc2f0e2a85/src/google_music/clients/mobileclient.py#L323-L347 |
thebigmunch/google-music | src/google_music/clients/mobileclient.py | MobileClient.listen_now_dismissed_items | def listen_now_dismissed_items(self):
"""Get a listing of items dismissed from Listen Now tab."""
response = self._call(
mc_calls.ListenNowGetDismissedItems
)
dismissed_items = response.body.get('items', [])
return dismissed_items | python | def listen_now_dismissed_items(self):
"""Get a listing of items dismissed from Listen Now tab."""
response = self._call(
mc_calls.ListenNowGetDismissedItems
)
dismissed_items = response.body.get('items', [])
return dismissed_items | Get a listing of items dismissed from Listen Now tab. | https://github.com/thebigmunch/google-music/blob/d8a94dab462a1f063fbc1152187a73dc2f0e2a85/src/google_music/clients/mobileclient.py#L349-L357 |
thebigmunch/google-music | src/google_music/clients/mobileclient.py | MobileClient.listen_now_items | def listen_now_items(self):
"""Get a listing of Listen Now items.
Note:
This does not include situations;
use the :meth:`situations` method instead.
Returns:
dict: With ``albums`` and ``stations`` keys of listen now items.
"""
response = self._call(
mc_calls.ListenNowGetListenNowItems
)
listen_now_item_list = response.body.get('listennow_items', [])
listen_now_items = defaultdict(list)
for item in listen_now_item_list:
type_ = f"{ListenNowItemType(item['type']).name}s"
listen_now_items[type_].append(item)
return dict(listen_now_items) | python | def listen_now_items(self):
"""Get a listing of Listen Now items.
Note:
This does not include situations;
use the :meth:`situations` method instead.
Returns:
dict: With ``albums`` and ``stations`` keys of listen now items.
"""
response = self._call(
mc_calls.ListenNowGetListenNowItems
)
listen_now_item_list = response.body.get('listennow_items', [])
listen_now_items = defaultdict(list)
for item in listen_now_item_list:
type_ = f"{ListenNowItemType(item['type']).name}s"
listen_now_items[type_].append(item)
return dict(listen_now_items) | Get a listing of Listen Now items.
Note:
This does not include situations;
use the :meth:`situations` method instead.
Returns:
dict: With ``albums`` and ``stations`` keys of listen now items. | https://github.com/thebigmunch/google-music/blob/d8a94dab462a1f063fbc1152187a73dc2f0e2a85/src/google_music/clients/mobileclient.py#L359-L380 |
thebigmunch/google-music | src/google_music/clients/mobileclient.py | MobileClient.playlist_song | def playlist_song(self, playlist_song_id):
"""Get information about a playlist song.
Note:
This returns the playlist entry information only.
For full song metadata, use :meth:`song` with
the ``'trackId'`` field.
Parameters:
playlist_song_id (str): A playlist song ID.
Returns:
dict: Playlist song information.
"""
playlist_song_info = next(
(
playlist_song
for playlist in self.playlists(include_songs=True)
for playlist_song in playlist['tracks']
if playlist_song['id'] == playlist_song_id
),
None
)
return playlist_song_info | python | def playlist_song(self, playlist_song_id):
"""Get information about a playlist song.
Note:
This returns the playlist entry information only.
For full song metadata, use :meth:`song` with
the ``'trackId'`` field.
Parameters:
playlist_song_id (str): A playlist song ID.
Returns:
dict: Playlist song information.
"""
playlist_song_info = next(
(
playlist_song
for playlist in self.playlists(include_songs=True)
for playlist_song in playlist['tracks']
if playlist_song['id'] == playlist_song_id
),
None
)
return playlist_song_info | Get information about a playlist song.
Note:
This returns the playlist entry information only.
For full song metadata, use :meth:`song` with
the ``'trackId'`` field.
Parameters:
playlist_song_id (str): A playlist song ID.
Returns:
dict: Playlist song information. | https://github.com/thebigmunch/google-music/blob/d8a94dab462a1f063fbc1152187a73dc2f0e2a85/src/google_music/clients/mobileclient.py#L394-L419 |
thebigmunch/google-music | src/google_music/clients/mobileclient.py | MobileClient.playlist_song_add | def playlist_song_add(
self,
song,
playlist,
*,
after=None,
before=None,
index=None,
position=None
):
"""Add a song to a playlist.
Note:
* Provide no optional arguments to add to end.
* Provide playlist song dicts for ``after`` and/or ``before``.
* Provide a zero-based ``index``.
* Provide a one-based ``position``.
Songs are inserted *at* given index or position.
It's also possible to add to the end by using
``len(songs)`` for index or ``len(songs) + 1`` for position.
Parameters:
song (dict): A song dict.
playlist (dict): A playlist dict.
after (dict, Optional): A playlist song dict ``songs`` will follow.
before (dict, Optional): A playlist song dict ``songs`` will precede.
index (int, Optional): The zero-based index position to insert ``song``.
position (int, Optional): The one-based position to insert ``song``.
Returns:
dict: Playlist dict including songs.
"""
prev, next_ = get_ple_prev_next(
self.playlist_songs(playlist),
after=after,
before=before,
index=index,
position=position
)
if 'storeId' in song:
song_id = song['storeId']
elif 'trackId' in song:
song_id = song['trackId']
else:
song_id = song['id']
mutation = mc_calls.PlaylistEntriesBatch.create(
song_id, playlist['id'],
preceding_entry_id=prev.get('id'),
following_entry_id=next_.get('id')
)
self._call(mc_calls.PlaylistEntriesBatch, mutation)
return self.playlist(playlist['id'], include_songs=True) | python | def playlist_song_add(
self,
song,
playlist,
*,
after=None,
before=None,
index=None,
position=None
):
"""Add a song to a playlist.
Note:
* Provide no optional arguments to add to end.
* Provide playlist song dicts for ``after`` and/or ``before``.
* Provide a zero-based ``index``.
* Provide a one-based ``position``.
Songs are inserted *at* given index or position.
It's also possible to add to the end by using
``len(songs)`` for index or ``len(songs) + 1`` for position.
Parameters:
song (dict): A song dict.
playlist (dict): A playlist dict.
after (dict, Optional): A playlist song dict ``songs`` will follow.
before (dict, Optional): A playlist song dict ``songs`` will precede.
index (int, Optional): The zero-based index position to insert ``song``.
position (int, Optional): The one-based position to insert ``song``.
Returns:
dict: Playlist dict including songs.
"""
prev, next_ = get_ple_prev_next(
self.playlist_songs(playlist),
after=after,
before=before,
index=index,
position=position
)
if 'storeId' in song:
song_id = song['storeId']
elif 'trackId' in song:
song_id = song['trackId']
else:
song_id = song['id']
mutation = mc_calls.PlaylistEntriesBatch.create(
song_id, playlist['id'],
preceding_entry_id=prev.get('id'),
following_entry_id=next_.get('id')
)
self._call(mc_calls.PlaylistEntriesBatch, mutation)
return self.playlist(playlist['id'], include_songs=True) | Add a song to a playlist.
Note:
* Provide no optional arguments to add to end.
* Provide playlist song dicts for ``after`` and/or ``before``.
* Provide a zero-based ``index``.
* Provide a one-based ``position``.
Songs are inserted *at* given index or position.
It's also possible to add to the end by using
``len(songs)`` for index or ``len(songs) + 1`` for position.
Parameters:
song (dict): A song dict.
playlist (dict): A playlist dict.
after (dict, Optional): A playlist song dict ``songs`` will follow.
before (dict, Optional): A playlist song dict ``songs`` will precede.
index (int, Optional): The zero-based index position to insert ``song``.
position (int, Optional): The one-based position to insert ``song``.
Returns:
dict: Playlist dict including songs. | https://github.com/thebigmunch/google-music/blob/d8a94dab462a1f063fbc1152187a73dc2f0e2a85/src/google_music/clients/mobileclient.py#L421-L477 |
thebigmunch/google-music | src/google_music/clients/mobileclient.py | MobileClient.playlist_songs_add | def playlist_songs_add(
self,
songs,
playlist,
*,
after=None,
before=None,
index=None,
position=None
):
"""Add songs to a playlist.
Note:
* Provide no optional arguments to add to end.
* Provide playlist song dicts for ``after`` and/or ``before``.
* Provide a zero-based ``index``.
* Provide a one-based ``position``.
Songs are inserted *at* given index or position.
It's also possible to add to the end by using
``len(songs)`` for index or ``len(songs) + 1`` for position.
Parameters:
songs (list): A list of song dicts.
playlist (dict): A playlist dict.
after (dict, Optional): A playlist song dict ``songs`` will follow.
before (dict, Optional): A playlist song dict ``songs`` will precede.
index (int, Optional): The zero-based index position to insert ``songs``.
position (int, Optional): The one-based position to insert ``songs``.
Returns:
dict: Playlist dict including songs.
"""
playlist_songs = self.playlist_songs(playlist)
prev, next_ = get_ple_prev_next(
playlist_songs,
after=after,
before=before,
index=index,
position=position
)
songs_len = len(songs)
for i, song in enumerate(songs):
if 'storeId' in song:
song_id = song['storeId']
elif 'trackId' in song:
song_id = song['trackId']
else:
song_id = song['id']
mutation = mc_calls.PlaylistEntriesBatch.create(
song_id, playlist['id'],
preceding_entry_id=prev.get('id'),
following_entry_id=next_.get('id')
)
response = self._call(mc_calls.PlaylistEntriesBatch, mutation)
result = response.body['mutate_response'][0]
# TODO: Proper exception on failure.
if result['response_code'] != 'OK':
break
if i < songs_len - 1:
while True:
prev = self.playlist_song(result['id'])
if prev:
break
return self.playlist(playlist['id'], include_songs=True) | python | def playlist_songs_add(
self,
songs,
playlist,
*,
after=None,
before=None,
index=None,
position=None
):
"""Add songs to a playlist.
Note:
* Provide no optional arguments to add to end.
* Provide playlist song dicts for ``after`` and/or ``before``.
* Provide a zero-based ``index``.
* Provide a one-based ``position``.
Songs are inserted *at* given index or position.
It's also possible to add to the end by using
``len(songs)`` for index or ``len(songs) + 1`` for position.
Parameters:
songs (list): A list of song dicts.
playlist (dict): A playlist dict.
after (dict, Optional): A playlist song dict ``songs`` will follow.
before (dict, Optional): A playlist song dict ``songs`` will precede.
index (int, Optional): The zero-based index position to insert ``songs``.
position (int, Optional): The one-based position to insert ``songs``.
Returns:
dict: Playlist dict including songs.
"""
playlist_songs = self.playlist_songs(playlist)
prev, next_ = get_ple_prev_next(
playlist_songs,
after=after,
before=before,
index=index,
position=position
)
songs_len = len(songs)
for i, song in enumerate(songs):
if 'storeId' in song:
song_id = song['storeId']
elif 'trackId' in song:
song_id = song['trackId']
else:
song_id = song['id']
mutation = mc_calls.PlaylistEntriesBatch.create(
song_id, playlist['id'],
preceding_entry_id=prev.get('id'),
following_entry_id=next_.get('id')
)
response = self._call(mc_calls.PlaylistEntriesBatch, mutation)
result = response.body['mutate_response'][0]
# TODO: Proper exception on failure.
if result['response_code'] != 'OK':
break
if i < songs_len - 1:
while True:
prev = self.playlist_song(result['id'])
if prev:
break
return self.playlist(playlist['id'], include_songs=True) | Add songs to a playlist.
Note:
* Provide no optional arguments to add to end.
* Provide playlist song dicts for ``after`` and/or ``before``.
* Provide a zero-based ``index``.
* Provide a one-based ``position``.
Songs are inserted *at* given index or position.
It's also possible to add to the end by using
``len(songs)`` for index or ``len(songs) + 1`` for position.
Parameters:
songs (list): A list of song dicts.
playlist (dict): A playlist dict.
after (dict, Optional): A playlist song dict ``songs`` will follow.
before (dict, Optional): A playlist song dict ``songs`` will precede.
index (int, Optional): The zero-based index position to insert ``songs``.
position (int, Optional): The one-based position to insert ``songs``.
Returns:
dict: Playlist dict including songs. | https://github.com/thebigmunch/google-music/blob/d8a94dab462a1f063fbc1152187a73dc2f0e2a85/src/google_music/clients/mobileclient.py#L479-L550 |
thebigmunch/google-music | src/google_music/clients/mobileclient.py | MobileClient.playlist_song_delete | def playlist_song_delete(self, playlist_song):
"""Delete song from playlist.
Parameters:
playlist_song (str): A playlist song dict.
Returns:
dict: Playlist dict including songs.
"""
self.playlist_songs_delete([playlist_song])
return self.playlist(playlist_song['playlistId'], include_songs=True) | python | def playlist_song_delete(self, playlist_song):
"""Delete song from playlist.
Parameters:
playlist_song (str): A playlist song dict.
Returns:
dict: Playlist dict including songs.
"""
self.playlist_songs_delete([playlist_song])
return self.playlist(playlist_song['playlistId'], include_songs=True) | Delete song from playlist.
Parameters:
playlist_song (str): A playlist song dict.
Returns:
dict: Playlist dict including songs. | https://github.com/thebigmunch/google-music/blob/d8a94dab462a1f063fbc1152187a73dc2f0e2a85/src/google_music/clients/mobileclient.py#L552-L564 |
thebigmunch/google-music | src/google_music/clients/mobileclient.py | MobileClient.playlist_songs_delete | def playlist_songs_delete(self, playlist_songs):
"""Delete songs from playlist.
Parameters:
playlist_songs (list): A list of playlist song dicts.
Returns:
dict: Playlist dict including songs.
"""
if not more_itertools.all_equal(
playlist_song['playlistId']
for playlist_song in playlist_songs
):
raise ValueError(
"All 'playlist_songs' must be from the same playlist."
)
mutations = [mc_calls.PlaylistEntriesBatch.delete(playlist_song['id']) for playlist_song in playlist_songs]
self._call(mc_calls.PlaylistEntriesBatch, mutations)
return self.playlist(playlist_songs[0]['playlistId'], include_songs=True) | python | def playlist_songs_delete(self, playlist_songs):
"""Delete songs from playlist.
Parameters:
playlist_songs (list): A list of playlist song dicts.
Returns:
dict: Playlist dict including songs.
"""
if not more_itertools.all_equal(
playlist_song['playlistId']
for playlist_song in playlist_songs
):
raise ValueError(
"All 'playlist_songs' must be from the same playlist."
)
mutations = [mc_calls.PlaylistEntriesBatch.delete(playlist_song['id']) for playlist_song in playlist_songs]
self._call(mc_calls.PlaylistEntriesBatch, mutations)
return self.playlist(playlist_songs[0]['playlistId'], include_songs=True) | Delete songs from playlist.
Parameters:
playlist_songs (list): A list of playlist song dicts.
Returns:
dict: Playlist dict including songs. | https://github.com/thebigmunch/google-music/blob/d8a94dab462a1f063fbc1152187a73dc2f0e2a85/src/google_music/clients/mobileclient.py#L566-L587 |
thebigmunch/google-music | src/google_music/clients/mobileclient.py | MobileClient.playlist_song_move | def playlist_song_move(
self,
playlist_song,
*,
after=None,
before=None,
index=None,
position=None
):
"""Move a song in a playlist.
Note:
* Provide no optional arguments to move to end.
* Provide playlist song dicts for ``after`` and/or ``before``.
* Provide a zero-based ``index``.
* Provide a one-based ``position``.
Songs are inserted *at* given index or position.
It's also possible to move to the end by using
``len(songs)`` for index or ``len(songs) + 1`` for position.
Parameters:
playlist_song (dict): A playlist song dict.
after (dict, Optional): A playlist song dict ``songs`` will follow.
before (dict, Optional): A playlist song dict ``songs`` will precede.
index (int, Optional): The zero-based index position to insert ``song``.
position (int, Optional): The one-based position to insert ``song``.
Returns:
dict: Playlist dict including songs.
"""
playlist_songs = self.playlist(
playlist_song['playlistId'],
include_songs=True
)['tracks']
prev, next_ = get_ple_prev_next(
playlist_songs,
after=after,
before=before,
index=index,
position=position
)
mutation = mc_calls.PlaylistEntriesBatch.update(
playlist_song,
preceding_entry_id=prev.get('id'),
following_entry_id=next_.get('id')
)
self._call(mc_calls.PlaylistEntriesBatch, mutation)
return self.playlist(playlist_song['playlistId'], include_songs=True) | python | def playlist_song_move(
self,
playlist_song,
*,
after=None,
before=None,
index=None,
position=None
):
"""Move a song in a playlist.
Note:
* Provide no optional arguments to move to end.
* Provide playlist song dicts for ``after`` and/or ``before``.
* Provide a zero-based ``index``.
* Provide a one-based ``position``.
Songs are inserted *at* given index or position.
It's also possible to move to the end by using
``len(songs)`` for index or ``len(songs) + 1`` for position.
Parameters:
playlist_song (dict): A playlist song dict.
after (dict, Optional): A playlist song dict ``songs`` will follow.
before (dict, Optional): A playlist song dict ``songs`` will precede.
index (int, Optional): The zero-based index position to insert ``song``.
position (int, Optional): The one-based position to insert ``song``.
Returns:
dict: Playlist dict including songs.
"""
playlist_songs = self.playlist(
playlist_song['playlistId'],
include_songs=True
)['tracks']
prev, next_ = get_ple_prev_next(
playlist_songs,
after=after,
before=before,
index=index,
position=position
)
mutation = mc_calls.PlaylistEntriesBatch.update(
playlist_song,
preceding_entry_id=prev.get('id'),
following_entry_id=next_.get('id')
)
self._call(mc_calls.PlaylistEntriesBatch, mutation)
return self.playlist(playlist_song['playlistId'], include_songs=True) | Move a song in a playlist.
Note:
* Provide no optional arguments to move to end.
* Provide playlist song dicts for ``after`` and/or ``before``.
* Provide a zero-based ``index``.
* Provide a one-based ``position``.
Songs are inserted *at* given index or position.
It's also possible to move to the end by using
``len(songs)`` for index or ``len(songs) + 1`` for position.
Parameters:
playlist_song (dict): A playlist song dict.
after (dict, Optional): A playlist song dict ``songs`` will follow.
before (dict, Optional): A playlist song dict ``songs`` will precede.
index (int, Optional): The zero-based index position to insert ``song``.
position (int, Optional): The one-based position to insert ``song``.
Returns:
dict: Playlist dict including songs. | https://github.com/thebigmunch/google-music/blob/d8a94dab462a1f063fbc1152187a73dc2f0e2a85/src/google_music/clients/mobileclient.py#L589-L641 |
thebigmunch/google-music | src/google_music/clients/mobileclient.py | MobileClient.playlist_songs_move | def playlist_songs_move(
self,
playlist_songs,
*,
after=None,
before=None,
index=None,
position=None
):
"""Move songs in a playlist.
Note:
* Provide no optional arguments to move to end.
* Provide playlist song dicts for ``after`` and/or ``before``.
* Provide a zero-based ``index``.
* Provide a one-based ``position``.
Songs are inserted *at* given index or position.
It's also possible to move to the end by using
``len(songs)`` for index or ``len(songs) + 1`` for position.
Parameters:
playlist_songs (list): A list of playlist song dicts.
after (dict, Optional): A playlist song dict ``songs`` will follow.
before (dict, Optional): A playlist song dict ``songs`` will precede.
index (int, Optional): The zero-based index position to insert ``songs``.
position (int, Optional): The one-based position to insert ``songs``.
Returns:
dict: Playlist dict including songs.
"""
if not more_itertools.all_equal(
playlist_song['playlistId']
for playlist_song in playlist_songs
):
raise ValueError(
"All 'playlist_songs' must be from the same playlist."
)
playlist = self.playlist(
playlist_songs[0]['playlistId'],
include_songs=True
)
prev, next_ = get_ple_prev_next(
playlist['tracks'],
after=after,
before=before,
index=index,
position=position
)
playlist_songs_len = len(playlist_songs)
for i, playlist_song in enumerate(playlist_songs):
mutation = mc_calls.PlaylistEntriesBatch.update(
playlist_song,
preceding_entry_id=prev.get('id'),
following_entry_id=next_.get('id')
)
response = self._call(mc_calls.PlaylistEntriesBatch, mutation)
result = response.body['mutate_response'][0]
# TODO: Proper exception on failure.
if result['response_code'] != 'OK':
break
if i < playlist_songs_len - 1:
while True:
prev = self.playlist_song(result['id'])
if prev:
break
return self.playlist(playlist_songs[0]['playlistId'], include_songs=True) | python | def playlist_songs_move(
self,
playlist_songs,
*,
after=None,
before=None,
index=None,
position=None
):
"""Move songs in a playlist.
Note:
* Provide no optional arguments to move to end.
* Provide playlist song dicts for ``after`` and/or ``before``.
* Provide a zero-based ``index``.
* Provide a one-based ``position``.
Songs are inserted *at* given index or position.
It's also possible to move to the end by using
``len(songs)`` for index or ``len(songs) + 1`` for position.
Parameters:
playlist_songs (list): A list of playlist song dicts.
after (dict, Optional): A playlist song dict ``songs`` will follow.
before (dict, Optional): A playlist song dict ``songs`` will precede.
index (int, Optional): The zero-based index position to insert ``songs``.
position (int, Optional): The one-based position to insert ``songs``.
Returns:
dict: Playlist dict including songs.
"""
if not more_itertools.all_equal(
playlist_song['playlistId']
for playlist_song in playlist_songs
):
raise ValueError(
"All 'playlist_songs' must be from the same playlist."
)
playlist = self.playlist(
playlist_songs[0]['playlistId'],
include_songs=True
)
prev, next_ = get_ple_prev_next(
playlist['tracks'],
after=after,
before=before,
index=index,
position=position
)
playlist_songs_len = len(playlist_songs)
for i, playlist_song in enumerate(playlist_songs):
mutation = mc_calls.PlaylistEntriesBatch.update(
playlist_song,
preceding_entry_id=prev.get('id'),
following_entry_id=next_.get('id')
)
response = self._call(mc_calls.PlaylistEntriesBatch, mutation)
result = response.body['mutate_response'][0]
# TODO: Proper exception on failure.
if result['response_code'] != 'OK':
break
if i < playlist_songs_len - 1:
while True:
prev = self.playlist_song(result['id'])
if prev:
break
return self.playlist(playlist_songs[0]['playlistId'], include_songs=True) | Move songs in a playlist.
Note:
* Provide no optional arguments to move to end.
* Provide playlist song dicts for ``after`` and/or ``before``.
* Provide a zero-based ``index``.
* Provide a one-based ``position``.
Songs are inserted *at* given index or position.
It's also possible to move to the end by using
``len(songs)`` for index or ``len(songs) + 1`` for position.
Parameters:
playlist_songs (list): A list of playlist song dicts.
after (dict, Optional): A playlist song dict ``songs`` will follow.
before (dict, Optional): A playlist song dict ``songs`` will precede.
index (int, Optional): The zero-based index position to insert ``songs``.
position (int, Optional): The one-based position to insert ``songs``.
Returns:
dict: Playlist dict including songs. | https://github.com/thebigmunch/google-music/blob/d8a94dab462a1f063fbc1152187a73dc2f0e2a85/src/google_music/clients/mobileclient.py#L643-L716 |
thebigmunch/google-music | src/google_music/clients/mobileclient.py | MobileClient.playlist_songs | def playlist_songs(self, playlist):
"""Get a listing of songs from a playlist.
Paramters:
playlist (dict): A playlist dict.
Returns:
list: Playlist song dicts.
"""
playlist_type = playlist.get('type')
playlist_song_list = []
if playlist_type in ('USER_GENERATED', None):
start_token = None
playlist_song_list = []
while True:
response = self._call(
mc_calls.PlaylistEntryFeed,
max_results=49995,
start_token=start_token
)
items = response.body.get('data', {}).get('items', [])
if items:
playlist_song_list.extend(items)
start_token = response.body.get('nextPageToken')
if start_token is None:
break
elif playlist_type == 'SHARED':
playlist_share_token = playlist['shareToken']
start_token = None
playlist_song_list = []
while True:
response = self._call(
mc_calls.PlaylistEntriesShared,
playlist_share_token,
max_results=49995,
start_token=start_token
)
entry = response.body['entries'][0]
items = entry.get('playlistEntry', [])
if items:
playlist_song_list.extend(items)
start_token = entry.get('nextPageToken')
if start_token is None:
break
playlist_song_list.sort(key=itemgetter('absolutePosition'))
return playlist_song_list | python | def playlist_songs(self, playlist):
"""Get a listing of songs from a playlist.
Paramters:
playlist (dict): A playlist dict.
Returns:
list: Playlist song dicts.
"""
playlist_type = playlist.get('type')
playlist_song_list = []
if playlist_type in ('USER_GENERATED', None):
start_token = None
playlist_song_list = []
while True:
response = self._call(
mc_calls.PlaylistEntryFeed,
max_results=49995,
start_token=start_token
)
items = response.body.get('data', {}).get('items', [])
if items:
playlist_song_list.extend(items)
start_token = response.body.get('nextPageToken')
if start_token is None:
break
elif playlist_type == 'SHARED':
playlist_share_token = playlist['shareToken']
start_token = None
playlist_song_list = []
while True:
response = self._call(
mc_calls.PlaylistEntriesShared,
playlist_share_token,
max_results=49995,
start_token=start_token
)
entry = response.body['entries'][0]
items = entry.get('playlistEntry', [])
if items:
playlist_song_list.extend(items)
start_token = entry.get('nextPageToken')
if start_token is None:
break
playlist_song_list.sort(key=itemgetter('absolutePosition'))
return playlist_song_list | Get a listing of songs from a playlist.
Paramters:
playlist (dict): A playlist dict.
Returns:
list: Playlist song dicts. | https://github.com/thebigmunch/google-music/blob/d8a94dab462a1f063fbc1152187a73dc2f0e2a85/src/google_music/clients/mobileclient.py#L718-L772 |
thebigmunch/google-music | src/google_music/clients/mobileclient.py | MobileClient.playlist | def playlist(self, playlist_id, *, include_songs=False):
"""Get information about a playlist.
Parameters:
playlist_id (str): A playlist ID.
include_songs (bool, Optional): Include songs from
the playlist in the returned dict.
Default: ``False``
Returns:
dict: Playlist information.
"""
playlist_info = next(
(
playlist
for playlist in self.playlists(include_songs=include_songs)
if playlist['id'] == playlist_id
),
None
)
return playlist_info | python | def playlist(self, playlist_id, *, include_songs=False):
"""Get information about a playlist.
Parameters:
playlist_id (str): A playlist ID.
include_songs (bool, Optional): Include songs from
the playlist in the returned dict.
Default: ``False``
Returns:
dict: Playlist information.
"""
playlist_info = next(
(
playlist
for playlist in self.playlists(include_songs=include_songs)
if playlist['id'] == playlist_id
),
None
)
return playlist_info | Get information about a playlist.
Parameters:
playlist_id (str): A playlist ID.
include_songs (bool, Optional): Include songs from
the playlist in the returned dict.
Default: ``False``
Returns:
dict: Playlist information. | https://github.com/thebigmunch/google-music/blob/d8a94dab462a1f063fbc1152187a73dc2f0e2a85/src/google_music/clients/mobileclient.py#L774-L796 |
thebigmunch/google-music | src/google_music/clients/mobileclient.py | MobileClient.playlist_create | def playlist_create(
self,
name,
description='',
*,
make_public=False,
songs=None
):
"""Create a playlist.
Parameters:
name (str): Name to give the playlist.
description (str): Description to give the playlist.
make_public (bool, Optional): If ``True`` and account has a subscription,
make playlist public.
Default: ``False``
songs (list, Optional): A list of song dicts to add to the playlist.
Returns:
dict: Playlist information.
"""
share_state = 'PUBLIC' if make_public else 'PRIVATE'
playlist = self._call(
mc_calls.PlaylistsCreate,
name,
description,
share_state
).body
if songs:
playlist = self.playlist_songs_add(songs, playlist)
return playlist | python | def playlist_create(
self,
name,
description='',
*,
make_public=False,
songs=None
):
"""Create a playlist.
Parameters:
name (str): Name to give the playlist.
description (str): Description to give the playlist.
make_public (bool, Optional): If ``True`` and account has a subscription,
make playlist public.
Default: ``False``
songs (list, Optional): A list of song dicts to add to the playlist.
Returns:
dict: Playlist information.
"""
share_state = 'PUBLIC' if make_public else 'PRIVATE'
playlist = self._call(
mc_calls.PlaylistsCreate,
name,
description,
share_state
).body
if songs:
playlist = self.playlist_songs_add(songs, playlist)
return playlist | Create a playlist.
Parameters:
name (str): Name to give the playlist.
description (str): Description to give the playlist.
make_public (bool, Optional): If ``True`` and account has a subscription,
make playlist public.
Default: ``False``
songs (list, Optional): A list of song dicts to add to the playlist.
Returns:
dict: Playlist information. | https://github.com/thebigmunch/google-music/blob/d8a94dab462a1f063fbc1152187a73dc2f0e2a85/src/google_music/clients/mobileclient.py#L798-L832 |
thebigmunch/google-music | src/google_music/clients/mobileclient.py | MobileClient.playlist_edit | def playlist_edit(self, playlist, *, name=None, description=None, public=None):
"""Edit playlist(s).
Parameters:
playlist (dict): A playlist dict.
name (str): Name to give the playlist.
description (str, Optional): Description to give the playlist.
make_public (bool, Optional): If ``True`` and account has a subscription,
make playlist public.
Default: ``False``
Returns:
dict: Playlist information.
"""
if all(
value is None
for value in (name, description, public)
):
raise ValueError(
'At least one of name, description, or public must be provided'
)
playlist_id = playlist['id']
playlist = self.playlist(playlist_id)
name = name if name is not None else playlist['name']
description = (
description if description is not None else playlist['description']
)
share_state = 'PUBLIC' if public else playlist['accessControlled']
playlist = self._call(
mc_calls.PlaylistsUpdate,
playlist_id,
name,
description,
share_state
).body
return playlist | python | def playlist_edit(self, playlist, *, name=None, description=None, public=None):
"""Edit playlist(s).
Parameters:
playlist (dict): A playlist dict.
name (str): Name to give the playlist.
description (str, Optional): Description to give the playlist.
make_public (bool, Optional): If ``True`` and account has a subscription,
make playlist public.
Default: ``False``
Returns:
dict: Playlist information.
"""
if all(
value is None
for value in (name, description, public)
):
raise ValueError(
'At least one of name, description, or public must be provided'
)
playlist_id = playlist['id']
playlist = self.playlist(playlist_id)
name = name if name is not None else playlist['name']
description = (
description if description is not None else playlist['description']
)
share_state = 'PUBLIC' if public else playlist['accessControlled']
playlist = self._call(
mc_calls.PlaylistsUpdate,
playlist_id,
name,
description,
share_state
).body
return playlist | Edit playlist(s).
Parameters:
playlist (dict): A playlist dict.
name (str): Name to give the playlist.
description (str, Optional): Description to give the playlist.
make_public (bool, Optional): If ``True`` and account has a subscription,
make playlist public.
Default: ``False``
Returns:
dict: Playlist information. | https://github.com/thebigmunch/google-music/blob/d8a94dab462a1f063fbc1152187a73dc2f0e2a85/src/google_music/clients/mobileclient.py#L847-L887 |
thebigmunch/google-music | src/google_music/clients/mobileclient.py | MobileClient.playlist_subscribe | def playlist_subscribe(self, playlist):
"""Subscribe to a public playlist.
Parameters:
playlist (dict): A public playlist dict.
Returns:
dict: Playlist information.
"""
mutation = mc_calls.PlaylistBatch.create(
playlist['name'],
playlist['description'],
'SHARED',
owner_name=playlist.get('ownerName', ''),
share_token=playlist['shareToken']
)
response_body = self._call(
mc_calls.PlaylistBatch,
mutation
).body
playlist_id = response_body['mutate_response'][0]['id']
return self.playlist(playlist_id) | python | def playlist_subscribe(self, playlist):
"""Subscribe to a public playlist.
Parameters:
playlist (dict): A public playlist dict.
Returns:
dict: Playlist information.
"""
mutation = mc_calls.PlaylistBatch.create(
playlist['name'],
playlist['description'],
'SHARED',
owner_name=playlist.get('ownerName', ''),
share_token=playlist['shareToken']
)
response_body = self._call(
mc_calls.PlaylistBatch,
mutation
).body
playlist_id = response_body['mutate_response'][0]['id']
return self.playlist(playlist_id) | Subscribe to a public playlist.
Parameters:
playlist (dict): A public playlist dict.
Returns:
dict: Playlist information. | https://github.com/thebigmunch/google-music/blob/d8a94dab462a1f063fbc1152187a73dc2f0e2a85/src/google_music/clients/mobileclient.py#L889-L914 |
thebigmunch/google-music | src/google_music/clients/mobileclient.py | MobileClient.playlists | def playlists(self, *, include_songs=False):
"""Get a listing of library playlists.
Parameters:
include_songs (bool, Optional): Include songs in the returned playlist dicts.
Default: ``False``.
Returns:
list: A list of playlist dicts.
"""
playlist_list = []
for chunk in self.playlists_iter(page_size=49995):
for playlist in chunk:
if include_songs:
playlist['tracks'] = self.playlist_songs(playlist)
playlist_list.append(playlist)
return playlist_list | python | def playlists(self, *, include_songs=False):
"""Get a listing of library playlists.
Parameters:
include_songs (bool, Optional): Include songs in the returned playlist dicts.
Default: ``False``.
Returns:
list: A list of playlist dicts.
"""
playlist_list = []
for chunk in self.playlists_iter(page_size=49995):
for playlist in chunk:
if include_songs:
playlist['tracks'] = self.playlist_songs(playlist)
playlist_list.append(playlist)
return playlist_list | Get a listing of library playlists.
Parameters:
include_songs (bool, Optional): Include songs in the returned playlist dicts.
Default: ``False``.
Returns:
list: A list of playlist dicts. | https://github.com/thebigmunch/google-music/blob/d8a94dab462a1f063fbc1152187a73dc2f0e2a85/src/google_music/clients/mobileclient.py#L926-L945 |
thebigmunch/google-music | src/google_music/clients/mobileclient.py | MobileClient.playlists_iter | def playlists_iter(self, *, start_token=None, page_size=250):
"""Get a paged iterator of library playlists.
Parameters:
start_token (str): The token of the page to return.
Default: Not sent to get first page.
page_size (int, Optional): The maximum number of results per returned page.
Max allowed is ``49995``.
Default: ``250``
Yields:
list: Playlist dicts.
"""
start_token = None
while True:
response = self._call(
mc_calls.PlaylistFeed,
max_results=page_size,
start_token=start_token
)
items = response.body.get('data', {}).get('items', [])
if items:
yield items
start_token = response.body.get('nextPageToken')
if start_token is None:
break | python | def playlists_iter(self, *, start_token=None, page_size=250):
"""Get a paged iterator of library playlists.
Parameters:
start_token (str): The token of the page to return.
Default: Not sent to get first page.
page_size (int, Optional): The maximum number of results per returned page.
Max allowed is ``49995``.
Default: ``250``
Yields:
list: Playlist dicts.
"""
start_token = None
while True:
response = self._call(
mc_calls.PlaylistFeed,
max_results=page_size,
start_token=start_token
)
items = response.body.get('data', {}).get('items', [])
if items:
yield items
start_token = response.body.get('nextPageToken')
if start_token is None:
break | Get a paged iterator of library playlists.
Parameters:
start_token (str): The token of the page to return.
Default: Not sent to get first page.
page_size (int, Optional): The maximum number of results per returned page.
Max allowed is ``49995``.
Default: ``250``
Yields:
list: Playlist dicts. | https://github.com/thebigmunch/google-music/blob/d8a94dab462a1f063fbc1152187a73dc2f0e2a85/src/google_music/clients/mobileclient.py#L947-L976 |
thebigmunch/google-music | src/google_music/clients/mobileclient.py | MobileClient.podcast | def podcast(self, podcast_series_id, *, max_episodes=50):
"""Get information about a podcast series.
Parameters:
podcast_series_id (str): A podcast series ID.
max_episodes (int, Optional): Include up to given number of episodes in returned dict.
Default: ``50``
Returns:
dict: Podcast series information.
"""
podcast_info = self._call(
mc_calls.PodcastFetchSeries,
podcast_series_id,
max_episodes=max_episodes
).body
return podcast_info | python | def podcast(self, podcast_series_id, *, max_episodes=50):
"""Get information about a podcast series.
Parameters:
podcast_series_id (str): A podcast series ID.
max_episodes (int, Optional): Include up to given number of episodes in returned dict.
Default: ``50``
Returns:
dict: Podcast series information.
"""
podcast_info = self._call(
mc_calls.PodcastFetchSeries,
podcast_series_id,
max_episodes=max_episodes
).body
return podcast_info | Get information about a podcast series.
Parameters:
podcast_series_id (str): A podcast series ID.
max_episodes (int, Optional): Include up to given number of episodes in returned dict.
Default: ``50``
Returns:
dict: Podcast series information. | https://github.com/thebigmunch/google-music/blob/d8a94dab462a1f063fbc1152187a73dc2f0e2a85/src/google_music/clients/mobileclient.py#L978-L996 |
thebigmunch/google-music | src/google_music/clients/mobileclient.py | MobileClient.podcasts | def podcasts(self, *, device_id=None):
"""Get a listing of subsribed podcast series.
Paramaters:
device_id (str, Optional): A mobile device ID.
Default: Use ``device_id`` of the :class:`MobileClient` instance.
Returns:
list: Podcast series dict.
"""
if device_id is None:
device_id = self.device_id
podcast_list = []
for chunk in self.podcasts_iter(device_id=device_id, page_size=49995):
podcast_list.extend(chunk)
return podcast_list | python | def podcasts(self, *, device_id=None):
"""Get a listing of subsribed podcast series.
Paramaters:
device_id (str, Optional): A mobile device ID.
Default: Use ``device_id`` of the :class:`MobileClient` instance.
Returns:
list: Podcast series dict.
"""
if device_id is None:
device_id = self.device_id
podcast_list = []
for chunk in self.podcasts_iter(device_id=device_id, page_size=49995):
podcast_list.extend(chunk)
return podcast_list | Get a listing of subsribed podcast series.
Paramaters:
device_id (str, Optional): A mobile device ID.
Default: Use ``device_id`` of the :class:`MobileClient` instance.
Returns:
list: Podcast series dict. | https://github.com/thebigmunch/google-music/blob/d8a94dab462a1f063fbc1152187a73dc2f0e2a85/src/google_music/clients/mobileclient.py#L998-L1016 |
thebigmunch/google-music | src/google_music/clients/mobileclient.py | MobileClient.podcasts_iter | def podcasts_iter(self, *, device_id=None, page_size=250):
"""Get a paged iterator of subscribed podcast series.
Parameters:
device_id (str, Optional): A mobile device ID.
Default: Use ``device_id`` of the :class:`MobileClient` instance.
page_size (int, Optional): The maximum number of results per returned page.
Max allowed is ``49995``.
Default: ``250``
Yields:
list: Podcast series dicts.
"""
if device_id is None:
device_id = self.device_id
start_token = None
prev_items = None
while True:
response = self._call(
mc_calls.PodcastSeries,
device_id,
max_results=page_size,
start_token=start_token
)
items = response.body.get('data', {}).get('items', [])
# Google does some weird shit.
if items != prev_items:
subscribed_podcasts = [
item
for item in items
if item.get('userPreferences', {}).get('subscribed')
]
yield subscribed_podcasts
prev_items = items
else:
break
start_token = response.body.get('nextPageToken')
if start_token is None:
break | python | def podcasts_iter(self, *, device_id=None, page_size=250):
"""Get a paged iterator of subscribed podcast series.
Parameters:
device_id (str, Optional): A mobile device ID.
Default: Use ``device_id`` of the :class:`MobileClient` instance.
page_size (int, Optional): The maximum number of results per returned page.
Max allowed is ``49995``.
Default: ``250``
Yields:
list: Podcast series dicts.
"""
if device_id is None:
device_id = self.device_id
start_token = None
prev_items = None
while True:
response = self._call(
mc_calls.PodcastSeries,
device_id,
max_results=page_size,
start_token=start_token
)
items = response.body.get('data', {}).get('items', [])
# Google does some weird shit.
if items != prev_items:
subscribed_podcasts = [
item
for item in items
if item.get('userPreferences', {}).get('subscribed')
]
yield subscribed_podcasts
prev_items = items
else:
break
start_token = response.body.get('nextPageToken')
if start_token is None:
break | Get a paged iterator of subscribed podcast series.
Parameters:
device_id (str, Optional): A mobile device ID.
Default: Use ``device_id`` of the :class:`MobileClient` instance.
page_size (int, Optional): The maximum number of results per returned page.
Max allowed is ``49995``.
Default: ``250``
Yields:
list: Podcast series dicts. | https://github.com/thebigmunch/google-music/blob/d8a94dab462a1f063fbc1152187a73dc2f0e2a85/src/google_music/clients/mobileclient.py#L1018-L1063 |
thebigmunch/google-music | src/google_music/clients/mobileclient.py | MobileClient.podcast_episode | def podcast_episode(self, podcast_episode_id):
"""Get information about a podcast_episode.
Parameters:
podcast_episode_id (str): A podcast episode ID.
Returns:
dict: Podcast episode information.
"""
response = self._call(
mc_calls.PodcastFetchEpisode,
podcast_episode_id
)
podcast_episode_info = [
podcast_episode
for podcast_episode in response.body
if not podcast_episode['deleted']
]
return podcast_episode_info | python | def podcast_episode(self, podcast_episode_id):
"""Get information about a podcast_episode.
Parameters:
podcast_episode_id (str): A podcast episode ID.
Returns:
dict: Podcast episode information.
"""
response = self._call(
mc_calls.PodcastFetchEpisode,
podcast_episode_id
)
podcast_episode_info = [
podcast_episode
for podcast_episode in response.body
if not podcast_episode['deleted']
]
return podcast_episode_info | Get information about a podcast_episode.
Parameters:
podcast_episode_id (str): A podcast episode ID.
Returns:
dict: Podcast episode information. | https://github.com/thebigmunch/google-music/blob/d8a94dab462a1f063fbc1152187a73dc2f0e2a85/src/google_music/clients/mobileclient.py#L1065-L1085 |
thebigmunch/google-music | src/google_music/clients/mobileclient.py | MobileClient.podcast_episodes | def podcast_episodes(self, *, device_id=None):
"""Get a listing of podcast episodes for all subscribed podcasts.
Paramaters:
device_id (str, Optional): A mobile device ID.
Default: Use ``device_id`` of the :class:`MobileClient` instance.
Returns:
list: Podcast episode dicts.
"""
if device_id is None:
device_id = self.device_id
podcast_episode_list = []
for chunk in self.podcast_episodes_iter(
device_id=device_id,
page_size=49995
):
podcast_episode_list.extend(chunk)
return podcast_episode_list | python | def podcast_episodes(self, *, device_id=None):
"""Get a listing of podcast episodes for all subscribed podcasts.
Paramaters:
device_id (str, Optional): A mobile device ID.
Default: Use ``device_id`` of the :class:`MobileClient` instance.
Returns:
list: Podcast episode dicts.
"""
if device_id is None:
device_id = self.device_id
podcast_episode_list = []
for chunk in self.podcast_episodes_iter(
device_id=device_id,
page_size=49995
):
podcast_episode_list.extend(chunk)
return podcast_episode_list | Get a listing of podcast episodes for all subscribed podcasts.
Paramaters:
device_id (str, Optional): A mobile device ID.
Default: Use ``device_id`` of the :class:`MobileClient` instance.
Returns:
list: Podcast episode dicts. | https://github.com/thebigmunch/google-music/blob/d8a94dab462a1f063fbc1152187a73dc2f0e2a85/src/google_music/clients/mobileclient.py#L1087-L1108 |
thebigmunch/google-music | src/google_music/clients/mobileclient.py | MobileClient.podcast_episodes_iter | def podcast_episodes_iter(self, *, device_id=None, page_size=250):
"""Get a paged iterator of podcast episode for all subscribed podcasts.
Parameters:
device_id (str, Optional): A mobile device ID.
Default: Use ``device_id`` of the :class:`MobileClient` instance.
page_size (int, Optional): The maximum number of results per returned page.
Max allowed is ``49995``.
Default: ``250``
Yields:
list: Podcast episode dicts.
"""
if device_id is None:
device_id = self.device_id
start_token = None
prev_items = None
while True:
response = self._call(
mc_calls.PodcastEpisode,
device_id,
max_results=page_size,
start_token=start_token
)
items = response.body.get('data', {}).get('items', [])
# Google does some weird shit.
if items != prev_items:
yield items
prev_items = items
else:
break
start_token = response.body.get('nextPageToken')
if start_token is None:
break | python | def podcast_episodes_iter(self, *, device_id=None, page_size=250):
"""Get a paged iterator of podcast episode for all subscribed podcasts.
Parameters:
device_id (str, Optional): A mobile device ID.
Default: Use ``device_id`` of the :class:`MobileClient` instance.
page_size (int, Optional): The maximum number of results per returned page.
Max allowed is ``49995``.
Default: ``250``
Yields:
list: Podcast episode dicts.
"""
if device_id is None:
device_id = self.device_id
start_token = None
prev_items = None
while True:
response = self._call(
mc_calls.PodcastEpisode,
device_id,
max_results=page_size,
start_token=start_token
)
items = response.body.get('data', {}).get('items', [])
# Google does some weird shit.
if items != prev_items:
yield items
prev_items = items
else:
break
start_token = response.body.get('nextPageToken')
if start_token is None:
break | Get a paged iterator of podcast episode for all subscribed podcasts.
Parameters:
device_id (str, Optional): A mobile device ID.
Default: Use ``device_id`` of the :class:`MobileClient` instance.
page_size (int, Optional): The maximum number of results per returned page.
Max allowed is ``49995``.
Default: ``250``
Yields:
list: Podcast episode dicts. | https://github.com/thebigmunch/google-music/blob/d8a94dab462a1f063fbc1152187a73dc2f0e2a85/src/google_music/clients/mobileclient.py#L1110-L1149 |
thebigmunch/google-music | src/google_music/clients/mobileclient.py | MobileClient.search | def search(self, query, *, max_results=100, **kwargs):
"""Search Google Music and library for content.
Parameters:
query (str): Search text.
max_results (int, Optional): Maximum number of results per type per
location to retrieve. I.e up to 100 Google and 100 library
for a total of 200 for the default value.
Google only accepts values up to 100.
Default: ``100``
kwargs (bool, Optional): Any of ``albums``, ``artists``, ``genres``,
``playlists``, ``podcasts``, ``situations``, ``songs``, ``stations``,
``videos`` set to ``True`` will include that result type in the
returned dict.
Setting none of them will include all result types in the returned dict.
Returns:
dict: A dict of results separated into keys: ``'albums'``, ``'artists'``,
``'genres'``, ``'playlists'``, ```'podcasts'``, ``'situations'``,
``'songs'``, ``'stations'``, ``'videos'``.
Note:
Free account search is restricted so may not contain hits for all result types.
"""
results = defaultdict(list)
for type_, results_ in self.search_library(
query,
max_results=max_results,
**kwargs
).items():
results[type_].extend(results_)
for type_, results_ in self.search_google(
query,
max_results=max_results,
**kwargs
).items():
results[type_].extend(results_)
return dict(results) | python | def search(self, query, *, max_results=100, **kwargs):
"""Search Google Music and library for content.
Parameters:
query (str): Search text.
max_results (int, Optional): Maximum number of results per type per
location to retrieve. I.e up to 100 Google and 100 library
for a total of 200 for the default value.
Google only accepts values up to 100.
Default: ``100``
kwargs (bool, Optional): Any of ``albums``, ``artists``, ``genres``,
``playlists``, ``podcasts``, ``situations``, ``songs``, ``stations``,
``videos`` set to ``True`` will include that result type in the
returned dict.
Setting none of them will include all result types in the returned dict.
Returns:
dict: A dict of results separated into keys: ``'albums'``, ``'artists'``,
``'genres'``, ``'playlists'``, ```'podcasts'``, ``'situations'``,
``'songs'``, ``'stations'``, ``'videos'``.
Note:
Free account search is restricted so may not contain hits for all result types.
"""
results = defaultdict(list)
for type_, results_ in self.search_library(
query,
max_results=max_results,
**kwargs
).items():
results[type_].extend(results_)
for type_, results_ in self.search_google(
query,
max_results=max_results,
**kwargs
).items():
results[type_].extend(results_)
return dict(results) | Search Google Music and library for content.
Parameters:
query (str): Search text.
max_results (int, Optional): Maximum number of results per type per
location to retrieve. I.e up to 100 Google and 100 library
for a total of 200 for the default value.
Google only accepts values up to 100.
Default: ``100``
kwargs (bool, Optional): Any of ``albums``, ``artists``, ``genres``,
``playlists``, ``podcasts``, ``situations``, ``songs``, ``stations``,
``videos`` set to ``True`` will include that result type in the
returned dict.
Setting none of them will include all result types in the returned dict.
Returns:
dict: A dict of results separated into keys: ``'albums'``, ``'artists'``,
``'genres'``, ``'playlists'``, ```'podcasts'``, ``'situations'``,
``'songs'``, ``'stations'``, ``'videos'``.
Note:
Free account search is restricted so may not contain hits for all result types. | https://github.com/thebigmunch/google-music/blob/d8a94dab462a1f063fbc1152187a73dc2f0e2a85/src/google_music/clients/mobileclient.py#L1151-L1192 |
thebigmunch/google-music | src/google_music/clients/mobileclient.py | MobileClient.search_google | def search_google(self, query, *, max_results=100, **kwargs):
"""Search Google Music for content.
Parameters:
query (str): Search text.
max_results (int, Optional): Maximum number of results per type to retrieve.
Google only accepts values up to 100.
Default: ``100``
kwargs (bool, Optional): Any of ``albums``, ``artists``, ``genres``,
``playlists``, ``podcasts``, ``situations``, ``songs``, ``stations``,
``videos`` set to ``True`` will include that result type in the
returned dict.
Setting none of them will include all result types in the returned dict.
Returns:
dict: A dict of results separated into keys: ``'albums'``, ``'artists'``,
``'genres'``, ``'playlists'``, ```'podcasts'``, ``'situations'``,
``'songs'``, ``'stations'``, ``'videos'``.
Note:
Free account search is restricted so may not contain hits for all result types.
"""
response = self._call(
mc_calls.Query,
query,
max_results=max_results,
**kwargs
)
clusters = response.body.get('clusterDetail', [])
results = defaultdict(list)
for cluster in clusters:
result_type = QueryResultType(cluster['cluster']['type']).name
entries = cluster.get('entries', [])
if len(entries) > 0:
for entry in entries:
item_key = next(
key
for key in entry
if key not in ['cluster', 'score', 'type']
)
results[f"{result_type}s"].append(entry[item_key])
return dict(results) | python | def search_google(self, query, *, max_results=100, **kwargs):
"""Search Google Music for content.
Parameters:
query (str): Search text.
max_results (int, Optional): Maximum number of results per type to retrieve.
Google only accepts values up to 100.
Default: ``100``
kwargs (bool, Optional): Any of ``albums``, ``artists``, ``genres``,
``playlists``, ``podcasts``, ``situations``, ``songs``, ``stations``,
``videos`` set to ``True`` will include that result type in the
returned dict.
Setting none of them will include all result types in the returned dict.
Returns:
dict: A dict of results separated into keys: ``'albums'``, ``'artists'``,
``'genres'``, ``'playlists'``, ```'podcasts'``, ``'situations'``,
``'songs'``, ``'stations'``, ``'videos'``.
Note:
Free account search is restricted so may not contain hits for all result types.
"""
response = self._call(
mc_calls.Query,
query,
max_results=max_results,
**kwargs
)
clusters = response.body.get('clusterDetail', [])
results = defaultdict(list)
for cluster in clusters:
result_type = QueryResultType(cluster['cluster']['type']).name
entries = cluster.get('entries', [])
if len(entries) > 0:
for entry in entries:
item_key = next(
key
for key in entry
if key not in ['cluster', 'score', 'type']
)
results[f"{result_type}s"].append(entry[item_key])
return dict(results) | Search Google Music for content.
Parameters:
query (str): Search text.
max_results (int, Optional): Maximum number of results per type to retrieve.
Google only accepts values up to 100.
Default: ``100``
kwargs (bool, Optional): Any of ``albums``, ``artists``, ``genres``,
``playlists``, ``podcasts``, ``situations``, ``songs``, ``stations``,
``videos`` set to ``True`` will include that result type in the
returned dict.
Setting none of them will include all result types in the returned dict.
Returns:
dict: A dict of results separated into keys: ``'albums'``, ``'artists'``,
``'genres'``, ``'playlists'``, ```'podcasts'``, ``'situations'``,
``'songs'``, ``'stations'``, ``'videos'``.
Note:
Free account search is restricted so may not contain hits for all result types. | https://github.com/thebigmunch/google-music/blob/d8a94dab462a1f063fbc1152187a73dc2f0e2a85/src/google_music/clients/mobileclient.py#L1194-L1240 |
thebigmunch/google-music | src/google_music/clients/mobileclient.py | MobileClient.search_library | def search_library(self, query, *, max_results=100, **kwargs):
"""Search Google Music for content.
Parameters:
query (str): Search text.
max_results (int, Optional): Maximum number of results per type to retrieve.
Default: ``100``
kwargs (bool, Optional): Any of ``playlists``, ``podcasts``,
``songs``, ``stations``, ``videos`` set to ``True``
will include that result type in the returned dict.
Setting none of them will include all result types in the returned dict.
Returns:
dict: A dict of results separated into keys:
``'playlists'``, ``'podcasts'``, ``'songs'``, ``'stations'``.
"""
def match_fields(item, fields):
return any(
query.casefold() in item.get(field, '').casefold()
for field in fields
)
types = [
(
'playlists',
['description', 'name'],
self.playlists
),
(
'podcasts',
['author', 'description', 'title'],
self.podcasts
),
(
'songs',
['album', 'albumArtist', 'artist', 'composer', 'genre', 'title'],
self.songs
),
(
'stations',
['byline', 'description', 'name'],
self.stations
),
]
results = {}
for type_, fields, func in types:
if (not kwargs) or (type_ in kwargs):
results[type_] = [
item
for item in func()
if match_fields(item, fields)
][:max_results]
return results | python | def search_library(self, query, *, max_results=100, **kwargs):
"""Search Google Music for content.
Parameters:
query (str): Search text.
max_results (int, Optional): Maximum number of results per type to retrieve.
Default: ``100``
kwargs (bool, Optional): Any of ``playlists``, ``podcasts``,
``songs``, ``stations``, ``videos`` set to ``True``
will include that result type in the returned dict.
Setting none of them will include all result types in the returned dict.
Returns:
dict: A dict of results separated into keys:
``'playlists'``, ``'podcasts'``, ``'songs'``, ``'stations'``.
"""
def match_fields(item, fields):
return any(
query.casefold() in item.get(field, '').casefold()
for field in fields
)
types = [
(
'playlists',
['description', 'name'],
self.playlists
),
(
'podcasts',
['author', 'description', 'title'],
self.podcasts
),
(
'songs',
['album', 'albumArtist', 'artist', 'composer', 'genre', 'title'],
self.songs
),
(
'stations',
['byline', 'description', 'name'],
self.stations
),
]
results = {}
for type_, fields, func in types:
if (not kwargs) or (type_ in kwargs):
results[type_] = [
item
for item in func()
if match_fields(item, fields)
][:max_results]
return results | Search Google Music for content.
Parameters:
query (str): Search text.
max_results (int, Optional): Maximum number of results per type to retrieve.
Default: ``100``
kwargs (bool, Optional): Any of ``playlists``, ``podcasts``,
``songs``, ``stations``, ``videos`` set to ``True``
will include that result type in the returned dict.
Setting none of them will include all result types in the returned dict.
Returns:
dict: A dict of results separated into keys:
``'playlists'``, ``'podcasts'``, ``'songs'``, ``'stations'``. | https://github.com/thebigmunch/google-music/blob/d8a94dab462a1f063fbc1152187a73dc2f0e2a85/src/google_music/clients/mobileclient.py#L1242-L1298 |
thebigmunch/google-music | src/google_music/clients/mobileclient.py | MobileClient.search_suggestion | def search_suggestion(self, query):
"""Get search query suggestions for query.
Parameters:
query (str): Search text.
Returns:
list: Suggested query strings.
"""
response = self._call(
mc_calls.QuerySuggestion,
query
)
suggested_queries = response.body.get('suggested_queries', [])
return [
suggested_query['suggestion_string']
for suggested_query in suggested_queries
] | python | def search_suggestion(self, query):
"""Get search query suggestions for query.
Parameters:
query (str): Search text.
Returns:
list: Suggested query strings.
"""
response = self._call(
mc_calls.QuerySuggestion,
query
)
suggested_queries = response.body.get('suggested_queries', [])
return [
suggested_query['suggestion_string']
for suggested_query in suggested_queries
] | Get search query suggestions for query.
Parameters:
query (str): Search text.
Returns:
list: Suggested query strings. | https://github.com/thebigmunch/google-music/blob/d8a94dab462a1f063fbc1152187a73dc2f0e2a85/src/google_music/clients/mobileclient.py#L1300-L1319 |
thebigmunch/google-music | src/google_music/clients/mobileclient.py | MobileClient.shuffle_album | def shuffle_album(
self, album, *, num_songs=100, only_library=False, recently_played=None
):
"""Get a listing of album shuffle/mix songs.
Parameters:
album (dict): An album dict.
num_songs (int, Optional): The maximum number of songs to return from the station.
Default: ``100``
only_library (bool, Optional): Only return content from library.
Default: False
recently_played (list, Optional): A list of dicts in the form of {'id': '', 'type'}
where ``id`` is a song ID and ``type`` is 0 for a library song and 1 for a store song.
Returns:
list: List of album shuffle/mix songs.
"""
station_info = {
'seed': {
'albumId': album['albumId'],
'seedType': StationSeedType.album.value
},
'num_entries': num_songs,
'library_content_only': only_library,
}
if recently_played is not None:
station_info['recently_played'] = recently_played
response = self._call(
mc_calls.RadioStationFeed,
station_infos=[station_info]
)
station_feed = response.body.get('data', {}).get('stations', [])
try:
station = station_feed[0]
except IndexError:
station = {}
return station.get('tracks', []) | python | def shuffle_album(
self, album, *, num_songs=100, only_library=False, recently_played=None
):
"""Get a listing of album shuffle/mix songs.
Parameters:
album (dict): An album dict.
num_songs (int, Optional): The maximum number of songs to return from the station.
Default: ``100``
only_library (bool, Optional): Only return content from library.
Default: False
recently_played (list, Optional): A list of dicts in the form of {'id': '', 'type'}
where ``id`` is a song ID and ``type`` is 0 for a library song and 1 for a store song.
Returns:
list: List of album shuffle/mix songs.
"""
station_info = {
'seed': {
'albumId': album['albumId'],
'seedType': StationSeedType.album.value
},
'num_entries': num_songs,
'library_content_only': only_library,
}
if recently_played is not None:
station_info['recently_played'] = recently_played
response = self._call(
mc_calls.RadioStationFeed,
station_infos=[station_info]
)
station_feed = response.body.get('data', {}).get('stations', [])
try:
station = station_feed[0]
except IndexError:
station = {}
return station.get('tracks', []) | Get a listing of album shuffle/mix songs.
Parameters:
album (dict): An album dict.
num_songs (int, Optional): The maximum number of songs to return from the station.
Default: ``100``
only_library (bool, Optional): Only return content from library.
Default: False
recently_played (list, Optional): A list of dicts in the form of {'id': '', 'type'}
where ``id`` is a song ID and ``type`` is 0 for a library song and 1 for a store song.
Returns:
list: List of album shuffle/mix songs. | https://github.com/thebigmunch/google-music/blob/d8a94dab462a1f063fbc1152187a73dc2f0e2a85/src/google_music/clients/mobileclient.py#L1321-L1362 |
thebigmunch/google-music | src/google_music/clients/mobileclient.py | MobileClient.shuffle_artist | def shuffle_artist(
self,
artist,
*,
num_songs=100,
only_library=False,
recently_played=None,
only_artist=False
):
"""Get a listing of artist shuffle/mix songs.
Parameters:
artist (dict): An artist dict.
num_songs (int, Optional): The maximum number of songs to return from the station.
Default: ``100``
only_library (bool, Optional): Only return content from library.
Default: False
recently_played (list, Optional): A list of dicts in the form of {'id': '', 'type'}
where ``id`` is a song ID and ``type`` is 0 for a library song and 1 for a store song.
only_artist (bool, Optional): If ``True``, only return songs from the artist,
else return songs from artist and related artists.
Default: ``False``
Returns:
list: List of artist shuffle/mix songs.
"""
station_info = {
'num_entries': num_songs,
'library_content_only': only_library
}
if only_artist:
station_info['seed'] = {
'artistId': artist['artistId'],
'seedType': StationSeedType.artist_only.value
}
else:
station_info['seed'] = {
'artistId': artist['artistId'],
'seedType': StationSeedType.artist_related.value
}
if recently_played is not None:
station_info['recently_played'] = recently_played
response = self._call(
mc_calls.RadioStationFeed,
station_infos=[station_info]
)
station_feed = response.body.get('data', {}).get('stations', [])
try:
station = station_feed[0]
except IndexError:
station = {}
return station.get('tracks', []) | python | def shuffle_artist(
self,
artist,
*,
num_songs=100,
only_library=False,
recently_played=None,
only_artist=False
):
"""Get a listing of artist shuffle/mix songs.
Parameters:
artist (dict): An artist dict.
num_songs (int, Optional): The maximum number of songs to return from the station.
Default: ``100``
only_library (bool, Optional): Only return content from library.
Default: False
recently_played (list, Optional): A list of dicts in the form of {'id': '', 'type'}
where ``id`` is a song ID and ``type`` is 0 for a library song and 1 for a store song.
only_artist (bool, Optional): If ``True``, only return songs from the artist,
else return songs from artist and related artists.
Default: ``False``
Returns:
list: List of artist shuffle/mix songs.
"""
station_info = {
'num_entries': num_songs,
'library_content_only': only_library
}
if only_artist:
station_info['seed'] = {
'artistId': artist['artistId'],
'seedType': StationSeedType.artist_only.value
}
else:
station_info['seed'] = {
'artistId': artist['artistId'],
'seedType': StationSeedType.artist_related.value
}
if recently_played is not None:
station_info['recently_played'] = recently_played
response = self._call(
mc_calls.RadioStationFeed,
station_infos=[station_info]
)
station_feed = response.body.get('data', {}).get('stations', [])
try:
station = station_feed[0]
except IndexError:
station = {}
return station.get('tracks', []) | Get a listing of artist shuffle/mix songs.
Parameters:
artist (dict): An artist dict.
num_songs (int, Optional): The maximum number of songs to return from the station.
Default: ``100``
only_library (bool, Optional): Only return content from library.
Default: False
recently_played (list, Optional): A list of dicts in the form of {'id': '', 'type'}
where ``id`` is a song ID and ``type`` is 0 for a library song and 1 for a store song.
only_artist (bool, Optional): If ``True``, only return songs from the artist,
else return songs from artist and related artists.
Default: ``False``
Returns:
list: List of artist shuffle/mix songs. | https://github.com/thebigmunch/google-music/blob/d8a94dab462a1f063fbc1152187a73dc2f0e2a85/src/google_music/clients/mobileclient.py#L1364-L1421 |
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