repo stringlengths 7 55 | path stringlengths 4 127 | func_name stringlengths 1 88 | original_string stringlengths 75 19.8k | language stringclasses 1
value | code stringlengths 75 19.8k | code_tokens list | docstring stringlengths 3 17.3k | docstring_tokens list | sha stringlengths 40 40 | url stringlengths 87 242 | partition stringclasses 1
value |
|---|---|---|---|---|---|---|---|---|---|---|---|
zblz/naima | naima/radiative.py | PionDecay._diffsigma | def _diffsigma(self, Ep, Egamma):
"""
Differential cross section
dsigma/dEg = Amax(Tp) * F(Tp,Egamma)
"""
Tp = Ep - self._m_p
diffsigma = self._Amax(Tp) * self._F(Tp, Egamma)
if self.nuclear_enhancement:
diffsigma *= self._nuclear_factor(Tp)
... | python | def _diffsigma(self, Ep, Egamma):
"""
Differential cross section
dsigma/dEg = Amax(Tp) * F(Tp,Egamma)
"""
Tp = Ep - self._m_p
diffsigma = self._Amax(Tp) * self._F(Tp, Egamma)
if self.nuclear_enhancement:
diffsigma *= self._nuclear_factor(Tp)
... | [
"def",
"_diffsigma",
"(",
"self",
",",
"Ep",
",",
"Egamma",
")",
":",
"Tp",
"=",
"Ep",
"-",
"self",
".",
"_m_p",
"diffsigma",
"=",
"self",
".",
"_Amax",
"(",
"Tp",
")",
"*",
"self",
".",
"_F",
"(",
"Tp",
",",
"Egamma",
")",
"if",
"self",
".",
... | Differential cross section
dsigma/dEg = Amax(Tp) * F(Tp,Egamma) | [
"Differential",
"cross",
"section"
] | d6a6781d73bf58fd8269e8b0e3b70be22723cd5b | https://github.com/zblz/naima/blob/d6a6781d73bf58fd8269e8b0e3b70be22723cd5b/naima/radiative.py#L1513-L1526 | train |
zblz/naima | naima/radiative.py | PionDecay._nuclear_factor | def _nuclear_factor(self, Tp):
"""
Compute nuclear enhancement factor
"""
sigmaRpp = 10 * np.pi * 1e-27
sigmainel = self._sigma_inel(Tp)
sigmainel0 = self._sigma_inel(1e3) # at 1e3 GeV
f = sigmainel / sigmainel0
f2 = np.where(f > 1, f, 1.0)
G = 1.... | python | def _nuclear_factor(self, Tp):
"""
Compute nuclear enhancement factor
"""
sigmaRpp = 10 * np.pi * 1e-27
sigmainel = self._sigma_inel(Tp)
sigmainel0 = self._sigma_inel(1e3) # at 1e3 GeV
f = sigmainel / sigmainel0
f2 = np.where(f > 1, f, 1.0)
G = 1.... | [
"def",
"_nuclear_factor",
"(",
"self",
",",
"Tp",
")",
":",
"sigmaRpp",
"=",
"10",
"*",
"np",
".",
"pi",
"*",
"1e-27",
"sigmainel",
"=",
"self",
".",
"_sigma_inel",
"(",
"Tp",
")",
"sigmainel0",
"=",
"self",
".",
"_sigma_inel",
"(",
"1e3",
")",
"f",
... | Compute nuclear enhancement factor | [
"Compute",
"nuclear",
"enhancement",
"factor"
] | d6a6781d73bf58fd8269e8b0e3b70be22723cd5b | https://github.com/zblz/naima/blob/d6a6781d73bf58fd8269e8b0e3b70be22723cd5b/naima/radiative.py#L1528-L1555 | train |
zblz/naima | naima/radiative.py | PionDecayKelner06._Fgamma | def _Fgamma(self, x, Ep):
"""
KAB06 Eq.58
Note: Quantities are not used in this function
Parameters
----------
x : float
Egamma/Eprot
Ep : float
Eprot [TeV]
"""
L = np.log(Ep)
B = 1.30 + 0.14 * L + 0.011 * L ** 2 ... | python | def _Fgamma(self, x, Ep):
"""
KAB06 Eq.58
Note: Quantities are not used in this function
Parameters
----------
x : float
Egamma/Eprot
Ep : float
Eprot [TeV]
"""
L = np.log(Ep)
B = 1.30 + 0.14 * L + 0.011 * L ** 2 ... | [
"def",
"_Fgamma",
"(",
"self",
",",
"x",
",",
"Ep",
")",
":",
"L",
"=",
"np",
".",
"log",
"(",
"Ep",
")",
"B",
"=",
"1.30",
"+",
"0.14",
"*",
"L",
"+",
"0.011",
"*",
"L",
"**",
"2",
"beta",
"=",
"(",
"1.79",
"+",
"0.11",
"*",
"L",
"+",
... | KAB06 Eq.58
Note: Quantities are not used in this function
Parameters
----------
x : float
Egamma/Eprot
Ep : float
Eprot [TeV] | [
"KAB06",
"Eq",
".",
"58"
] | d6a6781d73bf58fd8269e8b0e3b70be22723cd5b | https://github.com/zblz/naima/blob/d6a6781d73bf58fd8269e8b0e3b70be22723cd5b/naima/radiative.py#L1673-L1699 | train |
zblz/naima | naima/radiative.py | PionDecayKelner06._sigma_inel | def _sigma_inel(self, Ep):
"""
Inelastic cross-section for p-p interaction. KAB06 Eq. 73, 79
Note: Quantities are not used in this function
Parameters
----------
Ep : float
Eprot [TeV]
Returns
-------
sigma_inel : float
I... | python | def _sigma_inel(self, Ep):
"""
Inelastic cross-section for p-p interaction. KAB06 Eq. 73, 79
Note: Quantities are not used in this function
Parameters
----------
Ep : float
Eprot [TeV]
Returns
-------
sigma_inel : float
I... | [
"def",
"_sigma_inel",
"(",
"self",
",",
"Ep",
")",
":",
"L",
"=",
"np",
".",
"log",
"(",
"Ep",
")",
"sigma",
"=",
"34.3",
"+",
"1.88",
"*",
"L",
"+",
"0.25",
"*",
"L",
"**",
"2",
"if",
"Ep",
"<=",
"0.1",
":",
"Eth",
"=",
"1.22e-3",
"sigma",
... | Inelastic cross-section for p-p interaction. KAB06 Eq. 73, 79
Note: Quantities are not used in this function
Parameters
----------
Ep : float
Eprot [TeV]
Returns
-------
sigma_inel : float
Inelastic cross-section for p-p interaction [1/c... | [
"Inelastic",
"cross",
"-",
"section",
"for",
"p",
"-",
"p",
"interaction",
".",
"KAB06",
"Eq",
".",
"73",
"79"
] | d6a6781d73bf58fd8269e8b0e3b70be22723cd5b | https://github.com/zblz/naima/blob/d6a6781d73bf58fd8269e8b0e3b70be22723cd5b/naima/radiative.py#L1701-L1723 | train |
zblz/naima | naima/radiative.py | PionDecayKelner06._photon_integrand | def _photon_integrand(self, x, Egamma):
"""
Integrand of Eq. 72
"""
try:
return (
self._sigma_inel(Egamma / x)
* self._particle_distribution((Egamma / x))
* self._Fgamma(x, Egamma / x)
/ x
)
e... | python | def _photon_integrand(self, x, Egamma):
"""
Integrand of Eq. 72
"""
try:
return (
self._sigma_inel(Egamma / x)
* self._particle_distribution((Egamma / x))
* self._Fgamma(x, Egamma / x)
/ x
)
e... | [
"def",
"_photon_integrand",
"(",
"self",
",",
"x",
",",
"Egamma",
")",
":",
"try",
":",
"return",
"(",
"self",
".",
"_sigma_inel",
"(",
"Egamma",
"/",
"x",
")",
"*",
"self",
".",
"_particle_distribution",
"(",
"(",
"Egamma",
"/",
"x",
")",
")",
"*",
... | Integrand of Eq. 72 | [
"Integrand",
"of",
"Eq",
".",
"72"
] | d6a6781d73bf58fd8269e8b0e3b70be22723cd5b | https://github.com/zblz/naima/blob/d6a6781d73bf58fd8269e8b0e3b70be22723cd5b/naima/radiative.py#L1725-L1737 | train |
zblz/naima | naima/radiative.py | PionDecayKelner06._calc_specpp_hiE | def _calc_specpp_hiE(self, Egamma):
"""
Spectrum computed as in Eq. 42 for Egamma >= 0.1 TeV
"""
# Fixed quad with n=40 is about 15 times faster and is always within
# 0.5% of the result of adaptive quad for Egamma>0.1
# WARNING: It also produces artifacts for steep distr... | python | def _calc_specpp_hiE(self, Egamma):
"""
Spectrum computed as in Eq. 42 for Egamma >= 0.1 TeV
"""
# Fixed quad with n=40 is about 15 times faster and is always within
# 0.5% of the result of adaptive quad for Egamma>0.1
# WARNING: It also produces artifacts for steep distr... | [
"def",
"_calc_specpp_hiE",
"(",
"self",
",",
"Egamma",
")",
":",
"from",
"scipy",
".",
"integrate",
"import",
"quad",
"Egamma",
"=",
"Egamma",
".",
"to",
"(",
"\"TeV\"",
")",
".",
"value",
"specpp",
"=",
"(",
"c",
".",
"cgs",
".",
"value",
"*",
"quad... | Spectrum computed as in Eq. 42 for Egamma >= 0.1 TeV | [
"Spectrum",
"computed",
"as",
"in",
"Eq",
".",
"42",
"for",
"Egamma",
">",
"=",
"0",
".",
"1",
"TeV"
] | d6a6781d73bf58fd8269e8b0e3b70be22723cd5b | https://github.com/zblz/naima/blob/d6a6781d73bf58fd8269e8b0e3b70be22723cd5b/naima/radiative.py#L1739-L1765 | train |
zblz/naima | naima/radiative.py | PionDecayKelner06._calc_specpp_loE | def _calc_specpp_loE(self, Egamma):
"""
Delta-functional approximation for low energies Egamma < 0.1 TeV
"""
from scipy.integrate import quad
Egamma = Egamma.to("TeV").value
Epimin = Egamma + self._m_pi ** 2 / (4 * Egamma)
result = (
2
* ... | python | def _calc_specpp_loE(self, Egamma):
"""
Delta-functional approximation for low energies Egamma < 0.1 TeV
"""
from scipy.integrate import quad
Egamma = Egamma.to("TeV").value
Epimin = Egamma + self._m_pi ** 2 / (4 * Egamma)
result = (
2
* ... | [
"def",
"_calc_specpp_loE",
"(",
"self",
",",
"Egamma",
")",
":",
"from",
"scipy",
".",
"integrate",
"import",
"quad",
"Egamma",
"=",
"Egamma",
".",
"to",
"(",
"\"TeV\"",
")",
".",
"value",
"Epimin",
"=",
"Egamma",
"+",
"self",
".",
"_m_pi",
"**",
"2",
... | Delta-functional approximation for low energies Egamma < 0.1 TeV | [
"Delta",
"-",
"functional",
"approximation",
"for",
"low",
"energies",
"Egamma",
"<",
"0",
".",
"1",
"TeV"
] | d6a6781d73bf58fd8269e8b0e3b70be22723cd5b | https://github.com/zblz/naima/blob/d6a6781d73bf58fd8269e8b0e3b70be22723cd5b/naima/radiative.py#L1783-L1799 | train |
projecthamster/hamster-lib | hamster_lib/helpers/config_helpers.py | get_config_path | def get_config_path(appdirs=DEFAULT_APPDIRS, file_name=DEFAULT_CONFIG_FILENAME):
"""
Return the path where the config file is stored.
Args:
app_name (text_type, optional): Name of the application, defaults to
``'projecthamster``. Allows you to use your own application specific
names... | python | def get_config_path(appdirs=DEFAULT_APPDIRS, file_name=DEFAULT_CONFIG_FILENAME):
"""
Return the path where the config file is stored.
Args:
app_name (text_type, optional): Name of the application, defaults to
``'projecthamster``. Allows you to use your own application specific
names... | [
"def",
"get_config_path",
"(",
"appdirs",
"=",
"DEFAULT_APPDIRS",
",",
"file_name",
"=",
"DEFAULT_CONFIG_FILENAME",
")",
":",
"return",
"os",
".",
"path",
".",
"join",
"(",
"appdirs",
".",
"user_config_dir",
",",
"file_name",
")"
] | Return the path where the config file is stored.
Args:
app_name (text_type, optional): Name of the application, defaults to
``'projecthamster``. Allows you to use your own application specific
namespace if you wish.
file_name (text_type, optional): Name of the config file. Defaults ... | [
"Return",
"the",
"path",
"where",
"the",
"config",
"file",
"is",
"stored",
"."
] | bc34c822c239a6fa0cde3a4f90b0d00506fb5a4f | https://github.com/projecthamster/hamster-lib/blob/bc34c822c239a6fa0cde3a4f90b0d00506fb5a4f/hamster_lib/helpers/config_helpers.py#L141-L155 | train |
projecthamster/hamster-lib | hamster_lib/helpers/config_helpers.py | write_config_file | def write_config_file(config_instance, appdirs=DEFAULT_APPDIRS,
file_name=DEFAULT_CONFIG_FILENAME):
"""
Write a ConfigParser instance to file at the correct location.
Args:
config_instance: Config instance to safe to file.
appdirs (HamsterAppDirs, optional): ``HamsterAppDirs`` insta... | python | def write_config_file(config_instance, appdirs=DEFAULT_APPDIRS,
file_name=DEFAULT_CONFIG_FILENAME):
"""
Write a ConfigParser instance to file at the correct location.
Args:
config_instance: Config instance to safe to file.
appdirs (HamsterAppDirs, optional): ``HamsterAppDirs`` insta... | [
"def",
"write_config_file",
"(",
"config_instance",
",",
"appdirs",
"=",
"DEFAULT_APPDIRS",
",",
"file_name",
"=",
"DEFAULT_CONFIG_FILENAME",
")",
":",
"path",
"=",
"get_config_path",
"(",
"appdirs",
",",
"file_name",
")",
"with",
"open",
"(",
"path",
",",
"'w'"... | Write a ConfigParser instance to file at the correct location.
Args:
config_instance: Config instance to safe to file.
appdirs (HamsterAppDirs, optional): ``HamsterAppDirs`` instance storing app/user specific
path information.
file_name (text_type, optional): Name of the config ... | [
"Write",
"a",
"ConfigParser",
"instance",
"to",
"file",
"at",
"the",
"correct",
"location",
"."
] | bc34c822c239a6fa0cde3a4f90b0d00506fb5a4f | https://github.com/projecthamster/hamster-lib/blob/bc34c822c239a6fa0cde3a4f90b0d00506fb5a4f/hamster_lib/helpers/config_helpers.py#L158-L177 | train |
projecthamster/hamster-lib | hamster_lib/helpers/config_helpers.py | load_config_file | def load_config_file(appdirs=DEFAULT_APPDIRS, file_name=DEFAULT_CONFIG_FILENAME,
fallback_config_instance=None):
"""
Retrieve config information from file at default location.
If no config file is found a new one will be created either with ``fallback_config_instance``
as content or if none is ... | python | def load_config_file(appdirs=DEFAULT_APPDIRS, file_name=DEFAULT_CONFIG_FILENAME,
fallback_config_instance=None):
"""
Retrieve config information from file at default location.
If no config file is found a new one will be created either with ``fallback_config_instance``
as content or if none is ... | [
"def",
"load_config_file",
"(",
"appdirs",
"=",
"DEFAULT_APPDIRS",
",",
"file_name",
"=",
"DEFAULT_CONFIG_FILENAME",
",",
"fallback_config_instance",
"=",
"None",
")",
":",
"if",
"not",
"fallback_config_instance",
":",
"fallback_config_instance",
"=",
"backend_config_to_c... | Retrieve config information from file at default location.
If no config file is found a new one will be created either with ``fallback_config_instance``
as content or if none is provided with the result of ``get_default_backend_config``.
Args:
appdirs (HamsterAppDirs, optional): ``HamsterAppDirs``... | [
"Retrieve",
"config",
"information",
"from",
"file",
"at",
"default",
"location",
"."
] | bc34c822c239a6fa0cde3a4f90b0d00506fb5a4f | https://github.com/projecthamster/hamster-lib/blob/bc34c822c239a6fa0cde3a4f90b0d00506fb5a4f/hamster_lib/helpers/config_helpers.py#L180-L211 | train |
projecthamster/hamster-lib | hamster_lib/helpers/config_helpers.py | get_default_backend_config | def get_default_backend_config(appdirs):
"""
Return a default config dictionary.
Args:
appdirs (HamsterAppDirs): ``HamsterAppDirs`` instance encapsulating the apps details.
Returns:
dict: Dictionary with a default configuration.
Note:
Those defaults are independent of the ... | python | def get_default_backend_config(appdirs):
"""
Return a default config dictionary.
Args:
appdirs (HamsterAppDirs): ``HamsterAppDirs`` instance encapsulating the apps details.
Returns:
dict: Dictionary with a default configuration.
Note:
Those defaults are independent of the ... | [
"def",
"get_default_backend_config",
"(",
"appdirs",
")",
":",
"return",
"{",
"'store'",
":",
"'sqlalchemy'",
",",
"'day_start'",
":",
"datetime",
".",
"time",
"(",
"5",
",",
"30",
",",
"0",
")",
",",
"'fact_min_delta'",
":",
"1",
",",
"'tmpfile_path'",
":... | Return a default config dictionary.
Args:
appdirs (HamsterAppDirs): ``HamsterAppDirs`` instance encapsulating the apps details.
Returns:
dict: Dictionary with a default configuration.
Note:
Those defaults are independent of the particular config-store. | [
"Return",
"a",
"default",
"config",
"dictionary",
"."
] | bc34c822c239a6fa0cde3a4f90b0d00506fb5a4f | https://github.com/projecthamster/hamster-lib/blob/bc34c822c239a6fa0cde3a4f90b0d00506fb5a4f/hamster_lib/helpers/config_helpers.py#L214-L234 | train |
projecthamster/hamster-lib | hamster_lib/helpers/config_helpers.py | backend_config_to_configparser | def backend_config_to_configparser(config):
"""
Return a ConfigParser instance representing a given backend config dictionary.
Args:
config (dict): Dictionary of config key/value pairs.
Returns:
SafeConfigParser: SafeConfigParser instance representing config.
Note:
We do n... | python | def backend_config_to_configparser(config):
"""
Return a ConfigParser instance representing a given backend config dictionary.
Args:
config (dict): Dictionary of config key/value pairs.
Returns:
SafeConfigParser: SafeConfigParser instance representing config.
Note:
We do n... | [
"def",
"backend_config_to_configparser",
"(",
"config",
")",
":",
"def",
"get_store",
"(",
")",
":",
"return",
"config",
".",
"get",
"(",
"'store'",
")",
"def",
"get_day_start",
"(",
")",
":",
"day_start",
"=",
"config",
".",
"get",
"(",
"'day_start'",
")"... | Return a ConfigParser instance representing a given backend config dictionary.
Args:
config (dict): Dictionary of config key/value pairs.
Returns:
SafeConfigParser: SafeConfigParser instance representing config.
Note:
We do not provide *any* validation about mandatory values what ... | [
"Return",
"a",
"ConfigParser",
"instance",
"representing",
"a",
"given",
"backend",
"config",
"dictionary",
"."
] | bc34c822c239a6fa0cde3a4f90b0d00506fb5a4f | https://github.com/projecthamster/hamster-lib/blob/bc34c822c239a6fa0cde3a4f90b0d00506fb5a4f/hamster_lib/helpers/config_helpers.py#L239-L302 | train |
projecthamster/hamster-lib | hamster_lib/helpers/config_helpers.py | configparser_to_backend_config | def configparser_to_backend_config(cp_instance):
"""
Return a config dict generated from a configparser instance.
This functions main purpose is to ensure config dict values are properly typed.
Note:
This can be used with any ``ConfigParser`` backend instance not just the default one
i... | python | def configparser_to_backend_config(cp_instance):
"""
Return a config dict generated from a configparser instance.
This functions main purpose is to ensure config dict values are properly typed.
Note:
This can be used with any ``ConfigParser`` backend instance not just the default one
i... | [
"def",
"configparser_to_backend_config",
"(",
"cp_instance",
")",
":",
"def",
"get_store",
"(",
")",
":",
"store",
"=",
"cp_instance",
".",
"get",
"(",
"'Backend'",
",",
"'store'",
")",
"if",
"store",
"not",
"in",
"hamster_lib",
".",
"REGISTERED_BACKENDS",
"."... | Return a config dict generated from a configparser instance.
This functions main purpose is to ensure config dict values are properly typed.
Note:
This can be used with any ``ConfigParser`` backend instance not just the default one
in order to extract its config.
If a key is not found ... | [
"Return",
"a",
"config",
"dict",
"generated",
"from",
"a",
"configparser",
"instance",
"."
] | bc34c822c239a6fa0cde3a4f90b0d00506fb5a4f | https://github.com/projecthamster/hamster-lib/blob/bc34c822c239a6fa0cde3a4f90b0d00506fb5a4f/hamster_lib/helpers/config_helpers.py#L310-L381 | train |
projecthamster/hamster-lib | hamster_lib/helpers/config_helpers.py | HamsterAppDirs.user_data_dir | def user_data_dir(self):
"""Return ``user_data_dir``."""
directory = appdirs.user_data_dir(self.appname, self.appauthor,
version=self.version, roaming=self.roaming)
if self.create:
self._ensure_directory_exists(directory)
return directory | python | def user_data_dir(self):
"""Return ``user_data_dir``."""
directory = appdirs.user_data_dir(self.appname, self.appauthor,
version=self.version, roaming=self.roaming)
if self.create:
self._ensure_directory_exists(directory)
return directory | [
"def",
"user_data_dir",
"(",
"self",
")",
":",
"directory",
"=",
"appdirs",
".",
"user_data_dir",
"(",
"self",
".",
"appname",
",",
"self",
".",
"appauthor",
",",
"version",
"=",
"self",
".",
"version",
",",
"roaming",
"=",
"self",
".",
"roaming",
")",
... | Return ``user_data_dir``. | [
"Return",
"user_data_dir",
"."
] | bc34c822c239a6fa0cde3a4f90b0d00506fb5a4f | https://github.com/projecthamster/hamster-lib/blob/bc34c822c239a6fa0cde3a4f90b0d00506fb5a4f/hamster_lib/helpers/config_helpers.py#L76-L82 | train |
projecthamster/hamster-lib | hamster_lib/helpers/config_helpers.py | HamsterAppDirs.site_config_dir | def site_config_dir(self):
"""Return ``site_config_dir``."""
directory = appdirs.site_config_dir(self.appname, self.appauthor,
version=self.version, multipath=self.multipath)
if self.create:
self._ensure_directory_exists(directory)
return director... | python | def site_config_dir(self):
"""Return ``site_config_dir``."""
directory = appdirs.site_config_dir(self.appname, self.appauthor,
version=self.version, multipath=self.multipath)
if self.create:
self._ensure_directory_exists(directory)
return director... | [
"def",
"site_config_dir",
"(",
"self",
")",
":",
"directory",
"=",
"appdirs",
".",
"site_config_dir",
"(",
"self",
".",
"appname",
",",
"self",
".",
"appauthor",
",",
"version",
"=",
"self",
".",
"version",
",",
"multipath",
"=",
"self",
".",
"multipath",
... | Return ``site_config_dir``. | [
"Return",
"site_config_dir",
"."
] | bc34c822c239a6fa0cde3a4f90b0d00506fb5a4f | https://github.com/projecthamster/hamster-lib/blob/bc34c822c239a6fa0cde3a4f90b0d00506fb5a4f/hamster_lib/helpers/config_helpers.py#L103-L109 | train |
projecthamster/hamster-lib | hamster_lib/helpers/config_helpers.py | HamsterAppDirs.user_cache_dir | def user_cache_dir(self):
"""Return ``user_cache_dir``."""
directory = appdirs.user_cache_dir(self.appname, self.appauthor,
version=self.version)
if self.create:
self._ensure_directory_exists(directory)
return directory | python | def user_cache_dir(self):
"""Return ``user_cache_dir``."""
directory = appdirs.user_cache_dir(self.appname, self.appauthor,
version=self.version)
if self.create:
self._ensure_directory_exists(directory)
return directory | [
"def",
"user_cache_dir",
"(",
"self",
")",
":",
"directory",
"=",
"appdirs",
".",
"user_cache_dir",
"(",
"self",
".",
"appname",
",",
"self",
".",
"appauthor",
",",
"version",
"=",
"self",
".",
"version",
")",
"if",
"self",
".",
"create",
":",
"self",
... | Return ``user_cache_dir``. | [
"Return",
"user_cache_dir",
"."
] | bc34c822c239a6fa0cde3a4f90b0d00506fb5a4f | https://github.com/projecthamster/hamster-lib/blob/bc34c822c239a6fa0cde3a4f90b0d00506fb5a4f/hamster_lib/helpers/config_helpers.py#L112-L118 | train |
projecthamster/hamster-lib | hamster_lib/helpers/config_helpers.py | HamsterAppDirs._ensure_directory_exists | def _ensure_directory_exists(self, directory):
"""Ensure that the passed path exists."""
if not os.path.lexists(directory):
os.makedirs(directory)
return directory | python | def _ensure_directory_exists(self, directory):
"""Ensure that the passed path exists."""
if not os.path.lexists(directory):
os.makedirs(directory)
return directory | [
"def",
"_ensure_directory_exists",
"(",
"self",
",",
"directory",
")",
":",
"if",
"not",
"os",
".",
"path",
".",
"lexists",
"(",
"directory",
")",
":",
"os",
".",
"makedirs",
"(",
"directory",
")",
"return",
"directory"
] | Ensure that the passed path exists. | [
"Ensure",
"that",
"the",
"passed",
"path",
"exists",
"."
] | bc34c822c239a6fa0cde3a4f90b0d00506fb5a4f | https://github.com/projecthamster/hamster-lib/blob/bc34c822c239a6fa0cde3a4f90b0d00506fb5a4f/hamster_lib/helpers/config_helpers.py#L129-L133 | train |
projecthamster/hamster-lib | hamster_lib/helpers/helpers.py | _load_tmp_fact | def _load_tmp_fact(filepath):
"""
Load an 'ongoing fact' from a given location.
Args:
filepath: Full path to the tmpfile location.
Returns:
hamster_lib.Fact: ``Fact`` representing the 'ongoing fact'. Returns ``False``
if no file was found.
Raises:
TypeError: If... | python | def _load_tmp_fact(filepath):
"""
Load an 'ongoing fact' from a given location.
Args:
filepath: Full path to the tmpfile location.
Returns:
hamster_lib.Fact: ``Fact`` representing the 'ongoing fact'. Returns ``False``
if no file was found.
Raises:
TypeError: If... | [
"def",
"_load_tmp_fact",
"(",
"filepath",
")",
":",
"from",
"hamster_lib",
"import",
"Fact",
"try",
":",
"with",
"open",
"(",
"filepath",
",",
"'rb'",
")",
"as",
"fobj",
":",
"fact",
"=",
"pickle",
".",
"load",
"(",
"fobj",
")",
"except",
"IOError",
":... | Load an 'ongoing fact' from a given location.
Args:
filepath: Full path to the tmpfile location.
Returns:
hamster_lib.Fact: ``Fact`` representing the 'ongoing fact'. Returns ``False``
if no file was found.
Raises:
TypeError: If for some reason our stored instance is no... | [
"Load",
"an",
"ongoing",
"fact",
"from",
"a",
"given",
"location",
"."
] | bc34c822c239a6fa0cde3a4f90b0d00506fb5a4f | https://github.com/projecthamster/hamster-lib/blob/bc34c822c239a6fa0cde3a4f90b0d00506fb5a4f/hamster_lib/helpers/helpers.py#L33-L62 | train |
projecthamster/hamster-lib | hamster_lib/helpers/helpers.py | parse_raw_fact | def parse_raw_fact(raw_fact):
"""
Extract semantically meaningful sub-components from a ``raw fact`` text.
Args:
raw_fact (text_type): ``raw fact`` text to be parsed.
Returns:
dict: dict with sub-components as values.
"""
def at_split(string):
"""
Return everyth... | python | def parse_raw_fact(raw_fact):
"""
Extract semantically meaningful sub-components from a ``raw fact`` text.
Args:
raw_fact (text_type): ``raw fact`` text to be parsed.
Returns:
dict: dict with sub-components as values.
"""
def at_split(string):
"""
Return everyth... | [
"def",
"parse_raw_fact",
"(",
"raw_fact",
")",
":",
"def",
"at_split",
"(",
"string",
")",
":",
"result",
"=",
"string",
".",
"split",
"(",
"'@'",
",",
"1",
")",
"length",
"=",
"len",
"(",
"result",
")",
"if",
"length",
"==",
"1",
":",
"front",
","... | Extract semantically meaningful sub-components from a ``raw fact`` text.
Args:
raw_fact (text_type): ``raw fact`` text to be parsed.
Returns:
dict: dict with sub-components as values. | [
"Extract",
"semantically",
"meaningful",
"sub",
"-",
"components",
"from",
"a",
"raw",
"fact",
"text",
"."
] | bc34c822c239a6fa0cde3a4f90b0d00506fb5a4f | https://github.com/projecthamster/hamster-lib/blob/bc34c822c239a6fa0cde3a4f90b0d00506fb5a4f/hamster_lib/helpers/helpers.py#L65-L147 | train |
zblz/naima | naima/analysis.py | read_run | def read_run(filename, modelfn=None):
"""
Read chain from a hdf5 saved with `save_run`.
This function will also read the labels, data table, and metadata blobs
stored in the original sampler. If you want to use the result object with
`plot_fit` and setting the ``e_range`` parameter, you must provid... | python | def read_run(filename, modelfn=None):
"""
Read chain from a hdf5 saved with `save_run`.
This function will also read the labels, data table, and metadata blobs
stored in the original sampler. If you want to use the result object with
`plot_fit` and setting the ``e_range`` parameter, you must provid... | [
"def",
"read_run",
"(",
"filename",
",",
"modelfn",
"=",
"None",
")",
":",
"result",
"=",
"_result",
"(",
")",
"result",
".",
"modelfn",
"=",
"modelfn",
"result",
".",
"run_info",
"=",
"{",
"}",
"f",
"=",
"h5py",
".",
"File",
"(",
"filename",
",",
... | Read chain from a hdf5 saved with `save_run`.
This function will also read the labels, data table, and metadata blobs
stored in the original sampler. If you want to use the result object with
`plot_fit` and setting the ``e_range`` parameter, you must provide the
model function with the `modelfn` argume... | [
"Read",
"chain",
"from",
"a",
"hdf5",
"saved",
"with",
"save_run",
"."
] | d6a6781d73bf58fd8269e8b0e3b70be22723cd5b | https://github.com/zblz/naima/blob/d6a6781d73bf58fd8269e8b0e3b70be22723cd5b/naima/analysis.py#L530-L627 | train |
zblz/naima | examples/RXJ1713_IC_minimal.py | ElectronIC | def ElectronIC(pars, data):
"""
Define particle distribution model, radiative model, and return model flux
at data energy values
"""
ECPL = ExponentialCutoffPowerLaw(
pars[0] / u.eV, 10.0 * u.TeV, pars[1], 10 ** pars[2] * u.TeV
)
IC = InverseCompton(ECPL, seed_photon_fields=["CMB"])... | python | def ElectronIC(pars, data):
"""
Define particle distribution model, radiative model, and return model flux
at data energy values
"""
ECPL = ExponentialCutoffPowerLaw(
pars[0] / u.eV, 10.0 * u.TeV, pars[1], 10 ** pars[2] * u.TeV
)
IC = InverseCompton(ECPL, seed_photon_fields=["CMB"])... | [
"def",
"ElectronIC",
"(",
"pars",
",",
"data",
")",
":",
"ECPL",
"=",
"ExponentialCutoffPowerLaw",
"(",
"pars",
"[",
"0",
"]",
"/",
"u",
".",
"eV",
",",
"10.0",
"*",
"u",
".",
"TeV",
",",
"pars",
"[",
"1",
"]",
",",
"10",
"**",
"pars",
"[",
"2"... | Define particle distribution model, radiative model, and return model flux
at data energy values | [
"Define",
"particle",
"distribution",
"model",
"radiative",
"model",
"and",
"return",
"model",
"flux",
"at",
"data",
"energy",
"values"
] | d6a6781d73bf58fd8269e8b0e3b70be22723cd5b | https://github.com/zblz/naima/blob/d6a6781d73bf58fd8269e8b0e3b70be22723cd5b/examples/RXJ1713_IC_minimal.py#L13-L24 | train |
zblz/naima | naima/sherpa_models.py | SherpaModelECPL._pdist | def _pdist(p):
""" Return PL or ECPL instance based on parameters p """
index, ref, ampl, cutoff, beta = p[:5]
if cutoff == 0.0:
pdist = models.PowerLaw(
ampl * 1e30 * u.Unit("1/eV"), ref * u.TeV, index
)
else:
pdist = models.Exponentia... | python | def _pdist(p):
""" Return PL or ECPL instance based on parameters p """
index, ref, ampl, cutoff, beta = p[:5]
if cutoff == 0.0:
pdist = models.PowerLaw(
ampl * 1e30 * u.Unit("1/eV"), ref * u.TeV, index
)
else:
pdist = models.Exponentia... | [
"def",
"_pdist",
"(",
"p",
")",
":",
"index",
",",
"ref",
",",
"ampl",
",",
"cutoff",
",",
"beta",
"=",
"p",
"[",
":",
"5",
"]",
"if",
"cutoff",
"==",
"0.0",
":",
"pdist",
"=",
"models",
".",
"PowerLaw",
"(",
"ampl",
"*",
"1e30",
"*",
"u",
".... | Return PL or ECPL instance based on parameters p | [
"Return",
"PL",
"or",
"ECPL",
"instance",
"based",
"on",
"parameters",
"p"
] | d6a6781d73bf58fd8269e8b0e3b70be22723cd5b | https://github.com/zblz/naima/blob/d6a6781d73bf58fd8269e8b0e3b70be22723cd5b/naima/sherpa_models.py#L103-L118 | train |
zblz/naima | naima/plot.py | plot_chain | def plot_chain(sampler, p=None, **kwargs):
"""Generate a diagnostic plot of the sampler chains.
Parameters
----------
sampler : `emcee.EnsembleSampler`
Sampler containing the chains to be plotted.
p : int (optional)
Index of the parameter to plot. If omitted, all chains are plotted.... | python | def plot_chain(sampler, p=None, **kwargs):
"""Generate a diagnostic plot of the sampler chains.
Parameters
----------
sampler : `emcee.EnsembleSampler`
Sampler containing the chains to be plotted.
p : int (optional)
Index of the parameter to plot. If omitted, all chains are plotted.... | [
"def",
"plot_chain",
"(",
"sampler",
",",
"p",
"=",
"None",
",",
"**",
"kwargs",
")",
":",
"if",
"p",
"is",
"None",
":",
"npars",
"=",
"sampler",
".",
"chain",
".",
"shape",
"[",
"-",
"1",
"]",
"for",
"pp",
"in",
"six",
".",
"moves",
".",
"rang... | Generate a diagnostic plot of the sampler chains.
Parameters
----------
sampler : `emcee.EnsembleSampler`
Sampler containing the chains to be plotted.
p : int (optional)
Index of the parameter to plot. If omitted, all chains are plotted.
last_step : bool (optional)
Whether t... | [
"Generate",
"a",
"diagnostic",
"plot",
"of",
"the",
"sampler",
"chains",
"."
] | d6a6781d73bf58fd8269e8b0e3b70be22723cd5b | https://github.com/zblz/naima/blob/d6a6781d73bf58fd8269e8b0e3b70be22723cd5b/naima/plot.py#L34-L60 | train |
zblz/naima | naima/plot.py | _read_or_calc_samples | def _read_or_calc_samples(
sampler,
modelidx=0,
n_samples=100,
last_step=False,
e_range=None,
e_npoints=100,
threads=None,
):
"""Get samples from blob or compute them from chain and sampler.modelfn
"""
if not e_range:
# return the results saved in blobs
modelx, m... | python | def _read_or_calc_samples(
sampler,
modelidx=0,
n_samples=100,
last_step=False,
e_range=None,
e_npoints=100,
threads=None,
):
"""Get samples from blob or compute them from chain and sampler.modelfn
"""
if not e_range:
# return the results saved in blobs
modelx, m... | [
"def",
"_read_or_calc_samples",
"(",
"sampler",
",",
"modelidx",
"=",
"0",
",",
"n_samples",
"=",
"100",
",",
"last_step",
"=",
"False",
",",
"e_range",
"=",
"None",
",",
"e_npoints",
"=",
"100",
",",
"threads",
"=",
"None",
",",
")",
":",
"if",
"not",... | Get samples from blob or compute them from chain and sampler.modelfn | [
"Get",
"samples",
"from",
"blob",
"or",
"compute",
"them",
"from",
"chain",
"and",
"sampler",
".",
"modelfn"
] | d6a6781d73bf58fd8269e8b0e3b70be22723cd5b | https://github.com/zblz/naima/blob/d6a6781d73bf58fd8269e8b0e3b70be22723cd5b/naima/plot.py#L368-L421 | train |
zblz/naima | naima/plot.py | _calc_ML | def _calc_ML(sampler, modelidx=0, e_range=None, e_npoints=100):
"""Get ML model from blob or compute them from chain and sampler.modelfn
"""
ML, MLp, MLerr, ML_model = find_ML(sampler, modelidx)
if e_range is not None:
# prepare bogus data for calculation
e_range = validate_array(
... | python | def _calc_ML(sampler, modelidx=0, e_range=None, e_npoints=100):
"""Get ML model from blob or compute them from chain and sampler.modelfn
"""
ML, MLp, MLerr, ML_model = find_ML(sampler, modelidx)
if e_range is not None:
# prepare bogus data for calculation
e_range = validate_array(
... | [
"def",
"_calc_ML",
"(",
"sampler",
",",
"modelidx",
"=",
"0",
",",
"e_range",
"=",
"None",
",",
"e_npoints",
"=",
"100",
")",
":",
"ML",
",",
"MLp",
",",
"MLerr",
",",
"ML_model",
"=",
"find_ML",
"(",
"sampler",
",",
"modelidx",
")",
"if",
"e_range",... | Get ML model from blob or compute them from chain and sampler.modelfn | [
"Get",
"ML",
"model",
"from",
"blob",
"or",
"compute",
"them",
"from",
"chain",
"and",
"sampler",
".",
"modelfn"
] | d6a6781d73bf58fd8269e8b0e3b70be22723cd5b | https://github.com/zblz/naima/blob/d6a6781d73bf58fd8269e8b0e3b70be22723cd5b/naima/plot.py#L424-L466 | train |
zblz/naima | naima/plot.py | _plot_MLmodel | def _plot_MLmodel(ax, sampler, modelidx, e_range, e_npoints, e_unit, sed):
"""compute and plot ML model"""
ML, MLp, MLerr, ML_model = _calc_ML(
sampler, modelidx, e_range=e_range, e_npoints=e_npoints
)
f_unit, sedf = sed_conversion(ML_model[0], ML_model[1].unit, sed)
ax.loglog(
ML_... | python | def _plot_MLmodel(ax, sampler, modelidx, e_range, e_npoints, e_unit, sed):
"""compute and plot ML model"""
ML, MLp, MLerr, ML_model = _calc_ML(
sampler, modelidx, e_range=e_range, e_npoints=e_npoints
)
f_unit, sedf = sed_conversion(ML_model[0], ML_model[1].unit, sed)
ax.loglog(
ML_... | [
"def",
"_plot_MLmodel",
"(",
"ax",
",",
"sampler",
",",
"modelidx",
",",
"e_range",
",",
"e_npoints",
",",
"e_unit",
",",
"sed",
")",
":",
"ML",
",",
"MLp",
",",
"MLerr",
",",
"ML_model",
"=",
"_calc_ML",
"(",
"sampler",
",",
"modelidx",
",",
"e_range"... | compute and plot ML model | [
"compute",
"and",
"plot",
"ML",
"model"
] | d6a6781d73bf58fd8269e8b0e3b70be22723cd5b | https://github.com/zblz/naima/blob/d6a6781d73bf58fd8269e8b0e3b70be22723cd5b/naima/plot.py#L536-L550 | train |
zblz/naima | naima/plot.py | plot_CI | def plot_CI(
ax,
sampler,
modelidx=0,
sed=True,
confs=[3, 1, 0.5],
e_unit=u.eV,
label=None,
e_range=None,
e_npoints=100,
threads=None,
last_step=False,
):
"""Plot confidence interval.
Parameters
----------
ax : `matplotlib.Axes`
Axes to plot on.
s... | python | def plot_CI(
ax,
sampler,
modelidx=0,
sed=True,
confs=[3, 1, 0.5],
e_unit=u.eV,
label=None,
e_range=None,
e_npoints=100,
threads=None,
last_step=False,
):
"""Plot confidence interval.
Parameters
----------
ax : `matplotlib.Axes`
Axes to plot on.
s... | [
"def",
"plot_CI",
"(",
"ax",
",",
"sampler",
",",
"modelidx",
"=",
"0",
",",
"sed",
"=",
"True",
",",
"confs",
"=",
"[",
"3",
",",
"1",
",",
"0.5",
"]",
",",
"e_unit",
"=",
"u",
".",
"eV",
",",
"label",
"=",
"None",
",",
"e_range",
"=",
"None... | Plot confidence interval.
Parameters
----------
ax : `matplotlib.Axes`
Axes to plot on.
sampler : `emcee.EnsembleSampler`
Sampler
modelidx : int, optional
Model index. Default is 0
sed : bool, optional
Whether to plot SED or differential spectrum. If `None`, the ... | [
"Plot",
"confidence",
"interval",
"."
] | d6a6781d73bf58fd8269e8b0e3b70be22723cd5b | https://github.com/zblz/naima/blob/d6a6781d73bf58fd8269e8b0e3b70be22723cd5b/naima/plot.py#L553-L625 | train |
zblz/naima | naima/plot.py | plot_samples | def plot_samples(
ax,
sampler,
modelidx=0,
sed=True,
n_samples=100,
e_unit=u.eV,
e_range=None,
e_npoints=100,
threads=None,
label=None,
last_step=False,
):
"""Plot a number of samples from the sampler chain.
Parameters
----------
ax : `matplotlib.Axes`
... | python | def plot_samples(
ax,
sampler,
modelidx=0,
sed=True,
n_samples=100,
e_unit=u.eV,
e_range=None,
e_npoints=100,
threads=None,
label=None,
last_step=False,
):
"""Plot a number of samples from the sampler chain.
Parameters
----------
ax : `matplotlib.Axes`
... | [
"def",
"plot_samples",
"(",
"ax",
",",
"sampler",
",",
"modelidx",
"=",
"0",
",",
"sed",
"=",
"True",
",",
"n_samples",
"=",
"100",
",",
"e_unit",
"=",
"u",
".",
"eV",
",",
"e_range",
"=",
"None",
",",
"e_npoints",
"=",
"100",
",",
"threads",
"=",
... | Plot a number of samples from the sampler chain.
Parameters
----------
ax : `matplotlib.Axes`
Axes to plot on.
sampler : `emcee.EnsembleSampler`
Sampler
modelidx : int, optional
Model index. Default is 0
sed : bool, optional
Whether to plot SED or differential sp... | [
"Plot",
"a",
"number",
"of",
"samples",
"from",
"the",
"sampler",
"chain",
"."
] | d6a6781d73bf58fd8269e8b0e3b70be22723cd5b | https://github.com/zblz/naima/blob/d6a6781d73bf58fd8269e8b0e3b70be22723cd5b/naima/plot.py#L628-L700 | train |
zblz/naima | naima/plot.py | find_ML | def find_ML(sampler, modelidx):
"""
Find Maximum Likelihood parameters as those in the chain with a highest log
probability.
"""
index = np.unravel_index(
np.argmax(sampler.lnprobability), sampler.lnprobability.shape
)
MLp = sampler.chain[index]
if modelidx is not None and hasatt... | python | def find_ML(sampler, modelidx):
"""
Find Maximum Likelihood parameters as those in the chain with a highest log
probability.
"""
index = np.unravel_index(
np.argmax(sampler.lnprobability), sampler.lnprobability.shape
)
MLp = sampler.chain[index]
if modelidx is not None and hasatt... | [
"def",
"find_ML",
"(",
"sampler",
",",
"modelidx",
")",
":",
"index",
"=",
"np",
".",
"unravel_index",
"(",
"np",
".",
"argmax",
"(",
"sampler",
".",
"lnprobability",
")",
",",
"sampler",
".",
"lnprobability",
".",
"shape",
")",
"MLp",
"=",
"sampler",
... | Find Maximum Likelihood parameters as those in the chain with a highest log
probability. | [
"Find",
"Maximum",
"Likelihood",
"parameters",
"as",
"those",
"in",
"the",
"chain",
"with",
"a",
"highest",
"log",
"probability",
"."
] | d6a6781d73bf58fd8269e8b0e3b70be22723cd5b | https://github.com/zblz/naima/blob/d6a6781d73bf58fd8269e8b0e3b70be22723cd5b/naima/plot.py#L703-L738 | train |
zblz/naima | naima/plot.py | plot_blob | def plot_blob(
sampler, blobidx=0, label=None, last_step=False, figure=None, **kwargs
):
"""
Plot a metadata blob as a fit to spectral data or value distribution
Additional ``kwargs`` are passed to `plot_fit`.
Parameters
----------
sampler : `emcee.EnsembleSampler`
Sampler with a s... | python | def plot_blob(
sampler, blobidx=0, label=None, last_step=False, figure=None, **kwargs
):
"""
Plot a metadata blob as a fit to spectral data or value distribution
Additional ``kwargs`` are passed to `plot_fit`.
Parameters
----------
sampler : `emcee.EnsembleSampler`
Sampler with a s... | [
"def",
"plot_blob",
"(",
"sampler",
",",
"blobidx",
"=",
"0",
",",
"label",
"=",
"None",
",",
"last_step",
"=",
"False",
",",
"figure",
"=",
"None",
",",
"**",
"kwargs",
")",
":",
"modelx",
",",
"model",
"=",
"_process_blob",
"(",
"sampler",
",",
"bl... | Plot a metadata blob as a fit to spectral data or value distribution
Additional ``kwargs`` are passed to `plot_fit`.
Parameters
----------
sampler : `emcee.EnsembleSampler`
Sampler with a stored chain.
blobidx : int, optional
Metadata blob index to plot.
label : str, optional
... | [
"Plot",
"a",
"metadata",
"blob",
"as",
"a",
"fit",
"to",
"spectral",
"data",
"or",
"value",
"distribution"
] | d6a6781d73bf58fd8269e8b0e3b70be22723cd5b | https://github.com/zblz/naima/blob/d6a6781d73bf58fd8269e8b0e3b70be22723cd5b/naima/plot.py#L741-L782 | train |
zblz/naima | naima/plot.py | _plot_ulims | def _plot_ulims(
ax, x, y, xerr, color, capsize=5, height_fraction=0.25, elinewidth=2
):
"""
Plot upper limits as arrows with cap at value of upper limit.
uplim behaviour has been fixed in matplotlib 1.4
"""
ax.errorbar(
x, y, xerr=xerr, ls="", color=color, elinewidth=elinewidth, capsiz... | python | def _plot_ulims(
ax, x, y, xerr, color, capsize=5, height_fraction=0.25, elinewidth=2
):
"""
Plot upper limits as arrows with cap at value of upper limit.
uplim behaviour has been fixed in matplotlib 1.4
"""
ax.errorbar(
x, y, xerr=xerr, ls="", color=color, elinewidth=elinewidth, capsiz... | [
"def",
"_plot_ulims",
"(",
"ax",
",",
"x",
",",
"y",
",",
"xerr",
",",
"color",
",",
"capsize",
"=",
"5",
",",
"height_fraction",
"=",
"0.25",
",",
"elinewidth",
"=",
"2",
")",
":",
"ax",
".",
"errorbar",
"(",
"x",
",",
"y",
",",
"xerr",
"=",
"... | Plot upper limits as arrows with cap at value of upper limit.
uplim behaviour has been fixed in matplotlib 1.4 | [
"Plot",
"upper",
"limits",
"as",
"arrows",
"with",
"cap",
"at",
"value",
"of",
"upper",
"limit",
"."
] | d6a6781d73bf58fd8269e8b0e3b70be22723cd5b | https://github.com/zblz/naima/blob/d6a6781d73bf58fd8269e8b0e3b70be22723cd5b/naima/plot.py#L1044-L1083 | train |
zblz/naima | naima/plot.py | plot_data | def plot_data(
input_data,
xlabel=None,
ylabel=None,
sed=True,
figure=None,
e_unit=None,
ulim_opts={},
errorbar_opts={},
):
"""
Plot spectral data.
Parameters
----------
input_data : `emcee.EnsembleSampler`, `astropy.table.Table`, or `dict`
Spectral data to p... | python | def plot_data(
input_data,
xlabel=None,
ylabel=None,
sed=True,
figure=None,
e_unit=None,
ulim_opts={},
errorbar_opts={},
):
"""
Plot spectral data.
Parameters
----------
input_data : `emcee.EnsembleSampler`, `astropy.table.Table`, or `dict`
Spectral data to p... | [
"def",
"plot_data",
"(",
"input_data",
",",
"xlabel",
"=",
"None",
",",
"ylabel",
"=",
"None",
",",
"sed",
"=",
"True",
",",
"figure",
"=",
"None",
",",
"e_unit",
"=",
"None",
",",
"ulim_opts",
"=",
"{",
"}",
",",
"errorbar_opts",
"=",
"{",
"}",
",... | Plot spectral data.
Parameters
----------
input_data : `emcee.EnsembleSampler`, `astropy.table.Table`, or `dict`
Spectral data to plot. Can be given as a data table, a dict generated
with `validate_data_table` or a `emcee.EnsembleSampler` with a data
property.
xlabel : str, opti... | [
"Plot",
"spectral",
"data",
"."
] | d6a6781d73bf58fd8269e8b0e3b70be22723cd5b | https://github.com/zblz/naima/blob/d6a6781d73bf58fd8269e8b0e3b70be22723cd5b/naima/plot.py#L1282-L1376 | train |
zblz/naima | naima/plot.py | plot_distribution | def plot_distribution(samples, label, figure=None):
""" Plot a distribution and print statistics about it"""
from scipy import stats
import matplotlib.pyplot as plt
quant = [16, 50, 84]
quantiles = dict(six.moves.zip(quant, np.percentile(samples, quant)))
std = np.std(samples)
if isinstan... | python | def plot_distribution(samples, label, figure=None):
""" Plot a distribution and print statistics about it"""
from scipy import stats
import matplotlib.pyplot as plt
quant = [16, 50, 84]
quantiles = dict(six.moves.zip(quant, np.percentile(samples, quant)))
std = np.std(samples)
if isinstan... | [
"def",
"plot_distribution",
"(",
"samples",
",",
"label",
",",
"figure",
"=",
"None",
")",
":",
"from",
"scipy",
"import",
"stats",
"import",
"matplotlib",
".",
"pyplot",
"as",
"plt",
"quant",
"=",
"[",
"16",
",",
"50",
",",
"84",
"]",
"quantiles",
"="... | Plot a distribution and print statistics about it | [
"Plot",
"a",
"distribution",
"and",
"print",
"statistics",
"about",
"it"
] | d6a6781d73bf58fd8269e8b0e3b70be22723cd5b | https://github.com/zblz/naima/blob/d6a6781d73bf58fd8269e8b0e3b70be22723cd5b/naima/plot.py#L1379-L1469 | train |
zblz/naima | naima/plot.py | plot_corner | def plot_corner(sampler, show_ML=True, **kwargs):
"""
A plot that summarizes the parameter samples by showing them as individual
histograms and 2D histograms against each other. The maximum likelihood
parameter vector is indicated by a cross.
This function is a thin wrapper around `corner.corner`, ... | python | def plot_corner(sampler, show_ML=True, **kwargs):
"""
A plot that summarizes the parameter samples by showing them as individual
histograms and 2D histograms against each other. The maximum likelihood
parameter vector is indicated by a cross.
This function is a thin wrapper around `corner.corner`, ... | [
"def",
"plot_corner",
"(",
"sampler",
",",
"show_ML",
"=",
"True",
",",
"**",
"kwargs",
")",
":",
"import",
"matplotlib",
".",
"pyplot",
"as",
"plt",
"oldlw",
"=",
"plt",
".",
"rcParams",
"[",
"\"lines.linewidth\"",
"]",
"plt",
".",
"rcParams",
"[",
"\"l... | A plot that summarizes the parameter samples by showing them as individual
histograms and 2D histograms against each other. The maximum likelihood
parameter vector is indicated by a cross.
This function is a thin wrapper around `corner.corner`, found at
https://github.com/corner/corner.py.
Paramet... | [
"A",
"plot",
"that",
"summarizes",
"the",
"parameter",
"samples",
"by",
"showing",
"them",
"as",
"individual",
"histograms",
"and",
"2D",
"histograms",
"against",
"each",
"other",
".",
"The",
"maximum",
"likelihood",
"parameter",
"vector",
"is",
"indicated",
"by... | d6a6781d73bf58fd8269e8b0e3b70be22723cd5b | https://github.com/zblz/naima/blob/d6a6781d73bf58fd8269e8b0e3b70be22723cd5b/naima/plot.py#L1472-L1520 | train |
flowersteam/explauto | explauto/sensorimotor_model/forward/nn.py | NNForwardModel.predict_given_context | def predict_given_context(self, x, c, c_dims):
"""Provide a prediction of x with context c on dimensions c_dims in the output space being S - c_dims
@param x an array of float of length dim_x
@return predicted y as np.array of float
"""
assert len(c) == len(c_dims)
_... | python | def predict_given_context(self, x, c, c_dims):
"""Provide a prediction of x with context c on dimensions c_dims in the output space being S - c_dims
@param x an array of float of length dim_x
@return predicted y as np.array of float
"""
assert len(c) == len(c_dims)
_... | [
"def",
"predict_given_context",
"(",
"self",
",",
"x",
",",
"c",
",",
"c_dims",
")",
":",
"assert",
"len",
"(",
"c",
")",
"==",
"len",
"(",
"c_dims",
")",
"_",
",",
"index",
"=",
"self",
".",
"dataset",
".",
"nn_dims",
"(",
"x",
",",
"c",
",",
... | Provide a prediction of x with context c on dimensions c_dims in the output space being S - c_dims
@param x an array of float of length dim_x
@return predicted y as np.array of float | [
"Provide",
"a",
"prediction",
"of",
"x",
"with",
"context",
"c",
"on",
"dimensions",
"c_dims",
"in",
"the",
"output",
"space",
"being",
"S",
"-",
"c_dims"
] | cf0f81ecb9f6412f7276a95bd27359000e1e26b6 | https://github.com/flowersteam/explauto/blob/cf0f81ecb9f6412f7276a95bd27359000e1e26b6/explauto/sensorimotor_model/forward/nn.py#L35-L43 | train |
flowersteam/explauto | explauto/models/pydmps/dmp_rhythmic.py | DMPs_rhythmic.gen_front_term | def gen_front_term(self, x, dmp_num):
"""Generates the front term on the forcing term.
For rhythmic DMPs it's non-diminishing, so this
function is just a placeholder to return 1.
x float: the current value of the canonical system
dmp_num int: the index of the current dmp
... | python | def gen_front_term(self, x, dmp_num):
"""Generates the front term on the forcing term.
For rhythmic DMPs it's non-diminishing, so this
function is just a placeholder to return 1.
x float: the current value of the canonical system
dmp_num int: the index of the current dmp
... | [
"def",
"gen_front_term",
"(",
"self",
",",
"x",
",",
"dmp_num",
")",
":",
"if",
"isinstance",
"(",
"x",
",",
"np",
".",
"ndarray",
")",
":",
"return",
"np",
".",
"ones",
"(",
"x",
".",
"shape",
")",
"return",
"1"
] | Generates the front term on the forcing term.
For rhythmic DMPs it's non-diminishing, so this
function is just a placeholder to return 1.
x float: the current value of the canonical system
dmp_num int: the index of the current dmp | [
"Generates",
"the",
"front",
"term",
"on",
"the",
"forcing",
"term",
".",
"For",
"rhythmic",
"DMPs",
"it",
"s",
"non",
"-",
"diminishing",
"so",
"this",
"function",
"is",
"just",
"a",
"placeholder",
"to",
"return",
"1",
"."
] | cf0f81ecb9f6412f7276a95bd27359000e1e26b6 | https://github.com/flowersteam/explauto/blob/cf0f81ecb9f6412f7276a95bd27359000e1e26b6/explauto/models/pydmps/dmp_rhythmic.py#L48-L59 | train |
flowersteam/explauto | explauto/sensorimotor_model/forward/lwr.py | LWLRForwardModel.predict_y | def predict_y(self, xq, sigma=None, k=None):
"""Provide a prediction of xq in the output space
@param xq an array of float of length dim_x
@param estimated_sigma if False (default), sigma_sq=self.sigma_sq, else it is estimated from the neighbor distances in self._weights(.)
"""
... | python | def predict_y(self, xq, sigma=None, k=None):
"""Provide a prediction of xq in the output space
@param xq an array of float of length dim_x
@param estimated_sigma if False (default), sigma_sq=self.sigma_sq, else it is estimated from the neighbor distances in self._weights(.)
"""
... | [
"def",
"predict_y",
"(",
"self",
",",
"xq",
",",
"sigma",
"=",
"None",
",",
"k",
"=",
"None",
")",
":",
"assert",
"len",
"(",
"xq",
")",
"==",
"self",
".",
"dataset",
".",
"dim_x",
"sigma_sq",
"=",
"self",
".",
"sigma_sq",
"if",
"sigma",
"is",
"N... | Provide a prediction of xq in the output space
@param xq an array of float of length dim_x
@param estimated_sigma if False (default), sigma_sq=self.sigma_sq, else it is estimated from the neighbor distances in self._weights(.) | [
"Provide",
"a",
"prediction",
"of",
"xq",
"in",
"the",
"output",
"space"
] | cf0f81ecb9f6412f7276a95bd27359000e1e26b6 | https://github.com/flowersteam/explauto/blob/cf0f81ecb9f6412f7276a95bd27359000e1e26b6/explauto/sensorimotor_model/forward/lwr.py#L89-L115 | train |
flowersteam/explauto | explauto/sensorimotor_model/forward/lwr.py | LWLRForwardModel.predict_dims | def predict_dims(self, q, dims_x, dims_y, dims_out, sigma=None, k=None):
"""Provide a prediction of q in the output space
@param xq an array of float of length dim_x
@param estimated_sigma if False (default), sigma_sq=self.sigma_sq, else it is estimated from the neighbor distances in self._we... | python | def predict_dims(self, q, dims_x, dims_y, dims_out, sigma=None, k=None):
"""Provide a prediction of q in the output space
@param xq an array of float of length dim_x
@param estimated_sigma if False (default), sigma_sq=self.sigma_sq, else it is estimated from the neighbor distances in self._we... | [
"def",
"predict_dims",
"(",
"self",
",",
"q",
",",
"dims_x",
",",
"dims_y",
",",
"dims_out",
",",
"sigma",
"=",
"None",
",",
"k",
"=",
"None",
")",
":",
"assert",
"len",
"(",
"q",
")",
"==",
"len",
"(",
"dims_x",
")",
"+",
"len",
"(",
"dims_y",
... | Provide a prediction of q in the output space
@param xq an array of float of length dim_x
@param estimated_sigma if False (default), sigma_sq=self.sigma_sq, else it is estimated from the neighbor distances in self._weights(.) | [
"Provide",
"a",
"prediction",
"of",
"q",
"in",
"the",
"output",
"space"
] | cf0f81ecb9f6412f7276a95bd27359000e1e26b6 | https://github.com/flowersteam/explauto/blob/cf0f81ecb9f6412f7276a95bd27359000e1e26b6/explauto/sensorimotor_model/forward/lwr.py#L126-L153 | train |
flowersteam/explauto | explauto/experiment/log.py | ExperimentLog.scatter_plot | def scatter_plot(self, ax, topic_dims, t=None, ms_limits=True, **kwargs_plot):
""" 2D or 3D scatter plot.
:param axes ax: matplotlib axes (use Axes3D if 3D data)
:param tuple topic_dims: list of (topic, dims) tuples, where topic is a string and dims is a list of dimensions to be plotte... | python | def scatter_plot(self, ax, topic_dims, t=None, ms_limits=True, **kwargs_plot):
""" 2D or 3D scatter plot.
:param axes ax: matplotlib axes (use Axes3D if 3D data)
:param tuple topic_dims: list of (topic, dims) tuples, where topic is a string and dims is a list of dimensions to be plotte... | [
"def",
"scatter_plot",
"(",
"self",
",",
"ax",
",",
"topic_dims",
",",
"t",
"=",
"None",
",",
"ms_limits",
"=",
"True",
",",
"**",
"kwargs_plot",
")",
":",
"plot_specs",
"=",
"{",
"'marker'",
":",
"'o'",
",",
"'linestyle'",
":",
"'None'",
"}",
"plot_sp... | 2D or 3D scatter plot.
:param axes ax: matplotlib axes (use Axes3D if 3D data)
:param tuple topic_dims: list of (topic, dims) tuples, where topic is a string and dims is a list of dimensions to be plotted for that topic.
:param int t: time indexes to be plotted
:param... | [
"2D",
"or",
"3D",
"scatter",
"plot",
"."
] | cf0f81ecb9f6412f7276a95bd27359000e1e26b6 | https://github.com/flowersteam/explauto/blob/cf0f81ecb9f6412f7276a95bd27359000e1e26b6/explauto/experiment/log.py#L67-L93 | train |
flowersteam/explauto | explauto/interest_model/tree.py | Tree.get_nodes | def get_nodes(self):
"""
Get the list of all nodes.
"""
return self.fold_up(lambda n, fl, fg: [n] + fl + fg, lambda leaf: [leaf]) | python | def get_nodes(self):
"""
Get the list of all nodes.
"""
return self.fold_up(lambda n, fl, fg: [n] + fl + fg, lambda leaf: [leaf]) | [
"def",
"get_nodes",
"(",
"self",
")",
":",
"return",
"self",
".",
"fold_up",
"(",
"lambda",
"n",
",",
"fl",
",",
"fg",
":",
"[",
"n",
"]",
"+",
"fl",
"+",
"fg",
",",
"lambda",
"leaf",
":",
"[",
"leaf",
"]",
")"
] | Get the list of all nodes. | [
"Get",
"the",
"list",
"of",
"all",
"nodes",
"."
] | cf0f81ecb9f6412f7276a95bd27359000e1e26b6 | https://github.com/flowersteam/explauto/blob/cf0f81ecb9f6412f7276a95bd27359000e1e26b6/explauto/interest_model/tree.py#L189-L194 | train |
flowersteam/explauto | explauto/interest_model/tree.py | Tree.get_leaves | def get_leaves(self):
"""
Get the list of all leaves.
"""
return self.fold_up(lambda n, fl, fg: fl + fg, lambda leaf: [leaf]) | python | def get_leaves(self):
"""
Get the list of all leaves.
"""
return self.fold_up(lambda n, fl, fg: fl + fg, lambda leaf: [leaf]) | [
"def",
"get_leaves",
"(",
"self",
")",
":",
"return",
"self",
".",
"fold_up",
"(",
"lambda",
"n",
",",
"fl",
",",
"fg",
":",
"fl",
"+",
"fg",
",",
"lambda",
"leaf",
":",
"[",
"leaf",
"]",
")"
] | Get the list of all leaves. | [
"Get",
"the",
"list",
"of",
"all",
"leaves",
"."
] | cf0f81ecb9f6412f7276a95bd27359000e1e26b6 | https://github.com/flowersteam/explauto/blob/cf0f81ecb9f6412f7276a95bd27359000e1e26b6/explauto/interest_model/tree.py#L197-L202 | train |
flowersteam/explauto | explauto/interest_model/tree.py | Tree.pt2leaf | def pt2leaf(self, x):
"""
Get the leaf which domain contains x.
"""
if self.leafnode:
return self
else:
if x[self.split_dim] < self.split_value:
return self.lower.pt2leaf(x)
else:
return self.greater.pt2... | python | def pt2leaf(self, x):
"""
Get the leaf which domain contains x.
"""
if self.leafnode:
return self
else:
if x[self.split_dim] < self.split_value:
return self.lower.pt2leaf(x)
else:
return self.greater.pt2... | [
"def",
"pt2leaf",
"(",
"self",
",",
"x",
")",
":",
"if",
"self",
".",
"leafnode",
":",
"return",
"self",
"else",
":",
"if",
"x",
"[",
"self",
".",
"split_dim",
"]",
"<",
"self",
".",
"split_value",
":",
"return",
"self",
".",
"lower",
".",
"pt2leaf... | Get the leaf which domain contains x. | [
"Get",
"the",
"leaf",
"which",
"domain",
"contains",
"x",
"."
] | cf0f81ecb9f6412f7276a95bd27359000e1e26b6 | https://github.com/flowersteam/explauto/blob/cf0f81ecb9f6412f7276a95bd27359000e1e26b6/explauto/interest_model/tree.py#L221-L232 | train |
flowersteam/explauto | explauto/interest_model/tree.py | Tree.sample_random | def sample_random(self):
"""
Sample a point in a random leaf.
"""
if self.sampling_mode['volume']:
# Choose a leaf weighted by volume, randomly
if self.leafnode:
return self.sample_bounds()
else:
split_ratio =... | python | def sample_random(self):
"""
Sample a point in a random leaf.
"""
if self.sampling_mode['volume']:
# Choose a leaf weighted by volume, randomly
if self.leafnode:
return self.sample_bounds()
else:
split_ratio =... | [
"def",
"sample_random",
"(",
"self",
")",
":",
"if",
"self",
".",
"sampling_mode",
"[",
"'volume'",
"]",
":",
"if",
"self",
".",
"leafnode",
":",
"return",
"self",
".",
"sample_bounds",
"(",
")",
"else",
":",
"split_ratio",
"=",
"(",
"(",
"self",
".",
... | Sample a point in a random leaf. | [
"Sample",
"a",
"point",
"in",
"a",
"random",
"leaf",
"."
] | cf0f81ecb9f6412f7276a95bd27359000e1e26b6 | https://github.com/flowersteam/explauto/blob/cf0f81ecb9f6412f7276a95bd27359000e1e26b6/explauto/interest_model/tree.py#L244-L262 | train |
flowersteam/explauto | explauto/interest_model/tree.py | Tree.sample_greedy | def sample_greedy(self):
"""
Sample a point in the leaf with the max progress.
"""
if self.leafnode:
return self.sample_bounds()
else:
lp = self.lower.max_leaf_progress
gp = self.greater.max_leaf_progress
maxp =... | python | def sample_greedy(self):
"""
Sample a point in the leaf with the max progress.
"""
if self.leafnode:
return self.sample_bounds()
else:
lp = self.lower.max_leaf_progress
gp = self.greater.max_leaf_progress
maxp =... | [
"def",
"sample_greedy",
"(",
"self",
")",
":",
"if",
"self",
".",
"leafnode",
":",
"return",
"self",
".",
"sample_bounds",
"(",
")",
"else",
":",
"lp",
"=",
"self",
".",
"lower",
".",
"max_leaf_progress",
"gp",
"=",
"self",
".",
"greater",
".",
"max_le... | Sample a point in the leaf with the max progress. | [
"Sample",
"a",
"point",
"in",
"the",
"leaf",
"with",
"the",
"max",
"progress",
"."
] | cf0f81ecb9f6412f7276a95bd27359000e1e26b6 | https://github.com/flowersteam/explauto/blob/cf0f81ecb9f6412f7276a95bd27359000e1e26b6/explauto/interest_model/tree.py#L265-L288 | train |
flowersteam/explauto | explauto/interest_model/tree.py | Tree.progress_all | def progress_all(self):
"""
Competence progress of the overall tree.
"""
return self.progress_idxs(range(np.shape(self.get_data_x())[0] - self.progress_win_size,
np.shape(self.get_data_x())[0])) | python | def progress_all(self):
"""
Competence progress of the overall tree.
"""
return self.progress_idxs(range(np.shape(self.get_data_x())[0] - self.progress_win_size,
np.shape(self.get_data_x())[0])) | [
"def",
"progress_all",
"(",
"self",
")",
":",
"return",
"self",
".",
"progress_idxs",
"(",
"range",
"(",
"np",
".",
"shape",
"(",
"self",
".",
"get_data_x",
"(",
")",
")",
"[",
"0",
"]",
"-",
"self",
".",
"progress_win_size",
",",
"np",
".",
"shape",... | Competence progress of the overall tree. | [
"Competence",
"progress",
"of",
"the",
"overall",
"tree",
"."
] | cf0f81ecb9f6412f7276a95bd27359000e1e26b6 | https://github.com/flowersteam/explauto/blob/cf0f81ecb9f6412f7276a95bd27359000e1e26b6/explauto/interest_model/tree.py#L372-L378 | train |
flowersteam/explauto | explauto/interest_model/tree.py | Tree.progress_idxs | def progress_idxs(self, idxs):
"""
Competence progress on points of given indexes.
"""
if self.progress_measure == 'abs_deriv_cov':
if len(idxs) <= 1:
return 0
else:
idxs = sorted(idxs)[- self.progress_win_size:]
... | python | def progress_idxs(self, idxs):
"""
Competence progress on points of given indexes.
"""
if self.progress_measure == 'abs_deriv_cov':
if len(idxs) <= 1:
return 0
else:
idxs = sorted(idxs)[- self.progress_win_size:]
... | [
"def",
"progress_idxs",
"(",
"self",
",",
"idxs",
")",
":",
"if",
"self",
".",
"progress_measure",
"==",
"'abs_deriv_cov'",
":",
"if",
"len",
"(",
"idxs",
")",
"<=",
"1",
":",
"return",
"0",
"else",
":",
"idxs",
"=",
"sorted",
"(",
"idxs",
")",
"[",
... | Competence progress on points of given indexes. | [
"Competence",
"progress",
"on",
"points",
"of",
"given",
"indexes",
"."
] | cf0f81ecb9f6412f7276a95bd27359000e1e26b6 | https://github.com/flowersteam/explauto/blob/cf0f81ecb9f6412f7276a95bd27359000e1e26b6/explauto/interest_model/tree.py#L381-L411 | train |
flowersteam/explauto | explauto/interest_model/tree.py | Tree.fold_up | def fold_up(self, f_inter, f_leaf):
"""
Apply recursively the function f_inter from leaves to root, begining with function f_leaf on leaves.
"""
return f_leaf(self) if self.leafnode else f_inter(self,
self.lower.fold_up(f... | python | def fold_up(self, f_inter, f_leaf):
"""
Apply recursively the function f_inter from leaves to root, begining with function f_leaf on leaves.
"""
return f_leaf(self) if self.leafnode else f_inter(self,
self.lower.fold_up(f... | [
"def",
"fold_up",
"(",
"self",
",",
"f_inter",
",",
"f_leaf",
")",
":",
"return",
"f_leaf",
"(",
"self",
")",
"if",
"self",
".",
"leafnode",
"else",
"f_inter",
"(",
"self",
",",
"self",
".",
"lower",
".",
"fold_up",
"(",
"f_inter",
",",
"f_leaf",
")"... | Apply recursively the function f_inter from leaves to root, begining with function f_leaf on leaves. | [
"Apply",
"recursively",
"the",
"function",
"f_inter",
"from",
"leaves",
"to",
"root",
"begining",
"with",
"function",
"f_leaf",
"on",
"leaves",
"."
] | cf0f81ecb9f6412f7276a95bd27359000e1e26b6 | https://github.com/flowersteam/explauto/blob/cf0f81ecb9f6412f7276a95bd27359000e1e26b6/explauto/interest_model/tree.py#L736-L743 | train |
flowersteam/explauto | explauto/sensorimotor_model/bayesian_optimisation.py | F_to_minimize.f | def f(self, m):
''' Function to minimize '''
M = reshape(m,self.input_dim)
n = M.shape[0]
if M.shape[1] != self.input_dim:
return 'Wrong input dimension'
else:
S = self.environment.update(M)
m = M[0]
s = S[0]
# Store the... | python | def f(self, m):
''' Function to minimize '''
M = reshape(m,self.input_dim)
n = M.shape[0]
if M.shape[1] != self.input_dim:
return 'Wrong input dimension'
else:
S = self.environment.update(M)
m = M[0]
s = S[0]
# Store the... | [
"def",
"f",
"(",
"self",
",",
"m",
")",
":",
"M",
"=",
"reshape",
"(",
"m",
",",
"self",
".",
"input_dim",
")",
"n",
"=",
"M",
".",
"shape",
"[",
"0",
"]",
"if",
"M",
".",
"shape",
"[",
"1",
"]",
"!=",
"self",
".",
"input_dim",
":",
"return... | Function to minimize | [
"Function",
"to",
"minimize"
] | cf0f81ecb9f6412f7276a95bd27359000e1e26b6 | https://github.com/flowersteam/explauto/blob/cf0f81ecb9f6412f7276a95bd27359000e1e26b6/explauto/sensorimotor_model/bayesian_optimisation.py#L25-L37 | train |
flowersteam/explauto | explauto/sensorimotor_model/inverse/inverse.py | InverseModel.from_forward | def from_forward(cls, fmodel, **kwargs):
"""Construst an inverse model from a forward model and constraints.
"""
im = cls(fmodel.dim_x, fmodel.dim_y, **kwargs)
im.fmodel = fmodel
return im | python | def from_forward(cls, fmodel, **kwargs):
"""Construst an inverse model from a forward model and constraints.
"""
im = cls(fmodel.dim_x, fmodel.dim_y, **kwargs)
im.fmodel = fmodel
return im | [
"def",
"from_forward",
"(",
"cls",
",",
"fmodel",
",",
"**",
"kwargs",
")",
":",
"im",
"=",
"cls",
"(",
"fmodel",
".",
"dim_x",
",",
"fmodel",
".",
"dim_y",
",",
"**",
"kwargs",
")",
"im",
".",
"fmodel",
"=",
"fmodel",
"return",
"im"
] | Construst an inverse model from a forward model and constraints. | [
"Construst",
"an",
"inverse",
"model",
"from",
"a",
"forward",
"model",
"and",
"constraints",
"."
] | cf0f81ecb9f6412f7276a95bd27359000e1e26b6 | https://github.com/flowersteam/explauto/blob/cf0f81ecb9f6412f7276a95bd27359000e1e26b6/explauto/sensorimotor_model/inverse/inverse.py#L18-L23 | train |
flowersteam/explauto | explauto/sensorimotor_model/inverse/inverse.py | InverseModel._random_x | def _random_x(self):
"""If the database is empty, generate a random vector."""
return (tuple(random.random() for _ in range(self.fmodel.dim_x)),) | python | def _random_x(self):
"""If the database is empty, generate a random vector."""
return (tuple(random.random() for _ in range(self.fmodel.dim_x)),) | [
"def",
"_random_x",
"(",
"self",
")",
":",
"return",
"(",
"tuple",
"(",
"random",
".",
"random",
"(",
")",
"for",
"_",
"in",
"range",
"(",
"self",
".",
"fmodel",
".",
"dim_x",
")",
")",
",",
")"
] | If the database is empty, generate a random vector. | [
"If",
"the",
"database",
"is",
"empty",
"generate",
"a",
"random",
"vector",
"."
] | cf0f81ecb9f6412f7276a95bd27359000e1e26b6 | https://github.com/flowersteam/explauto/blob/cf0f81ecb9f6412f7276a95bd27359000e1e26b6/explauto/sensorimotor_model/inverse/inverse.py#L48-L50 | train |
flowersteam/explauto | explauto/sensorimotor_model/inverse/inverse.py | InverseModel.config | def config(self):
"""Return a string with the configuration"""
return ", ".join('%s:%s' % (key, value) for key, value in self.conf.items()) | python | def config(self):
"""Return a string with the configuration"""
return ", ".join('%s:%s' % (key, value) for key, value in self.conf.items()) | [
"def",
"config",
"(",
"self",
")",
":",
"return",
"\", \"",
".",
"join",
"(",
"'%s:%s'",
"%",
"(",
"key",
",",
"value",
")",
"for",
"key",
",",
"value",
"in",
"self",
".",
"conf",
".",
"items",
"(",
")",
")"
] | Return a string with the configuration | [
"Return",
"a",
"string",
"with",
"the",
"configuration"
] | cf0f81ecb9f6412f7276a95bd27359000e1e26b6 | https://github.com/flowersteam/explauto/blob/cf0f81ecb9f6412f7276a95bd27359000e1e26b6/explauto/sensorimotor_model/inverse/inverse.py#L104-L106 | train |
flowersteam/explauto | explauto/experiment/experiment.py | Experiment.run | def run(self, n_iter=-1, bg=False):
""" Run the experiment.
:param int n_iter: Number of run iterations, by default will run until the last evaluation step.
:param bool bg: whether to run in background (using a Thread)
"""
if n_iter == -1:
if not self.eval_a... | python | def run(self, n_iter=-1, bg=False):
""" Run the experiment.
:param int n_iter: Number of run iterations, by default will run until the last evaluation step.
:param bool bg: whether to run in background (using a Thread)
"""
if n_iter == -1:
if not self.eval_a... | [
"def",
"run",
"(",
"self",
",",
"n_iter",
"=",
"-",
"1",
",",
"bg",
"=",
"False",
")",
":",
"if",
"n_iter",
"==",
"-",
"1",
":",
"if",
"not",
"self",
".",
"eval_at",
":",
"raise",
"ValueError",
"(",
"'Set n_iter or define evaluate_at.'",
")",
"n_iter",... | Run the experiment.
:param int n_iter: Number of run iterations, by default will run until the last evaluation step.
:param bool bg: whether to run in background (using a Thread) | [
"Run",
"the",
"experiment",
"."
] | cf0f81ecb9f6412f7276a95bd27359000e1e26b6 | https://github.com/flowersteam/explauto/blob/cf0f81ecb9f6412f7276a95bd27359000e1e26b6/explauto/experiment/experiment.py#L55-L74 | train |
flowersteam/explauto | explauto/experiment/experiment.py | Experiment.evaluate_at | def evaluate_at(self, eval_at, testcases, mode=None):
""" Sets the evaluation interation indices.
:param list eval_at: iteration indices where an evaluation should be performed
:param numpy.array testcases: testcases used for evaluation
"""
self.eval_at = eval_at
... | python | def evaluate_at(self, eval_at, testcases, mode=None):
""" Sets the evaluation interation indices.
:param list eval_at: iteration indices where an evaluation should be performed
:param numpy.array testcases: testcases used for evaluation
"""
self.eval_at = eval_at
... | [
"def",
"evaluate_at",
"(",
"self",
",",
"eval_at",
",",
"testcases",
",",
"mode",
"=",
"None",
")",
":",
"self",
".",
"eval_at",
"=",
"eval_at",
"self",
".",
"log",
".",
"eval_at",
"=",
"eval_at",
"if",
"mode",
"is",
"None",
":",
"if",
"self",
".",
... | Sets the evaluation interation indices.
:param list eval_at: iteration indices where an evaluation should be performed
:param numpy.array testcases: testcases used for evaluation | [
"Sets",
"the",
"evaluation",
"interation",
"indices",
"."
] | cf0f81ecb9f6412f7276a95bd27359000e1e26b6 | https://github.com/flowersteam/explauto/blob/cf0f81ecb9f6412f7276a95bd27359000e1e26b6/explauto/experiment/experiment.py#L149-L168 | train |
flowersteam/explauto | explauto/models/pydmps/cs.py | CanonicalSystem.rollout | def rollout(self, **kwargs):
"""Generate x for open loop movements.
"""
if kwargs.has_key('tau'):
timesteps = int(self.timesteps / kwargs['tau'])
else:
timesteps = self.timesteps
self.x_track = np.zeros(timesteps)
self.reset_state()
... | python | def rollout(self, **kwargs):
"""Generate x for open loop movements.
"""
if kwargs.has_key('tau'):
timesteps = int(self.timesteps / kwargs['tau'])
else:
timesteps = self.timesteps
self.x_track = np.zeros(timesteps)
self.reset_state()
... | [
"def",
"rollout",
"(",
"self",
",",
"**",
"kwargs",
")",
":",
"if",
"kwargs",
".",
"has_key",
"(",
"'tau'",
")",
":",
"timesteps",
"=",
"int",
"(",
"self",
".",
"timesteps",
"/",
"kwargs",
"[",
"'tau'",
"]",
")",
"else",
":",
"timesteps",
"=",
"sel... | Generate x for open loop movements. | [
"Generate",
"x",
"for",
"open",
"loop",
"movements",
"."
] | cf0f81ecb9f6412f7276a95bd27359000e1e26b6 | https://github.com/flowersteam/explauto/blob/cf0f81ecb9f6412f7276a95bd27359000e1e26b6/explauto/models/pydmps/cs.py#L48-L62 | train |
flowersteam/explauto | explauto/utils/utils.py | discrete_random_draw | def discrete_random_draw(data, nb=1):
''' Code from Steve Nguyen'''
data = np.array(data)
if not data.any():
data = np.ones_like(data)
data = data/data.sum()
xk = np.arange(len(data))
custm = stats.rv_discrete(name='custm', values=(xk, data))
return custm.rvs(size=nb) | python | def discrete_random_draw(data, nb=1):
''' Code from Steve Nguyen'''
data = np.array(data)
if not data.any():
data = np.ones_like(data)
data = data/data.sum()
xk = np.arange(len(data))
custm = stats.rv_discrete(name='custm', values=(xk, data))
return custm.rvs(size=nb) | [
"def",
"discrete_random_draw",
"(",
"data",
",",
"nb",
"=",
"1",
")",
":",
"data",
"=",
"np",
".",
"array",
"(",
"data",
")",
"if",
"not",
"data",
".",
"any",
"(",
")",
":",
"data",
"=",
"np",
".",
"ones_like",
"(",
"data",
")",
"data",
"=",
"d... | Code from Steve Nguyen | [
"Code",
"from",
"Steve",
"Nguyen"
] | cf0f81ecb9f6412f7276a95bd27359000e1e26b6 | https://github.com/flowersteam/explauto/blob/cf0f81ecb9f6412f7276a95bd27359000e1e26b6/explauto/utils/utils.py#L46-L54 | train |
flowersteam/explauto | explauto/sensorimotor_model/inverse/optimize.py | OptimizedInverseModel.from_dataset | def from_dataset(cls, dataset, constraints = (), **kwargs):
"""Construct a optimized inverse model from an existing dataset.
A LWLR forward model is constructed by default.
"""
fm = LWLRForwardModel(dataset.dim_x, dataset.dim_y, **kwargs)
fm.dataset = dataset
im = cls.fro... | python | def from_dataset(cls, dataset, constraints = (), **kwargs):
"""Construct a optimized inverse model from an existing dataset.
A LWLR forward model is constructed by default.
"""
fm = LWLRForwardModel(dataset.dim_x, dataset.dim_y, **kwargs)
fm.dataset = dataset
im = cls.fro... | [
"def",
"from_dataset",
"(",
"cls",
",",
"dataset",
",",
"constraints",
"=",
"(",
")",
",",
"**",
"kwargs",
")",
":",
"fm",
"=",
"LWLRForwardModel",
"(",
"dataset",
".",
"dim_x",
",",
"dataset",
".",
"dim_y",
",",
"**",
"kwargs",
")",
"fm",
".",
"data... | Construct a optimized inverse model from an existing dataset.
A LWLR forward model is constructed by default. | [
"Construct",
"a",
"optimized",
"inverse",
"model",
"from",
"an",
"existing",
"dataset",
".",
"A",
"LWLR",
"forward",
"model",
"is",
"constructed",
"by",
"default",
"."
] | cf0f81ecb9f6412f7276a95bd27359000e1e26b6 | https://github.com/flowersteam/explauto/blob/cf0f81ecb9f6412f7276a95bd27359000e1e26b6/explauto/sensorimotor_model/inverse/optimize.py#L12-L19 | train |
flowersteam/explauto | explauto/sensorimotor_model/inverse/optimize.py | OptimizedInverseModel._setuplimits | def _setuplimits(self, constraints):
"""Setup the limits for every initialiser."""
self.constraints = tuple(constraints)
assert len(self.constraints) == self.fmodel.dim_x
self.goal = None | python | def _setuplimits(self, constraints):
"""Setup the limits for every initialiser."""
self.constraints = tuple(constraints)
assert len(self.constraints) == self.fmodel.dim_x
self.goal = None | [
"def",
"_setuplimits",
"(",
"self",
",",
"constraints",
")",
":",
"self",
".",
"constraints",
"=",
"tuple",
"(",
"constraints",
")",
"assert",
"len",
"(",
"self",
".",
"constraints",
")",
"==",
"self",
".",
"fmodel",
".",
"dim_x",
"self",
".",
"goal",
... | Setup the limits for every initialiser. | [
"Setup",
"the",
"limits",
"for",
"every",
"initialiser",
"."
] | cf0f81ecb9f6412f7276a95bd27359000e1e26b6 | https://github.com/flowersteam/explauto/blob/cf0f81ecb9f6412f7276a95bd27359000e1e26b6/explauto/sensorimotor_model/inverse/optimize.py#L37-L41 | train |
flowersteam/explauto | explauto/sensorimotor_model/inverse/optimize.py | OptimizedInverseModel._error | def _error(self, x):
"""Error function.
Once self.y_desired has been defined, compute the error
of input x using the forward model.
"""
y_pred = self.fmodel.predict_y(x)
err_v = y_pred - self.goal
error = sum(e*e for e in err_v)
return error | python | def _error(self, x):
"""Error function.
Once self.y_desired has been defined, compute the error
of input x using the forward model.
"""
y_pred = self.fmodel.predict_y(x)
err_v = y_pred - self.goal
error = sum(e*e for e in err_v)
return error | [
"def",
"_error",
"(",
"self",
",",
"x",
")",
":",
"y_pred",
"=",
"self",
".",
"fmodel",
".",
"predict_y",
"(",
"x",
")",
"err_v",
"=",
"y_pred",
"-",
"self",
".",
"goal",
"error",
"=",
"sum",
"(",
"e",
"*",
"e",
"for",
"e",
"in",
"err_v",
")",
... | Error function.
Once self.y_desired has been defined, compute the error
of input x using the forward model. | [
"Error",
"function",
".",
"Once",
"self",
".",
"y_desired",
"has",
"been",
"defined",
"compute",
"the",
"error",
"of",
"input",
"x",
"using",
"the",
"forward",
"model",
"."
] | cf0f81ecb9f6412f7276a95bd27359000e1e26b6 | https://github.com/flowersteam/explauto/blob/cf0f81ecb9f6412f7276a95bd27359000e1e26b6/explauto/sensorimotor_model/inverse/optimize.py#L46-L54 | train |
flowersteam/explauto | explauto/sensorimotor_model/inverse/optimize.py | OptimizedInverseModel._error_dm | def _error_dm(self, m, dm, s):
"""Error function.
Once self.goal has been defined, compute the error
of input using the generalized forward model.
"""
pred = self.fmodel.predict_given_context(np.hstack((m, dm)), s, range(len(s)))
err_v = pred - self.goal
error = ... | python | def _error_dm(self, m, dm, s):
"""Error function.
Once self.goal has been defined, compute the error
of input using the generalized forward model.
"""
pred = self.fmodel.predict_given_context(np.hstack((m, dm)), s, range(len(s)))
err_v = pred - self.goal
error = ... | [
"def",
"_error_dm",
"(",
"self",
",",
"m",
",",
"dm",
",",
"s",
")",
":",
"pred",
"=",
"self",
".",
"fmodel",
".",
"predict_given_context",
"(",
"np",
".",
"hstack",
"(",
"(",
"m",
",",
"dm",
")",
")",
",",
"s",
",",
"range",
"(",
"len",
"(",
... | Error function.
Once self.goal has been defined, compute the error
of input using the generalized forward model. | [
"Error",
"function",
".",
"Once",
"self",
".",
"goal",
"has",
"been",
"defined",
"compute",
"the",
"error",
"of",
"input",
"using",
"the",
"generalized",
"forward",
"model",
"."
] | cf0f81ecb9f6412f7276a95bd27359000e1e26b6 | https://github.com/flowersteam/explauto/blob/cf0f81ecb9f6412f7276a95bd27359000e1e26b6/explauto/sensorimotor_model/inverse/optimize.py#L56-L64 | train |
flowersteam/explauto | explauto/models/gaussian.py | Gaussian.generate | def generate(self, number=None):
"""Generates vectors from the Gaussian.
@param number: optional, if given, generates more than one vector.
@returns: generated vector(s), either as a one dimensional array
(shape (d,)) if number is not set, or as a two dimensional array
... | python | def generate(self, number=None):
"""Generates vectors from the Gaussian.
@param number: optional, if given, generates more than one vector.
@returns: generated vector(s), either as a one dimensional array
(shape (d,)) if number is not set, or as a two dimensional array
... | [
"def",
"generate",
"(",
"self",
",",
"number",
"=",
"None",
")",
":",
"if",
"number",
"is",
"None",
":",
"return",
"numpy",
".",
"random",
".",
"multivariate_normal",
"(",
"self",
".",
"mu",
",",
"self",
".",
"sigma",
")",
"else",
":",
"return",
"num... | Generates vectors from the Gaussian.
@param number: optional, if given, generates more than one vector.
@returns: generated vector(s), either as a one dimensional array
(shape (d,)) if number is not set, or as a two dimensional array
(shape (n, d)) if n is given as number param... | [
"Generates",
"vectors",
"from",
"the",
"Gaussian",
"."
] | cf0f81ecb9f6412f7276a95bd27359000e1e26b6 | https://github.com/flowersteam/explauto/blob/cf0f81ecb9f6412f7276a95bd27359000e1e26b6/explauto/models/gaussian.py#L31-L43 | train |
flowersteam/explauto | explauto/models/gaussian.py | Gaussian.log_normal | def log_normal(self, x):
"""
Returns the log density of probability of x or the one dimensional
array of all log probabilities if many vectors are given.
@param x : may be of (n,) shape
"""
d = self.mu.shape[0]
xc = x - self.mu
if len(x.shape) == 1:
... | python | def log_normal(self, x):
"""
Returns the log density of probability of x or the one dimensional
array of all log probabilities if many vectors are given.
@param x : may be of (n,) shape
"""
d = self.mu.shape[0]
xc = x - self.mu
if len(x.shape) == 1:
... | [
"def",
"log_normal",
"(",
"self",
",",
"x",
")",
":",
"d",
"=",
"self",
".",
"mu",
".",
"shape",
"[",
"0",
"]",
"xc",
"=",
"x",
"-",
"self",
".",
"mu",
"if",
"len",
"(",
"x",
".",
"shape",
")",
"==",
"1",
":",
"exp_term",
"=",
"numpy",
".",... | Returns the log density of probability of x or the one dimensional
array of all log probabilities if many vectors are given.
@param x : may be of (n,) shape | [
"Returns",
"the",
"log",
"density",
"of",
"probability",
"of",
"x",
"or",
"the",
"one",
"dimensional",
"array",
"of",
"all",
"log",
"probabilities",
"if",
"many",
"vectors",
"are",
"given",
"."
] | cf0f81ecb9f6412f7276a95bd27359000e1e26b6 | https://github.com/flowersteam/explauto/blob/cf0f81ecb9f6412f7276a95bd27359000e1e26b6/explauto/models/gaussian.py#L53-L67 | train |
flowersteam/explauto | explauto/models/gaussian.py | Gaussian.cond_gaussian | def cond_gaussian(self, dims, v):
"""
Returns mean and variance of the conditional probability
defined by a set of dimension and at a given vector.
@param dims : set of dimension to which respect conditional
probability is taken
@param v : vector defining the positi... | python | def cond_gaussian(self, dims, v):
"""
Returns mean and variance of the conditional probability
defined by a set of dimension and at a given vector.
@param dims : set of dimension to which respect conditional
probability is taken
@param v : vector defining the positi... | [
"def",
"cond_gaussian",
"(",
"self",
",",
"dims",
",",
"v",
")",
":",
"(",
"d",
",",
"c",
",",
"b",
",",
"a",
")",
"=",
"numpy",
".",
"split_matrix",
"(",
"self",
".",
"sigma",
",",
"dims",
")",
"(",
"mu2",
",",
"mu1",
")",
"=",
"numpy",
".",... | Returns mean and variance of the conditional probability
defined by a set of dimension and at a given vector.
@param dims : set of dimension to which respect conditional
probability is taken
@param v : vector defining the position where the conditional
probability is ta... | [
"Returns",
"mean",
"and",
"variance",
"of",
"the",
"conditional",
"probability",
"defined",
"by",
"a",
"set",
"of",
"dimension",
"and",
"at",
"a",
"given",
"vector",
"."
] | cf0f81ecb9f6412f7276a95bd27359000e1e26b6 | https://github.com/flowersteam/explauto/blob/cf0f81ecb9f6412f7276a95bd27359000e1e26b6/explauto/models/gaussian.py#L69-L85 | train |
flowersteam/explauto | explauto/environment/environment.py | Environment.from_configuration | def from_configuration(cls, env_name, config_name='default'):
""" Environment factory from name and configuration strings.
:param str env_name: the name string of the environment
:param str config_name: the configuration string for env_name
Environment name strings are available using... | python | def from_configuration(cls, env_name, config_name='default'):
""" Environment factory from name and configuration strings.
:param str env_name: the name string of the environment
:param str config_name: the configuration string for env_name
Environment name strings are available using... | [
"def",
"from_configuration",
"(",
"cls",
",",
"env_name",
",",
"config_name",
"=",
"'default'",
")",
":",
"env_cls",
",",
"env_configs",
",",
"_",
"=",
"environments",
"[",
"env_name",
"]",
"return",
"env_cls",
"(",
"**",
"env_configs",
"[",
"config_name",
"... | Environment factory from name and configuration strings.
:param str env_name: the name string of the environment
:param str config_name: the configuration string for env_name
Environment name strings are available using::
from explauto.environment import environments
... | [
"Environment",
"factory",
"from",
"name",
"and",
"configuration",
"strings",
"."
] | cf0f81ecb9f6412f7276a95bd27359000e1e26b6 | https://github.com/flowersteam/explauto/blob/cf0f81ecb9f6412f7276a95bd27359000e1e26b6/explauto/environment/environment.py#L31-L66 | train |
flowersteam/explauto | explauto/environment/environment.py | Environment.update | def update(self, m_ag, reset=True, log=True):
""" Computes sensorimotor values from motor orders.
:param numpy.array m_ag: a motor command with shape (self.conf.m_ndims, ) or a set of n motor commands of shape (n, self.conf.m_ndims)
:param bool log: emit the motor and sensory values for loggin... | python | def update(self, m_ag, reset=True, log=True):
""" Computes sensorimotor values from motor orders.
:param numpy.array m_ag: a motor command with shape (self.conf.m_ndims, ) or a set of n motor commands of shape (n, self.conf.m_ndims)
:param bool log: emit the motor and sensory values for loggin... | [
"def",
"update",
"(",
"self",
",",
"m_ag",
",",
"reset",
"=",
"True",
",",
"log",
"=",
"True",
")",
":",
"if",
"reset",
":",
"self",
".",
"reset",
"(",
")",
"if",
"len",
"(",
"array",
"(",
"m_ag",
")",
".",
"shape",
")",
"==",
"1",
":",
"s",
... | Computes sensorimotor values from motor orders.
:param numpy.array m_ag: a motor command with shape (self.conf.m_ndims, ) or a set of n motor commands of shape (n, self.conf.m_ndims)
:param bool log: emit the motor and sensory values for logging purpose (default: True).
:returns: an array of ... | [
"Computes",
"sensorimotor",
"values",
"from",
"motor",
"orders",
"."
] | cf0f81ecb9f6412f7276a95bd27359000e1e26b6 | https://github.com/flowersteam/explauto/blob/cf0f81ecb9f6412f7276a95bd27359000e1e26b6/explauto/environment/environment.py#L78-L99 | train |
flowersteam/explauto | explauto/models/dataset.py | Databag.reset | def reset(self):
"""Reset the dataset to zero elements."""
self.data = []
self.size = 0
self.kdtree = None # KDTree
self.nn_ready = False | python | def reset(self):
"""Reset the dataset to zero elements."""
self.data = []
self.size = 0
self.kdtree = None # KDTree
self.nn_ready = False | [
"def",
"reset",
"(",
"self",
")",
":",
"self",
".",
"data",
"=",
"[",
"]",
"self",
".",
"size",
"=",
"0",
"self",
".",
"kdtree",
"=",
"None",
"self",
".",
"nn_ready",
"=",
"False"
] | Reset the dataset to zero elements. | [
"Reset",
"the",
"dataset",
"to",
"zero",
"elements",
"."
] | cf0f81ecb9f6412f7276a95bd27359000e1e26b6 | https://github.com/flowersteam/explauto/blob/cf0f81ecb9f6412f7276a95bd27359000e1e26b6/explauto/models/dataset.py#L32-L37 | train |
flowersteam/explauto | explauto/models/dataset.py | Databag._build_tree | def _build_tree(self):
"""Build the KDTree for the observed data
"""
if not self.nn_ready:
self.kdtree = scipy.spatial.cKDTree(self.data)
self.nn_ready = True | python | def _build_tree(self):
"""Build the KDTree for the observed data
"""
if not self.nn_ready:
self.kdtree = scipy.spatial.cKDTree(self.data)
self.nn_ready = True | [
"def",
"_build_tree",
"(",
"self",
")",
":",
"if",
"not",
"self",
".",
"nn_ready",
":",
"self",
".",
"kdtree",
"=",
"scipy",
".",
"spatial",
".",
"cKDTree",
"(",
"self",
".",
"data",
")",
"self",
".",
"nn_ready",
"=",
"True"
] | Build the KDTree for the observed data | [
"Build",
"the",
"KDTree",
"for",
"the",
"observed",
"data"
] | cf0f81ecb9f6412f7276a95bd27359000e1e26b6 | https://github.com/flowersteam/explauto/blob/cf0f81ecb9f6412f7276a95bd27359000e1e26b6/explauto/models/dataset.py#L73-L78 | train |
flowersteam/explauto | explauto/models/dataset.py | Dataset.from_data | def from_data(cls, data):
""" Create a dataset from an array of data, infering the dimension from the datapoint """
if len(data) == 0:
raise ValueError("data array is empty.")
dim_x, dim_y = len(data[0][0]), len(data[0][1])
dataset = cls(dim_x, dim_y)
for x, y in data... | python | def from_data(cls, data):
""" Create a dataset from an array of data, infering the dimension from the datapoint """
if len(data) == 0:
raise ValueError("data array is empty.")
dim_x, dim_y = len(data[0][0]), len(data[0][1])
dataset = cls(dim_x, dim_y)
for x, y in data... | [
"def",
"from_data",
"(",
"cls",
",",
"data",
")",
":",
"if",
"len",
"(",
"data",
")",
"==",
"0",
":",
"raise",
"ValueError",
"(",
"\"data array is empty.\"",
")",
"dim_x",
",",
"dim_y",
"=",
"len",
"(",
"data",
"[",
"0",
"]",
"[",
"0",
"]",
")",
... | Create a dataset from an array of data, infering the dimension from the datapoint | [
"Create",
"a",
"dataset",
"from",
"an",
"array",
"of",
"data",
"infering",
"the",
"dimension",
"from",
"the",
"datapoint"
] | cf0f81ecb9f6412f7276a95bd27359000e1e26b6 | https://github.com/flowersteam/explauto/blob/cf0f81ecb9f6412f7276a95bd27359000e1e26b6/explauto/models/dataset.py#L88-L97 | train |
flowersteam/explauto | explauto/models/dataset.py | Dataset.from_xy | def from_xy(cls, x_array, y_array):
""" Create a dataset from two arrays of data.
:note: infering the dimensions for the first elements of each array.
"""
if len(x_array) == 0:
raise ValueError("data array is empty.")
dim_x, dim_y = len(x_array[0]), len(y_array[0... | python | def from_xy(cls, x_array, y_array):
""" Create a dataset from two arrays of data.
:note: infering the dimensions for the first elements of each array.
"""
if len(x_array) == 0:
raise ValueError("data array is empty.")
dim_x, dim_y = len(x_array[0]), len(y_array[0... | [
"def",
"from_xy",
"(",
"cls",
",",
"x_array",
",",
"y_array",
")",
":",
"if",
"len",
"(",
"x_array",
")",
"==",
"0",
":",
"raise",
"ValueError",
"(",
"\"data array is empty.\"",
")",
"dim_x",
",",
"dim_y",
"=",
"len",
"(",
"x_array",
"[",
"0",
"]",
"... | Create a dataset from two arrays of data.
:note: infering the dimensions for the first elements of each array. | [
"Create",
"a",
"dataset",
"from",
"two",
"arrays",
"of",
"data",
"."
] | cf0f81ecb9f6412f7276a95bd27359000e1e26b6 | https://github.com/flowersteam/explauto/blob/cf0f81ecb9f6412f7276a95bd27359000e1e26b6/explauto/models/dataset.py#L100-L112 | train |
flowersteam/explauto | explauto/models/dataset.py | Dataset.nn_x | def nn_x(self, x, k=1, radius=np.inf, eps=0.0, p=2):
"""Find the k nearest neighbors of x in the observed input data
@see Databag.nn() for argument description
@return distance and indexes of found nearest neighbors.
"""
assert len(x) == self.dim_x
k_x = min(k, self.size... | python | def nn_x(self, x, k=1, radius=np.inf, eps=0.0, p=2):
"""Find the k nearest neighbors of x in the observed input data
@see Databag.nn() for argument description
@return distance and indexes of found nearest neighbors.
"""
assert len(x) == self.dim_x
k_x = min(k, self.size... | [
"def",
"nn_x",
"(",
"self",
",",
"x",
",",
"k",
"=",
"1",
",",
"radius",
"=",
"np",
".",
"inf",
",",
"eps",
"=",
"0.0",
",",
"p",
"=",
"2",
")",
":",
"assert",
"len",
"(",
"x",
")",
"==",
"self",
".",
"dim_x",
"k_x",
"=",
"min",
"(",
"k",... | Find the k nearest neighbors of x in the observed input data
@see Databag.nn() for argument description
@return distance and indexes of found nearest neighbors. | [
"Find",
"the",
"k",
"nearest",
"neighbors",
"of",
"x",
"in",
"the",
"observed",
"input",
"data"
] | cf0f81ecb9f6412f7276a95bd27359000e1e26b6 | https://github.com/flowersteam/explauto/blob/cf0f81ecb9f6412f7276a95bd27359000e1e26b6/explauto/models/dataset.py#L204-L213 | train |
flowersteam/explauto | explauto/agent/agent.py | Agent.from_classes | def from_classes(cls,
im_model_cls, im_model_config, expl_dims,
sm_model_cls, sm_model_config, inf_dims,
m_mins, m_maxs, s_mins, s_maxs, n_bootstrap=0, context_mode=None):
"""Initialize agent class
:param class im_model_cls: a subclass of InterestedMod... | python | def from_classes(cls,
im_model_cls, im_model_config, expl_dims,
sm_model_cls, sm_model_config, inf_dims,
m_mins, m_maxs, s_mins, s_maxs, n_bootstrap=0, context_mode=None):
"""Initialize agent class
:param class im_model_cls: a subclass of InterestedMod... | [
"def",
"from_classes",
"(",
"cls",
",",
"im_model_cls",
",",
"im_model_config",
",",
"expl_dims",
",",
"sm_model_cls",
",",
"sm_model_config",
",",
"inf_dims",
",",
"m_mins",
",",
"m_maxs",
",",
"s_mins",
",",
"s_maxs",
",",
"n_bootstrap",
"=",
"0",
",",
"co... | Initialize agent class
:param class im_model_cls: a subclass of InterestedModel, as those registered in the interest_model package
:param dict im_model_config: a configuration dict as those registered in the interest_model package
:param list expl_dims: the sensorimotor dimensions where explo... | [
"Initialize",
"agent",
"class"
] | cf0f81ecb9f6412f7276a95bd27359000e1e26b6 | https://github.com/flowersteam/explauto/blob/cf0f81ecb9f6412f7276a95bd27359000e1e26b6/explauto/agent/agent.py#L31-L59 | train |
flowersteam/explauto | explauto/agent/agent.py | Agent.choose | def choose(self, context_ms=None):
""" Returns a point chosen by the interest model
"""
try:
if self.context_mode is None:
x = self.interest_model.sample()
else:
if self.context_mode["mode"] == 'mdmsds':
if self.expl_dim... | python | def choose(self, context_ms=None):
""" Returns a point chosen by the interest model
"""
try:
if self.context_mode is None:
x = self.interest_model.sample()
else:
if self.context_mode["mode"] == 'mdmsds':
if self.expl_dim... | [
"def",
"choose",
"(",
"self",
",",
"context_ms",
"=",
"None",
")",
":",
"try",
":",
"if",
"self",
".",
"context_mode",
"is",
"None",
":",
"x",
"=",
"self",
".",
"interest_model",
".",
"sample",
"(",
")",
"else",
":",
"if",
"self",
".",
"context_mode"... | Returns a point chosen by the interest model | [
"Returns",
"a",
"point",
"chosen",
"by",
"the",
"interest",
"model"
] | cf0f81ecb9f6412f7276a95bd27359000e1e26b6 | https://github.com/flowersteam/explauto/blob/cf0f81ecb9f6412f7276a95bd27359000e1e26b6/explauto/agent/agent.py#L62-L84 | train |
flowersteam/explauto | explauto/agent/agent.py | Agent.infer | def infer(self, expl_dims, inf_dims, x):
""" Use the sensorimotor model to compute the expected value on inf_dims given that the value on expl_dims is x.
.. note:: This corresponds to a prediction if expl_dims=self.conf.m_dims and inf_dims=self.conf.s_dims and to inverse prediction if expl_dims=self.co... | python | def infer(self, expl_dims, inf_dims, x):
""" Use the sensorimotor model to compute the expected value on inf_dims given that the value on expl_dims is x.
.. note:: This corresponds to a prediction if expl_dims=self.conf.m_dims and inf_dims=self.conf.s_dims and to inverse prediction if expl_dims=self.co... | [
"def",
"infer",
"(",
"self",
",",
"expl_dims",
",",
"inf_dims",
",",
"x",
")",
":",
"try",
":",
"if",
"self",
".",
"n_bootstrap",
">",
"0",
":",
"self",
".",
"n_bootstrap",
"-=",
"1",
"raise",
"ExplautoBootstrapError",
"y",
"=",
"self",
".",
"sensorimo... | Use the sensorimotor model to compute the expected value on inf_dims given that the value on expl_dims is x.
.. note:: This corresponds to a prediction if expl_dims=self.conf.m_dims and inf_dims=self.conf.s_dims and to inverse prediction if expl_dims=self.conf.s_dims and inf_dims=self.conf.m_dims. | [
"Use",
"the",
"sensorimotor",
"model",
"to",
"compute",
"the",
"expected",
"value",
"on",
"inf_dims",
"given",
"that",
"the",
"value",
"on",
"expl_dims",
"is",
"x",
"."
] | cf0f81ecb9f6412f7276a95bd27359000e1e26b6 | https://github.com/flowersteam/explauto/blob/cf0f81ecb9f6412f7276a95bd27359000e1e26b6/explauto/agent/agent.py#L86-L101 | train |
flowersteam/explauto | explauto/environment/poppy/poppy_env.py | PoppyEnvironment.compute_motor_command | def compute_motor_command(self, m_ag):
""" Compute the motor command by restricting it to the bounds. """
m_env = bounds_min_max(m_ag, self.conf.m_mins, self.conf.m_maxs)
return m_env | python | def compute_motor_command(self, m_ag):
""" Compute the motor command by restricting it to the bounds. """
m_env = bounds_min_max(m_ag, self.conf.m_mins, self.conf.m_maxs)
return m_env | [
"def",
"compute_motor_command",
"(",
"self",
",",
"m_ag",
")",
":",
"m_env",
"=",
"bounds_min_max",
"(",
"m_ag",
",",
"self",
".",
"conf",
".",
"m_mins",
",",
"self",
".",
"conf",
".",
"m_maxs",
")",
"return",
"m_env"
] | Compute the motor command by restricting it to the bounds. | [
"Compute",
"the",
"motor",
"command",
"by",
"restricting",
"it",
"to",
"the",
"bounds",
"."
] | cf0f81ecb9f6412f7276a95bd27359000e1e26b6 | https://github.com/flowersteam/explauto/blob/cf0f81ecb9f6412f7276a95bd27359000e1e26b6/explauto/environment/poppy/poppy_env.py#L42-L45 | train |
flowersteam/explauto | explauto/environment/poppy/poppy_env.py | PoppyEnvironment.compute_sensori_effect | def compute_sensori_effect(self, m_env):
""" Move the robot motors and retrieve the tracked object position. """
pos = {m.name: pos for m, pos in zip(self.motors, m_env)}
self.robot.goto_position(pos, self.move_duration, wait=True)
# This allows to actually apply a motor command
... | python | def compute_sensori_effect(self, m_env):
""" Move the robot motors and retrieve the tracked object position. """
pos = {m.name: pos for m, pos in zip(self.motors, m_env)}
self.robot.goto_position(pos, self.move_duration, wait=True)
# This allows to actually apply a motor command
... | [
"def",
"compute_sensori_effect",
"(",
"self",
",",
"m_env",
")",
":",
"pos",
"=",
"{",
"m",
".",
"name",
":",
"pos",
"for",
"m",
",",
"pos",
"in",
"zip",
"(",
"self",
".",
"motors",
",",
"m_env",
")",
"}",
"self",
".",
"robot",
".",
"goto_position"... | Move the robot motors and retrieve the tracked object position. | [
"Move",
"the",
"robot",
"motors",
"and",
"retrieve",
"the",
"tracked",
"object",
"position",
"."
] | cf0f81ecb9f6412f7276a95bd27359000e1e26b6 | https://github.com/flowersteam/explauto/blob/cf0f81ecb9f6412f7276a95bd27359000e1e26b6/explauto/environment/poppy/poppy_env.py#L47-L55 | train |
flowersteam/explauto | explauto/sensorimotor_model/learner.py | Learner.infer_order | def infer_order(self, goal, **kwargs):
"""Infer an order in order to obtain an effect close to goal, given the
observation data of the model.
:arg goal: a goal vector compatible with self.Sfeats
:rtype: same as goal (if list, tuple, or pandas.Series, else tuple)
"""
x ... | python | def infer_order(self, goal, **kwargs):
"""Infer an order in order to obtain an effect close to goal, given the
observation data of the model.
:arg goal: a goal vector compatible with self.Sfeats
:rtype: same as goal (if list, tuple, or pandas.Series, else tuple)
"""
x ... | [
"def",
"infer_order",
"(",
"self",
",",
"goal",
",",
"**",
"kwargs",
")",
":",
"x",
"=",
"self",
".",
"imodel",
".",
"infer_x",
"(",
"np",
".",
"array",
"(",
"self",
".",
"_pre_y",
"(",
"goal",
")",
")",
",",
"**",
"kwargs",
")",
"[",
"0",
"]",... | Infer an order in order to obtain an effect close to goal, given the
observation data of the model.
:arg goal: a goal vector compatible with self.Sfeats
:rtype: same as goal (if list, tuple, or pandas.Series, else tuple) | [
"Infer",
"an",
"order",
"in",
"order",
"to",
"obtain",
"an",
"effect",
"close",
"to",
"goal",
"given",
"the",
"observation",
"data",
"of",
"the",
"model",
"."
] | cf0f81ecb9f6412f7276a95bd27359000e1e26b6 | https://github.com/flowersteam/explauto/blob/cf0f81ecb9f6412f7276a95bd27359000e1e26b6/explauto/sensorimotor_model/learner.py#L55-L63 | train |
flowersteam/explauto | explauto/sensorimotor_model/learner.py | Learner.predict_effect | def predict_effect(self, order, **kwargs):
"""Predict the effect of a goal.
:arg order: an order vector compatible with self.Mfeats
:rtype: same as order (if list, tuple, or pandas.Series, else tuple)
"""
y = self.imodel.fmodel.predict_y(np.array(self._pre_x(order)), **kwargs)... | python | def predict_effect(self, order, **kwargs):
"""Predict the effect of a goal.
:arg order: an order vector compatible with self.Mfeats
:rtype: same as order (if list, tuple, or pandas.Series, else tuple)
"""
y = self.imodel.fmodel.predict_y(np.array(self._pre_x(order)), **kwargs)... | [
"def",
"predict_effect",
"(",
"self",
",",
"order",
",",
"**",
"kwargs",
")",
":",
"y",
"=",
"self",
".",
"imodel",
".",
"fmodel",
".",
"predict_y",
"(",
"np",
".",
"array",
"(",
"self",
".",
"_pre_x",
"(",
"order",
")",
")",
",",
"**",
"kwargs",
... | Predict the effect of a goal.
:arg order: an order vector compatible with self.Mfeats
:rtype: same as order (if list, tuple, or pandas.Series, else tuple) | [
"Predict",
"the",
"effect",
"of",
"a",
"goal",
"."
] | cf0f81ecb9f6412f7276a95bd27359000e1e26b6 | https://github.com/flowersteam/explauto/blob/cf0f81ecb9f6412f7276a95bd27359000e1e26b6/explauto/sensorimotor_model/learner.py#L65-L72 | train |
flowersteam/explauto | explauto/sensorimotor_model/inverse/sciopt.py | ScipyInverseModel._enforce_bounds | def _enforce_bounds(self, x):
""""Enforce the bounds on x if only infinitesimal violations occurs"""
assert len(x) == len(self.bounds)
x_enforced = []
for x_i, (lb, ub) in zip(x, self.bounds):
if x_i < lb:
if x_i > lb - (ub-lb)/1e10:
x_enfo... | python | def _enforce_bounds(self, x):
""""Enforce the bounds on x if only infinitesimal violations occurs"""
assert len(x) == len(self.bounds)
x_enforced = []
for x_i, (lb, ub) in zip(x, self.bounds):
if x_i < lb:
if x_i > lb - (ub-lb)/1e10:
x_enfo... | [
"def",
"_enforce_bounds",
"(",
"self",
",",
"x",
")",
":",
"assert",
"len",
"(",
"x",
")",
"==",
"len",
"(",
"self",
".",
"bounds",
")",
"x_enforced",
"=",
"[",
"]",
"for",
"x_i",
",",
"(",
"lb",
",",
"ub",
")",
"in",
"zip",
"(",
"x",
",",
"s... | Enforce the bounds on x if only infinitesimal violations occurs | [
"Enforce",
"the",
"bounds",
"on",
"x",
"if",
"only",
"infinitesimal",
"violations",
"occurs"
] | cf0f81ecb9f6412f7276a95bd27359000e1e26b6 | https://github.com/flowersteam/explauto/blob/cf0f81ecb9f6412f7276a95bd27359000e1e26b6/explauto/sensorimotor_model/inverse/sciopt.py#L67-L84 | train |
david-cortes/hpfrec | hpfrec/__init__.py | HPF.fit | def fit(self, counts_df, val_set=None):
"""
Fit Hierarchical Poisson Model to sparse count data
Fits a hierarchical Poisson model to count data using mean-field approximation with either
full-batch coordinate-ascent or mini-batch stochastic coordinate-ascent.
Note
----
DataFrames and arrays passed to ... | python | def fit(self, counts_df, val_set=None):
"""
Fit Hierarchical Poisson Model to sparse count data
Fits a hierarchical Poisson model to count data using mean-field approximation with either
full-batch coordinate-ascent or mini-batch stochastic coordinate-ascent.
Note
----
DataFrames and arrays passed to ... | [
"def",
"fit",
"(",
"self",
",",
"counts_df",
",",
"val_set",
"=",
"None",
")",
":",
"if",
"self",
".",
"stop_crit",
"==",
"'val-llk'",
":",
"if",
"val_set",
"is",
"None",
":",
"raise",
"ValueError",
"(",
"\"If 'stop_crit' is set to 'val-llk', must provide a vali... | Fit Hierarchical Poisson Model to sparse count data
Fits a hierarchical Poisson model to count data using mean-field approximation with either
full-batch coordinate-ascent or mini-batch stochastic coordinate-ascent.
Note
----
DataFrames and arrays passed to '.fit' might be modified inplace - if this is a ... | [
"Fit",
"Hierarchical",
"Poisson",
"Model",
"to",
"sparse",
"count",
"data"
] | cf0b18aa03e189f822b582d59c23f7862593289e | https://github.com/david-cortes/hpfrec/blob/cf0b18aa03e189f822b582d59c23f7862593289e/hpfrec/__init__.py#L344-L416 | train |
david-cortes/hpfrec | hpfrec/__init__.py | HPF.predict_factors | def predict_factors(self, counts_df, maxiter=10, ncores=1, random_seed=1, stop_thr=1e-3, return_all=False):
"""
Gets latent factors for a user given her item counts
This is similar to obtaining topics for a document in LDA.
Note
----
This function will NOT modify any of the item parameters.
Note
----... | python | def predict_factors(self, counts_df, maxiter=10, ncores=1, random_seed=1, stop_thr=1e-3, return_all=False):
"""
Gets latent factors for a user given her item counts
This is similar to obtaining topics for a document in LDA.
Note
----
This function will NOT modify any of the item parameters.
Note
----... | [
"def",
"predict_factors",
"(",
"self",
",",
"counts_df",
",",
"maxiter",
"=",
"10",
",",
"ncores",
"=",
"1",
",",
"random_seed",
"=",
"1",
",",
"stop_thr",
"=",
"1e-3",
",",
"return_all",
"=",
"False",
")",
":",
"ncores",
",",
"random_seed",
",",
"stop... | Gets latent factors for a user given her item counts
This is similar to obtaining topics for a document in LDA.
Note
----
This function will NOT modify any of the item parameters.
Note
----
This function only works with one user at a time.
Note
----
This function is prone to producing all NaNs v... | [
"Gets",
"latent",
"factors",
"for",
"a",
"user",
"given",
"her",
"item",
"counts"
] | cf0b18aa03e189f822b582d59c23f7862593289e | https://github.com/david-cortes/hpfrec/blob/cf0b18aa03e189f822b582d59c23f7862593289e/hpfrec/__init__.py#L947-L1019 | train |
flowersteam/explauto | explauto/sensorimotor_model/inverse/jacobian.py | JacobianInverseModel.infer_x | def infer_x(self, y_desired, **kwargs):
"""Provide an inversion of yq in the input space
@param yq an array of float of length dim_y
"""
sigma = kwargs.get('sigma', self.sigma)
k = kwargs.get('k', self.k)
xq = self._guess_x(y_desired, k =... | python | def infer_x(self, y_desired, **kwargs):
"""Provide an inversion of yq in the input space
@param yq an array of float of length dim_y
"""
sigma = kwargs.get('sigma', self.sigma)
k = kwargs.get('k', self.k)
xq = self._guess_x(y_desired, k =... | [
"def",
"infer_x",
"(",
"self",
",",
"y_desired",
",",
"**",
"kwargs",
")",
":",
"sigma",
"=",
"kwargs",
".",
"get",
"(",
"'sigma'",
",",
"self",
".",
"sigma",
")",
"k",
"=",
"kwargs",
".",
"get",
"(",
"'k'",
",",
"self",
".",
"k",
")",
"xq",
"=... | Provide an inversion of yq in the input space
@param yq an array of float of length dim_y | [
"Provide",
"an",
"inversion",
"of",
"yq",
"in",
"the",
"input",
"space"
] | cf0f81ecb9f6412f7276a95bd27359000e1e26b6 | https://github.com/flowersteam/explauto/blob/cf0f81ecb9f6412f7276a95bd27359000e1e26b6/explauto/sensorimotor_model/inverse/jacobian.py#L19-L60 | train |
flowersteam/explauto | explauto/experiment/pool.py | ExperimentPool.from_settings_product | def from_settings_product(cls, environments, babblings,
interest_models, sensorimotor_models,
evaluate_at,
testcases=None, same_testcases=False):
""" Creates a ExperimentPool with the product of all the given settings.
... | python | def from_settings_product(cls, environments, babblings,
interest_models, sensorimotor_models,
evaluate_at,
testcases=None, same_testcases=False):
""" Creates a ExperimentPool with the product of all the given settings.
... | [
"def",
"from_settings_product",
"(",
"cls",
",",
"environments",
",",
"babblings",
",",
"interest_models",
",",
"sensorimotor_models",
",",
"evaluate_at",
",",
"testcases",
"=",
"None",
",",
"same_testcases",
"=",
"False",
")",
":",
"l",
"=",
"itertools",
".",
... | Creates a ExperimentPool with the product of all the given settings.
:param environments: e.g. [('simple_arm', 'default'), ('simple_arm', 'high_dimensional')]
:type environments: list of (environment name, config name)
:param babblings: e.g. ['motor', 'goal']
:type babli... | [
"Creates",
"a",
"ExperimentPool",
"with",
"the",
"product",
"of",
"all",
"the",
"given",
"settings",
"."
] | cf0f81ecb9f6412f7276a95bd27359000e1e26b6 | https://github.com/flowersteam/explauto/blob/cf0f81ecb9f6412f7276a95bd27359000e1e26b6/explauto/experiment/pool.py#L48-L73 | train |
flowersteam/explauto | explauto/environment/npendulum/npendulum.py | NPendulumEnvironment.compute_sensori_effect | def compute_sensori_effect(self, m):
""" This function generates the end effector position at the end of the movement.
.. note:: The duration of the movement is 1000*self.dt
.. note:: To use basis functions rather than step functions, set :use_basis_functions: to 1
"""
if self.... | python | def compute_sensori_effect(self, m):
""" This function generates the end effector position at the end of the movement.
.. note:: The duration of the movement is 1000*self.dt
.. note:: To use basis functions rather than step functions, set :use_basis_functions: to 1
"""
if self.... | [
"def",
"compute_sensori_effect",
"(",
"self",
",",
"m",
")",
":",
"if",
"self",
".",
"use_basis_functions",
":",
"func",
"=",
"simulation",
".",
"step",
"(",
"m",
",",
"1000",
"*",
"self",
".",
"dt",
")",
"else",
":",
"traj",
"=",
"self",
".",
"bf",
... | This function generates the end effector position at the end of the movement.
.. note:: The duration of the movement is 1000*self.dt
.. note:: To use basis functions rather than step functions, set :use_basis_functions: to 1 | [
"This",
"function",
"generates",
"the",
"end",
"effector",
"position",
"at",
"the",
"end",
"of",
"the",
"movement",
"."
] | cf0f81ecb9f6412f7276a95bd27359000e1e26b6 | https://github.com/flowersteam/explauto/blob/cf0f81ecb9f6412f7276a95bd27359000e1e26b6/explauto/environment/npendulum/npendulum.py#L33-L50 | train |
flowersteam/explauto | explauto/environment/npendulum/npendulum.py | NPendulumEnvironment.animate_pendulum | def animate_pendulum(self, m, path="anim_npendulum"):
"""This function generates few images at different instants in order to animate the pendulum.
..note:: this function destroys and creates the folder :anim_npendulum:.
"""
if os.path.exists(path):
shutil.rmtree(path)
... | python | def animate_pendulum(self, m, path="anim_npendulum"):
"""This function generates few images at different instants in order to animate the pendulum.
..note:: this function destroys and creates the folder :anim_npendulum:.
"""
if os.path.exists(path):
shutil.rmtree(path)
... | [
"def",
"animate_pendulum",
"(",
"self",
",",
"m",
",",
"path",
"=",
"\"anim_npendulum\"",
")",
":",
"if",
"os",
".",
"path",
".",
"exists",
"(",
"path",
")",
":",
"shutil",
".",
"rmtree",
"(",
"path",
")",
"os",
".",
"makedirs",
"(",
"path",
")",
"... | This function generates few images at different instants in order to animate the pendulum.
..note:: this function destroys and creates the folder :anim_npendulum:. | [
"This",
"function",
"generates",
"few",
"images",
"at",
"different",
"instants",
"in",
"order",
"to",
"animate",
"the",
"pendulum",
"."
] | cf0f81ecb9f6412f7276a95bd27359000e1e26b6 | https://github.com/flowersteam/explauto/blob/cf0f81ecb9f6412f7276a95bd27359000e1e26b6/explauto/environment/npendulum/npendulum.py#L76-L90 | train |
flowersteam/explauto | explauto/sensorimotor_model/inverse/cma.py | _fileToMatrix | def _fileToMatrix(file_name):
"""rudimentary method to read in data from a file"""
# TODO: np.loadtxt() might be an alternative
# try:
if 1 < 3:
lres = []
for line in open(file_name, 'r').readlines():
if len(line) > 0 and line[0] not in ('%', '#'):
lres.ap... | python | def _fileToMatrix(file_name):
"""rudimentary method to read in data from a file"""
# TODO: np.loadtxt() might be an alternative
# try:
if 1 < 3:
lres = []
for line in open(file_name, 'r').readlines():
if len(line) > 0 and line[0] not in ('%', '#'):
lres.ap... | [
"def",
"_fileToMatrix",
"(",
"file_name",
")",
":",
"if",
"1",
"<",
"3",
":",
"lres",
"=",
"[",
"]",
"for",
"line",
"in",
"open",
"(",
"file_name",
",",
"'r'",
")",
".",
"readlines",
"(",
")",
":",
"if",
"len",
"(",
"line",
")",
">",
"0",
"and"... | rudimentary method to read in data from a file | [
"rudimentary",
"method",
"to",
"read",
"in",
"data",
"from",
"a",
"file"
] | cf0f81ecb9f6412f7276a95bd27359000e1e26b6 | https://github.com/flowersteam/explauto/blob/cf0f81ecb9f6412f7276a95bd27359000e1e26b6/explauto/sensorimotor_model/inverse/cma.py#L6827-L6841 | train |
flowersteam/explauto | explauto/sensorimotor_model/inverse/cma.py | pprint | def pprint(to_be_printed):
"""nicely formated print"""
try:
import pprint as pp
# generate an instance PrettyPrinter
# pp.PrettyPrinter().pprint(to_be_printed)
pp.pprint(to_be_printed)
except ImportError:
if isinstance(to_be_printed, dict):
print('{')
... | python | def pprint(to_be_printed):
"""nicely formated print"""
try:
import pprint as pp
# generate an instance PrettyPrinter
# pp.PrettyPrinter().pprint(to_be_printed)
pp.pprint(to_be_printed)
except ImportError:
if isinstance(to_be_printed, dict):
print('{')
... | [
"def",
"pprint",
"(",
"to_be_printed",
")",
":",
"try",
":",
"import",
"pprint",
"as",
"pp",
"pp",
".",
"pprint",
"(",
"to_be_printed",
")",
"except",
"ImportError",
":",
"if",
"isinstance",
"(",
"to_be_printed",
",",
"dict",
")",
":",
"print",
"(",
"'{'... | nicely formated print | [
"nicely",
"formated",
"print"
] | cf0f81ecb9f6412f7276a95bd27359000e1e26b6 | https://github.com/flowersteam/explauto/blob/cf0f81ecb9f6412f7276a95bd27359000e1e26b6/explauto/sensorimotor_model/inverse/cma.py#L7863-L7881 | train |
flowersteam/explauto | explauto/sensorimotor_model/inverse/cma.py | felli | def felli(x):
"""unbound test function, needed to test multiprocessor"""
return sum(1e6**(np.arange(len(x)) / (len(x) - 1)) * (np.array(x, copy=False))**2) | python | def felli(x):
"""unbound test function, needed to test multiprocessor"""
return sum(1e6**(np.arange(len(x)) / (len(x) - 1)) * (np.array(x, copy=False))**2) | [
"def",
"felli",
"(",
"x",
")",
":",
"return",
"sum",
"(",
"1e6",
"**",
"(",
"np",
".",
"arange",
"(",
"len",
"(",
"x",
")",
")",
"/",
"(",
"len",
"(",
"x",
")",
"-",
"1",
")",
")",
"*",
"(",
"np",
".",
"array",
"(",
"x",
",",
"copy",
"=... | unbound test function, needed to test multiprocessor | [
"unbound",
"test",
"function",
"needed",
"to",
"test",
"multiprocessor"
] | cf0f81ecb9f6412f7276a95bd27359000e1e26b6 | https://github.com/flowersteam/explauto/blob/cf0f81ecb9f6412f7276a95bd27359000e1e26b6/explauto/sensorimotor_model/inverse/cma.py#L8577-L8579 | train |
flowersteam/explauto | explauto/sensorimotor_model/inverse/cma.py | BestSolution.update | def update(self, arx, xarchive=None, arf=None, evals=None):
"""checks for better solutions in list `arx`.
Based on the smallest corresponding value in `arf`,
alternatively, `update` may be called with a `BestSolution`
instance like ``update(another_best_solution)`` in which case
... | python | def update(self, arx, xarchive=None, arf=None, evals=None):
"""checks for better solutions in list `arx`.
Based on the smallest corresponding value in `arf`,
alternatively, `update` may be called with a `BestSolution`
instance like ``update(another_best_solution)`` in which case
... | [
"def",
"update",
"(",
"self",
",",
"arx",
",",
"xarchive",
"=",
"None",
",",
"arf",
"=",
"None",
",",
"evals",
"=",
"None",
")",
":",
"if",
"isinstance",
"(",
"arx",
",",
"BestSolution",
")",
":",
"if",
"self",
".",
"evalsall",
"is",
"None",
":",
... | checks for better solutions in list `arx`.
Based on the smallest corresponding value in `arf`,
alternatively, `update` may be called with a `BestSolution`
instance like ``update(another_best_solution)`` in which case
the better solution becomes the current best.
`xarchive` is u... | [
"checks",
"for",
"better",
"solutions",
"in",
"list",
"arx",
"."
] | cf0f81ecb9f6412f7276a95bd27359000e1e26b6 | https://github.com/flowersteam/explauto/blob/cf0f81ecb9f6412f7276a95bd27359000e1e26b6/explauto/sensorimotor_model/inverse/cma.py#L813-L851 | train |
flowersteam/explauto | explauto/sensorimotor_model/inverse/cma.py | BoundaryHandlerBase.repair | def repair(self, x, copy_if_changed=True, copy_always=False):
"""projects infeasible values on the domain bound, might be
overwritten by derived class """
if copy_always:
x = array(x, copy=True)
copy = False
else:
copy = copy_if_changed
if self... | python | def repair(self, x, copy_if_changed=True, copy_always=False):
"""projects infeasible values on the domain bound, might be
overwritten by derived class """
if copy_always:
x = array(x, copy=True)
copy = False
else:
copy = copy_if_changed
if self... | [
"def",
"repair",
"(",
"self",
",",
"x",
",",
"copy_if_changed",
"=",
"True",
",",
"copy_always",
"=",
"False",
")",
":",
"if",
"copy_always",
":",
"x",
"=",
"array",
"(",
"x",
",",
"copy",
"=",
"True",
")",
"copy",
"=",
"False",
"else",
":",
"copy"... | projects infeasible values on the domain bound, might be
overwritten by derived class | [
"projects",
"infeasible",
"values",
"on",
"the",
"domain",
"bound",
"might",
"be",
"overwritten",
"by",
"derived",
"class"
] | cf0f81ecb9f6412f7276a95bd27359000e1e26b6 | https://github.com/flowersteam/explauto/blob/cf0f81ecb9f6412f7276a95bd27359000e1e26b6/explauto/sensorimotor_model/inverse/cma.py#L906-L926 | train |
flowersteam/explauto | explauto/sensorimotor_model/inverse/cma.py | BoundaryHandlerBase.has_bounds | def has_bounds(self):
"""return True, if any variable is bounded"""
bounds = self.bounds
if bounds in (None, [None, None]):
return False
for ib, bound in enumerate(bounds):
if bound is not None:
sign_ = 2 * ib - 1
for bound_i in bou... | python | def has_bounds(self):
"""return True, if any variable is bounded"""
bounds = self.bounds
if bounds in (None, [None, None]):
return False
for ib, bound in enumerate(bounds):
if bound is not None:
sign_ = 2 * ib - 1
for bound_i in bou... | [
"def",
"has_bounds",
"(",
"self",
")",
":",
"bounds",
"=",
"self",
".",
"bounds",
"if",
"bounds",
"in",
"(",
"None",
",",
"[",
"None",
",",
"None",
"]",
")",
":",
"return",
"False",
"for",
"ib",
",",
"bound",
"in",
"enumerate",
"(",
"bounds",
")",
... | return True, if any variable is bounded | [
"return",
"True",
"if",
"any",
"variable",
"is",
"bounded"
] | cf0f81ecb9f6412f7276a95bd27359000e1e26b6 | https://github.com/flowersteam/explauto/blob/cf0f81ecb9f6412f7276a95bd27359000e1e26b6/explauto/sensorimotor_model/inverse/cma.py#L954-L965 | train |
flowersteam/explauto | explauto/sensorimotor_model/inverse/cma.py | BoundaryHandlerBase.is_in_bounds | def is_in_bounds(self, x):
"""not yet tested"""
if self.bounds is None:
return True
for ib in [0, 1]:
if self.bounds[ib] is None:
continue
for i in rglen(x):
idx = min([i, len(self.bounds[ib]) - 1])
if self.bound... | python | def is_in_bounds(self, x):
"""not yet tested"""
if self.bounds is None:
return True
for ib in [0, 1]:
if self.bounds[ib] is None:
continue
for i in rglen(x):
idx = min([i, len(self.bounds[ib]) - 1])
if self.bound... | [
"def",
"is_in_bounds",
"(",
"self",
",",
"x",
")",
":",
"if",
"self",
".",
"bounds",
"is",
"None",
":",
"return",
"True",
"for",
"ib",
"in",
"[",
"0",
",",
"1",
"]",
":",
"if",
"self",
".",
"bounds",
"[",
"ib",
"]",
"is",
"None",
":",
"continue... | not yet tested | [
"not",
"yet",
"tested"
] | cf0f81ecb9f6412f7276a95bd27359000e1e26b6 | https://github.com/flowersteam/explauto/blob/cf0f81ecb9f6412f7276a95bd27359000e1e26b6/explauto/sensorimotor_model/inverse/cma.py#L967-L979 | train |
flowersteam/explauto | explauto/sensorimotor_model/inverse/cma.py | BoundTransform.repair | def repair(self, x, copy_if_changed=True, copy_always=False):
"""transforms ``x`` into the bounded domain.
``copy_always`` option might disappear.
"""
copy = copy_if_changed
if copy_always:
x = array(x, copy=True)
copy = False
if self.bounds is N... | python | def repair(self, x, copy_if_changed=True, copy_always=False):
"""transforms ``x`` into the bounded domain.
``copy_always`` option might disappear.
"""
copy = copy_if_changed
if copy_always:
x = array(x, copy=True)
copy = False
if self.bounds is N... | [
"def",
"repair",
"(",
"self",
",",
"x",
",",
"copy_if_changed",
"=",
"True",
",",
"copy_always",
"=",
"False",
")",
":",
"copy",
"=",
"copy_if_changed",
"if",
"copy_always",
":",
"x",
"=",
"array",
"(",
"x",
",",
"copy",
"=",
"True",
")",
"copy",
"="... | transforms ``x`` into the bounded domain.
``copy_always`` option might disappear. | [
"transforms",
"x",
"into",
"the",
"bounded",
"domain",
"."
] | cf0f81ecb9f6412f7276a95bd27359000e1e26b6 | https://github.com/flowersteam/explauto/blob/cf0f81ecb9f6412f7276a95bd27359000e1e26b6/explauto/sensorimotor_model/inverse/cma.py#L1063-L1076 | train |
flowersteam/explauto | explauto/sensorimotor_model/inverse/cma.py | BoundPenalty.repair | def repair(self, x, copy_if_changed=True, copy_always=False):
"""sets out-of-bounds components of ``x`` on the bounds.
"""
# TODO (old data): CPU(N,lam,iter=20,200,100): 3.3s of 8s for two bounds, 1.8s of 6.5s for one bound
# remark: np.max([bounds[0], x]) is about 40 times slower than ... | python | def repair(self, x, copy_if_changed=True, copy_always=False):
"""sets out-of-bounds components of ``x`` on the bounds.
"""
# TODO (old data): CPU(N,lam,iter=20,200,100): 3.3s of 8s for two bounds, 1.8s of 6.5s for one bound
# remark: np.max([bounds[0], x]) is about 40 times slower than ... | [
"def",
"repair",
"(",
"self",
",",
"x",
",",
"copy_if_changed",
"=",
"True",
",",
"copy_always",
"=",
"False",
")",
":",
"copy",
"=",
"copy_if_changed",
"if",
"copy_always",
":",
"x",
"=",
"array",
"(",
"x",
",",
"copy",
"=",
"True",
")",
"bounds",
"... | sets out-of-bounds components of ``x`` on the bounds. | [
"sets",
"out",
"-",
"of",
"-",
"bounds",
"components",
"of",
"x",
"on",
"the",
"bounds",
"."
] | cf0f81ecb9f6412f7276a95bd27359000e1e26b6 | https://github.com/flowersteam/explauto/blob/cf0f81ecb9f6412f7276a95bd27359000e1e26b6/explauto/sensorimotor_model/inverse/cma.py#L1137-L1167 | train |
flowersteam/explauto | explauto/sensorimotor_model/inverse/cma.py | BoundPenalty.update | def update(self, function_values, es):
"""updates the weights for computing a boundary penalty.
Arguments
---------
`function_values`
all function values of recent population of solutions
`es`
`CMAEvolutionStrategy` object instance, in particular
... | python | def update(self, function_values, es):
"""updates the weights for computing a boundary penalty.
Arguments
---------
`function_values`
all function values of recent population of solutions
`es`
`CMAEvolutionStrategy` object instance, in particular
... | [
"def",
"update",
"(",
"self",
",",
"function_values",
",",
"es",
")",
":",
"if",
"self",
".",
"bounds",
"is",
"None",
"or",
"(",
"self",
".",
"bounds",
"[",
"0",
"]",
"is",
"None",
"and",
"self",
".",
"bounds",
"[",
"1",
"]",
"is",
"None",
")",
... | updates the weights for computing a boundary penalty.
Arguments
---------
`function_values`
all function values of recent population of solutions
`es`
`CMAEvolutionStrategy` object instance, in particular
mean and variances and the methods from the at... | [
"updates",
"the",
"weights",
"for",
"computing",
"a",
"boundary",
"penalty",
"."
] | cf0f81ecb9f6412f7276a95bd27359000e1e26b6 | https://github.com/flowersteam/explauto/blob/cf0f81ecb9f6412f7276a95bd27359000e1e26b6/explauto/sensorimotor_model/inverse/cma.py#L1224-L1294 | train |
flowersteam/explauto | explauto/sensorimotor_model/inverse/cma.py | BoxConstraintsTransformationBase.initialize | def initialize(self):
"""initialize in base class"""
self._lb = [b[0] for b in self.bounds] # can be done more efficiently?
self._ub = [b[1] for b in self.bounds] | python | def initialize(self):
"""initialize in base class"""
self._lb = [b[0] for b in self.bounds] # can be done more efficiently?
self._ub = [b[1] for b in self.bounds] | [
"def",
"initialize",
"(",
"self",
")",
":",
"self",
".",
"_lb",
"=",
"[",
"b",
"[",
"0",
"]",
"for",
"b",
"in",
"self",
".",
"bounds",
"]",
"self",
".",
"_ub",
"=",
"[",
"b",
"[",
"1",
"]",
"for",
"b",
"in",
"self",
".",
"bounds",
"]"
] | initialize in base class | [
"initialize",
"in",
"base",
"class"
] | cf0f81ecb9f6412f7276a95bd27359000e1e26b6 | https://github.com/flowersteam/explauto/blob/cf0f81ecb9f6412f7276a95bd27359000e1e26b6/explauto/sensorimotor_model/inverse/cma.py#L1321-L1324 | train |
flowersteam/explauto | explauto/sensorimotor_model/inverse/cma.py | BoxConstraintsLinQuadTransformation.is_feasible_i | def is_feasible_i(self, x, i):
"""return True if value ``x`` is in the invertible domain of
variable ``i``
"""
lb = self._lb[self._index(i)]
ub = self._ub[self._index(i)]
al = self._al[self._index(i)]
au = self._au[self._index(i)]
return lb - al < x < ub ... | python | def is_feasible_i(self, x, i):
"""return True if value ``x`` is in the invertible domain of
variable ``i``
"""
lb = self._lb[self._index(i)]
ub = self._ub[self._index(i)]
al = self._al[self._index(i)]
au = self._au[self._index(i)]
return lb - al < x < ub ... | [
"def",
"is_feasible_i",
"(",
"self",
",",
"x",
",",
"i",
")",
":",
"lb",
"=",
"self",
".",
"_lb",
"[",
"self",
".",
"_index",
"(",
"i",
")",
"]",
"ub",
"=",
"self",
".",
"_ub",
"[",
"self",
".",
"_index",
"(",
"i",
")",
"]",
"al",
"=",
"sel... | return True if value ``x`` is in the invertible domain of
variable ``i`` | [
"return",
"True",
"if",
"value",
"x",
"is",
"in",
"the",
"invertible",
"domain",
"of",
"variable",
"i"
] | cf0f81ecb9f6412f7276a95bd27359000e1e26b6 | https://github.com/flowersteam/explauto/blob/cf0f81ecb9f6412f7276a95bd27359000e1e26b6/explauto/sensorimotor_model/inverse/cma.py#L1513-L1522 | train |
flowersteam/explauto | explauto/sensorimotor_model/inverse/cma.py | BoxConstraintsLinQuadTransformation._transform_i | def _transform_i(self, x, i):
"""return transform of x in component i"""
x = self._shift_or_mirror_into_invertible_i(x, i)
lb = self._lb[self._index(i)]
ub = self._ub[self._index(i)]
al = self._al[self._index(i)]
au = self._au[self._index(i)]
if x < lb + al:
... | python | def _transform_i(self, x, i):
"""return transform of x in component i"""
x = self._shift_or_mirror_into_invertible_i(x, i)
lb = self._lb[self._index(i)]
ub = self._ub[self._index(i)]
al = self._al[self._index(i)]
au = self._au[self._index(i)]
if x < lb + al:
... | [
"def",
"_transform_i",
"(",
"self",
",",
"x",
",",
"i",
")",
":",
"x",
"=",
"self",
".",
"_shift_or_mirror_into_invertible_i",
"(",
"x",
",",
"i",
")",
"lb",
"=",
"self",
".",
"_lb",
"[",
"self",
".",
"_index",
"(",
"i",
")",
"]",
"ub",
"=",
"sel... | return transform of x in component i | [
"return",
"transform",
"of",
"x",
"in",
"component",
"i"
] | cf0f81ecb9f6412f7276a95bd27359000e1e26b6 | https://github.com/flowersteam/explauto/blob/cf0f81ecb9f6412f7276a95bd27359000e1e26b6/explauto/sensorimotor_model/inverse/cma.py#L1582-L1597 | train |
flowersteam/explauto | explauto/sensorimotor_model/inverse/cma.py | BoxConstraintsLinQuadTransformation._inverse_i | def _inverse_i(self, y, i):
"""return inverse of y in component i"""
lb = self._lb[self._index(i)]
ub = self._ub[self._index(i)]
al = self._al[self._index(i)]
au = self._au[self._index(i)]
if 1 < 3:
if not lb <= y <= ub:
raise ValueError('argum... | python | def _inverse_i(self, y, i):
"""return inverse of y in component i"""
lb = self._lb[self._index(i)]
ub = self._ub[self._index(i)]
al = self._al[self._index(i)]
au = self._au[self._index(i)]
if 1 < 3:
if not lb <= y <= ub:
raise ValueError('argum... | [
"def",
"_inverse_i",
"(",
"self",
",",
"y",
",",
"i",
")",
":",
"lb",
"=",
"self",
".",
"_lb",
"[",
"self",
".",
"_index",
"(",
"i",
")",
"]",
"ub",
"=",
"self",
".",
"_ub",
"[",
"self",
".",
"_index",
"(",
"i",
")",
"]",
"al",
"=",
"self",... | return inverse of y in component i | [
"return",
"inverse",
"of",
"y",
"in",
"component",
"i"
] | cf0f81ecb9f6412f7276a95bd27359000e1e26b6 | https://github.com/flowersteam/explauto/blob/cf0f81ecb9f6412f7276a95bd27359000e1e26b6/explauto/sensorimotor_model/inverse/cma.py#L1598-L1612 | train |
flowersteam/explauto | explauto/sensorimotor_model/inverse/cma.py | GenoPheno.pheno | def pheno(self, x, into_bounds=None, copy=True, copy_always=False,
archive=None, iteration=None):
"""maps the genotypic input argument into the phenotypic space,
see help for class `GenoPheno`
Details
-------
If ``copy``, values from ``x`` are copied if changed und... | python | def pheno(self, x, into_bounds=None, copy=True, copy_always=False,
archive=None, iteration=None):
"""maps the genotypic input argument into the phenotypic space,
see help for class `GenoPheno`
Details
-------
If ``copy``, values from ``x`` are copied if changed und... | [
"def",
"pheno",
"(",
"self",
",",
"x",
",",
"into_bounds",
"=",
"None",
",",
"copy",
"=",
"True",
",",
"copy_always",
"=",
"False",
",",
"archive",
"=",
"None",
",",
"iteration",
"=",
"None",
")",
":",
"input_type",
"=",
"type",
"(",
"x",
")",
"if"... | maps the genotypic input argument into the phenotypic space,
see help for class `GenoPheno`
Details
-------
If ``copy``, values from ``x`` are copied if changed under the transformation. | [
"maps",
"the",
"genotypic",
"input",
"argument",
"into",
"the",
"phenotypic",
"space",
"see",
"help",
"for",
"class",
"GenoPheno"
] | cf0f81ecb9f6412f7276a95bd27359000e1e26b6 | https://github.com/flowersteam/explauto/blob/cf0f81ecb9f6412f7276a95bd27359000e1e26b6/explauto/sensorimotor_model/inverse/cma.py#L1753-L1806 | train |
flowersteam/explauto | explauto/sensorimotor_model/inverse/cma.py | GenoPheno.geno | def geno(self, y, from_bounds=None,
copy_if_changed=True, copy_always=False,
repair=None, archive=None):
"""maps the phenotypic input argument into the genotypic space,
that is, computes essentially the inverse of ``pheno``.
By default a copy is made only to prevent to... | python | def geno(self, y, from_bounds=None,
copy_if_changed=True, copy_always=False,
repair=None, archive=None):
"""maps the phenotypic input argument into the genotypic space,
that is, computes essentially the inverse of ``pheno``.
By default a copy is made only to prevent to... | [
"def",
"geno",
"(",
"self",
",",
"y",
",",
"from_bounds",
"=",
"None",
",",
"copy_if_changed",
"=",
"True",
",",
"copy_always",
"=",
"False",
",",
"repair",
"=",
"None",
",",
"archive",
"=",
"None",
")",
":",
"if",
"from_bounds",
"is",
"None",
":",
"... | maps the phenotypic input argument into the genotypic space,
that is, computes essentially the inverse of ``pheno``.
By default a copy is made only to prevent to modify ``y``.
The inverse of the user-defined transformation (if any)
is only needed if external solutions are injected, it ... | [
"maps",
"the",
"phenotypic",
"input",
"argument",
"into",
"the",
"genotypic",
"space",
"that",
"is",
"computes",
"essentially",
"the",
"inverse",
"of",
"pheno",
"."
] | cf0f81ecb9f6412f7276a95bd27359000e1e26b6 | https://github.com/flowersteam/explauto/blob/cf0f81ecb9f6412f7276a95bd27359000e1e26b6/explauto/sensorimotor_model/inverse/cma.py#L1808-L1889 | train |
flowersteam/explauto | explauto/sensorimotor_model/inverse/cma.py | OOOptimizer.optimize | def optimize(self, objective_fct, iterations=None, min_iterations=1,
args=(), verb_disp=None, logger=None, call_back=None):
"""find minimizer of `objective_fct`.
CAVEAT: the return value for `optimize` has changed to ``self``.
Arguments
---------
`objectiv... | python | def optimize(self, objective_fct, iterations=None, min_iterations=1,
args=(), verb_disp=None, logger=None, call_back=None):
"""find minimizer of `objective_fct`.
CAVEAT: the return value for `optimize` has changed to ``self``.
Arguments
---------
`objectiv... | [
"def",
"optimize",
"(",
"self",
",",
"objective_fct",
",",
"iterations",
"=",
"None",
",",
"min_iterations",
"=",
"1",
",",
"args",
"=",
"(",
")",
",",
"verb_disp",
"=",
"None",
",",
"logger",
"=",
"None",
",",
"call_back",
"=",
"None",
")",
":",
"as... | find minimizer of `objective_fct`.
CAVEAT: the return value for `optimize` has changed to ``self``.
Arguments
---------
`objective_fct`
function be to minimized
`iterations`
number of (maximal) iterations, while ``not self.stop()``
... | [
"find",
"minimizer",
"of",
"objective_fct",
"."
] | cf0f81ecb9f6412f7276a95bd27359000e1e26b6 | https://github.com/flowersteam/explauto/blob/cf0f81ecb9f6412f7276a95bd27359000e1e26b6/explauto/sensorimotor_model/inverse/cma.py#L2010-L2106 | train |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.