repository_name
stringlengths
7
55
func_path_in_repository
stringlengths
4
223
func_name
stringlengths
1
134
whole_func_string
stringlengths
75
104k
language
stringclasses
1 value
func_code_string
stringlengths
75
104k
func_code_tokens
sequencelengths
19
28.4k
func_documentation_string
stringlengths
1
46.9k
func_documentation_tokens
sequencelengths
1
1.97k
split_name
stringclasses
1 value
func_code_url
stringlengths
87
315
jleinonen/pytmatrix
pytmatrix/radar.py
Kdp
def Kdp(scatterer): """ Specific differential phase (K_dp) for the current setup. Args: scatterer: a Scatterer instance. Returns: K_dp [deg/km]. NOTE: This only returns the correct value if the particle diameter and wavelength are given in [mm]. The scatterer object should be set to forward scattering geometry before calling this function. """ if (scatterer.thet0 != scatterer.thet) or \ (scatterer.phi0 != scatterer.phi): raise ValueError("A forward scattering geometry is needed to " + \ "compute the specific differential phase.") S = scatterer.get_S() return 1e-3 * (180.0/np.pi) * scatterer.wavelength * (S[1,1]-S[0,0]).real
python
def Kdp(scatterer): """ Specific differential phase (K_dp) for the current setup. Args: scatterer: a Scatterer instance. Returns: K_dp [deg/km]. NOTE: This only returns the correct value if the particle diameter and wavelength are given in [mm]. The scatterer object should be set to forward scattering geometry before calling this function. """ if (scatterer.thet0 != scatterer.thet) or \ (scatterer.phi0 != scatterer.phi): raise ValueError("A forward scattering geometry is needed to " + \ "compute the specific differential phase.") S = scatterer.get_S() return 1e-3 * (180.0/np.pi) * scatterer.wavelength * (S[1,1]-S[0,0]).real
[ "def", "Kdp", "(", "scatterer", ")", ":", "if", "(", "scatterer", ".", "thet0", "!=", "scatterer", ".", "thet", ")", "or", "(", "scatterer", ".", "phi0", "!=", "scatterer", ".", "phi", ")", ":", "raise", "ValueError", "(", "\"A forward scattering geometry is needed to \"", "+", "\"compute the specific differential phase.\"", ")", "S", "=", "scatterer", ".", "get_S", "(", ")", "return", "1e-3", "*", "(", "180.0", "/", "np", ".", "pi", ")", "*", "scatterer", ".", "wavelength", "*", "(", "S", "[", "1", ",", "1", "]", "-", "S", "[", "0", ",", "0", "]", ")", ".", "real" ]
Specific differential phase (K_dp) for the current setup. Args: scatterer: a Scatterer instance. Returns: K_dp [deg/km]. NOTE: This only returns the correct value if the particle diameter and wavelength are given in [mm]. The scatterer object should be set to forward scattering geometry before calling this function.
[ "Specific", "differential", "phase", "(", "K_dp", ")", "for", "the", "current", "setup", "." ]
train
https://github.com/jleinonen/pytmatrix/blob/8803507fe5332786feab105fa74acf63e7121718/pytmatrix/radar.py#L112-L133
jleinonen/pytmatrix
pytmatrix/refractive.py
mg_refractive
def mg_refractive(m, mix): """Maxwell-Garnett EMA for the refractive index. Args: m: Tuple of the complex refractive indices of the media. mix: Tuple of the volume fractions of the media, len(mix)==len(m) (if sum(mix)!=1, these are taken relative to sum(mix)) Returns: The Maxwell-Garnett approximation for the complex refractive index of the effective medium If len(m)==2, the first element is taken as the matrix and the second as the inclusion. If len(m)>2, the media are mixed recursively so that the last element is used as the inclusion and the second to last as the matrix, then this mixture is used as the last element on the next iteration, and so on. """ if len(m) == 2: cF = float(mix[1]) / (mix[0]+mix[1]) * \ (m[1]**2-m[0]**2) / (m[1]**2+2*m[0]**2) er = m[0]**2 * (1.0+2.0*cF) / (1.0-cF) m = np.sqrt(er) else: m_last = mg_refractive(m[-2:], mix[-2:]) mix_last = mix[-2] + mix[-1] m = mg_refractive(m[:-2] + (m_last,), mix[:-2] + (mix_last,)) return m
python
def mg_refractive(m, mix): """Maxwell-Garnett EMA for the refractive index. Args: m: Tuple of the complex refractive indices of the media. mix: Tuple of the volume fractions of the media, len(mix)==len(m) (if sum(mix)!=1, these are taken relative to sum(mix)) Returns: The Maxwell-Garnett approximation for the complex refractive index of the effective medium If len(m)==2, the first element is taken as the matrix and the second as the inclusion. If len(m)>2, the media are mixed recursively so that the last element is used as the inclusion and the second to last as the matrix, then this mixture is used as the last element on the next iteration, and so on. """ if len(m) == 2: cF = float(mix[1]) / (mix[0]+mix[1]) * \ (m[1]**2-m[0]**2) / (m[1]**2+2*m[0]**2) er = m[0]**2 * (1.0+2.0*cF) / (1.0-cF) m = np.sqrt(er) else: m_last = mg_refractive(m[-2:], mix[-2:]) mix_last = mix[-2] + mix[-1] m = mg_refractive(m[:-2] + (m_last,), mix[:-2] + (mix_last,)) return m
[ "def", "mg_refractive", "(", "m", ",", "mix", ")", ":", "if", "len", "(", "m", ")", "==", "2", ":", "cF", "=", "float", "(", "mix", "[", "1", "]", ")", "/", "(", "mix", "[", "0", "]", "+", "mix", "[", "1", "]", ")", "*", "(", "m", "[", "1", "]", "**", "2", "-", "m", "[", "0", "]", "**", "2", ")", "/", "(", "m", "[", "1", "]", "**", "2", "+", "2", "*", "m", "[", "0", "]", "**", "2", ")", "er", "=", "m", "[", "0", "]", "**", "2", "*", "(", "1.0", "+", "2.0", "*", "cF", ")", "/", "(", "1.0", "-", "cF", ")", "m", "=", "np", ".", "sqrt", "(", "er", ")", "else", ":", "m_last", "=", "mg_refractive", "(", "m", "[", "-", "2", ":", "]", ",", "mix", "[", "-", "2", ":", "]", ")", "mix_last", "=", "mix", "[", "-", "2", "]", "+", "mix", "[", "-", "1", "]", "m", "=", "mg_refractive", "(", "m", "[", ":", "-", "2", "]", "+", "(", "m_last", ",", ")", ",", "mix", "[", ":", "-", "2", "]", "+", "(", "mix_last", ",", ")", ")", "return", "m" ]
Maxwell-Garnett EMA for the refractive index. Args: m: Tuple of the complex refractive indices of the media. mix: Tuple of the volume fractions of the media, len(mix)==len(m) (if sum(mix)!=1, these are taken relative to sum(mix)) Returns: The Maxwell-Garnett approximation for the complex refractive index of the effective medium If len(m)==2, the first element is taken as the matrix and the second as the inclusion. If len(m)>2, the media are mixed recursively so that the last element is used as the inclusion and the second to last as the matrix, then this mixture is used as the last element on the next iteration, and so on.
[ "Maxwell", "-", "Garnett", "EMA", "for", "the", "refractive", "index", "." ]
train
https://github.com/jleinonen/pytmatrix/blob/8803507fe5332786feab105fa74acf63e7121718/pytmatrix/refractive.py#L29-L57
jleinonen/pytmatrix
pytmatrix/refractive.py
bruggeman_refractive
def bruggeman_refractive(m, mix): """Bruggeman EMA for the refractive index. For instructions, see mg_refractive in this module, except this routine only works for two components. """ f1 = mix[0]/sum(mix) f2 = mix[1]/sum(mix) e1 = m[0]**2 e2 = m[1]**2 a = -2*(f1+f2) b = (2*f1*e1 - f1*e2 + 2*f2*e2 - f2*e1) c = (f1+f2)*e1*e2 e_eff = (-b - np.sqrt(b**2-4*a*c))/(2*a) return np.sqrt(e_eff)
python
def bruggeman_refractive(m, mix): """Bruggeman EMA for the refractive index. For instructions, see mg_refractive in this module, except this routine only works for two components. """ f1 = mix[0]/sum(mix) f2 = mix[1]/sum(mix) e1 = m[0]**2 e2 = m[1]**2 a = -2*(f1+f2) b = (2*f1*e1 - f1*e2 + 2*f2*e2 - f2*e1) c = (f1+f2)*e1*e2 e_eff = (-b - np.sqrt(b**2-4*a*c))/(2*a) return np.sqrt(e_eff)
[ "def", "bruggeman_refractive", "(", "m", ",", "mix", ")", ":", "f1", "=", "mix", "[", "0", "]", "/", "sum", "(", "mix", ")", "f2", "=", "mix", "[", "1", "]", "/", "sum", "(", "mix", ")", "e1", "=", "m", "[", "0", "]", "**", "2", "e2", "=", "m", "[", "1", "]", "**", "2", "a", "=", "-", "2", "*", "(", "f1", "+", "f2", ")", "b", "=", "(", "2", "*", "f1", "*", "e1", "-", "f1", "*", "e2", "+", "2", "*", "f2", "*", "e2", "-", "f2", "*", "e1", ")", "c", "=", "(", "f1", "+", "f2", ")", "*", "e1", "*", "e2", "e_eff", "=", "(", "-", "b", "-", "np", ".", "sqrt", "(", "b", "**", "2", "-", "4", "*", "a", "*", "c", ")", ")", "/", "(", "2", "*", "a", ")", "return", "np", ".", "sqrt", "(", "e_eff", ")" ]
Bruggeman EMA for the refractive index. For instructions, see mg_refractive in this module, except this routine only works for two components.
[ "Bruggeman", "EMA", "for", "the", "refractive", "index", "." ]
train
https://github.com/jleinonen/pytmatrix/blob/8803507fe5332786feab105fa74acf63e7121718/pytmatrix/refractive.py#L60-L74
jleinonen/pytmatrix
pytmatrix/refractive.py
ice_refractive
def ice_refractive(file): """ Interpolator for the refractive indices of ice. Inputs: File to read the refractive index lookup table from. This is supplied as "ice_refr.dat", retrieved from http://www.atmos.washington.edu/ice_optical_constants/ Returns: A callable object that takes as parameters the wavelength [mm] and the snow density [g/cm^3]. """ D = np.loadtxt(file) log_wl = np.log10(D[:,0]/1000) re = D[:,1] log_im = np.log10(D[:,2]) iobj_re = interpolate.interp1d(log_wl, re) iobj_log_im = interpolate.interp1d(log_wl, log_im) def ref(wl, snow_density): lwl = np.log10(wl) try: len(lwl) except TypeError: mi_sqr = complex(iobj_re(lwl), 10**iobj_log_im(lwl))**2 else: mi_sqr = np.array([complex(a,b) for (a,b) in zip(iobj_re(lwl), 10**iobj_log_im(lwl))])**2 c = (mi_sqr-1)/(mi_sqr+2) * snow_density/ice_density return np.sqrt( (1+2*c) / (1-c) ) return ref
python
def ice_refractive(file): """ Interpolator for the refractive indices of ice. Inputs: File to read the refractive index lookup table from. This is supplied as "ice_refr.dat", retrieved from http://www.atmos.washington.edu/ice_optical_constants/ Returns: A callable object that takes as parameters the wavelength [mm] and the snow density [g/cm^3]. """ D = np.loadtxt(file) log_wl = np.log10(D[:,0]/1000) re = D[:,1] log_im = np.log10(D[:,2]) iobj_re = interpolate.interp1d(log_wl, re) iobj_log_im = interpolate.interp1d(log_wl, log_im) def ref(wl, snow_density): lwl = np.log10(wl) try: len(lwl) except TypeError: mi_sqr = complex(iobj_re(lwl), 10**iobj_log_im(lwl))**2 else: mi_sqr = np.array([complex(a,b) for (a,b) in zip(iobj_re(lwl), 10**iobj_log_im(lwl))])**2 c = (mi_sqr-1)/(mi_sqr+2) * snow_density/ice_density return np.sqrt( (1+2*c) / (1-c) ) return ref
[ "def", "ice_refractive", "(", "file", ")", ":", "D", "=", "np", ".", "loadtxt", "(", "file", ")", "log_wl", "=", "np", ".", "log10", "(", "D", "[", ":", ",", "0", "]", "/", "1000", ")", "re", "=", "D", "[", ":", ",", "1", "]", "log_im", "=", "np", ".", "log10", "(", "D", "[", ":", ",", "2", "]", ")", "iobj_re", "=", "interpolate", ".", "interp1d", "(", "log_wl", ",", "re", ")", "iobj_log_im", "=", "interpolate", ".", "interp1d", "(", "log_wl", ",", "log_im", ")", "def", "ref", "(", "wl", ",", "snow_density", ")", ":", "lwl", "=", "np", ".", "log10", "(", "wl", ")", "try", ":", "len", "(", "lwl", ")", "except", "TypeError", ":", "mi_sqr", "=", "complex", "(", "iobj_re", "(", "lwl", ")", ",", "10", "**", "iobj_log_im", "(", "lwl", ")", ")", "**", "2", "else", ":", "mi_sqr", "=", "np", ".", "array", "(", "[", "complex", "(", "a", ",", "b", ")", "for", "(", "a", ",", "b", ")", "in", "zip", "(", "iobj_re", "(", "lwl", ")", ",", "10", "**", "iobj_log_im", "(", "lwl", ")", ")", "]", ")", "**", "2", "c", "=", "(", "mi_sqr", "-", "1", ")", "/", "(", "mi_sqr", "+", "2", ")", "*", "snow_density", "/", "ice_density", "return", "np", ".", "sqrt", "(", "(", "1", "+", "2", "*", "c", ")", "/", "(", "1", "-", "c", ")", ")", "return", "ref" ]
Interpolator for the refractive indices of ice. Inputs: File to read the refractive index lookup table from. This is supplied as "ice_refr.dat", retrieved from http://www.atmos.washington.edu/ice_optical_constants/ Returns: A callable object that takes as parameters the wavelength [mm] and the snow density [g/cm^3].
[ "Interpolator", "for", "the", "refractive", "indices", "of", "ice", "." ]
train
https://github.com/jleinonen/pytmatrix/blob/8803507fe5332786feab105fa74acf63e7121718/pytmatrix/refractive.py#L107-L142
jleinonen/pytmatrix
pytmatrix/tmatrix_aux.py
dsr_thurai_2007
def dsr_thurai_2007(D_eq): """ Drop shape relationship function from Thurai2007 (http://dx.doi.org/10.1175/JTECH2051.1) paper. Arguments: D_eq: Drop volume-equivalent diameter (mm) Returns: r: The vertical-to-horizontal drop axis ratio. Note: the Scatterer class expects horizontal to vertical, so you should pass 1/dsr_thurai_2007 """ if D_eq < 0.7: return 1.0 elif D_eq < 1.5: return 1.173 - 0.5165*D_eq + 0.4698*D_eq**2 - 0.1317*D_eq**3 - \ 8.5e-3*D_eq**4 else: return 1.065 - 6.25e-2*D_eq - 3.99e-3*D_eq**2 + 7.66e-4*D_eq**3 - \ 4.095e-5*D_eq**4
python
def dsr_thurai_2007(D_eq): """ Drop shape relationship function from Thurai2007 (http://dx.doi.org/10.1175/JTECH2051.1) paper. Arguments: D_eq: Drop volume-equivalent diameter (mm) Returns: r: The vertical-to-horizontal drop axis ratio. Note: the Scatterer class expects horizontal to vertical, so you should pass 1/dsr_thurai_2007 """ if D_eq < 0.7: return 1.0 elif D_eq < 1.5: return 1.173 - 0.5165*D_eq + 0.4698*D_eq**2 - 0.1317*D_eq**3 - \ 8.5e-3*D_eq**4 else: return 1.065 - 6.25e-2*D_eq - 3.99e-3*D_eq**2 + 7.66e-4*D_eq**3 - \ 4.095e-5*D_eq**4
[ "def", "dsr_thurai_2007", "(", "D_eq", ")", ":", "if", "D_eq", "<", "0.7", ":", "return", "1.0", "elif", "D_eq", "<", "1.5", ":", "return", "1.173", "-", "0.5165", "*", "D_eq", "+", "0.4698", "*", "D_eq", "**", "2", "-", "0.1317", "*", "D_eq", "**", "3", "-", "8.5e-3", "*", "D_eq", "**", "4", "else", ":", "return", "1.065", "-", "6.25e-2", "*", "D_eq", "-", "3.99e-3", "*", "D_eq", "**", "2", "+", "7.66e-4", "*", "D_eq", "**", "3", "-", "4.095e-5", "*", "D_eq", "**", "4" ]
Drop shape relationship function from Thurai2007 (http://dx.doi.org/10.1175/JTECH2051.1) paper. Arguments: D_eq: Drop volume-equivalent diameter (mm) Returns: r: The vertical-to-horizontal drop axis ratio. Note: the Scatterer class expects horizontal to vertical, so you should pass 1/dsr_thurai_2007
[ "Drop", "shape", "relationship", "function", "from", "Thurai2007", "(", "http", ":", "//", "dx", ".", "doi", ".", "org", "/", "10", ".", "1175", "/", "JTECH2051", ".", "1", ")", "paper", ".", "Arguments", ":", "D_eq", ":", "Drop", "volume", "-", "equivalent", "diameter", "(", "mm", ")" ]
train
https://github.com/jleinonen/pytmatrix/blob/8803507fe5332786feab105fa74acf63e7121718/pytmatrix/tmatrix_aux.py#L47-L66
edublancas/sklearn-evaluation
sklearn_evaluation/compute.py
feature_importances
def feature_importances(data, top_n=None, feature_names=None): """ Get and order feature importances from a scikit-learn model or from an array-like structure. If data is a scikit-learn model with sub-estimators (e.g. RandomForest, AdaBoost) the function will compute the standard deviation of each feature. Parameters ---------- data : sklearn model or array-like structure Object to get the data from. top_n : int Only get results for the top_n features. feature_names : array-like Feature_names Returns ------- numpy structured array Returns a numpy structured array with the data. Columns are feature_name, importance and std_ if an sklearn model with sub-estimators was passed in data. """ # data can be either a sklearn estimator or an iterator with # the actual importances values, try to get the values try: imp = data.feature_importances_ except: imp = np.array(data) # in case the user passed an estimator, it may have an estimators_ # attribute, which includes importnaces for every sub-estimator # get them if possible try: sub_imp = np.array([e.feature_importances_ for e in data.estimators_]) # calculate std std = np.std(sub_imp, axis=0) except: std = None # get the number of features n_features = len(imp) # check that the data has the correct format if top_n and top_n > n_features: raise ValueError(('top_n ({}) cannot be greater than the number of' ' features ({})'.format(top_n, n_features))) if top_n and top_n < 1: raise ValueError('top_n cannot be less than 1') if feature_names and len(feature_names) != n_features: raise ValueError(('feature_names ({}) must match the number of' ' features ({})'.format(len(feature_names), n_features))) # if the user did not pass feature names create generic names if feature_names is None: feature_names = ['Feature {}'.format(n) for n in range(1, n_features+1)] feature_names = np.array(feature_names) else: feature_names = np.array(feature_names) # order the data according to the importance for the feature idx = np.argsort(imp)[::-1] imp = imp[idx] feature_names = feature_names[idx] if std is not None: std = std[idx] # build the structured array if std is not None: names = 'feature_name,importance,std_' res = np.core.records.fromarrays([feature_names, imp, std], names=names) else: names = 'feature_name,importance' res = np.core.records.fromarrays([feature_names, imp], names=names) # get subset if top_n is not none if top_n: res = res[:top_n] return res
python
def feature_importances(data, top_n=None, feature_names=None): """ Get and order feature importances from a scikit-learn model or from an array-like structure. If data is a scikit-learn model with sub-estimators (e.g. RandomForest, AdaBoost) the function will compute the standard deviation of each feature. Parameters ---------- data : sklearn model or array-like structure Object to get the data from. top_n : int Only get results for the top_n features. feature_names : array-like Feature_names Returns ------- numpy structured array Returns a numpy structured array with the data. Columns are feature_name, importance and std_ if an sklearn model with sub-estimators was passed in data. """ # data can be either a sklearn estimator or an iterator with # the actual importances values, try to get the values try: imp = data.feature_importances_ except: imp = np.array(data) # in case the user passed an estimator, it may have an estimators_ # attribute, which includes importnaces for every sub-estimator # get them if possible try: sub_imp = np.array([e.feature_importances_ for e in data.estimators_]) # calculate std std = np.std(sub_imp, axis=0) except: std = None # get the number of features n_features = len(imp) # check that the data has the correct format if top_n and top_n > n_features: raise ValueError(('top_n ({}) cannot be greater than the number of' ' features ({})'.format(top_n, n_features))) if top_n and top_n < 1: raise ValueError('top_n cannot be less than 1') if feature_names and len(feature_names) != n_features: raise ValueError(('feature_names ({}) must match the number of' ' features ({})'.format(len(feature_names), n_features))) # if the user did not pass feature names create generic names if feature_names is None: feature_names = ['Feature {}'.format(n) for n in range(1, n_features+1)] feature_names = np.array(feature_names) else: feature_names = np.array(feature_names) # order the data according to the importance for the feature idx = np.argsort(imp)[::-1] imp = imp[idx] feature_names = feature_names[idx] if std is not None: std = std[idx] # build the structured array if std is not None: names = 'feature_name,importance,std_' res = np.core.records.fromarrays([feature_names, imp, std], names=names) else: names = 'feature_name,importance' res = np.core.records.fromarrays([feature_names, imp], names=names) # get subset if top_n is not none if top_n: res = res[:top_n] return res
[ "def", "feature_importances", "(", "data", ",", "top_n", "=", "None", ",", "feature_names", "=", "None", ")", ":", "# data can be either a sklearn estimator or an iterator with", "# the actual importances values, try to get the values", "try", ":", "imp", "=", "data", ".", "feature_importances_", "except", ":", "imp", "=", "np", ".", "array", "(", "data", ")", "# in case the user passed an estimator, it may have an estimators_", "# attribute, which includes importnaces for every sub-estimator", "# get them if possible", "try", ":", "sub_imp", "=", "np", ".", "array", "(", "[", "e", ".", "feature_importances_", "for", "e", "in", "data", ".", "estimators_", "]", ")", "# calculate std", "std", "=", "np", ".", "std", "(", "sub_imp", ",", "axis", "=", "0", ")", "except", ":", "std", "=", "None", "# get the number of features", "n_features", "=", "len", "(", "imp", ")", "# check that the data has the correct format", "if", "top_n", "and", "top_n", ">", "n_features", ":", "raise", "ValueError", "(", "(", "'top_n ({}) cannot be greater than the number of'", "' features ({})'", ".", "format", "(", "top_n", ",", "n_features", ")", ")", ")", "if", "top_n", "and", "top_n", "<", "1", ":", "raise", "ValueError", "(", "'top_n cannot be less than 1'", ")", "if", "feature_names", "and", "len", "(", "feature_names", ")", "!=", "n_features", ":", "raise", "ValueError", "(", "(", "'feature_names ({}) must match the number of'", "' features ({})'", ".", "format", "(", "len", "(", "feature_names", ")", ",", "n_features", ")", ")", ")", "# if the user did not pass feature names create generic names", "if", "feature_names", "is", "None", ":", "feature_names", "=", "[", "'Feature {}'", ".", "format", "(", "n", ")", "for", "n", "in", "range", "(", "1", ",", "n_features", "+", "1", ")", "]", "feature_names", "=", "np", ".", "array", "(", "feature_names", ")", "else", ":", "feature_names", "=", "np", ".", "array", "(", "feature_names", ")", "# order the data according to the importance for the feature", "idx", "=", "np", ".", "argsort", "(", "imp", ")", "[", ":", ":", "-", "1", "]", "imp", "=", "imp", "[", "idx", "]", "feature_names", "=", "feature_names", "[", "idx", "]", "if", "std", "is", "not", "None", ":", "std", "=", "std", "[", "idx", "]", "# build the structured array", "if", "std", "is", "not", "None", ":", "names", "=", "'feature_name,importance,std_'", "res", "=", "np", ".", "core", ".", "records", ".", "fromarrays", "(", "[", "feature_names", ",", "imp", ",", "std", "]", ",", "names", "=", "names", ")", "else", ":", "names", "=", "'feature_name,importance'", "res", "=", "np", ".", "core", ".", "records", ".", "fromarrays", "(", "[", "feature_names", ",", "imp", "]", ",", "names", "=", "names", ")", "# get subset if top_n is not none", "if", "top_n", ":", "res", "=", "res", "[", ":", "top_n", "]", "return", "res" ]
Get and order feature importances from a scikit-learn model or from an array-like structure. If data is a scikit-learn model with sub-estimators (e.g. RandomForest, AdaBoost) the function will compute the standard deviation of each feature. Parameters ---------- data : sklearn model or array-like structure Object to get the data from. top_n : int Only get results for the top_n features. feature_names : array-like Feature_names Returns ------- numpy structured array Returns a numpy structured array with the data. Columns are feature_name, importance and std_ if an sklearn model with sub-estimators was passed in data.
[ "Get", "and", "order", "feature", "importances", "from", "a", "scikit", "-", "learn", "model", "or", "from", "an", "array", "-", "like", "structure", "." ]
train
https://github.com/edublancas/sklearn-evaluation/blob/79ee6e4dfe911b5a5a9b78a5caaed7c73eef6f39/sklearn_evaluation/compute.py#L4-L89
edublancas/sklearn-evaluation
sklearn_evaluation/util.py
_group_by
def _group_by(data, criteria): """ Group objects in data using a function or a key """ if isinstance(criteria, str): criteria_str = criteria def criteria(x): return x[criteria_str] res = defaultdict(list) for element in data: key = criteria(element) res[key].append(element) return res
python
def _group_by(data, criteria): """ Group objects in data using a function or a key """ if isinstance(criteria, str): criteria_str = criteria def criteria(x): return x[criteria_str] res = defaultdict(list) for element in data: key = criteria(element) res[key].append(element) return res
[ "def", "_group_by", "(", "data", ",", "criteria", ")", ":", "if", "isinstance", "(", "criteria", ",", "str", ")", ":", "criteria_str", "=", "criteria", "def", "criteria", "(", "x", ")", ":", "return", "x", "[", "criteria_str", "]", "res", "=", "defaultdict", "(", "list", ")", "for", "element", "in", "data", ":", "key", "=", "criteria", "(", "element", ")", "res", "[", "key", "]", ".", "append", "(", "element", ")", "return", "res" ]
Group objects in data using a function or a key
[ "Group", "objects", "in", "data", "using", "a", "function", "or", "a", "key" ]
train
https://github.com/edublancas/sklearn-evaluation/blob/79ee6e4dfe911b5a5a9b78a5caaed7c73eef6f39/sklearn_evaluation/util.py#L37-L51
edublancas/sklearn-evaluation
sklearn_evaluation/util.py
_get_params_value
def _get_params_value(params): """ Given an iterator (k1, k2), returns a function that when called with an object obj returns a tuple of the form: ((k1, obj.parameters[k1]), (k2, obj.parameters[k2])) """ # sort params for consistency ord_params = sorted(params) def fn(obj): l = [] for p in ord_params: try: l.append((p, obj.parameters[p])) except: raise ValueError('{} is not a valid parameter'.format(p)) return tuple(l) return fn
python
def _get_params_value(params): """ Given an iterator (k1, k2), returns a function that when called with an object obj returns a tuple of the form: ((k1, obj.parameters[k1]), (k2, obj.parameters[k2])) """ # sort params for consistency ord_params = sorted(params) def fn(obj): l = [] for p in ord_params: try: l.append((p, obj.parameters[p])) except: raise ValueError('{} is not a valid parameter'.format(p)) return tuple(l) return fn
[ "def", "_get_params_value", "(", "params", ")", ":", "# sort params for consistency", "ord_params", "=", "sorted", "(", "params", ")", "def", "fn", "(", "obj", ")", ":", "l", "=", "[", "]", "for", "p", "in", "ord_params", ":", "try", ":", "l", ".", "append", "(", "(", "p", ",", "obj", ".", "parameters", "[", "p", "]", ")", ")", "except", ":", "raise", "ValueError", "(", "'{} is not a valid parameter'", ".", "format", "(", "p", ")", ")", "return", "tuple", "(", "l", ")", "return", "fn" ]
Given an iterator (k1, k2), returns a function that when called with an object obj returns a tuple of the form: ((k1, obj.parameters[k1]), (k2, obj.parameters[k2]))
[ "Given", "an", "iterator", "(", "k1", "k2", ")", "returns", "a", "function", "that", "when", "called", "with", "an", "object", "obj", "returns", "a", "tuple", "of", "the", "form", ":", "((", "k1", "obj", ".", "parameters", "[", "k1", "]", ")", "(", "k2", "obj", ".", "parameters", "[", "k2", "]", "))" ]
train
https://github.com/edublancas/sklearn-evaluation/blob/79ee6e4dfe911b5a5a9b78a5caaed7c73eef6f39/sklearn_evaluation/util.py#L54-L71
edublancas/sklearn-evaluation
sklearn_evaluation/util.py
_product
def _product(k, v): """ Perform the product between two objects even if they don't support iteration """ if not _can_iterate(k): k = [k] if not _can_iterate(v): v = [v] return list(product(k, v))
python
def _product(k, v): """ Perform the product between two objects even if they don't support iteration """ if not _can_iterate(k): k = [k] if not _can_iterate(v): v = [v] return list(product(k, v))
[ "def", "_product", "(", "k", ",", "v", ")", ":", "if", "not", "_can_iterate", "(", "k", ")", ":", "k", "=", "[", "k", "]", "if", "not", "_can_iterate", "(", "v", ")", ":", "v", "=", "[", "v", "]", "return", "list", "(", "product", "(", "k", ",", "v", ")", ")" ]
Perform the product between two objects even if they don't support iteration
[ "Perform", "the", "product", "between", "two", "objects", "even", "if", "they", "don", "t", "support", "iteration" ]
train
https://github.com/edublancas/sklearn-evaluation/blob/79ee6e4dfe911b5a5a9b78a5caaed7c73eef6f39/sklearn_evaluation/util.py#L80-L89
edublancas/sklearn-evaluation
sklearn_evaluation/util.py
_mapping_to_tuple_pairs
def _mapping_to_tuple_pairs(d): """ Convert a mapping object (such as a dictionary) to tuple pairs, using its keys and values to generate the pairs and then generating all possible combinations between those e.g. {1: (1,2,3)} -> (((1, 1),), ((1, 2),), ((1, 3),)) """ # order the keys, this will prevent different implementations of Python, # return different results from the same dictionary since the order of # iteration depends on it t = [] ord_keys = sorted(d.keys()) for k in ord_keys: t.append(_product(k, d[k])) return tuple(product(*t))
python
def _mapping_to_tuple_pairs(d): """ Convert a mapping object (such as a dictionary) to tuple pairs, using its keys and values to generate the pairs and then generating all possible combinations between those e.g. {1: (1,2,3)} -> (((1, 1),), ((1, 2),), ((1, 3),)) """ # order the keys, this will prevent different implementations of Python, # return different results from the same dictionary since the order of # iteration depends on it t = [] ord_keys = sorted(d.keys()) for k in ord_keys: t.append(_product(k, d[k])) return tuple(product(*t))
[ "def", "_mapping_to_tuple_pairs", "(", "d", ")", ":", "# order the keys, this will prevent different implementations of Python,", "# return different results from the same dictionary since the order of", "# iteration depends on it", "t", "=", "[", "]", "ord_keys", "=", "sorted", "(", "d", ".", "keys", "(", ")", ")", "for", "k", "in", "ord_keys", ":", "t", ".", "append", "(", "_product", "(", "k", ",", "d", "[", "k", "]", ")", ")", "return", "tuple", "(", "product", "(", "*", "t", ")", ")" ]
Convert a mapping object (such as a dictionary) to tuple pairs, using its keys and values to generate the pairs and then generating all possible combinations between those e.g. {1: (1,2,3)} -> (((1, 1),), ((1, 2),), ((1, 3),))
[ "Convert", "a", "mapping", "object", "(", "such", "as", "a", "dictionary", ")", "to", "tuple", "pairs", "using", "its", "keys", "and", "values", "to", "generate", "the", "pairs", "and", "then", "generating", "all", "possible", "combinations", "between", "those", "e", ".", "g", ".", "{", "1", ":", "(", "1", "2", "3", ")", "}", "-", ">", "(((", "1", "1", ")", ")", "((", "1", "2", ")", ")", "((", "1", "3", ")", "))" ]
train
https://github.com/edublancas/sklearn-evaluation/blob/79ee6e4dfe911b5a5a9b78a5caaed7c73eef6f39/sklearn_evaluation/util.py#L92-L106
edublancas/sklearn-evaluation
sklearn_evaluation/plot/learning_curve.py
learning_curve
def learning_curve(train_scores, test_scores, train_sizes, ax=None): """Plot a learning curve Plot a metric vs number of examples for the training and test set Parameters ---------- train_scores : array-like Scores for the training set test_scores : array-like Scores for the test set train_sizes : array-like Relative or absolute numbers of training examples used to generate the learning curve ax : matplotlib Axes Axes object to draw the plot onto, otherwise uses current Axes Returns ------- ax: matplotlib Axes Axes containing the plot Examples -------- .. plot:: ../../examples/learning_curve.py """ if ax is None: ax = plt.gca() ax.grid() ax.set_title("Learning Curve") ax.set_xlabel("Training examples") ax.set_ylabel("Score mean") train_scores_mean = np.mean(train_scores, axis=1) train_scores_std = np.std(train_scores, axis=1) test_scores_mean = np.mean(test_scores, axis=1) test_scores_std = np.std(test_scores, axis=1) ax.fill_between(train_sizes, train_scores_mean - train_scores_std, train_scores_mean + train_scores_std, alpha=0.1, color="r") ax.fill_between(train_sizes, test_scores_mean - test_scores_std, test_scores_mean + test_scores_std, alpha=0.1, color="g") ax.plot(train_sizes, train_scores_mean, 'o-', color="r", label="Training score") ax.plot(train_sizes, test_scores_mean, 'o-', color="g", label="Cross-validation score") ax.legend(loc="best") ax.margins(0.05) return ax
python
def learning_curve(train_scores, test_scores, train_sizes, ax=None): """Plot a learning curve Plot a metric vs number of examples for the training and test set Parameters ---------- train_scores : array-like Scores for the training set test_scores : array-like Scores for the test set train_sizes : array-like Relative or absolute numbers of training examples used to generate the learning curve ax : matplotlib Axes Axes object to draw the plot onto, otherwise uses current Axes Returns ------- ax: matplotlib Axes Axes containing the plot Examples -------- .. plot:: ../../examples/learning_curve.py """ if ax is None: ax = plt.gca() ax.grid() ax.set_title("Learning Curve") ax.set_xlabel("Training examples") ax.set_ylabel("Score mean") train_scores_mean = np.mean(train_scores, axis=1) train_scores_std = np.std(train_scores, axis=1) test_scores_mean = np.mean(test_scores, axis=1) test_scores_std = np.std(test_scores, axis=1) ax.fill_between(train_sizes, train_scores_mean - train_scores_std, train_scores_mean + train_scores_std, alpha=0.1, color="r") ax.fill_between(train_sizes, test_scores_mean - test_scores_std, test_scores_mean + test_scores_std, alpha=0.1, color="g") ax.plot(train_sizes, train_scores_mean, 'o-', color="r", label="Training score") ax.plot(train_sizes, test_scores_mean, 'o-', color="g", label="Cross-validation score") ax.legend(loc="best") ax.margins(0.05) return ax
[ "def", "learning_curve", "(", "train_scores", ",", "test_scores", ",", "train_sizes", ",", "ax", "=", "None", ")", ":", "if", "ax", "is", "None", ":", "ax", "=", "plt", ".", "gca", "(", ")", "ax", ".", "grid", "(", ")", "ax", ".", "set_title", "(", "\"Learning Curve\"", ")", "ax", ".", "set_xlabel", "(", "\"Training examples\"", ")", "ax", ".", "set_ylabel", "(", "\"Score mean\"", ")", "train_scores_mean", "=", "np", ".", "mean", "(", "train_scores", ",", "axis", "=", "1", ")", "train_scores_std", "=", "np", ".", "std", "(", "train_scores", ",", "axis", "=", "1", ")", "test_scores_mean", "=", "np", ".", "mean", "(", "test_scores", ",", "axis", "=", "1", ")", "test_scores_std", "=", "np", ".", "std", "(", "test_scores", ",", "axis", "=", "1", ")", "ax", ".", "fill_between", "(", "train_sizes", ",", "train_scores_mean", "-", "train_scores_std", ",", "train_scores_mean", "+", "train_scores_std", ",", "alpha", "=", "0.1", ",", "color", "=", "\"r\"", ")", "ax", ".", "fill_between", "(", "train_sizes", ",", "test_scores_mean", "-", "test_scores_std", ",", "test_scores_mean", "+", "test_scores_std", ",", "alpha", "=", "0.1", ",", "color", "=", "\"g\"", ")", "ax", ".", "plot", "(", "train_sizes", ",", "train_scores_mean", ",", "'o-'", ",", "color", "=", "\"r\"", ",", "label", "=", "\"Training score\"", ")", "ax", ".", "plot", "(", "train_sizes", ",", "test_scores_mean", ",", "'o-'", ",", "color", "=", "\"g\"", ",", "label", "=", "\"Cross-validation score\"", ")", "ax", ".", "legend", "(", "loc", "=", "\"best\"", ")", "ax", ".", "margins", "(", "0.05", ")", "return", "ax" ]
Plot a learning curve Plot a metric vs number of examples for the training and test set Parameters ---------- train_scores : array-like Scores for the training set test_scores : array-like Scores for the test set train_sizes : array-like Relative or absolute numbers of training examples used to generate the learning curve ax : matplotlib Axes Axes object to draw the plot onto, otherwise uses current Axes Returns ------- ax: matplotlib Axes Axes containing the plot Examples -------- .. plot:: ../../examples/learning_curve.py
[ "Plot", "a", "learning", "curve" ]
train
https://github.com/edublancas/sklearn-evaluation/blob/79ee6e4dfe911b5a5a9b78a5caaed7c73eef6f39/sklearn_evaluation/plot/learning_curve.py#L5-L59
edublancas/sklearn-evaluation
sklearn_evaluation/plot/precision_recall.py
precision_recall
def precision_recall(y_true, y_score, ax=None): """ Plot precision-recall curve. Parameters ---------- y_true : array-like, shape = [n_samples] Correct target values (ground truth). y_score : array-like, shape = [n_samples] or [n_samples, 2] for binary classification or [n_samples, n_classes] for multiclass Target scores (estimator predictions). ax : matplotlib Axes Axes object to draw the plot onto, otherwise uses current Axes Notes ----- It is assumed that the y_score parameter columns are in order. For example, if ``y_true = [2, 2, 1, 0, 0, 1, 2]``, then the first column in y_score must countain the scores for class 0, second column for class 1 and so on. Returns ------- ax: matplotlib Axes Axes containing the plot Examples -------- .. plot:: ../../examples/precision_recall.py """ if any((val is None for val in (y_true, y_score))): raise ValueError('y_true and y_score are needed to plot ' 'Precision-Recall') if ax is None: ax = plt.gca() # get the number of classes from y_score y_score_is_vector = is_column_vector(y_score) or is_row_vector(y_score) if y_score_is_vector: n_classes = 2 else: _, n_classes = y_score.shape # check data shape? if n_classes > 2: # convert y_true to binary format y_true_bin = label_binarize(y_true, classes=np.unique(y_true)) _precision_recall_multi(y_true_bin, y_score, ax=ax) for i in range(n_classes): _precision_recall(y_true_bin[:, i], y_score[:, i], ax=ax) else: if y_score_is_vector: _precision_recall(y_true, y_score, ax) else: _precision_recall(y_true, y_score[:, 1], ax) # raise error if n_classes = 1? return ax
python
def precision_recall(y_true, y_score, ax=None): """ Plot precision-recall curve. Parameters ---------- y_true : array-like, shape = [n_samples] Correct target values (ground truth). y_score : array-like, shape = [n_samples] or [n_samples, 2] for binary classification or [n_samples, n_classes] for multiclass Target scores (estimator predictions). ax : matplotlib Axes Axes object to draw the plot onto, otherwise uses current Axes Notes ----- It is assumed that the y_score parameter columns are in order. For example, if ``y_true = [2, 2, 1, 0, 0, 1, 2]``, then the first column in y_score must countain the scores for class 0, second column for class 1 and so on. Returns ------- ax: matplotlib Axes Axes containing the plot Examples -------- .. plot:: ../../examples/precision_recall.py """ if any((val is None for val in (y_true, y_score))): raise ValueError('y_true and y_score are needed to plot ' 'Precision-Recall') if ax is None: ax = plt.gca() # get the number of classes from y_score y_score_is_vector = is_column_vector(y_score) or is_row_vector(y_score) if y_score_is_vector: n_classes = 2 else: _, n_classes = y_score.shape # check data shape? if n_classes > 2: # convert y_true to binary format y_true_bin = label_binarize(y_true, classes=np.unique(y_true)) _precision_recall_multi(y_true_bin, y_score, ax=ax) for i in range(n_classes): _precision_recall(y_true_bin[:, i], y_score[:, i], ax=ax) else: if y_score_is_vector: _precision_recall(y_true, y_score, ax) else: _precision_recall(y_true, y_score[:, 1], ax) # raise error if n_classes = 1? return ax
[ "def", "precision_recall", "(", "y_true", ",", "y_score", ",", "ax", "=", "None", ")", ":", "if", "any", "(", "(", "val", "is", "None", "for", "val", "in", "(", "y_true", ",", "y_score", ")", ")", ")", ":", "raise", "ValueError", "(", "'y_true and y_score are needed to plot '", "'Precision-Recall'", ")", "if", "ax", "is", "None", ":", "ax", "=", "plt", ".", "gca", "(", ")", "# get the number of classes from y_score", "y_score_is_vector", "=", "is_column_vector", "(", "y_score", ")", "or", "is_row_vector", "(", "y_score", ")", "if", "y_score_is_vector", ":", "n_classes", "=", "2", "else", ":", "_", ",", "n_classes", "=", "y_score", ".", "shape", "# check data shape?", "if", "n_classes", ">", "2", ":", "# convert y_true to binary format", "y_true_bin", "=", "label_binarize", "(", "y_true", ",", "classes", "=", "np", ".", "unique", "(", "y_true", ")", ")", "_precision_recall_multi", "(", "y_true_bin", ",", "y_score", ",", "ax", "=", "ax", ")", "for", "i", "in", "range", "(", "n_classes", ")", ":", "_precision_recall", "(", "y_true_bin", "[", ":", ",", "i", "]", ",", "y_score", "[", ":", ",", "i", "]", ",", "ax", "=", "ax", ")", "else", ":", "if", "y_score_is_vector", ":", "_precision_recall", "(", "y_true", ",", "y_score", ",", "ax", ")", "else", ":", "_precision_recall", "(", "y_true", ",", "y_score", "[", ":", ",", "1", "]", ",", "ax", ")", "# raise error if n_classes = 1?", "return", "ax" ]
Plot precision-recall curve. Parameters ---------- y_true : array-like, shape = [n_samples] Correct target values (ground truth). y_score : array-like, shape = [n_samples] or [n_samples, 2] for binary classification or [n_samples, n_classes] for multiclass Target scores (estimator predictions). ax : matplotlib Axes Axes object to draw the plot onto, otherwise uses current Axes Notes ----- It is assumed that the y_score parameter columns are in order. For example, if ``y_true = [2, 2, 1, 0, 0, 1, 2]``, then the first column in y_score must countain the scores for class 0, second column for class 1 and so on. Returns ------- ax: matplotlib Axes Axes containing the plot Examples -------- .. plot:: ../../examples/precision_recall.py
[ "Plot", "precision", "-", "recall", "curve", "." ]
train
https://github.com/edublancas/sklearn-evaluation/blob/79ee6e4dfe911b5a5a9b78a5caaed7c73eef6f39/sklearn_evaluation/plot/precision_recall.py#L9-L70
edublancas/sklearn-evaluation
sklearn_evaluation/plot/precision_recall.py
_precision_recall
def _precision_recall(y_true, y_score, ax=None): """ Plot precision-recall curve. Parameters ---------- y_true : array-like, shape = [n_samples] Correct target values (ground truth). y_score : array-like, shape = [n_samples] Target scores (estimator predictions). ax : matplotlib Axes Axes object to draw the plot onto, otherwise uses current Axes Returns ------- ax: matplotlib Axes Axes containing the plot """ precision, recall, _ = precision_recall_curve(y_true, y_score) average_precision = average_precision_score(y_true, y_score) if ax is None: ax = plt.gca() ax.plot(recall, precision, label=('Precision-Recall curve: AUC={0:0.2f}' .format(average_precision))) _set_ax_settings(ax) return ax
python
def _precision_recall(y_true, y_score, ax=None): """ Plot precision-recall curve. Parameters ---------- y_true : array-like, shape = [n_samples] Correct target values (ground truth). y_score : array-like, shape = [n_samples] Target scores (estimator predictions). ax : matplotlib Axes Axes object to draw the plot onto, otherwise uses current Axes Returns ------- ax: matplotlib Axes Axes containing the plot """ precision, recall, _ = precision_recall_curve(y_true, y_score) average_precision = average_precision_score(y_true, y_score) if ax is None: ax = plt.gca() ax.plot(recall, precision, label=('Precision-Recall curve: AUC={0:0.2f}' .format(average_precision))) _set_ax_settings(ax) return ax
[ "def", "_precision_recall", "(", "y_true", ",", "y_score", ",", "ax", "=", "None", ")", ":", "precision", ",", "recall", ",", "_", "=", "precision_recall_curve", "(", "y_true", ",", "y_score", ")", "average_precision", "=", "average_precision_score", "(", "y_true", ",", "y_score", ")", "if", "ax", "is", "None", ":", "ax", "=", "plt", ".", "gca", "(", ")", "ax", ".", "plot", "(", "recall", ",", "precision", ",", "label", "=", "(", "'Precision-Recall curve: AUC={0:0.2f}'", ".", "format", "(", "average_precision", ")", ")", ")", "_set_ax_settings", "(", "ax", ")", "return", "ax" ]
Plot precision-recall curve. Parameters ---------- y_true : array-like, shape = [n_samples] Correct target values (ground truth). y_score : array-like, shape = [n_samples] Target scores (estimator predictions). ax : matplotlib Axes Axes object to draw the plot onto, otherwise uses current Axes Returns ------- ax: matplotlib Axes Axes containing the plot
[ "Plot", "precision", "-", "recall", "curve", "." ]
train
https://github.com/edublancas/sklearn-evaluation/blob/79ee6e4dfe911b5a5a9b78a5caaed7c73eef6f39/sklearn_evaluation/plot/precision_recall.py#L73-L101
edublancas/sklearn-evaluation
sklearn_evaluation/plot/precision_recall.py
_precision_recall_multi
def _precision_recall_multi(y_true, y_score, ax=None): """ Plot precision-recall curve. Parameters ---------- y_true : array-like, shape = [n_samples, n_classes] Correct target values (ground truth). y_score : array-like, shape = [n_samples, n_classes] Target scores (estimator predictions). ax : matplotlib Axes Axes object to draw the plot onto, otherwise uses current Axes Returns ------- ax: matplotlib Axes Axes containing the plot """ # Compute micro-average ROC curve and ROC area precision, recall, _ = precision_recall_curve(y_true.ravel(), y_score.ravel()) avg_prec = average_precision_score(y_true, y_score, average="micro") if ax is None: ax = plt.gca() ax.plot(recall, precision, label=('micro-average Precision-recall curve (area = {0:0.2f})' .format(avg_prec))) _set_ax_settings(ax) return ax
python
def _precision_recall_multi(y_true, y_score, ax=None): """ Plot precision-recall curve. Parameters ---------- y_true : array-like, shape = [n_samples, n_classes] Correct target values (ground truth). y_score : array-like, shape = [n_samples, n_classes] Target scores (estimator predictions). ax : matplotlib Axes Axes object to draw the plot onto, otherwise uses current Axes Returns ------- ax: matplotlib Axes Axes containing the plot """ # Compute micro-average ROC curve and ROC area precision, recall, _ = precision_recall_curve(y_true.ravel(), y_score.ravel()) avg_prec = average_precision_score(y_true, y_score, average="micro") if ax is None: ax = plt.gca() ax.plot(recall, precision, label=('micro-average Precision-recall curve (area = {0:0.2f})' .format(avg_prec))) _set_ax_settings(ax) return ax
[ "def", "_precision_recall_multi", "(", "y_true", ",", "y_score", ",", "ax", "=", "None", ")", ":", "# Compute micro-average ROC curve and ROC area", "precision", ",", "recall", ",", "_", "=", "precision_recall_curve", "(", "y_true", ".", "ravel", "(", ")", ",", "y_score", ".", "ravel", "(", ")", ")", "avg_prec", "=", "average_precision_score", "(", "y_true", ",", "y_score", ",", "average", "=", "\"micro\"", ")", "if", "ax", "is", "None", ":", "ax", "=", "plt", ".", "gca", "(", ")", "ax", ".", "plot", "(", "recall", ",", "precision", ",", "label", "=", "(", "'micro-average Precision-recall curve (area = {0:0.2f})'", ".", "format", "(", "avg_prec", ")", ")", ")", "_set_ax_settings", "(", "ax", ")", "return", "ax" ]
Plot precision-recall curve. Parameters ---------- y_true : array-like, shape = [n_samples, n_classes] Correct target values (ground truth). y_score : array-like, shape = [n_samples, n_classes] Target scores (estimator predictions). ax : matplotlib Axes Axes object to draw the plot onto, otherwise uses current Axes Returns ------- ax: matplotlib Axes Axes containing the plot
[ "Plot", "precision", "-", "recall", "curve", "." ]
train
https://github.com/edublancas/sklearn-evaluation/blob/79ee6e4dfe911b5a5a9b78a5caaed7c73eef6f39/sklearn_evaluation/plot/precision_recall.py#L104-L136
edublancas/sklearn-evaluation
sklearn_evaluation/metrics.py
precision_at
def precision_at(y_true, y_score, proportion, ignore_nas=False): ''' Calculates precision at a given proportion. Only supports binary classification. ''' # Sort scores in descending order scores_sorted = np.sort(y_score)[::-1] # Based on the proportion, get the index to split the data # if value is negative, return 0 cutoff_index = max(int(len(y_true) * proportion) - 1, 0) # Get the cutoff value cutoff_value = scores_sorted[cutoff_index] # Convert scores to binary, by comparing them with the cutoff value scores_binary = np.array([int(y >= cutoff_value) for y in y_score]) # Calculate precision using sklearn function if ignore_nas: precision = __precision(y_true, scores_binary) else: precision = precision_score(y_true, scores_binary) return precision, cutoff_value
python
def precision_at(y_true, y_score, proportion, ignore_nas=False): ''' Calculates precision at a given proportion. Only supports binary classification. ''' # Sort scores in descending order scores_sorted = np.sort(y_score)[::-1] # Based on the proportion, get the index to split the data # if value is negative, return 0 cutoff_index = max(int(len(y_true) * proportion) - 1, 0) # Get the cutoff value cutoff_value = scores_sorted[cutoff_index] # Convert scores to binary, by comparing them with the cutoff value scores_binary = np.array([int(y >= cutoff_value) for y in y_score]) # Calculate precision using sklearn function if ignore_nas: precision = __precision(y_true, scores_binary) else: precision = precision_score(y_true, scores_binary) return precision, cutoff_value
[ "def", "precision_at", "(", "y_true", ",", "y_score", ",", "proportion", ",", "ignore_nas", "=", "False", ")", ":", "# Sort scores in descending order", "scores_sorted", "=", "np", ".", "sort", "(", "y_score", ")", "[", ":", ":", "-", "1", "]", "# Based on the proportion, get the index to split the data", "# if value is negative, return 0", "cutoff_index", "=", "max", "(", "int", "(", "len", "(", "y_true", ")", "*", "proportion", ")", "-", "1", ",", "0", ")", "# Get the cutoff value", "cutoff_value", "=", "scores_sorted", "[", "cutoff_index", "]", "# Convert scores to binary, by comparing them with the cutoff value", "scores_binary", "=", "np", ".", "array", "(", "[", "int", "(", "y", ">=", "cutoff_value", ")", "for", "y", "in", "y_score", "]", ")", "# Calculate precision using sklearn function", "if", "ignore_nas", ":", "precision", "=", "__precision", "(", "y_true", ",", "scores_binary", ")", "else", ":", "precision", "=", "precision_score", "(", "y_true", ",", "scores_binary", ")", "return", "precision", ",", "cutoff_value" ]
Calculates precision at a given proportion. Only supports binary classification.
[ "Calculates", "precision", "at", "a", "given", "proportion", ".", "Only", "supports", "binary", "classification", "." ]
train
https://github.com/edublancas/sklearn-evaluation/blob/79ee6e4dfe911b5a5a9b78a5caaed7c73eef6f39/sklearn_evaluation/metrics.py#L7-L29
edublancas/sklearn-evaluation
sklearn_evaluation/metrics.py
__precision
def __precision(y_true, y_pred): ''' Precision metric tolerant to unlabeled data in y_true, NA values are ignored for the precision calculation ''' # make copies of the arrays to avoid modifying the original ones y_true = np.copy(y_true) y_pred = np.copy(y_pred) # precision = tp/(tp+fp) # True nehatives do not affect precision value, so for every missing # value in y_true, replace it with 0 and also replace the value # in y_pred with 0 is_nan = np.isnan(y_true) y_true[is_nan] = 0 y_pred[is_nan] = 0 precision = precision_score(y_true, y_pred) return precision
python
def __precision(y_true, y_pred): ''' Precision metric tolerant to unlabeled data in y_true, NA values are ignored for the precision calculation ''' # make copies of the arrays to avoid modifying the original ones y_true = np.copy(y_true) y_pred = np.copy(y_pred) # precision = tp/(tp+fp) # True nehatives do not affect precision value, so for every missing # value in y_true, replace it with 0 and also replace the value # in y_pred with 0 is_nan = np.isnan(y_true) y_true[is_nan] = 0 y_pred[is_nan] = 0 precision = precision_score(y_true, y_pred) return precision
[ "def", "__precision", "(", "y_true", ",", "y_pred", ")", ":", "# make copies of the arrays to avoid modifying the original ones", "y_true", "=", "np", ".", "copy", "(", "y_true", ")", "y_pred", "=", "np", ".", "copy", "(", "y_pred", ")", "# precision = tp/(tp+fp)", "# True nehatives do not affect precision value, so for every missing", "# value in y_true, replace it with 0 and also replace the value", "# in y_pred with 0", "is_nan", "=", "np", ".", "isnan", "(", "y_true", ")", "y_true", "[", "is_nan", "]", "=", "0", "y_pred", "[", "is_nan", "]", "=", "0", "precision", "=", "precision_score", "(", "y_true", ",", "y_pred", ")", "return", "precision" ]
Precision metric tolerant to unlabeled data in y_true, NA values are ignored for the precision calculation
[ "Precision", "metric", "tolerant", "to", "unlabeled", "data", "in", "y_true", "NA", "values", "are", "ignored", "for", "the", "precision", "calculation" ]
train
https://github.com/edublancas/sklearn-evaluation/blob/79ee6e4dfe911b5a5a9b78a5caaed7c73eef6f39/sklearn_evaluation/metrics.py#L51-L68
edublancas/sklearn-evaluation
sklearn_evaluation/metrics.py
labels_at
def labels_at(y_true, y_score, proportion, normalize=False): ''' Return the number of labels encountered in the top X proportion ''' # Get indexes of scores sorted in descending order indexes = np.argsort(y_score)[::-1] # Sort true values in the same order y_true_sorted = y_true[indexes] # Grab top x proportion of true values cutoff_index = max(int(len(y_true_sorted) * proportion) - 1, 0) # add one to index to grab values including that index y_true_top = y_true_sorted[:cutoff_index+1] # Count the number of non-nas in the top x proportion # we are returning a count so it should be an int values = int((~np.isnan(y_true_top)).sum()) if normalize: values = float(values)/(~np.isnan(y_true)).sum() return values
python
def labels_at(y_true, y_score, proportion, normalize=False): ''' Return the number of labels encountered in the top X proportion ''' # Get indexes of scores sorted in descending order indexes = np.argsort(y_score)[::-1] # Sort true values in the same order y_true_sorted = y_true[indexes] # Grab top x proportion of true values cutoff_index = max(int(len(y_true_sorted) * proportion) - 1, 0) # add one to index to grab values including that index y_true_top = y_true_sorted[:cutoff_index+1] # Count the number of non-nas in the top x proportion # we are returning a count so it should be an int values = int((~np.isnan(y_true_top)).sum()) if normalize: values = float(values)/(~np.isnan(y_true)).sum() return values
[ "def", "labels_at", "(", "y_true", ",", "y_score", ",", "proportion", ",", "normalize", "=", "False", ")", ":", "# Get indexes of scores sorted in descending order", "indexes", "=", "np", ".", "argsort", "(", "y_score", ")", "[", ":", ":", "-", "1", "]", "# Sort true values in the same order", "y_true_sorted", "=", "y_true", "[", "indexes", "]", "# Grab top x proportion of true values", "cutoff_index", "=", "max", "(", "int", "(", "len", "(", "y_true_sorted", ")", "*", "proportion", ")", "-", "1", ",", "0", ")", "# add one to index to grab values including that index", "y_true_top", "=", "y_true_sorted", "[", ":", "cutoff_index", "+", "1", "]", "# Count the number of non-nas in the top x proportion", "# we are returning a count so it should be an int", "values", "=", "int", "(", "(", "~", "np", ".", "isnan", "(", "y_true_top", ")", ")", ".", "sum", "(", ")", ")", "if", "normalize", ":", "values", "=", "float", "(", "values", ")", "/", "(", "~", "np", ".", "isnan", "(", "y_true", ")", ")", ".", "sum", "(", ")", "return", "values" ]
Return the number of labels encountered in the top X proportion
[ "Return", "the", "number", "of", "labels", "encountered", "in", "the", "top", "X", "proportion" ]
train
https://github.com/edublancas/sklearn-evaluation/blob/79ee6e4dfe911b5a5a9b78a5caaed7c73eef6f39/sklearn_evaluation/metrics.py#L100-L122
edublancas/sklearn-evaluation
sklearn_evaluation/plot/validation_curve.py
validation_curve
def validation_curve(train_scores, test_scores, param_range, param_name=None, semilogx=False, ax=None): """Plot a validation curve Plot a metric vs hyperpameter values for the training and test set Parameters ---------- train_scores : array-like Scores for the training set test_scores : array-like Scores for the test set param_range : array-like Hyperparameter values used to generate the curve param_range : str Hyperparameter name semilgo : bool Sets a log scale on the x axis ax : matplotlib Axes Axes object to draw the plot onto, otherwise uses current Axes Returns ------- ax: matplotlib Axes Axes containing the plot Examples -------- .. plot:: ../../examples/validation_curve.py """ if ax is None: ax = plt.gca() if semilogx: ax.set_xscale('log') train_scores_mean = np.mean(train_scores, axis=1) train_scores_std = np.std(train_scores, axis=1) test_scores_mean = np.mean(test_scores, axis=1) test_scores_std = np.std(test_scores, axis=1) ax.set_title("Validation Curve") ax.set_ylabel("Score mean") if param_name: ax.set_xlabel(param_name) ax.plot(param_range, train_scores_mean, label="Training score", color="r") ax.plot(param_range, test_scores_mean, label="Cross-validation score", color="g") ax.fill_between(param_range, train_scores_mean - train_scores_std, train_scores_mean + train_scores_std, alpha=0.2, color="r") ax.fill_between(param_range, test_scores_mean - test_scores_std, test_scores_mean + test_scores_std, alpha=0.2, color="g") ax.legend(loc="best") ax.margins(0.05) return ax
python
def validation_curve(train_scores, test_scores, param_range, param_name=None, semilogx=False, ax=None): """Plot a validation curve Plot a metric vs hyperpameter values for the training and test set Parameters ---------- train_scores : array-like Scores for the training set test_scores : array-like Scores for the test set param_range : array-like Hyperparameter values used to generate the curve param_range : str Hyperparameter name semilgo : bool Sets a log scale on the x axis ax : matplotlib Axes Axes object to draw the plot onto, otherwise uses current Axes Returns ------- ax: matplotlib Axes Axes containing the plot Examples -------- .. plot:: ../../examples/validation_curve.py """ if ax is None: ax = plt.gca() if semilogx: ax.set_xscale('log') train_scores_mean = np.mean(train_scores, axis=1) train_scores_std = np.std(train_scores, axis=1) test_scores_mean = np.mean(test_scores, axis=1) test_scores_std = np.std(test_scores, axis=1) ax.set_title("Validation Curve") ax.set_ylabel("Score mean") if param_name: ax.set_xlabel(param_name) ax.plot(param_range, train_scores_mean, label="Training score", color="r") ax.plot(param_range, test_scores_mean, label="Cross-validation score", color="g") ax.fill_between(param_range, train_scores_mean - train_scores_std, train_scores_mean + train_scores_std, alpha=0.2, color="r") ax.fill_between(param_range, test_scores_mean - test_scores_std, test_scores_mean + test_scores_std, alpha=0.2, color="g") ax.legend(loc="best") ax.margins(0.05) return ax
[ "def", "validation_curve", "(", "train_scores", ",", "test_scores", ",", "param_range", ",", "param_name", "=", "None", ",", "semilogx", "=", "False", ",", "ax", "=", "None", ")", ":", "if", "ax", "is", "None", ":", "ax", "=", "plt", ".", "gca", "(", ")", "if", "semilogx", ":", "ax", ".", "set_xscale", "(", "'log'", ")", "train_scores_mean", "=", "np", ".", "mean", "(", "train_scores", ",", "axis", "=", "1", ")", "train_scores_std", "=", "np", ".", "std", "(", "train_scores", ",", "axis", "=", "1", ")", "test_scores_mean", "=", "np", ".", "mean", "(", "test_scores", ",", "axis", "=", "1", ")", "test_scores_std", "=", "np", ".", "std", "(", "test_scores", ",", "axis", "=", "1", ")", "ax", ".", "set_title", "(", "\"Validation Curve\"", ")", "ax", ".", "set_ylabel", "(", "\"Score mean\"", ")", "if", "param_name", ":", "ax", ".", "set_xlabel", "(", "param_name", ")", "ax", ".", "plot", "(", "param_range", ",", "train_scores_mean", ",", "label", "=", "\"Training score\"", ",", "color", "=", "\"r\"", ")", "ax", ".", "plot", "(", "param_range", ",", "test_scores_mean", ",", "label", "=", "\"Cross-validation score\"", ",", "color", "=", "\"g\"", ")", "ax", ".", "fill_between", "(", "param_range", ",", "train_scores_mean", "-", "train_scores_std", ",", "train_scores_mean", "+", "train_scores_std", ",", "alpha", "=", "0.2", ",", "color", "=", "\"r\"", ")", "ax", ".", "fill_between", "(", "param_range", ",", "test_scores_mean", "-", "test_scores_std", ",", "test_scores_mean", "+", "test_scores_std", ",", "alpha", "=", "0.2", ",", "color", "=", "\"g\"", ")", "ax", ".", "legend", "(", "loc", "=", "\"best\"", ")", "ax", ".", "margins", "(", "0.05", ")", "return", "ax" ]
Plot a validation curve Plot a metric vs hyperpameter values for the training and test set Parameters ---------- train_scores : array-like Scores for the training set test_scores : array-like Scores for the test set param_range : array-like Hyperparameter values used to generate the curve param_range : str Hyperparameter name semilgo : bool Sets a log scale on the x axis ax : matplotlib Axes Axes object to draw the plot onto, otherwise uses current Axes Returns ------- ax: matplotlib Axes Axes containing the plot Examples -------- .. plot:: ../../examples/validation_curve.py
[ "Plot", "a", "validation", "curve" ]
train
https://github.com/edublancas/sklearn-evaluation/blob/79ee6e4dfe911b5a5a9b78a5caaed7c73eef6f39/sklearn_evaluation/plot/validation_curve.py#L5-L66
edublancas/sklearn-evaluation
sklearn_evaluation/plot/classification.py
confusion_matrix
def confusion_matrix(y_true, y_pred, target_names=None, normalize=False, cmap=None, ax=None): """ Plot confustion matrix. Parameters ---------- y_true : array-like, shape = [n_samples] Correct target values (ground truth). y_pred : array-like, shape = [n_samples] Target predicted classes (estimator predictions). target_names : list List containing the names of the target classes. List must be in order e.g. ``['Label for class 0', 'Label for class 1']``. If ``None``, generic labels will be generated e.g. ``['Class 0', 'Class 1']`` ax: matplotlib Axes Axes object to draw the plot onto, otherwise uses current Axes normalize : bool Normalize the confusion matrix cmap : matplotlib Colormap If ``None`` uses a modified version of matplotlib's OrRd colormap. Returns ------- ax: matplotlib Axes Axes containing the plot Examples -------- .. plot:: ../../examples/confusion_matrix.py """ if any((val is None for val in (y_true, y_pred))): raise ValueError("y_true and y_pred are needed to plot confusion " "matrix") # calculate how many names you expect values = set(y_true).union(set(y_pred)) expected_len = len(values) if target_names and (expected_len != len(target_names)): raise ValueError(('Data cointains {} different values, but target' ' names contains {} values.'.format(expected_len, len(target_names) ))) # if the user didn't pass target_names, create generic ones if not target_names: values = list(values) values.sort() target_names = ['Class {}'.format(v) for v in values] cm = sk_confusion_matrix(y_true, y_pred) if normalize: cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis] np.set_printoptions(precision=2) if ax is None: ax = plt.gca() # this (y, x) may sound counterintuitive. The reason is that # in a matrix cell (i, j) is in row=i and col=j, translating that # to an x, y plane (which matplotlib uses to plot), we need to use # i as the y coordinate (how many steps down) and j as the x coordinate # how many steps to the right. for (y, x), v in np.ndenumerate(cm): try: label = '{:.2}'.format(v) except: label = v ax.text(x, y, label, horizontalalignment='center', verticalalignment='center') if cmap is None: cmap = default_heatmap() im = ax.imshow(cm, interpolation='nearest', cmap=cmap) plt.colorbar(im, ax=ax) tick_marks = np.arange(len(target_names)) ax.set_xticks(tick_marks) ax.set_xticklabels(target_names) ax.set_yticks(tick_marks) ax.set_yticklabels(target_names) title = 'Confusion matrix' if normalize: title += ' (normalized)' ax.set_title(title) ax.set_ylabel('True label') ax.set_xlabel('Predicted label') return ax
python
def confusion_matrix(y_true, y_pred, target_names=None, normalize=False, cmap=None, ax=None): """ Plot confustion matrix. Parameters ---------- y_true : array-like, shape = [n_samples] Correct target values (ground truth). y_pred : array-like, shape = [n_samples] Target predicted classes (estimator predictions). target_names : list List containing the names of the target classes. List must be in order e.g. ``['Label for class 0', 'Label for class 1']``. If ``None``, generic labels will be generated e.g. ``['Class 0', 'Class 1']`` ax: matplotlib Axes Axes object to draw the plot onto, otherwise uses current Axes normalize : bool Normalize the confusion matrix cmap : matplotlib Colormap If ``None`` uses a modified version of matplotlib's OrRd colormap. Returns ------- ax: matplotlib Axes Axes containing the plot Examples -------- .. plot:: ../../examples/confusion_matrix.py """ if any((val is None for val in (y_true, y_pred))): raise ValueError("y_true and y_pred are needed to plot confusion " "matrix") # calculate how many names you expect values = set(y_true).union(set(y_pred)) expected_len = len(values) if target_names and (expected_len != len(target_names)): raise ValueError(('Data cointains {} different values, but target' ' names contains {} values.'.format(expected_len, len(target_names) ))) # if the user didn't pass target_names, create generic ones if not target_names: values = list(values) values.sort() target_names = ['Class {}'.format(v) for v in values] cm = sk_confusion_matrix(y_true, y_pred) if normalize: cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis] np.set_printoptions(precision=2) if ax is None: ax = plt.gca() # this (y, x) may sound counterintuitive. The reason is that # in a matrix cell (i, j) is in row=i and col=j, translating that # to an x, y plane (which matplotlib uses to plot), we need to use # i as the y coordinate (how many steps down) and j as the x coordinate # how many steps to the right. for (y, x), v in np.ndenumerate(cm): try: label = '{:.2}'.format(v) except: label = v ax.text(x, y, label, horizontalalignment='center', verticalalignment='center') if cmap is None: cmap = default_heatmap() im = ax.imshow(cm, interpolation='nearest', cmap=cmap) plt.colorbar(im, ax=ax) tick_marks = np.arange(len(target_names)) ax.set_xticks(tick_marks) ax.set_xticklabels(target_names) ax.set_yticks(tick_marks) ax.set_yticklabels(target_names) title = 'Confusion matrix' if normalize: title += ' (normalized)' ax.set_title(title) ax.set_ylabel('True label') ax.set_xlabel('Predicted label') return ax
[ "def", "confusion_matrix", "(", "y_true", ",", "y_pred", ",", "target_names", "=", "None", ",", "normalize", "=", "False", ",", "cmap", "=", "None", ",", "ax", "=", "None", ")", ":", "if", "any", "(", "(", "val", "is", "None", "for", "val", "in", "(", "y_true", ",", "y_pred", ")", ")", ")", ":", "raise", "ValueError", "(", "\"y_true and y_pred are needed to plot confusion \"", "\"matrix\"", ")", "# calculate how many names you expect", "values", "=", "set", "(", "y_true", ")", ".", "union", "(", "set", "(", "y_pred", ")", ")", "expected_len", "=", "len", "(", "values", ")", "if", "target_names", "and", "(", "expected_len", "!=", "len", "(", "target_names", ")", ")", ":", "raise", "ValueError", "(", "(", "'Data cointains {} different values, but target'", "' names contains {} values.'", ".", "format", "(", "expected_len", ",", "len", "(", "target_names", ")", ")", ")", ")", "# if the user didn't pass target_names, create generic ones", "if", "not", "target_names", ":", "values", "=", "list", "(", "values", ")", "values", ".", "sort", "(", ")", "target_names", "=", "[", "'Class {}'", ".", "format", "(", "v", ")", "for", "v", "in", "values", "]", "cm", "=", "sk_confusion_matrix", "(", "y_true", ",", "y_pred", ")", "if", "normalize", ":", "cm", "=", "cm", ".", "astype", "(", "'float'", ")", "/", "cm", ".", "sum", "(", "axis", "=", "1", ")", "[", ":", ",", "np", ".", "newaxis", "]", "np", ".", "set_printoptions", "(", "precision", "=", "2", ")", "if", "ax", "is", "None", ":", "ax", "=", "plt", ".", "gca", "(", ")", "# this (y, x) may sound counterintuitive. The reason is that", "# in a matrix cell (i, j) is in row=i and col=j, translating that", "# to an x, y plane (which matplotlib uses to plot), we need to use", "# i as the y coordinate (how many steps down) and j as the x coordinate", "# how many steps to the right.", "for", "(", "y", ",", "x", ")", ",", "v", "in", "np", ".", "ndenumerate", "(", "cm", ")", ":", "try", ":", "label", "=", "'{:.2}'", ".", "format", "(", "v", ")", "except", ":", "label", "=", "v", "ax", ".", "text", "(", "x", ",", "y", ",", "label", ",", "horizontalalignment", "=", "'center'", ",", "verticalalignment", "=", "'center'", ")", "if", "cmap", "is", "None", ":", "cmap", "=", "default_heatmap", "(", ")", "im", "=", "ax", ".", "imshow", "(", "cm", ",", "interpolation", "=", "'nearest'", ",", "cmap", "=", "cmap", ")", "plt", ".", "colorbar", "(", "im", ",", "ax", "=", "ax", ")", "tick_marks", "=", "np", ".", "arange", "(", "len", "(", "target_names", ")", ")", "ax", ".", "set_xticks", "(", "tick_marks", ")", "ax", ".", "set_xticklabels", "(", "target_names", ")", "ax", ".", "set_yticks", "(", "tick_marks", ")", "ax", ".", "set_yticklabels", "(", "target_names", ")", "title", "=", "'Confusion matrix'", "if", "normalize", ":", "title", "+=", "' (normalized)'", "ax", ".", "set_title", "(", "title", ")", "ax", ".", "set_ylabel", "(", "'True label'", ")", "ax", ".", "set_xlabel", "(", "'Predicted label'", ")", "return", "ax" ]
Plot confustion matrix. Parameters ---------- y_true : array-like, shape = [n_samples] Correct target values (ground truth). y_pred : array-like, shape = [n_samples] Target predicted classes (estimator predictions). target_names : list List containing the names of the target classes. List must be in order e.g. ``['Label for class 0', 'Label for class 1']``. If ``None``, generic labels will be generated e.g. ``['Class 0', 'Class 1']`` ax: matplotlib Axes Axes object to draw the plot onto, otherwise uses current Axes normalize : bool Normalize the confusion matrix cmap : matplotlib Colormap If ``None`` uses a modified version of matplotlib's OrRd colormap. Returns ------- ax: matplotlib Axes Axes containing the plot Examples -------- .. plot:: ../../examples/confusion_matrix.py
[ "Plot", "confustion", "matrix", "." ]
train
https://github.com/edublancas/sklearn-evaluation/blob/79ee6e4dfe911b5a5a9b78a5caaed7c73eef6f39/sklearn_evaluation/plot/classification.py#L13-L105
edublancas/sklearn-evaluation
sklearn_evaluation/plot/classification.py
feature_importances
def feature_importances(data, top_n=None, feature_names=None, ax=None): """ Get and order feature importances from a scikit-learn model or from an array-like structure. If data is a scikit-learn model with sub-estimators (e.g. RandomForest, AdaBoost) the function will compute the standard deviation of each feature. Parameters ---------- data : sklearn model or array-like structure Object to get the data from. top_n : int Only get results for the top_n features. feature_names : array-like Feature names ax : matplotlib Axes Axes object to draw the plot onto, otherwise uses current Axes Returns ------- ax: matplotlib Axes Axes containing the plot Examples -------- .. plot:: ../../examples/feature_importances.py """ if data is None: raise ValueError('data is needed to plot feature importances. ' 'When plotting using the evaluator you need to pass ' 'an estimator ') # If no feature_names is provided, assign numbers res = compute.feature_importances(data, top_n, feature_names) # number of features returned n_feats = len(res) if ax is None: ax = plt.gca() ax.set_title("Feature importances") try: ax.bar(range(n_feats), res.importance, yerr=res.std_, color='red', align="center") except: ax.bar(range(n_feats), res.importance, color='red', align="center") ax.set_xticks(range(n_feats)) ax.set_xticklabels(res.feature_name) ax.set_xlim([-1, n_feats]) return ax
python
def feature_importances(data, top_n=None, feature_names=None, ax=None): """ Get and order feature importances from a scikit-learn model or from an array-like structure. If data is a scikit-learn model with sub-estimators (e.g. RandomForest, AdaBoost) the function will compute the standard deviation of each feature. Parameters ---------- data : sklearn model or array-like structure Object to get the data from. top_n : int Only get results for the top_n features. feature_names : array-like Feature names ax : matplotlib Axes Axes object to draw the plot onto, otherwise uses current Axes Returns ------- ax: matplotlib Axes Axes containing the plot Examples -------- .. plot:: ../../examples/feature_importances.py """ if data is None: raise ValueError('data is needed to plot feature importances. ' 'When plotting using the evaluator you need to pass ' 'an estimator ') # If no feature_names is provided, assign numbers res = compute.feature_importances(data, top_n, feature_names) # number of features returned n_feats = len(res) if ax is None: ax = plt.gca() ax.set_title("Feature importances") try: ax.bar(range(n_feats), res.importance, yerr=res.std_, color='red', align="center") except: ax.bar(range(n_feats), res.importance, color='red', align="center") ax.set_xticks(range(n_feats)) ax.set_xticklabels(res.feature_name) ax.set_xlim([-1, n_feats]) return ax
[ "def", "feature_importances", "(", "data", ",", "top_n", "=", "None", ",", "feature_names", "=", "None", ",", "ax", "=", "None", ")", ":", "if", "data", "is", "None", ":", "raise", "ValueError", "(", "'data is needed to plot feature importances. '", "'When plotting using the evaluator you need to pass '", "'an estimator '", ")", "# If no feature_names is provided, assign numbers", "res", "=", "compute", ".", "feature_importances", "(", "data", ",", "top_n", ",", "feature_names", ")", "# number of features returned", "n_feats", "=", "len", "(", "res", ")", "if", "ax", "is", "None", ":", "ax", "=", "plt", ".", "gca", "(", ")", "ax", ".", "set_title", "(", "\"Feature importances\"", ")", "try", ":", "ax", ".", "bar", "(", "range", "(", "n_feats", ")", ",", "res", ".", "importance", ",", "yerr", "=", "res", ".", "std_", ",", "color", "=", "'red'", ",", "align", "=", "\"center\"", ")", "except", ":", "ax", ".", "bar", "(", "range", "(", "n_feats", ")", ",", "res", ".", "importance", ",", "color", "=", "'red'", ",", "align", "=", "\"center\"", ")", "ax", ".", "set_xticks", "(", "range", "(", "n_feats", ")", ")", "ax", ".", "set_xticklabels", "(", "res", ".", "feature_name", ")", "ax", ".", "set_xlim", "(", "[", "-", "1", ",", "n_feats", "]", ")", "return", "ax" ]
Get and order feature importances from a scikit-learn model or from an array-like structure. If data is a scikit-learn model with sub-estimators (e.g. RandomForest, AdaBoost) the function will compute the standard deviation of each feature. Parameters ---------- data : sklearn model or array-like structure Object to get the data from. top_n : int Only get results for the top_n features. feature_names : array-like Feature names ax : matplotlib Axes Axes object to draw the plot onto, otherwise uses current Axes Returns ------- ax: matplotlib Axes Axes containing the plot Examples -------- .. plot:: ../../examples/feature_importances.py
[ "Get", "and", "order", "feature", "importances", "from", "a", "scikit", "-", "learn", "model", "or", "from", "an", "array", "-", "like", "structure", ".", "If", "data", "is", "a", "scikit", "-", "learn", "model", "with", "sub", "-", "estimators", "(", "e", ".", "g", ".", "RandomForest", "AdaBoost", ")", "the", "function", "will", "compute", "the", "standard", "deviation", "of", "each", "feature", "." ]
train
https://github.com/edublancas/sklearn-evaluation/blob/79ee6e4dfe911b5a5a9b78a5caaed7c73eef6f39/sklearn_evaluation/plot/classification.py#L113-L166
edublancas/sklearn-evaluation
sklearn_evaluation/plot/classification.py
precision_at_proportions
def precision_at_proportions(y_true, y_score, ax=None): """ Plot precision values at different proportions. Parameters ---------- y_true : array-like Correct target values (ground truth). y_score : array-like Target scores (estimator predictions). ax : matplotlib Axes Axes object to draw the plot onto, otherwise uses current Axes Returns ------- ax: matplotlib Axes Axes containing the plot """ if any((val is None for val in (y_true, y_score))): raise ValueError('y_true and y_score are needed to plot precision at ' 'proportions') if ax is None: ax = plt.gca() y_score_is_vector = is_column_vector(y_score) or is_row_vector(y_score) if not y_score_is_vector: y_score = y_score[:, 1] # Calculate points proportions = [0.01 * i for i in range(1, 101)] precs_and_cutoffs = [precision_at(y_true, y_score, p) for p in proportions] precs, cutoffs = zip(*precs_and_cutoffs) # Plot and set nice defaults for title and axis labels ax.plot(proportions, precs) ax.set_title('Precision at various proportions') ax.set_ylabel('Precision') ax.set_xlabel('Proportion') ticks = [0.1 * i for i in range(1, 11)] ax.set_xticks(ticks) ax.set_xticklabels(ticks) ax.set_yticks(ticks) ax.set_yticklabels(ticks) ax.set_ylim([0, 1.0]) ax.set_xlim([0, 1.0]) return ax
python
def precision_at_proportions(y_true, y_score, ax=None): """ Plot precision values at different proportions. Parameters ---------- y_true : array-like Correct target values (ground truth). y_score : array-like Target scores (estimator predictions). ax : matplotlib Axes Axes object to draw the plot onto, otherwise uses current Axes Returns ------- ax: matplotlib Axes Axes containing the plot """ if any((val is None for val in (y_true, y_score))): raise ValueError('y_true and y_score are needed to plot precision at ' 'proportions') if ax is None: ax = plt.gca() y_score_is_vector = is_column_vector(y_score) or is_row_vector(y_score) if not y_score_is_vector: y_score = y_score[:, 1] # Calculate points proportions = [0.01 * i for i in range(1, 101)] precs_and_cutoffs = [precision_at(y_true, y_score, p) for p in proportions] precs, cutoffs = zip(*precs_and_cutoffs) # Plot and set nice defaults for title and axis labels ax.plot(proportions, precs) ax.set_title('Precision at various proportions') ax.set_ylabel('Precision') ax.set_xlabel('Proportion') ticks = [0.1 * i for i in range(1, 11)] ax.set_xticks(ticks) ax.set_xticklabels(ticks) ax.set_yticks(ticks) ax.set_yticklabels(ticks) ax.set_ylim([0, 1.0]) ax.set_xlim([0, 1.0]) return ax
[ "def", "precision_at_proportions", "(", "y_true", ",", "y_score", ",", "ax", "=", "None", ")", ":", "if", "any", "(", "(", "val", "is", "None", "for", "val", "in", "(", "y_true", ",", "y_score", ")", ")", ")", ":", "raise", "ValueError", "(", "'y_true and y_score are needed to plot precision at '", "'proportions'", ")", "if", "ax", "is", "None", ":", "ax", "=", "plt", ".", "gca", "(", ")", "y_score_is_vector", "=", "is_column_vector", "(", "y_score", ")", "or", "is_row_vector", "(", "y_score", ")", "if", "not", "y_score_is_vector", ":", "y_score", "=", "y_score", "[", ":", ",", "1", "]", "# Calculate points", "proportions", "=", "[", "0.01", "*", "i", "for", "i", "in", "range", "(", "1", ",", "101", ")", "]", "precs_and_cutoffs", "=", "[", "precision_at", "(", "y_true", ",", "y_score", ",", "p", ")", "for", "p", "in", "proportions", "]", "precs", ",", "cutoffs", "=", "zip", "(", "*", "precs_and_cutoffs", ")", "# Plot and set nice defaults for title and axis labels", "ax", ".", "plot", "(", "proportions", ",", "precs", ")", "ax", ".", "set_title", "(", "'Precision at various proportions'", ")", "ax", ".", "set_ylabel", "(", "'Precision'", ")", "ax", ".", "set_xlabel", "(", "'Proportion'", ")", "ticks", "=", "[", "0.1", "*", "i", "for", "i", "in", "range", "(", "1", ",", "11", ")", "]", "ax", ".", "set_xticks", "(", "ticks", ")", "ax", ".", "set_xticklabels", "(", "ticks", ")", "ax", ".", "set_yticks", "(", "ticks", ")", "ax", ".", "set_yticklabels", "(", "ticks", ")", "ax", ".", "set_ylim", "(", "[", "0", ",", "1.0", "]", ")", "ax", ".", "set_xlim", "(", "[", "0", ",", "1.0", "]", ")", "return", "ax" ]
Plot precision values at different proportions. Parameters ---------- y_true : array-like Correct target values (ground truth). y_score : array-like Target scores (estimator predictions). ax : matplotlib Axes Axes object to draw the plot onto, otherwise uses current Axes Returns ------- ax: matplotlib Axes Axes containing the plot
[ "Plot", "precision", "values", "at", "different", "proportions", "." ]
train
https://github.com/edublancas/sklearn-evaluation/blob/79ee6e4dfe911b5a5a9b78a5caaed7c73eef6f39/sklearn_evaluation/plot/classification.py#L169-L216
edublancas/sklearn-evaluation
sklearn_evaluation/table.py
feature_importances
def feature_importances(data, top_n=None, feature_names=None): """ Get and order feature importances from a scikit-learn model or from an array-like structure. If data is a scikit-learn model with sub-estimators (e.g. RandomForest, AdaBoost) the function will compute the standard deviation of each feature. Parameters ---------- data : sklearn model or array-like structure Object to get the data from. top_n : int Only get results for the top_n features. feature_names : array-like Feature_names Returns ------- table Table object with the data. Columns are feature_name, importance (`std_` only included for models with sub-estimators) """ if data is None: raise ValueError('data is needed to tabulate feature importances. ' 'When plotting using the evaluator you need to pass ' 'an estimator ') res = compute.feature_importances(data, top_n, feature_names) return Table(res, res.dtype.names)
python
def feature_importances(data, top_n=None, feature_names=None): """ Get and order feature importances from a scikit-learn model or from an array-like structure. If data is a scikit-learn model with sub-estimators (e.g. RandomForest, AdaBoost) the function will compute the standard deviation of each feature. Parameters ---------- data : sklearn model or array-like structure Object to get the data from. top_n : int Only get results for the top_n features. feature_names : array-like Feature_names Returns ------- table Table object with the data. Columns are feature_name, importance (`std_` only included for models with sub-estimators) """ if data is None: raise ValueError('data is needed to tabulate feature importances. ' 'When plotting using the evaluator you need to pass ' 'an estimator ') res = compute.feature_importances(data, top_n, feature_names) return Table(res, res.dtype.names)
[ "def", "feature_importances", "(", "data", ",", "top_n", "=", "None", ",", "feature_names", "=", "None", ")", ":", "if", "data", "is", "None", ":", "raise", "ValueError", "(", "'data is needed to tabulate feature importances. '", "'When plotting using the evaluator you need to pass '", "'an estimator '", ")", "res", "=", "compute", ".", "feature_importances", "(", "data", ",", "top_n", ",", "feature_names", ")", "return", "Table", "(", "res", ",", "res", ".", "dtype", ".", "names", ")" ]
Get and order feature importances from a scikit-learn model or from an array-like structure. If data is a scikit-learn model with sub-estimators (e.g. RandomForest, AdaBoost) the function will compute the standard deviation of each feature. Parameters ---------- data : sklearn model or array-like structure Object to get the data from. top_n : int Only get results for the top_n features. feature_names : array-like Feature_names Returns ------- table Table object with the data. Columns are feature_name, importance (`std_` only included for models with sub-estimators)
[ "Get", "and", "order", "feature", "importances", "from", "a", "scikit", "-", "learn", "model", "or", "from", "an", "array", "-", "like", "structure", "." ]
train
https://github.com/edublancas/sklearn-evaluation/blob/79ee6e4dfe911b5a5a9b78a5caaed7c73eef6f39/sklearn_evaluation/table.py#L30-L62
edublancas/sklearn-evaluation
docs/sphinxext/ipython_sphinxext/ipython_directive.py
block_parser
def block_parser(part, rgxin, rgxout, fmtin, fmtout): """ part is a string of ipython text, comprised of at most one input, one output, comments, and blank lines. The block parser parses the text into a list of:: blocks = [ (TOKEN0, data0), (TOKEN1, data1), ...] where TOKEN is one of [COMMENT | INPUT | OUTPUT ] and data is, depending on the type of token:: COMMENT : the comment string INPUT: the (DECORATOR, INPUT_LINE, REST) where DECORATOR: the input decorator (or None) INPUT_LINE: the input as string (possibly multi-line) REST : any stdout generated by the input line (not OUTPUT) OUTPUT: the output string, possibly multi-line """ block = [] lines = part.split('\n') N = len(lines) i = 0 decorator = None while 1: if i==N: # nothing left to parse -- the last line break line = lines[i] i += 1 line_stripped = line.strip() if line_stripped.startswith('#'): block.append((COMMENT, line)) continue if line_stripped.startswith('@'): # Here is where we assume there is, at most, one decorator. # Might need to rethink this. decorator = line_stripped continue # does this look like an input line? matchin = rgxin.match(line) if matchin: lineno, inputline = int(matchin.group(1)), matchin.group(2) # the ....: continuation string continuation = ' %s:'%''.join(['.']*(len(str(lineno))+2)) Nc = len(continuation) # input lines can continue on for more than one line, if # we have a '\' line continuation char or a function call # echo line 'print'. The input line can only be # terminated by the end of the block or an output line, so # we parse out the rest of the input line if it is # multiline as well as any echo text rest = [] while i<N: # look ahead; if the next line is blank, or a comment, or # an output line, we're done nextline = lines[i] matchout = rgxout.match(nextline) #print "nextline=%s, continuation=%s, starts=%s"%(nextline, continuation, nextline.startswith(continuation)) if matchout or nextline.startswith('#'): break elif nextline.startswith(continuation): # The default ipython_rgx* treat the space following the colon as optional. # However, If the space is there we must consume it or code # employing the cython_magic extension will fail to execute. # # This works with the default ipython_rgx* patterns, # If you modify them, YMMV. nextline = nextline[Nc:] if nextline and nextline[0] == ' ': nextline = nextline[1:] inputline += '\n' + nextline else: rest.append(nextline) i+= 1 block.append((INPUT, (decorator, inputline, '\n'.join(rest)))) continue # if it looks like an output line grab all the text to the end # of the block matchout = rgxout.match(line) if matchout: lineno, output = int(matchout.group(1)), matchout.group(2) if i<N-1: output = '\n'.join([output] + lines[i:]) block.append((OUTPUT, output)) break return block
python
def block_parser(part, rgxin, rgxout, fmtin, fmtout): """ part is a string of ipython text, comprised of at most one input, one output, comments, and blank lines. The block parser parses the text into a list of:: blocks = [ (TOKEN0, data0), (TOKEN1, data1), ...] where TOKEN is one of [COMMENT | INPUT | OUTPUT ] and data is, depending on the type of token:: COMMENT : the comment string INPUT: the (DECORATOR, INPUT_LINE, REST) where DECORATOR: the input decorator (or None) INPUT_LINE: the input as string (possibly multi-line) REST : any stdout generated by the input line (not OUTPUT) OUTPUT: the output string, possibly multi-line """ block = [] lines = part.split('\n') N = len(lines) i = 0 decorator = None while 1: if i==N: # nothing left to parse -- the last line break line = lines[i] i += 1 line_stripped = line.strip() if line_stripped.startswith('#'): block.append((COMMENT, line)) continue if line_stripped.startswith('@'): # Here is where we assume there is, at most, one decorator. # Might need to rethink this. decorator = line_stripped continue # does this look like an input line? matchin = rgxin.match(line) if matchin: lineno, inputline = int(matchin.group(1)), matchin.group(2) # the ....: continuation string continuation = ' %s:'%''.join(['.']*(len(str(lineno))+2)) Nc = len(continuation) # input lines can continue on for more than one line, if # we have a '\' line continuation char or a function call # echo line 'print'. The input line can only be # terminated by the end of the block or an output line, so # we parse out the rest of the input line if it is # multiline as well as any echo text rest = [] while i<N: # look ahead; if the next line is blank, or a comment, or # an output line, we're done nextline = lines[i] matchout = rgxout.match(nextline) #print "nextline=%s, continuation=%s, starts=%s"%(nextline, continuation, nextline.startswith(continuation)) if matchout or nextline.startswith('#'): break elif nextline.startswith(continuation): # The default ipython_rgx* treat the space following the colon as optional. # However, If the space is there we must consume it or code # employing the cython_magic extension will fail to execute. # # This works with the default ipython_rgx* patterns, # If you modify them, YMMV. nextline = nextline[Nc:] if nextline and nextline[0] == ' ': nextline = nextline[1:] inputline += '\n' + nextline else: rest.append(nextline) i+= 1 block.append((INPUT, (decorator, inputline, '\n'.join(rest)))) continue # if it looks like an output line grab all the text to the end # of the block matchout = rgxout.match(line) if matchout: lineno, output = int(matchout.group(1)), matchout.group(2) if i<N-1: output = '\n'.join([output] + lines[i:]) block.append((OUTPUT, output)) break return block
[ "def", "block_parser", "(", "part", ",", "rgxin", ",", "rgxout", ",", "fmtin", ",", "fmtout", ")", ":", "block", "=", "[", "]", "lines", "=", "part", ".", "split", "(", "'\\n'", ")", "N", "=", "len", "(", "lines", ")", "i", "=", "0", "decorator", "=", "None", "while", "1", ":", "if", "i", "==", "N", ":", "# nothing left to parse -- the last line", "break", "line", "=", "lines", "[", "i", "]", "i", "+=", "1", "line_stripped", "=", "line", ".", "strip", "(", ")", "if", "line_stripped", ".", "startswith", "(", "'#'", ")", ":", "block", ".", "append", "(", "(", "COMMENT", ",", "line", ")", ")", "continue", "if", "line_stripped", ".", "startswith", "(", "'@'", ")", ":", "# Here is where we assume there is, at most, one decorator.", "# Might need to rethink this.", "decorator", "=", "line_stripped", "continue", "# does this look like an input line?", "matchin", "=", "rgxin", ".", "match", "(", "line", ")", "if", "matchin", ":", "lineno", ",", "inputline", "=", "int", "(", "matchin", ".", "group", "(", "1", ")", ")", ",", "matchin", ".", "group", "(", "2", ")", "# the ....: continuation string", "continuation", "=", "' %s:'", "%", "''", ".", "join", "(", "[", "'.'", "]", "*", "(", "len", "(", "str", "(", "lineno", ")", ")", "+", "2", ")", ")", "Nc", "=", "len", "(", "continuation", ")", "# input lines can continue on for more than one line, if", "# we have a '\\' line continuation char or a function call", "# echo line 'print'. The input line can only be", "# terminated by the end of the block or an output line, so", "# we parse out the rest of the input line if it is", "# multiline as well as any echo text", "rest", "=", "[", "]", "while", "i", "<", "N", ":", "# look ahead; if the next line is blank, or a comment, or", "# an output line, we're done", "nextline", "=", "lines", "[", "i", "]", "matchout", "=", "rgxout", ".", "match", "(", "nextline", ")", "#print \"nextline=%s, continuation=%s, starts=%s\"%(nextline, continuation, nextline.startswith(continuation))", "if", "matchout", "or", "nextline", ".", "startswith", "(", "'#'", ")", ":", "break", "elif", "nextline", ".", "startswith", "(", "continuation", ")", ":", "# The default ipython_rgx* treat the space following the colon as optional.", "# However, If the space is there we must consume it or code", "# employing the cython_magic extension will fail to execute.", "#", "# This works with the default ipython_rgx* patterns,", "# If you modify them, YMMV.", "nextline", "=", "nextline", "[", "Nc", ":", "]", "if", "nextline", "and", "nextline", "[", "0", "]", "==", "' '", ":", "nextline", "=", "nextline", "[", "1", ":", "]", "inputline", "+=", "'\\n'", "+", "nextline", "else", ":", "rest", ".", "append", "(", "nextline", ")", "i", "+=", "1", "block", ".", "append", "(", "(", "INPUT", ",", "(", "decorator", ",", "inputline", ",", "'\\n'", ".", "join", "(", "rest", ")", ")", ")", ")", "continue", "# if it looks like an output line grab all the text to the end", "# of the block", "matchout", "=", "rgxout", ".", "match", "(", "line", ")", "if", "matchout", ":", "lineno", ",", "output", "=", "int", "(", "matchout", ".", "group", "(", "1", ")", ")", ",", "matchout", ".", "group", "(", "2", ")", "if", "i", "<", "N", "-", "1", ":", "output", "=", "'\\n'", ".", "join", "(", "[", "output", "]", "+", "lines", "[", "i", ":", "]", ")", "block", ".", "append", "(", "(", "OUTPUT", ",", "output", ")", ")", "break", "return", "block" ]
part is a string of ipython text, comprised of at most one input, one output, comments, and blank lines. The block parser parses the text into a list of:: blocks = [ (TOKEN0, data0), (TOKEN1, data1), ...] where TOKEN is one of [COMMENT | INPUT | OUTPUT ] and data is, depending on the type of token:: COMMENT : the comment string INPUT: the (DECORATOR, INPUT_LINE, REST) where DECORATOR: the input decorator (or None) INPUT_LINE: the input as string (possibly multi-line) REST : any stdout generated by the input line (not OUTPUT) OUTPUT: the output string, possibly multi-line
[ "part", "is", "a", "string", "of", "ipython", "text", "comprised", "of", "at", "most", "one", "input", "one", "output", "comments", "and", "blank", "lines", ".", "The", "block", "parser", "parses", "the", "text", "into", "a", "list", "of", "::" ]
train
https://github.com/edublancas/sklearn-evaluation/blob/79ee6e4dfe911b5a5a9b78a5caaed7c73eef6f39/docs/sphinxext/ipython_sphinxext/ipython_directive.py#L165-L266
edublancas/sklearn-evaluation
docs/sphinxext/ipython_sphinxext/ipython_directive.py
EmbeddedSphinxShell.process_block
def process_block(self, block): """ process block from the block_parser and return a list of processed lines """ ret = [] output = None input_lines = None lineno = self.IP.execution_count input_prompt = self.promptin % lineno output_prompt = self.promptout % lineno image_file = None image_directive = None found_input = False for token, data in block: if token == COMMENT: out_data = self.process_comment(data) elif token == INPUT: found_input = True (out_data, input_lines, output, is_doctest, decorator, image_file, image_directive) = \ self.process_input(data, input_prompt, lineno) elif token == OUTPUT: if not found_input: TAB = ' ' * 4 linenumber = 0 source = 'Unavailable' content = 'Unavailable' if self.directive: linenumber = self.directive.state.document.current_line source = self.directive.state.document.current_source content = self.directive.content # Add tabs and join into a single string. content = '\n'.join([TAB + line for line in content]) e = ('\n\nInvalid block: Block contains an output prompt ' 'without an input prompt.\n\n' 'Document source: {0}\n\n' 'Content begins at line {1}: \n\n{2}\n\n' 'Problematic block within content: \n\n{TAB}{3}\n\n') e = e.format(source, linenumber, content, block, TAB=TAB) # Write, rather than include in exception, since Sphinx # will truncate tracebacks. sys.stdout.write(e) raise RuntimeError('An invalid block was detected.') out_data = \ self.process_output(data, output_prompt, input_lines, output, is_doctest, decorator, image_file) if out_data: # Then there was user submitted output in verbatim mode. # We need to remove the last element of `ret` that was # added in `process_input`, as it is '' and would introduce # an undesirable newline. assert(ret[-1] == '') del ret[-1] if out_data: ret.extend(out_data) # save the image files if image_file is not None: self.save_image(image_file) return ret, image_directive
python
def process_block(self, block): """ process block from the block_parser and return a list of processed lines """ ret = [] output = None input_lines = None lineno = self.IP.execution_count input_prompt = self.promptin % lineno output_prompt = self.promptout % lineno image_file = None image_directive = None found_input = False for token, data in block: if token == COMMENT: out_data = self.process_comment(data) elif token == INPUT: found_input = True (out_data, input_lines, output, is_doctest, decorator, image_file, image_directive) = \ self.process_input(data, input_prompt, lineno) elif token == OUTPUT: if not found_input: TAB = ' ' * 4 linenumber = 0 source = 'Unavailable' content = 'Unavailable' if self.directive: linenumber = self.directive.state.document.current_line source = self.directive.state.document.current_source content = self.directive.content # Add tabs and join into a single string. content = '\n'.join([TAB + line for line in content]) e = ('\n\nInvalid block: Block contains an output prompt ' 'without an input prompt.\n\n' 'Document source: {0}\n\n' 'Content begins at line {1}: \n\n{2}\n\n' 'Problematic block within content: \n\n{TAB}{3}\n\n') e = e.format(source, linenumber, content, block, TAB=TAB) # Write, rather than include in exception, since Sphinx # will truncate tracebacks. sys.stdout.write(e) raise RuntimeError('An invalid block was detected.') out_data = \ self.process_output(data, output_prompt, input_lines, output, is_doctest, decorator, image_file) if out_data: # Then there was user submitted output in verbatim mode. # We need to remove the last element of `ret` that was # added in `process_input`, as it is '' and would introduce # an undesirable newline. assert(ret[-1] == '') del ret[-1] if out_data: ret.extend(out_data) # save the image files if image_file is not None: self.save_image(image_file) return ret, image_directive
[ "def", "process_block", "(", "self", ",", "block", ")", ":", "ret", "=", "[", "]", "output", "=", "None", "input_lines", "=", "None", "lineno", "=", "self", ".", "IP", ".", "execution_count", "input_prompt", "=", "self", ".", "promptin", "%", "lineno", "output_prompt", "=", "self", ".", "promptout", "%", "lineno", "image_file", "=", "None", "image_directive", "=", "None", "found_input", "=", "False", "for", "token", ",", "data", "in", "block", ":", "if", "token", "==", "COMMENT", ":", "out_data", "=", "self", ".", "process_comment", "(", "data", ")", "elif", "token", "==", "INPUT", ":", "found_input", "=", "True", "(", "out_data", ",", "input_lines", ",", "output", ",", "is_doctest", ",", "decorator", ",", "image_file", ",", "image_directive", ")", "=", "self", ".", "process_input", "(", "data", ",", "input_prompt", ",", "lineno", ")", "elif", "token", "==", "OUTPUT", ":", "if", "not", "found_input", ":", "TAB", "=", "' '", "*", "4", "linenumber", "=", "0", "source", "=", "'Unavailable'", "content", "=", "'Unavailable'", "if", "self", ".", "directive", ":", "linenumber", "=", "self", ".", "directive", ".", "state", ".", "document", ".", "current_line", "source", "=", "self", ".", "directive", ".", "state", ".", "document", ".", "current_source", "content", "=", "self", ".", "directive", ".", "content", "# Add tabs and join into a single string.", "content", "=", "'\\n'", ".", "join", "(", "[", "TAB", "+", "line", "for", "line", "in", "content", "]", ")", "e", "=", "(", "'\\n\\nInvalid block: Block contains an output prompt '", "'without an input prompt.\\n\\n'", "'Document source: {0}\\n\\n'", "'Content begins at line {1}: \\n\\n{2}\\n\\n'", "'Problematic block within content: \\n\\n{TAB}{3}\\n\\n'", ")", "e", "=", "e", ".", "format", "(", "source", ",", "linenumber", ",", "content", ",", "block", ",", "TAB", "=", "TAB", ")", "# Write, rather than include in exception, since Sphinx", "# will truncate tracebacks.", "sys", ".", "stdout", ".", "write", "(", "e", ")", "raise", "RuntimeError", "(", "'An invalid block was detected.'", ")", "out_data", "=", "self", ".", "process_output", "(", "data", ",", "output_prompt", ",", "input_lines", ",", "output", ",", "is_doctest", ",", "decorator", ",", "image_file", ")", "if", "out_data", ":", "# Then there was user submitted output in verbatim mode.", "# We need to remove the last element of `ret` that was", "# added in `process_input`, as it is '' and would introduce", "# an undesirable newline.", "assert", "(", "ret", "[", "-", "1", "]", "==", "''", ")", "del", "ret", "[", "-", "1", "]", "if", "out_data", ":", "ret", ".", "extend", "(", "out_data", ")", "# save the image files", "if", "image_file", "is", "not", "None", ":", "self", ".", "save_image", "(", "image_file", ")", "return", "ret", ",", "image_directive" ]
process block from the block_parser and return a list of processed lines
[ "process", "block", "from", "the", "block_parser", "and", "return", "a", "list", "of", "processed", "lines" ]
train
https://github.com/edublancas/sklearn-evaluation/blob/79ee6e4dfe911b5a5a9b78a5caaed7c73eef6f39/docs/sphinxext/ipython_sphinxext/ipython_directive.py#L640-L708
edublancas/sklearn-evaluation
sklearn_evaluation/plot/grid_search.py
grid_search
def grid_search(grid_scores, change, subset=None, kind='line', cmap=None, ax=None): """ Plot results from a sklearn grid search by changing two parameters at most. Parameters ---------- grid_scores : list of named tuples Results from a sklearn grid search (get them using the `grid_scores_` parameter) change : str or iterable with len<=2 Parameter to change subset : dictionary-like parameter-value(s) pairs to subset from grid_scores. (e.g. ``{'n_estimartors': [1, 10]}``), if None all combinations will be used. kind : ['line', 'bar'] This only applies whe change is a single parameter. Changes the type of plot cmap : matplotlib Colormap This only applies when change are two parameters. Colormap used for the matrix. If None uses a modified version of matplotlib's OrRd colormap. ax: matplotlib Axes Axes object to draw the plot onto, otherwise uses current Axes Returns ------- ax: matplotlib Axes Axes containing the plot Examples -------- .. plot:: ../../examples/grid_search.py """ if change is None: raise ValueError(('change can\'t be None, you need to select at least' ' one value to make the plot.')) if ax is None: ax = plt.gca() if cmap is None: cmap = default_heatmap() if isinstance(change, string_types) or len(change) == 1: return _grid_search_single(grid_scores, change, subset, kind, ax) elif len(change) == 2: return _grid_search_double(grid_scores, change, subset, cmap, ax) else: raise ValueError('change must have length 1 or 2 or be a string')
python
def grid_search(grid_scores, change, subset=None, kind='line', cmap=None, ax=None): """ Plot results from a sklearn grid search by changing two parameters at most. Parameters ---------- grid_scores : list of named tuples Results from a sklearn grid search (get them using the `grid_scores_` parameter) change : str or iterable with len<=2 Parameter to change subset : dictionary-like parameter-value(s) pairs to subset from grid_scores. (e.g. ``{'n_estimartors': [1, 10]}``), if None all combinations will be used. kind : ['line', 'bar'] This only applies whe change is a single parameter. Changes the type of plot cmap : matplotlib Colormap This only applies when change are two parameters. Colormap used for the matrix. If None uses a modified version of matplotlib's OrRd colormap. ax: matplotlib Axes Axes object to draw the plot onto, otherwise uses current Axes Returns ------- ax: matplotlib Axes Axes containing the plot Examples -------- .. plot:: ../../examples/grid_search.py """ if change is None: raise ValueError(('change can\'t be None, you need to select at least' ' one value to make the plot.')) if ax is None: ax = plt.gca() if cmap is None: cmap = default_heatmap() if isinstance(change, string_types) or len(change) == 1: return _grid_search_single(grid_scores, change, subset, kind, ax) elif len(change) == 2: return _grid_search_double(grid_scores, change, subset, cmap, ax) else: raise ValueError('change must have length 1 or 2 or be a string')
[ "def", "grid_search", "(", "grid_scores", ",", "change", ",", "subset", "=", "None", ",", "kind", "=", "'line'", ",", "cmap", "=", "None", ",", "ax", "=", "None", ")", ":", "if", "change", "is", "None", ":", "raise", "ValueError", "(", "(", "'change can\\'t be None, you need to select at least'", "' one value to make the plot.'", ")", ")", "if", "ax", "is", "None", ":", "ax", "=", "plt", ".", "gca", "(", ")", "if", "cmap", "is", "None", ":", "cmap", "=", "default_heatmap", "(", ")", "if", "isinstance", "(", "change", ",", "string_types", ")", "or", "len", "(", "change", ")", "==", "1", ":", "return", "_grid_search_single", "(", "grid_scores", ",", "change", ",", "subset", ",", "kind", ",", "ax", ")", "elif", "len", "(", "change", ")", "==", "2", ":", "return", "_grid_search_double", "(", "grid_scores", ",", "change", ",", "subset", ",", "cmap", ",", "ax", ")", "else", ":", "raise", "ValueError", "(", "'change must have length 1 or 2 or be a string'", ")" ]
Plot results from a sklearn grid search by changing two parameters at most. Parameters ---------- grid_scores : list of named tuples Results from a sklearn grid search (get them using the `grid_scores_` parameter) change : str or iterable with len<=2 Parameter to change subset : dictionary-like parameter-value(s) pairs to subset from grid_scores. (e.g. ``{'n_estimartors': [1, 10]}``), if None all combinations will be used. kind : ['line', 'bar'] This only applies whe change is a single parameter. Changes the type of plot cmap : matplotlib Colormap This only applies when change are two parameters. Colormap used for the matrix. If None uses a modified version of matplotlib's OrRd colormap. ax: matplotlib Axes Axes object to draw the plot onto, otherwise uses current Axes Returns ------- ax: matplotlib Axes Axes containing the plot Examples -------- .. plot:: ../../examples/grid_search.py
[ "Plot", "results", "from", "a", "sklearn", "grid", "search", "by", "changing", "two", "parameters", "at", "most", "." ]
train
https://github.com/edublancas/sklearn-evaluation/blob/79ee6e4dfe911b5a5a9b78a5caaed7c73eef6f39/sklearn_evaluation/plot/grid_search.py#L12-L64
edublancas/sklearn-evaluation
sklearn_evaluation/evaluator.py
ClassifierEvaluator.confusion_matrix
def confusion_matrix(self): """Confusion matrix plot """ return plot.confusion_matrix(self.y_true, self.y_pred, self.target_names, ax=_gen_ax())
python
def confusion_matrix(self): """Confusion matrix plot """ return plot.confusion_matrix(self.y_true, self.y_pred, self.target_names, ax=_gen_ax())
[ "def", "confusion_matrix", "(", "self", ")", ":", "return", "plot", ".", "confusion_matrix", "(", "self", ".", "y_true", ",", "self", ".", "y_pred", ",", "self", ".", "target_names", ",", "ax", "=", "_gen_ax", "(", ")", ")" ]
Confusion matrix plot
[ "Confusion", "matrix", "plot" ]
train
https://github.com/edublancas/sklearn-evaluation/blob/79ee6e4dfe911b5a5a9b78a5caaed7c73eef6f39/sklearn_evaluation/evaluator.py#L85-L89
edublancas/sklearn-evaluation
sklearn_evaluation/evaluator.py
ClassifierEvaluator.roc
def roc(self): """ROC plot """ return plot.roc(self.y_true, self.y_score, ax=_gen_ax())
python
def roc(self): """ROC plot """ return plot.roc(self.y_true, self.y_score, ax=_gen_ax())
[ "def", "roc", "(", "self", ")", ":", "return", "plot", ".", "roc", "(", "self", ".", "y_true", ",", "self", ".", "y_score", ",", "ax", "=", "_gen_ax", "(", ")", ")" ]
ROC plot
[ "ROC", "plot" ]
train
https://github.com/edublancas/sklearn-evaluation/blob/79ee6e4dfe911b5a5a9b78a5caaed7c73eef6f39/sklearn_evaluation/evaluator.py#L92-L95
edublancas/sklearn-evaluation
sklearn_evaluation/evaluator.py
ClassifierEvaluator.precision_recall
def precision_recall(self): """Precision-recall plot """ return plot.precision_recall(self.y_true, self.y_score, ax=_gen_ax())
python
def precision_recall(self): """Precision-recall plot """ return plot.precision_recall(self.y_true, self.y_score, ax=_gen_ax())
[ "def", "precision_recall", "(", "self", ")", ":", "return", "plot", ".", "precision_recall", "(", "self", ".", "y_true", ",", "self", ".", "y_score", ",", "ax", "=", "_gen_ax", "(", ")", ")" ]
Precision-recall plot
[ "Precision", "-", "recall", "plot" ]
train
https://github.com/edublancas/sklearn-evaluation/blob/79ee6e4dfe911b5a5a9b78a5caaed7c73eef6f39/sklearn_evaluation/evaluator.py#L98-L101
edublancas/sklearn-evaluation
sklearn_evaluation/evaluator.py
ClassifierEvaluator.feature_importances
def feature_importances(self): """Feature importances plot """ return plot.feature_importances(self.estimator, feature_names=self.feature_names, ax=_gen_ax())
python
def feature_importances(self): """Feature importances plot """ return plot.feature_importances(self.estimator, feature_names=self.feature_names, ax=_gen_ax())
[ "def", "feature_importances", "(", "self", ")", ":", "return", "plot", ".", "feature_importances", "(", "self", ".", "estimator", ",", "feature_names", "=", "self", ".", "feature_names", ",", "ax", "=", "_gen_ax", "(", ")", ")" ]
Feature importances plot
[ "Feature", "importances", "plot" ]
train
https://github.com/edublancas/sklearn-evaluation/blob/79ee6e4dfe911b5a5a9b78a5caaed7c73eef6f39/sklearn_evaluation/evaluator.py#L104-L109
edublancas/sklearn-evaluation
sklearn_evaluation/evaluator.py
ClassifierEvaluator.feature_importances_table
def feature_importances_table(self): """Feature importances table """ from . import table return table.feature_importances(self.estimator, feature_names=self.feature_names)
python
def feature_importances_table(self): """Feature importances table """ from . import table return table.feature_importances(self.estimator, feature_names=self.feature_names)
[ "def", "feature_importances_table", "(", "self", ")", ":", "from", ".", "import", "table", "return", "table", ".", "feature_importances", "(", "self", ".", "estimator", ",", "feature_names", "=", "self", ".", "feature_names", ")" ]
Feature importances table
[ "Feature", "importances", "table" ]
train
https://github.com/edublancas/sklearn-evaluation/blob/79ee6e4dfe911b5a5a9b78a5caaed7c73eef6f39/sklearn_evaluation/evaluator.py#L112-L118
edublancas/sklearn-evaluation
sklearn_evaluation/evaluator.py
ClassifierEvaluator.precision_at_proportions
def precision_at_proportions(self): """Precision at proportions plot """ return plot.precision_at_proportions(self.y_true, self.y_score, ax=_gen_ax())
python
def precision_at_proportions(self): """Precision at proportions plot """ return plot.precision_at_proportions(self.y_true, self.y_score, ax=_gen_ax())
[ "def", "precision_at_proportions", "(", "self", ")", ":", "return", "plot", ".", "precision_at_proportions", "(", "self", ".", "y_true", ",", "self", ".", "y_score", ",", "ax", "=", "_gen_ax", "(", ")", ")" ]
Precision at proportions plot
[ "Precision", "at", "proportions", "plot" ]
train
https://github.com/edublancas/sklearn-evaluation/blob/79ee6e4dfe911b5a5a9b78a5caaed7c73eef6f39/sklearn_evaluation/evaluator.py#L121-L125
edublancas/sklearn-evaluation
sklearn_evaluation/evaluator.py
ClassifierEvaluator.generate_report
def generate_report(self, template, path=None, style=None): """ Generate HTML report Parameters ---------- template : markdown-formatted string or path to the template file used for rendering the report. Any attribute of this object can be included in the report using the {tag} format. e.g.'# Report{estimator_name}{roc}{precision_recall}'. Apart from every attribute, you can also use {date} and {date_utc} tags to include the date for the report generation using local and UTC timezones repectively. path : str Path to save the HTML report. If None, the function will return the HTML code. style: str Path to a css file to apply style to the report. If None, no style will be applied Returns ------- report: str Returns the contents of the report if path is None. """ from .report import generate return generate(self, template, path, style)
python
def generate_report(self, template, path=None, style=None): """ Generate HTML report Parameters ---------- template : markdown-formatted string or path to the template file used for rendering the report. Any attribute of this object can be included in the report using the {tag} format. e.g.'# Report{estimator_name}{roc}{precision_recall}'. Apart from every attribute, you can also use {date} and {date_utc} tags to include the date for the report generation using local and UTC timezones repectively. path : str Path to save the HTML report. If None, the function will return the HTML code. style: str Path to a css file to apply style to the report. If None, no style will be applied Returns ------- report: str Returns the contents of the report if path is None. """ from .report import generate return generate(self, template, path, style)
[ "def", "generate_report", "(", "self", ",", "template", ",", "path", "=", "None", ",", "style", "=", "None", ")", ":", "from", ".", "report", "import", "generate", "return", "generate", "(", "self", ",", "template", ",", "path", ",", "style", ")" ]
Generate HTML report Parameters ---------- template : markdown-formatted string or path to the template file used for rendering the report. Any attribute of this object can be included in the report using the {tag} format. e.g.'# Report{estimator_name}{roc}{precision_recall}'. Apart from every attribute, you can also use {date} and {date_utc} tags to include the date for the report generation using local and UTC timezones repectively. path : str Path to save the HTML report. If None, the function will return the HTML code. style: str Path to a css file to apply style to the report. If None, no style will be applied Returns ------- report: str Returns the contents of the report if path is None.
[ "Generate", "HTML", "report" ]
train
https://github.com/edublancas/sklearn-evaluation/blob/79ee6e4dfe911b5a5a9b78a5caaed7c73eef6f39/sklearn_evaluation/evaluator.py#L127-L157
edublancas/sklearn-evaluation
sklearn_evaluation/plot/roc.py
roc
def roc(y_true, y_score, ax=None): """ Plot ROC curve. Parameters ---------- y_true : array-like, shape = [n_samples] Correct target values (ground truth). y_score : array-like, shape = [n_samples] or [n_samples, 2] for binary classification or [n_samples, n_classes] for multiclass Target scores (estimator predictions). ax: matplotlib Axes Axes object to draw the plot onto, otherwise uses current Axes Notes ----- It is assumed that the y_score parameter columns are in order. For example, if ``y_true = [2, 2, 1, 0, 0, 1, 2]``, then the first column in y_score must countain the scores for class 0, second column for class 1 and so on. Returns ------- ax: matplotlib Axes Axes containing the plot Examples -------- .. plot:: ../../examples/roc.py """ if any((val is None for val in (y_true, y_score))): raise ValueError("y_true and y_score are needed to plot ROC") if ax is None: ax = plt.gca() # get the number of classes based on the shape of y_score y_score_is_vector = is_column_vector(y_score) or is_row_vector(y_score) if y_score_is_vector: n_classes = 2 else: _, n_classes = y_score.shape # check data shape? if n_classes > 2: # convert y_true to binary format y_true_bin = label_binarize(y_true, classes=np.unique(y_true)) _roc_multi(y_true_bin, y_score, ax=ax) for i in range(n_classes): _roc(y_true_bin[:, i], y_score[:, i], ax=ax) else: if y_score_is_vector: _roc(y_true, y_score, ax) else: _roc(y_true, y_score[:, 1], ax) # raise error if n_classes = 1? return ax
python
def roc(y_true, y_score, ax=None): """ Plot ROC curve. Parameters ---------- y_true : array-like, shape = [n_samples] Correct target values (ground truth). y_score : array-like, shape = [n_samples] or [n_samples, 2] for binary classification or [n_samples, n_classes] for multiclass Target scores (estimator predictions). ax: matplotlib Axes Axes object to draw the plot onto, otherwise uses current Axes Notes ----- It is assumed that the y_score parameter columns are in order. For example, if ``y_true = [2, 2, 1, 0, 0, 1, 2]``, then the first column in y_score must countain the scores for class 0, second column for class 1 and so on. Returns ------- ax: matplotlib Axes Axes containing the plot Examples -------- .. plot:: ../../examples/roc.py """ if any((val is None for val in (y_true, y_score))): raise ValueError("y_true and y_score are needed to plot ROC") if ax is None: ax = plt.gca() # get the number of classes based on the shape of y_score y_score_is_vector = is_column_vector(y_score) or is_row_vector(y_score) if y_score_is_vector: n_classes = 2 else: _, n_classes = y_score.shape # check data shape? if n_classes > 2: # convert y_true to binary format y_true_bin = label_binarize(y_true, classes=np.unique(y_true)) _roc_multi(y_true_bin, y_score, ax=ax) for i in range(n_classes): _roc(y_true_bin[:, i], y_score[:, i], ax=ax) else: if y_score_is_vector: _roc(y_true, y_score, ax) else: _roc(y_true, y_score[:, 1], ax) # raise error if n_classes = 1? return ax
[ "def", "roc", "(", "y_true", ",", "y_score", ",", "ax", "=", "None", ")", ":", "if", "any", "(", "(", "val", "is", "None", "for", "val", "in", "(", "y_true", ",", "y_score", ")", ")", ")", ":", "raise", "ValueError", "(", "\"y_true and y_score are needed to plot ROC\"", ")", "if", "ax", "is", "None", ":", "ax", "=", "plt", ".", "gca", "(", ")", "# get the number of classes based on the shape of y_score", "y_score_is_vector", "=", "is_column_vector", "(", "y_score", ")", "or", "is_row_vector", "(", "y_score", ")", "if", "y_score_is_vector", ":", "n_classes", "=", "2", "else", ":", "_", ",", "n_classes", "=", "y_score", ".", "shape", "# check data shape?", "if", "n_classes", ">", "2", ":", "# convert y_true to binary format", "y_true_bin", "=", "label_binarize", "(", "y_true", ",", "classes", "=", "np", ".", "unique", "(", "y_true", ")", ")", "_roc_multi", "(", "y_true_bin", ",", "y_score", ",", "ax", "=", "ax", ")", "for", "i", "in", "range", "(", "n_classes", ")", ":", "_roc", "(", "y_true_bin", "[", ":", ",", "i", "]", ",", "y_score", "[", ":", ",", "i", "]", ",", "ax", "=", "ax", ")", "else", ":", "if", "y_score_is_vector", ":", "_roc", "(", "y_true", ",", "y_score", ",", "ax", ")", "else", ":", "_roc", "(", "y_true", ",", "y_score", "[", ":", ",", "1", "]", ",", "ax", ")", "# raise error if n_classes = 1?", "return", "ax" ]
Plot ROC curve. Parameters ---------- y_true : array-like, shape = [n_samples] Correct target values (ground truth). y_score : array-like, shape = [n_samples] or [n_samples, 2] for binary classification or [n_samples, n_classes] for multiclass Target scores (estimator predictions). ax: matplotlib Axes Axes object to draw the plot onto, otherwise uses current Axes Notes ----- It is assumed that the y_score parameter columns are in order. For example, if ``y_true = [2, 2, 1, 0, 0, 1, 2]``, then the first column in y_score must countain the scores for class 0, second column for class 1 and so on. Returns ------- ax: matplotlib Axes Axes containing the plot Examples -------- .. plot:: ../../examples/roc.py
[ "Plot", "ROC", "curve", "." ]
train
https://github.com/edublancas/sklearn-evaluation/blob/79ee6e4dfe911b5a5a9b78a5caaed7c73eef6f39/sklearn_evaluation/plot/roc.py#L9-L69
edublancas/sklearn-evaluation
sklearn_evaluation/plot/roc.py
_roc
def _roc(y_true, y_score, ax=None): """ Plot ROC curve for binary classification. Parameters ---------- y_true : array-like, shape = [n_samples] Correct target values (ground truth). y_score : array-like, shape = [n_samples] Target scores (estimator predictions). ax: matplotlib Axes Axes object to draw the plot onto, otherwise uses current Axes Returns ------- ax: matplotlib Axes Axes containing the plot """ # check dimensions fpr, tpr, _ = roc_curve(y_true, y_score) roc_auc = auc(fpr, tpr) ax.plot(fpr, tpr, label=('ROC curve (area = {0:0.2f})'.format(roc_auc))) _set_ax_settings(ax) return ax
python
def _roc(y_true, y_score, ax=None): """ Plot ROC curve for binary classification. Parameters ---------- y_true : array-like, shape = [n_samples] Correct target values (ground truth). y_score : array-like, shape = [n_samples] Target scores (estimator predictions). ax: matplotlib Axes Axes object to draw the plot onto, otherwise uses current Axes Returns ------- ax: matplotlib Axes Axes containing the plot """ # check dimensions fpr, tpr, _ = roc_curve(y_true, y_score) roc_auc = auc(fpr, tpr) ax.plot(fpr, tpr, label=('ROC curve (area = {0:0.2f})'.format(roc_auc))) _set_ax_settings(ax) return ax
[ "def", "_roc", "(", "y_true", ",", "y_score", ",", "ax", "=", "None", ")", ":", "# check dimensions", "fpr", ",", "tpr", ",", "_", "=", "roc_curve", "(", "y_true", ",", "y_score", ")", "roc_auc", "=", "auc", "(", "fpr", ",", "tpr", ")", "ax", ".", "plot", "(", "fpr", ",", "tpr", ",", "label", "=", "(", "'ROC curve (area = {0:0.2f})'", ".", "format", "(", "roc_auc", ")", ")", ")", "_set_ax_settings", "(", "ax", ")", "return", "ax" ]
Plot ROC curve for binary classification. Parameters ---------- y_true : array-like, shape = [n_samples] Correct target values (ground truth). y_score : array-like, shape = [n_samples] Target scores (estimator predictions). ax: matplotlib Axes Axes object to draw the plot onto, otherwise uses current Axes Returns ------- ax: matplotlib Axes Axes containing the plot
[ "Plot", "ROC", "curve", "for", "binary", "classification", "." ]
train
https://github.com/edublancas/sklearn-evaluation/blob/79ee6e4dfe911b5a5a9b78a5caaed7c73eef6f39/sklearn_evaluation/plot/roc.py#L72-L98
edublancas/sklearn-evaluation
sklearn_evaluation/plot/roc.py
_roc_multi
def _roc_multi(y_true, y_score, ax=None): """ Plot ROC curve for multi classification. Parameters ---------- y_true : array-like, shape = [n_samples, n_classes] Correct target values (ground truth). y_score : array-like, shape = [n_samples, n_classes] Target scores (estimator predictions). ax: matplotlib Axes Axes object to draw the plot onto, otherwise uses current Axes Returns ------- ax: matplotlib Axes Axes containing the plot """ # Compute micro-average ROC curve and ROC area fpr, tpr, _ = roc_curve(y_true.ravel(), y_score.ravel()) roc_auc = auc(fpr, tpr) if ax is None: ax = plt.gca() ax.plot(fpr, tpr, label=('micro-average ROC curve (area = {0:0.2f})' .format(roc_auc))) _set_ax_settings(ax) return ax
python
def _roc_multi(y_true, y_score, ax=None): """ Plot ROC curve for multi classification. Parameters ---------- y_true : array-like, shape = [n_samples, n_classes] Correct target values (ground truth). y_score : array-like, shape = [n_samples, n_classes] Target scores (estimator predictions). ax: matplotlib Axes Axes object to draw the plot onto, otherwise uses current Axes Returns ------- ax: matplotlib Axes Axes containing the plot """ # Compute micro-average ROC curve and ROC area fpr, tpr, _ = roc_curve(y_true.ravel(), y_score.ravel()) roc_auc = auc(fpr, tpr) if ax is None: ax = plt.gca() ax.plot(fpr, tpr, label=('micro-average ROC curve (area = {0:0.2f})' .format(roc_auc))) _set_ax_settings(ax) return ax
[ "def", "_roc_multi", "(", "y_true", ",", "y_score", ",", "ax", "=", "None", ")", ":", "# Compute micro-average ROC curve and ROC area", "fpr", ",", "tpr", ",", "_", "=", "roc_curve", "(", "y_true", ".", "ravel", "(", ")", ",", "y_score", ".", "ravel", "(", ")", ")", "roc_auc", "=", "auc", "(", "fpr", ",", "tpr", ")", "if", "ax", "is", "None", ":", "ax", "=", "plt", ".", "gca", "(", ")", "ax", ".", "plot", "(", "fpr", ",", "tpr", ",", "label", "=", "(", "'micro-average ROC curve (area = {0:0.2f})'", ".", "format", "(", "roc_auc", ")", ")", ")", "_set_ax_settings", "(", "ax", ")", "return", "ax" ]
Plot ROC curve for multi classification. Parameters ---------- y_true : array-like, shape = [n_samples, n_classes] Correct target values (ground truth). y_score : array-like, shape = [n_samples, n_classes] Target scores (estimator predictions). ax: matplotlib Axes Axes object to draw the plot onto, otherwise uses current Axes Returns ------- ax: matplotlib Axes Axes containing the plot
[ "Plot", "ROC", "curve", "for", "multi", "classification", "." ]
train
https://github.com/edublancas/sklearn-evaluation/blob/79ee6e4dfe911b5a5a9b78a5caaed7c73eef6f39/sklearn_evaluation/plot/roc.py#L101-L130
hubo1016/vlcp
vlcp/utils/redisclient.py
RedisClient.get_connection
async def get_connection(self, container): ''' Get an exclusive connection, useful for blocked commands and transactions. You must call release or shutdown (not recommanded) to return the connection after use. :param container: routine container :returns: RedisClientBase object, with some commands same as RedisClient like execute_command, batch_execute, register_script etc. ''' if self._connpool: conn = self._connpool.pop() return RedisClientBase(conn, self) else: conn = self._create_client(container) await RedisClientBase._get_connection(self, container, conn) await self._protocol.send_command(conn, container, 'SELECT', str(self.db)) return RedisClientBase(conn, self)
python
async def get_connection(self, container): ''' Get an exclusive connection, useful for blocked commands and transactions. You must call release or shutdown (not recommanded) to return the connection after use. :param container: routine container :returns: RedisClientBase object, with some commands same as RedisClient like execute_command, batch_execute, register_script etc. ''' if self._connpool: conn = self._connpool.pop() return RedisClientBase(conn, self) else: conn = self._create_client(container) await RedisClientBase._get_connection(self, container, conn) await self._protocol.send_command(conn, container, 'SELECT', str(self.db)) return RedisClientBase(conn, self)
[ "async", "def", "get_connection", "(", "self", ",", "container", ")", ":", "if", "self", ".", "_connpool", ":", "conn", "=", "self", ".", "_connpool", ".", "pop", "(", ")", "return", "RedisClientBase", "(", "conn", ",", "self", ")", "else", ":", "conn", "=", "self", ".", "_create_client", "(", "container", ")", "await", "RedisClientBase", ".", "_get_connection", "(", "self", ",", "container", ",", "conn", ")", "await", "self", ".", "_protocol", ".", "send_command", "(", "conn", ",", "container", ",", "'SELECT'", ",", "str", "(", "self", ".", "db", ")", ")", "return", "RedisClientBase", "(", "conn", ",", "self", ")" ]
Get an exclusive connection, useful for blocked commands and transactions. You must call release or shutdown (not recommanded) to return the connection after use. :param container: routine container :returns: RedisClientBase object, with some commands same as RedisClient like execute_command, batch_execute, register_script etc.
[ "Get", "an", "exclusive", "connection", "useful", "for", "blocked", "commands", "and", "transactions", ".", "You", "must", "call", "release", "or", "shutdown", "(", "not", "recommanded", ")", "to", "return", "the", "connection", "after", "use", ".", ":", "param", "container", ":", "routine", "container", ":", "returns", ":", "RedisClientBase", "object", "with", "some", "commands", "same", "as", "RedisClient", "like", "execute_command", "batch_execute", "register_script", "etc", "." ]
train
https://github.com/hubo1016/vlcp/blob/239055229ec93a99cc7e15208075724ccf543bd1/vlcp/utils/redisclient.py#L257-L275
hubo1016/vlcp
vlcp/utils/redisclient.py
RedisClient.execute_command
async def execute_command(self, container, *args): ''' Execute command on Redis server: - For (P)SUBSCRIBE/(P)UNSUBSCRIBE, the command is sent to the subscribe connection. It is recommended to use (p)subscribe/(p)unsubscribe method instead of directly call the command - For BLPOP, BRPOP, BRPOPLPUSH, the command is sent to a separated connection. The connection is recycled after command returns. - For other commands, the command is sent to the default connection. ''' if args: cmd = _str(args[0]).upper() if cmd in ('SUBSCRIBE', 'UNSUBSCRIBE', 'PSUBSCRIBE', 'PUNSUBSCRIBE'): await self._get_subscribe_connection(container) return await self._protocol.execute_command(self._subscribeconn, container, *args) elif cmd in ('BLPOP', 'BRPOP', 'BRPOPLPUSH'): c = await self.get_connection(container) with c.context(container): return await c.execute_command(container, *args) return await RedisClientBase.execute_command(self, container, *args)
python
async def execute_command(self, container, *args): ''' Execute command on Redis server: - For (P)SUBSCRIBE/(P)UNSUBSCRIBE, the command is sent to the subscribe connection. It is recommended to use (p)subscribe/(p)unsubscribe method instead of directly call the command - For BLPOP, BRPOP, BRPOPLPUSH, the command is sent to a separated connection. The connection is recycled after command returns. - For other commands, the command is sent to the default connection. ''' if args: cmd = _str(args[0]).upper() if cmd in ('SUBSCRIBE', 'UNSUBSCRIBE', 'PSUBSCRIBE', 'PUNSUBSCRIBE'): await self._get_subscribe_connection(container) return await self._protocol.execute_command(self._subscribeconn, container, *args) elif cmd in ('BLPOP', 'BRPOP', 'BRPOPLPUSH'): c = await self.get_connection(container) with c.context(container): return await c.execute_command(container, *args) return await RedisClientBase.execute_command(self, container, *args)
[ "async", "def", "execute_command", "(", "self", ",", "container", ",", "*", "args", ")", ":", "if", "args", ":", "cmd", "=", "_str", "(", "args", "[", "0", "]", ")", ".", "upper", "(", ")", "if", "cmd", "in", "(", "'SUBSCRIBE'", ",", "'UNSUBSCRIBE'", ",", "'PSUBSCRIBE'", ",", "'PUNSUBSCRIBE'", ")", ":", "await", "self", ".", "_get_subscribe_connection", "(", "container", ")", "return", "await", "self", ".", "_protocol", ".", "execute_command", "(", "self", ".", "_subscribeconn", ",", "container", ",", "*", "args", ")", "elif", "cmd", "in", "(", "'BLPOP'", ",", "'BRPOP'", ",", "'BRPOPLPUSH'", ")", ":", "c", "=", "await", "self", ".", "get_connection", "(", "container", ")", "with", "c", ".", "context", "(", "container", ")", ":", "return", "await", "c", ".", "execute_command", "(", "container", ",", "*", "args", ")", "return", "await", "RedisClientBase", ".", "execute_command", "(", "self", ",", "container", ",", "*", "args", ")" ]
Execute command on Redis server: - For (P)SUBSCRIBE/(P)UNSUBSCRIBE, the command is sent to the subscribe connection. It is recommended to use (p)subscribe/(p)unsubscribe method instead of directly call the command - For BLPOP, BRPOP, BRPOPLPUSH, the command is sent to a separated connection. The connection is recycled after command returns. - For other commands, the command is sent to the default connection.
[ "Execute", "command", "on", "Redis", "server", ":", "-", "For", "(", "P", ")", "SUBSCRIBE", "/", "(", "P", ")", "UNSUBSCRIBE", "the", "command", "is", "sent", "to", "the", "subscribe", "connection", ".", "It", "is", "recommended", "to", "use", "(", "p", ")", "subscribe", "/", "(", "p", ")", "unsubscribe", "method", "instead", "of", "directly", "call", "the", "command", "-", "For", "BLPOP", "BRPOP", "BRPOPLPUSH", "the", "command", "is", "sent", "to", "a", "separated", "connection", ".", "The", "connection", "is", "recycled", "after", "command", "returns", ".", "-", "For", "other", "commands", "the", "command", "is", "sent", "to", "the", "default", "connection", "." ]
train
https://github.com/hubo1016/vlcp/blob/239055229ec93a99cc7e15208075724ccf543bd1/vlcp/utils/redisclient.py#L296-L314
hubo1016/vlcp
vlcp/utils/redisclient.py
RedisClient.subscribe
async def subscribe(self, container, *keys): ''' Subscribe to specified channels :param container: routine container :param *keys: subscribed channels :returns: list of event matchers for the specified channels ''' await self._get_subscribe_connection(container) realkeys = [] for k in keys: count = self._subscribecounter.get(k, 0) if count == 0: realkeys.append(k) self._subscribecounter[k] = count + 1 if realkeys: await self._protocol.execute_command(self._subscribeconn, container, 'SUBSCRIBE', *realkeys) return [self._protocol.subscribematcher(self._subscribeconn, k, None, RedisSubscribeMessageEvent.MESSAGE) for k in keys]
python
async def subscribe(self, container, *keys): ''' Subscribe to specified channels :param container: routine container :param *keys: subscribed channels :returns: list of event matchers for the specified channels ''' await self._get_subscribe_connection(container) realkeys = [] for k in keys: count = self._subscribecounter.get(k, 0) if count == 0: realkeys.append(k) self._subscribecounter[k] = count + 1 if realkeys: await self._protocol.execute_command(self._subscribeconn, container, 'SUBSCRIBE', *realkeys) return [self._protocol.subscribematcher(self._subscribeconn, k, None, RedisSubscribeMessageEvent.MESSAGE) for k in keys]
[ "async", "def", "subscribe", "(", "self", ",", "container", ",", "*", "keys", ")", ":", "await", "self", ".", "_get_subscribe_connection", "(", "container", ")", "realkeys", "=", "[", "]", "for", "k", "in", "keys", ":", "count", "=", "self", ".", "_subscribecounter", ".", "get", "(", "k", ",", "0", ")", "if", "count", "==", "0", ":", "realkeys", ".", "append", "(", "k", ")", "self", ".", "_subscribecounter", "[", "k", "]", "=", "count", "+", "1", "if", "realkeys", ":", "await", "self", ".", "_protocol", ".", "execute_command", "(", "self", ".", "_subscribeconn", ",", "container", ",", "'SUBSCRIBE'", ",", "*", "realkeys", ")", "return", "[", "self", ".", "_protocol", ".", "subscribematcher", "(", "self", ".", "_subscribeconn", ",", "k", ",", "None", ",", "RedisSubscribeMessageEvent", ".", "MESSAGE", ")", "for", "k", "in", "keys", "]" ]
Subscribe to specified channels :param container: routine container :param *keys: subscribed channels :returns: list of event matchers for the specified channels
[ "Subscribe", "to", "specified", "channels", ":", "param", "container", ":", "routine", "container", ":", "param", "*", "keys", ":", "subscribed", "channels", ":", "returns", ":", "list", "of", "event", "matchers", "for", "the", "specified", "channels" ]
train
https://github.com/hubo1016/vlcp/blob/239055229ec93a99cc7e15208075724ccf543bd1/vlcp/utils/redisclient.py#L316-L335
hubo1016/vlcp
vlcp/utils/redisclient.py
RedisClient.unsubscribe
async def unsubscribe(self, container, *keys): ''' Unsubscribe specified channels. Every subscribed key should be unsubscribed exactly once, even if duplicated subscribed. :param container: routine container :param \*keys: subscribed channels ''' await self._get_subscribe_connection(container) realkeys = [] for k in keys: count = self._subscribecounter.get(k, 0) if count <= 1: realkeys.append(k) try: del self._subscribecounter[k] except KeyError: pass else: self._subscribecounter[k] = count - 1 if realkeys: await self._protocol.execute_command(self._subscribeconn, container, 'UNSUBSCRIBE', *realkeys)
python
async def unsubscribe(self, container, *keys): ''' Unsubscribe specified channels. Every subscribed key should be unsubscribed exactly once, even if duplicated subscribed. :param container: routine container :param \*keys: subscribed channels ''' await self._get_subscribe_connection(container) realkeys = [] for k in keys: count = self._subscribecounter.get(k, 0) if count <= 1: realkeys.append(k) try: del self._subscribecounter[k] except KeyError: pass else: self._subscribecounter[k] = count - 1 if realkeys: await self._protocol.execute_command(self._subscribeconn, container, 'UNSUBSCRIBE', *realkeys)
[ "async", "def", "unsubscribe", "(", "self", ",", "container", ",", "*", "keys", ")", ":", "await", "self", ".", "_get_subscribe_connection", "(", "container", ")", "realkeys", "=", "[", "]", "for", "k", "in", "keys", ":", "count", "=", "self", ".", "_subscribecounter", ".", "get", "(", "k", ",", "0", ")", "if", "count", "<=", "1", ":", "realkeys", ".", "append", "(", "k", ")", "try", ":", "del", "self", ".", "_subscribecounter", "[", "k", "]", "except", "KeyError", ":", "pass", "else", ":", "self", ".", "_subscribecounter", "[", "k", "]", "=", "count", "-", "1", "if", "realkeys", ":", "await", "self", ".", "_protocol", ".", "execute_command", "(", "self", ".", "_subscribeconn", ",", "container", ",", "'UNSUBSCRIBE'", ",", "*", "realkeys", ")" ]
Unsubscribe specified channels. Every subscribed key should be unsubscribed exactly once, even if duplicated subscribed. :param container: routine container :param \*keys: subscribed channels
[ "Unsubscribe", "specified", "channels", ".", "Every", "subscribed", "key", "should", "be", "unsubscribed", "exactly", "once", "even", "if", "duplicated", "subscribed", ".", ":", "param", "container", ":", "routine", "container", ":", "param", "\\", "*", "keys", ":", "subscribed", "channels" ]
train
https://github.com/hubo1016/vlcp/blob/239055229ec93a99cc7e15208075724ccf543bd1/vlcp/utils/redisclient.py#L337-L358
hubo1016/vlcp
vlcp/utils/redisclient.py
RedisClient.psubscribe
async def psubscribe(self, container, *keys): ''' Subscribe to specified globs :param container: routine container :param \*keys: subscribed globs :returns: list of event matchers for the specified globs ''' await self._get_subscribe_connection(container) realkeys = [] for k in keys: count = self._psubscribecounter.get(k, 0) if count == 0: realkeys.append(k) self._psubscribecounter[k] = count + 1 await self._protocol.execute_command(self._subscribeconn, container, 'PSUBSCRIBE', *realkeys) return [self._protocol.subscribematcher(self._subscribeconn, k, None, RedisSubscribeMessageEvent.PMESSAGE) for k in keys]
python
async def psubscribe(self, container, *keys): ''' Subscribe to specified globs :param container: routine container :param \*keys: subscribed globs :returns: list of event matchers for the specified globs ''' await self._get_subscribe_connection(container) realkeys = [] for k in keys: count = self._psubscribecounter.get(k, 0) if count == 0: realkeys.append(k) self._psubscribecounter[k] = count + 1 await self._protocol.execute_command(self._subscribeconn, container, 'PSUBSCRIBE', *realkeys) return [self._protocol.subscribematcher(self._subscribeconn, k, None, RedisSubscribeMessageEvent.PMESSAGE) for k in keys]
[ "async", "def", "psubscribe", "(", "self", ",", "container", ",", "*", "keys", ")", ":", "await", "self", ".", "_get_subscribe_connection", "(", "container", ")", "realkeys", "=", "[", "]", "for", "k", "in", "keys", ":", "count", "=", "self", ".", "_psubscribecounter", ".", "get", "(", "k", ",", "0", ")", "if", "count", "==", "0", ":", "realkeys", ".", "append", "(", "k", ")", "self", ".", "_psubscribecounter", "[", "k", "]", "=", "count", "+", "1", "await", "self", ".", "_protocol", ".", "execute_command", "(", "self", ".", "_subscribeconn", ",", "container", ",", "'PSUBSCRIBE'", ",", "*", "realkeys", ")", "return", "[", "self", ".", "_protocol", ".", "subscribematcher", "(", "self", ".", "_subscribeconn", ",", "k", ",", "None", ",", "RedisSubscribeMessageEvent", ".", "PMESSAGE", ")", "for", "k", "in", "keys", "]" ]
Subscribe to specified globs :param container: routine container :param \*keys: subscribed globs :returns: list of event matchers for the specified globs
[ "Subscribe", "to", "specified", "globs", ":", "param", "container", ":", "routine", "container", ":", "param", "\\", "*", "keys", ":", "subscribed", "globs", ":", "returns", ":", "list", "of", "event", "matchers", "for", "the", "specified", "globs" ]
train
https://github.com/hubo1016/vlcp/blob/239055229ec93a99cc7e15208075724ccf543bd1/vlcp/utils/redisclient.py#L360-L378
hubo1016/vlcp
vlcp/utils/redisclient.py
RedisClient.shutdown
async def shutdown(self, container, force=False): ''' Shutdown all connections. Exclusive connections created by get_connection will shutdown after release() ''' p = self._connpool self._connpool = [] self._shutdown = True if self._defaultconn: p.append(self._defaultconn) self._defaultconn = None if self._subscribeconn: p.append(self._subscribeconn) self._subscribeconn = None await container.execute_all([self._shutdown_conn(container, o, force) for o in p])
python
async def shutdown(self, container, force=False): ''' Shutdown all connections. Exclusive connections created by get_connection will shutdown after release() ''' p = self._connpool self._connpool = [] self._shutdown = True if self._defaultconn: p.append(self._defaultconn) self._defaultconn = None if self._subscribeconn: p.append(self._subscribeconn) self._subscribeconn = None await container.execute_all([self._shutdown_conn(container, o, force) for o in p])
[ "async", "def", "shutdown", "(", "self", ",", "container", ",", "force", "=", "False", ")", ":", "p", "=", "self", ".", "_connpool", "self", ".", "_connpool", "=", "[", "]", "self", ".", "_shutdown", "=", "True", "if", "self", ".", "_defaultconn", ":", "p", ".", "append", "(", "self", ".", "_defaultconn", ")", "self", ".", "_defaultconn", "=", "None", "if", "self", ".", "_subscribeconn", ":", "p", ".", "append", "(", "self", ".", "_subscribeconn", ")", "self", ".", "_subscribeconn", "=", "None", "await", "container", ".", "execute_all", "(", "[", "self", ".", "_shutdown_conn", "(", "container", ",", "o", ",", "force", ")", "for", "o", "in", "p", "]", ")" ]
Shutdown all connections. Exclusive connections created by get_connection will shutdown after release()
[ "Shutdown", "all", "connections", ".", "Exclusive", "connections", "created", "by", "get_connection", "will", "shutdown", "after", "release", "()" ]
train
https://github.com/hubo1016/vlcp/blob/239055229ec93a99cc7e15208075724ccf543bd1/vlcp/utils/redisclient.py#L399-L413
hubo1016/vlcp
vlcp/utils/redisclient.py
RedisClient.subscribe_state_matcher
def subscribe_state_matcher(self, container, connected = True): ''' Return a matcher to match the subscribe connection status. :param container: a routine container. NOTICE: this method is not a routine. :param connected: if True, the matcher matches connection up. If False, the matcher matches connection down. :returns: an event matcher. ''' if not self._subscribeconn: self._subscribeconn = self._create_client(container) return RedisConnectionStateEvent.createMatcher( RedisConnectionStateEvent.CONNECTION_UP if connected else RedisConnectionStateEvent.CONNECTION_DOWN, self._subscribeconn )
python
def subscribe_state_matcher(self, container, connected = True): ''' Return a matcher to match the subscribe connection status. :param container: a routine container. NOTICE: this method is not a routine. :param connected: if True, the matcher matches connection up. If False, the matcher matches connection down. :returns: an event matcher. ''' if not self._subscribeconn: self._subscribeconn = self._create_client(container) return RedisConnectionStateEvent.createMatcher( RedisConnectionStateEvent.CONNECTION_UP if connected else RedisConnectionStateEvent.CONNECTION_DOWN, self._subscribeconn )
[ "def", "subscribe_state_matcher", "(", "self", ",", "container", ",", "connected", "=", "True", ")", ":", "if", "not", "self", ".", "_subscribeconn", ":", "self", ".", "_subscribeconn", "=", "self", ".", "_create_client", "(", "container", ")", "return", "RedisConnectionStateEvent", ".", "createMatcher", "(", "RedisConnectionStateEvent", ".", "CONNECTION_UP", "if", "connected", "else", "RedisConnectionStateEvent", ".", "CONNECTION_DOWN", ",", "self", ".", "_subscribeconn", ")" ]
Return a matcher to match the subscribe connection status. :param container: a routine container. NOTICE: this method is not a routine. :param connected: if True, the matcher matches connection up. If False, the matcher matches connection down. :returns: an event matcher.
[ "Return", "a", "matcher", "to", "match", "the", "subscribe", "connection", "status", ".", ":", "param", "container", ":", "a", "routine", "container", ".", "NOTICE", ":", "this", "method", "is", "not", "a", "routine", ".", ":", "param", "connected", ":", "if", "True", "the", "matcher", "matches", "connection", "up", ".", "If", "False", "the", "matcher", "matches", "connection", "down", ".", ":", "returns", ":", "an", "event", "matcher", "." ]
train
https://github.com/hubo1016/vlcp/blob/239055229ec93a99cc7e15208075724ccf543bd1/vlcp/utils/redisclient.py#L438-L454
hubo1016/vlcp
vlcp/service/connection/zookeeperdb.py
_escape_path
def _escape_path(key): ''' Replace '/', '\\' in key ''' return _tobytes(key).replace(b'$', b'$_').replace(b'/', b'$+').replace(b'\\', b'$$')
python
def _escape_path(key): ''' Replace '/', '\\' in key ''' return _tobytes(key).replace(b'$', b'$_').replace(b'/', b'$+').replace(b'\\', b'$$')
[ "def", "_escape_path", "(", "key", ")", ":", "return", "_tobytes", "(", "key", ")", ".", "replace", "(", "b'$'", ",", "b'$_'", ")", ".", "replace", "(", "b'/'", ",", "b'$+'", ")", ".", "replace", "(", "b'\\\\'", ",", "b'$$'", ")" ]
Replace '/', '\\' in key
[ "Replace", "/", "\\\\", "in", "key" ]
train
https://github.com/hubo1016/vlcp/blob/239055229ec93a99cc7e15208075724ccf543bd1/vlcp/service/connection/zookeeperdb.py#L56-L60
hubo1016/vlcp
vlcp/utils/dhcp.py
reassemble_options
def reassemble_options(payload): ''' Reassemble partial options to options, returns a list of dhcp_option DHCP options are basically `|tag|length|value|` structure. When an option is longer than 255 bytes, it can be splitted into multiple structures with the same tag. The splitted structures must be joined back to get the original option. `dhcp_option_partial` is used to present the splitted options, and `dhcp_option` is used for reassembled option. ''' options = [] option_indices = {} def process_option_list(partials): for p in partials: if p.tag == OPTION_END: break if p.tag == OPTION_PAD: continue if p.tag in option_indices: # Reassemble the data options[option_indices[p.tag]][1].append(p.data) else: options.append((p.tag, [p.data])) option_indices[p.tag] = len(options) - 1 # First process options field process_option_list(payload.options) if OPTION_OVERLOAD in option_indices: # There is an overload option data = b''.join(options[option_indices[OPTION_OVERLOAD]][1]) overload_option = dhcp_overload.create(data) if overload_option & OVERLOAD_FILE: process_option_list(dhcp_option_partial[0].create(payload.file)) if overload_option & OVERLOAD_SNAME: process_option_list(dhcp_option_partial[0].create(payload.sname)) def _create_dhcp_option(tag, data): opt = dhcp_option(tag = tag) opt._setextra(data) opt._autosubclass() return opt return [_create_dhcp_option(tag, b''.join(data)) for tag,data in options]
python
def reassemble_options(payload): ''' Reassemble partial options to options, returns a list of dhcp_option DHCP options are basically `|tag|length|value|` structure. When an option is longer than 255 bytes, it can be splitted into multiple structures with the same tag. The splitted structures must be joined back to get the original option. `dhcp_option_partial` is used to present the splitted options, and `dhcp_option` is used for reassembled option. ''' options = [] option_indices = {} def process_option_list(partials): for p in partials: if p.tag == OPTION_END: break if p.tag == OPTION_PAD: continue if p.tag in option_indices: # Reassemble the data options[option_indices[p.tag]][1].append(p.data) else: options.append((p.tag, [p.data])) option_indices[p.tag] = len(options) - 1 # First process options field process_option_list(payload.options) if OPTION_OVERLOAD in option_indices: # There is an overload option data = b''.join(options[option_indices[OPTION_OVERLOAD]][1]) overload_option = dhcp_overload.create(data) if overload_option & OVERLOAD_FILE: process_option_list(dhcp_option_partial[0].create(payload.file)) if overload_option & OVERLOAD_SNAME: process_option_list(dhcp_option_partial[0].create(payload.sname)) def _create_dhcp_option(tag, data): opt = dhcp_option(tag = tag) opt._setextra(data) opt._autosubclass() return opt return [_create_dhcp_option(tag, b''.join(data)) for tag,data in options]
[ "def", "reassemble_options", "(", "payload", ")", ":", "options", "=", "[", "]", "option_indices", "=", "{", "}", "def", "process_option_list", "(", "partials", ")", ":", "for", "p", "in", "partials", ":", "if", "p", ".", "tag", "==", "OPTION_END", ":", "break", "if", "p", ".", "tag", "==", "OPTION_PAD", ":", "continue", "if", "p", ".", "tag", "in", "option_indices", ":", "# Reassemble the data", "options", "[", "option_indices", "[", "p", ".", "tag", "]", "]", "[", "1", "]", ".", "append", "(", "p", ".", "data", ")", "else", ":", "options", ".", "append", "(", "(", "p", ".", "tag", ",", "[", "p", ".", "data", "]", ")", ")", "option_indices", "[", "p", ".", "tag", "]", "=", "len", "(", "options", ")", "-", "1", "# First process options field", "process_option_list", "(", "payload", ".", "options", ")", "if", "OPTION_OVERLOAD", "in", "option_indices", ":", "# There is an overload option", "data", "=", "b''", ".", "join", "(", "options", "[", "option_indices", "[", "OPTION_OVERLOAD", "]", "]", "[", "1", "]", ")", "overload_option", "=", "dhcp_overload", ".", "create", "(", "data", ")", "if", "overload_option", "&", "OVERLOAD_FILE", ":", "process_option_list", "(", "dhcp_option_partial", "[", "0", "]", ".", "create", "(", "payload", ".", "file", ")", ")", "if", "overload_option", "&", "OVERLOAD_SNAME", ":", "process_option_list", "(", "dhcp_option_partial", "[", "0", "]", ".", "create", "(", "payload", ".", "sname", ")", ")", "def", "_create_dhcp_option", "(", "tag", ",", "data", ")", ":", "opt", "=", "dhcp_option", "(", "tag", "=", "tag", ")", "opt", ".", "_setextra", "(", "data", ")", "opt", ".", "_autosubclass", "(", ")", "return", "opt", "return", "[", "_create_dhcp_option", "(", "tag", ",", "b''", ".", "join", "(", "data", ")", ")", "for", "tag", ",", "data", "in", "options", "]" ]
Reassemble partial options to options, returns a list of dhcp_option DHCP options are basically `|tag|length|value|` structure. When an option is longer than 255 bytes, it can be splitted into multiple structures with the same tag. The splitted structures must be joined back to get the original option. `dhcp_option_partial` is used to present the splitted options, and `dhcp_option` is used for reassembled option.
[ "Reassemble", "partial", "options", "to", "options", "returns", "a", "list", "of", "dhcp_option", "DHCP", "options", "are", "basically", "|tag|length|value|", "structure", ".", "When", "an", "option", "is", "longer", "than", "255", "bytes", "it", "can", "be", "splitted", "into", "multiple", "structures", "with", "the", "same", "tag", ".", "The", "splitted", "structures", "must", "be", "joined", "back", "to", "get", "the", "original", "option", ".", "dhcp_option_partial", "is", "used", "to", "present", "the", "splitted", "options", "and", "dhcp_option", "is", "used", "for", "reassembled", "option", "." ]
train
https://github.com/hubo1016/vlcp/blob/239055229ec93a99cc7e15208075724ccf543bd1/vlcp/utils/dhcp.py#L248-L289
hubo1016/vlcp
vlcp/utils/dhcp.py
build_options
def build_options(payload, options, maxsize = 576, overload = OVERLOAD_FILE | OVERLOAD_SNAME, allowpartial = True): ''' Split a list of options This is the reverse operation of `reassemble_options`, it splits `dhcp_option` into `dhcp_option_partial` if necessary, and set overload option if field overloading is used. :param options: a list of `dhcp_option` :param maxsize: Limit the maximum DHCP message size. If options cannot fit into the DHCP message, specified fields are overloaded for options. If options cannot fit after overloading, extra options are DROPPED if allowpartial = True. It is important to sort the dhcp options by priority. :param overload: fields that are allowed to be overloaded :param allowpartial: When options cannot fit into the DHCP message, allow the rest options to be dropped. :return: Number of options that are dropped i.e. `options[:-return_value]` are dropped ''' if maxsize < 576: maxsize = 576 max_options_size = maxsize - 240 # Ignore OPTION_PAD and OPTION_END options = [o for o in options if o.tag not in (OPTION_PAD, OPTION_END)] # Only preserve data option_data = [(o.tag, o._tobytes()[2:]) for o in options] def split_options(option_data, limits): """ Split options into multiple fields :param option_data: list of (tag, data) pair :param limits: list of int for limit of each field (excluding PAD and END) :return: number of options that are dropped """ # List of (dhcp_option_partial, option_not_finished) partial_options = [] buffers = [0] if not options: return ([], 0) def create_result(): # Remove any unfinished partial options while partial_options and partial_options[-1][1]: partial_options.pop() buffers.append(len(partial_options)) r = [[po for po,_ in partial_options[buffers[i]:buffers[i+1]]] for i in range(0, len(buffers) - 1)] # Remove empty fields while r and not r[-1]: r.pop() return r # Current field used size current_size = 0 limit_iter = iter(limits) try: next_limit = next(limit_iter) except (StopIteration, GeneratorExit): return ([], False) for i, (tag, data) in enumerate(option_data): # Current used data size data_size = 0 # Do not split very small options on boundary, this may prevent some broken DHCP clients/servers # to cause problem nosplit = (len(data) <= 32) while True: # next partial option size should be: # 1. no more than the current field limit (minus 1-byte tag and 1-byte length) # 2. no more than the single dhcp_option_partial data limit (255 due to single byte length) # 3. no more than the rest data size next_size = min(next_limit - current_size - 2, 255, len(data) - data_size) if next_size < 0 or (next_size == 0 and data_size < len(data)) \ or (next_size < len(data) - data_size and nosplit): # Cannot put this part of data on the current field, find the next field try: next_limit = next(limit_iter) except (StopIteration, GeneratorExit): return (create_result(), len(option_data) - i) # Record field boundary buffers.append(len(partial_options)) current_size = 0 else: # Put this partial option on current field partial_options.append((dhcp_option_partial(tag = tag, data = data[data_size : data_size + next_size]), (next_size < len(data) - data_size))) data_size += next_size current_size += next_size + 2 if data_size >= len(data): # finished current option break return (create_result(), 0) # First try to fit all options in options field # preserve a byte for OPTION_END result, not_finished = split_options(option_data, [max_options_size - 1]) if not_finished: if overload & (OVERLOAD_FILE | OVERLOAD_SNAME): # Try overload # minus a overload option (1-byte tag, 1-byte lenght, 1-byte dhcp_overload) and 1-byte OPTION_END limits = [max_options_size - 4] if overload & OVERLOAD_FILE: # preserve a byte for OPTION_END limits.append(127) if overload & OVERLOAD_SNAME: # preserve a byte for OPTION_END limits.append(63) result2, not_finished2 = split_options(option_data, limits) # Only overload if we have a better result if len(result2) > 1: result = result2 not_finished = not_finished2 if not allowpartial and not_finished: raise ValueError("%d options cannot fit into a DHCP message" % (not_finished,)) if not result: return not_finished elif len(result) <= 1: # No overload payload.options = result[0] + [dhcp_option_partial(tag = OPTION_END)] else: overload_option = 0 if len(result) >= 2 and result[1]: overload_option |= OVERLOAD_FILE # overload file field payload.file = dhcp_option_partial[0].tobytes(result[1] + [dhcp_option_partial(tag = OPTION_END)]) if len(result) >= 3 and result[2]: overload_option |= OVERLOAD_SNAME # overload sname field payload.sname = dhcp_option_partial[0].tobytes(result[2] + [dhcp_option_partial(tag = OPTION_END)]) # Put an overload option before any other options payload.options = [dhcp_option_partial(tag = OPTION_OVERLOAD, data = dhcp_overload.tobytes(overload_option))] \ + result[0] + [dhcp_option_partial(tag = OPTION_END)] return not_finished
python
def build_options(payload, options, maxsize = 576, overload = OVERLOAD_FILE | OVERLOAD_SNAME, allowpartial = True): ''' Split a list of options This is the reverse operation of `reassemble_options`, it splits `dhcp_option` into `dhcp_option_partial` if necessary, and set overload option if field overloading is used. :param options: a list of `dhcp_option` :param maxsize: Limit the maximum DHCP message size. If options cannot fit into the DHCP message, specified fields are overloaded for options. If options cannot fit after overloading, extra options are DROPPED if allowpartial = True. It is important to sort the dhcp options by priority. :param overload: fields that are allowed to be overloaded :param allowpartial: When options cannot fit into the DHCP message, allow the rest options to be dropped. :return: Number of options that are dropped i.e. `options[:-return_value]` are dropped ''' if maxsize < 576: maxsize = 576 max_options_size = maxsize - 240 # Ignore OPTION_PAD and OPTION_END options = [o for o in options if o.tag not in (OPTION_PAD, OPTION_END)] # Only preserve data option_data = [(o.tag, o._tobytes()[2:]) for o in options] def split_options(option_data, limits): """ Split options into multiple fields :param option_data: list of (tag, data) pair :param limits: list of int for limit of each field (excluding PAD and END) :return: number of options that are dropped """ # List of (dhcp_option_partial, option_not_finished) partial_options = [] buffers = [0] if not options: return ([], 0) def create_result(): # Remove any unfinished partial options while partial_options and partial_options[-1][1]: partial_options.pop() buffers.append(len(partial_options)) r = [[po for po,_ in partial_options[buffers[i]:buffers[i+1]]] for i in range(0, len(buffers) - 1)] # Remove empty fields while r and not r[-1]: r.pop() return r # Current field used size current_size = 0 limit_iter = iter(limits) try: next_limit = next(limit_iter) except (StopIteration, GeneratorExit): return ([], False) for i, (tag, data) in enumerate(option_data): # Current used data size data_size = 0 # Do not split very small options on boundary, this may prevent some broken DHCP clients/servers # to cause problem nosplit = (len(data) <= 32) while True: # next partial option size should be: # 1. no more than the current field limit (minus 1-byte tag and 1-byte length) # 2. no more than the single dhcp_option_partial data limit (255 due to single byte length) # 3. no more than the rest data size next_size = min(next_limit - current_size - 2, 255, len(data) - data_size) if next_size < 0 or (next_size == 0 and data_size < len(data)) \ or (next_size < len(data) - data_size and nosplit): # Cannot put this part of data on the current field, find the next field try: next_limit = next(limit_iter) except (StopIteration, GeneratorExit): return (create_result(), len(option_data) - i) # Record field boundary buffers.append(len(partial_options)) current_size = 0 else: # Put this partial option on current field partial_options.append((dhcp_option_partial(tag = tag, data = data[data_size : data_size + next_size]), (next_size < len(data) - data_size))) data_size += next_size current_size += next_size + 2 if data_size >= len(data): # finished current option break return (create_result(), 0) # First try to fit all options in options field # preserve a byte for OPTION_END result, not_finished = split_options(option_data, [max_options_size - 1]) if not_finished: if overload & (OVERLOAD_FILE | OVERLOAD_SNAME): # Try overload # minus a overload option (1-byte tag, 1-byte lenght, 1-byte dhcp_overload) and 1-byte OPTION_END limits = [max_options_size - 4] if overload & OVERLOAD_FILE: # preserve a byte for OPTION_END limits.append(127) if overload & OVERLOAD_SNAME: # preserve a byte for OPTION_END limits.append(63) result2, not_finished2 = split_options(option_data, limits) # Only overload if we have a better result if len(result2) > 1: result = result2 not_finished = not_finished2 if not allowpartial and not_finished: raise ValueError("%d options cannot fit into a DHCP message" % (not_finished,)) if not result: return not_finished elif len(result) <= 1: # No overload payload.options = result[0] + [dhcp_option_partial(tag = OPTION_END)] else: overload_option = 0 if len(result) >= 2 and result[1]: overload_option |= OVERLOAD_FILE # overload file field payload.file = dhcp_option_partial[0].tobytes(result[1] + [dhcp_option_partial(tag = OPTION_END)]) if len(result) >= 3 and result[2]: overload_option |= OVERLOAD_SNAME # overload sname field payload.sname = dhcp_option_partial[0].tobytes(result[2] + [dhcp_option_partial(tag = OPTION_END)]) # Put an overload option before any other options payload.options = [dhcp_option_partial(tag = OPTION_OVERLOAD, data = dhcp_overload.tobytes(overload_option))] \ + result[0] + [dhcp_option_partial(tag = OPTION_END)] return not_finished
[ "def", "build_options", "(", "payload", ",", "options", ",", "maxsize", "=", "576", ",", "overload", "=", "OVERLOAD_FILE", "|", "OVERLOAD_SNAME", ",", "allowpartial", "=", "True", ")", ":", "if", "maxsize", "<", "576", ":", "maxsize", "=", "576", "max_options_size", "=", "maxsize", "-", "240", "# Ignore OPTION_PAD and OPTION_END", "options", "=", "[", "o", "for", "o", "in", "options", "if", "o", ".", "tag", "not", "in", "(", "OPTION_PAD", ",", "OPTION_END", ")", "]", "# Only preserve data", "option_data", "=", "[", "(", "o", ".", "tag", ",", "o", ".", "_tobytes", "(", ")", "[", "2", ":", "]", ")", "for", "o", "in", "options", "]", "def", "split_options", "(", "option_data", ",", "limits", ")", ":", "\"\"\"\n Split options into multiple fields\n \n :param option_data: list of (tag, data) pair\n \n :param limits: list of int for limit of each field (excluding PAD and END)\n \n :return: number of options that are dropped\n \"\"\"", "# List of (dhcp_option_partial, option_not_finished)", "partial_options", "=", "[", "]", "buffers", "=", "[", "0", "]", "if", "not", "options", ":", "return", "(", "[", "]", ",", "0", ")", "def", "create_result", "(", ")", ":", "# Remove any unfinished partial options", "while", "partial_options", "and", "partial_options", "[", "-", "1", "]", "[", "1", "]", ":", "partial_options", ".", "pop", "(", ")", "buffers", ".", "append", "(", "len", "(", "partial_options", ")", ")", "r", "=", "[", "[", "po", "for", "po", ",", "_", "in", "partial_options", "[", "buffers", "[", "i", "]", ":", "buffers", "[", "i", "+", "1", "]", "]", "]", "for", "i", "in", "range", "(", "0", ",", "len", "(", "buffers", ")", "-", "1", ")", "]", "# Remove empty fields", "while", "r", "and", "not", "r", "[", "-", "1", "]", ":", "r", ".", "pop", "(", ")", "return", "r", "# Current field used size", "current_size", "=", "0", "limit_iter", "=", "iter", "(", "limits", ")", "try", ":", "next_limit", "=", "next", "(", "limit_iter", ")", "except", "(", "StopIteration", ",", "GeneratorExit", ")", ":", "return", "(", "[", "]", ",", "False", ")", "for", "i", ",", "(", "tag", ",", "data", ")", "in", "enumerate", "(", "option_data", ")", ":", "# Current used data size", "data_size", "=", "0", "# Do not split very small options on boundary, this may prevent some broken DHCP clients/servers", "# to cause problem", "nosplit", "=", "(", "len", "(", "data", ")", "<=", "32", ")", "while", "True", ":", "# next partial option size should be:", "# 1. no more than the current field limit (minus 1-byte tag and 1-byte length)", "# 2. no more than the single dhcp_option_partial data limit (255 due to single byte length)", "# 3. no more than the rest data size", "next_size", "=", "min", "(", "next_limit", "-", "current_size", "-", "2", ",", "255", ",", "len", "(", "data", ")", "-", "data_size", ")", "if", "next_size", "<", "0", "or", "(", "next_size", "==", "0", "and", "data_size", "<", "len", "(", "data", ")", ")", "or", "(", "next_size", "<", "len", "(", "data", ")", "-", "data_size", "and", "nosplit", ")", ":", "# Cannot put this part of data on the current field, find the next field", "try", ":", "next_limit", "=", "next", "(", "limit_iter", ")", "except", "(", "StopIteration", ",", "GeneratorExit", ")", ":", "return", "(", "create_result", "(", ")", ",", "len", "(", "option_data", ")", "-", "i", ")", "# Record field boundary", "buffers", ".", "append", "(", "len", "(", "partial_options", ")", ")", "current_size", "=", "0", "else", ":", "# Put this partial option on current field", "partial_options", ".", "append", "(", "(", "dhcp_option_partial", "(", "tag", "=", "tag", ",", "data", "=", "data", "[", "data_size", ":", "data_size", "+", "next_size", "]", ")", ",", "(", "next_size", "<", "len", "(", "data", ")", "-", "data_size", ")", ")", ")", "data_size", "+=", "next_size", "current_size", "+=", "next_size", "+", "2", "if", "data_size", ">=", "len", "(", "data", ")", ":", "# finished current option", "break", "return", "(", "create_result", "(", ")", ",", "0", ")", "# First try to fit all options in options field", "# preserve a byte for OPTION_END", "result", ",", "not_finished", "=", "split_options", "(", "option_data", ",", "[", "max_options_size", "-", "1", "]", ")", "if", "not_finished", ":", "if", "overload", "&", "(", "OVERLOAD_FILE", "|", "OVERLOAD_SNAME", ")", ":", "# Try overload", "# minus a overload option (1-byte tag, 1-byte lenght, 1-byte dhcp_overload) and 1-byte OPTION_END", "limits", "=", "[", "max_options_size", "-", "4", "]", "if", "overload", "&", "OVERLOAD_FILE", ":", "# preserve a byte for OPTION_END", "limits", ".", "append", "(", "127", ")", "if", "overload", "&", "OVERLOAD_SNAME", ":", "# preserve a byte for OPTION_END", "limits", ".", "append", "(", "63", ")", "result2", ",", "not_finished2", "=", "split_options", "(", "option_data", ",", "limits", ")", "# Only overload if we have a better result", "if", "len", "(", "result2", ")", ">", "1", ":", "result", "=", "result2", "not_finished", "=", "not_finished2", "if", "not", "allowpartial", "and", "not_finished", ":", "raise", "ValueError", "(", "\"%d options cannot fit into a DHCP message\"", "%", "(", "not_finished", ",", ")", ")", "if", "not", "result", ":", "return", "not_finished", "elif", "len", "(", "result", ")", "<=", "1", ":", "# No overload", "payload", ".", "options", "=", "result", "[", "0", "]", "+", "[", "dhcp_option_partial", "(", "tag", "=", "OPTION_END", ")", "]", "else", ":", "overload_option", "=", "0", "if", "len", "(", "result", ")", ">=", "2", "and", "result", "[", "1", "]", ":", "overload_option", "|=", "OVERLOAD_FILE", "# overload file field", "payload", ".", "file", "=", "dhcp_option_partial", "[", "0", "]", ".", "tobytes", "(", "result", "[", "1", "]", "+", "[", "dhcp_option_partial", "(", "tag", "=", "OPTION_END", ")", "]", ")", "if", "len", "(", "result", ")", ">=", "3", "and", "result", "[", "2", "]", ":", "overload_option", "|=", "OVERLOAD_SNAME", "# overload sname field", "payload", ".", "sname", "=", "dhcp_option_partial", "[", "0", "]", ".", "tobytes", "(", "result", "[", "2", "]", "+", "[", "dhcp_option_partial", "(", "tag", "=", "OPTION_END", ")", "]", ")", "# Put an overload option before any other options", "payload", ".", "options", "=", "[", "dhcp_option_partial", "(", "tag", "=", "OPTION_OVERLOAD", ",", "data", "=", "dhcp_overload", ".", "tobytes", "(", "overload_option", ")", ")", "]", "+", "result", "[", "0", "]", "+", "[", "dhcp_option_partial", "(", "tag", "=", "OPTION_END", ")", "]", "return", "not_finished" ]
Split a list of options This is the reverse operation of `reassemble_options`, it splits `dhcp_option` into `dhcp_option_partial` if necessary, and set overload option if field overloading is used. :param options: a list of `dhcp_option` :param maxsize: Limit the maximum DHCP message size. If options cannot fit into the DHCP message, specified fields are overloaded for options. If options cannot fit after overloading, extra options are DROPPED if allowpartial = True. It is important to sort the dhcp options by priority. :param overload: fields that are allowed to be overloaded :param allowpartial: When options cannot fit into the DHCP message, allow the rest options to be dropped. :return: Number of options that are dropped i.e. `options[:-return_value]` are dropped
[ "Split", "a", "list", "of", "options", "This", "is", "the", "reverse", "operation", "of", "reassemble_options", "it", "splits", "dhcp_option", "into", "dhcp_option_partial", "if", "necessary", "and", "set", "overload", "option", "if", "field", "overloading", "is", "used", ".", ":", "param", "options", ":", "a", "list", "of", "dhcp_option", ":", "param", "maxsize", ":", "Limit", "the", "maximum", "DHCP", "message", "size", ".", "If", "options", "cannot", "fit", "into", "the", "DHCP", "message", "specified", "fields", "are", "overloaded", "for", "options", ".", "If", "options", "cannot", "fit", "after", "overloading", "extra", "options", "are", "DROPPED", "if", "allowpartial", "=", "True", ".", "It", "is", "important", "to", "sort", "the", "dhcp", "options", "by", "priority", ".", ":", "param", "overload", ":", "fields", "that", "are", "allowed", "to", "be", "overloaded", ":", "param", "allowpartial", ":", "When", "options", "cannot", "fit", "into", "the", "DHCP", "message", "allow", "the", "rest", "options", "to", "be", "dropped", ".", ":", "return", ":", "Number", "of", "options", "that", "are", "dropped", "i", ".", "e", ".", "options", "[", ":", "-", "return_value", "]", "are", "dropped" ]
train
https://github.com/hubo1016/vlcp/blob/239055229ec93a99cc7e15208075724ccf543bd1/vlcp/utils/dhcp.py#L291-L424
hubo1016/vlcp
vlcp/utils/dhcp.py
create_option_from_value
def create_option_from_value(tag, value): """ Set DHCP option with human friendly value """ dhcp_option.parser() fake_opt = dhcp_option(tag = tag) for c in dhcp_option.subclasses: if c.criteria(fake_opt): if hasattr(c, '_parse_from_value'): return c(tag = tag, value = c._parse_from_value(value)) else: raise ValueError('Invalid DHCP option ' + str(tag) + ": " + repr(value)) else: fake_opt._setextra(_tobytes(value)) return fake_opt
python
def create_option_from_value(tag, value): """ Set DHCP option with human friendly value """ dhcp_option.parser() fake_opt = dhcp_option(tag = tag) for c in dhcp_option.subclasses: if c.criteria(fake_opt): if hasattr(c, '_parse_from_value'): return c(tag = tag, value = c._parse_from_value(value)) else: raise ValueError('Invalid DHCP option ' + str(tag) + ": " + repr(value)) else: fake_opt._setextra(_tobytes(value)) return fake_opt
[ "def", "create_option_from_value", "(", "tag", ",", "value", ")", ":", "dhcp_option", ".", "parser", "(", ")", "fake_opt", "=", "dhcp_option", "(", "tag", "=", "tag", ")", "for", "c", "in", "dhcp_option", ".", "subclasses", ":", "if", "c", ".", "criteria", "(", "fake_opt", ")", ":", "if", "hasattr", "(", "c", ",", "'_parse_from_value'", ")", ":", "return", "c", "(", "tag", "=", "tag", ",", "value", "=", "c", ".", "_parse_from_value", "(", "value", ")", ")", "else", ":", "raise", "ValueError", "(", "'Invalid DHCP option '", "+", "str", "(", "tag", ")", "+", "\": \"", "+", "repr", "(", "value", ")", ")", "else", ":", "fake_opt", ".", "_setextra", "(", "_tobytes", "(", "value", ")", ")", "return", "fake_opt" ]
Set DHCP option with human friendly value
[ "Set", "DHCP", "option", "with", "human", "friendly", "value" ]
train
https://github.com/hubo1016/vlcp/blob/239055229ec93a99cc7e15208075724ccf543bd1/vlcp/utils/dhcp.py#L427-L441
hubo1016/vlcp
vlcp/utils/dhcp.py
create_dhcp_options
def create_dhcp_options(input_dict, ignoreError = False, generateNone = False): """ Try best to create dhcp_options from human friendly values, ignoring invalid values """ retdict = {} for k,v in dict(input_dict).items(): try: if generateNone and v is None: retdict[k] = None else: try: retdict[k] = create_option_from_value(k, v) except _EmptyOptionException: if generateNone: retdict[k] = None except Exception: if ignoreError: continue else: raise return retdict
python
def create_dhcp_options(input_dict, ignoreError = False, generateNone = False): """ Try best to create dhcp_options from human friendly values, ignoring invalid values """ retdict = {} for k,v in dict(input_dict).items(): try: if generateNone and v is None: retdict[k] = None else: try: retdict[k] = create_option_from_value(k, v) except _EmptyOptionException: if generateNone: retdict[k] = None except Exception: if ignoreError: continue else: raise return retdict
[ "def", "create_dhcp_options", "(", "input_dict", ",", "ignoreError", "=", "False", ",", "generateNone", "=", "False", ")", ":", "retdict", "=", "{", "}", "for", "k", ",", "v", "in", "dict", "(", "input_dict", ")", ".", "items", "(", ")", ":", "try", ":", "if", "generateNone", "and", "v", "is", "None", ":", "retdict", "[", "k", "]", "=", "None", "else", ":", "try", ":", "retdict", "[", "k", "]", "=", "create_option_from_value", "(", "k", ",", "v", ")", "except", "_EmptyOptionException", ":", "if", "generateNone", ":", "retdict", "[", "k", "]", "=", "None", "except", "Exception", ":", "if", "ignoreError", ":", "continue", "else", ":", "raise", "return", "retdict" ]
Try best to create dhcp_options from human friendly values, ignoring invalid values
[ "Try", "best", "to", "create", "dhcp_options", "from", "human", "friendly", "values", "ignoring", "invalid", "values" ]
train
https://github.com/hubo1016/vlcp/blob/239055229ec93a99cc7e15208075724ccf543bd1/vlcp/utils/dhcp.py#L444-L465
hubo1016/vlcp
vlcp/event/event.py
with_indices
def with_indices(*args): ''' Create indices for an event class. Every event class must be decorated with this decorator. ''' def decorator(cls): for c in cls.__bases__: if hasattr(c, '_indicesNames'): cls._classnameIndex = c._classnameIndex + 1 for i in range(0, cls._classnameIndex): setattr(cls, '_classname' + str(i), getattr(c, '_classname' + str(i))) setattr(cls, '_classname' + str(cls._classnameIndex), cls._getTypename()) cls._indicesNames = c._indicesNames + ('_classname' + str(cls._classnameIndex),) + args cls._generateTemplate() return cls cls._classnameIndex = -1 cls._indicesNames = args cls._generateTemplate() return cls return decorator
python
def with_indices(*args): ''' Create indices for an event class. Every event class must be decorated with this decorator. ''' def decorator(cls): for c in cls.__bases__: if hasattr(c, '_indicesNames'): cls._classnameIndex = c._classnameIndex + 1 for i in range(0, cls._classnameIndex): setattr(cls, '_classname' + str(i), getattr(c, '_classname' + str(i))) setattr(cls, '_classname' + str(cls._classnameIndex), cls._getTypename()) cls._indicesNames = c._indicesNames + ('_classname' + str(cls._classnameIndex),) + args cls._generateTemplate() return cls cls._classnameIndex = -1 cls._indicesNames = args cls._generateTemplate() return cls return decorator
[ "def", "with_indices", "(", "*", "args", ")", ":", "def", "decorator", "(", "cls", ")", ":", "for", "c", "in", "cls", ".", "__bases__", ":", "if", "hasattr", "(", "c", ",", "'_indicesNames'", ")", ":", "cls", ".", "_classnameIndex", "=", "c", ".", "_classnameIndex", "+", "1", "for", "i", "in", "range", "(", "0", ",", "cls", ".", "_classnameIndex", ")", ":", "setattr", "(", "cls", ",", "'_classname'", "+", "str", "(", "i", ")", ",", "getattr", "(", "c", ",", "'_classname'", "+", "str", "(", "i", ")", ")", ")", "setattr", "(", "cls", ",", "'_classname'", "+", "str", "(", "cls", ".", "_classnameIndex", ")", ",", "cls", ".", "_getTypename", "(", ")", ")", "cls", ".", "_indicesNames", "=", "c", ".", "_indicesNames", "+", "(", "'_classname'", "+", "str", "(", "cls", ".", "_classnameIndex", ")", ",", ")", "+", "args", "cls", ".", "_generateTemplate", "(", ")", "return", "cls", "cls", ".", "_classnameIndex", "=", "-", "1", "cls", ".", "_indicesNames", "=", "args", "cls", ".", "_generateTemplate", "(", ")", "return", "cls", "return", "decorator" ]
Create indices for an event class. Every event class must be decorated with this decorator.
[ "Create", "indices", "for", "an", "event", "class", ".", "Every", "event", "class", "must", "be", "decorated", "with", "this", "decorator", "." ]
train
https://github.com/hubo1016/vlcp/blob/239055229ec93a99cc7e15208075724ccf543bd1/vlcp/event/event.py#L208-L226
hubo1016/vlcp
vlcp/event/event.py
Diff_.two_way_difference
def two_way_difference(self, b, extra_add = (), extra_remove = ()): """ Return (self - b, b - self) """ if self is b: return ((), ()) if isinstance(b, DiffRef_): extra_remove = extra_remove + b.add b = b.origin if extra_add == extra_remove: extra_add = extra_remove = () if isinstance(b, Diff_): if self.base is b.base: first = self.add + b.remove second = self.remove + b.add elif self.base is b: first = self.add second = self.remove elif b.base is self: first = b.remove second = b.add else: first = self second = b else: first = self second = b if not first and not extra_add: return ((), tuple(second) + tuple(extra_remove)) elif not second and not extra_remove: return (tuple(first) + tuple(extra_add), ()) else: first = set(first) first.update(extra_add) second = set(second) second.update(extra_remove) return tuple(first.difference(second)), tuple(second.difference(first))
python
def two_way_difference(self, b, extra_add = (), extra_remove = ()): """ Return (self - b, b - self) """ if self is b: return ((), ()) if isinstance(b, DiffRef_): extra_remove = extra_remove + b.add b = b.origin if extra_add == extra_remove: extra_add = extra_remove = () if isinstance(b, Diff_): if self.base is b.base: first = self.add + b.remove second = self.remove + b.add elif self.base is b: first = self.add second = self.remove elif b.base is self: first = b.remove second = b.add else: first = self second = b else: first = self second = b if not first and not extra_add: return ((), tuple(second) + tuple(extra_remove)) elif not second and not extra_remove: return (tuple(first) + tuple(extra_add), ()) else: first = set(first) first.update(extra_add) second = set(second) second.update(extra_remove) return tuple(first.difference(second)), tuple(second.difference(first))
[ "def", "two_way_difference", "(", "self", ",", "b", ",", "extra_add", "=", "(", ")", ",", "extra_remove", "=", "(", ")", ")", ":", "if", "self", "is", "b", ":", "return", "(", "(", ")", ",", "(", ")", ")", "if", "isinstance", "(", "b", ",", "DiffRef_", ")", ":", "extra_remove", "=", "extra_remove", "+", "b", ".", "add", "b", "=", "b", ".", "origin", "if", "extra_add", "==", "extra_remove", ":", "extra_add", "=", "extra_remove", "=", "(", ")", "if", "isinstance", "(", "b", ",", "Diff_", ")", ":", "if", "self", ".", "base", "is", "b", ".", "base", ":", "first", "=", "self", ".", "add", "+", "b", ".", "remove", "second", "=", "self", ".", "remove", "+", "b", ".", "add", "elif", "self", ".", "base", "is", "b", ":", "first", "=", "self", ".", "add", "second", "=", "self", ".", "remove", "elif", "b", ".", "base", "is", "self", ":", "first", "=", "b", ".", "remove", "second", "=", "b", ".", "add", "else", ":", "first", "=", "self", "second", "=", "b", "else", ":", "first", "=", "self", "second", "=", "b", "if", "not", "first", "and", "not", "extra_add", ":", "return", "(", "(", ")", ",", "tuple", "(", "second", ")", "+", "tuple", "(", "extra_remove", ")", ")", "elif", "not", "second", "and", "not", "extra_remove", ":", "return", "(", "tuple", "(", "first", ")", "+", "tuple", "(", "extra_add", ")", ",", "(", ")", ")", "else", ":", "first", "=", "set", "(", "first", ")", "first", ".", "update", "(", "extra_add", ")", "second", "=", "set", "(", "second", ")", "second", ".", "update", "(", "extra_remove", ")", "return", "tuple", "(", "first", ".", "difference", "(", "second", ")", ")", ",", "tuple", "(", "second", ".", "difference", "(", "first", ")", ")" ]
Return (self - b, b - self)
[ "Return", "(", "self", "-", "b", "b", "-", "self", ")" ]
train
https://github.com/hubo1016/vlcp/blob/239055229ec93a99cc7e15208075724ccf543bd1/vlcp/event/event.py#L166-L202
hubo1016/vlcp
vlcp/event/event.py
Event.getTypename
def getTypename(cls): ''' :returns: return the proper name to match ''' if cls is Event: return None else: for c in cls.__bases__: if issubclass(c, Event): if c is Event: return cls._getTypename() else: return c.getTypename()
python
def getTypename(cls): ''' :returns: return the proper name to match ''' if cls is Event: return None else: for c in cls.__bases__: if issubclass(c, Event): if c is Event: return cls._getTypename() else: return c.getTypename()
[ "def", "getTypename", "(", "cls", ")", ":", "if", "cls", "is", "Event", ":", "return", "None", "else", ":", "for", "c", "in", "cls", ".", "__bases__", ":", "if", "issubclass", "(", "c", ",", "Event", ")", ":", "if", "c", "is", "Event", ":", "return", "cls", ".", "_getTypename", "(", ")", "else", ":", "return", "c", ".", "getTypename", "(", ")" ]
:returns: return the proper name to match
[ ":", "returns", ":", "return", "the", "proper", "name", "to", "match" ]
train
https://github.com/hubo1016/vlcp/blob/239055229ec93a99cc7e15208075724ccf543bd1/vlcp/event/event.py#L292-L304
hubo1016/vlcp
vlcp/event/event.py
Event.createMatcher
def createMatcher(cls, *args, **kwargs): ''' :param _ismatch: user-defined function ismatch(event) for matching test :param \*args: indices :param \*\*kwargs: index_name=index_value for matching criteria ''' if kwargs and not args: return EventMatcher(tuple(getattr(cls, ind) if ind[:10] == '_classname' else kwargs.get(ind) for ind in cls.indicesNames()), kwargs.get('_ismatch')) else: return EventMatcher(tuple(cls._generateIndices(args)), kwargs.get('_ismatch'))
python
def createMatcher(cls, *args, **kwargs): ''' :param _ismatch: user-defined function ismatch(event) for matching test :param \*args: indices :param \*\*kwargs: index_name=index_value for matching criteria ''' if kwargs and not args: return EventMatcher(tuple(getattr(cls, ind) if ind[:10] == '_classname' else kwargs.get(ind) for ind in cls.indicesNames()), kwargs.get('_ismatch')) else: return EventMatcher(tuple(cls._generateIndices(args)), kwargs.get('_ismatch'))
[ "def", "createMatcher", "(", "cls", ",", "*", "args", ",", "*", "*", "kwargs", ")", ":", "if", "kwargs", "and", "not", "args", ":", "return", "EventMatcher", "(", "tuple", "(", "getattr", "(", "cls", ",", "ind", ")", "if", "ind", "[", ":", "10", "]", "==", "'_classname'", "else", "kwargs", ".", "get", "(", "ind", ")", "for", "ind", "in", "cls", ".", "indicesNames", "(", ")", ")", ",", "kwargs", ".", "get", "(", "'_ismatch'", ")", ")", "else", ":", "return", "EventMatcher", "(", "tuple", "(", "cls", ".", "_generateIndices", "(", "args", ")", ")", ",", "kwargs", ".", "get", "(", "'_ismatch'", ")", ")" ]
:param _ismatch: user-defined function ismatch(event) for matching test :param \*args: indices :param \*\*kwargs: index_name=index_value for matching criteria
[ ":", "param", "_ismatch", ":", "user", "-", "defined", "function", "ismatch", "(", "event", ")", "for", "matching", "test", ":", "param", "\\", "*", "args", ":", "indices", ":", "param", "\\", "*", "\\", "*", "kwargs", ":", "index_name", "=", "index_value", "for", "matching", "criteria" ]
train
https://github.com/hubo1016/vlcp/blob/239055229ec93a99cc7e15208075724ccf543bd1/vlcp/event/event.py#L331-L340
hubo1016/vlcp
vlcp/event/ratelimiter.py
RateLimiter.limit
async def limit(self, use = 1): """ Acquire "resources", wait until enough "resources" are acquired. For each loop, `limit` number of "resources" are permitted. :param use: number of "resouces" to be used. :return: True if is limited """ c = self._counter self._counter = c + use if self._task is None: self._task = self._container.subroutine(self._limiter_task(), False) if c >= self._bottom_line: # Limited await RateLimitingEvent.createMatcher(self, c // self._limit) return True else: return False
python
async def limit(self, use = 1): """ Acquire "resources", wait until enough "resources" are acquired. For each loop, `limit` number of "resources" are permitted. :param use: number of "resouces" to be used. :return: True if is limited """ c = self._counter self._counter = c + use if self._task is None: self._task = self._container.subroutine(self._limiter_task(), False) if c >= self._bottom_line: # Limited await RateLimitingEvent.createMatcher(self, c // self._limit) return True else: return False
[ "async", "def", "limit", "(", "self", ",", "use", "=", "1", ")", ":", "c", "=", "self", ".", "_counter", "self", ".", "_counter", "=", "c", "+", "use", "if", "self", ".", "_task", "is", "None", ":", "self", ".", "_task", "=", "self", ".", "_container", ".", "subroutine", "(", "self", ".", "_limiter_task", "(", ")", ",", "False", ")", "if", "c", ">=", "self", ".", "_bottom_line", ":", "# Limited", "await", "RateLimitingEvent", ".", "createMatcher", "(", "self", ",", "c", "//", "self", ".", "_limit", ")", "return", "True", "else", ":", "return", "False" ]
Acquire "resources", wait until enough "resources" are acquired. For each loop, `limit` number of "resources" are permitted. :param use: number of "resouces" to be used. :return: True if is limited
[ "Acquire", "resources", "wait", "until", "enough", "resources", "are", "acquired", ".", "For", "each", "loop", "limit", "number", "of", "resources", "are", "permitted", ".", ":", "param", "use", ":", "number", "of", "resouces", "to", "be", "used", ".", ":", "return", ":", "True", "if", "is", "limited" ]
train
https://github.com/hubo1016/vlcp/blob/239055229ec93a99cc7e15208075724ccf543bd1/vlcp/event/ratelimiter.py#L55-L73
hubo1016/vlcp
vlcp/utils/connector.py
TaskPool.run_task
async def run_task(self, container, task, newthread = False): "Run task() in task pool. Raise an exception or return the return value" e = TaskEvent(self, task=task, newthread = newthread) await container.wait_for_send(e) ev = await TaskDoneEvent.createMatcher(e) if hasattr(ev, 'exception'): raise ev.exception else: return ev.result
python
async def run_task(self, container, task, newthread = False): "Run task() in task pool. Raise an exception or return the return value" e = TaskEvent(self, task=task, newthread = newthread) await container.wait_for_send(e) ev = await TaskDoneEvent.createMatcher(e) if hasattr(ev, 'exception'): raise ev.exception else: return ev.result
[ "async", "def", "run_task", "(", "self", ",", "container", ",", "task", ",", "newthread", "=", "False", ")", ":", "e", "=", "TaskEvent", "(", "self", ",", "task", "=", "task", ",", "newthread", "=", "newthread", ")", "await", "container", ".", "wait_for_send", "(", "e", ")", "ev", "=", "await", "TaskDoneEvent", ".", "createMatcher", "(", "e", ")", "if", "hasattr", "(", "ev", ",", "'exception'", ")", ":", "raise", "ev", ".", "exception", "else", ":", "return", "ev", ".", "result" ]
Run task() in task pool. Raise an exception or return the return value
[ "Run", "task", "()", "in", "task", "pool", ".", "Raise", "an", "exception", "or", "return", "the", "return", "value" ]
train
https://github.com/hubo1016/vlcp/blob/239055229ec93a99cc7e15208075724ccf543bd1/vlcp/utils/connector.py#L450-L458
hubo1016/vlcp
vlcp/utils/connector.py
TaskPool.run_gen_task
async def run_gen_task(self, container, gentask, newthread = True): "Run generator gentask() in task pool, yield customized events" e = TaskEvent(self, gen_task = gentask, newthread = newthread) await container.wait_for_send(e) ev = await TaskDoneEvent.createMatcher(e) if hasattr(ev, 'exception'): raise ev.exception else: return ev.result
python
async def run_gen_task(self, container, gentask, newthread = True): "Run generator gentask() in task pool, yield customized events" e = TaskEvent(self, gen_task = gentask, newthread = newthread) await container.wait_for_send(e) ev = await TaskDoneEvent.createMatcher(e) if hasattr(ev, 'exception'): raise ev.exception else: return ev.result
[ "async", "def", "run_gen_task", "(", "self", ",", "container", ",", "gentask", ",", "newthread", "=", "True", ")", ":", "e", "=", "TaskEvent", "(", "self", ",", "gen_task", "=", "gentask", ",", "newthread", "=", "newthread", ")", "await", "container", ".", "wait_for_send", "(", "e", ")", "ev", "=", "await", "TaskDoneEvent", ".", "createMatcher", "(", "e", ")", "if", "hasattr", "(", "ev", ",", "'exception'", ")", ":", "raise", "ev", ".", "exception", "else", ":", "return", "ev", ".", "result" ]
Run generator gentask() in task pool, yield customized events
[ "Run", "generator", "gentask", "()", "in", "task", "pool", "yield", "customized", "events" ]
train
https://github.com/hubo1016/vlcp/blob/239055229ec93a99cc7e15208075724ccf543bd1/vlcp/utils/connector.py#L462-L470
hubo1016/vlcp
vlcp/utils/connector.py
TaskPool.run_async_task
async def run_async_task(self, container, asynctask, newthread = True): "Run asynctask(sender) in task pool, call sender(events) to send customized events, return result" e = TaskEvent(self, async_task = asynctask, newthread = newthread) await container.wait_for_send(e) ev = await TaskDoneEvent.createMatcher(e) if hasattr(ev, 'exception'): raise ev.exception else: return ev.result
python
async def run_async_task(self, container, asynctask, newthread = True): "Run asynctask(sender) in task pool, call sender(events) to send customized events, return result" e = TaskEvent(self, async_task = asynctask, newthread = newthread) await container.wait_for_send(e) ev = await TaskDoneEvent.createMatcher(e) if hasattr(ev, 'exception'): raise ev.exception else: return ev.result
[ "async", "def", "run_async_task", "(", "self", ",", "container", ",", "asynctask", ",", "newthread", "=", "True", ")", ":", "e", "=", "TaskEvent", "(", "self", ",", "async_task", "=", "asynctask", ",", "newthread", "=", "newthread", ")", "await", "container", ".", "wait_for_send", "(", "e", ")", "ev", "=", "await", "TaskDoneEvent", ".", "createMatcher", "(", "e", ")", "if", "hasattr", "(", "ev", ",", "'exception'", ")", ":", "raise", "ev", ".", "exception", "else", ":", "return", "ev", ".", "result" ]
Run asynctask(sender) in task pool, call sender(events) to send customized events, return result
[ "Run", "asynctask", "(", "sender", ")", "in", "task", "pool", "call", "sender", "(", "events", ")", "to", "send", "customized", "events", "return", "result" ]
train
https://github.com/hubo1016/vlcp/blob/239055229ec93a99cc7e15208075724ccf543bd1/vlcp/utils/connector.py#L474-L482
hubo1016/vlcp
vlcp/event/lock.py
Lock.lock
async def lock(self, container = None): "Wait for lock acquire" if container is None: container = RoutineContainer.get_container(self.scheduler) if self.locked: pass elif self.lockroutine: await LockedEvent.createMatcher(self) else: await container.wait_for_send(LockEvent(self.context, self.key, self)) self.locked = True
python
async def lock(self, container = None): "Wait for lock acquire" if container is None: container = RoutineContainer.get_container(self.scheduler) if self.locked: pass elif self.lockroutine: await LockedEvent.createMatcher(self) else: await container.wait_for_send(LockEvent(self.context, self.key, self)) self.locked = True
[ "async", "def", "lock", "(", "self", ",", "container", "=", "None", ")", ":", "if", "container", "is", "None", ":", "container", "=", "RoutineContainer", ".", "get_container", "(", "self", ".", "scheduler", ")", "if", "self", ".", "locked", ":", "pass", "elif", "self", ".", "lockroutine", ":", "await", "LockedEvent", ".", "createMatcher", "(", "self", ")", "else", ":", "await", "container", ".", "wait_for_send", "(", "LockEvent", "(", "self", ".", "context", ",", "self", ".", "key", ",", "self", ")", ")", "self", ".", "locked", "=", "True" ]
Wait for lock acquire
[ "Wait", "for", "lock", "acquire" ]
train
https://github.com/hubo1016/vlcp/blob/239055229ec93a99cc7e15208075724ccf543bd1/vlcp/event/lock.py#L54-L64
hubo1016/vlcp
vlcp/event/lock.py
Lock.trylock
def trylock(self): "Try to acquire lock and return True; if cannot acquire the lock at this moment, return False." if self.locked: return True if self.lockroutine: return False waiter = self.scheduler.send(LockEvent(self.context, self.key, self)) if waiter: return False else: self.locked = True return True
python
def trylock(self): "Try to acquire lock and return True; if cannot acquire the lock at this moment, return False." if self.locked: return True if self.lockroutine: return False waiter = self.scheduler.send(LockEvent(self.context, self.key, self)) if waiter: return False else: self.locked = True return True
[ "def", "trylock", "(", "self", ")", ":", "if", "self", ".", "locked", ":", "return", "True", "if", "self", ".", "lockroutine", ":", "return", "False", "waiter", "=", "self", ".", "scheduler", ".", "send", "(", "LockEvent", "(", "self", ".", "context", ",", "self", ".", "key", ",", "self", ")", ")", "if", "waiter", ":", "return", "False", "else", ":", "self", ".", "locked", "=", "True", "return", "True" ]
Try to acquire lock and return True; if cannot acquire the lock at this moment, return False.
[ "Try", "to", "acquire", "lock", "and", "return", "True", ";", "if", "cannot", "acquire", "the", "lock", "at", "this", "moment", "return", "False", "." ]
train
https://github.com/hubo1016/vlcp/blob/239055229ec93a99cc7e15208075724ccf543bd1/vlcp/event/lock.py#L65-L76
hubo1016/vlcp
vlcp/event/lock.py
Lock.beginlock
def beginlock(self, container): "Start to acquire lock in another routine. Call trylock or lock later to acquire the lock. Call unlock to cancel the lock routine" if self.locked: return True if self.lockroutine: return False self.lockroutine = container.subroutine(self._lockroutine(container), False) return self.locked
python
def beginlock(self, container): "Start to acquire lock in another routine. Call trylock or lock later to acquire the lock. Call unlock to cancel the lock routine" if self.locked: return True if self.lockroutine: return False self.lockroutine = container.subroutine(self._lockroutine(container), False) return self.locked
[ "def", "beginlock", "(", "self", ",", "container", ")", ":", "if", "self", ".", "locked", ":", "return", "True", "if", "self", ".", "lockroutine", ":", "return", "False", "self", ".", "lockroutine", "=", "container", ".", "subroutine", "(", "self", ".", "_lockroutine", "(", "container", ")", ",", "False", ")", "return", "self", ".", "locked" ]
Start to acquire lock in another routine. Call trylock or lock later to acquire the lock. Call unlock to cancel the lock routine
[ "Start", "to", "acquire", "lock", "in", "another", "routine", ".", "Call", "trylock", "or", "lock", "later", "to", "acquire", "the", "lock", ".", "Call", "unlock", "to", "cancel", "the", "lock", "routine" ]
train
https://github.com/hubo1016/vlcp/blob/239055229ec93a99cc7e15208075724ccf543bd1/vlcp/event/lock.py#L80-L87
hubo1016/vlcp
vlcp/event/lock.py
Lock.unlock
def unlock(self): "Unlock the key" if self.lockroutine: self.lockroutine.close() self.lockroutine = None if self.locked: self.locked = False self.scheduler.ignore(LockEvent.createMatcher(self.context, self.key, self))
python
def unlock(self): "Unlock the key" if self.lockroutine: self.lockroutine.close() self.lockroutine = None if self.locked: self.locked = False self.scheduler.ignore(LockEvent.createMatcher(self.context, self.key, self))
[ "def", "unlock", "(", "self", ")", ":", "if", "self", ".", "lockroutine", ":", "self", ".", "lockroutine", ".", "close", "(", ")", "self", ".", "lockroutine", "=", "None", "if", "self", ".", "locked", ":", "self", ".", "locked", "=", "False", "self", ".", "scheduler", ".", "ignore", "(", "LockEvent", ".", "createMatcher", "(", "self", ".", "context", ",", "self", ".", "key", ",", "self", ")", ")" ]
Unlock the key
[ "Unlock", "the", "key" ]
train
https://github.com/hubo1016/vlcp/blob/239055229ec93a99cc7e15208075724ccf543bd1/vlcp/event/lock.py#L88-L95
hubo1016/vlcp
vlcp/event/lock.py
Semaphore.create
def create(self): """ Create the subqueue to change the default behavior of Lock to semaphore. """ self.queue = self.scheduler.queue.addSubQueue(self.priority, LockEvent.createMatcher(self.context, self.key), maxdefault = self.size, defaultQueueClass = CBQueue.AutoClassQueue.initHelper('locker', subqueuelimit = 1))
python
def create(self): """ Create the subqueue to change the default behavior of Lock to semaphore. """ self.queue = self.scheduler.queue.addSubQueue(self.priority, LockEvent.createMatcher(self.context, self.key), maxdefault = self.size, defaultQueueClass = CBQueue.AutoClassQueue.initHelper('locker', subqueuelimit = 1))
[ "def", "create", "(", "self", ")", ":", "self", ".", "queue", "=", "self", ".", "scheduler", ".", "queue", ".", "addSubQueue", "(", "self", ".", "priority", ",", "LockEvent", ".", "createMatcher", "(", "self", ".", "context", ",", "self", ".", "key", ")", ",", "maxdefault", "=", "self", ".", "size", ",", "defaultQueueClass", "=", "CBQueue", ".", "AutoClassQueue", ".", "initHelper", "(", "'locker'", ",", "subqueuelimit", "=", "1", ")", ")" ]
Create the subqueue to change the default behavior of Lock to semaphore.
[ "Create", "the", "subqueue", "to", "change", "the", "default", "behavior", "of", "Lock", "to", "semaphore", "." ]
train
https://github.com/hubo1016/vlcp/blob/239055229ec93a99cc7e15208075724ccf543bd1/vlcp/event/lock.py#L141-L146
hubo1016/vlcp
vlcp/event/lock.py
Semaphore.destroy
async def destroy(self, container = None): """ Destroy the created subqueue to change the behavior back to Lock """ if container is None: container = RoutineContainer(self.scheduler) if self.queue is not None: await container.syscall_noreturn(syscall_removequeue(self.scheduler.queue, self.queue)) self.queue = None
python
async def destroy(self, container = None): """ Destroy the created subqueue to change the behavior back to Lock """ if container is None: container = RoutineContainer(self.scheduler) if self.queue is not None: await container.syscall_noreturn(syscall_removequeue(self.scheduler.queue, self.queue)) self.queue = None
[ "async", "def", "destroy", "(", "self", ",", "container", "=", "None", ")", ":", "if", "container", "is", "None", ":", "container", "=", "RoutineContainer", "(", "self", ".", "scheduler", ")", "if", "self", ".", "queue", "is", "not", "None", ":", "await", "container", ".", "syscall_noreturn", "(", "syscall_removequeue", "(", "self", ".", "scheduler", ".", "queue", ",", "self", ".", "queue", ")", ")", "self", ".", "queue", "=", "None" ]
Destroy the created subqueue to change the behavior back to Lock
[ "Destroy", "the", "created", "subqueue", "to", "change", "the", "behavior", "back", "to", "Lock" ]
train
https://github.com/hubo1016/vlcp/blob/239055229ec93a99cc7e15208075724ccf543bd1/vlcp/event/lock.py#L147-L155
hubo1016/vlcp
vlcp/event/connection.py
Connection.shutdown
async def shutdown(self, force = False, connmark = -1): ''' Can call without delegate ''' if connmark is None: connmark = self.connmark self.scheduler.emergesend(ConnectionControlEvent(self, ConnectionControlEvent.SHUTDOWN, force, connmark))
python
async def shutdown(self, force = False, connmark = -1): ''' Can call without delegate ''' if connmark is None: connmark = self.connmark self.scheduler.emergesend(ConnectionControlEvent(self, ConnectionControlEvent.SHUTDOWN, force, connmark))
[ "async", "def", "shutdown", "(", "self", ",", "force", "=", "False", ",", "connmark", "=", "-", "1", ")", ":", "if", "connmark", "is", "None", ":", "connmark", "=", "self", ".", "connmark", "self", ".", "scheduler", ".", "emergesend", "(", "ConnectionControlEvent", "(", "self", ",", "ConnectionControlEvent", ".", "SHUTDOWN", ",", "force", ",", "connmark", ")", ")" ]
Can call without delegate
[ "Can", "call", "without", "delegate" ]
train
https://github.com/hubo1016/vlcp/blob/239055229ec93a99cc7e15208075724ccf543bd1/vlcp/event/connection.py#L367-L373
hubo1016/vlcp
vlcp/event/connection.py
Connection.reconnect
async def reconnect(self, force = True, connmark = None): ''' Can call without delegate ''' if connmark is None: connmark = self.connmark self.scheduler.emergesend(ConnectionControlEvent(self, ConnectionControlEvent.RECONNECT, force, connmark))
python
async def reconnect(self, force = True, connmark = None): ''' Can call without delegate ''' if connmark is None: connmark = self.connmark self.scheduler.emergesend(ConnectionControlEvent(self, ConnectionControlEvent.RECONNECT, force, connmark))
[ "async", "def", "reconnect", "(", "self", ",", "force", "=", "True", ",", "connmark", "=", "None", ")", ":", "if", "connmark", "is", "None", ":", "connmark", "=", "self", ".", "connmark", "self", ".", "scheduler", ".", "emergesend", "(", "ConnectionControlEvent", "(", "self", ",", "ConnectionControlEvent", ".", "RECONNECT", ",", "force", ",", "connmark", ")", ")" ]
Can call without delegate
[ "Can", "call", "without", "delegate" ]
train
https://github.com/hubo1016/vlcp/blob/239055229ec93a99cc7e15208075724ccf543bd1/vlcp/event/connection.py#L374-L380
hubo1016/vlcp
vlcp/event/connection.py
Connection.reset
async def reset(self, force = True, connmark = None): ''' Can call without delegate ''' if connmark is None: connmark = self.connmark self.scheduler.emergesend(ConnectionControlEvent(self, ConnectionControlEvent.RESET, force, connmark))
python
async def reset(self, force = True, connmark = None): ''' Can call without delegate ''' if connmark is None: connmark = self.connmark self.scheduler.emergesend(ConnectionControlEvent(self, ConnectionControlEvent.RESET, force, connmark))
[ "async", "def", "reset", "(", "self", ",", "force", "=", "True", ",", "connmark", "=", "None", ")", ":", "if", "connmark", "is", "None", ":", "connmark", "=", "self", ".", "connmark", "self", ".", "scheduler", ".", "emergesend", "(", "ConnectionControlEvent", "(", "self", ",", "ConnectionControlEvent", ".", "RESET", ",", "force", ",", "connmark", ")", ")" ]
Can call without delegate
[ "Can", "call", "without", "delegate" ]
train
https://github.com/hubo1016/vlcp/blob/239055229ec93a99cc7e15208075724ccf543bd1/vlcp/event/connection.py#L381-L387
hubo1016/vlcp
vlcp/event/connection.py
Connection.write
async def write(self, event, ignoreException = True): ''' Can call without delegate ''' connmark = self.connmark if self.connected: def _until(): if not self.connected or self.connmark != connmark: return True r = await self.wait_for_send(event, until=_until) if r: if ignoreException: return else: raise else: if not ignoreException: raise ConnectionResetException
python
async def write(self, event, ignoreException = True): ''' Can call without delegate ''' connmark = self.connmark if self.connected: def _until(): if not self.connected or self.connmark != connmark: return True r = await self.wait_for_send(event, until=_until) if r: if ignoreException: return else: raise else: if not ignoreException: raise ConnectionResetException
[ "async", "def", "write", "(", "self", ",", "event", ",", "ignoreException", "=", "True", ")", ":", "connmark", "=", "self", ".", "connmark", "if", "self", ".", "connected", ":", "def", "_until", "(", ")", ":", "if", "not", "self", ".", "connected", "or", "self", ".", "connmark", "!=", "connmark", ":", "return", "True", "r", "=", "await", "self", ".", "wait_for_send", "(", "event", ",", "until", "=", "_until", ")", "if", "r", ":", "if", "ignoreException", ":", "return", "else", ":", "raise", "else", ":", "if", "not", "ignoreException", ":", "raise", "ConnectionResetException" ]
Can call without delegate
[ "Can", "call", "without", "delegate" ]
train
https://github.com/hubo1016/vlcp/blob/239055229ec93a99cc7e15208075724ccf543bd1/vlcp/event/connection.py#L388-L405
hubo1016/vlcp
vlcp/event/connection.py
TcpServer.shutdown
async def shutdown(self, connmark = -1): ''' Can call without delegate ''' if connmark is None: connmark = self.connmark self.scheduler.emergesend(ConnectionControlEvent(self, ConnectionControlEvent.SHUTDOWN, True, connmark))
python
async def shutdown(self, connmark = -1): ''' Can call without delegate ''' if connmark is None: connmark = self.connmark self.scheduler.emergesend(ConnectionControlEvent(self, ConnectionControlEvent.SHUTDOWN, True, connmark))
[ "async", "def", "shutdown", "(", "self", ",", "connmark", "=", "-", "1", ")", ":", "if", "connmark", "is", "None", ":", "connmark", "=", "self", ".", "connmark", "self", ".", "scheduler", ".", "emergesend", "(", "ConnectionControlEvent", "(", "self", ",", "ConnectionControlEvent", ".", "SHUTDOWN", ",", "True", ",", "connmark", ")", ")" ]
Can call without delegate
[ "Can", "call", "without", "delegate" ]
train
https://github.com/hubo1016/vlcp/blob/239055229ec93a99cc7e15208075724ccf543bd1/vlcp/event/connection.py#L1056-L1062
hubo1016/vlcp
vlcp/event/connection.py
TcpServer.stoplisten
async def stoplisten(self, connmark = -1): ''' Can call without delegate ''' if connmark is None: connmark = self.connmark self.scheduler.emergesend(ConnectionControlEvent(self, ConnectionControlEvent.STOPLISTEN, True, connmark))
python
async def stoplisten(self, connmark = -1): ''' Can call without delegate ''' if connmark is None: connmark = self.connmark self.scheduler.emergesend(ConnectionControlEvent(self, ConnectionControlEvent.STOPLISTEN, True, connmark))
[ "async", "def", "stoplisten", "(", "self", ",", "connmark", "=", "-", "1", ")", ":", "if", "connmark", "is", "None", ":", "connmark", "=", "self", ".", "connmark", "self", ".", "scheduler", ".", "emergesend", "(", "ConnectionControlEvent", "(", "self", ",", "ConnectionControlEvent", ".", "STOPLISTEN", ",", "True", ",", "connmark", ")", ")" ]
Can call without delegate
[ "Can", "call", "without", "delegate" ]
train
https://github.com/hubo1016/vlcp/blob/239055229ec93a99cc7e15208075724ccf543bd1/vlcp/event/connection.py#L1064-L1070
hubo1016/vlcp
vlcp/event/connection.py
TcpServer.startlisten
async def startlisten(self, connmark = -1): ''' Can call without delegate ''' if connmark is None: connmark = self.connmark self.scheduler.emergesend(ConnectionControlEvent(self, ConnectionControlEvent.STARTLISTEN, True, connmark))
python
async def startlisten(self, connmark = -1): ''' Can call without delegate ''' if connmark is None: connmark = self.connmark self.scheduler.emergesend(ConnectionControlEvent(self, ConnectionControlEvent.STARTLISTEN, True, connmark))
[ "async", "def", "startlisten", "(", "self", ",", "connmark", "=", "-", "1", ")", ":", "if", "connmark", "is", "None", ":", "connmark", "=", "self", ".", "connmark", "self", ".", "scheduler", ".", "emergesend", "(", "ConnectionControlEvent", "(", "self", ",", "ConnectionControlEvent", ".", "STARTLISTEN", ",", "True", ",", "connmark", ")", ")" ]
Can call without delegate
[ "Can", "call", "without", "delegate" ]
train
https://github.com/hubo1016/vlcp/blob/239055229ec93a99cc7e15208075724ccf543bd1/vlcp/event/connection.py#L1072-L1078
hubo1016/vlcp
vlcp/start.py
default_start
def default_start(): """ Use `sys.argv` for starting parameters. This is the entry-point of `vlcp-start` """ (config, daemon, pidfile, startup, fork) = parsearg() if config is None: if os.path.isfile('/etc/vlcp.conf'): config = '/etc/vlcp.conf' else: print('/etc/vlcp.conf is not found; start without configurations.') elif not config: config = None main(config, startup, daemon, pidfile, fork)
python
def default_start(): """ Use `sys.argv` for starting parameters. This is the entry-point of `vlcp-start` """ (config, daemon, pidfile, startup, fork) = parsearg() if config is None: if os.path.isfile('/etc/vlcp.conf'): config = '/etc/vlcp.conf' else: print('/etc/vlcp.conf is not found; start without configurations.') elif not config: config = None main(config, startup, daemon, pidfile, fork)
[ "def", "default_start", "(", ")", ":", "(", "config", ",", "daemon", ",", "pidfile", ",", "startup", ",", "fork", ")", "=", "parsearg", "(", ")", "if", "config", "is", "None", ":", "if", "os", ".", "path", ".", "isfile", "(", "'/etc/vlcp.conf'", ")", ":", "config", "=", "'/etc/vlcp.conf'", "else", ":", "print", "(", "'/etc/vlcp.conf is not found; start without configurations.'", ")", "elif", "not", "config", ":", "config", "=", "None", "main", "(", "config", ",", "startup", ",", "daemon", ",", "pidfile", ",", "fork", ")" ]
Use `sys.argv` for starting parameters. This is the entry-point of `vlcp-start`
[ "Use", "sys", ".", "argv", "for", "starting", "parameters", ".", "This", "is", "the", "entry", "-", "point", "of", "vlcp", "-", "start" ]
train
https://github.com/hubo1016/vlcp/blob/239055229ec93a99cc7e15208075724ccf543bd1/vlcp/start.py#L61-L73
hubo1016/vlcp
vlcp/utils/webclient.py
Response.close
def close(self): "Stop the output stream, but further download will still perform" if self.stream: self.stream.close(self.scheduler) self.stream = None
python
def close(self): "Stop the output stream, but further download will still perform" if self.stream: self.stream.close(self.scheduler) self.stream = None
[ "def", "close", "(", "self", ")", ":", "if", "self", ".", "stream", ":", "self", ".", "stream", ".", "close", "(", "self", ".", "scheduler", ")", "self", ".", "stream", "=", "None" ]
Stop the output stream, but further download will still perform
[ "Stop", "the", "output", "stream", "but", "further", "download", "will", "still", "perform" ]
train
https://github.com/hubo1016/vlcp/blob/239055229ec93a99cc7e15208075724ccf543bd1/vlcp/utils/webclient.py#L314-L318
hubo1016/vlcp
vlcp/utils/webclient.py
Response.shutdown
async def shutdown(self): "Force stop the output stream, if there are more data to download, shutdown the connection" if self.stream: if not self.stream.dataeof and not self.stream.dataerror: self.stream.close(self.scheduler) await self.connection.shutdown() else: self.stream.close(self.scheduler) self.stream = None
python
async def shutdown(self): "Force stop the output stream, if there are more data to download, shutdown the connection" if self.stream: if not self.stream.dataeof and not self.stream.dataerror: self.stream.close(self.scheduler) await self.connection.shutdown() else: self.stream.close(self.scheduler) self.stream = None
[ "async", "def", "shutdown", "(", "self", ")", ":", "if", "self", ".", "stream", ":", "if", "not", "self", ".", "stream", ".", "dataeof", "and", "not", "self", ".", "stream", ".", "dataerror", ":", "self", ".", "stream", ".", "close", "(", "self", ".", "scheduler", ")", "await", "self", ".", "connection", ".", "shutdown", "(", ")", "else", ":", "self", ".", "stream", ".", "close", "(", "self", ".", "scheduler", ")", "self", ".", "stream", "=", "None" ]
Force stop the output stream, if there are more data to download, shutdown the connection
[ "Force", "stop", "the", "output", "stream", "if", "there", "are", "more", "data", "to", "download", "shutdown", "the", "connection" ]
train
https://github.com/hubo1016/vlcp/blob/239055229ec93a99cc7e15208075724ccf543bd1/vlcp/utils/webclient.py#L319-L327
hubo1016/vlcp
vlcp/utils/networkplugin.py
createphysicalnetwork
def createphysicalnetwork(type, create_processor = partial(default_processor, excluding=('id', 'type')), reorder_dict = default_iterate_dict): """ :param type: physical network type :param create_processor: create_processor(physicalnetwork, walk, write, \*, parameters) """ # create an new physical network def walker(walk, write, timestamp, parameters_dict): for key, parameters in reorder_dict(parameters_dict): try: value = walk(key) except KeyError: pass else: id_ = parameters['id'] new_network = create_new(PhysicalNetwork, value, id_) new_network.type = type create_processor(new_network, walk, write, parameters=parameters) write(key, new_network) new_networkmap = PhysicalNetworkMap.create_instance(id_) new_networkmap.network = new_network.create_weakreference() write(new_networkmap.getkey(), new_networkmap) # Save into network set try: physet = walk(PhysicalNetworkSet.default_key()) except KeyError: pass else: physet.set.dataset().add(new_network.create_weakreference()) write(physet.getkey(), physet) return walker
python
def createphysicalnetwork(type, create_processor = partial(default_processor, excluding=('id', 'type')), reorder_dict = default_iterate_dict): """ :param type: physical network type :param create_processor: create_processor(physicalnetwork, walk, write, \*, parameters) """ # create an new physical network def walker(walk, write, timestamp, parameters_dict): for key, parameters in reorder_dict(parameters_dict): try: value = walk(key) except KeyError: pass else: id_ = parameters['id'] new_network = create_new(PhysicalNetwork, value, id_) new_network.type = type create_processor(new_network, walk, write, parameters=parameters) write(key, new_network) new_networkmap = PhysicalNetworkMap.create_instance(id_) new_networkmap.network = new_network.create_weakreference() write(new_networkmap.getkey(), new_networkmap) # Save into network set try: physet = walk(PhysicalNetworkSet.default_key()) except KeyError: pass else: physet.set.dataset().add(new_network.create_weakreference()) write(physet.getkey(), physet) return walker
[ "def", "createphysicalnetwork", "(", "type", ",", "create_processor", "=", "partial", "(", "default_processor", ",", "excluding", "=", "(", "'id'", ",", "'type'", ")", ")", ",", "reorder_dict", "=", "default_iterate_dict", ")", ":", "# create an new physical network", "def", "walker", "(", "walk", ",", "write", ",", "timestamp", ",", "parameters_dict", ")", ":", "for", "key", ",", "parameters", "in", "reorder_dict", "(", "parameters_dict", ")", ":", "try", ":", "value", "=", "walk", "(", "key", ")", "except", "KeyError", ":", "pass", "else", ":", "id_", "=", "parameters", "[", "'id'", "]", "new_network", "=", "create_new", "(", "PhysicalNetwork", ",", "value", ",", "id_", ")", "new_network", ".", "type", "=", "type", "create_processor", "(", "new_network", ",", "walk", ",", "write", ",", "parameters", "=", "parameters", ")", "write", "(", "key", ",", "new_network", ")", "new_networkmap", "=", "PhysicalNetworkMap", ".", "create_instance", "(", "id_", ")", "new_networkmap", ".", "network", "=", "new_network", ".", "create_weakreference", "(", ")", "write", "(", "new_networkmap", ".", "getkey", "(", ")", ",", "new_networkmap", ")", "# Save into network set", "try", ":", "physet", "=", "walk", "(", "PhysicalNetworkSet", ".", "default_key", "(", ")", ")", "except", "KeyError", ":", "pass", "else", ":", "physet", ".", "set", ".", "dataset", "(", ")", ".", "add", "(", "new_network", ".", "create_weakreference", "(", ")", ")", "write", "(", "physet", ".", "getkey", "(", ")", ",", "physet", ")", "return", "walker" ]
:param type: physical network type :param create_processor: create_processor(physicalnetwork, walk, write, \*, parameters)
[ ":", "param", "type", ":", "physical", "network", "type", ":", "param", "create_processor", ":", "create_processor", "(", "physicalnetwork", "walk", "write", "\\", "*", "parameters", ")" ]
train
https://github.com/hubo1016/vlcp/blob/239055229ec93a99cc7e15208075724ccf543bd1/vlcp/utils/networkplugin.py#L43-L76
hubo1016/vlcp
vlcp/utils/networkplugin.py
deletephysicalnetwork
def deletephysicalnetwork(check_processor = default_physicalnetwork_delete_check, reorder_dict = default_iterate_dict): """ :param check_processor: check_processor(physicalnetwork, physicalnetworkmap, walk, write, \*, parameters) """ def walker(walk, write, timestamp, parameters_dict): for key, parameters in reorder_dict(parameters_dict): try: value = walk(key) except KeyError: pass else: id_ = parameters['id'] try: phy_map = walk(PhysicalNetworkMap.default_key(id_)) except KeyError: pass else: check_processor(value, phy_map, walk, write, parameters=parameters) write(phy_map.getkey(), None) try: phynetset = walk(PhysicalNetworkSet.default_key()) except KeyError: pass else: phynetset.set.dataset().discard(value.create_weakreference()) write(phynetset.getkey(), phynetset) write(key, None) return walker
python
def deletephysicalnetwork(check_processor = default_physicalnetwork_delete_check, reorder_dict = default_iterate_dict): """ :param check_processor: check_processor(physicalnetwork, physicalnetworkmap, walk, write, \*, parameters) """ def walker(walk, write, timestamp, parameters_dict): for key, parameters in reorder_dict(parameters_dict): try: value = walk(key) except KeyError: pass else: id_ = parameters['id'] try: phy_map = walk(PhysicalNetworkMap.default_key(id_)) except KeyError: pass else: check_processor(value, phy_map, walk, write, parameters=parameters) write(phy_map.getkey(), None) try: phynetset = walk(PhysicalNetworkSet.default_key()) except KeyError: pass else: phynetset.set.dataset().discard(value.create_weakreference()) write(phynetset.getkey(), phynetset) write(key, None) return walker
[ "def", "deletephysicalnetwork", "(", "check_processor", "=", "default_physicalnetwork_delete_check", ",", "reorder_dict", "=", "default_iterate_dict", ")", ":", "def", "walker", "(", "walk", ",", "write", ",", "timestamp", ",", "parameters_dict", ")", ":", "for", "key", ",", "parameters", "in", "reorder_dict", "(", "parameters_dict", ")", ":", "try", ":", "value", "=", "walk", "(", "key", ")", "except", "KeyError", ":", "pass", "else", ":", "id_", "=", "parameters", "[", "'id'", "]", "try", ":", "phy_map", "=", "walk", "(", "PhysicalNetworkMap", ".", "default_key", "(", "id_", ")", ")", "except", "KeyError", ":", "pass", "else", ":", "check_processor", "(", "value", ",", "phy_map", ",", "walk", ",", "write", ",", "parameters", "=", "parameters", ")", "write", "(", "phy_map", ".", "getkey", "(", ")", ",", "None", ")", "try", ":", "phynetset", "=", "walk", "(", "PhysicalNetworkSet", ".", "default_key", "(", ")", ")", "except", "KeyError", ":", "pass", "else", ":", "phynetset", ".", "set", ".", "dataset", "(", ")", ".", "discard", "(", "value", ".", "create_weakreference", "(", ")", ")", "write", "(", "phynetset", ".", "getkey", "(", ")", ",", "phynetset", ")", "write", "(", "key", ",", "None", ")", "return", "walker" ]
:param check_processor: check_processor(physicalnetwork, physicalnetworkmap, walk, write, \*, parameters)
[ ":", "param", "check_processor", ":", "check_processor", "(", "physicalnetwork", "physicalnetworkmap", "walk", "write", "\\", "*", "parameters", ")" ]
train
https://github.com/hubo1016/vlcp/blob/239055229ec93a99cc7e15208075724ccf543bd1/vlcp/utils/networkplugin.py#L104-L132
hubo1016/vlcp
vlcp/utils/networkplugin.py
createphysicalport
def createphysicalport(create_processor = partial(default_processor, excluding=('vhost', 'systemid', 'bridge', 'name', 'physicalnetwork')), reorder_dict = default_iterate_dict): """ :param create_processor: create_processor(physicalport, physicalnetwork, physicalnetworkmap, walk, write, \*, parameters) """ def walker(walk, write, timestamp, parameters_dict): for key, parameters in reorder_dict(parameters_dict): try: value = walk(key) except KeyError: pass else: p = create_new(PhysicalPort, value, parameters['vhost'], parameters['systemid'], parameters['bridge'], parameters['name']) try: physicalnetwork = walk(PhysicalNetwork.default_key(parameters['physicalnetwork'])) except KeyError: pass else: # Should already been check from outside p.physicalnetwork = physicalnetwork.create_reference() try: phymap = walk(PhysicalNetworkMap._network.leftkey(physicalnetwork)) except KeyError: pass else: create_processor(p, physicalnetwork, phymap, walk, write, parameters=parameters) phymap.ports.dataset().add(p.create_weakreference()) write(phymap.getkey(), phymap) try: phyportset = walk(PhysicalPortSet.default_key()) except KeyError: pass else: phyportset.set.dataset().add(p.create_weakreference()) write(phyportset.getkey(), phyportset) write(p.getkey(), p) return walker
python
def createphysicalport(create_processor = partial(default_processor, excluding=('vhost', 'systemid', 'bridge', 'name', 'physicalnetwork')), reorder_dict = default_iterate_dict): """ :param create_processor: create_processor(physicalport, physicalnetwork, physicalnetworkmap, walk, write, \*, parameters) """ def walker(walk, write, timestamp, parameters_dict): for key, parameters in reorder_dict(parameters_dict): try: value = walk(key) except KeyError: pass else: p = create_new(PhysicalPort, value, parameters['vhost'], parameters['systemid'], parameters['bridge'], parameters['name']) try: physicalnetwork = walk(PhysicalNetwork.default_key(parameters['physicalnetwork'])) except KeyError: pass else: # Should already been check from outside p.physicalnetwork = physicalnetwork.create_reference() try: phymap = walk(PhysicalNetworkMap._network.leftkey(physicalnetwork)) except KeyError: pass else: create_processor(p, physicalnetwork, phymap, walk, write, parameters=parameters) phymap.ports.dataset().add(p.create_weakreference()) write(phymap.getkey(), phymap) try: phyportset = walk(PhysicalPortSet.default_key()) except KeyError: pass else: phyportset.set.dataset().add(p.create_weakreference()) write(phyportset.getkey(), phyportset) write(p.getkey(), p) return walker
[ "def", "createphysicalport", "(", "create_processor", "=", "partial", "(", "default_processor", ",", "excluding", "=", "(", "'vhost'", ",", "'systemid'", ",", "'bridge'", ",", "'name'", ",", "'physicalnetwork'", ")", ")", ",", "reorder_dict", "=", "default_iterate_dict", ")", ":", "def", "walker", "(", "walk", ",", "write", ",", "timestamp", ",", "parameters_dict", ")", ":", "for", "key", ",", "parameters", "in", "reorder_dict", "(", "parameters_dict", ")", ":", "try", ":", "value", "=", "walk", "(", "key", ")", "except", "KeyError", ":", "pass", "else", ":", "p", "=", "create_new", "(", "PhysicalPort", ",", "value", ",", "parameters", "[", "'vhost'", "]", ",", "parameters", "[", "'systemid'", "]", ",", "parameters", "[", "'bridge'", "]", ",", "parameters", "[", "'name'", "]", ")", "try", ":", "physicalnetwork", "=", "walk", "(", "PhysicalNetwork", ".", "default_key", "(", "parameters", "[", "'physicalnetwork'", "]", ")", ")", "except", "KeyError", ":", "pass", "else", ":", "# Should already been check from outside", "p", ".", "physicalnetwork", "=", "physicalnetwork", ".", "create_reference", "(", ")", "try", ":", "phymap", "=", "walk", "(", "PhysicalNetworkMap", ".", "_network", ".", "leftkey", "(", "physicalnetwork", ")", ")", "except", "KeyError", ":", "pass", "else", ":", "create_processor", "(", "p", ",", "physicalnetwork", ",", "phymap", ",", "walk", ",", "write", ",", "parameters", "=", "parameters", ")", "phymap", ".", "ports", ".", "dataset", "(", ")", ".", "add", "(", "p", ".", "create_weakreference", "(", ")", ")", "write", "(", "phymap", ".", "getkey", "(", ")", ",", "phymap", ")", "try", ":", "phyportset", "=", "walk", "(", "PhysicalPortSet", ".", "default_key", "(", ")", ")", "except", "KeyError", ":", "pass", "else", ":", "phyportset", ".", "set", ".", "dataset", "(", ")", ".", "add", "(", "p", ".", "create_weakreference", "(", ")", ")", "write", "(", "phyportset", ".", "getkey", "(", ")", ",", "phyportset", ")", "write", "(", "p", ".", "getkey", "(", ")", ",", "p", ")", "return", "walker" ]
:param create_processor: create_processor(physicalport, physicalnetwork, physicalnetworkmap, walk, write, \*, parameters)
[ ":", "param", "create_processor", ":", "create_processor", "(", "physicalport", "physicalnetwork", "physicalnetworkmap", "walk", "write", "\\", "*", "parameters", ")" ]
train
https://github.com/hubo1016/vlcp/blob/239055229ec93a99cc7e15208075724ccf543bd1/vlcp/utils/networkplugin.py#L141-L180
hubo1016/vlcp
vlcp/utils/networkplugin.py
updatephysicalport
def updatephysicalport(update_processor = partial(default_processor, excluding=('vhost', 'systemid', 'bridge', 'name'), disabled=('physicalnetwork',)), reorder_dict = default_iterate_dict ): """ :param update_processor: update_processor(physcialport, walk, write, \*, parameters) """ def walker(walk, write, timestamp, parameters_dict): for key, parameters in reorder_dict(parameters_dict): try: value = walk(key) except KeyError: pass else: if update_processor(value, walk, write, parameters=parameters): write(key, value) return walker
python
def updatephysicalport(update_processor = partial(default_processor, excluding=('vhost', 'systemid', 'bridge', 'name'), disabled=('physicalnetwork',)), reorder_dict = default_iterate_dict ): """ :param update_processor: update_processor(physcialport, walk, write, \*, parameters) """ def walker(walk, write, timestamp, parameters_dict): for key, parameters in reorder_dict(parameters_dict): try: value = walk(key) except KeyError: pass else: if update_processor(value, walk, write, parameters=parameters): write(key, value) return walker
[ "def", "updatephysicalport", "(", "update_processor", "=", "partial", "(", "default_processor", ",", "excluding", "=", "(", "'vhost'", ",", "'systemid'", ",", "'bridge'", ",", "'name'", ")", ",", "disabled", "=", "(", "'physicalnetwork'", ",", ")", ")", ",", "reorder_dict", "=", "default_iterate_dict", ")", ":", "def", "walker", "(", "walk", ",", "write", ",", "timestamp", ",", "parameters_dict", ")", ":", "for", "key", ",", "parameters", "in", "reorder_dict", "(", "parameters_dict", ")", ":", "try", ":", "value", "=", "walk", "(", "key", ")", "except", "KeyError", ":", "pass", "else", ":", "if", "update_processor", "(", "value", ",", "walk", ",", "write", ",", "parameters", "=", "parameters", ")", ":", "write", "(", "key", ",", "value", ")", "return", "walker" ]
:param update_processor: update_processor(physcialport, walk, write, \*, parameters)
[ ":", "param", "update_processor", ":", "update_processor", "(", "physcialport", "walk", "write", "\\", "*", "parameters", ")" ]
train
https://github.com/hubo1016/vlcp/blob/239055229ec93a99cc7e15208075724ccf543bd1/vlcp/utils/networkplugin.py#L183-L200
hubo1016/vlcp
vlcp/utils/networkplugin.py
deletephysicalport
def deletephysicalport(check_processor=_false_processor, reorder_dict = default_iterate_dict): """ :param check_processor: check_processor(physicalport, physicalnetwork, physicalnetworkmap, walk, write \*, parameters) """ def walker(walk, write, timestamp, parameters_dict): for key, parameters in reorder_dict(parameters_dict): try: value = walk(key) except KeyError: pass else: try: phynet = walk(value.physicalnetwork.getkey()) except KeyError: pass else: try: phymap = walk(PhysicalNetworkMap._network.leftkey(phynet)) except KeyError: pass else: check_processor(value, phynet, phymap, walk, write, parameters=parameters) phymap.ports.dataset().discard(value.create_weakreference()) write(phymap.getkey(), phymap) try: physet = walk(PhysicalPortSet.default_key()) except KeyError: pass else: physet.set.dataset().discard(value.create_weakreference()) write(physet.getkey(), physet) write(key, None) return walker
python
def deletephysicalport(check_processor=_false_processor, reorder_dict = default_iterate_dict): """ :param check_processor: check_processor(physicalport, physicalnetwork, physicalnetworkmap, walk, write \*, parameters) """ def walker(walk, write, timestamp, parameters_dict): for key, parameters in reorder_dict(parameters_dict): try: value = walk(key) except KeyError: pass else: try: phynet = walk(value.physicalnetwork.getkey()) except KeyError: pass else: try: phymap = walk(PhysicalNetworkMap._network.leftkey(phynet)) except KeyError: pass else: check_processor(value, phynet, phymap, walk, write, parameters=parameters) phymap.ports.dataset().discard(value.create_weakreference()) write(phymap.getkey(), phymap) try: physet = walk(PhysicalPortSet.default_key()) except KeyError: pass else: physet.set.dataset().discard(value.create_weakreference()) write(physet.getkey(), physet) write(key, None) return walker
[ "def", "deletephysicalport", "(", "check_processor", "=", "_false_processor", ",", "reorder_dict", "=", "default_iterate_dict", ")", ":", "def", "walker", "(", "walk", ",", "write", ",", "timestamp", ",", "parameters_dict", ")", ":", "for", "key", ",", "parameters", "in", "reorder_dict", "(", "parameters_dict", ")", ":", "try", ":", "value", "=", "walk", "(", "key", ")", "except", "KeyError", ":", "pass", "else", ":", "try", ":", "phynet", "=", "walk", "(", "value", ".", "physicalnetwork", ".", "getkey", "(", ")", ")", "except", "KeyError", ":", "pass", "else", ":", "try", ":", "phymap", "=", "walk", "(", "PhysicalNetworkMap", ".", "_network", ".", "leftkey", "(", "phynet", ")", ")", "except", "KeyError", ":", "pass", "else", ":", "check_processor", "(", "value", ",", "phynet", ",", "phymap", ",", "walk", ",", "write", ",", "parameters", "=", "parameters", ")", "phymap", ".", "ports", ".", "dataset", "(", ")", ".", "discard", "(", "value", ".", "create_weakreference", "(", ")", ")", "write", "(", "phymap", ".", "getkey", "(", ")", ",", "phymap", ")", "try", ":", "physet", "=", "walk", "(", "PhysicalPortSet", ".", "default_key", "(", ")", ")", "except", "KeyError", ":", "pass", "else", ":", "physet", ".", "set", ".", "dataset", "(", ")", ".", "discard", "(", "value", ".", "create_weakreference", "(", ")", ")", "write", "(", "physet", ".", "getkey", "(", ")", ",", "physet", ")", "write", "(", "key", ",", "None", ")", "return", "walker" ]
:param check_processor: check_processor(physicalport, physicalnetwork, physicalnetworkmap, walk, write \*, parameters)
[ ":", "param", "check_processor", ":", "check_processor", "(", "physicalport", "physicalnetwork", "physicalnetworkmap", "walk", "write", "\\", "*", "parameters", ")" ]
train
https://github.com/hubo1016/vlcp/blob/239055229ec93a99cc7e15208075724ccf543bd1/vlcp/utils/networkplugin.py#L203-L237
hubo1016/vlcp
vlcp/utils/networkplugin.py
createlogicalnetwork
def createlogicalnetwork(create_processor = partial(default_processor, excluding=('id', 'physicalnetwork')), reorder_dict = default_iterate_dict): """ :param create_processor: create_processor(logicalnetwork, logicalnetworkmap, physicalnetwork, physicalnetworkmap, walk, write, \*, parameters) """ def walker(walk, write, timestamp, parameters_dict): for key, parameters in reorder_dict(parameters_dict): try: value = walk(key) except KeyError: pass else: id_ = parameters['id'] lognet = create_new(LogicalNetwork, value, id_) logmap = LogicalNetworkMap.create_instance(id_) logmap.network = lognet.create_reference() try: phynet = walk(PhysicalNetwork.default_key(parameters['physicalnetwork'])) except KeyError: pass else: lognet.physicalnetwork = phynet.create_reference() try: phymap = walk(PhysicalNetworkMap._network.leftkey(phynet)) except KeyError: pass else: create_processor(lognet, logmap, phynet, phymap, walk, write, parameters=parameters) phymap.logicnetworks.dataset().add(lognet.create_weakreference()) write(phymap.getkey(), phymap) write(lognet.getkey(), lognet) write(logmap.getkey(), logmap) try: logicalnetworkset = walk(LogicalNetworkSet.default_key()) except KeyError: pass else: logicalnetworkset.set.dataset().add(lognet.create_weakreference()) write(logicalnetworkset.getkey(), logicalnetworkset) return walker
python
def createlogicalnetwork(create_processor = partial(default_processor, excluding=('id', 'physicalnetwork')), reorder_dict = default_iterate_dict): """ :param create_processor: create_processor(logicalnetwork, logicalnetworkmap, physicalnetwork, physicalnetworkmap, walk, write, \*, parameters) """ def walker(walk, write, timestamp, parameters_dict): for key, parameters in reorder_dict(parameters_dict): try: value = walk(key) except KeyError: pass else: id_ = parameters['id'] lognet = create_new(LogicalNetwork, value, id_) logmap = LogicalNetworkMap.create_instance(id_) logmap.network = lognet.create_reference() try: phynet = walk(PhysicalNetwork.default_key(parameters['physicalnetwork'])) except KeyError: pass else: lognet.physicalnetwork = phynet.create_reference() try: phymap = walk(PhysicalNetworkMap._network.leftkey(phynet)) except KeyError: pass else: create_processor(lognet, logmap, phynet, phymap, walk, write, parameters=parameters) phymap.logicnetworks.dataset().add(lognet.create_weakreference()) write(phymap.getkey(), phymap) write(lognet.getkey(), lognet) write(logmap.getkey(), logmap) try: logicalnetworkset = walk(LogicalNetworkSet.default_key()) except KeyError: pass else: logicalnetworkset.set.dataset().add(lognet.create_weakreference()) write(logicalnetworkset.getkey(), logicalnetworkset) return walker
[ "def", "createlogicalnetwork", "(", "create_processor", "=", "partial", "(", "default_processor", ",", "excluding", "=", "(", "'id'", ",", "'physicalnetwork'", ")", ")", ",", "reorder_dict", "=", "default_iterate_dict", ")", ":", "def", "walker", "(", "walk", ",", "write", ",", "timestamp", ",", "parameters_dict", ")", ":", "for", "key", ",", "parameters", "in", "reorder_dict", "(", "parameters_dict", ")", ":", "try", ":", "value", "=", "walk", "(", "key", ")", "except", "KeyError", ":", "pass", "else", ":", "id_", "=", "parameters", "[", "'id'", "]", "lognet", "=", "create_new", "(", "LogicalNetwork", ",", "value", ",", "id_", ")", "logmap", "=", "LogicalNetworkMap", ".", "create_instance", "(", "id_", ")", "logmap", ".", "network", "=", "lognet", ".", "create_reference", "(", ")", "try", ":", "phynet", "=", "walk", "(", "PhysicalNetwork", ".", "default_key", "(", "parameters", "[", "'physicalnetwork'", "]", ")", ")", "except", "KeyError", ":", "pass", "else", ":", "lognet", ".", "physicalnetwork", "=", "phynet", ".", "create_reference", "(", ")", "try", ":", "phymap", "=", "walk", "(", "PhysicalNetworkMap", ".", "_network", ".", "leftkey", "(", "phynet", ")", ")", "except", "KeyError", ":", "pass", "else", ":", "create_processor", "(", "lognet", ",", "logmap", ",", "phynet", ",", "phymap", ",", "walk", ",", "write", ",", "parameters", "=", "parameters", ")", "phymap", ".", "logicnetworks", ".", "dataset", "(", ")", ".", "add", "(", "lognet", ".", "create_weakreference", "(", ")", ")", "write", "(", "phymap", ".", "getkey", "(", ")", ",", "phymap", ")", "write", "(", "lognet", ".", "getkey", "(", ")", ",", "lognet", ")", "write", "(", "logmap", ".", "getkey", "(", ")", ",", "logmap", ")", "try", ":", "logicalnetworkset", "=", "walk", "(", "LogicalNetworkSet", ".", "default_key", "(", ")", ")", "except", "KeyError", ":", "pass", "else", ":", "logicalnetworkset", ".", "set", ".", "dataset", "(", ")", ".", "add", "(", "lognet", ".", "create_weakreference", "(", ")", ")", "write", "(", "logicalnetworkset", ".", "getkey", "(", ")", ",", "logicalnetworkset", ")", "return", "walker" ]
:param create_processor: create_processor(logicalnetwork, logicalnetworkmap, physicalnetwork, physicalnetworkmap, walk, write, \*, parameters)
[ ":", "param", "create_processor", ":", "create_processor", "(", "logicalnetwork", "logicalnetworkmap", "physicalnetwork", "physicalnetworkmap", "walk", "write", "\\", "*", "parameters", ")" ]
train
https://github.com/hubo1016/vlcp/blob/239055229ec93a99cc7e15208075724ccf543bd1/vlcp/utils/networkplugin.py#L247-L287
hubo1016/vlcp
vlcp/utils/networkplugin.py
deletelogicalnetwork
def deletelogicalnetwork(check_processor=default_logicalnetwork_delete_check, reorder_dict = default_iterate_dict): """ :param check_processor: check_processor(logicalnetwork, logicalnetworkmap, physicalnetwork, physicalnetworkmap, walk, write, \*, parameters) """ def walker(walk, write, timestamp, parameters_dict): for key, parameters in reorder_dict(parameters_dict): try: value = walk(key) except KeyError: pass else: try: logmap = walk(LogicalNetworkMap._network.leftkey(key)) except KeyError: pass else: try: phynet = walk(value.physicalnetwork.getkey()) except KeyError: pass else: try: phymap = walk(PhysicalNetworkMap._network.leftkey(phynet)) except KeyError: pass else: check_processor(value, logmap, phynet, phymap, walk, write, parameters=parameters) phymap.logicnetworks.dataset().discard(value.create_weakreference()) write(phymap.getkey(), phymap) write(key, None) write(logmap.getkey(), None) try: logicalnetworkset = walk(LogicalNetworkSet.default_key()) except KeyError: pass else: logicalnetworkset.set.dataset().discard(value.create_weakreference()) write(logicalnetworkset.getkey(), logicalnetworkset) return walker
python
def deletelogicalnetwork(check_processor=default_logicalnetwork_delete_check, reorder_dict = default_iterate_dict): """ :param check_processor: check_processor(logicalnetwork, logicalnetworkmap, physicalnetwork, physicalnetworkmap, walk, write, \*, parameters) """ def walker(walk, write, timestamp, parameters_dict): for key, parameters in reorder_dict(parameters_dict): try: value = walk(key) except KeyError: pass else: try: logmap = walk(LogicalNetworkMap._network.leftkey(key)) except KeyError: pass else: try: phynet = walk(value.physicalnetwork.getkey()) except KeyError: pass else: try: phymap = walk(PhysicalNetworkMap._network.leftkey(phynet)) except KeyError: pass else: check_processor(value, logmap, phynet, phymap, walk, write, parameters=parameters) phymap.logicnetworks.dataset().discard(value.create_weakreference()) write(phymap.getkey(), phymap) write(key, None) write(logmap.getkey(), None) try: logicalnetworkset = walk(LogicalNetworkSet.default_key()) except KeyError: pass else: logicalnetworkset.set.dataset().discard(value.create_weakreference()) write(logicalnetworkset.getkey(), logicalnetworkset) return walker
[ "def", "deletelogicalnetwork", "(", "check_processor", "=", "default_logicalnetwork_delete_check", ",", "reorder_dict", "=", "default_iterate_dict", ")", ":", "def", "walker", "(", "walk", ",", "write", ",", "timestamp", ",", "parameters_dict", ")", ":", "for", "key", ",", "parameters", "in", "reorder_dict", "(", "parameters_dict", ")", ":", "try", ":", "value", "=", "walk", "(", "key", ")", "except", "KeyError", ":", "pass", "else", ":", "try", ":", "logmap", "=", "walk", "(", "LogicalNetworkMap", ".", "_network", ".", "leftkey", "(", "key", ")", ")", "except", "KeyError", ":", "pass", "else", ":", "try", ":", "phynet", "=", "walk", "(", "value", ".", "physicalnetwork", ".", "getkey", "(", ")", ")", "except", "KeyError", ":", "pass", "else", ":", "try", ":", "phymap", "=", "walk", "(", "PhysicalNetworkMap", ".", "_network", ".", "leftkey", "(", "phynet", ")", ")", "except", "KeyError", ":", "pass", "else", ":", "check_processor", "(", "value", ",", "logmap", ",", "phynet", ",", "phymap", ",", "walk", ",", "write", ",", "parameters", "=", "parameters", ")", "phymap", ".", "logicnetworks", ".", "dataset", "(", ")", ".", "discard", "(", "value", ".", "create_weakreference", "(", ")", ")", "write", "(", "phymap", ".", "getkey", "(", ")", ",", "phymap", ")", "write", "(", "key", ",", "None", ")", "write", "(", "logmap", ".", "getkey", "(", ")", ",", "None", ")", "try", ":", "logicalnetworkset", "=", "walk", "(", "LogicalNetworkSet", ".", "default_key", "(", ")", ")", "except", "KeyError", ":", "pass", "else", ":", "logicalnetworkset", ".", "set", ".", "dataset", "(", ")", ".", "discard", "(", "value", ".", "create_weakreference", "(", ")", ")", "write", "(", "logicalnetworkset", ".", "getkey", "(", ")", ",", "logicalnetworkset", ")", "return", "walker" ]
:param check_processor: check_processor(logicalnetwork, logicalnetworkmap, physicalnetwork, physicalnetworkmap, walk, write, \*, parameters)
[ ":", "param", "check_processor", ":", "check_processor", "(", "logicalnetwork", "logicalnetworkmap", "physicalnetwork", "physicalnetworkmap", "walk", "write", "\\", "*", "parameters", ")" ]
train
https://github.com/hubo1016/vlcp/blob/239055229ec93a99cc7e15208075724ccf543bd1/vlcp/utils/networkplugin.py#L315-L356
hubo1016/vlcp
vlcp/utils/flowupdater.py
FlowUpdater.restart_walk
async def restart_walk(self): """ Force a re-walk """ if not self._restartwalk: self._restartwalk = True await self.wait_for_send(FlowUpdaterNotification(self, FlowUpdaterNotification.STARTWALK))
python
async def restart_walk(self): """ Force a re-walk """ if not self._restartwalk: self._restartwalk = True await self.wait_for_send(FlowUpdaterNotification(self, FlowUpdaterNotification.STARTWALK))
[ "async", "def", "restart_walk", "(", "self", ")", ":", "if", "not", "self", ".", "_restartwalk", ":", "self", ".", "_restartwalk", "=", "True", "await", "self", ".", "wait_for_send", "(", "FlowUpdaterNotification", "(", "self", ",", "FlowUpdaterNotification", ".", "STARTWALK", ")", ")" ]
Force a re-walk
[ "Force", "a", "re", "-", "walk" ]
train
https://github.com/hubo1016/vlcp/blob/239055229ec93a99cc7e15208075724ccf543bd1/vlcp/utils/flowupdater.py#L106-L112
hubo1016/vlcp
vlcp/utils/flowupdater.py
FlowUpdater._dataobject_update_detect
async def _dataobject_update_detect(self, _initialkeys, _savedresult): """ Coroutine that wait for retrieved value update notification """ def expr(newvalues, updatedvalues): if any(v.getkey() in _initialkeys for v in updatedvalues if v is not None): return True else: return self.shouldupdate(newvalues, updatedvalues) while True: updatedvalues, _ = await multiwaitif(_savedresult, self, expr, True) if not self._updatedset: self.scheduler.emergesend(FlowUpdaterNotification(self, FlowUpdaterNotification.DATAUPDATED)) self._updatedset.update(updatedvalues)
python
async def _dataobject_update_detect(self, _initialkeys, _savedresult): """ Coroutine that wait for retrieved value update notification """ def expr(newvalues, updatedvalues): if any(v.getkey() in _initialkeys for v in updatedvalues if v is not None): return True else: return self.shouldupdate(newvalues, updatedvalues) while True: updatedvalues, _ = await multiwaitif(_savedresult, self, expr, True) if not self._updatedset: self.scheduler.emergesend(FlowUpdaterNotification(self, FlowUpdaterNotification.DATAUPDATED)) self._updatedset.update(updatedvalues)
[ "async", "def", "_dataobject_update_detect", "(", "self", ",", "_initialkeys", ",", "_savedresult", ")", ":", "def", "expr", "(", "newvalues", ",", "updatedvalues", ")", ":", "if", "any", "(", "v", ".", "getkey", "(", ")", "in", "_initialkeys", "for", "v", "in", "updatedvalues", "if", "v", "is", "not", "None", ")", ":", "return", "True", "else", ":", "return", "self", ".", "shouldupdate", "(", "newvalues", ",", "updatedvalues", ")", "while", "True", ":", "updatedvalues", ",", "_", "=", "await", "multiwaitif", "(", "_savedresult", ",", "self", ",", "expr", ",", "True", ")", "if", "not", "self", ".", "_updatedset", ":", "self", ".", "scheduler", ".", "emergesend", "(", "FlowUpdaterNotification", "(", "self", ",", "FlowUpdaterNotification", ".", "DATAUPDATED", ")", ")", "self", ".", "_updatedset", ".", "update", "(", "updatedvalues", ")" ]
Coroutine that wait for retrieved value update notification
[ "Coroutine", "that", "wait", "for", "retrieved", "value", "update", "notification" ]
train
https://github.com/hubo1016/vlcp/blob/239055229ec93a99cc7e15208075724ccf543bd1/vlcp/utils/flowupdater.py#L114-L127
hubo1016/vlcp
vlcp/utils/flowupdater.py
FlowUpdater.updateobjects
def updateobjects(self, updatedvalues): """ Force a update notification on specified objects, even if they are not actually updated in ObjectDB """ if not self._updatedset: self.scheduler.emergesend(FlowUpdaterNotification(self, FlowUpdaterNotification.DATAUPDATED)) self._updatedset.update(set(updatedvalues).intersection(self._savedresult))
python
def updateobjects(self, updatedvalues): """ Force a update notification on specified objects, even if they are not actually updated in ObjectDB """ if not self._updatedset: self.scheduler.emergesend(FlowUpdaterNotification(self, FlowUpdaterNotification.DATAUPDATED)) self._updatedset.update(set(updatedvalues).intersection(self._savedresult))
[ "def", "updateobjects", "(", "self", ",", "updatedvalues", ")", ":", "if", "not", "self", ".", "_updatedset", ":", "self", ".", "scheduler", ".", "emergesend", "(", "FlowUpdaterNotification", "(", "self", ",", "FlowUpdaterNotification", ".", "DATAUPDATED", ")", ")", "self", ".", "_updatedset", ".", "update", "(", "set", "(", "updatedvalues", ")", ".", "intersection", "(", "self", ".", "_savedresult", ")", ")" ]
Force a update notification on specified objects, even if they are not actually updated in ObjectDB
[ "Force", "a", "update", "notification", "on", "specified", "objects", "even", "if", "they", "are", "not", "actually", "updated", "in", "ObjectDB" ]
train
https://github.com/hubo1016/vlcp/blob/239055229ec93a99cc7e15208075724ccf543bd1/vlcp/utils/flowupdater.py#L129-L136
hubo1016/vlcp
vlcp/utils/flowupdater.py
FlowUpdater._flowupdater
async def _flowupdater(self): """ Coroutine calling `updateflow()` """ lastresult = set(v for v in self._savedresult if v is not None and not v.isdeleted()) flowupdate = FlowUpdaterNotification.createMatcher(self, FlowUpdaterNotification.FLOWUPDATE) while True: currentresult = [v for v in self._savedresult if v is not None and not v.isdeleted()] # Calculating differences additems = [] updateditems = [] updatedset2 = self._updatedset2 for v in currentresult: if v not in lastresult: additems.append(v) else: lastresult.remove(v) if v in updatedset2: # Updated updateditems.append(v) removeitems = lastresult self._updatedset2.clear() # Save current result for next difference lastresult = set(currentresult) if not additems and not removeitems and not updateditems: await flowupdate continue await self.updateflow(self._connection, set(additems), removeitems, set(updateditems))
python
async def _flowupdater(self): """ Coroutine calling `updateflow()` """ lastresult = set(v for v in self._savedresult if v is not None and not v.isdeleted()) flowupdate = FlowUpdaterNotification.createMatcher(self, FlowUpdaterNotification.FLOWUPDATE) while True: currentresult = [v for v in self._savedresult if v is not None and not v.isdeleted()] # Calculating differences additems = [] updateditems = [] updatedset2 = self._updatedset2 for v in currentresult: if v not in lastresult: additems.append(v) else: lastresult.remove(v) if v in updatedset2: # Updated updateditems.append(v) removeitems = lastresult self._updatedset2.clear() # Save current result for next difference lastresult = set(currentresult) if not additems and not removeitems and not updateditems: await flowupdate continue await self.updateflow(self._connection, set(additems), removeitems, set(updateditems))
[ "async", "def", "_flowupdater", "(", "self", ")", ":", "lastresult", "=", "set", "(", "v", "for", "v", "in", "self", ".", "_savedresult", "if", "v", "is", "not", "None", "and", "not", "v", ".", "isdeleted", "(", ")", ")", "flowupdate", "=", "FlowUpdaterNotification", ".", "createMatcher", "(", "self", ",", "FlowUpdaterNotification", ".", "FLOWUPDATE", ")", "while", "True", ":", "currentresult", "=", "[", "v", "for", "v", "in", "self", ".", "_savedresult", "if", "v", "is", "not", "None", "and", "not", "v", ".", "isdeleted", "(", ")", "]", "# Calculating differences", "additems", "=", "[", "]", "updateditems", "=", "[", "]", "updatedset2", "=", "self", ".", "_updatedset2", "for", "v", "in", "currentresult", ":", "if", "v", "not", "in", "lastresult", ":", "additems", ".", "append", "(", "v", ")", "else", ":", "lastresult", ".", "remove", "(", "v", ")", "if", "v", "in", "updatedset2", ":", "# Updated", "updateditems", ".", "append", "(", "v", ")", "removeitems", "=", "lastresult", "self", ".", "_updatedset2", ".", "clear", "(", ")", "# Save current result for next difference", "lastresult", "=", "set", "(", "currentresult", ")", "if", "not", "additems", "and", "not", "removeitems", "and", "not", "updateditems", ":", "await", "flowupdate", "continue", "await", "self", ".", "updateflow", "(", "self", ".", "_connection", ",", "set", "(", "additems", ")", ",", "removeitems", ",", "set", "(", "updateditems", ")", ")" ]
Coroutine calling `updateflow()`
[ "Coroutine", "calling", "updateflow", "()" ]
train
https://github.com/hubo1016/vlcp/blob/239055229ec93a99cc7e15208075724ccf543bd1/vlcp/utils/flowupdater.py#L138-L165
hubo1016/vlcp
vlcp/utils/flowupdater.py
FlowUpdater.main
async def main(self): """ Main coroutine """ try: lastkeys = set() dataupdate = FlowUpdaterNotification.createMatcher(self, FlowUpdaterNotification.DATAUPDATED) startwalk = FlowUpdaterNotification.createMatcher(self, FlowUpdaterNotification.STARTWALK) self.subroutine(self._flowupdater(), False, '_flowupdateroutine') # Cache updated objects presave_update = set() while True: self._restartwalk = False presave_update.update(self._updatedset) self._updatedset.clear() _initialkeys = set(self._initialkeys) try: walk_result = await call_api(self, 'objectdb', 'walk', {'keys': self._initialkeys, 'walkerdict': self._walkerdict, 'requestid': (self._requstid, self._requestindex)}) except Exception: self._logger.warning("Flow updater %r walk step failed, conn = %r", self, self._connection, exc_info=True) # Cleanup await call_api(self, 'objectdb', 'unwatchall', {'requestid': (self._requstid, self._requestindex)}) await self.wait_with_timeout(2) self._requestindex += 1 if self._restartwalk: continue if self._updatedset: if any(v.getkey() in _initialkeys for v in self._updatedset): # During walk, there are other initial keys that are updated # To make sure we get the latest result, restart the walk continue lastkeys = set(self._savedkeys) _savedkeys, _savedresult = walk_result removekeys = tuple(lastkeys.difference(_savedkeys)) self.reset_initialkeys(_savedkeys, _savedresult) _initialkeys = set(self._initialkeys) if self._dataupdateroutine: self.terminate(self._dataupdateroutine) # Start detecting updates self.subroutine(self._dataobject_update_detect(_initialkeys, _savedresult), False, "_dataupdateroutine") # Set the updates back (potentially merged with newly updated objects) self._updatedset.update(v for v in presave_update) presave_update.clear() await self.walkcomplete(_savedkeys, _savedresult) if removekeys: await call_api(self, 'objectdb', 'munwatch', {'keys': removekeys, 'requestid': (self._requstid, self._requestindex)}) # Transfer updated objects to updatedset2 before a flow update notification # This helps to make `walkcomplete` executes before `updateflow` # # But notice that since there is only a single data object copy in all the program, # it is impossible to hide the change completely during `updateflow` self._updatedset2.update(self._updatedset) self._updatedset.clear() self._savedkeys = _savedkeys self._savedresult = _savedresult await self.wait_for_send(FlowUpdaterNotification(self, FlowUpdaterNotification.FLOWUPDATE)) while not self._restartwalk: if self._updatedset: if any(v.getkey() in _initialkeys for v in self._updatedset): break else: self._updatedset2.update(self._updatedset) self._updatedset.clear() self.scheduler.emergesend(FlowUpdaterNotification(self, FlowUpdaterNotification.FLOWUPDATE)) await M_(dataupdate, startwalk) except Exception: self._logger.exception("Flow updater %r stops update by an exception, conn = %r", self, self._connection) raise finally: self.subroutine(send_api(self, 'objectdb', 'unwatchall', {'requestid': (self._requstid, self._requestindex)}), False) if self._flowupdateroutine: self.terminate(self._flowupdateroutine) self._flowupdateroutine = None if self._dataupdateroutine: self.terminate(self._dataupdateroutine) self._dataupdateroutine = None
python
async def main(self): """ Main coroutine """ try: lastkeys = set() dataupdate = FlowUpdaterNotification.createMatcher(self, FlowUpdaterNotification.DATAUPDATED) startwalk = FlowUpdaterNotification.createMatcher(self, FlowUpdaterNotification.STARTWALK) self.subroutine(self._flowupdater(), False, '_flowupdateroutine') # Cache updated objects presave_update = set() while True: self._restartwalk = False presave_update.update(self._updatedset) self._updatedset.clear() _initialkeys = set(self._initialkeys) try: walk_result = await call_api(self, 'objectdb', 'walk', {'keys': self._initialkeys, 'walkerdict': self._walkerdict, 'requestid': (self._requstid, self._requestindex)}) except Exception: self._logger.warning("Flow updater %r walk step failed, conn = %r", self, self._connection, exc_info=True) # Cleanup await call_api(self, 'objectdb', 'unwatchall', {'requestid': (self._requstid, self._requestindex)}) await self.wait_with_timeout(2) self._requestindex += 1 if self._restartwalk: continue if self._updatedset: if any(v.getkey() in _initialkeys for v in self._updatedset): # During walk, there are other initial keys that are updated # To make sure we get the latest result, restart the walk continue lastkeys = set(self._savedkeys) _savedkeys, _savedresult = walk_result removekeys = tuple(lastkeys.difference(_savedkeys)) self.reset_initialkeys(_savedkeys, _savedresult) _initialkeys = set(self._initialkeys) if self._dataupdateroutine: self.terminate(self._dataupdateroutine) # Start detecting updates self.subroutine(self._dataobject_update_detect(_initialkeys, _savedresult), False, "_dataupdateroutine") # Set the updates back (potentially merged with newly updated objects) self._updatedset.update(v for v in presave_update) presave_update.clear() await self.walkcomplete(_savedkeys, _savedresult) if removekeys: await call_api(self, 'objectdb', 'munwatch', {'keys': removekeys, 'requestid': (self._requstid, self._requestindex)}) # Transfer updated objects to updatedset2 before a flow update notification # This helps to make `walkcomplete` executes before `updateflow` # # But notice that since there is only a single data object copy in all the program, # it is impossible to hide the change completely during `updateflow` self._updatedset2.update(self._updatedset) self._updatedset.clear() self._savedkeys = _savedkeys self._savedresult = _savedresult await self.wait_for_send(FlowUpdaterNotification(self, FlowUpdaterNotification.FLOWUPDATE)) while not self._restartwalk: if self._updatedset: if any(v.getkey() in _initialkeys for v in self._updatedset): break else: self._updatedset2.update(self._updatedset) self._updatedset.clear() self.scheduler.emergesend(FlowUpdaterNotification(self, FlowUpdaterNotification.FLOWUPDATE)) await M_(dataupdate, startwalk) except Exception: self._logger.exception("Flow updater %r stops update by an exception, conn = %r", self, self._connection) raise finally: self.subroutine(send_api(self, 'objectdb', 'unwatchall', {'requestid': (self._requstid, self._requestindex)}), False) if self._flowupdateroutine: self.terminate(self._flowupdateroutine) self._flowupdateroutine = None if self._dataupdateroutine: self.terminate(self._dataupdateroutine) self._dataupdateroutine = None
[ "async", "def", "main", "(", "self", ")", ":", "try", ":", "lastkeys", "=", "set", "(", ")", "dataupdate", "=", "FlowUpdaterNotification", ".", "createMatcher", "(", "self", ",", "FlowUpdaterNotification", ".", "DATAUPDATED", ")", "startwalk", "=", "FlowUpdaterNotification", ".", "createMatcher", "(", "self", ",", "FlowUpdaterNotification", ".", "STARTWALK", ")", "self", ".", "subroutine", "(", "self", ".", "_flowupdater", "(", ")", ",", "False", ",", "'_flowupdateroutine'", ")", "# Cache updated objects", "presave_update", "=", "set", "(", ")", "while", "True", ":", "self", ".", "_restartwalk", "=", "False", "presave_update", ".", "update", "(", "self", ".", "_updatedset", ")", "self", ".", "_updatedset", ".", "clear", "(", ")", "_initialkeys", "=", "set", "(", "self", ".", "_initialkeys", ")", "try", ":", "walk_result", "=", "await", "call_api", "(", "self", ",", "'objectdb'", ",", "'walk'", ",", "{", "'keys'", ":", "self", ".", "_initialkeys", ",", "'walkerdict'", ":", "self", ".", "_walkerdict", ",", "'requestid'", ":", "(", "self", ".", "_requstid", ",", "self", ".", "_requestindex", ")", "}", ")", "except", "Exception", ":", "self", ".", "_logger", ".", "warning", "(", "\"Flow updater %r walk step failed, conn = %r\"", ",", "self", ",", "self", ".", "_connection", ",", "exc_info", "=", "True", ")", "# Cleanup", "await", "call_api", "(", "self", ",", "'objectdb'", ",", "'unwatchall'", ",", "{", "'requestid'", ":", "(", "self", ".", "_requstid", ",", "self", ".", "_requestindex", ")", "}", ")", "await", "self", ".", "wait_with_timeout", "(", "2", ")", "self", ".", "_requestindex", "+=", "1", "if", "self", ".", "_restartwalk", ":", "continue", "if", "self", ".", "_updatedset", ":", "if", "any", "(", "v", ".", "getkey", "(", ")", "in", "_initialkeys", "for", "v", "in", "self", ".", "_updatedset", ")", ":", "# During walk, there are other initial keys that are updated", "# To make sure we get the latest result, restart the walk", "continue", "lastkeys", "=", "set", "(", "self", ".", "_savedkeys", ")", "_savedkeys", ",", "_savedresult", "=", "walk_result", "removekeys", "=", "tuple", "(", "lastkeys", ".", "difference", "(", "_savedkeys", ")", ")", "self", ".", "reset_initialkeys", "(", "_savedkeys", ",", "_savedresult", ")", "_initialkeys", "=", "set", "(", "self", ".", "_initialkeys", ")", "if", "self", ".", "_dataupdateroutine", ":", "self", ".", "terminate", "(", "self", ".", "_dataupdateroutine", ")", "# Start detecting updates", "self", ".", "subroutine", "(", "self", ".", "_dataobject_update_detect", "(", "_initialkeys", ",", "_savedresult", ")", ",", "False", ",", "\"_dataupdateroutine\"", ")", "# Set the updates back (potentially merged with newly updated objects)", "self", ".", "_updatedset", ".", "update", "(", "v", "for", "v", "in", "presave_update", ")", "presave_update", ".", "clear", "(", ")", "await", "self", ".", "walkcomplete", "(", "_savedkeys", ",", "_savedresult", ")", "if", "removekeys", ":", "await", "call_api", "(", "self", ",", "'objectdb'", ",", "'munwatch'", ",", "{", "'keys'", ":", "removekeys", ",", "'requestid'", ":", "(", "self", ".", "_requstid", ",", "self", ".", "_requestindex", ")", "}", ")", "# Transfer updated objects to updatedset2 before a flow update notification", "# This helps to make `walkcomplete` executes before `updateflow`", "#", "# But notice that since there is only a single data object copy in all the program,", "# it is impossible to hide the change completely during `updateflow`", "self", ".", "_updatedset2", ".", "update", "(", "self", ".", "_updatedset", ")", "self", ".", "_updatedset", ".", "clear", "(", ")", "self", ".", "_savedkeys", "=", "_savedkeys", "self", ".", "_savedresult", "=", "_savedresult", "await", "self", ".", "wait_for_send", "(", "FlowUpdaterNotification", "(", "self", ",", "FlowUpdaterNotification", ".", "FLOWUPDATE", ")", ")", "while", "not", "self", ".", "_restartwalk", ":", "if", "self", ".", "_updatedset", ":", "if", "any", "(", "v", ".", "getkey", "(", ")", "in", "_initialkeys", "for", "v", "in", "self", ".", "_updatedset", ")", ":", "break", "else", ":", "self", ".", "_updatedset2", ".", "update", "(", "self", ".", "_updatedset", ")", "self", ".", "_updatedset", ".", "clear", "(", ")", "self", ".", "scheduler", ".", "emergesend", "(", "FlowUpdaterNotification", "(", "self", ",", "FlowUpdaterNotification", ".", "FLOWUPDATE", ")", ")", "await", "M_", "(", "dataupdate", ",", "startwalk", ")", "except", "Exception", ":", "self", ".", "_logger", ".", "exception", "(", "\"Flow updater %r stops update by an exception, conn = %r\"", ",", "self", ",", "self", ".", "_connection", ")", "raise", "finally", ":", "self", ".", "subroutine", "(", "send_api", "(", "self", ",", "'objectdb'", ",", "'unwatchall'", ",", "{", "'requestid'", ":", "(", "self", ".", "_requstid", ",", "self", ".", "_requestindex", ")", "}", ")", ",", "False", ")", "if", "self", ".", "_flowupdateroutine", ":", "self", ".", "terminate", "(", "self", ".", "_flowupdateroutine", ")", "self", ".", "_flowupdateroutine", "=", "None", "if", "self", ".", "_dataupdateroutine", ":", "self", ".", "terminate", "(", "self", ".", "_dataupdateroutine", ")", "self", ".", "_dataupdateroutine", "=", "None" ]
Main coroutine
[ "Main", "coroutine" ]
train
https://github.com/hubo1016/vlcp/blob/239055229ec93a99cc7e15208075724ccf543bd1/vlcp/utils/flowupdater.py#L167-L248
hubo1016/vlcp
vlcp/event/core.py
syscall_direct
def syscall_direct(*events): ''' Directly process these events. This should never be used for normal events. ''' def _syscall(scheduler, processor): for e in events: processor(e) return _syscall
python
def syscall_direct(*events): ''' Directly process these events. This should never be used for normal events. ''' def _syscall(scheduler, processor): for e in events: processor(e) return _syscall
[ "def", "syscall_direct", "(", "*", "events", ")", ":", "def", "_syscall", "(", "scheduler", ",", "processor", ")", ":", "for", "e", "in", "events", ":", "processor", "(", "e", ")", "return", "_syscall" ]
Directly process these events. This should never be used for normal events.
[ "Directly", "process", "these", "events", ".", "This", "should", "never", "be", "used", "for", "normal", "events", "." ]
train
https://github.com/hubo1016/vlcp/blob/239055229ec93a99cc7e15208075724ccf543bd1/vlcp/event/core.py#L520-L527
hubo1016/vlcp
vlcp/event/core.py
syscall_generator
def syscall_generator(generator): ''' Directly process events from a generator function. This should never be used for normal events. ''' def _syscall(scheduler, processor): for e in generator(): processor(e) return _syscall
python
def syscall_generator(generator): ''' Directly process events from a generator function. This should never be used for normal events. ''' def _syscall(scheduler, processor): for e in generator(): processor(e) return _syscall
[ "def", "syscall_generator", "(", "generator", ")", ":", "def", "_syscall", "(", "scheduler", ",", "processor", ")", ":", "for", "e", "in", "generator", "(", ")", ":", "processor", "(", "e", ")", "return", "_syscall" ]
Directly process events from a generator function. This should never be used for normal events.
[ "Directly", "process", "events", "from", "a", "generator", "function", ".", "This", "should", "never", "be", "used", "for", "normal", "events", "." ]
train
https://github.com/hubo1016/vlcp/blob/239055229ec93a99cc7e15208075724ccf543bd1/vlcp/event/core.py#L529-L536
hubo1016/vlcp
vlcp/event/core.py
syscall_clearqueue
def syscall_clearqueue(queue): ''' Clear a queue ''' def _syscall(scheduler, processor): qes, qees = queue.clear() events = scheduler.queue.unblockqueue(queue) for e in events: scheduler.eventtree.remove(e) for e in qes: processor(e) for e in qees: processor(e) return _syscall
python
def syscall_clearqueue(queue): ''' Clear a queue ''' def _syscall(scheduler, processor): qes, qees = queue.clear() events = scheduler.queue.unblockqueue(queue) for e in events: scheduler.eventtree.remove(e) for e in qes: processor(e) for e in qees: processor(e) return _syscall
[ "def", "syscall_clearqueue", "(", "queue", ")", ":", "def", "_syscall", "(", "scheduler", ",", "processor", ")", ":", "qes", ",", "qees", "=", "queue", ".", "clear", "(", ")", "events", "=", "scheduler", ".", "queue", ".", "unblockqueue", "(", "queue", ")", "for", "e", "in", "events", ":", "scheduler", ".", "eventtree", ".", "remove", "(", "e", ")", "for", "e", "in", "qes", ":", "processor", "(", "e", ")", "for", "e", "in", "qees", ":", "processor", "(", "e", ")", "return", "_syscall" ]
Clear a queue
[ "Clear", "a", "queue" ]
train
https://github.com/hubo1016/vlcp/blob/239055229ec93a99cc7e15208075724ccf543bd1/vlcp/event/core.py#L538-L551
hubo1016/vlcp
vlcp/event/core.py
syscall_removequeue
def syscall_removequeue(queue, index): ''' Remove subqueue `queue[index]` from queue. ''' def _syscall(scheduler, processor): events = scheduler.queue.unblockqueue(queue[index]) for e in events: scheduler.eventtree.remove(e) qes, qees = queue.removeSubQueue(index) for e in qes: processor(e) for e in qees: processor(e) return _syscall
python
def syscall_removequeue(queue, index): ''' Remove subqueue `queue[index]` from queue. ''' def _syscall(scheduler, processor): events = scheduler.queue.unblockqueue(queue[index]) for e in events: scheduler.eventtree.remove(e) qes, qees = queue.removeSubQueue(index) for e in qes: processor(e) for e in qees: processor(e) return _syscall
[ "def", "syscall_removequeue", "(", "queue", ",", "index", ")", ":", "def", "_syscall", "(", "scheduler", ",", "processor", ")", ":", "events", "=", "scheduler", ".", "queue", ".", "unblockqueue", "(", "queue", "[", "index", "]", ")", "for", "e", "in", "events", ":", "scheduler", ".", "eventtree", ".", "remove", "(", "e", ")", "qes", ",", "qees", "=", "queue", ".", "removeSubQueue", "(", "index", ")", "for", "e", "in", "qes", ":", "processor", "(", "e", ")", "for", "e", "in", "qees", ":", "processor", "(", "e", ")", "return", "_syscall" ]
Remove subqueue `queue[index]` from queue.
[ "Remove", "subqueue", "queue", "[", "index", "]", "from", "queue", "." ]
train
https://github.com/hubo1016/vlcp/blob/239055229ec93a99cc7e15208075724ccf543bd1/vlcp/event/core.py#L553-L566
hubo1016/vlcp
vlcp/event/core.py
syscall_clearremovequeue
def syscall_clearremovequeue(queue, index): ''' Clear the subqueue `queue[index]` and remove it from queue. ''' def _syscall(scheduler, processor): qes, qees = queue[index].clear() events = scheduler.queue.unblockqueue(queue[index]) for e in events: scheduler.eventtree.remove(e) qes2, qees2 = queue.removeSubQueue(index) for e in qes: processor(e) for e in qes2: processor(e) for e in qees: processor(e) for e in qees2: processor(e) return _syscall
python
def syscall_clearremovequeue(queue, index): ''' Clear the subqueue `queue[index]` and remove it from queue. ''' def _syscall(scheduler, processor): qes, qees = queue[index].clear() events = scheduler.queue.unblockqueue(queue[index]) for e in events: scheduler.eventtree.remove(e) qes2, qees2 = queue.removeSubQueue(index) for e in qes: processor(e) for e in qes2: processor(e) for e in qees: processor(e) for e in qees2: processor(e) return _syscall
[ "def", "syscall_clearremovequeue", "(", "queue", ",", "index", ")", ":", "def", "_syscall", "(", "scheduler", ",", "processor", ")", ":", "qes", ",", "qees", "=", "queue", "[", "index", "]", ".", "clear", "(", ")", "events", "=", "scheduler", ".", "queue", ".", "unblockqueue", "(", "queue", "[", "index", "]", ")", "for", "e", "in", "events", ":", "scheduler", ".", "eventtree", ".", "remove", "(", "e", ")", "qes2", ",", "qees2", "=", "queue", ".", "removeSubQueue", "(", "index", ")", "for", "e", "in", "qes", ":", "processor", "(", "e", ")", "for", "e", "in", "qes2", ":", "processor", "(", "e", ")", "for", "e", "in", "qees", ":", "processor", "(", "e", ")", "for", "e", "in", "qees2", ":", "processor", "(", "e", ")", "return", "_syscall" ]
Clear the subqueue `queue[index]` and remove it from queue.
[ "Clear", "the", "subqueue", "queue", "[", "index", "]", "and", "remove", "it", "from", "queue", "." ]
train
https://github.com/hubo1016/vlcp/blob/239055229ec93a99cc7e15208075724ccf543bd1/vlcp/event/core.py#L568-L586
hubo1016/vlcp
vlcp/event/core.py
Scheduler.register
def register(self, matchers, runnable): ''' Register an iterator(runnable) to scheduler and wait for events :param matchers: sequence of EventMatchers :param runnable: an iterator that accept send method :param daemon: if True, the runnable will be registered as a daemon. ''' if getattr(self, 'syscallfunc', None) is not None and getattr(self, 'syscallrunnable', None) is None: # Inject this register self.syscallrunnable = runnable else: for m in matchers: self.matchtree.insert(m, runnable) events = self.eventtree.findAndRemove(m) for e in events: self.queue.unblock(e) if m.indices[0] == PollEvent._classname0 and len(m.indices) >= 2: self.polling.onmatch(m.indices[1], None if len(m.indices) <= 2 else m.indices[2], True) self.registerIndex.setdefault(runnable, set()).update(matchers)
python
def register(self, matchers, runnable): ''' Register an iterator(runnable) to scheduler and wait for events :param matchers: sequence of EventMatchers :param runnable: an iterator that accept send method :param daemon: if True, the runnable will be registered as a daemon. ''' if getattr(self, 'syscallfunc', None) is not None and getattr(self, 'syscallrunnable', None) is None: # Inject this register self.syscallrunnable = runnable else: for m in matchers: self.matchtree.insert(m, runnable) events = self.eventtree.findAndRemove(m) for e in events: self.queue.unblock(e) if m.indices[0] == PollEvent._classname0 and len(m.indices) >= 2: self.polling.onmatch(m.indices[1], None if len(m.indices) <= 2 else m.indices[2], True) self.registerIndex.setdefault(runnable, set()).update(matchers)
[ "def", "register", "(", "self", ",", "matchers", ",", "runnable", ")", ":", "if", "getattr", "(", "self", ",", "'syscallfunc'", ",", "None", ")", "is", "not", "None", "and", "getattr", "(", "self", ",", "'syscallrunnable'", ",", "None", ")", "is", "None", ":", "# Inject this register", "self", ".", "syscallrunnable", "=", "runnable", "else", ":", "for", "m", "in", "matchers", ":", "self", ".", "matchtree", ".", "insert", "(", "m", ",", "runnable", ")", "events", "=", "self", ".", "eventtree", ".", "findAndRemove", "(", "m", ")", "for", "e", "in", "events", ":", "self", ".", "queue", ".", "unblock", "(", "e", ")", "if", "m", ".", "indices", "[", "0", "]", "==", "PollEvent", ".", "_classname0", "and", "len", "(", "m", ".", "indices", ")", ">=", "2", ":", "self", ".", "polling", ".", "onmatch", "(", "m", ".", "indices", "[", "1", "]", ",", "None", "if", "len", "(", "m", ".", "indices", ")", "<=", "2", "else", "m", ".", "indices", "[", "2", "]", ",", "True", ")", "self", ".", "registerIndex", ".", "setdefault", "(", "runnable", ",", "set", "(", ")", ")", ".", "update", "(", "matchers", ")" ]
Register an iterator(runnable) to scheduler and wait for events :param matchers: sequence of EventMatchers :param runnable: an iterator that accept send method :param daemon: if True, the runnable will be registered as a daemon.
[ "Register", "an", "iterator", "(", "runnable", ")", "to", "scheduler", "and", "wait", "for", "events", ":", "param", "matchers", ":", "sequence", "of", "EventMatchers", ":", "param", "runnable", ":", "an", "iterator", "that", "accept", "send", "method", ":", "param", "daemon", ":", "if", "True", "the", "runnable", "will", "be", "registered", "as", "a", "daemon", "." ]
train
https://github.com/hubo1016/vlcp/blob/239055229ec93a99cc7e15208075724ccf543bd1/vlcp/event/core.py#L128-L149
hubo1016/vlcp
vlcp/event/core.py
Scheduler.unregister
def unregister(self, matchers, runnable): ''' Unregister an iterator(runnable) and stop waiting for events :param matchers: sequence of EventMatchers :param runnable: an iterator that accept send method ''' for m in matchers: self.matchtree.remove(m, runnable) if m.indices[0] == PollEvent._classname0 and len(m.indices) >= 2: self.polling.onmatch(m.indices[1], None if len(m.indices) <= 2 else m.indices[2], False) self.registerIndex.setdefault(runnable, set()).difference_update(matchers)
python
def unregister(self, matchers, runnable): ''' Unregister an iterator(runnable) and stop waiting for events :param matchers: sequence of EventMatchers :param runnable: an iterator that accept send method ''' for m in matchers: self.matchtree.remove(m, runnable) if m.indices[0] == PollEvent._classname0 and len(m.indices) >= 2: self.polling.onmatch(m.indices[1], None if len(m.indices) <= 2 else m.indices[2], False) self.registerIndex.setdefault(runnable, set()).difference_update(matchers)
[ "def", "unregister", "(", "self", ",", "matchers", ",", "runnable", ")", ":", "for", "m", "in", "matchers", ":", "self", ".", "matchtree", ".", "remove", "(", "m", ",", "runnable", ")", "if", "m", ".", "indices", "[", "0", "]", "==", "PollEvent", ".", "_classname0", "and", "len", "(", "m", ".", "indices", ")", ">=", "2", ":", "self", ".", "polling", ".", "onmatch", "(", "m", ".", "indices", "[", "1", "]", ",", "None", "if", "len", "(", "m", ".", "indices", ")", "<=", "2", "else", "m", ".", "indices", "[", "2", "]", ",", "False", ")", "self", ".", "registerIndex", ".", "setdefault", "(", "runnable", ",", "set", "(", ")", ")", ".", "difference_update", "(", "matchers", ")" ]
Unregister an iterator(runnable) and stop waiting for events :param matchers: sequence of EventMatchers :param runnable: an iterator that accept send method
[ "Unregister", "an", "iterator", "(", "runnable", ")", "and", "stop", "waiting", "for", "events", ":", "param", "matchers", ":", "sequence", "of", "EventMatchers", ":", "param", "runnable", ":", "an", "iterator", "that", "accept", "send", "method" ]
train
https://github.com/hubo1016/vlcp/blob/239055229ec93a99cc7e15208075724ccf543bd1/vlcp/event/core.py#L150-L162
hubo1016/vlcp
vlcp/event/core.py
Scheduler.unregisterall
def unregisterall(self, runnable): ''' Unregister all matches and detach the runnable. Automatically called when runnable returns StopIteration. ''' if runnable in self.registerIndex: for m in self.registerIndex[runnable]: self.matchtree.remove(m, runnable) if m.indices[0] == PollEvent._classname0 and len(m.indices) >= 2: self.polling.onmatch(m.indices[1], None if len(m.indices) <= 2 else m.indices[2], False) del self.registerIndex[runnable] self.daemons.discard(runnable)
python
def unregisterall(self, runnable): ''' Unregister all matches and detach the runnable. Automatically called when runnable returns StopIteration. ''' if runnable in self.registerIndex: for m in self.registerIndex[runnable]: self.matchtree.remove(m, runnable) if m.indices[0] == PollEvent._classname0 and len(m.indices) >= 2: self.polling.onmatch(m.indices[1], None if len(m.indices) <= 2 else m.indices[2], False) del self.registerIndex[runnable] self.daemons.discard(runnable)
[ "def", "unregisterall", "(", "self", ",", "runnable", ")", ":", "if", "runnable", "in", "self", ".", "registerIndex", ":", "for", "m", "in", "self", ".", "registerIndex", "[", "runnable", "]", ":", "self", ".", "matchtree", ".", "remove", "(", "m", ",", "runnable", ")", "if", "m", ".", "indices", "[", "0", "]", "==", "PollEvent", ".", "_classname0", "and", "len", "(", "m", ".", "indices", ")", ">=", "2", ":", "self", ".", "polling", ".", "onmatch", "(", "m", ".", "indices", "[", "1", "]", ",", "None", "if", "len", "(", "m", ".", "indices", ")", "<=", "2", "else", "m", ".", "indices", "[", "2", "]", ",", "False", ")", "del", "self", ".", "registerIndex", "[", "runnable", "]", "self", ".", "daemons", ".", "discard", "(", "runnable", ")" ]
Unregister all matches and detach the runnable. Automatically called when runnable returns StopIteration.
[ "Unregister", "all", "matches", "and", "detach", "the", "runnable", ".", "Automatically", "called", "when", "runnable", "returns", "StopIteration", "." ]
train
https://github.com/hubo1016/vlcp/blob/239055229ec93a99cc7e15208075724ccf543bd1/vlcp/event/core.py#L163-L173
hubo1016/vlcp
vlcp/event/core.py
Scheduler.ignore
def ignore(self, matcher): ''' Unblock and ignore the matched events, if any. ''' events = self.eventtree.findAndRemove(matcher) for e in events: self.queue.unblock(e) e.canignore = True
python
def ignore(self, matcher): ''' Unblock and ignore the matched events, if any. ''' events = self.eventtree.findAndRemove(matcher) for e in events: self.queue.unblock(e) e.canignore = True
[ "def", "ignore", "(", "self", ",", "matcher", ")", ":", "events", "=", "self", ".", "eventtree", ".", "findAndRemove", "(", "matcher", ")", "for", "e", "in", "events", ":", "self", ".", "queue", ".", "unblock", "(", "e", ")", "e", ".", "canignore", "=", "True" ]
Unblock and ignore the matched events, if any.
[ "Unblock", "and", "ignore", "the", "matched", "events", "if", "any", "." ]
train
https://github.com/hubo1016/vlcp/blob/239055229ec93a99cc7e15208075724ccf543bd1/vlcp/event/core.py#L174-L181
hubo1016/vlcp
vlcp/event/core.py
Scheduler.quit
def quit(self, daemononly = False): ''' Send quit event to quit the main loop ''' if not self.quitting: self.quitting = True self.queue.append(SystemControlEvent(SystemControlEvent.QUIT, daemononly = daemononly), True)
python
def quit(self, daemononly = False): ''' Send quit event to quit the main loop ''' if not self.quitting: self.quitting = True self.queue.append(SystemControlEvent(SystemControlEvent.QUIT, daemononly = daemononly), True)
[ "def", "quit", "(", "self", ",", "daemononly", "=", "False", ")", ":", "if", "not", "self", ".", "quitting", ":", "self", ".", "quitting", "=", "True", "self", ".", "queue", ".", "append", "(", "SystemControlEvent", "(", "SystemControlEvent", ".", "QUIT", ",", "daemononly", "=", "daemononly", ")", ",", "True", ")" ]
Send quit event to quit the main loop
[ "Send", "quit", "event", "to", "quit", "the", "main", "loop" ]
train
https://github.com/hubo1016/vlcp/blob/239055229ec93a99cc7e15208075724ccf543bd1/vlcp/event/core.py#L182-L188
hubo1016/vlcp
vlcp/event/core.py
Scheduler.setTimer
def setTimer(self, start, interval = None): ''' Generate a TimerEvent on specified time :param start: offset for time from now (seconds), or datetime for a fixed time :param interval: if not None, the timer is regenerated by interval seconds. :returns: a timer handle to wait or cancel the timer ''' if isinstance(start, datetime): timestamp = (start - datetime.fromtimestamp(0)).total_seconds() else: timestamp = self.current_time + start if interval is not None: if not (interval > 0): raise ValueError('interval must be positive.') th = self.TimerHandle(timestamp, interval) self.timers.push(th, timestamp) return th
python
def setTimer(self, start, interval = None): ''' Generate a TimerEvent on specified time :param start: offset for time from now (seconds), or datetime for a fixed time :param interval: if not None, the timer is regenerated by interval seconds. :returns: a timer handle to wait or cancel the timer ''' if isinstance(start, datetime): timestamp = (start - datetime.fromtimestamp(0)).total_seconds() else: timestamp = self.current_time + start if interval is not None: if not (interval > 0): raise ValueError('interval must be positive.') th = self.TimerHandle(timestamp, interval) self.timers.push(th, timestamp) return th
[ "def", "setTimer", "(", "self", ",", "start", ",", "interval", "=", "None", ")", ":", "if", "isinstance", "(", "start", ",", "datetime", ")", ":", "timestamp", "=", "(", "start", "-", "datetime", ".", "fromtimestamp", "(", "0", ")", ")", ".", "total_seconds", "(", ")", "else", ":", "timestamp", "=", "self", ".", "current_time", "+", "start", "if", "interval", "is", "not", "None", ":", "if", "not", "(", "interval", ">", "0", ")", ":", "raise", "ValueError", "(", "'interval must be positive.'", ")", "th", "=", "self", ".", "TimerHandle", "(", "timestamp", ",", "interval", ")", "self", ".", "timers", ".", "push", "(", "th", ",", "timestamp", ")", "return", "th" ]
Generate a TimerEvent on specified time :param start: offset for time from now (seconds), or datetime for a fixed time :param interval: if not None, the timer is regenerated by interval seconds. :returns: a timer handle to wait or cancel the timer
[ "Generate", "a", "TimerEvent", "on", "specified", "time", ":", "param", "start", ":", "offset", "for", "time", "from", "now", "(", "seconds", ")", "or", "datetime", "for", "a", "fixed", "time", ":", "param", "interval", ":", "if", "not", "None", "the", "timer", "is", "regenerated", "by", "interval", "seconds", ".", ":", "returns", ":", "a", "timer", "handle", "to", "wait", "or", "cancel", "the", "timer" ]
train
https://github.com/hubo1016/vlcp/blob/239055229ec93a99cc7e15208075724ccf543bd1/vlcp/event/core.py#L201-L220
hubo1016/vlcp
vlcp/event/core.py
Scheduler.registerPolling
def registerPolling(self, fd, options = POLLING_IN|POLLING_OUT, daemon = False): ''' register a polling file descriptor :param fd: file descriptor or socket object :param options: bit mask flags. Polling object should ignore the incompatible flag. ''' self.polling.register(fd, options, daemon)
python
def registerPolling(self, fd, options = POLLING_IN|POLLING_OUT, daemon = False): ''' register a polling file descriptor :param fd: file descriptor or socket object :param options: bit mask flags. Polling object should ignore the incompatible flag. ''' self.polling.register(fd, options, daemon)
[ "def", "registerPolling", "(", "self", ",", "fd", ",", "options", "=", "POLLING_IN", "|", "POLLING_OUT", ",", "daemon", "=", "False", ")", ":", "self", ".", "polling", ".", "register", "(", "fd", ",", "options", ",", "daemon", ")" ]
register a polling file descriptor :param fd: file descriptor or socket object :param options: bit mask flags. Polling object should ignore the incompatible flag.
[ "register", "a", "polling", "file", "descriptor", ":", "param", "fd", ":", "file", "descriptor", "or", "socket", "object", ":", "param", "options", ":", "bit", "mask", "flags", ".", "Polling", "object", "should", "ignore", "the", "incompatible", "flag", "." ]
train
https://github.com/hubo1016/vlcp/blob/239055229ec93a99cc7e15208075724ccf543bd1/vlcp/event/core.py#L230-L238
hubo1016/vlcp
vlcp/event/core.py
Scheduler.unregisterPolling
def unregisterPolling(self, fd, daemon = False): ''' Unregister a polling file descriptor :param fd: file descriptor or socket object ''' self.polling.unregister(fd, daemon)
python
def unregisterPolling(self, fd, daemon = False): ''' Unregister a polling file descriptor :param fd: file descriptor or socket object ''' self.polling.unregister(fd, daemon)
[ "def", "unregisterPolling", "(", "self", ",", "fd", ",", "daemon", "=", "False", ")", ":", "self", ".", "polling", ".", "unregister", "(", "fd", ",", "daemon", ")" ]
Unregister a polling file descriptor :param fd: file descriptor or socket object
[ "Unregister", "a", "polling", "file", "descriptor", ":", "param", "fd", ":", "file", "descriptor", "or", "socket", "object" ]
train
https://github.com/hubo1016/vlcp/blob/239055229ec93a99cc7e15208075724ccf543bd1/vlcp/event/core.py#L246-L252
hubo1016/vlcp
vlcp/event/core.py
Scheduler.setDaemon
def setDaemon(self, runnable, isdaemon, noregister = False): ''' If a runnable is a daemon, it will not keep the main loop running. The main loop will end when all alived runnables are daemons. ''' if not noregister and runnable not in self.registerIndex: self.register((), runnable) if isdaemon: self.daemons.add(runnable) else: self.daemons.discard(runnable)
python
def setDaemon(self, runnable, isdaemon, noregister = False): ''' If a runnable is a daemon, it will not keep the main loop running. The main loop will end when all alived runnables are daemons. ''' if not noregister and runnable not in self.registerIndex: self.register((), runnable) if isdaemon: self.daemons.add(runnable) else: self.daemons.discard(runnable)
[ "def", "setDaemon", "(", "self", ",", "runnable", ",", "isdaemon", ",", "noregister", "=", "False", ")", ":", "if", "not", "noregister", "and", "runnable", "not", "in", "self", ".", "registerIndex", ":", "self", ".", "register", "(", "(", ")", ",", "runnable", ")", "if", "isdaemon", ":", "self", ".", "daemons", ".", "add", "(", "runnable", ")", "else", ":", "self", ".", "daemons", ".", "discard", "(", "runnable", ")" ]
If a runnable is a daemon, it will not keep the main loop running. The main loop will end when all alived runnables are daemons.
[ "If", "a", "runnable", "is", "a", "daemon", "it", "will", "not", "keep", "the", "main", "loop", "running", ".", "The", "main", "loop", "will", "end", "when", "all", "alived", "runnables", "are", "daemons", "." ]
train
https://github.com/hubo1016/vlcp/blob/239055229ec93a99cc7e15208075724ccf543bd1/vlcp/event/core.py#L259-L268
hubo1016/vlcp
vlcp/event/core.py
Scheduler.syscall
def syscall(self, func): ''' Call the func in core context (main loop). func should like:: def syscall_sample(scheduler, processor): something... where processor is a function which accept an event. When calling processor, scheduler directly process this event without sending it to queue. An event matcher is returned to the caller, and the caller should wait for the event immediately to get the return value from the system call. The SyscallReturnEvent will have 'retvalue' as the return value, or 'exception' as the exception thrown: (type, value, traceback) :param func: syscall function :returns: an event matcher to wait for the SyscallReturnEvent. If None is returned, a syscall is already scheduled; return to core context at first. ''' if getattr(self, 'syscallfunc', None) is not None: return None self.syscallfunc = func self.syscallmatcher = SyscallReturnEvent.createMatcher() return self.syscallmatcher
python
def syscall(self, func): ''' Call the func in core context (main loop). func should like:: def syscall_sample(scheduler, processor): something... where processor is a function which accept an event. When calling processor, scheduler directly process this event without sending it to queue. An event matcher is returned to the caller, and the caller should wait for the event immediately to get the return value from the system call. The SyscallReturnEvent will have 'retvalue' as the return value, or 'exception' as the exception thrown: (type, value, traceback) :param func: syscall function :returns: an event matcher to wait for the SyscallReturnEvent. If None is returned, a syscall is already scheduled; return to core context at first. ''' if getattr(self, 'syscallfunc', None) is not None: return None self.syscallfunc = func self.syscallmatcher = SyscallReturnEvent.createMatcher() return self.syscallmatcher
[ "def", "syscall", "(", "self", ",", "func", ")", ":", "if", "getattr", "(", "self", ",", "'syscallfunc'", ",", "None", ")", "is", "not", "None", ":", "return", "None", "self", ".", "syscallfunc", "=", "func", "self", ".", "syscallmatcher", "=", "SyscallReturnEvent", ".", "createMatcher", "(", ")", "return", "self", ".", "syscallmatcher" ]
Call the func in core context (main loop). func should like:: def syscall_sample(scheduler, processor): something... where processor is a function which accept an event. When calling processor, scheduler directly process this event without sending it to queue. An event matcher is returned to the caller, and the caller should wait for the event immediately to get the return value from the system call. The SyscallReturnEvent will have 'retvalue' as the return value, or 'exception' as the exception thrown: (type, value, traceback) :param func: syscall function :returns: an event matcher to wait for the SyscallReturnEvent. If None is returned, a syscall is already scheduled; return to core context at first.
[ "Call", "the", "func", "in", "core", "context", "(", "main", "loop", ")", ".", "func", "should", "like", "::", "def", "syscall_sample", "(", "scheduler", "processor", ")", ":", "something", "..." ]
train
https://github.com/hubo1016/vlcp/blob/239055229ec93a99cc7e15208075724ccf543bd1/vlcp/event/core.py#L269-L294
hubo1016/vlcp
vlcp/event/core.py
Scheduler.main
def main(self, installsignal = True, sendinit = True): ''' Start main loop ''' if installsignal: sigterm = signal(SIGTERM, self._quitsignal) sigint = signal(SIGINT, self._quitsignal) try: from signal import SIGUSR1 sigusr1 = signal(SIGUSR1, self._tracesignal) except Exception: pass try: if sendinit: self.queue.append(SystemControlEvent(SystemControlEvent.INIT), True) def processSyscall(): while self.syscallfunc is not None: r = getattr(self, 'syscallrunnable', None) if r is None: self.syscallfunc = None break try: try: retvalue = self.syscallfunc(self, processEvent) except Exception: (t, v, tr) = sys.exc_info() self.syscallfunc = None self.syscallrunnable = None r.send((SyscallReturnEvent(exception=(t, v, tr)), self.syscallmatcher)) else: self.syscallfunc = None self.syscallrunnable = None r.send((SyscallReturnEvent(retvalue=retvalue), self.syscallmatcher)) except StopIteration: self.unregisterall(r) except QuitException: self.unregisterall(r) except Exception: self.logger.exception('processing syscall failed with exception') self.unregisterall(r) def processEvent(event, emptys = ()): if self.debugging: self.logger.debug('Processing event %s', repr(event)) runnables = self.matchtree.matchesWithMatchers(event) for r, m in runnables: try: self.syscallfunc = None self.syscallrunnable = None if self.debugging: self.logger.debug('Send event to %r, matched with %r', r, m) r.send((event, m)) except StopIteration: self.unregisterall(r) except QuitException: self.unregisterall(r) except Exception: self.logger.exception('processing event %s failed with exception', repr(event)) self.unregisterall(r) processSyscall() if not event.canignore and not event.canignorenow(): self.eventtree.insert(event) self.queue.block(event, emptys) else: for e in emptys: processEvent(e) def processQueueEvent(event): """ Optimized with queue events """ if self.debugging: self.logger.debug('Processing event %s', repr(event)) is_valid = event.is_valid() if is_valid is None: processEvent(event) else: while event.is_valid(): result = self.matchtree.matchfirstwithmatcher(event) if result is None: break r, m = result try: self.syscallfunc = None self.syscallrunnable = None if self.debugging: self.logger.debug('Send event to %r, matched with %r', r, m) r.send((event, m)) except StopIteration: self.unregisterall(r) except QuitException: self.unregisterall(r) except Exception: self.logger.exception('processing event %s failed with exception', repr(event)) self.unregisterall(r) processSyscall() def processYields(): while self._pending_runnables: i = 0 while i < len(self._pending_runnables): r = self._pending_runnables[i] try: next(r) except StopIteration: self.unregisterall(r) except QuitException: self.unregisterall(r) except Exception: self.logger.exception('Resuming %r failed with exception', r) self.unregisterall(r) processSyscall() i += 1 del self._pending_runnables[:i] canquit = False self.logger.info('Main loop started') current_time = self.current_time = time() processYields() quitMatcher = SystemControlEvent.createMatcher(type=SystemControlEvent.QUIT) while len(self.registerIndex) > len(self.daemons): if self.debugging: self.logger.debug('Blocked events: %d', len(self.queue.blockEvents)) self.logger.debug('Blocked events list: %r', list(self.queue.blockEvents.keys())) if self.quitting: self.logger.debug('Routines still not quit: %r', list(self.registerIndex.keys())) if self.quitsignal: self.quit() if canquit and not self.queue.canPop() and not self.timers: if self.quitting: break else: self.quit(True) self.queue.append(SystemControlLowPriorityEvent(SystemControlLowPriorityEvent.LOOP), True) processedEvents = 0 while self.queue.canPop() and (self.processevents is None or processedEvents < self.processevents): e, qes, emptys = self.queue.pop() # Queue events will not enqueue again if not e.canignore and not e.canignorenow(): # The event might block, must process it first processEvent(e, emptys) for qe in qes: processQueueEvent(qe) else: for qe in qes: processQueueEvent(qe) processEvent(e, emptys) processYields() if quitMatcher.isMatch(e): if e.daemononly: runnables = list(self.daemons) else: runnables = list(self.registerIndex.keys()) for r in runnables: try: r.throw(QuitException) except StopIteration: self.unregisterall(r) except QuitException: self.unregisterall(r) except Exception: self.logger.exception('Runnable quit failed with exception') self.unregisterall(r) processSyscall() processYields() if self.quitsignal: self.quit() processedEvents += 1 if len(self.registerIndex) <= len(self.daemons): break end_time = time() if end_time - current_time > 1: self.logger.warning("An iteration takes %r seconds to process", end_time - current_time) if self.generatecontinue or self.queue.canPop(): wait = 0 elif not self.timers: wait = None else: wait = self.timers.top().timestamp - end_time if wait < 0: wait = 0 events, canquit = self.polling.pollEvents(wait) for e in events: self.queue.append(e, True) current_time = self.current_time = time() now = current_time + 0.1 while self.timers and self.timers.topPriority() < now: t = self.timers.top() if t.interval is not None: t.timestamp += t.interval self.timers.setpriority(t, t.timestamp) else: self.timers.pop() self.queue.append(TimerEvent(t), True) if self.generatecontinue: self.queue.append(SystemControlEvent(SystemControlEvent.CONTINUE), True) self.generatecontinue = False if self.registerIndex: if len(self.registerIndex) > len(self.daemons): self.logger.warning('Some runnables are not quit, doing cleanup') self.logger.warning('Runnables list: %r', set(self.registerIndex.keys()).difference(self.daemons)) for r in list(self.registerIndex.keys()): try: r.close() except Exception: self.logger.exception('Runnable quit failed with exception') finally: self.unregisterall(r) self.logger.info('Main loop quit normally') finally: if installsignal: signal(SIGTERM, sigterm) signal(SIGINT, sigint) try: signal(SIGUSR1, sigusr1) except Exception: pass
python
def main(self, installsignal = True, sendinit = True): ''' Start main loop ''' if installsignal: sigterm = signal(SIGTERM, self._quitsignal) sigint = signal(SIGINT, self._quitsignal) try: from signal import SIGUSR1 sigusr1 = signal(SIGUSR1, self._tracesignal) except Exception: pass try: if sendinit: self.queue.append(SystemControlEvent(SystemControlEvent.INIT), True) def processSyscall(): while self.syscallfunc is not None: r = getattr(self, 'syscallrunnable', None) if r is None: self.syscallfunc = None break try: try: retvalue = self.syscallfunc(self, processEvent) except Exception: (t, v, tr) = sys.exc_info() self.syscallfunc = None self.syscallrunnable = None r.send((SyscallReturnEvent(exception=(t, v, tr)), self.syscallmatcher)) else: self.syscallfunc = None self.syscallrunnable = None r.send((SyscallReturnEvent(retvalue=retvalue), self.syscallmatcher)) except StopIteration: self.unregisterall(r) except QuitException: self.unregisterall(r) except Exception: self.logger.exception('processing syscall failed with exception') self.unregisterall(r) def processEvent(event, emptys = ()): if self.debugging: self.logger.debug('Processing event %s', repr(event)) runnables = self.matchtree.matchesWithMatchers(event) for r, m in runnables: try: self.syscallfunc = None self.syscallrunnable = None if self.debugging: self.logger.debug('Send event to %r, matched with %r', r, m) r.send((event, m)) except StopIteration: self.unregisterall(r) except QuitException: self.unregisterall(r) except Exception: self.logger.exception('processing event %s failed with exception', repr(event)) self.unregisterall(r) processSyscall() if not event.canignore and not event.canignorenow(): self.eventtree.insert(event) self.queue.block(event, emptys) else: for e in emptys: processEvent(e) def processQueueEvent(event): """ Optimized with queue events """ if self.debugging: self.logger.debug('Processing event %s', repr(event)) is_valid = event.is_valid() if is_valid is None: processEvent(event) else: while event.is_valid(): result = self.matchtree.matchfirstwithmatcher(event) if result is None: break r, m = result try: self.syscallfunc = None self.syscallrunnable = None if self.debugging: self.logger.debug('Send event to %r, matched with %r', r, m) r.send((event, m)) except StopIteration: self.unregisterall(r) except QuitException: self.unregisterall(r) except Exception: self.logger.exception('processing event %s failed with exception', repr(event)) self.unregisterall(r) processSyscall() def processYields(): while self._pending_runnables: i = 0 while i < len(self._pending_runnables): r = self._pending_runnables[i] try: next(r) except StopIteration: self.unregisterall(r) except QuitException: self.unregisterall(r) except Exception: self.logger.exception('Resuming %r failed with exception', r) self.unregisterall(r) processSyscall() i += 1 del self._pending_runnables[:i] canquit = False self.logger.info('Main loop started') current_time = self.current_time = time() processYields() quitMatcher = SystemControlEvent.createMatcher(type=SystemControlEvent.QUIT) while len(self.registerIndex) > len(self.daemons): if self.debugging: self.logger.debug('Blocked events: %d', len(self.queue.blockEvents)) self.logger.debug('Blocked events list: %r', list(self.queue.blockEvents.keys())) if self.quitting: self.logger.debug('Routines still not quit: %r', list(self.registerIndex.keys())) if self.quitsignal: self.quit() if canquit and not self.queue.canPop() and not self.timers: if self.quitting: break else: self.quit(True) self.queue.append(SystemControlLowPriorityEvent(SystemControlLowPriorityEvent.LOOP), True) processedEvents = 0 while self.queue.canPop() and (self.processevents is None or processedEvents < self.processevents): e, qes, emptys = self.queue.pop() # Queue events will not enqueue again if not e.canignore and not e.canignorenow(): # The event might block, must process it first processEvent(e, emptys) for qe in qes: processQueueEvent(qe) else: for qe in qes: processQueueEvent(qe) processEvent(e, emptys) processYields() if quitMatcher.isMatch(e): if e.daemononly: runnables = list(self.daemons) else: runnables = list(self.registerIndex.keys()) for r in runnables: try: r.throw(QuitException) except StopIteration: self.unregisterall(r) except QuitException: self.unregisterall(r) except Exception: self.logger.exception('Runnable quit failed with exception') self.unregisterall(r) processSyscall() processYields() if self.quitsignal: self.quit() processedEvents += 1 if len(self.registerIndex) <= len(self.daemons): break end_time = time() if end_time - current_time > 1: self.logger.warning("An iteration takes %r seconds to process", end_time - current_time) if self.generatecontinue or self.queue.canPop(): wait = 0 elif not self.timers: wait = None else: wait = self.timers.top().timestamp - end_time if wait < 0: wait = 0 events, canquit = self.polling.pollEvents(wait) for e in events: self.queue.append(e, True) current_time = self.current_time = time() now = current_time + 0.1 while self.timers and self.timers.topPriority() < now: t = self.timers.top() if t.interval is not None: t.timestamp += t.interval self.timers.setpriority(t, t.timestamp) else: self.timers.pop() self.queue.append(TimerEvent(t), True) if self.generatecontinue: self.queue.append(SystemControlEvent(SystemControlEvent.CONTINUE), True) self.generatecontinue = False if self.registerIndex: if len(self.registerIndex) > len(self.daemons): self.logger.warning('Some runnables are not quit, doing cleanup') self.logger.warning('Runnables list: %r', set(self.registerIndex.keys()).difference(self.daemons)) for r in list(self.registerIndex.keys()): try: r.close() except Exception: self.logger.exception('Runnable quit failed with exception') finally: self.unregisterall(r) self.logger.info('Main loop quit normally') finally: if installsignal: signal(SIGTERM, sigterm) signal(SIGINT, sigint) try: signal(SIGUSR1, sigusr1) except Exception: pass
[ "def", "main", "(", "self", ",", "installsignal", "=", "True", ",", "sendinit", "=", "True", ")", ":", "if", "installsignal", ":", "sigterm", "=", "signal", "(", "SIGTERM", ",", "self", ".", "_quitsignal", ")", "sigint", "=", "signal", "(", "SIGINT", ",", "self", ".", "_quitsignal", ")", "try", ":", "from", "signal", "import", "SIGUSR1", "sigusr1", "=", "signal", "(", "SIGUSR1", ",", "self", ".", "_tracesignal", ")", "except", "Exception", ":", "pass", "try", ":", "if", "sendinit", ":", "self", ".", "queue", ".", "append", "(", "SystemControlEvent", "(", "SystemControlEvent", ".", "INIT", ")", ",", "True", ")", "def", "processSyscall", "(", ")", ":", "while", "self", ".", "syscallfunc", "is", "not", "None", ":", "r", "=", "getattr", "(", "self", ",", "'syscallrunnable'", ",", "None", ")", "if", "r", "is", "None", ":", "self", ".", "syscallfunc", "=", "None", "break", "try", ":", "try", ":", "retvalue", "=", "self", ".", "syscallfunc", "(", "self", ",", "processEvent", ")", "except", "Exception", ":", "(", "t", ",", "v", ",", "tr", ")", "=", "sys", ".", "exc_info", "(", ")", "self", ".", "syscallfunc", "=", "None", "self", ".", "syscallrunnable", "=", "None", "r", ".", "send", "(", "(", "SyscallReturnEvent", "(", "exception", "=", "(", "t", ",", "v", ",", "tr", ")", ")", ",", "self", ".", "syscallmatcher", ")", ")", "else", ":", "self", ".", "syscallfunc", "=", "None", "self", ".", "syscallrunnable", "=", "None", "r", ".", "send", "(", "(", "SyscallReturnEvent", "(", "retvalue", "=", "retvalue", ")", ",", "self", ".", "syscallmatcher", ")", ")", "except", "StopIteration", ":", "self", ".", "unregisterall", "(", "r", ")", "except", "QuitException", ":", "self", ".", "unregisterall", "(", "r", ")", "except", "Exception", ":", "self", ".", "logger", ".", "exception", "(", "'processing syscall failed with exception'", ")", "self", ".", "unregisterall", "(", "r", ")", "def", "processEvent", "(", "event", ",", "emptys", "=", "(", ")", ")", ":", "if", "self", ".", "debugging", ":", "self", ".", "logger", ".", "debug", "(", "'Processing event %s'", ",", "repr", "(", "event", ")", ")", "runnables", "=", "self", ".", "matchtree", ".", "matchesWithMatchers", "(", "event", ")", "for", "r", ",", "m", "in", "runnables", ":", "try", ":", "self", ".", "syscallfunc", "=", "None", "self", ".", "syscallrunnable", "=", "None", "if", "self", ".", "debugging", ":", "self", ".", "logger", ".", "debug", "(", "'Send event to %r, matched with %r'", ",", "r", ",", "m", ")", "r", ".", "send", "(", "(", "event", ",", "m", ")", ")", "except", "StopIteration", ":", "self", ".", "unregisterall", "(", "r", ")", "except", "QuitException", ":", "self", ".", "unregisterall", "(", "r", ")", "except", "Exception", ":", "self", ".", "logger", ".", "exception", "(", "'processing event %s failed with exception'", ",", "repr", "(", "event", ")", ")", "self", ".", "unregisterall", "(", "r", ")", "processSyscall", "(", ")", "if", "not", "event", ".", "canignore", "and", "not", "event", ".", "canignorenow", "(", ")", ":", "self", ".", "eventtree", ".", "insert", "(", "event", ")", "self", ".", "queue", ".", "block", "(", "event", ",", "emptys", ")", "else", ":", "for", "e", "in", "emptys", ":", "processEvent", "(", "e", ")", "def", "processQueueEvent", "(", "event", ")", ":", "\"\"\"\n Optimized with queue events\n \"\"\"", "if", "self", ".", "debugging", ":", "self", ".", "logger", ".", "debug", "(", "'Processing event %s'", ",", "repr", "(", "event", ")", ")", "is_valid", "=", "event", ".", "is_valid", "(", ")", "if", "is_valid", "is", "None", ":", "processEvent", "(", "event", ")", "else", ":", "while", "event", ".", "is_valid", "(", ")", ":", "result", "=", "self", ".", "matchtree", ".", "matchfirstwithmatcher", "(", "event", ")", "if", "result", "is", "None", ":", "break", "r", ",", "m", "=", "result", "try", ":", "self", ".", "syscallfunc", "=", "None", "self", ".", "syscallrunnable", "=", "None", "if", "self", ".", "debugging", ":", "self", ".", "logger", ".", "debug", "(", "'Send event to %r, matched with %r'", ",", "r", ",", "m", ")", "r", ".", "send", "(", "(", "event", ",", "m", ")", ")", "except", "StopIteration", ":", "self", ".", "unregisterall", "(", "r", ")", "except", "QuitException", ":", "self", ".", "unregisterall", "(", "r", ")", "except", "Exception", ":", "self", ".", "logger", ".", "exception", "(", "'processing event %s failed with exception'", ",", "repr", "(", "event", ")", ")", "self", ".", "unregisterall", "(", "r", ")", "processSyscall", "(", ")", "def", "processYields", "(", ")", ":", "while", "self", ".", "_pending_runnables", ":", "i", "=", "0", "while", "i", "<", "len", "(", "self", ".", "_pending_runnables", ")", ":", "r", "=", "self", ".", "_pending_runnables", "[", "i", "]", "try", ":", "next", "(", "r", ")", "except", "StopIteration", ":", "self", ".", "unregisterall", "(", "r", ")", "except", "QuitException", ":", "self", ".", "unregisterall", "(", "r", ")", "except", "Exception", ":", "self", ".", "logger", ".", "exception", "(", "'Resuming %r failed with exception'", ",", "r", ")", "self", ".", "unregisterall", "(", "r", ")", "processSyscall", "(", ")", "i", "+=", "1", "del", "self", ".", "_pending_runnables", "[", ":", "i", "]", "canquit", "=", "False", "self", ".", "logger", ".", "info", "(", "'Main loop started'", ")", "current_time", "=", "self", ".", "current_time", "=", "time", "(", ")", "processYields", "(", ")", "quitMatcher", "=", "SystemControlEvent", ".", "createMatcher", "(", "type", "=", "SystemControlEvent", ".", "QUIT", ")", "while", "len", "(", "self", ".", "registerIndex", ")", ">", "len", "(", "self", ".", "daemons", ")", ":", "if", "self", ".", "debugging", ":", "self", ".", "logger", ".", "debug", "(", "'Blocked events: %d'", ",", "len", "(", "self", ".", "queue", ".", "blockEvents", ")", ")", "self", ".", "logger", ".", "debug", "(", "'Blocked events list: %r'", ",", "list", "(", "self", ".", "queue", ".", "blockEvents", ".", "keys", "(", ")", ")", ")", "if", "self", ".", "quitting", ":", "self", ".", "logger", ".", "debug", "(", "'Routines still not quit: %r'", ",", "list", "(", "self", ".", "registerIndex", ".", "keys", "(", ")", ")", ")", "if", "self", ".", "quitsignal", ":", "self", ".", "quit", "(", ")", "if", "canquit", "and", "not", "self", ".", "queue", ".", "canPop", "(", ")", "and", "not", "self", ".", "timers", ":", "if", "self", ".", "quitting", ":", "break", "else", ":", "self", ".", "quit", "(", "True", ")", "self", ".", "queue", ".", "append", "(", "SystemControlLowPriorityEvent", "(", "SystemControlLowPriorityEvent", ".", "LOOP", ")", ",", "True", ")", "processedEvents", "=", "0", "while", "self", ".", "queue", ".", "canPop", "(", ")", "and", "(", "self", ".", "processevents", "is", "None", "or", "processedEvents", "<", "self", ".", "processevents", ")", ":", "e", ",", "qes", ",", "emptys", "=", "self", ".", "queue", ".", "pop", "(", ")", "# Queue events will not enqueue again", "if", "not", "e", ".", "canignore", "and", "not", "e", ".", "canignorenow", "(", ")", ":", "# The event might block, must process it first", "processEvent", "(", "e", ",", "emptys", ")", "for", "qe", "in", "qes", ":", "processQueueEvent", "(", "qe", ")", "else", ":", "for", "qe", "in", "qes", ":", "processQueueEvent", "(", "qe", ")", "processEvent", "(", "e", ",", "emptys", ")", "processYields", "(", ")", "if", "quitMatcher", ".", "isMatch", "(", "e", ")", ":", "if", "e", ".", "daemononly", ":", "runnables", "=", "list", "(", "self", ".", "daemons", ")", "else", ":", "runnables", "=", "list", "(", "self", ".", "registerIndex", ".", "keys", "(", ")", ")", "for", "r", "in", "runnables", ":", "try", ":", "r", ".", "throw", "(", "QuitException", ")", "except", "StopIteration", ":", "self", ".", "unregisterall", "(", "r", ")", "except", "QuitException", ":", "self", ".", "unregisterall", "(", "r", ")", "except", "Exception", ":", "self", ".", "logger", ".", "exception", "(", "'Runnable quit failed with exception'", ")", "self", ".", "unregisterall", "(", "r", ")", "processSyscall", "(", ")", "processYields", "(", ")", "if", "self", ".", "quitsignal", ":", "self", ".", "quit", "(", ")", "processedEvents", "+=", "1", "if", "len", "(", "self", ".", "registerIndex", ")", "<=", "len", "(", "self", ".", "daemons", ")", ":", "break", "end_time", "=", "time", "(", ")", "if", "end_time", "-", "current_time", ">", "1", ":", "self", ".", "logger", ".", "warning", "(", "\"An iteration takes %r seconds to process\"", ",", "end_time", "-", "current_time", ")", "if", "self", ".", "generatecontinue", "or", "self", ".", "queue", ".", "canPop", "(", ")", ":", "wait", "=", "0", "elif", "not", "self", ".", "timers", ":", "wait", "=", "None", "else", ":", "wait", "=", "self", ".", "timers", ".", "top", "(", ")", ".", "timestamp", "-", "end_time", "if", "wait", "<", "0", ":", "wait", "=", "0", "events", ",", "canquit", "=", "self", ".", "polling", ".", "pollEvents", "(", "wait", ")", "for", "e", "in", "events", ":", "self", ".", "queue", ".", "append", "(", "e", ",", "True", ")", "current_time", "=", "self", ".", "current_time", "=", "time", "(", ")", "now", "=", "current_time", "+", "0.1", "while", "self", ".", "timers", "and", "self", ".", "timers", ".", "topPriority", "(", ")", "<", "now", ":", "t", "=", "self", ".", "timers", ".", "top", "(", ")", "if", "t", ".", "interval", "is", "not", "None", ":", "t", ".", "timestamp", "+=", "t", ".", "interval", "self", ".", "timers", ".", "setpriority", "(", "t", ",", "t", ".", "timestamp", ")", "else", ":", "self", ".", "timers", ".", "pop", "(", ")", "self", ".", "queue", ".", "append", "(", "TimerEvent", "(", "t", ")", ",", "True", ")", "if", "self", ".", "generatecontinue", ":", "self", ".", "queue", ".", "append", "(", "SystemControlEvent", "(", "SystemControlEvent", ".", "CONTINUE", ")", ",", "True", ")", "self", ".", "generatecontinue", "=", "False", "if", "self", ".", "registerIndex", ":", "if", "len", "(", "self", ".", "registerIndex", ")", ">", "len", "(", "self", ".", "daemons", ")", ":", "self", ".", "logger", ".", "warning", "(", "'Some runnables are not quit, doing cleanup'", ")", "self", ".", "logger", ".", "warning", "(", "'Runnables list: %r'", ",", "set", "(", "self", ".", "registerIndex", ".", "keys", "(", ")", ")", ".", "difference", "(", "self", ".", "daemons", ")", ")", "for", "r", "in", "list", "(", "self", ".", "registerIndex", ".", "keys", "(", ")", ")", ":", "try", ":", "r", ".", "close", "(", ")", "except", "Exception", ":", "self", ".", "logger", ".", "exception", "(", "'Runnable quit failed with exception'", ")", "finally", ":", "self", ".", "unregisterall", "(", "r", ")", "self", ".", "logger", ".", "info", "(", "'Main loop quit normally'", ")", "finally", ":", "if", "installsignal", ":", "signal", "(", "SIGTERM", ",", "sigterm", ")", "signal", "(", "SIGINT", ",", "sigint", ")", "try", ":", "signal", "(", "SIGUSR1", ",", "sigusr1", ")", "except", "Exception", ":", "pass" ]
Start main loop
[ "Start", "main", "loop" ]
train
https://github.com/hubo1016/vlcp/blob/239055229ec93a99cc7e15208075724ccf543bd1/vlcp/event/core.py#L303-L518
hubo1016/vlcp
vlcp/service/connection/tcpserver.py
TcpServerBase.getservers
def getservers(self, vhost = None): ''' Return current servers :param vhost: return only servers of vhost if specified. '' to return only default servers. None for all servers. ''' if vhost is not None: return [s for s in self.connections if s.protocol.vhost == vhost] else: return list(self.connections)
python
def getservers(self, vhost = None): ''' Return current servers :param vhost: return only servers of vhost if specified. '' to return only default servers. None for all servers. ''' if vhost is not None: return [s for s in self.connections if s.protocol.vhost == vhost] else: return list(self.connections)
[ "def", "getservers", "(", "self", ",", "vhost", "=", "None", ")", ":", "if", "vhost", "is", "not", "None", ":", "return", "[", "s", "for", "s", "in", "self", ".", "connections", "if", "s", ".", "protocol", ".", "vhost", "==", "vhost", "]", "else", ":", "return", "list", "(", "self", ".", "connections", ")" ]
Return current servers :param vhost: return only servers of vhost if specified. '' to return only default servers. None for all servers.
[ "Return", "current", "servers", ":", "param", "vhost", ":", "return", "only", "servers", "of", "vhost", "if", "specified", ".", "to", "return", "only", "default", "servers", ".", "None", "for", "all", "servers", "." ]
train
https://github.com/hubo1016/vlcp/blob/239055229ec93a99cc7e15208075724ccf543bd1/vlcp/service/connection/tcpserver.py#L138-L148