Split (1)

train · 200k rows

input stringlengths 2.65k 237k	output stringclasses 1 value
#!/usr/bin/env python import numpy as np import scipy.io as io import matplotlib.pyplot as plt from scipy.stats import norm import argparse import copy import tqdm from hdphmm.utils import timeseries as ts def initialize(): parser = argparse.ArgumentParser(description='Generate timeseries with different underlying models') parser.add_argument('-t', '--type', default='AR', type=str, help="Underlying dynamical model. Only AR is fully" "implemented.") parser.add_argument('-d', '--ndraws', default=2000, type=int, help='Number of time steps to take') parser.add_argument('-n', '--ntraj', default=4, type=int, help='Number of trajetories to generate') parser.add_argument('-f', '--format', nargs='+', default='npz', type=str, help='Format of output array (mat or npz)') parser.add_argument('-nd', '--ndimensions', default=1, type=int, help='Number of dimensions of trajectory.') # Define transition matrix. Either provide your own, or let this script generate one parser.add_argument('-T', '--transition_matrix', nargs='+', action='append', type=float, help='Define a transition ' 'matrix. If provided, this will be used to determine the number of states. Each row of the ' 'transition matrix should be passed with a separate -T flag') parser.add_argument('-s', '--nstates', default=3, type=int, help='Number of states to switch between') parser.add_argument('-slip', '--slip', default=0.01, type=float, help='Determines how frequently things will ' 'switch states. A slip of 0 results in a transition matrix with only ones on the diagonal. ' 'Higher values of slip will give smaller and smaller ratios of diagonals to the rest.') # Autoregressive parameters parser.add_argument('-r', '--order', default=1, type=int, help='Autoregressive order (number of time lags that ' 'yt depends on.') parser.add_argument('-phis', '--phis', nargs='+', action='append', type=float, help='Define autoregressive' 'coefficients for each state. Coefficients for each state should be passed in order with ' 'separate -phis flags. If this is not specified, the coefficients will be randomly generated' 'for you.') # noise parameters parser.add_argument('-cov', '--covariance', nargs='+', action='append', type=float, help='Covariance matrix for ' 'each state. Pass matrix for each state with a separate flag.') # phantom state linking parser.add_argument('-l', '--link', action="store_true", help='Link together the independent trajetories with a' 'phantom state in between.') parser.add_argument('-pl', '--phantom_length', default=100, type=int, help='Number of time steps for phantom state ' 'linkage') return parser class StateError(Exception): """ Raised if an undefined reaction is attempted """ def __init__(self, message): super().__init__(message) class GenARData: def __init__(self, params=None, dim=3, transition_matrix=None, phis=None, nstates=None, slip=0.25, order=1, cov=None, stdmax=1, mu=None): """ Given a transition matrix, generate timeseries using Markov Chains :param params: a dictionary containing parameters of an AR HMM process. This is how one should pass the parameters generated by hdphmm.InfiniteHMM stored in InfiniteHMM.converged_params. :param dim: number of dimensions of trajectory data :param transition_matrix: a list of N N-length lists. Each list represents a row of the transition matrix. If \ None or False is passed, a transition matrix will be generated randomly :param phis: a list of N order-length lists of autoregressive coefficients for each state. In order of phi_1, \ phi_2 etc. If None, these will be randomly generated :param nstates: number of states :param slip: determines ratio of diagonal elements to the rest. A higher 'high' will give you a smaller ratio \ of diagonals to the rest and vice versa. A high of zero will result in an identity matrix, meaning there will \ be no transitions from the initial state :param order: autoregressive order. Only specified if type is 'AR' :param cov: covariance matrix of multivariate Gaussian white noise for each state. If None, a random covariance matrix will be generated :param stdmax: maximum standard deviation of Gaussian white noise. This is only used if stds=None :type params: dict :type dim: int :type transition_matrix: list of lists :type nstates: int :type slip: float :type order: int :type stds: list :type stdmax: float :type phis: list of lists """ self.T = None self.dwells = [] self.hops = [] if params is not None: self.T = params['T'] self.nstates = self.T.shape[1] self.phis = params['A'] self.cov = params['sigma'] self.pi_init = params['pi_init'] self.dim = self.cov.shape[1] self.order = self.phis.shape[1] self.mu = params['mu'] #self.count_matrix = self._get_count_matrix(params['z']) else: self.dim = dim self.order = order if transition_matrix is not None: self.T = np.array(transition_matrix) self.nstates = self.T.shape[1] # define number of states based on provided transition matrix else: if not nstates: raise StateError("If no transition matrix is provided, the number of states must be specified") self.nstates = nstates self.generate_transition_matrix(slip) self.phis = np.zeros([self.nstates, order, dim, dim]) if phis is not None: # only works for r = 1 for s in range(self.nstates): try: self.phis[s, 0, ...] = np.array(phis[s]).reshape(dim, dim) except IndexError: raise IndexError('You have not provided enough phi matrices for the number of requested states') else: # NOTE: for multidimensional case, off-diagonal terms in each phi coefficient matrix are set to zero. # I'm not sure what the stabilty rules are for the multidimensional case self.phis = np.zeros([1, order, dim, dim, self.nstates]) for s in range(self.nstates): self.phis[0, s, ...] = generate_ar_parameters(order, dim) self.cov = np.zeros([self.nstates, dim, dim]) if cov is None: for s in range(self.nstates): A = np.random.uniform(0, stdmax, size=(dim, dim)) self.cov[0, ..., s] = A @ A.T else: for s in range(self.nstates): self.cov[s, ...] = np.array(cov[s]).reshape(dim, dim) self.mu = np.zeros([self.nstates, self.dim]) if mu is not None: for s in range(self.nstates): self.mu[s, :] = mu[s] self.state_labels = np.arange(self.nstates) self.traj = None self.state_sequence = None def _get_count_matrix(self, z): nclusters = len(np.unique(z)) # need A, sigma, transition matrix, pi_init count_matrix = np.zeros([nclusters, nclusters]) found_states = np.unique(z) ndx_dict = {found_states[i]: i for i in range(len(found_states))} count_matrix = np.zeros([nclusters, nclusters]) for frame in range(1, nT - 1): # start at frame 1. May need to truncate more as equilibration transitioned_from = [ndx_dict[i] for i in ihmm_final.z[:, frame - 1]] transitioned_to = [ndx_dict[i] for i in ihmm_final.z[:, frame]] for pair in zip(transitioned_from, transitioned_to): count_matrix[pair[0], pair[1]] += 1 # for frame in range(1, z.shape[1]): # start at frame 1. May need to truncate more as equilibration # transitioned_from = z[:, frame - 1] # transitioned_to = z[:, frame] # for pair in zip(transitioned_from, transitioned_to): # count_matrix[pair[0], pair[1]] += 1 return count_matrix def generate_transition_matrix(self, high): """ generate a semi-random transition matrix :param high: determines ratio of diagonal elements to the rest. A higher 'high' will give you a smaller ratio of diagonals to the rest and vice versa. A high of zero will result in an identity matrix, meaning there will be no transitions from the initial state :type high: float """ T = np.eye(self.nstates) # start with identify matrix T += np.random.uniform(0, high, size=(self.nstates, self.nstates)) # add random draws from uniform distribution self.T = T / T.sum(axis=1, keepdims=1) # normalize so sum of rows is 1 def gen_trajectory(self, ndraws, ntraj, bound_dimensions=None, progress=True, state_no=None, resample_T=False, alpha=1): """ Generate time series with chosen underlying dynamics :param ndraws: number of sequential points to generate :param ntraj: number of independent trajectories to generate :param unbound_dimensions: indices of dimensions whose mean should not be fixed. :param progress: show progress bar :param state_no: if not None, generate a trajectory for the given state index :param resample_T: resample the rows of the transition matrix by drawing from a dirichlet process :param alpha: multiply rows of transition matrix by this number :type ndraws: int :type ntraj: int :type unbound_dimensions: NoneType, list or np.ndarray :type progress: bool :type state_no: int or NoneType :type resample_T: bool :type alpha: int or float """ return self._gen_ar_hmm(ndraws, ntraj, bound_dimensions=bound_dimensions, state_no=state_no, progress=progress, resample_T=resample_T, alpha=alpha) #return self._gen_ar_hmm_fixed_distance(10) def _gen_ar_hmm_fixed_distance(self, length, bound_dimensions=None): """ Generate a mean-zero autoregressive timeseries based on the transition matrix and autoregressive parameters. The timeseries is defined as: yt = \sum_{n=1}^{r} phi_n * y_{t-n} + \epsilon_t where r is autoregressive order and \epsilon_t is Gaussian white noise with state-dependent variance :param ndraws: number of points to generate for timeseries :param phis: autoregressive coefficients for each state (n_phis x n_states) :type ndraws: int :type phis: np.ndarray """ state_labels = np.arange(self.nstates) current_state = np.random.choice(state_labels, p=self.pi_init) previous_state = current_state mean = np.zeros(self.dim) traj = [np.array([0, 0]) for i in range(self.order)] # r, z zeroed = [np.array([0, 0]) for i in range(self.order)] # traj = np.zeros([self.order + 1, self.dim]) # zeroed = np.zeros_like(traj) tlen = self.order n = 0 # while np.abs(traj[-1][1]) < length: self.traj = np.zeros([4807, 100, 2]) for i in
:param async_req bool :param str id: (required) :param list[ApiParameter] parameters: :param str run_name: name to identify the run on the Kubeflow Pipelines UI, defaults to component name :return: ApiRunCodeResponse If the method is called asynchronously, returns the request thread. """ all_params = ['id', 'parameters', 'run_name'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method run_dataset" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'id' is set if ('id' not in params or params['id'] is None): raise ValueError("Missing the required parameter `id` when calling `run_dataset`") # noqa: E501 collection_formats = {} path_params = {} if 'id' in params: path_params['id'] = params['id'] # noqa: E501 query_params = [] if 'run_name' in params: query_params.append(('run_name', params['run_name'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params = None if 'parameters' in params: body_params = params['parameters'] # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/datasets/{id}/run', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='ApiRunCodeResponse', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def set_featured_datasets(self, dataset_ids, kwargs): # noqa: E501 """set_featured_datasets # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.set_featured_datasets(dataset_ids, async_req=True) >>> result = thread.get() :param async_req bool :param list[str] dataset_ids: Array of dataset IDs to be featured. (required) :return: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.set_featured_datasets_with_http_info(dataset_ids, kwargs) # noqa: E501 else: (data) = self.set_featured_datasets_with_http_info(dataset_ids, kwargs) # noqa: E501 return data def set_featured_datasets_with_http_info(self, dataset_ids, kwargs): # noqa: E501 """set_featured_datasets # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.set_featured_datasets_with_http_info(dataset_ids, async_req=True) >>> result = thread.get() :param async_req bool :param list[str] dataset_ids: Array of dataset IDs to be featured. (required) :return: None If the method is called asynchronously, returns the request thread. """ all_params = ['dataset_ids'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method set_featured_datasets" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'dataset_ids' is set if ('dataset_ids' not in params or params['dataset_ids'] is None): raise ValueError("Missing the required parameter `dataset_ids` when calling `set_featured_datasets`") # noqa: E501 collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'dataset_ids' in params: body_params = params['dataset_ids'] # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/datasets/featured', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def upload_dataset(self, uploadfile, kwargs): # noqa: E501 """upload_dataset # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.upload_dataset(uploadfile, async_req=True) >>> result = thread.get() :param async_req bool :param file uploadfile: The dataset YAML file to upload. Can be a GZip-compressed TAR file (.tgz, .tar.gz) or a YAML file (.yaml, .yml). Maximum size is 32MB. (required) :param str name: :return: ApiDataset If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.upload_dataset_with_http_info(uploadfile, kwargs) # noqa: E501 else: (data) = self.upload_dataset_with_http_info(uploadfile, kwargs) # noqa: E501 return data def upload_dataset_with_http_info(self, uploadfile, kwargs): # noqa: E501 """upload_dataset # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.upload_dataset_with_http_info(uploadfile, async_req=True) >>> result = thread.get() :param async_req bool :param file uploadfile: The dataset YAML file to upload. Can be a GZip-compressed TAR file (.tgz, .tar.gz) or a YAML file (.yaml, .yml). Maximum size is 32MB. (required) :param str name: :return: ApiDataset If the method is called asynchronously, returns the request thread. """ all_params = ['uploadfile', 'name'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method upload_dataset" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'uploadfile' is set if ('uploadfile' not in params or params['uploadfile'] is None): raise ValueError("Missing the required parameter `uploadfile` when calling `upload_dataset`") # noqa: E501 collection_formats = {} path_params = {} query_params = [] if 'name' in params: query_params.append(('name', params['name'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {} if 'uploadfile' in params: local_var_files['uploadfile'] = params['uploadfile'] # noqa: E501 body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['multipart/form-data']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/datasets/upload', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='ApiDataset', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def upload_dataset_file(self, id, uploadfile, kwargs): # noqa: E501 """upload_dataset_file # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.upload_dataset_file(id, uploadfile, async_req=True) >>> result = thread.get() :param async_req bool :param str id: The id of the dataset. (required) :param file uploadfile: The file to upload, overwriting existing. Can be a GZip-compressed TAR file (.tgz), a YAML file (.yaml), Python script (.py), or Markdown file (.md) (required) :return: ApiDataset If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.upload_dataset_file_with_http_info(id, uploadfile, kwargs) # noqa: E501 else: (data) = self.upload_dataset_file_with_http_info(id, uploadfile, kwargs) # noqa: E501 return data def upload_dataset_file_with_http_info(self, id, uploadfile, kwargs): # noqa: E501 """upload_dataset_file # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.upload_dataset_file_with_http_info(id, uploadfile, async_req=True) >>> result = thread.get() :param async_req bool :param str id: The id of the dataset. (required) :param file uploadfile: The file to upload, overwriting existing. Can be a GZip-compressed TAR file (.tgz), a YAML file (.yaml), Python script (.py), or Markdown file (.md) (required) :return: ApiDataset If the method is called asynchronously, returns the request thread. """ all_params = ['id', 'uploadfile'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method upload_dataset_file" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'id' is set if ('id' not in params or params['id'] is None): raise ValueError("Missing the required parameter `id` when calling `upload_dataset_file`") # noqa: E501 # verify the required parameter 'uploadfile' is set if ('uploadfile' not in params or params['uploadfile'] is None): raise ValueError("Missing the required parameter `uploadfile` when calling `upload_dataset_file`") # noqa: E501 collection_formats = {} path_params = {} if 'id' in params: path_params['id'] = params['id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} if 'uploadfile' in params: local_var_files['uploadfile'] = params['uploadfile'] # noqa: E501 body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['multipart/form-data']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/datasets/{id}/upload', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='ApiDataset', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def upload_dataset_from_url(self, url, **kwargs): # noqa: E501 """upload_dataset_from_url # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.upload_dataset_from_url(url, async_req=True) >>> result = thread.get() :param async_req bool :param
9 * m.b1400) m.e1 = Constraint(expr= m.x1 - 0.2 * m.x141 == 0) m.e2 = Constraint(expr= m.x2 - 0.2 * m.x142 == 0) m.e3 = Constraint(expr= m.x3 - 0.2 * m.x143 == 0) m.e4 = Constraint(expr= m.x4 - 0.2 * m.x144 == 0) m.e5 = Constraint(expr= m.x5 - 0.2 * m.x145 == 0) m.e6 = Constraint(expr= m.x6 - 0.2 * m.x146 == 0) m.e7 = Constraint(expr= m.x7 - 0.2 * m.x147 == 0) m.e8 = Constraint(expr= m.x8 - 0.2 * m.x148 == 0) m.e9 = Constraint(expr= m.x9 - 0.2 * m.x149 == 0) m.e10 = Constraint(expr= m.x10 - 0.2 * m.x150 == 0) m.e11 = Constraint(expr= m.x11 - 0.2 * m.x151 == 0) m.e12 = Constraint(expr= m.x12 - 0.2 * m.x152 == 0) m.e13 = Constraint(expr= m.x13 - 0.2 * m.x153 == 0) m.e14 = Constraint(expr= m.x14 - 0.2 * m.x154 == 0) m.e15 = Constraint(expr= m.x15 - 0.2 * m.x155 == 0) m.e16 = Constraint(expr= m.x16 - 0.2 * m.x156 == 0) m.e17 = Constraint(expr= m.x17 - 0.2 * m.x157 == 0) m.e18 = Constraint(expr= m.x18 - 0.2 * m.x158 == 0) m.e19 = Constraint(expr= m.x19 - 0.2 * m.x159 == 0) m.e20 = Constraint(expr= m.x20 - 0.2 * m.x160 == 0) m.e21 = Constraint(expr= m.x21 - 0.5 * m.x161 == 0) m.e22 = Constraint(expr= m.x22 - 0.5 * m.x162 == 0) m.e23 = Constraint(expr= m.x23 - 0.5 * m.x163 == 0) m.e24 = Constraint(expr= m.x24 - 0.5 * m.x164 == 0) m.e25 = Constraint(expr= m.x25 - 0.5 * m.x165 == 0) m.e26 = Constraint(expr= m.x26 - 0.5 * m.x166 == 0) m.e27 = Constraint(expr= m.x27 - 0.5 * m.x167 == 0) m.e28 = Constraint(expr= m.x28 - 0.5 * m.x168 == 0) m.e29 = Constraint(expr= m.x29 - 0.7 * m.x169 == 0) m.e30 = Constraint(expr= m.x30 - 0.7 * m.x170 == 0) m.e31 = Constraint(expr= m.x31 - 0.7 * m.x171 == 0) m.e32 = Constraint(expr= m.x32 - 0.7 * m.x172 == 0) m.e33 = Constraint(expr= m.x33 - 0.7 * m.x173 == 0) m.e34 = Constraint(expr= m.x34 - 0.7 * m.x174 == 0) m.e35 = Constraint(expr= m.x35 - 0.7 * m.x175 == 0) m.e36 = Constraint(expr= m.x36 - 0.7 * m.x176 == 0) m.e37 = Constraint(expr= m.x37 - 1.2 * m.x177 == 0) m.e38 = Constraint(expr= m.x38 - 1.2 * m.x178 == 0) m.e39 = Constraint(expr= m.x39 - 1.2 * m.x179 == 0) m.e40 = Constraint(expr= m.x40 - 1.2 * m.x180 == 0) m.e41 = Constraint(expr= m.x41 - 1.2 * m.x181 == 0) m.e42 = Constraint(expr= m.x42 - 1.2 * m.x182 == 0) m.e43 = Constraint(expr= m.x43 - 1.2 * m.x183 == 0) m.e44 = Constraint(expr= m.x44 - 1.2 * m.x184 == 0) m.e45 = Constraint(expr= m.x45 - 0.5 * m.x185 == 0) m.e46 = Constraint(expr= m.x46 - 0.5 * m.x186 == 0) m.e47 = Constraint(expr= m.x47 - 0.5 * m.x187 == 0) m.e48 = Constraint(expr= m.x48 - 0.5 * m.x188 == 0) m.e49 = Constraint(expr= m.x49 - 0.7 * m.x189 == 0) m.e50 = Constraint(expr= m.x50 - 0.7 * m.x190 == 0) m.e51 = Constraint(expr= m.x51 - 0.7 * m.x191 == 0) m.e52 = Constraint(expr= m.x52 - 0.7 * m.x192 == 0) m.e53 = Constraint(expr= m.x53 - 1.2 * m.x193 == 0) m.e54 = Constraint(expr= m.x54 - 1.2 * m.x194 == 0) m.e55 = Constraint(expr= m.x55 - 1.2 * m.x195 == 0) m.e56 = Constraint(expr= m.x56 - 1.2 * m.x196 == 0) m.e57 = Constraint(expr= m.x57 - 1.2 * m.x197 == 0) m.e58 = Constraint(expr= m.x58 - 1.2 * m.x198 == 0) m.e59 = Constraint(expr= m.x59 - 1.2 * m.x199 == 0) m.e60 = Constraint(expr= m.x60 - 1.2 * m.x200 == 0) m.e61 = Constraint(expr= m.x61 - 1.2 * m.x201 == 0) m.e62 = Constraint(expr= m.x62 - 1.2 * m.x202 == 0) m.e63 = Constraint(expr= m.x63 - 1.2 * m.x203 == 0) m.e64 = Constraint(expr= m.x64 - 1.2 * m.x204 == 0) m.e65 = Constraint(expr= m.x65 - 1.2 * m.x205 == 0) m.e66 = Constraint(expr= m.x66 - 1.2 * m.x206 == 0) m.e67 = Constraint(expr= m.x67 - 1.2 * m.x207 == 0) m.e68 = Constraint(expr= m.x68 - 1.2 * m.x208 == 0) m.e69 = Constraint(expr= m.x69 - 0.3 * m.x209 == 0) m.e70 = Constraint(expr= m.x70 - 0.3 * m.x210 == 0) m.e71 = Constraint(expr= m.x71 - 0.3 * m.x211 == 0) m.e72 = Constraint(expr= m.x72 - 0.3 * m.x212 == 0) m.e73 = Constraint(expr= m.x73 - 0.9 * m.x213 == 0) m.e74 = Constraint(expr= m.x74 - 0.9 * m.x214 == 0) m.e75 = Constraint(expr= m.x75 - 0.9 * m.x215 == 0) m.e76 = Constraint(expr= m.x76 - 0.9 * m.x216 == 0) m.e77 = Constraint(expr= m.x77 - 0.3 * m.x217 == 0) m.e78 = Constraint(expr= m.x78 - 0.3 * m.x218 == 0) m.e79 = Constraint(expr= m.x79 - 0.3 * m.x219 == 0) m.e80 = Constraint(expr= m.x80 - 0.3 * m.x220 == 0) m.e81 = Constraint(expr= m.x81 - 0.9 * m.x221 == 0) m.e82 = Constraint(expr= m.x82 - 0.9 * m.x222 == 0) m.e83 = Constraint(expr= m.x83 - 0.9 * m.x223 == 0) m.e84 = Constraint(expr= m.x84 - 0.9 * m.x224 == 0) m.e85 = Constraint(expr= m.x85 - 0.4 * m.x225 == 0) m.e86 = Constraint(expr= m.x86 - 0.4 * m.x226 == 0) m.e87 = Constraint(expr= m.x87 - 0.4 * m.x227 == 0) m.e88 = Constraint(expr= m.x88 - 0.4 * m.x228 == 0) m.e89 = Constraint(expr= m.x89 - 0.4 * m.x229 == 0) m.e90 = Constraint(expr= m.x90 - 0.4 * m.x230 == 0) m.e91 = Constraint(expr= m.x91 - 0.4 * m.x231 == 0) m.e92 = Constraint(expr= m.x92 - 0.4 * m.x232 == 0) m.e93 = Constraint(expr= m.x93 - 0.4 * m.x233 == 0) m.e94 = Constraint(expr= m.x94 - 0.4 * m.x234 == 0) m.e95 = Constraint(expr= m.x95 - 0.4 * m.x235 == 0) m.e96 = Constraint(expr= m.x96 - 0.4 * m.x236 == 0) m.e97 = Constraint(expr= m.x97 - 1.6 * m.x237 == 0) m.e98 = Constraint(expr= m.x98 - 1.6 * m.x238 == 0) m.e99 = Constraint(expr= m.x99 - 1.6 * m.x239 == 0) m.e100 = Constraint(expr= m.x100 - 1.6 * m.x240 == 0) m.e101 = Constraint(expr= m.x101 - 1.6 * m.x241 == 0) m.e102 = Constraint(expr= m.x102 - 1.6 * m.x242 == 0) m.e103 = Constraint(expr= m.x103 - 1.6 * m.x243 == 0) m.e104 = Constraint(expr= m.x104 - 1.6 * m.x244 == 0) m.e105 = Constraint(expr= m.x105 - 1.1 * m.x245 == 0) m.e106 = Constraint(expr= m.x106 - 1.1 * m.x246 == 0) m.e107 = Constraint(expr= m.x107 - 1.1 * m.x247 == 0) m.e108 = Constraint(expr= m.x108 - 1.1 * m.x248 == 0) m.e109 = Constraint(expr= m.x109 - 1.1 * m.x249 == 0) m.e110 = Constraint(expr= m.x110 - 1.1 * m.x250 == 0) m.e111 = Constraint(expr= m.x111 - 1.1 * m.x251 == 0) m.e112 = Constraint(expr= m.x112 - 1.1 * m.x252 == 0) m.e113 = Constraint(expr= m.x113 - 0.7 * m.x253 == 0) m.e114 = Constraint(expr= m.x114 - 0.7 * m.x254 == 0) m.e115 = Constraint(expr= m.x115 - 0.7 * m.x255 == 0) m.e116 = Constraint(expr= m.x116 - 0.7 * m.x256 == 0) m.e117 = Constraint(expr= m.x117 - 0.7 * m.x257 == 0) m.e118 = Constraint(expr= m.x118 - 0.7 * m.x258 == 0) m.e119 = Constraint(expr= m.x119 - 0.7 * m.x259 == 0) m.e120 = Constraint(expr= m.x120 - 0.7 * m.x260 == 0) m.e121 = Constraint(expr= m.x121 - 0.7 * m.x261 == 0) m.e122 = Constraint(expr= m.x122 - 0.7 * m.x262 == 0) m.e123 = Constraint(expr= m.x123 - 0.7 * m.x263 == 0) m.e124 = Constraint(expr= m.x124 - 0.7 * m.x264 == 0) m.e125 = Constraint(expr= m.x125 - 0.2 * m.x265 == 0) m.e126 = Constraint(expr= m.x126 - 0.2 * m.x266 == 0) m.e127 = Constraint(expr= m.x127 - 0.2 * m.x267 == 0) m.e128 = Constraint(expr= m.x128 - 0.2 * m.x268 == 0) m.e129 = Constraint(expr= m.x129 - 0.7 * m.x269 == 0) m.e130 = Constraint(expr= m.x130 - 0.7 * m.x270 == 0) m.e131 = Constraint(expr= m.x131 - 0.7 * m.x271 == 0) m.e132 = Constraint(expr= m.x132 - 0.7 * m.x272 == 0) m.e133 = Constraint(expr= m.x133 - 0.3 * m.x273 == 0) m.e134 = Constraint(expr= m.x134 - 0.3 * m.x274 == 0) m.e135 = Constraint(expr= m.x135 - 0.3 * m.x275 == 0) m.e136 = Constraint(expr= m.x136 - 0.3 * m.x276 == 0) m.e137 = Constraint(expr= m.x137 - 0.9 * m.x277 == 0) m.e138 = Constraint(expr= m.x138 - 0.9 * m.x278 == 0) m.e139 = Constraint(expr= m.x139 - 0.9 * m.x279 == 0) m.e140 = Constraint(expr= m.x140 - 0.9 * m.x280 == 0) m.e141 = Constraint(expr= m.x101 >= 1.2) m.e142 = Constraint(expr= m.x102 >= 1.15) m.e143 = Constraint(expr= m.x103 >= 1.1) m.e144 = Constraint(expr=
#!/usr/bin/env python # -- coding: utf-8 -- ############################################################################### # Copyright Kitware Inc. # # Licensed under the Apache License, Version 2.0 ( the "License" ); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ############################################################################### # flake8: noqa: E501 class TcgaCodes(object): DISEASE_STUDIES = { # 'Study Abbreviation': 'Study Name', 'LAML': 'Acute Myeloid Leukemia', 'ACC': 'Adrenocortical carcinoma', 'BLCA': 'Bladder Urothelial Carcinoma', 'LGG': 'Brain Lower Grade Glioma', 'BRCA': 'Breast invasive carcinoma', 'CESC': 'Cervical squamous cell carcinoma and endocervical adenocarcinoma', 'CHOL': 'Cholangiocarcinoma', 'LCML': 'Chronic Myelogenous Leukemia', 'COAD': 'Colon adenocarcinoma', 'CNTL': 'Controls', 'ESCA': 'Esophageal carcinoma ', 'FPPP': 'FFPE Pilot Phase II', 'GBM': 'Glioblastoma multiforme', 'HNSC': 'Head and Neck squamous cell carcinoma', 'KICH': 'Kidney Chromophobe', 'KIRC': 'Kidney renal clear cell carcinoma', 'KIRP': 'Kidney renal papillary cell carcinoma', 'LIHC': 'Liver hepatocellular carcinoma', 'LUAD': 'Lung adenocarcinoma', 'LUSC': 'Lung squamous cell carcinoma', 'DLBC': 'Lymphoid Neoplasm Diffuse Large B-cell Lymphoma', 'MESO': 'Mesothelioma', 'MISC': 'Miscellaneous', 'OV': 'Ovarian serous cystadenocarcinoma', 'PAAD': 'Pancreatic adenocarcinoma', 'PCPG': 'Pheochromocytoma and Paraganglioma', 'PRAD': 'Prostate adenocarcinoma', 'READ': 'Rectum adenocarcinoma', 'SARC': 'Sarcoma', 'SKCM': 'Skin Cutaneous Melanoma', 'STAD': 'Stomach adenocarcinoma', 'TGCT': 'Testicular Germ Cell Tumors', 'THYM': 'Thymoma', 'THCA': 'Thyroid carcinoma', 'UCS': 'Uterine Carcinosarcoma', 'UCEC': 'Uterine Corpus Endometrial Carcinoma', 'UVM': 'Uveal Melanoma', } REPOSITORY_TYPES = { 'bcr', # 'Biospecimen Core Resource' 'cgcc', 'gsc', } DATA_PROVIDERS = { 'biotab', # Clinical metadata, skip 'intgen.org', 'nationwidechildrens.org', 'genome.wustl.edu', 'supplemental' # unknown, appears under 'tumor/ov/bcr/', skip } DATA_TYPES = { 'bio', # XML format clinical metadata, skip 'biotab', # CSV format clinical metadata, skip 'pathology_reports', # PDF format pathology reports, skip 'diagnostic_images', # SVS format images, use 'tissue_images', # SVS format images, use 'minbio' # unknown, appears under 'tumor/gbm/bcr/intgen.org/', skip } SLIDE_LOCATION = { 'TS': 'Top Slide', 'MS': 'Middle Slide', 'BS': 'Bottom Slide', 'DX': 'Top Slide', } TISSUE_SOURCE_SITE = { # 'TSS Code': ('Source Site', 'Study Name', 'BCR'), '01': ('International Genomics Consortium', 'Ovarian serous cystadenocarcinoma', 'IGC'), '02': ('MD Anderson Cancer Center', 'Glioblastoma multiforme', 'IGC'), '04': ('Gynecologic Oncology Group', 'Ovarian serous cystadenocarcinoma', 'IGC'), '05': ('Indivumed', 'Lung adenocarcinoma', 'IGC'), '06': ('Henry Ford Hospital', 'Glioblastoma multiforme', 'IGC'), '07': ('TGen', 'Cell Line Control', 'IGC'), '08': ('UCSF', 'Glioblastoma multiforme', 'IGC'), '09': ('UCSF', 'Ovarian serous cystadenocarcinoma', 'IGC'), '10': ('MD Anderson Cancer Center', 'Ovarian serous cystadenocarcinoma', 'IGC'), '11': ('MD Anderson Cancer Center', 'Lung squamous cell carcinoma', 'IGC'), '12': ('Duke', 'Glioblastoma multiforme', 'IGC'), '13': ('Memorial Sloan Kettering', 'Ovarian serous cystadenocarcinoma', 'IGC'), '14': ('Emory University', 'Glioblastoma multiforme', 'IGC'), '15': ('Mayo Clinic - Rochester', 'Glioblastoma multiforme', 'IGC'), '16': ('Toronto Western Hospital', 'Glioblastoma multiforme', 'IGC'), '17': ('Washington University', 'Lung adenocarcinoma', 'IGC'), '18': ('Princess Margaret Hospital (Canada)', 'Lung squamous cell carcinoma', 'IGC'), '19': ('Case Western', 'Glioblastoma multiforme', 'IGC'), '1Z': ('Johns Hopkins', 'Thymoma', 'NCH'), '20': ('Fox Chase Cancer Center', 'Ovarian serous cystadenocarcinoma', 'IGC'), '21': ('Fox Chase Cancer Center', 'Lung squamous cell carcinoma', 'IGC'), '22': ('Mayo Clinic - Rochester', 'Lung squamous cell carcinoma', 'IGC'), '23': ('<NAME>', 'Ovarian serous cystadenocarcinoma', 'IGC'), '24': ('Washington University', 'Ovarian serous cystadenocarcinoma', 'IGC'), '25': ('<NAME> - Rochester', 'Ovarian serous cystadenocarcinoma', 'IGC'), '26': ('University of Florida', 'Glioblastoma multiforme', 'IGC'), '27': ('Milan - Italy, Fondazione IRCCS Instituto Neuroligico C. Besta', 'Glioblastoma multiforme', 'IGC'), '28': ('<NAME>', 'Glioblastoma multiforme', 'IGC'), '29': ('Duke', 'Ovarian serous cystadenocarcinoma', 'IGC'), '2A': ('Memorial Sloan Kettering Cancer Center', 'Prostate adenocarcinoma', 'NCH'), '2E': ('University of Kansas Medical Center', 'Uterine Corpus Endometrial Carcinoma', 'NCH'), '2F': ('Erasmus MC', 'Bladder Urothelial Carcinoma', 'NCH'), '2G': ('Erasmus MC', 'Testicular Germ Cell Tumors', 'NCH'), '2H': ('Erasmus MC', 'Esophageal carcinoma ', 'NCH'), '2J': ('<NAME>', 'Pancreatic adenocarcinoma', 'NCH'), '2K': ('Greenville Health System', 'Kidney renal papillary cell carcinoma', 'NCH'), '2L': ('Technical University of Munich', 'Pancreatic adenocarcinoma', 'NCH'), '2M': ('Technical University of Munich', 'Esophageal carcinoma ', 'NCH'), '2N': ('Technical University of Munich', 'Stomach adenocarcinoma', 'NCH'), '2P': ('University of California San Diego', 'Pancreatic adenocarcinoma', 'NCH'), '2V': ('University of California San Diego', 'Liver hepatocellular carcinoma', 'NCH'), '2W': ('University of New Mexico', 'Cervical squamous cell carcinoma and endocervical adenocarcinoma', 'NCH'), '2X': ('ABS IUPUI', 'Testicular Germ Cell Tumors', 'NCH'), '2Y': ('Moffitt Cancer Center', 'Liver hepatocellular carcinoma', 'NCH'), '2Z': ('Moffitt Cancer Center', 'Kidney renal papillary cell carcinoma', 'NCH'), '30': ('Harvard', 'Ovarian serous cystadenocarcinoma', 'IGC'), '31': ('Imperial College', 'Ovarian serous cystadenocarcinoma', 'IGC'), '32': ('St. Joseph\'s Hospital (AZ)', 'Glioblastoma multiforme', 'IGC'), '33': ('Johns Hopkins', 'Lung squamous cell carcinoma', 'IGC'), '34': ('University of Pittsburgh', 'Lung squamous cell carcinoma', 'IGC'), '35': ('Cureline', 'Lung adenocarcinoma', 'IGC'), '36': ('BC Cancer Agency', 'Ovarian serous cystadenocarcinoma', 'IGC'), '37': ('Cureline', 'Lung squamous cell carcinoma', 'IGC'), '38': ('UNC', 'Lung adenocarcinoma', 'IGC'), '39': ('MSKCC', 'Lung squamous cell carcinoma', 'IGC'), '3A': ('Moffitt Cancer Center', 'Pancreatic adenocarcinoma', 'NCH'), '3B': ('Moffitt Cancer Center', 'Sarcoma', 'NCH'), '3C': ('Columbia University', 'Breast invasive carcinoma', 'NCH'), '3E': ('Columbia University', 'Pancreatic adenocarcinoma', 'NCH'), '3G': ('MD Anderson Cancer Center', 'Thymoma', 'NCH'), '3H': ('MD Anderson Cancer Center', 'Mesothelioma', 'NCH'), '3J': ('Carle Cancer Center', 'Breast invasive carcinoma', 'NCH'), '3K': ('Boston Medical Center', 'Liver hepatocellular carcinoma', 'NCH'), '3L': ('Albert Einstein Medical Center', 'Colon adenocarcinoma', 'NCH'), '3M': ('University of Kansas Medical Center', 'Stomach adenocarcinoma', 'NCH'), '3N': ('Greenville Health System', 'Skin Cutaneous Melanoma', 'NCH'), '3P': ('Greenville Health System', 'Ovarian serous cystadenocarcinoma', 'NCH'), '3Q': ('Greenville Health Systems', 'Thymoma', 'NCH'), '3R': ('University of New Mexico', 'Sarcoma', 'NCH'), '3S': ('University of New Mexico', 'Thymoma', 'NCH'), '3T': ('Emory University', 'Thymoma', 'NCH'), '3U': ('University of Chicago', 'Mesothelioma', 'NCH'), '3W': ('University of California San Diego', 'Sarcoma', 'NCH'), '3X': ('Alberta Health Services', 'Cholangiocarcinoma', 'NCH'), '3Z': ('Mary Bird Perkins Cancer Center - Our Lady of the Lake', 'Kidney renal clear cell carcinoma', 'NCH'), '41': ('Christiana Healthcare', 'Glioblastoma multiforme', 'IGC'), '42': ('Christiana Healthcare', 'Ovarian serous cystadenocarcinoma', 'IGC'), '43': ('Christiana Healthcare', 'Lung squamous cell carcinoma', 'IGC'), '44': ('Christiana Healthcare', 'Lung adenocarcinoma', 'IGC'), '46': ('St. Joseph\'s Medical Center (MD)', 'Lung squamous cell carcinoma', 'IGC'), '49': ('Johns Hopkins', 'Lung adenocarcinoma', 'IGC'), '4A': ('Mary Bird Perkins Cancer Center - Our Lady of the Lake', 'Kidney renal papillary cell carcinoma', 'NCH'), '4B': ('Mary Bird Perkins Cancer Center - Our Lady of the Lake', 'Lung adenocarcinoma', 'NCH'), '4C': ('Mary Bird Perkins Cancer Center - Our Lady of the Lake', 'Thyroid carcinoma', 'NCH'), '4D': ('Molecular Response', 'Ovarian serous cystadenocarcinoma', 'NCH'), '4E': ('Molecular Response', 'Uterine Corpus Endometrial Carcinoma', 'NCH'), '4G': ('Sapienza University of Rome', 'Cholangiocarcinoma', 'NCH'), '4H': ('Proteogenex, Inc.', 'Breast invasive carcinoma', 'NCH'), '4J': ('Proteogenex, Inc.', 'Cervical squamous cell carcinoma and endocervical adenocarcinoma', 'NCH'), '4K': ('Proteogenex, Inc.', 'Testicular Germ Cell Tumors', 'NCH'), '4L': ('Proteogenex, Inc.', 'Prostate adenocarcinoma', 'NCH'), '4N': ('<NAME>kins Cancer Center - Our Lady of the Lake', 'Colon adenocarcinoma', 'NCH'), '4P': ('Duke University', 'Head and Neck squamous cell carcinoma', 'NCH'), '4Q': ('Duke University', 'Sarcoma', 'NCH'), '4R': ('Duke University', 'Liver hepatocellular carcinoma', 'NCH'), '4S': ('Duke University', 'Prostate adenocarcinoma', 'NCH'), '4T': ('Duke University', 'Colon adenocarcinoma', 'NCH'), '4V': ('Hospital Louis Pradel', 'Thymoma', 'NCH'), '4W': ('University of Miami', 'Glioblastoma multiforme', 'NCH'), '4X': ('Yale University', 'Thymoma', 'NCH'), '4Y': ('Medical College of Wisconsin', 'Sarcoma', 'NCH'), '4Z': ('Barretos Cancer Hospital', 'Bladder Urothelial Carcinoma', 'NCH'), '50': ('University of Pittsburgh', 'Lung adenocarcinoma', 'IGC'), '51': ('UNC', 'Lung squamous cell carcinoma', 'IGC'), '52': ('University of Miami', 'Lung squamous cell carcinoma', 'IGC'), '53': ('University of Miami', 'Lung adenocarcinoma', 'IGC'), '55': ('International Genomics Consortium', 'Lung adenocarcinoma', 'IGC'), '56': ('International Genomics Consortium', 'Lung squamous cell carcinoma', 'IGC'), '57': ('International Genomics Consortium', 'Ovarian serous cystadenocarcinoma', 'IGC'), '58': ('Thoraxklinik at University Hospital Heidelberg', 'Lung squamous cell carcinoma', 'IGC'), '59': ('Roswell Park', 'Ovarian serous cystadenocarcinoma', 'IGC'), '5A': ('Wake Forest University', 'Cholangiocarcinoma', 'NCH'), '5B': ('Medical College of Wisconsin', 'Uterine Corpus Endometrial Carcinoma', 'NCH'), '5C': ('Cureline', 'Liver hepatocellular carcinoma', 'NCH'), '5D': ('University of Miami', 'Sarcoma', 'NCH'), '5F': ('Duke University', 'Thyroid carcinoma', 'NCH'), '5G': ('Cleveland Clinic Foundation', 'Thymoma', 'NCH'), '5H': ('Retina Consultants Houston', 'Uveal Melanoma', 'NCH'), '5J': ('Cureline', 'Acute Myeloid Leukemia', 'NCH'), '5K': ('St. Joseph\'s Hospital AZ', 'Thymoma', 'NCH'),
EMPTY_BOARD = [ [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], ] EMPTY_BOARD_STRING = " \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n" BOARD_WITH_ROW_2_COLUMN_4_ALIVE = [ # 0 1 2 3 4 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], # 0 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], # 1 [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], # 2 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], ] BOARD_WITH_ROW_2_COLUMN_4_ALIVE_STRING = " \n \n * \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n" BOARD_WITH_SQUARE_AT_ROW_2_COLUMN_4_ALIVE = [ # 0 1 2 3 4 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], # 0 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], # 1 [0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], # 2 [0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0,
j, k, m)y(m, n, p) answer(i, j, k, n, p, z) self.failUnless(isEqual(result, answer, tol=tol), "Mismatch: %s != %s" % (result, answer)) return #--------------------------------------------------------------------------- # thirdranktensor . fourthranktensor #--------------------------------------------------------------------------- def testThirdRankTensorDotFourthRankTensor(self): for dim in dims: r3type = eval("ThirdRankTensor%id" % dim) r4type = eval("FourthRankTensor%id" % dim) r5type = eval("FifthRankTensor%id" % dim) x = fillRandom(r3type) y = fillRandom(r4type) result = innerProduct(x, y) answer = r5type() for i in xrange(dim): for j in xrange(dim): for k in xrange(dim): for m in xrange(dim): for n in xrange(dim): for p in xrange(dim): z = answer(i, j, m, n, p) + x(i, j, k)y(k, m, n, p) answer(i, j, m, n, p, z) self.failUnless(isEqual(result, answer, tol=tol), "Mismatch: %s != %s" % (result, answer)) return #--------------------------------------------------------------------------- # thirdranktensor . thirdranktensor #--------------------------------------------------------------------------- def testThirdRankTensorDotThirdRankTensor(self): for dim in dims: r3type = eval("ThirdRankTensor%id" % dim) r4type = eval("FourthRankTensor%id" % dim) x = fillRandom(r3type) y = fillRandom(r3type) result = innerProduct(x, y) answer = r4type() for i in xrange(dim): for j in xrange(dim): for k in xrange(dim): for m in xrange(dim): for n in xrange(dim): z = answer(i, j, m, n) + x(i, j, k)y(k, m, n) answer(i, j, m, n, z) self.failUnless(isEqual(result, answer, tol=tol), "Mismatch: %s != %s" % (result, answer)) return #=============================================================================== # Test class for outer product. #=============================================================================== class TestOuterProduct(unittest.TestCase): #--------------------------------------------------------------------------- # scalar x value #--------------------------------------------------------------------------- def testScalarOuterThing(self): for typestring in ("Vector%id", "Tensor%id", "SymTensor%id", "ThirdRankTensor%id"): for dim in dims: ttype = eval(typestring % dim) x = rangen.uniform(ranrange) y = fillRandom(ttype) result = outerProduct(x, y) answer = ttype() for i in xrange(ttype.numElements): answer[i] = xy[i] self.failUnless(isEqual(result, answer, tol=tol), "Mismatch for %s: %s != %s" % (ttype.__name__, result, answer)) return #--------------------------------------------------------------------------- # value x scalar #--------------------------------------------------------------------------- def testThingOuterScalar(self): for typestring in ("Vector%id", "Tensor%id", "SymTensor%id", "ThirdRankTensor%id"): for dim in dims: ttype = eval(typestring % dim) x = rangen.uniform(ranrange) y = fillRandom(ttype) result = outerProduct(y, x) answer = ttype() for i in xrange(ttype.numElements): answer[i] = xy[i] self.failUnless(isEqual(result, answer, tol=tol), "Mismatch for %s: %s != %s" % (ttype.__name__, result, answer)) return #--------------------------------------------------------------------------- # vector x vector #--------------------------------------------------------------------------- def testVectorOuterVector(self): for dim in dims: type = eval("Vector%id" % dim) ttype = eval("Tensor%id" % dim) x = fillRandom(type) y = fillRandom(type) result = outerProduct(x, y) answer = ttype() for i in xrange(dim): for j in xrange(dim): answer(i, j, x[i]y[j]) self.failUnless(isEqual(result, answer, tol=tol), "Mismatch: %s != %s" % (result, answer)) return #--------------------------------------------------------------------------- # tensor x vector #--------------------------------------------------------------------------- def testTensorOuterVector(self): for typestring in ("Tensor%id", "SymTensor%id"): for dim in dims: vtype = eval("Vector%id" % dim) ttype = eval(typestring % dim) trttype = eval("ThirdRankTensor%id" % dim) x = fillRandom(ttype) y = fillRandom(vtype) result = outerProduct(x, y) answer = trttype() for i in xrange(dim): for j in xrange(dim): for k in xrange(dim): answer(i, j, k, x(i,j)y(k)) self.failUnless(isEqual(result, answer, tol=tol), "Mismatch: %s != %s" % (result, answer)) return #--------------------------------------------------------------------------- # vector x tensor #--------------------------------------------------------------------------- def testVectorOuterTensor(self): for typestring in ("Tensor%id", "SymTensor%id"): for dim in dims: vtype = eval("Vector%id" % dim) ttype = eval(typestring % dim) trttype = eval("ThirdRankTensor%id" % dim) x = fillRandom(vtype) y = fillRandom(ttype) result = outerProduct(x, y) answer = trttype() for i in xrange(dim): for j in xrange(dim): for k in xrange(dim): answer(i, j, k, x(i)y(j,k)) self.failUnless(isEqual(result, answer, tol=tol), "Mismatch: %s != %s" % (result, answer)) return #=============================================================================== # Test class for double inner product. #=============================================================================== class TestDoubleInnerProduct(unittest.TestCase): #--------------------------------------------------------------------------- # tensor .. tensor #--------------------------------------------------------------------------- def testTensorDoubleDotTensor(self): for ttypestring1 in ("Tensor%id", "SymTensor%id"): for ttypestring2 in ("Tensor%id", "SymTensor%id"): for dim in dims: t1type = eval(ttypestring1 % dim) t2type = eval(ttypestring2 % dim) x = fillRandom(t1type) y = fillRandom(t2type) result = innerDoubleProduct(x, y) result2 = x.doubledot(y) answer = 0.0 for i in xrange(dim): for j in xrange(dim): answer += x(i,j)y(j,i) self.failUnless(abs(result - answer) < 1.0e-10, "Mismatch: %s != %s" % (result, answer)) self.failUnless(abs(result2 - answer) < 1.0e-10, "Mismatch: %s != %s" % (result2, answer)) return #--------------------------------------------------------------------------- # tensor .. thirdranktensor #--------------------------------------------------------------------------- def testTensorDoubleDotThirdRankTensor(self): for ttypestring in ("Tensor%id", "SymTensor%id"): for dim in dims: vtype = eval("Vector%id" % dim) r2type = eval(ttypestring % dim) r3type = eval("ThirdRankTensor%id" % dim) x = fillRandom(r2type) y = fillRandom(r3type) result = innerDoubleProduct(x, y) answer = vtype() for i in xrange(dim): for j in xrange(dim): for k in xrange(dim): answer[k] += x(i, j)y(j, i, k) self.failUnless(isEqual(result, answer, tol=tol), "Mismatch: %s != %s" % (result, answer)) return #--------------------------------------------------------------------------- # thirdranktensor .. tensor #--------------------------------------------------------------------------- def testThirdRankTensorDoubleDotTensor(self): for ttypestring in ("Tensor%id", "SymTensor%id"): for dim in dims: vtype = eval("Vector%id" % dim) r2type = eval(ttypestring % dim) r3type = eval("ThirdRankTensor%id" % dim) x = fillRandom(r3type) y = fillRandom(r2type) result = innerDoubleProduct(x, y) answer = vtype() for i in xrange(dim): for j in xrange(dim): for k in xrange(dim): answer[i] += x(i, j, k)y(k, j) self.failUnless(isEqual(result, answer, tol=tol), "Mismatch: %s != %s" % (result, answer)) return #--------------------------------------------------------------------------- # thirdranktensor .. thirdranktensor #--------------------------------------------------------------------------- def testThirdRankTensorDoubleDotThirdRankTensor(self): for dim in dims: r2type = eval("Tensor%id" % dim) r3type = eval("ThirdRankTensor%id" % dim) x = fillRandom(r3type) y = fillRandom(r3type) result = innerDoubleProduct(x, y) answer = r2type() for i in xrange(dim): for j in xrange(dim): for k in xrange(dim): for m in xrange(dim): z = answer(i,m) + x(i,j,k)y(k,j,m) answer(i,m,z) self.failUnless(isEqual(result, answer, tol=tol), "Mismatch: %s != %s" % (result, answer)) return #--------------------------------------------------------------------------- # tensor .. fourthranktensor #--------------------------------------------------------------------------- def testTensorDoubleDotFourthRankTensor(self): for ttypestring in ("Tensor%id", "SymTensor%id"): for dim in dims: ttype = eval("Tensor%id" % dim) r2type = eval(ttypestring % dim) r4type = eval("FourthRankTensor%id" % dim) x = fillRandom(r2type) y = fillRandom(r4type) result = innerDoubleProduct(x, y) answer = ttype() for i in xrange(dim): for j in xrange(dim): for k in xrange(dim): for m in xrange(dim): z = answer(k, m) + x(i, j)y(j, i, k, m) answer(k, m, z) self.failUnless(isEqual(result, answer, tol=tol), "Mismatch: %s != %s" % (result, answer)) return #--------------------------------------------------------------------------- # fourthranktensor .. tensor #--------------------------------------------------------------------------- def testFourthRankTensorDoubleDotTensor(self): for ttypestring in ("Tensor%id", "SymTensor%id"): for dim in dims: ttype = eval("Tensor%id" % dim) r2type = eval(ttypestring % dim) r4type = eval("FourthRankTensor%id" % dim) x = fillRandom(r4type) y = fillRandom(r2type) result = innerDoubleProduct(x, y) answer = ttype() for i in xrange(dim): for j in xrange(dim): for k in xrange(dim): for m in xrange(dim): z = answer(i, j) + x(i, j, k, m)y(m, k) answer(i, j, z) self.failUnless(isEqual(result, answer, tol=tol), "Mismatch: %s != %s" % (result, answer)) return #--------------------------------------------------------------------------- # thirdranktensor .. fourthranktensor #--------------------------------------------------------------------------- def testThirdRankTensorDoubleDotFourthRankTensor(self): for dim in dims: r3type = eval("ThirdRankTensor%id" % dim) r4type = eval("FourthRankTensor%id" % dim) x = fillRandom(r3type) y = fillRandom(r4type) result = innerDoubleProduct(x, y) answer = r3type() for i in xrange(dim): for j in xrange(dim): for k in xrange(dim): for m in xrange(dim): for n in xrange(dim): z = answer(i, m, n) + x(i, j, k)y(k, j, m, n) answer(i, m, n, z) self.failUnless(isEqual(result, answer, tol=tol), "Mismatch: %s != %s" % (result, answer)) return #--------------------------------------------------------------------------- # fourthranktensor .. thirdranktensor #--------------------------------------------------------------------------- def testFourthRankTensorDoubleDotThirdRankTensor(self): for dim in dims: r3type = eval("ThirdRankTensor%id" % dim) r4type = eval("FourthRankTensor%id" % dim) x = fillRandom(r4type) y = fillRandom(r3type) result = innerDoubleProduct(x, y) answer = r3type() for i in xrange(dim): for j in xrange(dim): for k in xrange(dim): for m in xrange(dim): for n in xrange(dim): z = answer(i, j, n) + x(i, j, k, m)y(m, k, n) answer(i, j, n, z) self.failUnless(isEqual(result, answer, tol=tol), "Mismatch: %s != %s" % (result, answer)) return #--------------------------------------------------------------------------- # fourthranktensor .. fourthranktensor #--------------------------------------------------------------------------- def testFourthRankTensorDoubleDotFourthRankTensor(self): for dim in dims: r4type = eval("FourthRankTensor%id" % dim) x = fillRandom(r4type) y = fillRandom(r4type) result = innerDoubleProduct(x, y) answer = r4type() for i in xrange(dim): for j in xrange(dim): for k in xrange(dim): for m in xrange(dim): for n in xrange(dim): for p in xrange(dim): z = answer(i, j, n, p) + x(i, j, k, m)*y(m, k, n, p) answer(i, j, n, p, z) self.failUnless(isEqual(result, answer, tol=tol), "Mismatch: %s != %s" % (result, answer)) return #--------------------------------------------------------------------------- # tensor .. fifthranktensor #--------------------------------------------------------------------------- def testTensorDoubleDotFifthRankTensor(self): for ttypestring in ("Tensor%id", "SymTensor%id"): for dim in dims: r2type = eval(ttypestring % dim) r3type = eval("ThirdRankTensor%id" % dim) r5type = eval("FifthRankTensor%id" % dim)
<reponame>ark0015/DetectorDesignSensitivities<filename>Functions/waveform_Wphase.py import numpy as np def Get_Waveform(source,pct_of_peak=0.01): """Uses Mass Ratio (q <= 18), aligned spins (abs(a/m)~0.85 or when q=1 abs(a/m)<0.98), fitting coefficients for QNM type, and sampling rate Returns the frequency, the Phenom amplitude of the inspiral-merger-ringdown Uses methods found in <https://arxiv.org/abs/1508.07253> and <https://arxiv.org/abs/1508.07250> Parameters ---------- source : object source object from StrainandNoise, contains all source parameters pct_of_peak : float, optional the percentange of the strain at merger that dictates the maximum frequency the waveform is calculated at in geometrized units (G=c=1) Returns ------- Mf : numpy array of floats the waveform frequencies in geometrized units (G=c=1) fullwaveform : numpy array of floats the waveform strain in geometrized units (G=c=1) """ f_low = source.f_low N = source.nfreqs q = source.q x1 = source.chi1 x2 = source.chi2 fitcoeffs = source._fitcoeffs #M = m1+m2 #Total Mass #q = m2/m1 #Mass Ratio: Paper tested up to 18 #eta = m1m2/M2 reduced mass: Paper tested up to 0.05 (q=18) eta = q/(q+1)2 x_PN = chi_PN(eta,x1,x2) #PN reduced spin parameter a_f = a_final(x1,x2,q,eta) #dimensionless spin ################## #Finds f_ringdown and f_damp from fit taken from <https://arxiv.org/abs/gr-qc/0512160> n = 0 #QNM indices l = 2 m = 2 numn = 3 #number of n's included in the table index = (l-2)(2l+1)numn + (l-m)numn + n f_fit = fitcoeffs[index][3:6] q_fit = fitcoeffs[index][6:9] omega_RD = f_fit[0]+f_fit[1](1-a_f)*f_fit[2] #M omega_{lmn} tau = 2(q_fit[0]+q_fit[1](1-a_f)q_fit[2])/omega_RD #tau_{lmn}/M = 2 Q_{lmn}/(M omega_{lmn}) ######################## f_RD = omega_RD/2/np.pi f_damp = 1/tau/2/np.pi Gamma1 = Lambda(eta,x_PN,4) Gamma2 = Lambda(eta,x_PN,5) Gamma3 = Lambda(eta,x_PN,6) f_peak = Calc_f_peak(f_RD,f_damp,[Gamma1,Gamma2,Gamma3]) f1 = 0.014 f3 = f_peak f2 = (f1+f3)/2 f1_phase = 0.018 f2_phase = 0.5f_RD cutoffFreq = Find_Cutoff_Freq(f_RD,f_damp,[Gamma1,Gamma2,Gamma3],pct_of_peak=pct_of_peak) #If lowest frequency is greater than cutoffFreq, then raise error. if f_low >= cutoffFreq: raise ValueError('Lower frequency bound (ie. f_low) must be lower than that of the merger ringdown.') Mf = np.logspace(np.log10(f_low),np.log10(cutoffFreq),N) #Mf_phase = np.logspace(log10(0.0035),log10(1.15f_RD),N) #Mf_phase = np.logspace(log10(0.0035),log10(0.12),N) v1 = A_insp(f1,eta,x1,x2,x_PN) v2 = Lambda(eta,x_PN,3) v3 = A_MR(f3,f_RD,f_damp,[Gamma1,Gamma2,Gamma3]) fund1 = DA_insp(f1,eta,x1,x2,x_PN) fund3 = DA_MR(f3,f_RD,f_damp,[Gamma1,Gamma2,Gamma3]) ############################# #Calculate Solutions to eqn 21 in intermediate region Del_solns = A_intermediate(f1,f2,f3,v1,v2,v3,fund1,fund3) # Solutions to eqn 21 ############################## #Calculate all sections of waveform and Paste together indxf1 = np.argmin(np.abs(Mf-f1)) indxfpeak = np.argmin(np.abs(Mf-f_peak)) tmpinspiral = A_norm(Mf[0:indxf1+1],eta)A_insp(Mf[0:indxf1+1],eta,x1,x2,x_PN) tmpintermediate = A_norm(Mf[indxf1+1:indxfpeak],eta)A_int(Mf[indxf1+1:indxfpeak],Del_solns) tmpmergerringdown = A_norm(Mf[indxfpeak:],eta)A_MR(Mf[indxfpeak:],f_RD,f_damp,[Gamma1,Gamma2,Gamma3]) fullwaveform = np.hstack((tmpinspiral,tmpintermediate,tmpmergerringdown)) ############################## #Calculate all section of waveform Phase indxf1_phase = np.argmin(np.abs(Mf-f1_phase)) indxf2_phase = np.argmin(np.abs(Mf-f2_phase)) tc=0.0 ############################## #Calculate Phase connections alpha0 and Beta0: dphi_ins = Dphi_ins(f1_phase,eta,x1,x2,x_PN,tc) phi_ins = Phi_ins(f1_phase,eta,x1,x2,x_PN,tc) beta1 = etadphi_ins - Dphi_int(f1_phase,eta,x_PN,0.0) beta0 = etaphi_ins - Phi_int(f1_phase,eta,x_PN,beta1,0.0) alpha1 = Dphi_int(f2_phase,eta,x_PN,beta1) - Dphi_MR(f2_phase,eta,x_PN,f_RD,f_damp,0.0) alpha0 = Phi_int(f2_phase,eta,x_PN,beta1,beta0) - Phi_MR(f2_phase,eta,x_PN,f_RD,f_damp,alpha1,0.0) dinspiral_phase = Dphi_ins(Mf[:indxf1_phase+1],eta,x1,x2,x_PN,tc) dintermediate_phase = (1/eta)Dphi_int(Mf[indxf1_phase+1:indxf2_phase],eta,x_PN,beta1) dmerger_ringdown_phase = (1/eta)Dphi_MR(Mf[indxf2_phase:],eta,x_PN,f_RD,f_damp,alpha1) inspiral_phase = Phi_ins(Mf[0:indxf1_phase+1],eta,x1,x2,x_PN,tc) intermediate_phase = (1/eta)Phi_int(Mf[indxf1_phase+1:indxf2_phase],eta,x_PN,beta1,beta0) merger_ringdown_phase = (1/eta)Phi_MR(Mf[indxf2_phase:],eta,x_PN,f_RD,f_damp,alpha1,alpha0) ############################ #Join subsections of phase and amplitude fullphase = np.hstack((inspiral_phase,intermediate_phase,merger_ringdown_phase)) return [Mf,fullwaveform,fullphase] def A_norm(freqs,eta): """Calculates the constant scaling factor A_0 Parameters ---------- freqs : array The frequencies in Natural units (Mf, G=c=1) of the waveform eta : float The reduced mass ratio """ const = np.sqrt(2eta/3/np.pi(1/3)) return constfreqs*-(7/6) def A_insp(freqs,eta,x1,x2,X_PN): """Calculates the Inspiral Amplitude Parameters ---------- freqs : array The frequencies in Natural units (Mf, G=c=1) of the waveform eta : float The reduced mass ratio x1 : float The dimensionless spin parameter abs(a/m) for black hole m1. x2 : float The dimensionless spin parameter abs(a/m) for black hole m2. x_PN : float The PN reduced spin parameter """ A_PN = 0.0 A_higher = 0.0 for i in range(7): A_PN = A_PN + PN_coeffs(eta,x1,x2,i)(np.pifreqs)(i/3) if i >= 1 and i <= 3: A_higher = A_higher + Lambda(eta,X_PN,i-1)freqs((6+i)/3) return (A_PN + A_higher) def DA_insp(freqs,eta,x1,x2,X_PN): """Calculates Derivative of the inspiral amplitude. Parameters ---------- freqs : array The frequencies in Natural units (Mf, G=c=1) of the waveform eta : float The reduced mass ratio x1 : float The dimensionless spin parameter abs(a/m) for black hole m1. x2 : float The dimensionless spin parameter abs(a/m) for black hole m2. x_PN : float The PN reduced spin parameter """ DA_PN = 0.0 DA_higher = 0.0 for i in range(7): PN_const = np.pi(i/3)(i/3)PN_coeffs(eta,x1,x2,i) DA_PN = DA_PN + PN_const(freqs)((i-3)/3) if i >= 1 and i <= 3: higher_const = ((6+i)/3)Lambda(eta,X_PN,i-1) DA_higher = DA_higher + higher_constfreqs((i+3)/3) return DA_PN + DA_higher def A_MR(freqs,f_RD,f_damp,Gammas): """Calculates the Normalized Merger-Ringdown Amplitude Parameters ---------- freqs : array The frequencies in Natural units (Mf, G=c=1) of the waveform f_RD : float Frequency of the Ringdown transition f_damp : float Damping frequency Gammas : array-like Normalizes lorentzian to correct shape """ varf = freqs-f_RD fg_d = Gammas[2]f_damp return (Gammas[0]fg_d)/(varf2+fg_d2)np.exp(-(Gammas[1]/fg_d)varf) def DA_MR(freqs,f_RD,f_damp,Gammas): """Calculates Derivative of the Merger-Ringdown Amplitude Parameters ---------- freqs : array The frequencies in Natural units (Mf, G=c=1) of the waveform f_RD : float Frequency of the Ringdown transition f_damp : float Damping frequency Gammas : array-like Normalizes lorentzian to correct shape """ varf = freqs-f_RD fg_d = Gammas[2]f_damp A_MR_0 = A_MR(freqs,f_RD,f_damp,Gammas) return -A_MR_0(2varf/(varf2+fg_d2)+Gammas[1]/fg_d) def A_intermediate(f1,f2,f3,v1,v2,v3,d1,d3): """Solves system of equations for intermediate amplitude matching""" Mat = np.array([[1., f1, f12, f13, f14],[1., f2, f22, f23, f24],[1., f3, f32, f33, f3*4], \ [0., 1., 2f1, 3f12, 4f1*3],[0., 1., 2f3, 3f32, 4f3*3]],dtype='float') a = np.array([v1,v2,v3,d1,d3],dtype='float') return np.linalg.solve(Mat,a) def A_int(freqs,delt): """Calculates the Intermediate Amplitude Parameters ---------- freqs : array The frequencies in Natural units (Mf, G=c=1) of the waveform delt : array Coefficient solutions to match the inspiral to the merger-ringdown portion of the waveform """ return (delt[0]+delt[1]freqs+delt[2]freqs2+delt[3]freqs*3+delt[4]freqs*4) ########################################################################### #Phase portion of waveform ########################################################################### def Phi_ins(freqs,eta,x1,x2,x_PN,t_c,phi_c,sigma0): """Calculates the Inspiral Phase Parameters ---------- freqs : array The frequencies in Natural units (Mf, G=c=1) of the waveform eta : float The reduced mass ratio x1 : float The dimensionless spin parameter abs(a/m) for black hole m1. x2 : float The dimensionless spin parameter abs(a/m) for black hole m2. x_PN : float The PN reduced spin parameter t_c : float Coalescence time?? """ #t_c = 0.0 #???? #phi_c = 0.0 #???????? #sigma0 = 0.0 #??? sigma1 = Lambda(eta,x_PN,7) sigma2 = Lambda(eta,x_PN,8) sigma3 = Lambda(eta,x_PN,9) sigma4 = Lambda(eta,x_PN,10) TF2_expansion = 0.0 TF2_const = 3/(128eta) piMf = np.pifreqs for i in range(5): #First four of summation, others need to be separate for log(piMf) factors. TF2_expansion = TF2_expansion + PN_coeffs_phase(eta,x1,x2,i)(piMf)((i-5)/3) TF2_expansion = (TF2_expansion + (1+np.log10(piMf))PN_coeffs_phase(eta,x1,x2,5) + (PN_coeffs_phase(eta,x1,x2,6) - (6848/63)np.log10(64piMf))(piMf)(1/3) + PN_coeffs_phase(eta,x1,x2,7)(piMf)*(2/3)) phi_TF2 = 2t_cpiMf - phi_c - np.pi/4 + TF2_constTF2_expansion return (phi_TF2 + (1/eta)(sigma0 + sigma1freqs + (3/4sigma2)freqs*(4/3) + (3/5sigma3)freqs(5/3) + (.5sigma4)freqs2)) def Dphi_ins(freqs,eta,x1,x2,x_PN,t_c): """Calculates the Inspiral Phase Derivative Parameters ---------- freqs : array The frequencies in Natural units (Mf, G=c=1) of the waveform eta : float The reduced mass ratio x1 : float The dimensionless spin parameter abs(a/m) for black hole m1. x2 : float The dimensionless spin parameter abs(a/m) for black hole m2. x_PN : float The PN reduced spin parameter t_c : float Coalescence time?? """ #t_c = -200.0 #???? sigma1 = Lambda(eta,x_PN,7) sigma2 = Lambda(eta,x_PN,8) sigma3 = Lambda(eta,x_PN,9) sigma4 = Lambda(eta,x_PN,10) TF2_const_deriv = 3/(128eta) TF2_expans_deriv = 0.0 for i in range(5): TF2_expans_deriv = TF2_expans_deriv + (PN_coeffs_phase(eta,x1,x2,i)np.pi((i-5)/3)((i-5)/3))freqs((i-8)/3) Dphi_TF2 = (2np.pit_c + TF2_const_deriv(TF2_expans_deriv + PN_coeffs_phase(eta,x1,x2,4)/freqs + (PN_coeffs_phase(eta,x1,x2,5)-(6848/63)(3+np.log10((64np.pi)freqs)))(np.pi*(1/3)/3)freqs*(-2/3) + ((2/3)np.pi*(2/3)PN_coeffs_phase(eta,x1,x2,6))freqs(-1/3))) return(Dphi_TF2 + (1/eta)(sigma1 + sigma2freqs(1/3) + sigma3freqs*(2/3) + sigma4freqs)) def Phi_MR(freqs,eta,x_PN,f_RD,f_damp,alpha1,alpha0): """Calculates the Normalized Merger-Ringdown Phase Parameters ---------- freqs : array The frequencies in Natural units (Mf, G=c=1) of the waveform eta : float The reduced mass ratio x_PN : float The PN reduced spin parameter f_RD : float Frequency of the Ringdown transition f_damp : float Damping frequency alpha1 : float ?? alpha0 : float ?? """ ########################### #Calculates phase for merger ringdown (eqn 14) alpha2 = Lambda(eta,x_PN,15) alpha3 = Lambda(eta,x_PN,16) alpha4 = Lambda(eta,x_PN,17) alpha5 = Lambda(eta,x_PN,18) lorentzian = (freqs - alpha5f_RD)/f_damp return (alpha0 + alpha1freqs - alpha2/freqs + (4./3.alpha3)freqs*(.75) + alpha4np.arctan(lorentzian)) def Dphi_MR(freqs,eta,x_PN,f_RD,f_damp,alpha1): """Calculates Derivative of the Merger-Ringdown Phase Derivative Parameters ---------- freqs : array The frequencies in Natural units (Mf, G=c=1) of the waveform eta : float The
# -- coding: utf-8 -- """ featherpmm.py: Extends feather file format with a paired file that contains extra metadata. If the feather file is /path/to/foo.feather, the metadata file is /path/to/foo.pmm. """ from __future__ import division import os import datetime import numpy as np import sys try: import pyarrow.feather as feather except ImportError: feather = None from pmmif import pmm PANDAS_STRING_DTYPE = np.dtype('O') PANDAS_BOOL_DTYPE = np.dtype('bool') PANDAS_FLOAT_DTYPE = np.dtype('float64') NULL_STRING = '∅' NULL_SUFFIX = '_' + NULL_STRING UTF8 = 'UTF-8' isPython2 = sys.version_info.major < 3 if isPython2: bytes_type = str # type: type unicode_type = unicode # type: type else: bytes_type = bytes unicode_type = str class Dataset(object): """ Container for a Pandas dataframe and a metadata object from PMM. Metadata fulfils two functions: 1. It allows association of 'intended' types of columns, in cases for which Pandas forces promotion. For example: - Integer columns that contain nulls are promoted to float in Pandas, but can be marked as 'integer' in the metadata. - Boolean columns that contain nulls are promoted to Object in Pandas, but can be marked as 'boolean' in the metadata. 2. It allows additional annotations to be associated with the dataframe, and with individual columns of the dataframe. - Tags (with optional values) - Descriptions A dataset object has two attributes: - df is an ordinary Pandas dataframe - md is a PMM Metadata object """ def __init__(self, df, md=None, name=None): """ Create Dataset object. - df is a Pandas dataframe. - md is a PMM metadata object, and is optional. - name is the name to be associated with the dataset, for the case where no metadata is provided. If no metadata is provided, the metadata is inferred from the dataframe. """ self.df = df self.md = md or _create_pmm_metadata(df, name) def add_field(self, name, col, pmmtype=None): """ Add a new field to the dataset. Adds the field to the Pandas dataframe, and declares its type in the PMM metadata. The type, if provided, must be one of: 'boolean', 'integer', 'real', 'string', 'datestamp'. If the type is not specified, the type is inferred from the dataframe. The advantages of using add_field, as opposed to just creating it directly in the dataframe, are: - the option of specifying an intended type, even if Pandas has promoted the type in the dataframe. - creating a corresponding entry in the metadata, to facilitate tagging, descriptions, etc. """ self.df[name] = col self.declare_field(name, pmmtype) def declare_field(self, name, pmmtype=None): """ Declare the type of a field in the dataset, which must already exist in the Pandas dataframe. This is intended for use when a dataframe has been created without any metadata (for example, from a CSV file), and then more detailed type information needs to be declared for existing fields. The type, if provided, must be one of the types described for add_field, above. """ if self.df[name].isnull().all(): # all null or no records if pmmtype == 'string': self.df[name] = self.df[name].astype(np.dtype('O')) elif pmmtype == 'datestamp': self.df[name] = (self.df[name] .astype(np.dtype('datetime64[ns]'))) fieldMetadata = _create_pmm_field(self.df[name], pmmtype=pmmtype) self.md.add_field(name, fieldMetadata) def tag_field(self, colname, tagname, value=None): """ Add a tag to a field. The tag can optionally have a value, but by default does not. The field must already exist in the metadata. If a value is provided, it must be of one of the following Python types: - None - bool - int - float - str - datetime.datetime or it can be a (potentially nested) list or dictionary over these types with string keys. """ self.md[colname].tags[tagname] = value def tag_dataset(self, tagname, value=None): """ Add a tag to the dataset. The tag can optionally have a value, but by default does not. If a value is provided, it must have one of the types described in tag_field above. """ self.md.tags[tagname] = value def update_metadata(self): """ Update the metadata to bring it into line with the dataset. After calling this method, all of the fields that exist in the dataset will now exist in the metadata too, and the metadata will not contain any fields that do not appear in the dataframe. It will infer types in the metadata for any fields that do not already have metadata, but will not alter the types of existing fields in the metadata. """ _reset_fields_from_dataframe(self) def merge_metadata(self, other, fields=None): """ After merging another dataset in (via pd.merge), we don't have any of the metadata associated with the fields that have come in from that other dataset. This function takes the 'other metadata' and brings it into the metadata for the main dataset. The new metadata wins if the fields exists in both if: 1. the fields parameter is left empty, or 2. the fields parameter is a list but field is not in the list """ newfields = [fx.name for fx in other.md.fields] for f in newfields: if (fields is None) or (f in fields): if f in [fx.name for fx in self.md.fields]: del self.md.fields[[fx.name for fx in self.md.fields].index(f)] _add_metadata_from_other_dataset(self.md, other.md) _reset_fields_from_dataframe(self) def append(self, other): """ Append another dataset to an existing one. The second dataframe is appended to the first one, and the metadata for any fields that only exist in the second one is added to the first one. """ self.df = self.df.append(other.df, ignore_index=True) _add_metadata_from_other_dataset(self.md, other.md) _reset_fields_from_dataframe(self) def read_dataframe(featherpath): """ Similar to feather.read_dataframe except that it also reads the corresponding .pmm file, if present, and returns a Dataset object rather than a dataframe. The Dataset object contains the Pandas dataframe in its df attribute, and the metadata in its md attribute. """ if feather is None: raise Exception('Feather-format is not available') df = feather.read_feather(featherpath) pmmpath, datasetname = _split_feather_path(featherpath) if os.path.exists(pmmpath): md = pmm.load(pmmpath) else: md = _create_pmm_metadata(df, datasetname) df = _recover_problematical_all_null_columns(Dataset(df, md)) return Dataset(df, md) def write_dataframe(dataset, featherpath): """ Similar to feather.write_dataframe except that it also writes a corresponding .pmm file, and expects a Dataset object rather than a dataframe. The Dataset object contains the Pandas dataframe in its df attribute, and the metadata in its md attribute. """ if feather is None: raise Exception('Feather-format is not available') pmmpath, datasetname = _split_feather_path(featherpath) if dataset.md is None: dataset.md = _create_pmm_metadata(dataset.df, datasetname) _reset_fields_from_dataframe(dataset) df = _sanitize_problematical_all_null_columns(dataset) try: # feather doesn't always write file correctly if it already exists if os.path.exists(featherpath): os.remove(featherpath) feather.write_feather(df, featherpath) dataset.md.save(pmmpath) except: # feather leaves dud files around if it fails to write if os.path.exists(featherpath): os.remove(featherpath) if os.path.exists(pmmpath): os.remove(pmmpath) raise # # The rest of the functions below are internal to this module, and should not # be called from outside. # def _sanitize_problematical_all_null_columns(ds): """ Feather doesn't like all-null string columns or all-null boolean columns, so this method transforms them before saving. They are transformed into float64 fields with NaN et every value, and the pandas column name gets a '_∅t' appended, where t is a type indicator --- b for boolean, s for string or u for unknown. """ origdf, md = ds.df, ds.md df = origdf[list(origdf)] # Copye nTransformed = 0 fieldnames = list(df.columns) nRecords = len(df.index) dfIsUnicode = fieldnames and type(fieldnames[0]) == unicode_type fn = _unicode_definite if dfIsUnicode else _utf8_definite null_suffix = fn(NULL_SUFFIX) for i, f in enumerate(fieldnames): if (df[f].dtype not in (PANDAS_STRING_DTYPE, PANDAS_BOOL_DTYPE) or df[f].notnull().sum() > 0): # includes bools with nulls continue if df[f].dtype == PANDAS_BOOL_DTYPE and nRecords > 0: continue typeChar = ('b' if md[f].type == 'boolean' else 's' if md[f].type == 'string' else 'u') altname = f + null_suffix + typeChar if altname in fieldnames: continue # already there. Whatever... # restore any all-null string fields df[altname] = np.array([np.nan] * nRecords, dtype=PANDAS_FLOAT_DTYPE) nTransformed += 1 fieldnames[i] = altname if nTransformed > 0: df = df[fieldnames] return df def _recover_problematical_all_null_columns(ds): """ Feather doesn't like all-null string columns or all-null boolean columns, so they are sanitized before saving; this untransforms them. """ df, md = ds.df, ds.md nTransformed = 0 fieldnames = list(df.columns) nRecords = len(df.index) dfIsUnicode = fieldnames and type(fieldnames[0]) == unicode_type fn = _unicode_definite if dfIsUnicode else _utf8_definite null_suffix = fn(NULL_SUFFIX) for i, f
<reponame>PrivateStorageio/SecureAccessTokenAuthorizer # Copyright 2022 PrivateStorage.io, LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import annotations """ A system for replicating local SQLite3 database state to remote storage. Theory of Operation =================== A function to wrap a ``sqlite3.Connection`` in a new type is provided. This new type provides facilities for accomplishing two goals: * It (can someday) presents an expanded connection interface which includes the ability to switch the database into "replicated" mode. This is an application-facing interface meant to be used when the application is ready to discharge its responsibilities in the replication process. * It (can someday) expose the usual cursor interface wrapped around the usual cursor behavior combined with extra logic to record statements which change the underlying database (DDL and DML statements). This recorded data then feeds into the above replication process once it is enabled. An application's responsibilities in the replication process are to arrange for remote storage of "snapshots" and "event streams". See the replication/recovery design document for details of these concepts. Once replication has been enabled, the application (can someday be) informed whenever the event stream changes (respecting database transactionality) and data can be shipped to remote storage as desired. It is essential to good replication performance that once replication is enabled all database-modifying actions are captured in the event stream. This is the reason for providing a ``sqlite3.Connection``-like object for use by application code rather than a separate side-car interface: it minimizes the opportunities for database changes which are overlooked by this replication system. """ __all__ = [ "ReplicationAlreadySetup", "fail_setup_replication", "setup_tahoe_lafs_replication", "with_replication", "statements_to_snapshot", "connection_to_statements", "snapshot", ] import os import re from enum import Enum from io import BytesIO from sqlite3 import Connection as _SQLite3Connection from sqlite3 import Cursor as _SQLite3Cursor from typing import ( IO, Any, Awaitable, Callable, ClassVar, Generator, Iterable, Iterator, Optional, Protocol, Sequence, ) import cbor2 from attrs import Factory, define, field, frozen from compose import compose from eliot import log_call from twisted.application.service import IService, Service from twisted.internet.defer import CancelledError, Deferred, DeferredQueue, succeed from twisted.logger import Logger from twisted.python.filepath import FilePath from twisted.python.lockfile import FilesystemLock from ._types import CapStr from .config import REPLICA_RWCAP_BASENAME, Config from .sql import Connection, Cursor, SQLRuntimeType, SQLType, statement_mutates from .tahoe import DataProvider, DirectoryEntry, ITahoeClient, attenuate_writecap # function which can set remote ZKAPAuthorizer state. Uploader = Callable[[str, DataProvider], Awaitable[None]] # function which can remove entries from ZKAPAuthorizer state. Pruner = Callable[[Callable[[str], bool]], Awaitable[None]] # functions which can list all entries in ZKAPAuthorizer state Lister = Callable[[], Awaitable[list[str]]] EntryLister = Callable[[], Awaitable[dict[str, DirectoryEntry]]] class SnapshotPolicy(Protocol): """ Encode policy rules about when to take and upload a new snapshot. """ def should_snapshot(self, snapshot_size: int, replica_sizes: list[int]) -> bool: """ Given the size of a new snapshot and the size of an existing replica (snapshot and event streams), is now a good time to take a new snapshot? """ SNAPSHOT_NAME = "snapshot" @frozen class Replica: """ Manage a specific replica. """ upload: Uploader prune: Pruner entry_lister: EntryLister async def list(self) -> list[str]: return list(await self.entry_lister()) class ReplicationJob(Enum): """ The kinds of jobs that the Replication queue knows about :ivar startup: The job that is run once when the replication service starts and which is responsible for inspecting local and remote state to determine if any actions are immediately necessary (even before any further local changes are made). :ivar event_stream: The job to upload a new event stream object. :ivar snapshot: The job to upload a new snapshot object and prune now-obsolete event stream objects. :ivar consider_snapshot: The job to inspect replica event stream and snapshot state and potentially schedule a new snapshot which will allow pruning of existing event streams. """ startup = 1 event_stream = 2 snapshot = 3 consider_snapshot = 4 @frozen class Change: """ Represent an item in a replication event stream :ivar sequence: The sequence number of this event. :ivar statement: The SQL statement associated with this event. :ivar important: Whether this change was "important" or not. :ivar arguments: Any arguments for the SQL statement. """ sequence: int statement: str arguments: Sequence[SQLType] = field(converter=tuple) important: bool @arguments.validator def _validate_arguments(self, attribute, value) -> None: """ Require that the value has as elements only values are legal SQL values. :note: attrs validators run after attrs converters. """ if all(isinstance(o, SQLRuntimeType) for o in value): return None raise ValueError("sequence contains values incompatible with SQL") @frozen class EventStream: """ A series of database operations represented as `Change` instances. :ivar version: An identifier for the schema of the serialized form of this event stream. This will appear inside the serialized form. A change to the schema will be accompanied with an increment to this value. """ changes: Sequence[Change] = field(converter=tuple) version: ClassVar[int] = 1 def highest_sequence(self) -> Optional[int]: """ :returns: the highest sequence number in this EventStream (or None if there are no events) """ if not self.changes: return None return max(change.sequence for change in self.changes) def to_bytes(self) -> IO[bytes]: """ :returns: a producer of bytes representing this EventStream. """ return BytesIO( cbor2.dumps( { "version": self.version, "events": tuple( ( event.sequence, event.statement, event.arguments, event.important, ) for event in self.changes ), } ) ) @classmethod def from_bytes(cls, stream: IO[bytes]) -> EventStream: """ :returns EventStream: an instance of EventStream from the given bytes (which should have been produced by a prior call to ``to_bytes``) """ data = cbor2.load(stream) serial_version = data.get("version", None) if serial_version != cls.version: raise ValueError( f"Unknown serialized event stream version {serial_version}" ) return cls( changes=[ # List comprehension has incompatible type List[Change]; expected List[_T_co] # https://github.com/python-attrs/attrs/issues/519 Change(args) # type: ignore for args in data["events"] ] ) class AlreadySettingUp(Exception): """ Another setup attempt is currently in progress. """ class ReplicationAlreadySetup(Exception): """ An attempt was made to setup of replication but it is already set up. """ async def fail_setup_replication(): """ A replication setup function that always fails. """ raise Exception("Test not set up for replication") async def setup_tahoe_lafs_replication(client: ITahoeClient) -> str: """ Configure the ZKAPAuthorizer plugin that lives in the Tahoe-LAFS node with the given configuration to replicate its state onto Tahoe-LAFS storage servers using that Tahoe-LAFS node. """ # Find the configuration path for this node's replica. config_path = client.get_private_path(REPLICA_RWCAP_BASENAME) # Take an advisory lock on the configuration path to avoid concurrency # shennanigans. config_lock = FilesystemLock(config_path.asTextMode().path + ".lock") if not config_lock.lock(): raise AlreadySettingUp() try: # Check to see if there is already configuration. if config_path.exists(): raise ReplicationAlreadySetup() # Create a directory with it rw_cap = await client.make_directory() # Store the resulting write-cap in the node's private directory config_path.setContent(rw_cap.encode("ascii")) finally: # On success and failure, release the lock since we're done with the # file for now. config_lock.unlock() # Attenuate it to a read-cap rocap = attenuate_writecap(rw_cap) # Return the read-cap return rocap def is_replication_setup(config: Config) -> bool: """ :return: ``True`` if and only if replication has previously been setup for the Tahoe-LAFS node associated with the given configuration. """ # Find the configuration path for this node's replica. return FilePath(config.get_private_path(REPLICA_RWCAP_BASENAME)).exists() def get_replica_rwcap(config: Config) -> CapStr: """ :return: a mutable directory capability for our replica. :raises: Exception if replication is not setup """ rwcap_file = FilePath(config.get_private_path(REPLICA_RWCAP_BASENAME)) return rwcap_file.getContent().decode("ascii") @define class _Important: """ A context-manager to set and unset the ._important flag on a _ReplicationCapableConnection """ _replication_cursor: _ReplicationCapableCursor def __enter__(self) -> None: self._replication_cursor._important = True def __exit__(self, args) -> None: self._replication_cursor._important = False return None def with_replication( connection: _SQLite3Connection, enable_replication: bool ) -> _ReplicationCapableConnection: """ Wrap the given connection in a layer which is capable of entering a "replication mode". In replication mode, the wrapper stores all changes made through the connection so that they are available to be replicated by another component. In normal mode, changes are not stored. :param connection: The SQLite3 connection to wrap. :param enable_replication: If ``True`` then the wrapper is placed in "replication mode" initially. Otherwise it
<filename>src/FinFET.py<gh_stars>1-10 #BSD 3-Clause License # #Copyright (c) 2019, The Regents of the University of Minnesota # #All rights reserved. # #Redistribution and use in source and binary forms, with or without #modification, are permitted provided that the following conditions are met: # #* Redistributions of source code must retain the above copyright notice, this # list of conditions and the following disclaimer. # #* Redistributions in binary form must reproduce the above copyright notice, # this list of conditions and the following disclaimer in the documentation # and/or other materials provided with the distribution. # #* Neither the name of the copyright holder nor the names of its # contributors may be used to endorse or promote products derived from # this software without specific prior written permission. # #THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" #AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE #IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE #DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE #FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL #DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR #SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER #CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, #OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE #OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. #!/usr/bin/env python3 # -- coding: utf-8 -- """ @author = <NAME> """ import math import json import argparse import numpy as np from thermalModel import thermalModel #direction convention #length is along x Dimesion 0 #width is along y Dimension 1 #height is along z Dimension 2 class FinFET: def __init__(self, TECH, MOS, n_gate, n_fin, f_model_param, f_tool_config): self.TECH = TECH self.MOS = MOS self.n_gate = n_gate self.n_fin = n_fin self.initialize(f_tool_config, f_model_param) self.create_model() def quant(self, a, reso): return math.ceil(a / reso) * reso def load_json(self, json_file): with open(json_file) as f: json_data = json.load(f) return json_data def initialize(self, file_tool_config, file_model_param): tool_config = self.load_json(file_tool_config) model_param = self.load_json(file_model_param) # properties l_chnl = model_param["dimensions"]["l_chnl"] t_gate = model_param["dimensions"]["t_gate"] #design dimensions t_substrate = model_param["dimensions"][ "t_substrate"] #thickness of substrate that has been modelled for after the point at which it is t_box = model_param["dimensions"][ "t_box"] #25 # thickness of box layer t_chnl = model_param["dimensions"][ "t_chnl"] #6 # thickness of channel, source and drain diffusions t_gox = model_param["dimensions"][ "t_gox"] #1 # thickness of gate oxide t_diff_ext = model_param["dimensions"][ "t_diff_ext"] #20 # height of the diffusion extension above the diffusion t_cont = model_param["dimensions"][ "t_cont"] #10 thickness of contact bar # across all fins e_gate = model_param["dimensions"][ "e_gate"] #10 # extension of gate out of diffusion l_gate_space = model_param["dimensions"][ "l_gate_space"] #35 # lenght of source and drain diffusion l_diff_ext = model_param["dimensions"][ "l_diff_ext"] #25 # length of the source and drain diffusion extension l_cont = model_param["dimensions"]["l_cont"] #10 #length of contact w_cont = model_param["dimensions"]["w_cont"] #10 #width of contact self.res = tool_config["resolution"] l_sp_diff_ext = tool_config[ "l_sp_diff_ext"] #5 # spacing between gate and either edge sp_edge = tool_config["sp_edge"] #5 # spacing to the edges t_sp_edge = tool_config["t_sp_edge"] #20 # spacing to the edges l_g2sd_junc = model_param["dimensions"]["l_g2sd_junc"] l_sd_junc = model_param["dimensions"]["l_sd_junc"] w_fin = model_param["dimensions"]["w_fin"] w_fin_space = model_param["dimensions"]["w_fin_space"] self.t_sub2gnd = tool_config[ "t_sub2gnd"] #475 # thickness of substrate to ground not represented in node self.t_cnt2gnd = tool_config[ "t_cnt2gnd"] #1000 # distance from contact to ground self.resx = self.res[0] self.resy = self.res[1] self.resz = self.res[2] resx = self.res[0] resy = self.res[1] resz = self.res[2] self.w_fin = self.quant(w_fin, resy) self.w_fin_space = self.quant(w_fin_space, resy) self.l_chnl = self.quant(l_chnl, resx) self.t_gate = self.quant(t_gate, resz) self.t_box = self.quant(t_box, resz) self.t_chnl = self.quant(t_chnl, resz) self.t_gox = self.quant(t_gox, resz) self.l_gox = self.quant(t_gox, resx) self.w_gox = self.quant(t_gox, resy) self.t_cont = self.quant(t_cont, resz) self.l_sp_edge = self.quant(sp_edge, resx) self.w_sp_edge = self.quant(sp_edge, resy) self.t_sp_edge = self.quant(t_sp_edge, resz) self.e_gate = self.quant(e_gate, resy) self.l_gate_space = self.quant(l_gate_space, resx) self.t_substrate = self.quant(t_substrate, resz) self.l_diff_ext = self.quant(l_diff_ext, resx) self.t_diff_ext = self.quant(t_diff_ext, resz) self.l_sp_diff_ext = self.quant(l_sp_diff_ext, resx) self.l_cont = self.quant(l_cont, resx) self.w_cont = self.quant(w_cont, resy) self.l_g2sd_junc = self.quant(l_g2sd_junc, resx) self.l_sd_junc = self.quant(l_sd_junc, resx) # t_sub2gnd and t_cnt2gnd do not need quatization as there are not used in mask assert self.TECH == 'SOI' or self.TECH == 'Bulk', "Undefined TECH type" self.length = 2self.l_sp_edge + 2(self.l_sd_junc + self.l_g2sd_junc) +\ (self.n_gate - 1)(self.l_chnl+self.l_gate_space) + self.l_chnl self.width = 2(self.w_sp_edge+ self.e_gate) +\ (self.n_fin -1)(2self.w_gox + self.w_fin + self.w_fin_space) +\ 2self.w_gox + self.w_fin self.height = self.t_substrate + self.t_box + self.t_chnl + self.t_cont +\ self.t_sp_edge + max(self.t_gox + self.t_gate, self.t_diff_ext) #print("%d %d %d %d %d"%(self.t_substrate, self.t_box, self.t_chnl,\ # self.t_sp_edge, max(self.t_gox + self.t_gate, self.t_diff_ext))) print("INFO: Model Dimensions LWH: %4.3f %4.3f %4.3f" % (self.length, self.width, self.height)) print("INFO: Resolution : %4.3e %4.3e %4.3e" % (self.resx, self.resy, self.resz)) self.device = thermalModel(length=self.length, width=self.width, height=self.height, resolution=self.res, n_fin=self.n_fin) self.device.set_device_parameters(channel_length=self.l_chnl, gate_thickness=self.t_gate, substrate2ground=self.t_sub2gnd, contact2ground=self.t_cnt2gnd, gate_oxide_thickness=self.t_gox) self.device.set_conductivity_table(file_model_param) print("INFO: Initialization complete") def create_substrate(self): self.device.create_substrate(thickness=self.t_substrate) or_z = self.t_substrate if self.TECH == 'SOI': #t_box origin = (0, 0, or_z) size = (self.length, self.width, self.t_box) self.device.create_t_box(origin, size) elif self.TECH == 'Bulk': origin = (0, 0, or_z) sz_y = self.w_sp_edge + self.e_gate + self.w_gox size = (self.length, sz_y, self.t_box) self.device.create_t_box(origin, size) for f in range(self.n_fin): #create the fin or_y = self.w_sp_edge + self.e_gate + self.w_gox +\ f(2self.w_gox + self.w_fin + self.w_fin_space) sz_y = self.w_fin origin = (0, or_y, or_z) size = (self.length, sz_y, self.t_box) self.device.create_diffusion(origin, size, self.MOS, finFET=1) #create the box or_y = or_y + sz_y origin = (0, or_y, or_z) if f == self.n_fin - 1: sz_y = self.width - or_y else: sz_y = 2 self.w_gox + self.w_fin_space size = (self.length, sz_y, self.t_box) self.device.create_t_box(origin, size) or_z = or_z + self.t_box return or_z def create_fins(self, or_x_in, or_z_in): #creates the fin with the surrounding gate and contact # or_x , or_z inputs #create source diffsion, channel and drain diffusions for the fin or_x = or_x_in or_z = or_z_in sz_z = self.t_chnl #source diffusion of fin for f in range(self.n_fin): sz_x = self.l_g2sd_junc or_y = self.w_sp_edge + self.e_gate + self.w_gox +\ f(2self.w_gox + self.w_fin + self.w_fin_space) sz_y = self.w_fin origin = (or_x, or_y, or_z) size = (sz_x, sz_y, sz_z) self.device.create_diffusion(origin, size, self.MOS, finFET=1) for n in range(self.n_gate): sz_x = self.l_chnl or_x = or_x_in + self.l_g2sd_junc + n * (self.l_chnl + self.l_gate_space) or_x_gate = or_x or_y = self.w_sp_edge #surround gate origin = (or_x, or_y, or_z) sz_y = self.e_gate size = (sz_x, sz_y, sz_z + self.t_gox) cond = self.device.cond['gate'] self.device.create_box(origin, size, cond) for f in range(self.n_fin): # surround gate oxide or_y = or_y + sz_y origin = (or_x, or_y, or_z) sz_y = self.w_gox size = (sz_x, sz_y, sz_z) cond = self.device.cond['SiO2'] self.device.create_box(origin, size, cond) #channel or_x = or_x_gate or_y = or_y + sz_y sz_y = self.w_fin origin = (or_x, or_y, or_z) self.device.create_channel(origin=origin, channel_width=sz_y, channel_depth=sz_z, d_type=self.MOS) # drain diffusion or_x = or_x_gate + self.l_chnl if n == self.n_gate - 1: sz_x = self.l_g2sd_junc else: sz_x = self.l_gate_space origin = (or_x, or_y, or_z) size = (sz_x, sz_y, sz_z) self.device.create_diffusion(origin, size, self.MOS, finFET=1) # surround gate oxide or_x = or_x_gate or_y = or_y + sz_y origin = (or_x, or_y, or_z) sz_x = self.l_chnl sz_y = self.w_gox size = (sz_x, sz_y, sz_z) cond = self.device.cond['SiO2'] self.device.create_box(origin, size, cond) #surround gate or_x = or_x_gate or_y = or_y + sz_y origin = (or_x, or_y, or_z) sz_x = self.l_chnl if f == self.n_fin - 1: sz_y = self.e_gate else: sz_y = self.w_fin_space size = (sz_x, sz_y, sz_z + self.t_gox) cond = self.device.cond['gate'] self.device.create_box(origin, size, cond) end_x = or_x_gate + self.l_chnl + self.l_g2sd_junc end_z = or_z + sz_z return end_x, end_z def create_SD_junction(self, or_x, or_z): #or_x input sz_z = self.t_chnl or_y = self.w_sp_edge + self.e_gate sz_y = (self.n_fin -1)(2self.w_gox + self.w_fin + self.w_fin_space)+\ 2self.w_gox + self.w_fin sz_x = self.l_sd_junc origin = (or_x, or_y, or_z) size = (sz_x, sz_y, sz_z) self.device.create_diffusion(origin, size, self.MOS) return or_x + sz_x, or_z + sz_z def create_gate_oxide(self, or_x, or_z): #gate oxide or_x_in = or_x or_y = self.w_sp_edge + self.e_gate sz_y = self.w_fin + 2 self.w_gox for n in range(self.n_gate): or_x = or_x_in +self. l_sd_junc + self.l_g2sd_junc +\ n*(self.l_chnl+self.l_gate_space) origin = (or_x, or_y, or_z) self.device.create_gate_oxide(origin=origin, channel_width=sz_y) end_x = or_x + self.l_chnl end_z = or_z + self.t_gox return
""" script to ease construction of CSDGM2-style metadata for an GeMS-style geodatabase. To use, Run ValidateDatabase to make sure that the database is complete and there are no missing DMU, Glossary, or DataSources entries In ArcCatalog, go to Customize>Options>Metadata and set Metadata Style to "FGDC CSDGM Metadata". OK and exit. In ArcCatalog, use the ArcGIS metadata editor to complete the record for the GeologicMap feature dataset. Save. NOTE THAT whatever errors or you create in this master metadata record will be faithfully propagated to metadata records for all parts of the geodatabase! Run script GeMS_MetadataCSDGM2_Arc10.1.py. This script will: Export the GeologicMap metadata record in CSDGM2 format Polish this metadata slightly for use as a master record For the geodatabase as a whole and for each entity (table, feature dataset, feature class) in the geodatabase: Copies the master record. Adds supplemental information (ArcGIS reports this in Resouce:Details) about the the GeMS standard and continents of the geodatabase. Adds a description of the entity taken from the GeMS documentation. Adds entity-attribute information taken from the GeMS documentation and the DMU, Glossary, and DataSources tables of the geodatabase. Writes this XML to a file in the directory that contains the geodatabase. Imports this XML into the geodatabase as metadata for the appropriate entity. Look at file <geodatabasename>-metadataLog.txt to see what parts of which metadata records need to be completed by hand. This will occur wherever you extend the database schema beyond the schema outlined in the GeMS documentation. *Note that this script provides for a file that automates description of your extensions to the GeMS schema so that you need not edit metadata by hand--see file my_GeMSDefinitions.py in the GeMS Scripts directory.* Inspect metadata records in ArcCatalog (the Description tab) to see that they are complete. Open saved XML files in browser to see that they are appropriate. Scan for duplicate entries. You want ISO metadata? Change your Metadata Style and fix records using the ArcCatalog metadata editor. Export as ISO of your flavor, insofar as ArcCatalog allows. Let us know how this works. Usage: prompt>GeMS_MetadataCSDGM2_Arc10.1.py <geodatabase> <NAME> and <NAME>, US Geological Survey <EMAIL>, <EMAIL> """ # 17 March 2017 Changed NCGMP09 to GeMS, etc. # 18 April 2017 Added utility functions, local definition-extension file # 12 August 2017 Modified to recognize GeoMaterial, GeoMaterialConfidence, and GeoMaterialDict. # Added number of rows in each table to gdb description in SupplementalInfo #Metadata conversion (ImportMetadata_conversion) is not supported in Pro as of 180926, but is on the roadmap. import arcpy, sys, os.path, copy, imp, glob from GeMS_Definition import enumeratedValueDomainFieldList, rangeDomainDict, unrepresentableDomainDict, attribDict, entityDict, GeoMatConfDict from GeMS_utilityFunctions import * from xml.dom.minidom import * versionString = 'GeMS_MetadataCSDGM2_Arc10.py, version of 10 December 2017' translator = arcpy.GetInstallInfo("desktop")["InstallDir"]+'Metadata/Translator/ARCGIS2FGDC.xml' debug = False ncgmp = 'GeMS' ncgmpFullRef = '"GeMS (Geologic Map Schema)--a standard format for digital publication of geologic maps, version 2.0", available at http://ngmdb.usgs.gov/Info/standards/GeMS/' eaoverviewCitation = 'Detailed descriptions of entities, attributes, and attribute values are given in metadata for constituent elements of this composite dataset. See also '+ncgmpFullRef+'.' gdbDesc0a = ' is a composite geodataset that conforms to '+ncgmpFullRef+'. ' gdbDesc0b = ' is part of a composite geodataset that conforms to '+ncgmpFullRef+'. ' gdbDesc2 = 'Metadata records associated with each element within the geodataset contain more detailed descriptions of their purposes, constituent entities, and attributes. ' gdbDesc3 = ('Two shapefile versions of the dataset are also available. The OPEN shapefile version consists '+ 'of shapefiles, DBF files, and delimited text files and retains all information in the native '+ 'geodatabase, but some programming will likely be necessary to assemble these components into '+ 'usable formats. The SIMPLE shapefile version consists only of shapefiles and is easily used, but '+ 'lacks some information present in the native geodatabase.') def __appendOrReplace(rootNode,newNode,nodeTag): if len(rootNode.getElementsByTagName(nodeTag)) == 0: rootNode.appendChild(newNode) else: rootNode.replaceChild(newNode,rootNode.getElementsByTagName(nodeTag)[0]) def __fieldNameList(fc): #Returns a list of field names from Field.name in arcpy.ListFields fldList = arcpy.ListFields(fc) nameList = [] for fld in fldList: if not fld.name in ('OBJECTID', 'SHAPE','Shape', 'Shape_Length', 'Shape_Area'): nameList.append(fld.name) return nameList def __findInlineRef(sourceID): # finds the Inline reference for each DataSource_ID query = '"DataSources_ID" = \'' + sourceID + '\'' rows = arcpy.SearchCursor(dataSources, query) row = next(rows) if not row is None: #return row.Inline return row.Source else: return "" def __newElement(dom,tag,text): nd = dom.createElement(tag) ndText = dom.createTextNode(text) nd.appendChild(ndText) return nd def __updateAttrDef(fld,dom): ##element tag names are ## attr = Attribute ## attrlabl = Attribute_Label ## attrdef = Attribute_Definition ## attrdefs = Attribute_Definition_Source labelNodes = dom.getElementsByTagName('attrlabl') for attrlabl in labelNodes: if attrlabl.firstChild.data == fld: attr = attrlabl.parentNode if fld.find('_ID') > -1: # substitute generic _ID field for specific attrdefText = attribDict['_ID'] else: attrdefText = attribDict[fld] attrdef = __newElement(dom,'attrdef',attrdefText) __appendOrReplace(attr,attrdef,'attrdef') attrdefs = __newElement(dom,'attrdefs',ncgmp) __appendOrReplace(attr,attrdefs,'attrdefs') return dom def __updateEdom(fld, defs, dom): ##element tag names are ## attr = Attribute ## attrdomv = Attribute_Domain_Values ## edom = Enumerated_Domain ## edomv = Enumerated_Domain_Value ## edomd = Enumerated_Domain_Definition ## edomvds = Enumerated_Domain_Value_Definition_Source labelNodes = dom.getElementsByTagName('attrlabl') for attrlabl in labelNodes: if attrlabl.firstChild.data == fld: attr = attrlabl.parentNode attrdomv = dom.createElement('attrdomv') for k in defs.items(): edom = dom.createElement('edom') edomv = __newElement(dom,'edomv',k[0]) edomvd = __newElement(dom,'edomvd',k[1][0]) edom.appendChild(edomv) edom.appendChild(edomvd) if len(k[1][1]) > 0: edomvds = __newElement(dom,'edomvds',k[1][1]) edom.appendChild(edomvds) attrdomv.appendChild(edom) __appendOrReplace(attr,attrdomv,'attrdomv') return dom def __updateEntityAttributes(fc, fldList, dom, logFile): """For each attribute (field) in fldList, adds attribute definition and definition source, classifies as range domain, unrepresentable-value domain or enumerated-value domain, and for range domains, adds rangemin, rangemax, and units; for unrepresentable value domains, adds unrepresentable value statement; for enumerated value domains: 1) Finds all controlled-vocabulary fields in the table sent to it 2) Builds a set of unique terms in each field, ie, the domain 3) Matches each domain value to an entry in the glossary 4) Builds a dictionary of term:(definition, source) items 5) Takes the dictionary items and put them into the metadata document as Attribute_Domain_Values Field MapUnit in table DescriptionOfMapUnits is treated as a special case. """ cantfindTerm = [] cantfindValue = [] for fld in fldList: addMsgAndPrint( ' Field: '+ fld) # if is _ID field or if field definition is available, update definition if fld.find('_ID') > -1 or fld in attribDict: dom = __updateAttrDef(fld,dom) else: cantfindTerm.append(fld) #if this is an _ID field if fld.find('_ID') > -1: dom = __updateUdom(fld,dom,unrepresentableDomainDict['_ID']) #if this is another unrepresentable-domain field if fld in unrepresentableDomainDict: dom = __updateUdom(fld,dom,unrepresentableDomainDict[fld]) #if this is a defined range-domain field elif fld in rangeDomainDict: dom = __updateRdom(fld,dom) #if this is MapUnit in DMU elif fld == 'MapUnit' and fc == 'DescriptionOfMapUnits': dom = __updateUdom(fld,dom,unrepresentableDomainDict['default']) #if this is a defined Enumerated Value Domain field elif fld in enumeratedValueDomainFieldList: valList = [] #create a search cursor on the field rows = arcpy.SearchCursor(fc,'','', fld) row = next(rows) #collect all values/terms in that field while row: if not row.getValue(fld) is None: valList.append(row.getValue(fld)) row = next(rows) #uniquify the list by converting it to a set object valList = set(valList) #create an empty dictionary object to hold the matches between the unique terms #and their definitions (grabbed from the glossary) defs = {} #for each unique term, try to create a search cursor of just one record where the term #matchs a Term field value from the glossary if fld == 'MapUnit' and fc != 'DescriptionOfMapUnits': for t in valList: query = '"MapUnit" = \'' + t + '\'' rows = arcpy.SearchCursor(DMU, query) row = next(rows) #if the searchcursor contains a row if row: #create an entry in the dictionary of term:[definition, source] key:value pairs #this is how we will enumerate through the enumerated_domain section defs[t] = [] if row.FullName != None: defs[t].append(row.FullName.encode('utf_8')) defs[t].append('this report, table DescriptionOfMapUnits') else: addMsgAndPrint('MapUnit = '+t+', FullName not defined') defs[t].append(row.Name.encode('utf_8')) defs[t].append('this report, table DescriptionOfMapUnits') else: if not t in ('',' '): cantfindValue.append([fld,t]) elif fld == 'GeoMaterialConfidence' and fc == 'DescriptionOfMapUnits': if debug: addMsgAndPrint('DMU / GeoMaterialsConfidence') defs = GeoMatConfDict elif fld == 'GeoMaterial' and fc == 'DescriptionOfMapUnits': if debug: addMsgAndPrint('DMU / GeoMaterials!') for t in valList: query = '"GeoMaterial" = \'' + t + '\'' if debug: addMsgAndPrint('query='+query) rows
type\: str pattern: ((\:\\|[0\-9a\-fA\-F]{0,4})\:)([0\-9a\-fA\-F]{0,4}\:){0,5}((([0\-9a\-fA\-F]{0,4}\:)?(\:\\|[0\-9a\-fA\-F]{0,4}))\\|(((25[0\-5]\\|2[0\-4][0\-9]\\|[01]?[0\-9]?[0\-9])\\.){3}(25[0\-5]\\|2[0\-4][0\-9]\\|[01]?[0\-9]?[0\-9])))(%[\\p{N}\\p{L}]+)? .. attribute:: metric_type (key) Metric type type\: int range: \-2147483648..2147483647 .. attribute:: source2 (key) Source of path 2 type\: union of the below types: type\: str pattern: (([0\-9]\\|[1\-9][0\-9]\\|1[0\-9][0\-9]\\|2[0\-4][0\-9]\\|25[0\-5])\\.){3}([0\-9]\\|[1\-9][0\-9]\\|1[0\-9][0\-9]\\|2[0\-4][0\-9]\\|25[0\-5])(%[\\p{N}\\p{L}]+)? type\: str pattern: ((\:\\|[0\-9a\-fA\-F]{0,4})\:)([0\-9a\-fA\-F]{0,4}\:){0,5}((([0\-9a\-fA\-F]{0,4}\:)?(\:\\|[0\-9a\-fA\-F]{0,4}))\\|(((25[0\-5]\\|2[0\-4][0\-9]\\|[01]?[0\-9]?[0\-9])\\.){3}(25[0\-5]\\|2[0\-4][0\-9]\\|[01]?[0\-9]?[0\-9])))(%[\\p{N}\\p{L}]+)? .. attribute:: destination2 (key) Destination of path 2 type\: union of the below types: type\: str pattern: (([0\-9]\\|[1\-9][0\-9]\\|1[0\-9][0\-9]\\|2[0\-4][0\-9]\\|25[0\-5])\\.){3}([0\-9]\\|[1\-9][0\-9]\\|1[0\-9][0\-9]\\|2[0\-4][0\-9]\\|25[0\-5])(%[\\p{N}\\p{L}]+)? type\: str pattern: ((\:\\|[0\-9a\-fA\-F]{0,4})\:)([0\-9a\-fA\-F]{0,4}\:){0,5}((([0\-9a\-fA\-F]{0,4}\:)?(\:\\|[0\-9a\-fA\-F]{0,4}))\\|(((25[0\-5]\\|2[0\-4][0\-9]\\|[01]?[0\-9]?[0\-9])\\.){3}(25[0\-5]\\|2[0\-4][0\-9]\\|[01]?[0\-9]?[0\-9])))(%[\\p{N}\\p{L}]+)? .. attribute:: disjoint_level (key) Disjointness level type\: int range: \-2147483648..2147483647 .. attribute:: disjoint_strict (key) Strict disjointness required type\: int range: \-2147483648..2147483647 .. attribute:: shortest_path (key) Whether path 1 or 2 should be shortest type\: int range: \-2147483648..2147483647 .. attribute:: headends_swapped Headends swapped type\: :py:class:`PceHeadendSwap <ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_xtc_oper.PceHeadendSwap>` .. attribute:: cspf_result CSPF Result type\: :py:class:`PceCspfRc <ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_xtc_oper.PceCspfRc>` .. attribute:: output_path Output PCE paths type\: list of :py:class:`OutputPath <ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_xtc_oper.Pce.Cspf.CspfPaths.CspfPath.OutputPath>` """ _prefix = 'infra-xtc-oper' _revision = '2017-08-24' def __init__(self): super(Pce.Cspf.CspfPaths.CspfPath, self).__init__() self.yang_name = "cspf-path" self.yang_parent_name = "cspf-paths" self.is_top_level_class = False self.has_list_ancestor = False self.ylist_key_names = ['af','source1','destination1','metric_type','source2','destination2','disjoint_level','disjoint_strict','shortest_path'] self._child_container_classes = OrderedDict([]) self._child_list_classes = OrderedDict([("output-path", ("output_path", Pce.Cspf.CspfPaths.CspfPath.OutputPath))]) self._leafs = OrderedDict([ ('af', YLeaf(YType.int32, 'af')), ('source1', YLeaf(YType.str, 'source1')), ('destination1', YLeaf(YType.str, 'destination1')), ('metric_type', YLeaf(YType.int32, 'metric-type')), ('source2', YLeaf(YType.str, 'source2')), ('destination2', YLeaf(YType.str, 'destination2')), ('disjoint_level', YLeaf(YType.int32, 'disjoint-level')), ('disjoint_strict', YLeaf(YType.int32, 'disjoint-strict')), ('shortest_path', YLeaf(YType.int32, 'shortest-path')), ('headends_swapped', YLeaf(YType.enumeration, 'headends-swapped')), ('cspf_result', YLeaf(YType.enumeration, 'cspf-result')), ]) self.af = None self.source1 = None self.destination1 = None self.metric_type = None self.source2 = None self.destination2 = None self.disjoint_level = None self.disjoint_strict = None self.shortest_path = None self.headends_swapped = None self.cspf_result = None self.output_path = YList(self) self._segment_path = lambda: "cspf-path" + "[af='" + str(self.af) + "']" + "[source1='" + str(self.source1) + "']" + "[destination1='" + str(self.destination1) + "']" + "[metric-type='" + str(self.metric_type) + "']" + "[source2='" + str(self.source2) + "']" + "[destination2='" + str(self.destination2) + "']" + "[disjoint-level='" + str(self.disjoint_level) + "']" + "[disjoint-strict='" + str(self.disjoint_strict) + "']" + "[shortest-path='" + str(self.shortest_path) + "']" self._absolute_path = lambda: "Cisco-IOS-XR-infra-xtc-oper:pce/cspf/cspf-paths/%s" % self._segment_path() def __setattr__(self, name, value): self._perform_setattr(Pce.Cspf.CspfPaths.CspfPath, ['af', 'source1', 'destination1', 'metric_type', 'source2', 'destination2', 'disjoint_level', 'disjoint_strict', 'shortest_path', 'headends_swapped', 'cspf_result'], name, value) class OutputPath(Entity): """ Output PCE paths .. attribute:: source Source of path type\: :py:class:`Source <ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_xtc_oper.Pce.Cspf.CspfPaths.CspfPath.OutputPath.Source>` .. attribute:: destination Destination of path type\: :py:class:`Destination <ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_xtc_oper.Pce.Cspf.CspfPaths.CspfPath.OutputPath.Destination>` .. attribute:: cost Cost type\: int range: 0..18446744073709551615 .. attribute:: hops Hop addresses type\: list of :py:class:`Hops <ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_xtc_oper.Pce.Cspf.CspfPaths.CspfPath.OutputPath.Hops>` """ _prefix = 'infra-xtc-oper' _revision = '2017-08-24' def __init__(self): super(Pce.Cspf.CspfPaths.CspfPath.OutputPath, self).__init__() self.yang_name = "output-path" self.yang_parent_name = "cspf-path" self.is_top_level_class = False self.has_list_ancestor = True self.ylist_key_names = [] self._child_container_classes = OrderedDict([("source", ("source", Pce.Cspf.CspfPaths.CspfPath.OutputPath.Source)), ("destination", ("destination", Pce.Cspf.CspfPaths.CspfPath.OutputPath.Destination))]) self._child_list_classes = OrderedDict([("hops", ("hops", Pce.Cspf.CspfPaths.CspfPath.OutputPath.Hops))]) self._leafs = OrderedDict([ ('cost', YLeaf(YType.uint64, 'cost')), ]) self.cost = None self.source = Pce.Cspf.CspfPaths.CspfPath.OutputPath.Source() self.source.parent = self self._children_name_map["source"] = "source" self._children_yang_names.add("source") self.destination = Pce.Cspf.CspfPaths.CspfPath.OutputPath.Destination() self.destination.parent = self self._children_name_map["destination"] = "destination" self._children_yang_names.add("destination") self.hops = YList(self) self._segment_path = lambda: "output-path" def __setattr__(self, name, value): self._perform_setattr(Pce.Cspf.CspfPaths.CspfPath.OutputPath, ['cost'], name, value) class Source(Entity): """ Source of path .. attribute:: af_name AFName type\: :py:class:`PceAfId <ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_xtc_oper.PceAfId>` .. attribute:: ipv4 IPv4 address type type\: str pattern: (([0\-9]\\|[1\-9][0\-9]\\|1[0\-9][0\-9]\\|2[0\-4][0\-9]\\|25[0\-5])\\.){3}([0\-9]\\|[1\-9][0\-9]\\|1[0\-9][0\-9]\\|2[0\-4][0\-9]\\|25[0\-5])(%[\\p{N}\\p{L}]+)? .. attribute:: ipv6 IPv6 address type type\: str pattern: ((\:\\|[0\-9a\-fA\-F]{0,4})\:)([0\-9a\-fA\-F]{0,4}\:){0,5}((([0\-9a\-fA\-F]{0,4}\:)?(\:\\|[0\-9a\-fA\-F]{0,4}))\\|(((25[0\-5]\\|2[0\-4][0\-9]\\|[01]?[0\-9]?[0\-9])\\.){3}(25[0\-5]\\|2[0\-4][0\-9]\\|[01]?[0\-9]?[0\-9])))(%[\\p{N}\\p{L}]+)? """ _prefix = 'infra-xtc-oper' _revision = '2017-08-24' def __init__(self): super(Pce.Cspf.CspfPaths.CspfPath.OutputPath.Source, self).__init__() self.yang_name = "source" self.yang_parent_name = "output-path" self.is_top_level_class = False self.has_list_ancestor = True self.ylist_key_names = [] self._child_container_classes = OrderedDict([]) self._child_list_classes = OrderedDict([]) self._leafs = OrderedDict([ ('af_name', YLeaf(YType.enumeration, 'af-name')), ('ipv4', YLeaf(YType.str, 'ipv4')), ('ipv6', YLeaf(YType.str, 'ipv6')), ]) self.af_name = None self.ipv4 = None self.ipv6 = None self._segment_path = lambda: "source" def __setattr__(self, name, value): self._perform_setattr(Pce.Cspf.CspfPaths.CspfPath.OutputPath.Source, ['af_name', 'ipv4', 'ipv6'], name, value) class Destination(Entity): """ Destination of path .. attribute:: af_name AFName type\: :py:class:`PceAfId <ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_xtc_oper.PceAfId>` .. attribute:: ipv4 IPv4 address type type\: str pattern: (([0\-9]\\|[1\-9][0\-9]\\|1[0\-9][0\-9]\\|2[0\-4][0\-9]\\|25[0\-5])\\.){3}([0\-9]\\|[1\-9][0\-9]\\|1[0\-9][0\-9]\\|2[0\-4][0\-9]\\|25[0\-5])(%[\\p{N}\\p{L}]+)? .. attribute:: ipv6 IPv6 address type type\: str pattern: ((\:\\|[0\-9a\-fA\-F]{0,4})\:)([0\-9a\-fA\-F]{0,4}\:){0,5}((([0\-9a\-fA\-F]{0,4}\:)?(\:\\|[0\-9a\-fA\-F]{0,4}))\\|(((25[0\-5]\\|2[0\-4][0\-9]\\|[01]?[0\-9]?[0\-9])\\.){3}(25[0\-5]\\|2[0\-4][0\-9]\\|[01]?[0\-9]?[0\-9])))(%[\\p{N}\\p{L}]+)? """ _prefix = 'infra-xtc-oper' _revision = '2017-08-24' def __init__(self): super(Pce.Cspf.CspfPaths.CspfPath.OutputPath.Destination, self).__init__() self.yang_name = "destination" self.yang_parent_name = "output-path" self.is_top_level_class = False self.has_list_ancestor = True self.ylist_key_names = [] self._child_container_classes = OrderedDict([]) self._child_list_classes = OrderedDict([]) self._leafs = OrderedDict([ ('af_name', YLeaf(YType.enumeration, 'af-name')), ('ipv4', YLeaf(YType.str, 'ipv4')), ('ipv6', YLeaf(YType.str, 'ipv6')), ]) self.af_name = None self.ipv4 = None self.ipv6 = None self._segment_path = lambda: "destination" def __setattr__(self, name, value): self._perform_setattr(Pce.Cspf.CspfPaths.CspfPath.OutputPath.Destination, ['af_name', 'ipv4', 'ipv6'], name, value) class Hops(Entity): """ Hop addresses .. attribute:: address_family Address Family type\: int range: 0..255 .. attribute:: ipv4_prefix IPv4 prefix type\: str pattern: (([0\-9]\\|[1\-9][0\-9]\\|1[0\-9][0\-9]\\|2[0\-4][0\-9]\\|25[0\-5])\\.){3}([0\-9]\\|[1\-9][0\-9]\\|1[0\-9][0\-9]\\|2[0\-4][0\-9]\\|25[0\-5])(%[\\p{N}\\p{L}]+)? .. attribute:: ipv6_prefix IPv6 prefix type\: str pattern: ((\:\\|[0\-9a\-fA\-F]{0,4})\:)([0\-9a\-fA\-F]{0,4}\:){0,5}((([0\-9a\-fA\-F]{0,4}\:)?(\:\\|[0\-9a\-fA\-F]{0,4}))\\|(((25[0\-5]\\|2[0\-4][0\-9]\\|[01]?[0\-9]?[0\-9])\\.){3}(25[0\-5]\\|2[0\-4][0\-9]\\|[01]?[0\-9]?[0\-9])))(%[\\p{N}\\p{L}]+)? """ _prefix = 'infra-xtc-oper' _revision = '2017-08-24' def __init__(self): super(Pce.Cspf.CspfPaths.CspfPath.OutputPath.Hops, self).__init__() self.yang_name = "hops" self.yang_parent_name = "output-path" self.is_top_level_class = False self.has_list_ancestor = True self.ylist_key_names = [] self._child_container_classes = OrderedDict([]) self._child_list_classes = OrderedDict([]) self._leafs = OrderedDict([ ('address_family', YLeaf(YType.uint8, 'address-family')), ('ipv4_prefix', YLeaf(YType.str, 'ipv4-prefix')), ('ipv6_prefix', YLeaf(YType.str, 'ipv6-prefix')), ]) self.address_family = None self.ipv4_prefix = None self.ipv6_prefix = None self._segment_path = lambda: "hops" def __setattr__(self, name, value): self._perform_setattr(Pce.Cspf.CspfPaths.CspfPath.OutputPath.Hops, ['address_family', 'ipv4_prefix', 'ipv6_prefix'], name, value) class TopologySummary(Entity): """ Node summary database in XTC .. attribute:: stats_topology_update Statistics on topology update type\: :py:class:`StatsTopologyUpdate <ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_xtc_oper.Pce.TopologySummary.StatsTopologyUpdate>` .. attribute:: nodes Number of PCE nodes type\: int range: 0..4294967295 .. attribute:: lookup_nodes Number of lookup nodes type\: int range: 0..4294967295 .. attribute:: prefixes Number of prefixes type\: int range: 0..4294967295 .. attribute:: prefix_sids Number of total prefix SIDs type\: int range: 0..4294967295 .. attribute:: regular_prefix_sids Number of reguar prefix SIDs type\: int range: 0..4294967295 .. attribute:: strict_prefix_sids Number of strict prefix SIDs type\: int range: 0..4294967295 .. attribute:: links Number of links type\: int range: 0..4294967295 .. attribute:: epe_links Number of EPE links type\: int range: 0..4294967295 .. attribute:: adjacency_sids Number of total adjacency SIDs type\: int range: 0..4294967295 .. attribute:: epesids Number of total EPE SIDs type\: int range: 0..4294967295 .. attribute:: protected_adjacency_sids Number of protected adjacency SIDs type\: int range: 0..4294967295 .. attribute:: un_protected_adjacency_sids Number of unprotected adjacency SIDs type\: int range: 0..4294967295 .. attribute:: topology_consistent True if topology is consistent type\: bool """ _prefix = 'infra-xtc-oper' _revision = '2017-08-24' def __init__(self): super(Pce.TopologySummary, self).__init__() self.yang_name = "topology-summary" self.yang_parent_name = "pce" self.is_top_level_class = False self.has_list_ancestor = False self.ylist_key_names = [] self._child_container_classes = OrderedDict([("stats-topology-update", ("stats_topology_update", Pce.TopologySummary.StatsTopologyUpdate))]) self._child_list_classes = OrderedDict([]) self._leafs = OrderedDict([ ('nodes', YLeaf(YType.uint32, 'nodes')), ('lookup_nodes', YLeaf(YType.uint32, 'lookup-nodes')), ('prefixes', YLeaf(YType.uint32, 'prefixes')), ('prefix_sids', YLeaf(YType.uint32, 'prefix-sids')), ('regular_prefix_sids', YLeaf(YType.uint32, 'regular-prefix-sids')), ('strict_prefix_sids', YLeaf(YType.uint32, 'strict-prefix-sids')), ('links', YLeaf(YType.uint32, 'links')), ('epe_links', YLeaf(YType.uint32, 'epe-links')), ('adjacency_sids', YLeaf(YType.uint32, 'adjacency-sids')), ('epesids', YLeaf(YType.uint32, 'epesids')), ('protected_adjacency_sids', YLeaf(YType.uint32, 'protected-adjacency-sids')), ('un_protected_adjacency_sids', YLeaf(YType.uint32, 'un-protected-adjacency-sids')), ('topology_consistent', YLeaf(YType.boolean, 'topology-consistent')), ]) self.nodes = None self.lookup_nodes = None self.prefixes = None self.prefix_sids = None self.regular_prefix_sids = None self.strict_prefix_sids = None self.links = None self.epe_links = None self.adjacency_sids = None self.epesids = None self.protected_adjacency_sids = None self.un_protected_adjacency_sids = None self.topology_consistent = None self.stats_topology_update = Pce.TopologySummary.StatsTopologyUpdate() self.stats_topology_update.parent = self self._children_name_map["stats_topology_update"] = "stats-topology-update" self._children_yang_names.add("stats-topology-update") self._segment_path = lambda: "topology-summary" self._absolute_path = lambda: "Cisco-IOS-XR-infra-xtc-oper:pce/%s" % self._segment_path() def __setattr__(self, name, value): self._perform_setattr(Pce.TopologySummary, ['nodes', 'lookup_nodes', 'prefixes', 'prefix_sids', 'regular_prefix_sids', 'strict_prefix_sids', 'links', 'epe_links', 'adjacency_sids', 'epesids', 'protected_adjacency_sids', 'un_protected_adjacency_sids', 'topology_consistent'], name, value) class StatsTopologyUpdate(Entity): """ Statistics on topology update .. attribute:: num_nodes_added Number of nodes added type\: int range: 0..4294967295 .. attribute:: num_nodes_deleted Number of nodes deleted type\: int range: 0..4294967295 .. attribute:: num_links_added Number of links added type\: int range: 0..4294967295 .. attribute:: num_links_deleted Number of links deleted type\: int range: 0..4294967295 .. attribute:: num_prefixes_added Number of prefixes added type\: int range: 0..4294967295 .. attribute:: num_prefixes_deleted Number of prefixes deleted type\: int range: 0..4294967295 """ _prefix = 'infra-xtc-oper' _revision = '2017-08-24' def
K.cast(K.less(inputs, 0), 'float32') * (K.exp(inputs - 1) * K.maximum(K.cast_to_floatx(0.0), K.minimum(K.cast_to_floatx(1.0), (inputs + 1.0)/2.0))) def get_config(self): base_config = super(HardElish, self).get_config() return dict(list(base_config.items()) def compute_output_shape(self, input_shape): return input_shape class BentID(Layer): ''' Bent's Identity Activation Function. .. math:: bentId(x) = x + \\frac{\\sqrt{x^{2}+1}-1}{2} Plot: .. figure:: _static/bent_id.png :align: center Shape: - Input: Arbitrary. Use the keyword argument `input_shape` (tuple of integers, does not include the samples axis) when using this layer as the first layer in a model. - Output: Same shape as the input. Examples: >>> X_input = Input(input_shape) >>> X = BentID()(X_input) ''' def __init__(self, kwargs): super(BentID, self).__init__(kwargs) self.supports_masking = True def call(self, inputs): return inputs + ((K.sqrt(K.pow(inputs,2)+1)-1)/2) def get_config(self): base_config = super(BentID, self).get_config() return dict(list(base_config.items()) def compute_output_shape(self, input_shape): return input_shape class WeightedTanh(Layer): ''' Weighted TanH Activation Function. .. math:: Weighted TanH(x, weight) = tanh(x * weight) Plot: .. figure:: _static/weighted_tanh.png :align: center Shape: - Input: Arbitrary. Use the keyword argument `input_shape` (tuple of integers, does not include the samples axis) when using this layer as the first layer in a model. - Output: Same shape as the input. Arguments: - weight: hyperparameter (default=1.0) Examples: >>> X_input = Input(input_shape) >>> X = WeightedTanh(weight=1.0)(X_input) ''' def __init__(self, weight=1.0, kwargs): super(WeightedTanh, self).__init__(kwargs) self.supports_masking = True self.weight = K.cast_to_floatx(weight) def call(self, inputs): return K.tanh(inputs * self.weight) def get_config(self): config = {'weight': float(self.weight)} base_config = super(WeightedTanh, self).get_config() return dict(list(base_config.items()) + list(config.items())) def compute_output_shape(self, input_shape): return input_shape class SineReLU(Layer): ''' Sine ReLU Activation Function. .. math:: SineReLU(x, \\epsilon) = \\left\\{\\begin{matrix} x , x > 0 \\\\ \\epsilon * (sin(x)-cos(x)), x \\leq 0 \\end{matrix}\\right. Plot: .. figure:: _static/sine_relu.png :align: center Shape: - Input: Arbitrary. Use the keyword argument `input_shape` (tuple of integers, does not include the samples axis) when using this layer as the first layer in a model. - Output: Same shape as the input. References: - See related Medium article: https://medium.com/@wilder.rodrigues/sinerelu-an-alternative-to-the-relu-activation-function-e46a6199997d Arguments: - epsilon: hyperparameter (default=0.01) Examples: >>> X_input = Input(input_shape) >>> X = SineReLU(epsilon=0.01)(X_input) ''' def __init__(self, epsilon=0.01, kwargs): super(SineReLU, self).__init__(kwargs) self.supports_masking = True self.epsilon = K.cast_to_floatx(epsilon) def call(self, inputs): return K.cast(K.greater_equal(inputs, 0), 'float32') * inputs + K.cast(K.less(inputs, 0), 'float32') * self.epsilon * (K.sin(inputs) - K.cos(inputs)) def get_config(self): config = {'epsilon': float(self.epsilon)} base_config = super(SineReLU, self).get_config() return dict(list(base_config.items()) + list(config.items())) def compute_output_shape(self, input_shape): return input_shape class ISRLU(Layer): ''' ISRLU Activation Function. .. math:: ISRLU(x)=\\left\\{\\begin{matrix} x, x\\geq 0 \\\\ x * (\\frac{1}{\\sqrt{1 + \\alphax^2}}), x <0 \\end{matrix}\\right. Plot: .. figure:: _static/isrlu.png :align: center Shape: - Input: Arbitrary. Use the keyword argument `input_shape` (tuple of integers, does not include the samples axis) when using this layer as the first layer in a model. - Output: Same shape as the input. Arguments: - alpha: hyperparameter α controls the value to which an ISRLU saturates for negative inputs (default = 1) References: - ISRLU paper: https://arxiv.org/pdf/1710.09967.pdf Examples: >>> X_input = Input(input_shape) >>> X = ISRLU(alpha=1.0)(X_input) ''' def __init__(self, alpha=1.0, kwargs): super(ISRLU, self).__init__(kwargs) self.supports_masking = True self.alpha = K.cast_to_floatx(alpha) def call(self, inputs): return K.cast(K.less(inputs, 0), 'float32') ISRU(alpha=self.alpha)(inputs) + K.cast(K.greater_equal(inputs, 0), 'float32') * inputs def get_config(self): config = {'alpha': float(self.alpha)} base_config = super(ISRLU, self).get_config() return dict(list(base_config.items()) + list(config.items())) def compute_output_shape(self, input_shape): return input_shape class SoftClipping(Layer): ''' Soft Clipping Activation Function. .. math:: SC(x) = 1 / \\alpha * log(\\frac{1 + e^{\\alpha * x}}{1 + e^{\\alpha * (x-1)}}) Plot: .. figure:: _static/sc.png :align: center Shape: - Input: Arbitrary. Use the keyword argument `input_shape` (tuple of integers, does not include the samples axis) when using this layer as the first layer in a model. - Output: Same shape as the input. Arguments: - alpha: hyper-parameter, which determines how close to linear the central region is and how sharply the linear region turns to the asymptotic values References: - See SC paper: https://arxiv.org/pdf/1810.11509.pdf Examples: >>> X_input = Input(input_shape) >>> X = SoftClipping(alpha=0.5)(X_input) ''' def __init__(self, alpha=0.5, kwargs): super(SoftClipping, self).__init__(kwargs) self.supports_masking = True self.alpha = K.cast_to_floatx(alpha) def call(self, inputs): return (1 / self.alpha) * K.log((1 + K.exp(self.alpha * inputs))/(1 + K.exp(self.alpha (inputs - 1)))) def get_config(self): config = {'alpha': float(self.alpha)} base_config = super(SoftClipping, self).get_config() return dict(list(base_config.items()) + list(config.items())) def compute_output_shape(self, input_shape): return input_shape class Aria2(Layer): ''' Aria-2 Activation Function. .. math:: Aria2(x, \\alpha, \\beta) = (1+e^{-\\betax})^{-\\alpha} Plot: .. figure:: _static/aria2.png :align: center Shape: - Input: Arbitrary. Use the keyword argument `input_shape` (tuple of integers, does not include the samples axis) when using this layer as the first layer in a model. - Output: Same shape as the input. Arguments: - alpha: hyper-parameter which has a two-fold effect; it reduces the curvature in 3rd quadrant as well as increases the curvature in first quadrant while lowering the value of activation (default = 1) - beta: the exponential growth rate (default = 0.5) References: - See Aria paper: https://arxiv.org/abs/1805.08878 Examples: >>> X_input = Input(input_shape) >>> X =Aria2(alpha=1.0, beta=0.5)(X_input) ''' def __init__(self, alpha=1.0, beta=0.5, kwargs): super(Aria2, self).__init__(kwargs) self.supports_masking = True self.alpha = K.cast_to_floatx(alpha) self.beta = K.cast_to_floatx(beta) def call(self, inputs): return K.pow((1 + K.exp(-self.beta * inputs)), -self.alpha) def get_config(self): config = {'alpha': float(self.alpha), 'beta': float(self.beta)} base_config = super(Aria2, self).get_config() return dict(list(base_config.items()) + list(config.items())) def compute_output_shape(self, input_shape): return input_shape class Celu(Layer): ''' CELU Activation Function. .. math:: CELU(x, \\alpha) = max(0,x) + min(0,\\alpha * (exp(x/ \\alpha)-1)) Shape: - Input: Arbitrary. Use the keyword argument `input_shape` (tuple of integers, does not include the samples axis) when using this layer as the first layer in a model. - Output: Same shape as the input. Arguments: - alpha: the α value for the CELU formulation (default=1.0) References: - See CELU paper: https://arxiv.org/abs/1704.07483 Examples: >>> X_input = Input(input_shape) >>> X = Celu(alpha=1.0)(X_input) ''' def __init__(self, alpha=1.0, kwargs): super(Celu, self).__init__(kwargs) self.supports_masking = True self.alpha = K.cast_to_floatx(alpha) def call(self, inputs): return K.cast(K.greater_equal(inputs, 0), 'float32') * inputs + K.cast(K.less(inputs, 0), 'float32') * self.alpha * (K.exp (inputs / self.alpha) - 1) def get_config(self): config = {'alpha': float(self.alpha)} base_config = super(Celu, self).get_config() return dict(list(base_config.items()) + list(config.items())) def compute_output_shape(self, input_shape): return input_shape class ReLU6(Layer): ''' RELU6 Activation Function. .. math:: RELU6(x) = min(max(0,x),6) Shape: - Input: Arbitrary. Use the keyword argument `input_shape` (tuple of integers, does not include the samples axis) when using this layer as the first layer in a model. - Output: Same shape as the input. References: - See RELU6 paper: http://www.cs.utoronto.ca/~kriz/conv-cifar10-aug2010.pdf Examples: >>> X_input = Input(input_shape) >>> X = ReLU6()(X_input) ''' def __init__(self, kwargs): super(ReLU6, self).__init__(kwargs) self.supports_masking = True def call(self, inputs): return K.cast(K.greater_equal(inputs, 6), 'float32') * 6 + K.cast(K.less(inputs, 6), 'float32') * K.relu(inputs) def get_config(self): base_config = super(ReLU6, self).get_config() return dict(list(base_config.items()) def compute_output_shape(self, input_shape): return input_shape class HardTanh(Layer): ''' Hard-TanH Activation Function. .. math:: Hard-TanH(x) = \\left\\{\\begin{matrix} 1, x > 1 \\\\ x , -1 \\leq x \\leq 1 \\\\ -1, x <- 1 \\end{matrix}\\right. Shape: - Input: Arbitrary. Use the keyword argument `input_shape` (tuple of integers, does not include the samples axis) when using this layer as the first layer in a model. - Output: Same shape as the input. Examples: >>> X_input = Input(input_shape) >>> X = HardTanh()(X_input) ''' def __init__(self, kwargs): super(HardTanh, self).__init__(kwargs) self.supports_masking = True def call(self, inputs): return K.cast(K.greater(inputs , 1), 'float32')\ + inputs * K.cast(K.less_equal(inputs, 1), 'float32') * K.cast(K.greater_equal(inputs, -1), 'float32') - K.cast(K.less(inputs, -1), 'float32') def get_config(self): base_config = super(HardTanh, self).get_config() return dict(list(base_config.items()) def compute_output_shape(self, input_shape): return input_shape class LogSigmoid(Layer): ''' Log-Sigmoid Activation Function. .. math:: Log-Sigmoid(x) = log (\\frac{1}{1+e^{-x}}) Shape: - Input: Arbitrary. Use the keyword argument `input_shape` (tuple of integers, does not include the samples axis) when using this layer as the first layer in a model. - Output: Same shape as the input. Examples: >>> X_input = Input(input_shape) >>> X = LogSigmoid()(X_input) ''' def __init__(self, kwargs): super(LogSigmoid, self).__init__(kwargs) self.supports_masking = True def call(self, inputs): return K.log(K.sigmoid(inputs)) def get_config(self): base_config = super(LogSigmoid, self).get_config() return dict(list(base_config.items()) def compute_output_shape(self, input_shape): return input_shape class TanhShrink(Layer): ''' TanH-Shrink Activation Function. .. math:: TanH-Shrink(x) = x - tanh(x) Shape: - Input: Arbitrary. Use the keyword argument `input_shape` (tuple of integers,
expected result (raised from the pexpect module). """ reporter.step('Getting device information...') self.__access_priv_exec_mode(child, eol, enable_password) try: # Get the name of the default drive. Depending on the device, it may be bootflash, # flash, slot (for linear memory cards), or disk (for CompactFlash disks) child.sendline('dir' + eol) child.expect_exact('dir') index = child.expect_exact(['More', self.device_prompts[1], ]) dir_list = str(child.before) if index == 0: # No need to get the whole directory listing, so break out child.sendcontrol('c') child.expect_exact(self.device_prompts[1]) default_file_system = dir_list.split( 'Directory of ')[1].split(':')[0].strip() if not default_file_system.startswith(('bootflash', 'flash', 'slot', 'disk',)): raise RuntimeError('Cannot get the device\'s working drive.') # If the drive is not formatted, a warning will appear, followed by another prompt. # Wait for it to pass, and get to the correct prompt index = child.expect_exact( ['before an image can be booted from this device', pexpect.TIMEOUT, ], timeout=5) if index == 0: child.expect_exact(self.device_prompts[1]) except (RuntimeError, IndexError) as ex: # RuntimeError = explicit, while IndexError = implicit if split index is out of range reporter.warn(ex.message) default_file_system = None try: # Get the IOS version child.sendline('show version \| include [IOSios] [Ss]oftware' + eol) # child.expect_exact('show version \| include') child.expect_exact(self.device_prompts[1]) software_ver = str(child.before).split( 'show version \| include [IOSios] [Ss]oftware\r')[1].split('\r')[0].strip() if not re.compile(r'[IOSios] [Ss]oftware').search(software_ver): raise RuntimeError('Cannot get the device\'s software version.') except (RuntimeError, IndexError) as ex: reporter.warn(ex.message) software_ver = None try: # Get the name of the device child.sendline('show inventory \| include DESCR:' + eol) # child.expect_exact('show inventory \| include DESCR:') child.expect_exact(self.device_prompts[1]) device_name = str(child.before).split( 'show inventory \| include DESCR:\r')[1].split('\r')[0].strip() if not re.compile(r'DESCR:').search(device_name): raise RuntimeError('Cannot get the device\'s name.') except (RuntimeError, IndexError) as ex: reporter.warn(ex.message) device_name = None try: # Get the serial number of the device child.sendline('show version \| include [Pp]rocessor [Bb]oard [IDid]' + eol) # child.expect_exact('show version \| include') child.expect_exact(self.device_prompts[1]) serial_num = str(child.before).split( 'show version \| include [Pp]rocessor [Bb]oard [IDid]\r')[1].split('\r')[0].strip() if not re.compile(r'[Pp]rocessor [Bb]oard [IDid]').search(serial_num): raise RuntimeError('Cannot get the device\'s serial number.') except (RuntimeError, IndexError) as ex: reporter.warn(ex.message) serial_num = None # Get rid of ANSI escape sequences ansi_seq = re.compile('(?:\\x1b\[)([\w;]+)(H)') default_file_system = ansi_seq.sub('', str(default_file_system)).strip() software_ver = ansi_seq.sub('', str(software_ver)).strip() device_name = ansi_seq.sub('', str(device_name)).strip() serial_num = ansi_seq.sub('', str(serial_num)).strip() reporter.success() return default_file_system, software_ver, device_name, serial_num def format_file_system(self, child, reporter, eol, device_file_system): """Format a file system (i.e., memory) on a network device. :param pexpect.spawn child: Connection in a child application object. :param labs.cisco.Reporter reporter: A reference to the popup GUI window that reports the status and progress of the script. :param str eol: EOL sequence (LF or CRLF) used by the connection. :param str device_file_system: File system to format. :return: None :rtype: None :raise ValueError: If an argument is invalid. :raise pexpect.ExceptionPexpect: If the result of a send command does not match the expected result (raised from the pexpect module). """ # Validate inputs if not device_file_system.startswith(('bootflash', 'flash', 'slot', 'disk',)): reporter.error() raise ValueError('Invalid Cisco file system name.') reporter.step('Formatting device memory...') self.__access_priv_exec_mode(child, eol) # Format the memory. Look for the final characters of the following strings: # 'Format operation may take a while. Continue? [confirm]' # 'Format operation will destroy all data in 'flash:'. Continue? [confirm]' # '66875392 bytes available (0 bytes used)' child.sendline('format {0}:'.format(device_file_system) + eol) index = 1 while index != 0: index = child.expect_exact( [pexpect.TIMEOUT, 'Continue? [confirm]', 'Enter volume ID', ], timeout=5) if index != 0: child.sendline(eol) child.expect_exact('Format of {0} complete'.format(device_file_system), timeout=120) child.sendline('show {0}'.format(device_file_system) + eol) child.expect_exact('(0 bytes used)') child.expect_exact(self.device_prompts[1]) reporter.success() def set_switch_priority(self, child, reporter, eol, switch_number=1, switch_priority=1, enable_password=None, commit=True): """Set the switch priority in the stack. :param pexpect.spawn child: Connection in a child application object. :param labs.cisco.Reporter reporter: A reference to the popup GUI window that reports the status and progress of the script. :param str eol: EOL sequence (LF or CRLF) used by the connection. :param switch_number: Switch reference number in the stack :param switch_priority: Switch priority in the stack; maximum is 15. The switch with the largest number (e.g., 15) becomes the master switch for the stack. :param str enable_password: Password to enable Privileged EXEC Mode from User EXEC Mode. :param bool commit: True to save changes to startup-config. :return: None :rtype: None :raise ValueError: If an argument is invalid. :raise pexpect.ExceptionPexpect: If the result of a send command does not match the expected result (raised from the pexpect module). """ reporter.step('Setting switch priority...') self.__access_priv_exec_mode(child, eol, enable_password=<PASSWORD>_password) # Validate inputs if not 1 <= switch_number <= 9: raise ValueError('Invalid switch stack member number.') validate_switch_priority(switch_priority) child.sendline('configure terminal' + eol) child.expect_exact(self.device_prompts[2]) child.sendline('switch {0} priority {1}'.format(switch_number, switch_priority)) index = 0 while index == 0: index = child.expect_exact( ['Do you want to continue', 'New Priority has been set successfully', ]) if index == 0: child.sendline(eol) child.sendline('end' + eol) child.expect_exact(self.device_prompts[1]) # Save changes if True if commit: self.save_running_configuration(child, eol, enable_password=enable_password) reporter.success() def set_switch_ip_addr(self, child, reporter, eol, vlan_name, vlan_port, new_ip_address, new_netmask, enable_password=None, commit=True): """Set a switch's IP address. :param pexpect.spawn child: Connection in a child application object. :param labs.cisco.Reporter reporter: A reference to the popup GUI window that reports the status and progress of the script. :param str eol: EOL sequence (LF or CRLF) used by the connection. :param str vlan_name: Virtual Local Area Network (VLAN) interface to configure. :param str vlan_port: Ethernet interface port name to configure and connect to VLAN. :param str new_ip_address: New IPv4 address for the device. :param str new_netmask: New netmask for the device. :param str enable_password: <PASSWORD> enable Privileged EXEC Mode from User EXEC Mode. :param bool commit: True to save changes to startup-config. :return: None :rtype: None :raise pexpect.ExceptionPexpect: If the result of a send command does not match the expected result (raised from the pexpect module). """ reporter.step('Setting the switch\'s IP address...') self.__access_priv_exec_mode(child, eol, enable_password=enable_password) # Validate inputs # FYI, vlan_port, while not validated, should start with F(ast), G(iga), etc. validate_ip_address(new_ip_address) validate_subnet_mask(new_netmask) child.sendline('configure terminal' + eol) child.expect_exact(self.device_prompts[2]) # Configure Ethernet port child.sendline('interface {0}'.format(vlan_port) + eol) child.expect_exact(self.device_prompts[3]) # Configure the VLAN membership mode child.sendline('switchport mode access' + eol) child.expect_exact(self.device_prompts[3]) # Assign the port to the VLAN child.sendline('switchport access {0}'.format(vlan_name) + eol) child.expect_exact(self.device_prompts[3]) # Set to forwarding state immediately, bypassing the listening and learning states # Used to prevent L2 switching loops when connecting to the remote host child.sendline('spanning-tree portfast' + eol) child.expect_exact(self.device_prompts[3]) child.sendline('no shutdown' + eol) child.expect_exact(self.device_prompts[3]) # Configure VLAN child.sendline('interface {0}'.format(vlan_name) + eol) child.expect_exact(self.device_prompts[3]) child.sendline('ip address {0} {1}'.format(new_ip_address, new_netmask) + eol) child.expect_exact(self.device_prompts[3]) child.sendline('no shutdown' + eol) child.expect_exact(self.device_prompts[3]) child.sendline('end' + eol) child.expect_exact(self.device_prompts[1]) # Save changes if True if commit: self.save_running_configuration(child, eol, enable_password=<PASSWORD>) reporter.success() def set_router_ip_addr(self, child, reporter, eol, ethernet_port, new_ip_address, new_netmask, enable_password=None, commit=True): """Set a router's IP address. :param pexpect.spawn child: Connection in a child application object. :param labs.cisco.Reporter reporter: A reference to the popup GUI window that reports the status and progress of the script. :param str eol: EOL sequence (LF or CRLF) used by the connection. :param str ethernet_port: Ethernet interface port name to configure. :param str new_ip_address: New IPv4 address for the device. :param str new_netmask: New netmask for the device. :param str enable_password: Password to enable Privileged EXEC Mode from User EXEC Mode. :param bool commit: True to save changes to startup-config. :return: None :rtype: None """ # Validate inputs # ethernet_port, while not validated, should start with F(ast), G(iga), etc. validate_ip_address(new_ip_address) validate_subnet_mask(new_netmask) reporter.step('Setting the router\'s IP address...') self.__access_priv_exec_mode(child, eol) child.sendline('configure terminal' + eol) child.expect_exact(self.device_prompts[2]) # Configure Ethernet port child.sendline('interface {0}'.format(ethernet_port) + eol) child.expect_exact(self.device_prompts[3]) child.sendline('ip address {0} {1}'.format(new_ip_address, new_netmask) + eol) child.expect_exact(self.device_prompts[3]) child.sendline('no shutdown' + eol) child.expect_exact(self.device_prompts[3]) child.sendline('end' + eol) child.expect_exact(self.device_prompts[1]) # Save changes if True if commit: self.save_running_configuration(child, eol, enable_password=<PASSWORD>_password) reporter.success() def ping_from_device(self, child, reporter, eol, destination_ip_addr, count=4, enable_password=None): """Check connectivity with another device. :param pexpect.spawn child: Connection in a child application object. :param labs.cisco.Reporter reporter: A reference to the popup GUI window that reports the status and progress of the script. :param str eol: EOL sequence (LF or CRLF) used by the connection. :param str destination_ip_addr: IPv4 address of the other device. :param int count: Number of
<gh_stars>1-10 #!/usr/bin/env python # coding: utf-8 # In[1]: """ Module containng custom Keras models and layers required for FlowNet architecture. """ try: import numpy as np import tensorflow as tf from tensorflow import keras from tensorflow.keras import backend as K except Exception as e: raise Exception("Error occured while importing dependency packages. More details:\n",e) __author__ = "<NAME>" __copyright__ = "Copyright 2020, FlowNet" __credits__ = ["<NAME>"] __license__ = "" __version__ = "0.1.0" __maintainer__ = "<NAME>" __email__ = "<EMAIL>" __status__ = "Development" class ForwardModel(tf.keras.Model): """ Model to construct FNN (Forward Neural Network) using custom Keras layers. Subclass of tf.keras.Model """ def __init__(self, space_dim=1, time_dep=False, output_dim=1, n_hid_lay=3, n_hid_nrn=20, act_func = "tanh", rhs_func = None): """ space_dim (int) -> Dimension of the space Omega where the PDE is defined. time_dep (bool) -> True if the problem is time dependent. output_dim (int) -> Dimension of the range of the solution to PDE. n_hid_layer (int) -> Number of hidden layers in the neural network. n_hid_nrn (int) -> Number of neurons in each hidden layer of the NN. act_func (string) -> Activation functions for each of the hidden layers. Has to be one of the members of keras.activations: could be one of {"tanh", "sigmoid", "elu", "relu", "exponential"} """ super(ForwardModel, self).__init__() #Defining class atributes self.space_dim = space_dim self.time_dep = time_dep self.output_dim = output_dim self.n_hid_lay = n_hid_lay self.n_hid_nrn = n_hid_nrn #Block of hidden layers self.hidden_block = [keras.layers.Dense( self.n_hid_nrn, activation=act_func, name="dense_"+str(i+1) ) for i in range(n_hid_lay)] #Final output layer self.final_layer = keras.layers.Dense(self.output_dim, name="final_layer") #Defining the rhs of PDE: P(u,delu) = f(x,t) if rhs_func != None: self.rhs_function = rhs_func else: self.rhs_function = lambda x: 0 def findGrad(self,func,input_space): """ Find gradient with respect to the domain Omega of the PDE. (tensor, tensor) -> Keras.Lambda layer arguments: ---------- func (tf tensor): function represented by tf tensor structure (Usually of size: data_size x dim_output_previous_layer). The func is usually the final output (solution u) coming out of a hidden layer input_space: argument with respect to which we need the partial derrivatives of func. Usually a list of input arguments representing the space dimension. Output: Keras.Lambda layer. Note that output of such a lambda layer will be a list of tensors with each element giving partial derrivative wrt to each element in argm. See tf.Keras.Lambda and tf.gradients for more details. """ try: return keras.layers.Lambda(lambda z: [tf.gradients(z[0],x_i, unconnected_gradients='zero') for x_i in z[1] ]) ([func, input_space]) except Exception as e: raise Exception("Error occured in finding the time derrivative lambda layer of type {} as follows: \n{}".format(type(e)),e) def findTimeDer(self,func,input_time): """ (tensor, tensor) -> Keras.Lambda layer arguments: ---------- func (tf tensor): function represented by tf tensor structure (Usually of size: data_size x dim_output_previous_layer). The func is usually the final output (solution u) coming out of a hidden layer input_time: TensorFlow tensor. This should be the element of the input list which corresponds to the time dimension. Used only if the problem is time_dependent. Output: Keras.Lambda layer. Note that output of such a lambda layer will be a tensor of size m x 1 representing the time derrivative of output func. See tf.Keras.Lambda and tf.gradients for more details. """ assert (self.time_dep), "Tried taking time derrivative even though the problem is not time dependent." try: return keras.layers.Lambda(lambda z: tf.gradients(z[0],z[1], unconnected_gradients='zero') [0]) ([func, input_time]) except Exception as e: raise Exception("Error occured in find gradient lambda layer of type {} as follows: \n{} ".format(type(e)),e) def findLaplace(self,first_der,input_space): """ (tensor, tensor) -> Keras.Lambda layer Returns lambda layer to find the laplacian of the solution to pde. arguments: ---------- first_der (tf tensor): function represented by tf tensor structure (Usually of size: data_size x dim_output_previous_layer). The func is input_space: argument with respect to which we need the partial derrivatives of func. Usually a list of input arguments representing the space dimension. Output: Keras.Lambda layer. This lambda layer outputs the laplacian of solution function u. See tf.Keras.Lambda and tf.gradients for more details. """ try: # list containng diagonal entries of hessian matrix. Note that tf.gradients #returns a list of tensors and hence thats why we have a [0] at the end of #the tf.gradients fucntion as tf.gradients(func,argm) [0] del_sq_layer = keras.layers.Lambda( lambda z: [ tf.gradients(z[0][i], z[1][i], unconnected_gradients='zero') [0] for i in range(len(z[1])) ] ) ([first_der,input_space]) return sum(del_sq_layer) except Exception as e: raise Exception("Error occured in find laplacian lambda layer of type {} as follows: \n{}".format(type(e)),e) #final layer representing the lhs P(x,t) of PDE P(x,t)=0 def findPdeLayer(self, laplacian, input_arg, time_der=0): """ (tensor, tensor, tensor) -> Keras.Lambda layer Returns lambda layer to find the actual pde P(u,delu,x,t) such that P(u,delu,x,t)=0. arguments: ---------- laplacian (tf tensor): laplacian with respect to space dim . input_arg: list of inputs corresponding to both space and time dimension. Last elemetn of the list corresponds to the temporal dimension. Output: Keras.Lambda layer. This lambda layer outputs the PDE P(u,delu, x,t). See tf.Keras.Lambda and tf.gradients for more details. """ try: # return keras.layers.Lambda(lambda z: z[0] - z[1] - tf.sin(z[2][0]+z[2][1]) - # 2z[2][2]tf.sin(z[2][0]+z[2][1])) ([time_der, laplacian, input_arg]) return keras.layers.Lambda(lambda z: z[0] - z[1] - self.rhs_function(input_arg)) ([time_der, laplacian, input_arg]) except Exception as e: raise Exception("Error occured in finding pde lambda layer of type {} as follows: \n{}".format(type(e)),e) def get_config(self): #getting basic config using the parent model class base_config = super().get_config() return {**base_config, "space_dim": self.space_dim, "time_dep": self.time_dep, "output_dim": self.output_dim, "n_hid_lay": self.n_hid_lay, "n_hid_nrn": self.n_hid_nrn, "act_func": self.act_func } def from_config(self, config, custom_objects): super().from_config(config) def call(self, inputs, training=False): """ Call function which wll be used while training, prediciton and evaluation of the ForwardModel. arguments: ---------- inputs (list of tensors) -> last element of the list corresponds to temporal diimension if self.time_dep = True. If possible, always feed the data from the data processing method in flowDataProcess module. training (bool) -> True if calling the function for training. False for prediction and evaluation. Value of triainng will be automatically taken care of by Keras. Note that inputs should always be given as a list with the last element of the list representing the dimension corresponding to time. """ if self.time_dep: try: assert(len(inputs) > 1) input_space = inputs[:-1] input_time = inputs[-1] except Exception as e: raise Exception("Error occured while separating spacial and temporal data from inputs, make sure that spacio-temporal data is being used to for training and x=[space_dim1,..,space_dimn,time_dim]. More details on error below:\n", type(e), e) else: input_space = inputs #concatening all the input data (space and time dimensions) making it #read to be passed to the hidden layers hidden_output = keras.layers.concatenate(inputs) #hidden layers for layer_id in range(self.n_hid_lay): hidden_output = self.hidden_block[layer_id] (hidden_output) #output layer, this is typically the solution function output_layer = self.final_layer(hidden_output) if training: #pde specific layers grad_layer = self.findGrad(output_layer, input_space) laplace_layer = self.findLaplace(grad_layer, input_space) if self.time_dep: time_der_layer = self.findTimeDer(output_layer, input_time) else: time_der_layer=0 pde_layer = self.findPdeLayer(laplace_layer, inputs, time_der_layer) return output_layer, pde_layer elif not training: #only outputting the function value if not tranining. return output_layer # In[3]: class Poission(ForwardModel): """ Doc string goes here """ def __init__(self, space_dim=1, perm_tensor=None, output_dim=1, n_hid_lay=3, n_hid_nrn=20, act_func = "tanh", rhs_func = None): """ talk about super initialization """ super().__init__(space_dim=space_dim, time_dep=False, output_dim=output_dim, n_hid_lay=n_hid_lay, n_hid_nrn=n_hid_nrn, act_func = act_func, rhs_func = rhs_func) self._perm_tensor = perm_tensor if perm_tensor else tf.eye(space_dim) #final layer representing the lhs P(x) of PDE P(x)=0 def findPdeLayer(self, laplacian, input_arg): """ (tensor, tensor, tensor) -> Keras.Lambda layer Returns lambda layer to find the actual pde P(u,delu,x,t) such that P(u,delu,x,t)=0. arguments: ---------- laplacian (tf tensor): laplacian with respect to space dim . input_arg: list of inputs corresponding to both space and time dimension. Last elemetn of the list corresponds to the temporal
next_case_line_number: int = 0 for line in code_except_decorator: # print(f'{line_number} : {line}') if "case " in line: next_case_line_number = line_number next_case = line.strip() if "done()" in line: match_obligations.add(line_number, next_case_line_number, next_case) line_number += 1 # print(match_obligations) def new_transition(self, event): if not hasattr(self, '__data_object__') or self.__data_object__ is None: self.__data_object__ = match_obligations result = transition_function(self, event) if isinstance(result, int): # print(f'++> {self.__data_object__}') self.__data_object__.remove(result) # print(f'--> {self.__data_object__}') if self.__data_object__.empty(): return ok # we are done else: return self # we are not done yet else: return result # something else (ok, error, or another state) return new_transition def initial(state_class: State) -> State: """ Decorator function which introduces the Boolean variable 'is_initial' (assigning it the value True, although that is not important) in its argument state. It allows us to annotate states as follows: @initial class Init(State): ... This has the same effect as: class Init(State): ... Init.is_initial = True :param state_class: the state to decorate. :return: the decorated state. """ state_class.is_initial = True return state_class def mk_state_vector(arg: State \| List[State]) -> List[State]: """ Turns a state or a list of states into a list of states. If the argument is a single state s, the singular state vector [s] is returned. If the argument is a list of states, it is returned unchanged. :param arg: a state or a list of states. :return: the list of states. """ if isinstance(arg, list): return arg else: return [arg] class Monitor: """ Any user defined monitor class must extend this class. It defines a monitor. """ def __init__(self): """ monitors: A monitor can have sub-monitors, stored in this variable. is_top_monitor: Is True iff. this monitor is the topmost monitor in a monitor hierarchy, that is: not a sub-monitor of another monitor. Used for printing purposes in that only the topmost monitor prints out certain debugging information. states: The state vector of the monitor: the set of all active states. states_indexed: Indexed states, used for slicing. errors: Detected errors during monitoring. event_count: Counts the events as they come in. option_show_state_event: When True, state and event will be printed on transition errors. option_print_summary: When True, a summary of the analysis is printed for the top monitor. """ self.monitors: List[Monitor] = [] self.is_top_monitor: bool = True self.states: Set[State] = set([]) self.states_indexed : Dict[object, Set[State]] = {} self.errors: List[str] = [] self.event_count: int = 0 self.option_show_state_event: bool = True self.option_print_summary: bool = True # Create always state if outermost transitions exist outer_transitions = inspect.getmembers(self, predicate=is_transition_method) if len(outer_transitions) > 0: always = type("Always", (AlwaysState,), {}) setattr(always, "is_initial", True) (name, method) = outer_transitions[0] setattr(always, name, method.__func__) setattr(self, "Always", always) # Locate all state classes (subclassing State): state_classes = inspect.getmembers(self, predicate=is_state_class) if len(state_classes) > 0: # Add initial states: initial_state_found = False for (state_name, state_class) in state_classes: if hasattr(state_class, 'is_initial'): self.add_state_to_state_vector(self.states, state_class()) initial_state_found = True if not initial_state_found: (name, the_first_class) = state_classes[0] self.add_state_to_state_vector(self.states, the_first_class()) # Debug initial states # print(f'Initial states of {self.get_monitor_name()}:') # for state in self.states: # print(state) # print() def set_event_count(self, initial_value: int): """ Sets the initial value of `event_count` to a different value than 0. This is used for example when processing CSV files, where there is a header row, which should be counted as an 'event' so that `event_count` will correspond to row number in the CSV file. :param initial_value: the initial value of `event_count`. """ self.event_count = initial_value def get_monitor_name(self) -> str: return self.__class__.__name__ def key(self, event) -> Optional[object]: """ Returns indexing key of event. Returns None by default but can be overwritten by user. :param event: event to extract index from. :return: the index of the event. """ return None def monitor_this(self, monitors: "Monitor"): """ Records one or more monitors as sub-monitors of this monitor. Each event submitted to this monitor is also submitted to the sub-monitors. Likewise when end() is called on this monitor, end() is also called on the sub-monitors. :param monitors: the monitors to record as sub-monitors. """ for monitor in monitors: monitor.is_top_monitor = False self.monitors.append(monitor) def is_relevant(self, event: Event) -> bool: """ Returns True if the event should be monitored. By default all submitted events are monitored. This method is meant to be overridden by the user. :param event: the incoming event. :return: True if the event should be monitored. """ return True def eval(self, event: Event): """ This method is used to submit events to the monitor. The monitor evaluates the event against the states in the state vector. The eval method is called recursively on sub-monitors, so it is only necessary to call it on the topmost monitor. :param event: the submitted event. """ global __monitor__ __monitor__ = self self.event_count += 1 if DEBUG_PROGRESS and self.is_top_monitor and self.event_count % DEBUG_PROGRESS == 0: debug(f'---------------------> {self.event_count}') if DEBUG and self.is_top_monitor: debug_frame("=", f'Event {self.event_count} {event}') for monitor in self.monitors: monitor.eval(event) if DEBUG: debug_frame("#", f'Monitor {self.get_monitor_name()}') if self.is_relevant(event): index = self.key(event) if index is None: new_states = self.eval_states(event, self.states) if new_states is not None: self.states = new_states for (idx, states) in self.states_indexed.items(): new_states = self.eval_states(event, states) if new_states is not None: self.states_indexed[idx] = new_states else: if index in self.states_indexed: states = self.states_indexed[index] else: states = self.states new_states = self.eval_states(event, states) if new_states is not None: self.states_indexed[index] = new_states if DEBUG: debug(f'\n{self}') def eval_states(self, event: Event, states: Set[State]) -> Optional[Set[State]]: """ Evaluates an event on each state in a set of states. :param event: the event to evaluate. :param states: the set of states to evaluate it on. :return: the resulting set of states. None is returned if no transitions fired. """ transition_triggered = False states_to_remove = set([]) states_to_add = set([]) new_states = set([]) for source_state in states: resulting_states = source_state.eval(event) # returns None or a list of states if DEBUG: debug(f'{source_state} results in {mk_string("[",", ","]", resulting_states)}') if resulting_states is not None: transition_triggered = True states_to_remove.add(source_state) for target_state in resulting_states: if target_state == ok: pass elif isinstance(target_state, ErrorState): self.report_transition_error(source_state, event, target_state.message) elif isinstance(target_state, InfoState): self.report_transition_information(source_state, event, target_state.message) else: self.add_state_to_state_vector(states_to_add, target_state) if transition_triggered: new_states = states new_states = new_states - states_to_remove new_states = new_states.union(states_to_add) return new_states else: return None def end(self): """ Terminates monitoring for the monitor. This includes looking for hot states of type HotState, which should not occur, and then printing out a summary of the verification. The end() method is called recursively on sub-monitors, so it only needs to be called on the top-most monitor. """ if self.is_top_monitor: print() print('Terminating monitoring!') print() for monitor in self.monitors: monitor.end() print_frame("+", f'Terminating monitor {self.get_monitor_name()}') for state in self.get_all_states(): if isinstance(state, HotState) or isinstance(state, HotNextState): self.report_end_error(f'terminates in hot state {state}') if self.is_top_monitor and self.option_print_summary: self.print_summary() def verify(self, trace: List[Event]): ''' Verifies a trace, which is a list of events. It calls eval on each event and calls end() at the end of the trace. :param trace: the trace. ''' for event in trace: self.eval(event) self.end() def __str__(self) -> str: monitor_name = self.__class__.__name__ suffix = " states:" bar_length = len(monitor_name) + len(suffix) result = f'{"-" bar_length}\n' result += monitor_name + suffix + "\n" for state in self.states: result += f'{state}\n' for (index, states) in self.states_indexed.items(): if states: result += f'index {index}:\n' for state in states: result += f' {state}\n' result += f'{"-" * bar_length}\n' return result def add_state_to_state_vector(self, states: Set[State], state: State): """ Adds a state to a state vector. Also sets the monitor field of the state to self (the monitor the state is part of). :param states: the state vector to add state to. :param state: the state to become initial states. """ state.set_monitor_to(self) states.add(state) def report_transition_error(self, state: State, event: Event, msg: str): """ Reports an error caused by taking a transition that results in an ErrorState. :param state: the state in which the transition is taken. :param event: the event that causes the transition to be taken. :param msg: the error message provided by user. """ message =
is visible. When the last pending initializer is removed, and no failing result is set, the initializers struct will be set to nil and the object is considered as initialized and visible to all clients. type: list contains: name: description: - name of the process that is responsible for initializing this object. type: str result: description: - If result is set with the Failure field, the object will be persisted to storage and then deleted, ensuring that other clients can observe the deletion. type: complex contains: api_version: description: - APIVersion defines the versioned schema of this representation of an object. Servers should convert recognized schemas to the latest internal value, and may reject unrecognized values. type: str code: description: - Suggested HTTP return code for this status, 0 if not set. type: int details: description: - Extended data associated with the reason. Each reason may define its own extended details. This field is optional and the data returned is not guaranteed to conform to any schema except that defined by the reason type. type: complex contains: causes: description: - The Causes array includes more details associated with the StatusReason failure. Not all StatusReasons may provide detailed causes. type: list contains: field: description: - 'The field of the resource that has caused this error, as named by its JSON serialization. May include dot and postfix notation for nested attributes. Arrays are zero-indexed. Fields may appear more than once in an array of causes due to fields having multiple errors. Optional. Examples: "name" - the field "name" on the current resource "items[0].name" - the field "name" on the first array entry in "items"' type: str message: description: - A human-readable description of the cause of the error. This field may be presented as-is to a reader. type: str reason: description: - A machine-readable description of the cause of the error. If this value is empty there is no information available. type: str group: description: - The group attribute of the resource associated with the status StatusReason. type: str kind: description: - The kind attribute of the resource associated with the status StatusReason. On some operations may differ from the requested resource Kind. type: str name: description: - The name attribute of the resource associated with the status StatusReason (when there is a single name which can be described). type: str retry_after_seconds: description: - If specified, the time in seconds before the operation should be retried. type: int uid: description: - UID of the resource. (when there is a single resource which can be described). type: str kind: description: - Kind is a string value representing the REST resource this object represents. Servers may infer this from the endpoint the client submits requests to. Cannot be updated. In CamelCase. type: str message: description: - A human-readable description of the status of this operation. type: str metadata: description: - Standard list metadata. type: complex contains: resource_version: description: - String that identifies the server's internal version of this object that can be used by clients to determine when objects have changed. Value must be treated as opaque by clients and passed unmodified back to the server. Populated by the system. Read-only. type: str self_link: description: - SelfLink is a URL representing this object. Populated by the system. Read-only. type: str reason: description: - A machine-readable description of why this operation is in the "Failure" status. If this value is empty there is no information available. A Reason clarifies an HTTP status code but does not override it. type: str status: description: - 'Status of the operation. One of: "Success" or "Failure".' type: str labels: description: - Map of string keys and values that can be used to organize and categorize (scope and select) objects. May match selectors of replication controllers and services. type: complex contains: str, str name: description: - Name must be unique within a namespace. Is required when creating resources, although some resources may allow a client to request the generation of an appropriate name automatically. Name is primarily intended for creation idempotence and configuration definition. Cannot be updated. type: str namespace: description: - Namespace defines the space within each name must be unique. An empty namespace is equivalent to the "default" namespace, but "default" is the canonical representation. Not all objects are required to be scoped to a namespace - the value of this field for those objects will be empty. Must be a DNS_LABEL. Cannot be updated. type: str owner_references: description: - List of objects depended by this object. If ALL objects in the list have been deleted, this object will be garbage collected. If this object is managed by a controller, then an entry in this list will point to this controller, with the controller field set to true. There cannot be more than one managing controller. type: list contains: api_version: description: - API version of the referent. type: str block_owner_deletion: description: - If true, AND if the owner has the "foregroundDeletion" finalizer, then the owner cannot be deleted from the key-value store until this reference is removed. Defaults to false. To set this field, a user needs "delete" permission of the owner, otherwise 422 (Unprocessable Entity) will be returned. type: bool controller: description: - If true, this reference points to the managing controller. type: bool kind: description: - Kind of the referent. type: str name: description: - Name of the referent. type: str uid: description: - UID of the referent. type: str resource_version: description: - An opaque value that represents the internal version of this object that can be used by clients to determine when objects have changed. May be used for optimistic concurrency, change detection, and the watch operation on a resource or set of resources. Clients must treat these values as opaque and passed unmodified back to the server. They may only be valid for a particular resource or set of resources. Populated by the system. Read-only. Value must be treated as opaque by clients and . type: str self_link: description: - SelfLink is a URL representing this object. Populated by the system. Read-only. type: str uid: description: - UID is the unique in time and space value for this object. It is typically generated by the server on successful creation of a resource and is not allowed to change on PUT operations. Populated by the system. Read-only. type: str spec: description: - spec holds all the input necessary to produce a new build, and the conditions when to trigger them. type: complex contains: completion_deadline_seconds: description: - completionDeadlineSeconds is an optional duration in seconds, counted from the time when a build pod gets scheduled in the system, that the build may be active on a node before the system actively tries to terminate the build; value must be positive integer type: int failed_builds_history_limit: description: - failedBuildsHistoryLimit is the number of old failed builds to retain. If not specified, all failed builds are retained. type: int node_selector: description: - nodeSelector is a selector which must be true for the build pod to fit on a node If nil, it can be overridden by default build nodeselector values for the cluster. If set to an empty map or a map with any values, default build nodeselector values are ignored. type: complex contains: str, str output: description: - output describes the Docker image the Strategy should produce. type: complex contains: image_labels: description: - imageLabels define a list of labels that are applied to the resulting image. If there are multiple labels with the same name then the last one in the list is used. type: list contains: name: description: - name defines the name of the label. It must have non-zero length. type: str value: description: - value defines the
<reponame>misakadam97/cell_mrcnn from os import listdir, mkdir, path from os.path import join, isdir, basename, split from glob import glob import numpy as np import skimage.draw from skimage.io import imread from cell_mrcnn.utils import correct_central_brightness, subtract_bg, convert_to_bit8, \ get_cell_mrcnn_path_from_config_file, get_image_description, \ preproc_pipeline, load_image import read_roi import gc import pandas as pd from datetime import datetime from shutil import copyfile # data directory # data_dir = get_cell_mrcnn_path_from_config_file() # dataset_dir = join(data_dir, 'annotated_datasets/') # Import Mask RCNN from cell_mrcnn import utils from PIL import Image def calculate_percentiles(im_paths): # takes ~2mins # on 2048x2048 images the below percentiles leave about _ pixels out: # 99: 42k # 99.9: 4.2k # 99.99: 420 # 99.999: 42 # 99.9999: 4 # 100: 0 , this is equal to max percentiles = [99, 99.9, 99.99, 99.999, 99.9999, 100] perc_df = pd.DataFrame(columns=percentiles, index=pd.RangeIndex(0, len(im_paths))) for i, im_path in enumerate(im_paths): im = imread(im_path) im = correct_central_brightness(im.astype(np.float)) im = subtract_bg(im) percentile_list = [] for perc in percentiles: percentile_list.append(np.percentile(im, perc)) perc_df.loc[i, :] = percentile_list perc_df.loc["min", :] = perc_df.min(axis=0) perc_df.loc["max", :] = perc_df.loc[perc_df.index[0:-1], :].max(axis=0) perc_df.loc["mean", :] = perc_df.loc[perc_df.index[0:-2], :].mean(axis=0) perc_df.loc["median", :] = perc_df.loc[perc_df.index[0:-3], :].median( axis=0) perc_df.loc["var", :] = perc_df.loc[perc_df.index[0:-4], :].var(axis=0) perc_df.loc["std", :] = perc_df.loc[perc_df.index[0:-5], :].std(axis=0) return perc_df def preprocess(channel_paths, output_folder, cutoffs): """ :param input_folder: folder of the raw images :param output_folder: preprocessed images will be saved here :param channel_paths: nested list of image paths. Can contain 1 or 2 lists. If it contains 2; 1st should be Venus, this is gona be the red channel; 2nd should be Cerulean, this will be the blue channel; and they should be sorted! :param cutoffs: cutoff for 8 bit conversion (order same as in channel paths) :return: """ if not isdir(output_folder): mkdir(output_folder) # If only Venus channel if len(channel_paths) == 1: venus_paths = channel_paths[0] for i, impath in enumerate(venus_paths): im = imread(impath) im_c = correct_central_brightness(im.astype(np.float16)) im_bg = subtract_bg(im_c) im8 = convert_to_bit8(im_bg, cutoffs[0]) fname = impath.split()[1] Image.fromarray(im8).save(join(output_folder, fname + '.png')) # If Venus and Cerulean elif len(channel_paths) == 2: red_paths, blue_paths = channel_paths[0], channel_paths[1] red_desc = [get_image_description(path)[1:] for path in red_paths] blue_desc = [get_image_description(path)[1:] for path in blue_paths] assert red_desc == blue_desc, 'images not sorted' for i, (red_path, blue_path) in enumerate(zip(red_paths, blue_paths)): print('\rCreating composite images: {}/{}' \ .format(i + 1, len(blue_paths)), end='...') try: red, blue = imread(red_path), imread(blue_path) comp = preproc_pipeline(red, blue) fname = split(red_path)[1].split('.')[0] Image.fromarray(comp).save(join(output_folder, fname + '.png')) except: print(f'image {i} processing failed') def transfer_w3_channel_images(data_dir): cit_dir = join(data_dir, 'w3/cit') cellmembrane_dir = join(data_dir, 'w3/cellmembrane') if not isdir(cit_dir): mkdir(cit_dit) if not isdir(cellmembrane_dir): mkdir(cellmembrane_dir) # get the path to all tif images im_paths = glob(join(data_dir, '*/.tif'), recursive=True) # select the non-thumbnail w3 images for i, im_path in enumerate(im_paths): print('\rSeparating cit. and cellmembrane w3 channel images: ', i + 1, '/', len(im_paths), end='') if 'thumb' in im_path: continue if im_path.split('_')[-1][:2] != 'w3': continue im = imread(im_path) im = convert_to_bit8(im) im = Image.fromarray(im) if 'cit' in im_path: im.save(join(cit_dir, im_path.split('/')[-1].split('.')[0] + '.png')) else: im.save(join(cellmembrane_dir, im_path.split('/')[-1].split('.')[0] + '.png')) def cell_groups_to_bg(image, roi_set): cell_group_rois = [roi_set[key] for key in roi_set.keys() if 'cell_group' in key] im = np.copy(image) for cg_roi in cell_group_rois: rr, cc = skimage.draw.polygon(cg_roi['y'], cg_roi['x']) mask = np.zeros((im.shape[0], im.shape[1]), dtype=np.uint8) mask[rr, cc] = 1 rand_bg = np.random.randint(im.mean() - im.std(), im.mean() + im.std(), size=mask.sum()) mask[rr, cc] = rand_bg im[rr, cc] = mask[rr, cc] return im def read_roi_or_roiset(impath): impath = impath.split('.')[0] # if rois are in a zip (image contains multiple rois) if path.isfile(join(impath + '.zip')): rois = read_roi.read_roi_zip( join(impath + '.zip')) return rois # if roi is in roi format(image only contains 1 roi) elif path.isfile(join(impath + '.roi')): rois = read_roi.read_roi_file(join(impath + '.roi')) return rois else: print("rois couldn't be found for:", impath) def calc_avg_pixel_value(image_paths, output_file=None): image = load_image(image_paths[0]) channel_n = image.shape[2] channel_mean_dict = {} for i in range(channel_n): channel_mean_dict[i] = [] for image_path in image_paths: image = load_image(image_path) for i in range(channel_n): channel_mean_dict[i].append(image[:, :, i].mean()) for i in range(channel_n): channel_mean_dict[i] = np.array(channel_mean_dict[i]).mean() means = [mean for mean in channel_mean_dict.values()] if output_file: with open(output_file, 'w') as f: for i, mean in enumerate(means): f.write('Channel {} mean: {}'.format(i+1, means[i])) return means def copy_annotated(input_folder, output_folder): """ creates a folder named the current datetime; and copies the roi.zip files and corresponding images from the input folder into it :param input_folder: :param output_folder: :return: """ # get a list of roi paths roi_paths = glob(join(input_folder, '.zip')) roi_paths.extend(glob(join(input_folder, '.roi'))) # get the corresponding image paths image_paths = [] for roi_path in roi_paths: image_paths.append(roi_path.split('.')[0] + '.png') date = datetime.now().strftime("%Y_%m_%d_%H_%M_%S") output_folder = join(output_folder, date) if not isdir(output_folder): mkdir(output_folder) for roi, im in zip(roi_paths, image_paths): copyfile(roi, join(output_folder, basename(roi))) copyfile(im, join(output_folder, basename(im))) return output_folder class CellTransformData(utils.Dataset): def load_cell(self, dataset_dir): """Load the cell dataset resize the images and rois to a uniform dimension. dataset_dir: Root directory of the dataset. subset: Subset to load: train or val """ # Add classes. We have only one class to add. self.add_class("hulab", 1, "cell") image_names = [file_name for file_name in listdir(dataset_dir) if 'png' in file_name] # todo: revisit this naming convention, come up w/ a unifrom system # just so that the orderly assigned id is in the utils. Dataset class # is in the same order as the numbers in the image names # image_names = [int(name.split('.png')[0]) for name in image_names] # image_names.sort() # image_names = [str(n) + '.png' for n in image_names] # Add images for image_name in image_names: id = image_name.split('.')[0] # Get the x, y coordinaets of points of the polygons that make up # the outline of each object instance. # RoI format: # roi = { # region1 : { # 'type' = 'freehand' # 'x' = [...] # 'y' = [...] # 'n' = 412 # 'width' = 0 # always 0, not informative # 'name' = the region name (same as the key) # 'position' = 0 # always 0, not informative # } # ...more regions # } rois = read_roi_or_roiset(join(dataset_dir, id)) im_path = join(dataset_dir, image_name) # load_mask() needs the image size to convert polygons to masks. # Unfortunately, RoI doesn't include it, so we must read # the image. This is only managable since the dataset is tiny. h, w = imread(im_path).shape[:2] self.add_image( "hulab", image_id=id, # use file name as a unique image id path=im_path, width=w, height=h, polygons=rois) def polygon_to_mask(self, image_id): """Generate instance masks for an image. Returns: masks: A bool array of shape [height, width, instance count] with one mask per instance. class_ids: a 1D array of class IDs of the instance masks. """ # If not a confonc dataset image, delegate to parent class. image_info = self.image_info[image_id] if image_info["source"] != "hulab": return super(self.__class__, self).load_mask(image_id) # Convert polygons to a bitmap mask of shape # [height, width, instance_count] info = self.image_info[image_id] # /proc/sys/vm/overcommit_memory has to be "1" for larger arrays mask = np.zeros([info["height"], info["width"], len(info["polygons"])], dtype=np.bool) for i, (key, vals) in enumerate(info["polygons"].items()): # Get indexes of pixels inside the polygon and set them to 1 rr, cc = skimage.draw.polygon(vals['y'], vals['x']) mask[rr, cc, i] = True del (rr, cc) gc.collect() # Return mask, and array of class IDs of each instance. Since we have # one class ID only, we return an array of 1s return mask, np.ones([mask.shape[-1]], dtype=np.int32) def split_to_trainval(self, dataset_dir): # set up the output directory's subfolders [mkdir(join(dataset_dir, folder)) for folder in ['train', 'val'] if folder not in listdir(dataset_dir)] np.random.seed(seed=54646) train_set = np.random.choice(self.image_ids, int(len(self.image_ids) * 0.7), replace=False) means = [] for image_id_ in self.image_ids: info = self.image_info[image_id_] im = self.load_image(image_id_) means.append(im.mean(axis=(0, 1))) mask = self.polygon_to_mask(image_id_)[0] id_ = info['id'] if image_id_ in train_set: output_dir = join(dataset_dir, 'train', str(id_)) else: output_dir = join(dataset_dir, 'val', str(id_)) if not isdir(output_dir): mkdir(output_dir) for m in range(mask.shape[2]): mask_ = Image.fromarray((mask[:, :, m] * 255).astype(np.uint8), mode='L') mask_ = mask_.convert(mode='1') mask_.save(join(output_dir, str(id_) + '_mask_' + str(m) \ + '.png')) copyfile(info['path'], join(output_dir, str(id_) + '.png')) means = np.array(means).mean(axis=0) print('Average pixel value(s) is(/are): {}'.format(means)) return means if __name__ == '__main__': # load the dataset dataset_path = join(dataset_dir, '2020_11_22_02_55_03') ds = CellTransformData() ds.load_cell(dataset_path) ds.prepare() ds.split_to_trainval(dataset_path) show_example = False if show_example: # test image_id = 12 image = ds.load_image(image_id) mask, class_ids = ds.polygon_to_mask(image_id) original_shape = image.shape # Resize image,
<gh_stars>1-10 from __future__ import print_function, division, absolute_import import numpy as np import scipy from scipy.misc import imsave, imread, imresize from sklearn.feature_extraction.image import reconstruct_from_patches_2d, extract_patches_2d from scipy.ndimage.filters import gaussian_filter from skimage.util.shape import view_as_windows from keras import backend as K import os import time #import cv2 ''' _image_scale_multiplier is a special variable which is used to alter image size. The default image size is 32x32. If a true upscaling model is used, then the input image size is 16x16, which not offer adequate training samples. ''' _image_scale_multiplier = 1 img_size = 256 * _image_scale_multiplier stride = 16 * _image_scale_multiplier assert (img_size 2) % (stride 2) == 0, "Number of images generated from strided subsample of the image needs to be \n" \ "a positive integer. Change stride such that : \n" \ "(img_size 2) / (stride 2) is a positive integer." input_path = r"input_images/" validation_path = r"val_images/" validation_set5_path = validation_path + "set5/" validation_set14_path = validation_path + "set14/" base_dataset_dir = os.path.expanduser("~") + "/Image Super Resolution Dataset/" output_path = base_dataset_dir + "train_images/train/" validation_output_path = base_dataset_dir + r"train_images/validation/" if not os.path.exists(output_path): os.makedirs(output_path) def transform_images(directory, output_directory, scaling_factor=2, max_nb_images=-1, true_upscale=False): index = 1 if not os.path.exists(output_directory + "X/"): os.makedirs(output_directory + "X/") if not os.path.exists(output_directory + "y/"): os.makedirs(output_directory + "y/") # For each image in input_images directory nb_images = len([name for name in os.listdir(directory)]) if max_nb_images != -1: print("Transforming %d images." % max_nb_images) else: assert max_nb_images <= nb_images, "Max number of images must be less than number of images in path" print("Transforming %d images." % (nb_images)) if nb_images == 0: print("Extract the training images or images from imageset_91.zip (found in the releases of the project) " "into a directory with the name 'input_images'") print("Extract the validation images or images from set5_validation.zip (found in the releases of the project) " "into a directory with the name 'val_images'") exit() for file in os.listdir(directory): img = imread(directory + file, mode='RGB') # Resize to 256 x 256 img = imresize(img, (img_size, img_size)) img=scipy.misc.imfilter(img,ftype='sharpen') # Create patches hr_patch_size = (16 * scaling_factor * _image_scale_multiplier) nb_hr_images = (img_size 2) // (stride 2) hr_samples = np.empty((nb_hr_images, hr_patch_size, hr_patch_size, 3)) image_subsample_iterator = subimage_generator(img, stride, hr_patch_size, nb_hr_images) stride_range = np.sqrt(nb_hr_images).astype(int) i = 0 for j in range(stride_range): for k in range(stride_range): hr_samples[i, :, :, :] = next(image_subsample_iterator) i += 1 lr_patch_size = 16 * _image_scale_multiplier t1 = time.time() # Create nb_hr_images 'X' and 'Y' sub-images of size hr_patch_size for each patch for i in range(nb_hr_images): ip = hr_samples[i] # Save ground truth image X imsave(output_directory + "/y/" + "%d_%d.png" % (index, i + 1), ip) # Apply Gaussian Blur to Y op = gaussian_filter(ip, sigma=0.5) print("CVVVVVVVVVVVVVVVV") #ip = np.array(ip, dtype=np.uint8) #op = cv2.bilateralFilter(ip,15,125,125) # Subsample by scaling factor to Y op = imresize(op, (lr_patch_size, lr_patch_size), interp='bicubic') if not true_upscale: # Upscale by scaling factor to Y op = imresize(op, (hr_patch_size, hr_patch_size), interp='bicubic') # Save Y imsave(output_directory + "/X/" + "%d_%d.png" % (index, i+1), op) print("Finished image %d in time %0.2f seconds. (%s)" % (index, time.time() - t1, file)) index += 1 if max_nb_images > 0 and index >= max_nb_images: print("Transformed maximum number of images. ") break print("Images transformed. Saved at directory : %s" % (output_directory)) def image_count(): return len([name for name in os.listdir(output_path + "X/")]) def val_image_count(): return len([name for name in os.listdir(validation_output_path + "X/")]) def subimage_generator(img, stride, patch_size, nb_hr_images): for _ in range(nb_hr_images): for x in range(0, img_size - patch_size, stride): for y in range(0, img_size - patch_size, stride): subimage = img[x : x + patch_size, y : y + patch_size, :] yield subimage def subimage_patch(img, stride, patch_size, nb_hr_images): heightini, widthini = img.shape[:2] #print(str(heightini)+'--'+str(widthini)) #j=0 for y in range(0, widthini , stride): #for y in range(0, heightini - patch_size, stride): for x in range(0, heightini , stride): if (x + patch_size)<widthini and (y + patch_size) <heightini: subimage = img[y : y + patch_size, x : x + patch_size, :] #height, width = subimage.shape[:2] #print(str(height)+'<<-->>'+str(width)) #print(str(x)+'--'+str(y)+'--'+str(x + patch_size)+'--'+str(y + patch_size)) #j += 1 yield subimage def make_patches(x, scale, patch_size, upscale=True, verbose=1): '''x shape: (num_channels, rows, cols)''' height, width = x.shape[:2] img_height =width * scale img_width = height * scale #x = imresize(x, (int(img_width/1.1), int(img_height/1.1) )) #imsave("intermediate.jpg", x) #x = imresize(x, (img_width,img_height)) #print("PATCH SIZE SIZE") #imsave("intermediateafter.jpg", x) #if upscale: x = imresize(x, (height * scale, width * scale), interp='bicubic') #if upscale: x = imresize(x, (height * scale, width * scale)) patches = extract_patches_2d(x, (patch_size, patch_size)) return patches def make_patchesOrig(x, scale, patch_size, upscale=False, verbose=1): '''x shape: (num_channels, rows, cols)''' height, width = x.shape[:2] if upscale: x = imresize(x, (height * scale, width * scale)) patches = extract_patches_2dv2(x, (patch_size, patch_size)) return patches def make_patchesStep(x, scale, patch_size, upscale=False,extraction_step=24, verbose=1): '''x shape: (num_channels, rows, cols)''' height, width = x.shape[:2] if upscale: x = imresize(x, (height * scale, width * scale)) patches = extract_patches_Step(x, (patch_size, patch_size),extraction_step) return patches def combine_patches(in_patches, out_shape, scale): '''Reconstruct an image from these `patches`''' print("wpatch") recon = reconstruct_from_patches_2d(in_patches, out_shape) return recon from itertools import product def reconstruct_from_patches_2dloc(patches, image_size): """Reconstruct the image from all of its patches. Patches are assumed to overlap and the image is constructed by filling in the patches from left to right, top to bottom, averaging the overlapping regions. Read more in the :ref:`User Guide <image_feature_extraction>`. Parameters ---------- patches : array, shape = (n_patches, patch_height, patch_width) or (n_patches, patch_height, patch_width, n_channels) The complete set of patches. If the patches contain colour information, channels are indexed along the last dimension: RGB patches would have `n_channels=3`. image_size : tuple of ints (image_height, image_width) or (image_height, image_width, n_channels) the size of the image that will be reconstructed Returns ------- image : array, shape = image_size the reconstructed image """ i_h, i_w = image_size[:2] p_h, p_w = patches.shape[1:3] img = np.zeros(image_size) # compute the dimensions of the patches array n_h = i_h - p_h + 1 n_w = i_w - p_w + 1 for p, (i, j) in zip(patches, product(range(n_h), range(n_w))): img[i:i + p_h, j:j + p_w] += p for i in range(i_h): for j in range(i_w): # divide by the amount of overlap # XXX: is this the most efficient way? memory-wise yes, cpu wise? img[i, j] /= float(min(i + 1, p_h, i_h - i) * min(j + 1, p_w, i_w - j)) return img # generate an overlap count map directly, this is fast Y, X = np.ogrid[0:i_h, 0:i_w] x_h = min(p_w, i_w - p_w) y_h = min(p_h, i_h - p_h) overlap_cnt = ((np.minimum(np.minimum(X+1,x_h), np.minimum(i_w-X,x_h))) (np.minimum(np.minimum(Y+1,y_h), np.minimum(i_h-Y,y_h))),1) return img/overlap_cnt def subimage_build_patch_global(img, stride, patch_size, nb_hr_images): heightini, widthini = img.shape[:2] print("///////////------") print(img.shape) #print(str(heightini)+'--'+str(widthini)) i=0 for y in range(0, widthini , stride): #for y in range(0, heightini - patch_size, stride): for x in range(0, heightini , stride): if (x + patch_size)<widthini and (y + patch_size) <heightini: i += 1 subimages= np.empty((i, patch_size, patch_size, 3)) j=0 for y in range(0, widthini , stride): #for y in range(0, heightini - patch_size, stride): for x in range(0, heightini , stride): if (x + patch_size)<widthini and (y + patch_size) <heightini: subimages[j, :, :, :] = img[y : y + patch_size, x : x + patch_size, :] #height, width = subimage.shape[:2] #print(str(height)+'<<-->>'+str(width)) #print(str(x)+'--'+str(y)+'--'+str(x + patch_size)+'--'+str(y + patch_size)) j += 1 #yield subimage print(i) return subimages def subimage_combine_patches_global(imgtrue , patches, stride, patch_size, scale): heighttrue, widthtrue = imgtrue.shape[:2] img = imresize(imgtrue, (heighttruescale, widthtruescale), interp='bicubic') heightini, widthini = img.shape[:2] print("///////////------") print(img.shape) print(patches.shape) j=0 for y in range(0, widthini , stride): #for y in range(0, heightini - patch_size, stride): for x in range(0, heightini , stride): if (x + patch_size)<widthini and (y + patch_size) <heightini: #subimages[j, :, :, :] = img[y : y + patch_size, x : x + patch_size, :] img[y : y + patch_size, x : x + patch_size, :]=patches[j, :, :, :] #height, width = subimage.shape[:2] #print(str(height)+'<<-->>'+str(width)) #print(str(x)+'--'+str(y)+'--'+str(x + patch_size)+'--'+str(y + patch_size)) j += 1 print(j) return img def image_generator(directory, scale_factor=2, target_shape=None, channels=3, small_train_images=False, shuffle=True, batch_size=32, seed=None): if not target_shape: if small_train_images: if K.image_dim_ordering() == "th": image_shape = (channels, 16 _image_scale_multiplier, 16 * _image_scale_multiplier) y_image_shape = (channels, 16 * scale_factor *
import logging import pickle from typing import Dict, List, Optional, Set, Union import click import hail as hl from gnomad.resources.grch38.gnomad import ( COHORTS_WITH_POP_STORED_AS_SUBPOP, HGDP_POPS, TGP_POPS, TGP_POP_NAMES, POPS, SEXES, SUBSETS, ) from gnomad.sample_qc.ancestry import POP_NAMES from gnomad.utils.filtering import remove_fields_from_constant from gnomad.utils.vcf import ( add_as_info_dict, adjust_vcf_incompatible_types, ALLELE_TYPE_FIELDS, AS_FIELDS, AS_VQSR_FIELDS, create_label_groups, ENTRIES, FAF_POPS, FORMAT_DICT, HISTS, INFO_DICT, IN_SILICO_ANNOTATIONS_INFO_DICT, make_info_dict, make_vcf_filter_dict, REGION_FLAG_FIELDS, RF_FIELDS, SITE_FIELDS, VQSR_FIELDS, ) from gnomad.variant_qc.pipeline import INBREEDING_COEFF_HARD_CUTOFF from gnomad.utils.annotations import region_flag_expr from joint_calling import utils, _version, resources from joint_calling.utils import get_validation_callback logger = logging.getLogger(__file__) logger.setLevel('INFO') # Add new site fields # NEW_SITE_FIELDS = [ # 'monoallelic', # 'transmitted_singleton', # ] # SITE_FIELDS.extend(NEW_SITE_FIELDS) # Remove original alleles for containing non-releasable alleles MISSING_ALLELE_TYPE_FIELDS = ['original_alleles', 'has_star'] ALLELE_TYPE_FIELDS = remove_fields_from_constant( ALLELE_TYPE_FIELDS, MISSING_ALLELE_TYPE_FIELDS ) # Remove SB (not included in VCF) and SOR (doesn't exist in v3.1) from site fields MISSING_SITES_FIELDS = ['SOR', 'SB'] SITE_FIELDS = remove_fields_from_constant(SITE_FIELDS, MISSING_SITES_FIELDS) # Remove AS_VarDP from AS fields MISSING_AS_FIELDS = ['AS_VarDP'] AS_FIELDS = remove_fields_from_constant(AS_FIELDS, MISSING_AS_FIELDS) # Make subset list (used in properly filling out VCF header descriptions and naming VCF info fields) SUBSET_LIST_FOR_VCF = SUBSETS.copy() SUBSET_LIST_FOR_VCF.append('') # Remove cohorts that have subpop frequencies stored as pop frequencies # Inclusion of these subsets significantly increases the size of storage in the VCFs because of the many subpops SUBSET_LIST_FOR_VCF = remove_fields_from_constant( SUBSET_LIST_FOR_VCF, COHORTS_WITH_POP_STORED_AS_SUBPOP ) # Remove decoy from region field flag MISSING_REGION_FIELDS = ['decoy'] REGION_FLAG_FIELDS = remove_fields_from_constant( REGION_FLAG_FIELDS, MISSING_REGION_FIELDS ) # All missing fields to remove from vcf info dict MISSING_INFO_FIELDS = ( MISSING_ALLELE_TYPE_FIELDS + MISSING_AS_FIELDS + MISSING_REGION_FIELDS + MISSING_SITES_FIELDS + RF_FIELDS ) # Remove unnecessary pop names from POP_NAMES dict POPS = {pop: POP_NAMES[pop] for pop in POPS} # Remove unnecessary pop names from FAF_POPS dict FAF_POPS = {pop: POP_NAMES[pop] for pop in FAF_POPS} # Get HGDP + TGP(KG) subset pop names HGDP_TGP_KEEP_POPS = TGP_POPS + HGDP_POPS HGDP_TGP_POPS = {} for pop in HGDP_TGP_KEEP_POPS: if pop in TGP_POP_NAMES: HGDP_TGP_POPS[pop] = TGP_POP_NAMES[pop] else: HGDP_TGP_POPS[pop] = pop.capitalize() # Used for HGDP + TGP subset MT VCF output only FORMAT_DICT.update( { 'RGQ': { 'Number': '1', 'Type': 'Integer', 'Description': 'Unconditional reference genotype confidence, encoded as a phred quality -10log10 p(genotype call is wrong)', } } ) @click.command() @click.version_option(_version.__version__) @click.option( '--mt', 'mt_path', required=True, callback=get_validation_callback(ext='mt', must_exist=True), help='path to the raw sparse Matrix Table generated by combine_gvcfs.py', ) @click.option( '--out-ht', 'out_ht_path', required=True, callback=get_validation_callback(ext='ht'), help='path to write Hail Table', ) @click.option( '--out-vcf-header-txt', 'out_vcf_header_txt_path', required=True, callback=get_validation_callback(ext='txt'), ) @click.option( '--public-subset', 'is_public_subset', is_flag=True, help='create a subset', ) @click.option( '--test', 'is_test', is_flag=True, required=True, help='Create release files using only 2 partitions on chr20, chrX, ' 'and chrY for testing purposes' ) @click.option( '--local-tmp-dir', 'local_tmp_dir', help='local directory for temporary files and Hail logs (must be local).', ) def main( mt_path: str, out_ht_path: str, out_vcf_header_txt_path: str, is_test: bool, is_public_subset: bool, local_tmp_dir: str, ): # pylint: disable=missing-function-docstring utils.init_hail(__file__, local_tmp_dir) mt = hl.read_matrix_table(mt_path) ht = mt.rows() if is_test: ht = filter_to_test(ht) # Setup of parameters and Table/MatrixTable parameter_dict = _build_parameter_dict(ht, is_public_subset) vcf_ht = _prepare_vcf_ht(ht, is_subset=is_public_subset) if is_public_subset: logger.info( 'Loading subset MT and annotating with the prepared VCF HT for VCF export...' ) entries_ht = mt.select_rows().select_entries(ENTRIES) vcf_ht = ht.annotate_rows(**vcf_ht[entries_ht.row_key]) logger.info('Cleaning up the VCF HT for final export...') vcf_ht = _cleanup_ht_for_vcf_export(vcf_ht) vcf_ht = vcf_ht.checkpoint(out_ht_path, overwrite=True) vcf_ht.describe() _prepare_vcf_header_dict( ht=ht, vcf_ht=vcf_ht, vcf_header_txt_path=out_vcf_header_txt_path, parameter_dict=parameter_dict, is_public_subset=is_public_subset, ) def _prepare_vcf_header_dict( ht, vcf_ht, vcf_header_txt_path, parameter_dict, is_public_subset, ): logger.info('Making histogram bin edges...') header_dict = prepare_vcf_header_dict( vcf_ht, subset_list=parameter_dict['subsets'], pops=parameter_dict['pops'], filtering_model_field=parameter_dict['filtering_model_field'], inbreeding_coeff_cutoff=ht.inbreeding_coeff_cutoff, ) if not is_public_subset: header_dict.pop('format') logger.info('Saving header dict to pickle...') with hl.hadoop_open(vcf_header_txt_path, 'wb') as p: pickle.dump(header_dict, p, protocol=pickle.HIGHEST_PROTOCOL) def populate_subset_info_dict( subset: str, description_text: str, pops: Dict[str, str] = POPS, faf_pops: Dict[str, str] = FAF_POPS, sexes: List[str] = SEXES, label_delimiter: str = '_', ) -> Dict[str, Dict[str, str]]: """ Call `make_info_dict` to populate INFO dictionary with specific sexes, population names, and filtering allele frequency (faf) pops for the requested subset. :param subset: Sample subset in dataset. :param description_text: Text describing the sample subset that should be added to the INFO description. :param pops: Dict of sample global population names for gnomAD genomes. Default is POPS. :param faf_pops: Dict with faf pop names (keys) and descriptions (values). Default is FAF_POPS. :param sexes: gnomAD sample sexes used in VCF export. Default is SEXES. :param label_delimiter: String to use as delimiter when making group label combinations. Default is '_'. :return: Dictionary containing Subset specific INFO header fields. """ vcf_info_dict = {} faf_label_groups = create_label_groups(pops=faf_pops, sexes=sexes) for label_group in faf_label_groups: vcf_info_dict.update( make_info_dict( prefix=subset, prefix_before_metric=True if 'gnomad' in subset else False, pop_names=faf_pops, label_groups=label_group, label_delimiter=label_delimiter, faf=True, description_text=description_text, ) ) label_groups = create_label_groups(pops=pops, sexes=sexes) for label_group in label_groups: vcf_info_dict.update( make_info_dict( prefix=subset, prefix_before_metric=True if 'gnomad' in subset else False, pop_names=pops, label_groups=label_group, label_delimiter=label_delimiter, description_text=description_text, ) ) # Add popmax to info dict vcf_info_dict.update( make_info_dict( prefix=subset, label_delimiter=label_delimiter, pop_names=pops, popmax=True, description_text=description_text, ) ) return vcf_info_dict def populate_info_dict( info_dict: Dict[str, Dict[str, str]] = INFO_DICT, subset_list: List[str] = SUBSETS, subset_pops: Dict[str, str] = POPS, gnomad_pops: Dict[str, str] = POPS, faf_pops: Dict[str, str] = FAF_POPS, sexes: List[str] = SEXES, in_silico_dict: Dict[str, Dict[str, str]] = IN_SILICO_ANNOTATIONS_INFO_DICT, label_delimiter: str = '_', bin_edges: Dict[str, str] = None, age_hist_data: str = None, ) -> Dict[str, Dict[str, str]]: """ Call `make_info_dict` and `make_hist_dict` to populate INFO dictionary with specific sexes, population names, and filtering allele frequency (faf) pops. Used during VCF export. Creates: - INFO fields for age histograms (bin freq, n_smaller, and n_larger for heterozygous and homozygous variant carriers) - INFO fields for popmax AC, AN, AF, nhomalt, and popmax population - INFO fields for AC, AN, AF, nhomalt for each combination of sample population, sex both for adj and raw data - INFO fields for filtering allele frequency (faf) annotations - INFO fields for variant histograms (hist_bin_freq for each histogram and hist_n_larger for DP histograms) :param bin_edges: Dictionary of variant annotation histograms and their associated bin edges. :param age_hist_data: Pipe-delimited string of age histograms, from `get_age_distributions`. :param info_dict: INFO dict to be populated. :param subset_list: List of sample subsets in dataset. Default is SUBSETS. :param subset_pops: Dict of sample global population names to use for all subsets in `subset_list` unless the subset is 'gnomad', in that case `gnomad_pops` is used. Default is POPS. :param gnomad_pops: Dict of sample global population names for gnomAD genomes. Default is POPS. :param faf_pops: Dict with faf pop names (keys) and descriptions (values). Default is FAF_POPS. :param sexes: gnomAD sample sexes used in VCF export. Default is SEXES. :param in_silico_dict: Dictionary of in silico predictor score descriptions. :param label_delimiter: String to use as delimiter when making group label combinations. :return: Updated INFO dictionary for VCF export. """ vcf_info_dict = info_dict.copy() # Remove MISSING_INFO_FIELDS from info dict for field in MISSING_INFO_FIELDS: vcf_info_dict.pop(field, None) # Add allele-specific fields to info dict, including AS_VQSR_FIELDS vcf_info_dict.update( add_as_info_dict(info_dict=info_dict, as_fields=AS_FIELDS + AS_VQSR_FIELDS) ) for subset in subset_list: if subset == 'gnomad': description_text = ' in gnomAD' pops = gnomad_pops else: description_text = '' if subset == '' else f' in {subset} subset' pops = subset_pops vcf_info_dict.update( populate_subset_info_dict( subset=subset, description_text=description_text, pops=pops, faf_pops=faf_pops, sexes=sexes, label_delimiter=label_delimiter, ) ) if age_hist_data: age_hist_data = '\|'.join(str(x) for x in age_hist_data) vcf_info_dict.update( make_info_dict( prefix='', label_delimiter=label_delimiter, bin_edges=bin_edges, popmax=True, age_hist_data=age_hist_data, ) ) # Add in silico prediction annotations to info_dict vcf_info_dict.update(in_silico_dict) return vcf_info_dict def make_info_expr( t: Union[hl.MatrixTable, hl.Table], hist_prefix: str = '', ) -> Dict[str, hl.expr.Expression]: """ Make Hail expression for variant annotations to be included in VCF INFO field. :param t: Table/MatrixTable containing variant annotations to be reformatted for VCF export. :param hist_prefix: Prefix to use for histograms. :return: Dictionary containing Hail expressions for relevant INFO annotations. :rtype: Dict[str, hl.expr.Expression] """ vcf_info_dict = {} # Add site-level annotations to vcf_info_dict for field in SITE_FIELDS: vcf_info_dict[field] = t['release_ht_info'][f'{field}'] # Add AS annotations to info dict for field in AS_FIELDS: vcf_info_dict[field] = t['release_ht_info'][f'{field}'] for field in VQSR_FIELDS: vcf_info_dict[field] = t['vqsr'][f'{field}'] # Add region_flag and allele_info fields to info dict for field in ALLELE_TYPE_FIELDS: vcf_info_dict[field] = t['allele_info'][f'{field}'] for field in REGION_FLAG_FIELDS: vcf_info_dict[field] = t['region_flag'][f'{field}'] # Add underscore to hist_prefix if it isn't empty if hist_prefix != '': hist_prefix += '_' # Histograms to export are: # gq_hist_alt, gq_hist_all, dp_hist_alt, dp_hist_all, ab_hist_alt # We previously dropped: # _n_smaller for all hists # _bin_edges for all hists # _n_larger for all hists EXCEPT DP hists for hist in HISTS: hist_type = f'{hist_prefix}qual_hists' hist_dict = { f'{hist}_bin_freq': hl.delimit(t[hist_type][hist].bin_freq, delimiter='\|'), } vcf_info_dict.update(hist_dict) if 'dp' in hist: vcf_info_dict.update({f'{hist}_n_larger': t[hist_type][hist].n_larger},) # Add in silico annotations to info dict vcf_info_dict['cadd_raw_score'] = t['cadd']['raw_score'] vcf_info_dict['cadd_phred']
<reponame>jeffvan-netsia/voltha_doc<filename>voltha/coordinator.py # # Copyright 2017 the original author or authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # """ Consul-based coordinator services """ from consul import ConsulException from consul.twisted import Consul from requests import ConnectionError from structlog import get_logger from twisted.internet import reactor from twisted.internet.defer import inlineCallbacks, returnValue, Deferred from twisted.internet.error import DNSLookupError from zope.interface import implementer from leader import Leader from common.utils.asleep import asleep from common.utils.message_queue import MessageQueue from voltha.registry import IComponent from worker import Worker from simplejson import dumps, loads from common.utils.deferred_utils import DeferredWithTimeout, TimeOutError log = get_logger() class StaleMembershipEntryException(Exception): pass @implementer(IComponent) class Coordinator(object): """ An app shall instantiate only one Coordinator (singleton). A single instance of this object shall take care of all external with consul, and via consul, all coordination activities with its clustered peers. Roles include: - registering an ephemeral membership entry (k/v record) in consul - participating in a symmetric leader election, and potentially assuming the leader's role. What leadership entails is not a concern for the coordination, it simply instantiates (and shuts down) a leader class when it gains (or looses) leadership. """ CONNECT_RETRY_INTERVAL_SEC = 1 RETRY_BACKOFF = [0.05, 0.1, 0.2, 0.5, 1, 2, 5] # Public methods: def __init__(self, internal_host_address, external_host_address, instance_id, rest_port, config, consul='localhost:8500', container_name_regex='^.\.([0-9]+)\..$'): log.info('initializing-coordinator') self.config = config['coordinator'] self.worker_config = config['worker'] self.leader_config = config['leader'] self.membership_watch_relatch_delay = config.get( 'membership_watch_relatch_delay', 0.1) self.tracking_loop_delay = self.config.get( 'tracking_loop_delay', 1) self.session_renewal_timeout = self.config.get( 'session_renewal_timeout', 5) self.session_renewal_loop_delay = self.config.get( 'session_renewal_loop_delay', 3) self.membership_maintenance_loop_delay = self.config.get( 'membership_maintenance_loop_delay', 5) self.session_time_to_live = self.config.get( 'session_time_to_live', 10) self.prefix = self.config.get('voltha_kv_prefix', 'service/voltha') self.leader_prefix = '/'.join((self.prefix, self.config.get( self.config['leader_key'], 'leader'))) self.membership_prefix = '/'.join((self.prefix, self.config.get( self.config['membership_key'], 'members'), '')) self.assignment_prefix = '/'.join((self.prefix, self.config.get( self.config['assignment_key'], 'assignments'), '')) self.workload_prefix = '/'.join((self.prefix, self.config.get( self.config['workload_key'], 'work'), '')) self.core_store_prefix = '/'.join((self.prefix, self.config.get( self.config['core_store_key'], 'data/core'))) self.core_store_assignment_key = self.core_store_prefix + \ '/assignment' self.core_storage_suffix = 'core_store' self.retries = 0 self.instance_id = instance_id self.internal_host_address = internal_host_address self.external_host_address = external_host_address self.rest_port = rest_port self.membership_record_key = self.membership_prefix + self.instance_id self.session_id = None self.i_am_leader = False self.leader_id = None # will be the instance id of the current leader self.shutting_down = False self.leader = None self.membership_callback = None self.worker = Worker(self.instance_id, self) self.host = consul.split(':')[0].strip() self.port = int(consul.split(':')[1].strip()) # TODO need to handle reconnect events properly self.consul = Consul(host=self.host, port=self.port) self.container_name_regex = container_name_regex self.wait_for_leader_deferreds = [] self.peers_mapping_queue = MessageQueue() def start(self): log.debug('starting') reactor.callLater(0, self._async_init) log.info('started') return self @inlineCallbacks def stop(self): log.debug('stopping') self.shutting_down = True yield self._delete_session() # this will delete the leader lock too yield self.worker.stop() if self.leader is not None: yield self.leader.stop() self.leader = None log.info('stopped') def wait_for_a_leader(self): """ Async wait till a leader is detected/elected. The deferred will be called with the leader's instance_id :return: Deferred. """ d = Deferred() if self.leader_id is not None: d.callback(self.leader_id) return d else: self.wait_for_leader_deferreds.append(d) return d # Wait for a core data id to be assigned to this voltha instance @inlineCallbacks def get_core_store_id_and_prefix(self): core_store_id = yield self.worker.get_core_store_id() returnValue((core_store_id, self.core_store_prefix)) def recv_peers_map(self): return self.peers_mapping_queue.get() def publish_peers_map_change(self, msg): self.peers_mapping_queue.put(msg) # Proxy methods for consul with retry support def kv_get(self, args, kw): return self._retry('GET', args, *kw) def kv_put(self, args, *kw): return self._retry('PUT', args, *kw) def kv_delete(self, args, *kw): return self._retry('DELETE', args, **kw) # Methods exposing key membership information @inlineCallbacks def get_members(self): """Return list of all members""" _, members = yield self.kv_get(self.membership_prefix, recurse=True) returnValue([member['Key'][len(self.membership_prefix):] for member in members]) # Private (internal) methods: @inlineCallbacks def _async_init(self): yield self._create_session() yield self._create_membership_record() yield self._start_leader_tracking() yield self.worker.start() def _backoff(self, msg): wait_time = self.RETRY_BACKOFF[min(self.retries, len(self.RETRY_BACKOFF) - 1)] self.retries += 1 log.info(msg, retry_in=wait_time) return asleep(wait_time) def _clear_backoff(self): if self.retries: log.info('reconnected-to-consul', after_retries=self.retries) self.retries = 0 @inlineCallbacks def _create_session(self): @inlineCallbacks def _create_session(): consul = yield self.get_consul() # create consul session self.session_id = yield consul.session.create( behavior='release', ttl=self.session_time_to_live, lock_delay=1) log.info('created-consul-session', session_id=self.session_id) self._start_session_tracking() yield self._retry(_create_session) @inlineCallbacks def _delete_session(self): try: yield self.consul.session.destroy(self.session_id) except Exception as e: log.exception('failed-to-delete-session', session_id=self.session_id) @inlineCallbacks def _create_membership_record(self): yield self._do_create_membership_record_with_retries() reactor.callLater(0, self._maintain_membership_record) @inlineCallbacks def _maintain_membership_record(self): try: while 1: valid_membership = yield self._assert_membership_record_valid() if not valid_membership: log.info('recreating-membership-before', session=self.session_id) yield self._do_create_membership_record_with_retries() log.info('recreating-membership-after', session=self.session_id) else: log.debug('valid-membership', session=self.session_id) # Async sleep before checking the membership record again yield asleep(self.membership_maintenance_loop_delay) except Exception, e: log.exception('unexpected-error-leader-trackin', e=e) finally: # except in shutdown, the loop must continue (after a short delay) if not self.shutting_down: reactor.callLater(self.membership_watch_relatch_delay, self._maintain_membership_record) def _create_membership_record_data(self): member_record = dict() member_record['status'] = 'alive' member_record['host_address'] = self.external_host_address return member_record @inlineCallbacks def _assert_membership_record_valid(self): try: log.info('membership-record-before') is_timeout, (_, record) = yield \ self.consul_get_with_timeout( key=self.membership_record_key, index=0, timeout=5) if is_timeout: returnValue(False) log.info('membership-record-after', record=record) if record is None or \ 'Session' not in record or \ record['Session'] != self.session_id: log.info('membership-record-change-detected', old_session=self.session_id, record=record) returnValue(False) else: returnValue(True) except Exception as e: log.exception('membership-validation-exception', e=e) returnValue(False) @inlineCallbacks def _do_create_membership_record_with_retries(self): while 1: log.info('recreating-membership', session=self.session_id) result = yield self._retry( 'PUT', self.membership_record_key, dumps(self._create_membership_record_data()), acquire=self.session_id) if result: log.info('new-membership-record-created', session=self.session_id) break else: log.warn('cannot-create-membership-record') yield self._backoff('stale-membership-record') def _start_session_tracking(self): reactor.callLater(0, self._session_tracking_loop) @inlineCallbacks def _session_tracking_loop(self): @inlineCallbacks def _redo_session(): log.info('_redo_session-before') yield self._delete_session() # Create a new consul connection/session with a TTL of 25 secs try: self.consul = Consul(host=self.host, port=self.port) self.session_id = yield self.consul.session.create( behavior='release', ttl=self.session_time_to_live, lock_delay=1) log.info('new-consul-session', session=self.session_id) except Exception as e: log.exception('could-not-create-a-consul-session', e=e) @inlineCallbacks def _renew_session(m_callback): try: log.debug('_renew_session-before') consul_ref = self.consul result = yield consul_ref.session.renew( session_id=self.session_id) log.info('just-renewed-session', result=result) if not m_callback.called: # Triggering callback will cancel the timeout timer log.info('trigger-callback-to-cancel-timout-timer') m_callback.callback(result) else: # Timeout event has already been called. Just ignore # this event log.info('renew-called-after-timout', new_consul_ref=self.consul, old_consul_ref=consul_ref) except Exception, e: # Let the invoking method receive a timeout log.exception('could-not-renew-session', e=e) try: while 1: log.debug('session-tracking-start') rcvd = DeferredWithTimeout( timeout=self.session_renewal_timeout) _renew_session(rcvd) try: _ = yield rcvd except TimeOutError as e: log.info('session-renew-timeout', e=e) # Redo the session yield _redo_session() except Exception as e: log.exception('session-renew-exception', e=e) else: log.debug('successfully-renewed-session') # Async sleep before the next session tracking yield asleep(self.session_renewal_loop_delay) except Exception as e: log.exception('renew-exception', e=e) finally: reactor.callLater(self.session_renewal_loop_delay, self._session_tracking_loop) def _start_leader_tracking(self): reactor.callLater(0, self._leadership_tracking_loop) @inlineCallbacks def _leadership_tracking_loop(self): try: # Attempt to acquire leadership lock. True indicates success; # False indicates there is already a leader. It's instance id # is then the value under the leader key service/voltha/leader. # attempt acquire leader lock log.info('leadership-attempt-before') result = yield self._retry('PUT', self.leader_prefix, self.instance_id, acquire=self.session_id) log.info('leadership-attempt-after') # read it back before being too happy; seeing our session id is a # proof and now we have the change id that we can use to reliably # track any changes. In an unlikely scenario where the leadership # key gets wiped out administratively since the previous line, # the returned record can be None. Handle it. (index, record) = yield self._retry('GET', self.leader_prefix) log.info('leader-prefix', i_am_leader=result, index=index, record=record) if record is not None: if result is True: if record['Session'] == self.session_id: yield self._assert_leadership() else: pass # confusion; need to retry leadership else: leader_id = record['Value'] yield self._assert_nonleadership(leader_id) # if record was none, we shall try leadership again last = record while last is not None: # this shall return only when update is made to leader key # or expires after 5 seconds wait is_timeout, (tmp_index, updated) = yield \ self.consul_get_with_timeout( key=self.leader_prefix, index=index, timeout=5) # Timeout means either there is a lost connectivity to # consul or there are no change to that key. Do nothing. if is_timeout: continue # After timeout event the index returned from # consul_get_with_timeout is None. If we are here it's not a # timeout, therefore the index is a valid one. index=tmp_index if updated is None or updated != last: log.info('leader-key-change', index=index, updated=updated, last=last) # leadership has changed or vacated (or forcefully # removed), apply now # If I was previoulsy the leader then assert a non # leadership role before going for election if self.i_am_leader: log.info('leaving-leaderdhip', leader=self.instance_id) yield self._assert_nonleadership(self.instance_id) break last = updated except Exception, e: log.exception('unexpected-error-leader-trackin', e=e) finally: # except in shutdown, the loop must continue (after a
# -- coding: utf-8 -- # # Copyright © Simphony Project Contributors # Licensed under the terms of the MIT License # (see simphony/__init__.py for details) import pytest import os from simphony.plugins.siepic.parser import load_spi #============================================================================== # Test the parser #============================================================================== EBeam_sequoiap_A_v2_result = { 'circuits': [ { 'name': 'EBeam_sequoiap_A_v2', 'ports': [ 'ebeam_gc_te1550$1_laser', 'ebeam_gc_te1550$1_detector1' ], 'subcircuits': 'EBeam_sequoiap_A_v2', 'params': [ { 'name': 'sch_x', 'value': -1.0 }, { 'name': 'sch_y', 'value': -1.0 } ] } ], 'subcircuits': [ { 'name': 'EBeam_sequoiap_A_v2', 'ports': ['ebeam_gc_te1550$1_laser', 'ebeam_gc_te1550$1_detector1'], 'components': [ { 'name': 'ebeam_y_1550_67', 'model': 'ebeam_y_1550', 'ports': ['N$80', 'N$81', 'N$82'], 'params': { 'library': 'Design kits/ebeam', 'lay_x': 0.00010077000000000001, 'lay_y': 0.00013824, 'sch_x': 8.339586207, 'sch_y': 11.440551724 } }, { 'name': 'ebeam_gc_te1550_68', 'model': 'ebeam_gc_te1550', 'ports': ['ebeam_gc_te1550$1_laser', 'N$80'], 'params': { 'library': 'Design kits/ebeam', 'lay_x': 7.687e-05, 'lay_y': 0.00013824, 'sch_x': 6.361655172, 'sch_y': 11.440551724 } }, { 'name': 'ebeam_gc_te1550_69', 'model': 'ebeam_gc_te1550', 'ports': ['ebeam_gc_te1550$1_detector1', 'N$83'], 'params': { 'library': 'Design kits/ebeam', 'lay_x': 7.687e-05, 'lay_y': 1.1240000000000002e-05, 'sch_x': 6.361655172, 'sch_y': 0.930206897 } }, { 'name': 'ebeam_y_1550_70', 'model': 'ebeam_y_1550', 'ports': ['N$83', 'N$85', 'N$84'], 'params': { 'library': 'Design kits/ebeam', 'lay_x': 0.00010077000000000001, 'lay_y': 1.1240000000000002e-05, 'sch_x': 8.339586207, 'sch_y': 0.930206897 } }, { 'name': 'ebeam_wg_integral_1550_72', 'model': 'ebeam_wg_integral_1550', 'ports': ['N$81', 'N$84'], 'params': { 'library': 'Design kits/ebeam', 'wg_length': 0.000189995, 'wg_width': 5e-07, 'points': '[[108.17,140.99],[138.469,140.99],[138.469,8.49],[108.17,8.49]]', 'radius': 5.0, 'lay_x': 0.000123694, 'lay_y': 7.474e-05, 'sch_x': 10.236744828, 'sch_y': 6.18537931 } }, { 'name': 'ebeam_wg_integral_1550_83', 'model': 'ebeam_wg_integral_1550', 'ports': ['N$82', 'N$85'], 'params': { 'library': 'Design kits/ebeam', 'wg_length': 0.000149995, 'wg_width': 5e-07, 'points': '[[104.92,389.16],[120.719,389.16],[120.719,267.66],[104.92,267.66]]', 'radius': 5.0, 'lay_x': 0.000116444, 'lay_y': 7.474e-05, 'sch_x': 9.636744828, 'sch_y': 6.18537931 } } ], 'params': { 'MC_uniformity_width': 0.0, 'MC_uniformity_thickness': 0.0, 'MC_resolution_x': 100.0, 'MC_resolution_y': 100.0, 'MC_grid': 1e-05, 'MC_non_uniform': 99.0 } } ], 'analyses': [ { 'definition': { 'input_unit': 'wavelength', 'input_parameter': 'start_and_stop' }, 'params': { 'minimum_loss': 80.0, 'analysis_type': 'scattering_data', 'multithreading': 'user_defined', 'number_of_threads': 1.0, 'orthogonal_identifier': 1.0, 'start': 1.5e-06, 'stop': 1.6e-06, 'number_of_points': 3000.0, 'input': ['EBeam_sequoiap_A_v2,ebeam_gc_te1550$1_detector1'], 'output': 'EBeam_sequoiap_A_v2,ebeam_gc_te1550$1_laser' } } ] } MZI4_result = { 'circuits': [ { 'name': 'MZI4', 'ports': ['ebeam_gc_te1550_detector2', 'ebeam_gc_te1550_laser1'], 'subcircuits': 'MZI4', 'params': [{'name': 'sch_x', 'value': -1.0}, {'name': 'sch_y', 'value': -1.0}] } ], 'subcircuits': [ { 'name': 'MZI4', 'ports': ['ebeam_gc_te1550_detector2', 'ebeam_gc_te1550_laser1'], 'components': [ { 'name': 'ebeam_y_1550_0', 'model': 'ebeam_y_1550', 'ports': ['N$0', 'N$2', 'N$1'], 'params': { 'library': 'Design kits/ebeam', 'lay_x': 7.4e-06, 'lay_y': 0.000127, 'sch_x': 0.478534829, 'sch_y': 8.212692343 } }, { 'name': 'ebeam_gc_te1550_1', 'model': 'ebeam_gc_te1550', 'ports': ['ebeam_gc_te1550_detector2', 'N$0'], 'params': { 'library': 'Design kits/ebeam', 'lay_x': -1.6500000000000005e-05, 'lay_y': 0.000127, 'sch_x': -1.067003336, 'sch_y': 8.212692343 } }, { 'name': 'ebeam_gc_te1550_2', 'model': 'ebeam_gc_te1550', 'ports': ['ebeam_gc_te1550_laser1', 'N$3'], 'params': { 'library': 'Design kits/ebeam', 'lay_x': -1.6500000000000005e-05, 'lay_y': 0.000254, 'sch_x': -1.067003336, 'sch_y': 16.425384686 } }, { 'name': 'ebeam_y_1550_3', 'model': 'ebeam_y_1550', 'ports': ['N$6', 'N$5', 'N$4'], 'params': { 'library': 'Design kits/ebeam', 'lay_x': 8.993e-05, 'lay_y': 0.000127, 'sch_x': 5.815491515, 'sch_y': 8.212692343, 'sch_f': 'true' } }, { 'name': 'ebeam_wg_integral_1550_4', 'model': 'ebeam_wg_integral_1550', 'ports': ['N$1', 'N$4'], 'params': { 'library': 'Design kits/ebeam', 'wg_length': 6.773e-05, 'wg_width': 5e-07, 'points': '[[14.8,124.25],[82.53,124.25]]', 'radius': 5.0, 'lay_x': 4.866500000000001e-05, 'lay_y': 0.00012425, 'sch_x': 3.147013172, 'sch_y': 8.034858453 } }, { 'name': 'ebeam_wg_integral_1550_5', 'model': 'ebeam_wg_integral_1550', 'ports': ['N$2', 'N$5'], 'params': { 'library': 'Design kits/ebeam', 'wg_length': 0.000297394, 'wg_width': 5e-07, 'points': '[[14.8,129.75],[28.64,129.75],[28.64,247.68],[75.36,247.68],[75.36,129.75],[82.53,129.75]]', 'radius': 5.0, 'lay_x': 4.866500000000001e-05, 'lay_y': 0.000188715, 'sch_x': 3.147013172, 'sch_y': 12.203608153 } }, { 'name': 'ebeam_wg_integral_1550_6', 'model': 'ebeam_wg_integral_1550', 'ports': ['N$6', 'N$3'], 'params': { 'library': 'Design kits/ebeam', 'wg_length': 0.000256152, 'wg_width': 5e-07, 'points': '[[97.33,127.0],[114.79,127.0],[114.79,254.0],[0.0,254.0]]', 'radius': 5.0, 'lay_x': 5.777e-05, 'lay_y': 0.0001905, 'sch_x': 3.735805013, 'sch_y': 12.319038514 } } ], 'params': { 'MC_uniformity_width': 0.0, 'MC_uniformity_thickness': 0.0, 'MC_resolution_x': 100.0, 'MC_resolution_y': 100.0, 'MC_grid': 1e-05, 'MC_non_uniform': 99.0 } } ], 'analyses': [ { 'definition': { 'input_unit': 'wavelength', 'input_parameter': 'start_and_stop' }, 'params': { 'minimum_loss': 80.0, 'analysis_type': 'scattering_data', 'multithreading': 'user_defined', 'number_of_threads': 1.0, 'orthogonal_identifier': 1.0, 'start': 1.5e-06, 'stop': 1.6e-06, 'number_of_points': 2000.0, 'input': ['MZI4,ebeam_gc_te1550_detector2'], 'output': 'MZI4,ebeam_gc_te1550_laser1' } } ] } top_result = { 'circuits': [ { 'name': 'top', 'ports': ['ebeam_gc_te1550_laser1', 'ebeam_gc_te1550_detector2', 'ebeam_gc_te1550_detector4', 'ebeam_gc_te1550_detector3'], 'subcircuits': 'top', 'params': [ {'name': 'sch_x', 'value': -1.0}, {'name': 'sch_y', 'value': -1.0} ] } ], 'subcircuits': [ { 'name': 'top', 'ports': ['ebeam_gc_te1550_laser1', 'ebeam_gc_te1550_detector2', 'ebeam_gc_te1550_detector4', 'ebeam_gc_te1550_detector3'], 'components': [ { 'name': 'ebeam_dc_te1550_0', 'model': 'ebeam_dc_te1550', 'ports': ['N$0', 'N$1', 'N$3', 'N$2'], 'params': {'library': 'Design kits/ebeam', 'wg_width': 5e-07, 'gap': 2e-07, 'radius': 5e-06, 'Lc': 1.5e-05, 'lay_x': 2.36e-06, 'lay_y': 1.2e-07, 'sch_x': 0.082235221, 'sch_y': 0.004181452} }, { 'name': 'ebeam_gc_te1550_1', 'model': 'ebeam_gc_te1550', 'ports': ['ebeam_gc_te1550_laser1', 'N$4'], 'params': {'library': 'Design kits/ebeam', 'lay_x': -0.00013533, 'lay_y': 1.475e-05, 'sch_x': -4.715632378, 'sch_y': 0.513970129} }, { 'name': 'ebeam_gc_te1550_2', 'model': 'ebeam_gc_te1550', 'ports': ['ebeam_gc_te1550_detector2', 'N$5'], 'params': {'library': 'Design kits/ebeam', 'lay_x': -0.00012984, 'lay_y': -7.662e-05, 'sch_x': -4.524330954, 'sch_y': -2.669857037} }, { 'name': 'ebeam_gc_te1550_3', 'model': 'ebeam_gc_te1550', 'ports': ['ebeam_gc_te1550_detector4', 'N$6'], 'params': {'library': 'Design kits/ebeam', 'lay_x': 9.456e-05, 'lay_y': -8.471e-05, 'sch_x': 3.294984096, 'sch_y': -2.951756586, 'sch_r': 180.0} }, { 'name': 'ebeam_gc_te1550_4', 'model': 'ebeam_gc_te1550', 'ports': ['ebeam_gc_te1550_detector3', 'N$7'], 'params': {'library': 'Design kits/ebeam', 'lay_x': 0.00013005, 'lay_y': 3.253e-05, 'sch_x': 4.531648495, 'sch_y': 1.133521919, 'sch_r': 180.0} }, { 'name': 'ebeam_wg_integral_1550_5', 'model': 'ebeam_wg_integral_1550', 'ports': ['N$0', 'N$5'], 'params': {'library': 'Design kits/ebeam', 'wg_length': 0.000173487, 'wg_width': 5e-07, 'points': '[[-11.14,-2.23],[-40.45,-2.23],[-40.45,-76.62],[-113.34,-76.62]]', 'radius': 5.0, 'lay_x': -6.224e-05, 'lay_y': -3.9425e-05, 'sch_x': -2.168779718, 'sch_y': -1.373781176} }, { 'name': 'ebeam_wg_integral_1550_6', 'model': 'ebeam_wg_integral_1550', 'ports': ['N$4', 'N$1'], 'params': {'library': 'Design kits/ebeam', 'wg_length': 0.000116867, 'wg_width': 5e-07, 'points': '[[-118.83,14.75],[-26.47,14.75],[-26.47,2.47],[-11.14,2.47]]', 'radius': 5.0, 'lay_x': -6.4985e-05, 'lay_y': 8.61e-06, 'sch_x': -2.26443043, 'sch_y': 0.300019174} }, { 'name': 'ebeam_wg_integral_1550_7', 'model': 'ebeam_wg_integral_1550', 'ports': ['N$8', 'N$2'], 'params': {'library': 'Design kits/ebeam', 'wg_length': 7.4217e-05, 'wg_width': 5e-07, 'points': '[[65.87,29.78],[36.16,29.78],[36.16,2.47],[15.86,2.47]]', 'radius': 5.0, 'lay_x': 4.0865e-05, 'lay_y': 1.6125e-05, 'sch_x': 1.423958598, 'sch_y': 0.561882599} }, { 'name': 'ebeam_wg_integral_1550_8', 'model': 'ebeam_wg_integral_1550', 'ports': ['N$3', 'N$6'], 'params': {'library': 'Design kits/ebeam', 'wg_length': 0.000141577, 'wg_width': 5e-07, 'points': '[[15.86,-2.23],[35.04,-2.23],[35.04,-84.71],[78.06,-84.71]]', 'radius': 5.0, 'lay_x': 4.696e-05, 'lay_y': -4.347000000000001e-05, 'sch_x': 1.636341509, 'sch_y': -1.51473095} }, { 'name': 'ebeam_y_1550_9', 'model': 'ebeam_y_1550', 'ports': ['N$8', 'N$10', 'N$9'], 'params': {'library': 'Design kits/ebeam', 'lay_x': 7.327e-05, 'lay_y': 2.978e-05, 'sch_x': 2.553124838, 'sch_y': 1.037696979} }, { 'name': 'ebeam_terminator_te1550_10', 'model': 'ebeam_terminator_te1550', 'ports': ['N$11'], 'params': {'library': 'Design kits/ebeam', 'lay_x': 9.14e-05, 'lay_y': 2.7e-07, 'sch_x': 3.184872529, 'sch_y': 0.009408267, 'sch_r': 270.0} }, { 'name': 'ebeam_wg_integral_1550_11', 'model': 'ebeam_wg_integral_1550', 'ports': ['N$9', 'N$11'], 'params': {'library': 'Design kits/ebeam', 'wg_length': 3.0488e-05, 'wg_width': 5e-07, 'points': '[[80.67,27.03],[91.4,27.03],[91.4,5.72]]', 'radius': 5.0, 'lay_x': 8.641e-05, 'lay_y': 1.675e-05, 'sch_x': 3.010993821, 'sch_y': 0.5836609940000002} }, { 'name': 'ebeam_wg_integral_1550_12', 'model': 'ebeam_wg_integral_1550', 'ports': ['N$10', 'N$7'], 'params': { 'library': 'Design kits/ebeam', 'wg_length': 3.288e-05, 'wg_width': 5e-07, 'points': '[[80.67,32.53],[113.55,32.53]]', 'radius': 5.0, 'lay_x': 9.711e-05, 'lay_y': 3.253e-05, 'sch_x': 3.383839949, 'sch_y': 1.133521919 } } ], 'params': { 'MC_uniformity_width': 0.0, 'MC_uniformity_thickness': 0.0, 'MC_resolution_x': 100.0, 'MC_resolution_y': 100.0, 'MC_grid': 1e-05, 'MC_non_uniform': 99.0 } } ], 'analyses': [ { 'definition': { 'input_unit': 'wavelength', 'input_parameter': 'start_and_stop' }, 'params': { 'minimum_loss': 80.0, 'analysis_type': 'scattering_data', 'multithreading': 'user_defined', 'number_of_threads': 1.0, 'orthogonal_identifier': 1.0, 'start': 1.5e-06, 'stop': 1.6e-06, 'number_of_points': 2000.0, 'input': ['top,ebeam_gc_te1550_detector2', 'top,ebeam_gc_te1550_detector3', 'top,ebeam_gc_te1550_detector4'], 'output': 'top,ebeam_gc_te1550_laser1' } } ] } def test_EBeam_sequoiap_A_v2(): filename = os.path.join(os.path.dirname(__file__), 'spice', 'EBeam_sequoiap_A_v2', 'EBeam_sequoiap_A_v2_main.spi') res = load_spi(filename) assert res == EBeam_sequoiap_A_v2_result def test_MZI4(): filename = os.path.join(os.path.dirname(__file__), 'spice', 'MZI4', 'MZI4_main.spi') res = load_spi(filename) assert res == MZI4_result def test_top(): filename =
"resource_field_ref") @property @pulumi.getter(name="secretKeyRef") def secret_key_ref(self) -> Optional['outputs.SeldonDeploymentSpecPredictorsSvcOrchSpecEnvValueFromSecretKeyRef']: """ Selects a key of a secret in the pod's namespace """ return pulumi.get(self, "secret_key_ref") def _translate_property(self, prop): return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop @pulumi.output_type class SeldonDeploymentSpecPredictorsSvcOrchSpecEnvValueFromConfigMapKeyRef(dict): """ Selects a key of a ConfigMap. """ def __init__(__self__, , key: str, name: Optional[str] = None, optional: Optional[bool] = None): """ Selects a key of a ConfigMap. :param str key: The key to select. :param str name: Name of the referent. More info: https://kubernetes.io/docs/concepts/overview/working-with-objects/names/#names TODO: Add other useful fields. apiVersion, kind, uid? :param bool optional: Specify whether the ConfigMap or its key must be defined """ pulumi.set(__self__, "key", key) if name is not None: pulumi.set(__self__, "name", name) if optional is not None: pulumi.set(__self__, "optional", optional) @property @pulumi.getter def key(self) -> str: """ The key to select. """ return pulumi.get(self, "key") @property @pulumi.getter def name(self) -> Optional[str]: """ Name of the referent. More info: https://kubernetes.io/docs/concepts/overview/working-with-objects/names/#names TODO: Add other useful fields. apiVersion, kind, uid? """ return pulumi.get(self, "name") @property @pulumi.getter def optional(self) -> Optional[bool]: """ Specify whether the ConfigMap or its key must be defined """ return pulumi.get(self, "optional") def _translate_property(self, prop): return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop @pulumi.output_type class SeldonDeploymentSpecPredictorsSvcOrchSpecEnvValueFromFieldRef(dict): """ Selects a field of the pod: supports metadata.name, metadata.namespace, metadata.labels, metadata.annotations, spec.nodeName, spec.serviceAccountName, status.hostIP, status.podIP, status.podIPs. """ def __init__(__self__, , field_path: str, api_version: Optional[str] = None): """ Selects a field of the pod: supports metadata.name, metadata.namespace, metadata.labels, metadata.annotations, spec.nodeName, spec.serviceAccountName, status.hostIP, status.podIP, status.podIPs. :param str field_path: Path of the field to select in the specified API version. :param str api_version: Version of the schema the FieldPath is written in terms of, defaults to "v1". """ pulumi.set(__self__, "field_path", field_path) if api_version is not None: pulumi.set(__self__, "api_version", api_version) @property @pulumi.getter(name="fieldPath") def field_path(self) -> str: """ Path of the field to select in the specified API version. """ return pulumi.get(self, "field_path") @property @pulumi.getter(name="apiVersion") def api_version(self) -> Optional[str]: """ Version of the schema the FieldPath is written in terms of, defaults to "v1". """ return pulumi.get(self, "api_version") def _translate_property(self, prop): return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop @pulumi.output_type class SeldonDeploymentSpecPredictorsSvcOrchSpecEnvValueFromResourceFieldRef(dict): """ Selects a resource of the container: only resources limits and requests (limits.cpu, limits.memory, limits.ephemeral-storage, requests.cpu, requests.memory and requests.ephemeral-storage) are currently supported. """ def __init__(__self__, , resource: str, container_name: Optional[str] = None, divisor: Optional['outputs.SeldonDeploymentSpecPredictorsSvcOrchSpecEnvValueFromResourceFieldRefDivisor'] = None): """ Selects a resource of the container: only resources limits and requests (limits.cpu, limits.memory, limits.ephemeral-storage, requests.cpu, requests.memory and requests.ephemeral-storage) are currently supported. :param str resource: Required: resource to select :param str container_name: Container name: required for volumes, optional for env vars :param 'SeldonDeploymentSpecPredictorsSvcOrchSpecEnvValueFromResourceFieldRefDivisorArgs' divisor: Specifies the output format of the exposed resources, defaults to "1" """ pulumi.set(__self__, "resource", resource) if container_name is not None: pulumi.set(__self__, "container_name", container_name) if divisor is not None: pulumi.set(__self__, "divisor", divisor) @property @pulumi.getter def resource(self) -> str: """ Required: resource to select """ return pulumi.get(self, "resource") @property @pulumi.getter(name="containerName") def container_name(self) -> Optional[str]: """ Container name: required for volumes, optional for env vars """ return pulumi.get(self, "container_name") @property @pulumi.getter def divisor(self) -> Optional['outputs.SeldonDeploymentSpecPredictorsSvcOrchSpecEnvValueFromResourceFieldRefDivisor']: """ Specifies the output format of the exposed resources, defaults to "1" """ return pulumi.get(self, "divisor") def _translate_property(self, prop): return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop @pulumi.output_type class SeldonDeploymentSpecPredictorsSvcOrchSpecEnvValueFromResourceFieldRefDivisor(dict): def __init__(__self__): pass def _translate_property(self, prop): return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop @pulumi.output_type class SeldonDeploymentSpecPredictorsSvcOrchSpecEnvValueFromSecretKeyRef(dict): """ Selects a key of a secret in the pod's namespace """ def __init__(__self__, , key: str, name: Optional[str] = None, optional: Optional[bool] = None): """ Selects a key of a secret in the pod's namespace :param str key: The key of the secret to select from. Must be a valid secret key. :param str name: Name of the referent. More info: https://kubernetes.io/docs/concepts/overview/working-with-objects/names/#names TODO: Add other useful fields. apiVersion, kind, uid? :param bool optional: Specify whether the Secret or its key must be defined """ pulumi.set(__self__, "key", key) if name is not None: pulumi.set(__self__, "name", name) if optional is not None: pulumi.set(__self__, "optional", optional) @property @pulumi.getter def key(self) -> str: """ The key of the secret to select from. Must be a valid secret key. """ return pulumi.get(self, "key") @property @pulumi.getter def name(self) -> Optional[str]: """ Name of the referent. More info: https://kubernetes.io/docs/concepts/overview/working-with-objects/names/#names TODO: Add other useful fields. apiVersion, kind, uid? """ return pulumi.get(self, "name") @property @pulumi.getter def optional(self) -> Optional[bool]: """ Specify whether the Secret or its key must be defined """ return pulumi.get(self, "optional") def _translate_property(self, prop): return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop @pulumi.output_type class SeldonDeploymentSpecPredictorsSvcOrchSpecResources(dict): """ ResourceRequirements describes the compute resource requirements. """ def __init__(__self__, , limits: Optional[Mapping[str, 'outputs.SeldonDeploymentSpecPredictorsSvcOrchSpecResourcesLimits']] = None, requests: Optional[Mapping[str, 'outputs.SeldonDeploymentSpecPredictorsSvcOrchSpecResourcesRequests']] = None): """ ResourceRequirements describes the compute resource requirements. :param Mapping[str, 'SeldonDeploymentSpecPredictorsSvcOrchSpecResourcesLimitsArgs'] limits: Limits describes the maximum amount of compute resources allowed. More info: https://kubernetes.io/docs/concepts/configuration/manage-compute-resources-container/ :param Mapping[str, 'SeldonDeploymentSpecPredictorsSvcOrchSpecResourcesRequestsArgs'] requests: Requests describes the minimum amount of compute resources required. If Requests is omitted for a container, it defaults to Limits if that is explicitly specified, otherwise to an implementation-defined value. More info: https://kubernetes.io/docs/concepts/configuration/manage-compute-resources-container/ """ if limits is not None: pulumi.set(__self__, "limits", limits) if requests is not None: pulumi.set(__self__, "requests", requests) @property @pulumi.getter def limits(self) -> Optional[Mapping[str, 'outputs.SeldonDeploymentSpecPredictorsSvcOrchSpecResourcesLimits']]: """ Limits describes the maximum amount of compute resources allowed. More info: https://kubernetes.io/docs/concepts/configuration/manage-compute-resources-container/ """ return pulumi.get(self, "limits") @property @pulumi.getter def requests(self) -> Optional[Mapping[str, 'outputs.SeldonDeploymentSpecPredictorsSvcOrchSpecResourcesRequests']]: """ Requests describes the minimum amount of compute resources required. If Requests is omitted for a container, it defaults to Limits if that is explicitly specified, otherwise to an implementation-defined value. More info: https://kubernetes.io/docs/concepts/configuration/manage-compute-resources-container/ """ return pulumi.get(self, "requests") def _translate_property(self, prop): return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop @pulumi.output_type class SeldonDeploymentSpecPredictorsSvcOrchSpecResourcesLimits(dict): def __init__(__self__): pass def _translate_property(self, prop): return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop @pulumi.output_type class SeldonDeploymentSpecPredictorsSvcOrchSpecResourcesRequests(dict): def __init__(__self__): pass def _translate_property(self, prop): return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop @pulumi.output_type class SeldonDeploymentStatus(dict): """ SeldonDeploymentStatus defines the observed state of SeldonDeployment """ def __init__(__self__, , address: Optional['outputs.SeldonDeploymentStatusAddress'] = None, deployment_status: Optional[Mapping[str, 'outputs.SeldonDeploymentStatusDeploymentStatus']] = None, description: Optional[str] = None, replicas: Optional[int] = None, service_status: Optional[Mapping[str, 'outputs.SeldonDeploymentStatusServiceStatus']] = None, state: Optional[str] = None): """ SeldonDeploymentStatus defines the observed state of SeldonDeployment :param 'SeldonDeploymentStatusAddressArgs' address: Addressable placeholder until duckv1 issue is fixed: https://github.com/kubernetes-sigs/controller-tools/issues/391 """ if address is not None: pulumi.set(__self__, "address", address) if deployment_status is not None: pulumi.set(__self__, "deployment_status", deployment_status) if description is not None: pulumi.set(__self__, "description", description) if replicas is not None: pulumi.set(__self__, "replicas", replicas) if service_status is not None: pulumi.set(__self__, "service_status", service_status) if state is not None: pulumi.set(__self__, "state", state) @property @pulumi.getter def address(self) -> Optional['outputs.SeldonDeploymentStatusAddress']: """ Addressable placeholder until duckv1 issue is fixed: https://github.com/kubernetes-sigs/controller-tools/issues/391 """ return pulumi.get(self, "address") @property @pulumi.getter(name="deploymentStatus") def deployment_status(self) -> Optional[Mapping[str, 'outputs.SeldonDeploymentStatusDeploymentStatus']]: return pulumi.get(self, "deployment_status") @property @pulumi.getter def description(self) -> Optional[str]: return pulumi.get(self, "description") @property @pulumi.getter def replicas(self) -> Optional[int]: return pulumi.get(self, "replicas") @property @pulumi.getter(name="serviceStatus") def service_status(self) -> Optional[Mapping[str, 'outputs.SeldonDeploymentStatusServiceStatus']]: return pulumi.get(self, "service_status") @property @pulumi.getter def state(self) -> Optional[str]: return pulumi.get(self, "state") def _translate_property(self, prop): return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop @pulumi.output_type class SeldonDeploymentStatusAddress(dict): """ Addressable placeholder until duckv1 issue is fixed: https://github.com/kubernetes-sigs/controller-tools/issues/391 """ def __init__(__self__, , url: Optional[str] = None): """ Addressable placeholder until duckv1 issue is fixed: https://github.com/kubernetes-sigs/controller-tools/issues/391 """ if url is not None: pulumi.set(__self__, "url", url) @property @pulumi.getter def url(self) -> Optional[str]: return pulumi.get(self, "url") def _translate_property(self, prop): return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop @pulumi.output_type class SeldonDeploymentStatusDeploymentStatus(dict): def __init__(__self__, , available_replicas: Optional[int] = None, description: Optional[str] = None, explainer_for: Optional[str] = None, name: Optional[str] = None, replicas: Optional[int] = None, status: Optional[str] = None): if available_replicas is not None: pulumi.set(__self__, "available_replicas", available_replicas) if description is not None: pulumi.set(__self__, "description", description) if explainer_for is not None: pulumi.set(__self__, "explainer_for", explainer_for) if name is not None: pulumi.set(__self__, "name", name) if replicas is not None: pulumi.set(__self__, "replicas", replicas) if status is not None: pulumi.set(__self__, "status", status) @property @pulumi.getter(name="availableReplicas") def available_replicas(self) -> Optional[int]: return pulumi.get(self, "available_replicas") @property @pulumi.getter def description(self) -> Optional[str]: return pulumi.get(self, "description") @property @pulumi.getter(name="explainerFor") def explainer_for(self) -> Optional[str]: return pulumi.get(self, "explainer_for") @property @pulumi.getter def name(self) -> Optional[str]: return pulumi.get(self, "name") @property @pulumi.getter def replicas(self) -> Optional[int]: return pulumi.get(self, "replicas") @property @pulumi.getter def status(self) -> Optional[str]: return pulumi.get(self, "status") def _translate_property(self, prop): return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop @pulumi.output_type class SeldonDeploymentStatusServiceStatus(dict): def __init__(__self__, *, explainer_for: Optional[str] = None, grpc_endpoint: Optional[str] = None, http_endpoint: Optional[str] = None,
# importando a biblioteca PySimpleGUI para a interface import PySimpleGUI as sg # Procurando brechas sobre como os usuários podem responder as perguntas. yes = ["S", "s", "sim"] no = ["N", "n", "nao", "não"] # Objetos espada = 0 flor = 0 # Primeira janela def janela_inicial(): sg.theme('Reddit') layout = [ [sg.Text('Seja bem vindo!')], [sg.Text('')], [sg.Text('Você está a entrar em uma aventura.')], [sg.Text('')], [sg.Text('Clique em ok para continuar')], [sg.Button('Sair'), sg.Button('Ok')] ] return sg.Window("Vamos ??", layout=layout, finalize=True, size=(300, 180)) # segunda janela def janela_comeco(): sg.theme('Reddit') layout = [ [sg.Text("Você é um plebeu e, em um dia, decide sair com seus amigos.\nVocês vão na taverna “O Bode Dourado”, na cidade de Odin.\nEsta cidade é um movimentado entreposto comercial a\nmeio caminho de todas as principais rotas mercantis\nentre os reinos do leste")], [sg.Text('...')], [sg.Text("Depois de uma noite de bebedeira com amigos,\nvocê acorda na manhã seguinte\nem uma floresta densa e úmida. " "\nCabeça girando e lutando contra a vontade de vomitar,\nvocê se levanta e se maravilha com o seu novo ambiente desconhecido. " "\nA paz desaparece rapidamente quando você ouve um som grotesco \nemitido atrás de você.\nUm Orc babando está correndo em sua direção. Você irá:")], [sg.Text("Pegue uma pedra próxima e jogue-a no Orc "), sg.Button('A')], [sg.Text("Deite-se e espere ser atacado. "), sg.Button('B')], [sg.Text("Correr! "), sg.Button('C')] ] return sg.Window("A história começa", layout=layout, finalize=True, size=(450, 400)) # escolha jogar pedra def janela_choicePedra(): sg.theme("Reddit") layout = [ [sg.Text("\nO Orc fica atordoado, mas recupera o controle.\nEle começa a correr em sua direção novamente. Você vai: ")], [sg.Text('Esconder-se atrás da rocha '), sg.Button('A')], [sg.Text('Jogar outra pedra'), sg.Button('B')], [sg.Text('Correr para uma caverna próxima'), sg.Button('C')] ] return sg.Window("Uma pedra ??", layout=layout, finalize=True) # escolha deitar e esperar ser atacado def janela_choiceDeitar(): sg.theme("Reddit") layout = [ [sg.Text('Essa foi rápida...\nVocê morreu')], [sg.Text('')], [sg.Button('Recomeçar??'), sg.Button('Sair..')] ] return sg.Window("Rápido como o vento", layout=layout, finalize=True, size=(300, 100)) # escolha correr def janela_choiceCorrer(): sg.theme("Reddit") layout = [ [sg.Text("\nVocê corre o mais rápido possível,\nmas a " "a velocidade do Orc é muito grande. Você irá: ")], [sg.Text('Esconder-se atrás da rocha '), sg.Button('A')], [sg.Text('Fazer uma armadilha, e então lutar'), sg.Button('B')], [sg.Text('Corra em direção a um cidade'), sg.Button('C')] ] return sg.Window("The Flash ?", layout=layout, finalize=True) # esxolha de jogar a pedra e acabar morrendo def janela_choicePedraF(): sg.theme("Reddit") layout = [ [sg.Text("\nVocê decide jogar outra pedra. " " A primeira pedra não deu muito dano.\nA pedra ricocheteou na cabeça e você erra. \nVocê morreu! ")], [sg.Text('')], [sg.Button('Recomeçar??'), sg.Button('Sair..')] ] return sg.Window("Não foi dessa vez", layout=layout, finalize=True) # escolha da caverna def janela_choiceCaverna(): sg.theme("Reddit") layout = [ [sg.Text("\nVocê estava hesitante, já que a caverna estava escura e sinistra. " " \nAntes de entrar totalmente, você nota você nota um báu parcialmente enterrado no chão. " " \nAo abrir, você encontra uma espada cintilante. Deseja pega-lá? Sim / Não? ")], [sg.Text('')], [sg.Input(key='resposta')], [sg.Button('Ok')] ] return sg.Window("A caverna pegar ou largar ", layout=layout, finalize=True) # escolha depois de pegar a espada def janela_choiceEspada(): sg.theme("Reddit") layout = [ [sg.Text( "\n O Orc continua a te perseguir, você está em apuros. O que fará em seguida? ")], [sg.Text('Se esconder em silêncio'), sg.Button('A')], [sg.Text('Lutar'), sg.Button('B')], [sg.Text('Correr'), sg.Button('C')] ] return sg.Window("E agora ??", layout=layout, finalize=True) # escolha de lutar após pegar a espada def janela_choiceEspada1(): sg.theme("Reddit") layout = [ [sg.Text("\nVocê ficou esperando. A espada cintilante atraiu " "o Orc,\nque pensou que você não era páreo.\nEnquanto ele caminhava " "cada vez mais perto,\nseu coração bate mais rápido. Como o Orc " "estendeu a mão para agarrar a espada,\nvocê perfurou a lâmina em " "seu peito. \n \n Você sobreviveu! ")], [sg.Button('Recomeçar??'), sg.Button('Sair..')] ] return sg.Window("Grande Final", layout=layout, finalize=True) # escolha de lutar sem ter pego a espada def janela_choiceEspada0(): sg.theme("Reddit") layout = [ [sg.Text( "\n Você deveria ter pegado aquela espada.\nVocê está indefeso. \n Você morreu! ")], [sg.Button('Recomeçar??'), sg.Button('Sair..')] ] return sg.Window("Por uma espada..", layout=layout, finalize=True) # escolha de ficar no escuro def janela_choiceEscuro(): sg.theme("Reddit") layout = [ [sg.Text("\nMesmo? Você vai se esconder no escuro?\nEu penso que " "Orcs podem ver muito bem no escuro, certo?\nNão tenho certeza, mas " "Vou com SIM, então ... \n Você morreu! ")], [sg.Button('Recomeçar??'), sg.Button('Sair..')] ] return sg.Window("Não muito inteligente ", layout=layout, finalize=True) # escolha de se esconder atrás da rocha def janela_choiceRocha(): sg.theme("Reddit") layout = [ [sg.Text('Você foi facilmente visto')], [sg.Text('Você morreu!')], [sg.Button('Recomeçar??'), sg.Button('Sair..')] ] return sg.Window("Não foi dessa vez", layout=layout, finalize=True, size=(270, 100)) # escolha de fazer armadilha def janela_choiceArmadilha(): sg.theme("Reddit") layout = [ [sg.Text('Você não é pareo para o orc')], [sg.Text('Você morreu')], [sg.Button('Recomeçar??'), sg.Button('Sair..')] ] return sg.Window("Não foi dessa vez", layout=layout, finalize=True, size=(270, 100)) # escolha de ir para cidade def janela_choiceCidade(): sg.theme("Reddit") layout = [ [sg.Text( "Você tenta acalmar sua respiração pesada enquanto se esconde " "\natrás de um edifício delapitado, esperando o Orc chegar " "\ncorrendo na esquina. Você nota uma flor roxa " "\nperto do seu pé. Você a pega? Sim / Não?")], [sg.Input(key='resposta')], [sg.Button('Ok')] ] return sg.Window("A cidade", layout=layout, finalize=True) # escolha de pegar a flor def janela_choiceFlor1(): sg.theme("Reddit") layout = [ [sg.Text("\nVocê rapidamente pega a flor roxa, de alguma forma " "\nesperando que isso pare o Orc. Pare! \nO Orc estava apenas procurando " "por amor. " "\nIsso foi bem estranho... Mas você sobreviveu! ")], [sg.Button('Recomeçar??'), sg.Button('Sair..')] ] return sg.Window("You win!!", layout=layout, finalize=True) # escolha de não pegar a flor def janela_choiceFlor0(): sg.theme("Reddit") layout = [ [sg.Text("\nTalvez você devesse ter pego a flor... " "\nVocê morre esmagado pelo Orc. ")], [sg.Button('Recomeçar??'), sg.Button('Sair..')] ] return sg.Window("You lose..", layout=layout, finalize=True) # definindo o valor das variaveis janela1, janela2 = janela_inicial(), None janela3 = None janela4 = None janela5 = None janela4 = None janela6 = None janelaX = None janelaC = None janelaAA = None # utilizando listas para definir o valor das variaveis yes = ["S", "s", "sim", "Sim"] no = ["N", "n", "nao", "não"] # estrutura de repetição while True: # definindo para as variaveis serem lidas em todas as janelas / verificando condições e escolhas window, event, values = sg.read_all_windows() # Escolha página inicial if window == janela1 and event == sg.WIN_CLOSED: break if window == janela1 and event == 'Sair': break if window == janela1 and event == 'Ok': janela2 = janela_comeco() janela1.hide() # inicio da história / segunda janela if window == janela2 and event == sg.WIN_CLOSED: break if window == janela2 and event == 'A': janelaAA = janela_choicePedra() janela2.hide() if window == janela2 and event == 'B': janela3 = janela_choiceDeitar() janela2.hide() if window == janela2 and event == 'C': janela3 = janela_choiceCorrer() janela2.hide() # janela choice A if window == janelaAA and event == sg.WIN_CLOSED: break if window == janelaAA and event == 'A': janela4 = janela_choiceRocha() janelaAA.hide() if window == janelaAA and event == 'B': janela4 = janela_choicePedraF() janelaAA.hide() if window == janelaAA and event == 'C': janela4 = janela_choiceCaverna() janelaAA.hide() # janela 4 if window == janela3 and event == sg.WIN_CLOSED: break if window == janela3 and event == 'A': janela5 = janela_choiceRocha() janela3.hide() if window == janela3 and event == 'B': janela5 = janela_choiceArmadilha() janela3.hide() if window == janela3 and event == 'C': janelaC = janela_choiceCidade() janela3.hide() #morte choice rocha c -> a if window == janela5 and event == sg.WIN_CLOSED: break if window == janela5 and event == ('Recomeçar??'): janela1 = janela_inicial() janela3.hide() if window == janela5 and event == ('Sair..'): break # morte choice rocha if window == janela4 and event == sg.WIN_CLOSED: break if window == janela4 and event == ('Recomeçar??'): janela1 = janela_inicial() janela4.hide() if window == janela4 and event == ('Sair..'): break # choice cidade if window == janelaC and event == sg.WIN_CLOSED: break if event == 'Ok': if window == janelaC and values['resposta'] == yes[0]: janela6 = janela_choiceFlor1() janelaC.hide() flor = 1 # adiciona a flor a seu inventario if window == janelaC and values['resposta'] == yes[1]: janela6 = janela_choiceFlor1() janelaC.hide() flor = 1 if window == janelaC and values['resposta'] == yes[2]: janela6 = janela_choiceFlor1() janelaC.hide() flor = 1 if window == janelaC and values['resposta'] == yes[3]: janela6 = janela_choiceFlor1() janelaC.hide() flor = 1 if window == janelaC and values['resposta'] == no[0]: flor = 0 janela6 = janela_choiceFlor0() janelaC.hide() if window == janelaC and
+ m.x94 == 0) m.c49 = Constraint(expr= - 40m.x24 + m.x96 == 0) m.c50 = Constraint(expr= - 40m.x25 + m.x98 == 0) m.c51 = Constraint(expr= - 40m.x26 + m.x100 == 0) m.c52 = Constraint(expr= - 40m.x2 + m.x53 == 0) m.c53 = Constraint(expr= - 40m.x3 + m.x55 == 0) m.c54 = Constraint(expr= - 40m.x4 + m.x57 == 0) m.c55 = Constraint(expr= - 40m.x5 + m.x59 == 0) m.c56 = Constraint(expr= - 40m.x6 + m.x61 == 0) m.c57 = Constraint(expr= - 40m.x7 + m.x63 == 0) m.c58 = Constraint(expr= - 40m.x8 + m.x65 == 0) m.c59 = Constraint(expr= - 40m.x9 + m.x67 == 0) m.c60 = Constraint(expr= - 40m.x10 + m.x69 == 0) m.c61 = Constraint(expr= - 40m.x11 + m.x71 == 0) m.c62 = Constraint(expr= - 40m.x12 + m.x73 == 0) m.c63 = Constraint(expr= - 40m.x13 + m.x75 == 0) m.c64 = Constraint(expr= - 40m.x14 + m.x77 == 0) m.c65 = Constraint(expr= - 40m.x15 + m.x79 == 0) m.c66 = Constraint(expr= - 40m.x16 + m.x81 == 0) m.c67 = Constraint(expr= - 40m.x17 + m.x83 == 0) m.c68 = Constraint(expr= - 40m.x18 + m.x85 == 0) m.c69 = Constraint(expr= - 40m.x19 + m.x87 == 0) m.c70 = Constraint(expr= - 40m.x20 + m.x89 == 0) m.c71 = Constraint(expr= - 40m.x21 + m.x91 == 0) m.c72 = Constraint(expr= - 40m.x22 + m.x93 == 0) m.c73 = Constraint(expr= - 40m.x23 + m.x95 == 0) m.c74 = Constraint(expr= - 40m.x24 + m.x97 == 0) m.c75 = Constraint(expr= - 40m.x25 + m.x99 == 0) m.c76 = Constraint(expr= - 40m.x26 + m.x101 == 0) m.c77 = Constraint(expr=m.x27(m.x130 - m.x127) - m.x102 == 0) m.c78 = Constraint(expr=m.x28(-0.574695771132691m.x127 - 0.287347885566345m.x128 + 0.766261028176921m.x129 + 0.574695771132691m.x136 + 0.287347885566345m.x137 - 0.766261028176921m.x138) - m.x103 == 0) m.c79 = Constraint(expr=m.x29(0.574695771132691m.x130 - 0.287347885566345m.x131 + 0.766261028176921m.x132 - 0.574695771132691m.x133 + 0.287347885566345m.x134 - 0.766261028176921m.x135) - m.x104 == 0) m.c80 = Constraint(expr=m.x30(0.287347885566345m.x128 - 0.574695771132691m.x127 + 0.766261028176921m.x129 + 0.574695771132691m.x139 - 0.287347885566345m.x140 - 0.766261028176921m.x141) - m.x105 == 0) m.c81 = Constraint(expr=m.x31(0.574695771132691m.x130 + 0.287347885566345m.x131 + 0.766261028176921m.x132 - 0.574695771132691m.x142 - 0.287347885566345m.x143 - 0.766261028176921m.x144) - m.x106 == 0) m.c82 = Constraint(expr=m.x32(0.936329177569045m.x132 - 0.351123441588392m.x131 + 0.351123441588392m.x137 - 0.936329177569045m.x138) - m.x107 == 0) m.c83 = Constraint(expr=m.x33(0.351123441588392m.x131 + 0.936329177569045m.x132 - 0.351123441588392m.x140 - 0.936329177569045m.x141) - m.x108 == 0) m.c84 = Constraint(expr=m.x34(0.936329177569045m.x129 - 0.351123441588392m.x128 + 0.351123441588392m.x134 - 0.936329177569045m.x135) - m.x109 == 0) m.c85 = Constraint(expr=m.x35(0.351123441588392m.x128 + 0.936329177569045m.x129 - 0.351123441588392m.x143 - 0.936329177569045m.x144) - m.x110 == 0) m.c86 = Constraint(expr=m.x36(m.x134 - m.x143) - m.x111 == 0) m.c87 = Constraint(expr=m.x37(m.x137 - m.x140) - m.x112 == 0) m.c88 = Constraint(expr=m.x38(m.x136 - m.x133) - m.x113 == 0) m.c89 = Constraint(expr=m.x39(m.x139 - m.x142) - m.x114 == 0) m.c90 = Constraint(expr=m.x40(0.345032779671177m.x133 + 0.75907211527659m.x134 + 0.552052447473883m.x135) - m.x115 == 0) m.c91 = Constraint(expr=m.x41(0.345032779671177m.x142 - 0.75907211527659m.x143 + 0.552052447473883m.x144) - m.x116 == 0) m.c92 = Constraint(expr=m.x42(0.75907211527659m.x137 - 0.345032779671177m.x136 + 0.552052447473883m.x138) - m.x117 == 0) m.c93 = Constraint(expr=m.x43(-0.345032779671177m.x139 - 0.75907211527659m.x140 + 0.552052447473883m.x141) - m.x118 == 0) m.c94 = Constraint(expr=m.x44(0.75907211527659m.x136 - 0.345032779671177m.x137 + 0.552052447473883m.x138) - m.x119 == 0) m.c95 = Constraint(expr=m.x45(-0.75907211527659m.x133 - 0.345032779671177m.x134 + 0.552052447473883m.x135) - m.x120 == 0) m.c96 = Constraint(expr=m.x46(0.75907211527659m.x139 + 0.345032779671177m.x140 + 0.552052447473883m.x141) - m.x121 == 0) m.c97 = Constraint(expr=m.x47(0.345032779671177m.x143 - 0.75907211527659m.x142 + 0.552052447473883m.x144) - m.x122 == 0) m.c98 = Constraint(expr=m.x48(0.468292905790847m.x142 + 0.468292905790847m.x143 + 0.749268649265355m.x144) - m.x123 == 0) m.c99 = Constraint(expr=m.x49(0.468292905790847m.x133 - 0.468292905790847m.x134 + 0.749268649265355m.x135) - m.x124 == 0) m.c100 = Constraint(expr=m.x50(-0.468292905790847m.x136 - 0.468292905790847m.x137 + 0.749268649265355m.x138) - m.x125 == 0) m.c101 = Constraint(expr=m.x51(0.468292905790847m.x140 - 0.468292905790847m.x139 + 0.749268649265355m.x141) - m.x126 == 0) m.c102 = Constraint(expr= - m.x102 - 0.574695771132691m.x103 - 0.574695771132691m.x105 == 1) m.c103 = Constraint(expr= - 0.287347885566345m.x103 + 0.287347885566345m.x105 - 0.351123441588392m.x109 + 0.351123441588392m.x110 == 10) m.c104 = Constraint(expr= 0.766261028176921m.x103 + 0.766261028176921m.x105 + 0.936329177569045m.x109 + 0.936329177569045m.x110 == -10) m.c105 = Constraint(expr= m.x102 + 0.574695771132691m.x104 + 0.574695771132691m.x106 == 0) m.c106 = Constraint(expr= - 0.287347885566345m.x104 + 0.287347885566345m.x106 - 0.351123441588392m.x107 + 0.351123441588392m.x108 == 10) m.c107 = Constraint(expr= 0.766261028176921m.x104 + 0.766261028176921m.x106 + 0.936329177569045m.x107 + 0.936329177569045m.x108 == -10) m.c108 = Constraint(expr= - 0.574695771132691m.x104 - m.x113 + 0.345032779671177m.x115 - 0.75907211527659m.x120 + 0.468292905790847m.x124 == 0.5) m.c109 = Constraint(expr= 0.287347885566345m.x104 + 0.351123441588392m.x109 + m.x111 + 0.75907211527659m.x115 - 0.345032779671177m.x120 - 0.468292905790847m.x124 == 0) m.c110 = Constraint(expr= - 0.766261028176921m.x104 - 0.936329177569045m.x109 + 0.552052447473883m.x115 + 0.552052447473883m.x120 + 0.749268649265355m.x124 == 0) m.c111 = Constraint(expr= 0.574695771132691m.x103 + m.x113 - 0.345032779671177m.x117 + 0.75907211527659m.x119 - 0.468292905790847m.x125 == 0) m.c112 = Constraint(expr= 0.287347885566345m.x103 + 0.351123441588392m.x107 + m.x112 + 0.75907211527659m.x117 - 0.345032779671177m.x119 - 0.468292905790847m.x125 == 0) m.c113 = Constraint(expr= - 0.766261028176921m.x103 - 0.936329177569045m.x107 + 0.552052447473883m.x117 + 0.552052447473883m.x119 + 0.749268649265355m.x125 == 0) m.c114 = Constraint(expr= 0.574695771132691m.x105 + m.x114 - 0.345032779671177m.x118 + 0.75907211527659m.x121 - 0.468292905790847m.x126 == 0) m.c115 = Constraint(expr= - 0.287347885566345m.x105 - 0.351123441588392m.x108 - m.x112 - 0.75907211527659m.x118 + 0.345032779671177m.x121 + 0.468292905790847m.x126 == 0) m.c116 = Constraint(expr= - 0.766261028176921m.x105 - 0.936329177569045m.x108 + 0.552052447473883m.x118 + 0.552052447473883m.x121 + 0.749268649265355m.x126 == 0) m.c117 = Constraint(expr= - 0.574695771132691m.x106 - m.x114 + 0.345032779671177m.x116 - 0.75907211527659m.x122 + 0.468292905790847m.x123 == 0.6) m.c118 = Constraint(expr= - 0.287347885566345m.x106 - 0.351123441588392m.x110 - m.x111 - 0.75907211527659m.x116 + 0.345032779671177m.x122 + 0.468292905790847m.x123 == 0) m.c119 = Constraint(expr= - 0.766261028176921m.x106 - 0.936329177569045m.x110 + 0.552052447473883m.x116 + 0.552052447473883m.x122 + 0.749268649265355m.x123 == 0) m.c120 = Constraint(expr= - m.x52 + m.x102 <= 0) m.c121 = Constraint(expr= - m.x53 - m.x102 <= 0) m.c122 = Constraint(expr= - m.x54 + m.x103 <= 0) m.c123 = Constraint(expr= - m.x55 - m.x103 <= 0) m.c124 = Constraint(expr= - m.x56 + m.x104 <= 0) m.c125 = Constraint(expr= - m.x57 - m.x104 <= 0) m.c126 = Constraint(expr= - m.x58 + m.x105 <= 0) m.c127 = Constraint(expr= - m.x59 - m.x105 <= 0) m.c128 = Constraint(expr= - m.x60 + m.x106 <= 0) m.c129 = Constraint(expr= - m.x61 - m.x106 <= 0) m.c130 = Constraint(expr= - m.x62 + m.x107 <= 0) m.c131 = Constraint(expr= - m.x63 - m.x107 <= 0) m.c132 = Constraint(expr= - m.x64 + m.x108 <= 0) m.c133 = Constraint(expr= - m.x65 - m.x108 <= 0) m.c134 = Constraint(expr= - m.x66 + m.x109 <= 0) m.c135 = Constraint(expr= - m.x67 - m.x109 <= 0) m.c136 = Constraint(expr= - m.x68 + m.x110 <= 0) m.c137 = Constraint(expr= - m.x69 - m.x110 <= 0) m.c138 = Constraint(expr= - m.x70 + m.x111 <= 0) m.c139 = Constraint(expr= - m.x71 - m.x111 <= 0) m.c140 = Constraint(expr= - m.x72 + m.x112 <= 0) m.c141 = Constraint(expr= - m.x73 - m.x112 <= 0) m.c142 = Constraint(expr= - m.x74 + m.x113 <= 0) m.c143 = Constraint(expr= - m.x75 - m.x113 <= 0) m.c144 = Constraint(expr= - m.x76 + m.x114 <= 0) m.c145 = Constraint(expr= - m.x77 - m.x114 <= 0) m.c146 = Constraint(expr= - m.x78 + m.x115 <= 0) m.c147 = Constraint(expr= - m.x79 - m.x115 <= 0) m.c148 = Constraint(expr= - m.x80 + m.x116 <= 0) m.c149 = Constraint(expr= - m.x81 - m.x116 <= 0) m.c150 = Constraint(expr= - m.x82 + m.x117 <= 0) m.c151 = Constraint(expr= - m.x83 - m.x117 <= 0) m.c152 = Constraint(expr= - m.x84 + m.x118 <= 0) m.c153 = Constraint(expr= - m.x85 - m.x118 <= 0) m.c154 = Constraint(expr= - m.x86 + m.x119 <= 0) m.c155 = Constraint(expr= - m.x87 - m.x119 <= 0) m.c156 = Constraint(expr= - m.x88 + m.x120 <= 0) m.c157 = Constraint(expr= - m.x89 - m.x120 <= 0) m.c158 = Constraint(expr= - m.x90 + m.x121 <= 0) m.c159 = Constraint(expr= - m.x91 - m.x121 <= 0) m.c160 = Constraint(expr= - m.x92 + m.x122 <= 0) m.c161 = Constraint(expr= - m.x93 - m.x122 <= 0) m.c162 = Constraint(expr= - m.x94 + m.x123 <= 0) m.c163 = Constraint(expr= - m.x95 - m.x123 <= 0) m.c164 = Constraint(expr= - m.x96 + m.x124 <= 0) m.c165 = Constraint(expr= - m.x97 - m.x124 <= 0) m.c166 = Constraint(expr= - m.x98 + m.x125 <= 0) m.c167 = Constraint(expr= - m.x99 - m.x125 <= 0) m.c168 = Constraint(expr= - m.x100 + m.x126 <= 0) m.c169 = Constraint(expr= - m.x101 - m.x126 <= 0) m.c170 = Constraint(expr= - m.x2 + 0.1m.b145 + 0.2m.b146 + 0.3m.b147 + 0.4m.b148 + 0.5m.b149 + 0.6m.b150 + 0.7m.b151 + 0.8m.b152 + 0.9m.b153 + m.b154 + 1.1m.b155 + 1.2m.b156 + 1.3m.b157 + 1.4m.b158 + 1.5m.b159 + 1.6m.b160 + 1.7m.b161 + 1.8m.b162 + 1.9m.b163 + 2m.b164 + 2.1m.b165 + 2.2m.b166 + 2.3m.b167 + 2.4m.b168 + 2.5m.b169 + 2.6m.b170 + 2.8m.b171 + 3m.b172 + 3.2m.b173 + 3.4m.b174 == 0) m.c171 = Constraint(expr= - m.x3 + 0.1m.b175 + 0.2m.b176 + 0.3m.b177 + 0.4m.b178 + 0.5m.b179 + 0.6m.b180 + 0.7m.b181 + 0.8m.b182 + 0.9m.b183 + m.b184 + 1.1m.b185 + 1.2m.b186 + 1.3m.b187 + 1.4m.b188 + 1.5m.b189 + 1.6m.b190 + 1.7m.b191 + 1.8m.b192 + 1.9m.b193 + 2m.b194 + 2.1m.b195 + 2.2m.b196 + 2.3m.b197 + 2.4m.b198 + 2.5m.b199 + 2.6m.b200 + 2.8m.b201 + 3m.b202 + 3.2m.b203 + 3.4*m.b204 == 0) m.c172 = Constraint(expr= -
<filename>scripts/addons/uvpackmaster2/panel_base.py<gh_stars>1-10 # ##### BEGIN GPL LICENSE BLOCK ##### # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License # as published by the Free Software Foundation; either version 2 # of the License, or (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to the Free Software Foundation, # Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. # # ##### END GPL LICENSE BLOCK ##### import multiprocessing from .prefs import get_prefs from .operator import * from .operator_islands import * from .operator_box import * from .operator_uv import * from .utils import * from .presets import * from .labels import UvpLabels from .register import UVP2_OT_SelectUvpEngine import bpy class UVP2_OT_SetRotStep(bpy.types.Operator): bl_idname = 'uvpackmaster2.set_rot_step' bl_label = 'Set Rotation Step' bl_description = "Set Rotation Step to one of the suggested values" rot_step : IntProperty( name='Rotation Step', description='', default=90) def execute(self, context): scene_props = context.scene.uvp2_props scene_props.rot_step = self.rot_step return {'FINISHED'} class UVP2_MT_SetRotStep(bpy.types.Menu): bl_idname = "UVP2_MT_SetRotStep" bl_label = "Set Rotation Step" STEPS = [1, 2, 3, 5, 6, 9, 10, 15, 18, 30, 45, 90, 180] def draw(self, context): layout = self.layout for step in self.STEPS: operator = layout.operator(UVP2_OT_SetRotStep.bl_idname, text=str(step)) operator.rot_step = step class UVP2_UL_DeviceList(bpy.types.UIList): def draw_item(self, context, layout, data, item, icon, active_data, active_propname, index): dev_name = str(item.name) row = layout.row() icon_id = 'NONE' if not item.supported: dev_name += ' ' + UvpLabels.FEATURE_NOT_SUPPORTED_MSG icon_id = UvpLabels.FEATURE_NOT_SUPPORTED_ICON row.label(text=dev_name, icon=icon_id) class UVP2_PT_Generic(bpy.types.Panel): def draw(self, context): self.prefs = get_prefs() self.scene_props = context.scene.uvp2_props self.draw_specific(context) def handle_prop(self, prop_name, supported, not_supported_msg, ui_elem): if supported: ui_elem.prop(self.scene_props, prop_name) else: ui_elem.enabled = False split = ui_elem.split(factor=0.4) col_s = split.column() col_s.prop(self.scene_props, prop_name) col_s = split.column() col_s.label(text=not_supported_msg) def handle_prop_enum(self, prop_name, prop_label, supported, not_supported_msg, ui_elem): prop_label_colon = prop_label + ':' if supported: ui_elem.label(text=prop_label_colon) else: split = ui_elem.split(factor=0.4) col_s = split.column() col_s.label(text=prop_label_colon) col_s = split.column() col_s.label(text=not_supported_msg) ui_elem.prop(self.scene_props, prop_name, text='') ui_elem.enabled = supported def messages_in_boxes(self, ui_elem, messages): for msg in messages: box = ui_elem.box() msg_split = split_by_chars(msg, 60) if len(msg_split) > 0: # box.separator() for msg_part in msg_split: box.label(text=msg_part) # box.separator() class UVP2_PT_MainBase(UVP2_PT_Generic): bl_idname = 'UVP2_PT_MainBase' bl_label = 'UVPackmaster2' bl_context = '' def draw_specific(self, context): layout = self.layout demo_suffix = " (DEMO)" if self.prefs.FEATURE_demo else '' row = layout.row() row.label(text=self.prefs.label_message) if not self.prefs.uvp_initialized: row.operator(UVP2_OT_UvpSetupHelp.bl_idname, icon='HELP', text='') row = layout.row() row2 = row.row() row2.enabled = False row2.prop(self.prefs, 'uvp_path') select_icon = 'FILEBROWSER' if is_blender28() else 'FILE_FOLDER' row.operator(UVP2_OT_SelectUvpEngine.bl_idname, icon=select_icon, text='') col = layout.column(align=True) col.separator() if in_debug_mode(): box = col.box() col2 = box.column(align=True) col2.label(text="Debug options:") row = col2.row(align=True) row.prop(self.prefs, "write_to_file") row = col2.row(align=True) row.prop(self.prefs, "wait_for_debugger") row = col2.row(align=True) row.prop(self.prefs, "seed") row = col2.row(align=True) row.prop(self.prefs, "test_param") col.separator() col.label(text="Engine operations:") row = col.row(align=True) row.enabled = self.prefs.FEATURE_overlap_check row.operator(UVP2_OT_OverlapCheckOperator.bl_idname) if not self.prefs.FEATURE_overlap_check: row.label(text=UvpLabels.FEATURE_NOT_SUPPORTED_MSG) col.operator(UVP2_OT_MeasureAreaOperator.bl_idname) # Validate operator row = col.row(align=True) row.enabled = self.prefs.FEATURE_validation row.operator(UVP2_OT_ValidateOperator.bl_idname, text=UVP2_OT_ValidateOperator.bl_label + demo_suffix) if not self.prefs.FEATURE_validation: row.label(text=UvpLabels.FEATURE_NOT_SUPPORTED_MSG) row = col.row(align=True) row.scale_y = 1.75 row.operator(UVP2_OT_PackOperator.bl_idname, text=UVP2_OT_PackOperator.bl_label + demo_suffix) col.label(text="Last operation status:") box = col.box() box.label(text=self.prefs['op_status'] if self.prefs['op_status'] != '' else '------') col.separator() if len(self.prefs['op_warnings']) > 0: row = col.row() row.label(text="WARNINGS:", icon=UvpLabels.FEATURE_NOT_SUPPORTED_ICON) self.messages_in_boxes(col, self.prefs['op_warnings']) col.separator() col.separator() col.label(text="Other operations:") row = col.row(align=True) row.operator(UVP2_OT_SplitOverlappingIslands.bl_idname) row = col.row(align=True) row.operator(UVP2_OT_UndoIslandSplit.bl_idname) row = col.row(align=True) row.operator(UVP2_OT_AdjustScaleToUnselected.bl_idname) if in_debug_mode(): row = col.row(align=True) row.operator(UVP2_OT_DebugIslands.bl_idname) col.separator() col.label(text='Option presets:') row = col.row(align=True) row.operator(UVP2_OT_SavePreset.bl_idname) row.operator(UVP2_OT_LoadPreset.bl_idname) row = col.row(align=True) row.operator(UVP2_OT_ResetToDefaults.bl_idname) class UVP2_PT_PackingDeviceBase(UVP2_PT_Generic): bl_label = 'Packing Devices' bl_context = '' bl_options = {'DEFAULT_CLOSED'} def draw_specific(self, context): layout = self.layout col = layout.column(align=True) col.template_list("UVP2_UL_DeviceList", "", self.prefs, "dev_array", self.prefs, "sel_dev_idx") box = col.box() box.label(text=UvpLabels.PACKING_DEVICE_WARNING) box.enabled = False # Multi device box = col.box() box.enabled = self.prefs.FEATURE_multi_device_pack row = box.row() self.handle_prop("multi_device_pack", self.prefs.FEATURE_multi_device_pack, UvpLabels.FEATURE_NOT_SUPPORTED_MSG, row) class UVP2_PT_BasicOptionsBase(UVP2_PT_Generic): bl_label = 'Basic Options' bl_context = '' def draw_specific(self, context): layout = self.layout col = layout.column(align=True) col.prop(self.prefs, "thread_count") col.prop(self.scene_props, "margin") col.prop(self.scene_props, "precision") # Rotation Resolution box = col.box() box.enabled = self.prefs.FEATURE_island_rotation row = box.row() # TODO: missing feature check row.prop(self.scene_props, "rot_enable") # box = col.box() row = box.row() row.enabled = self.scene_props.rot_enable row.prop(self.scene_props, "prerot_disable") row = col.row(align=True) row.enabled = self.scene_props.rot_enable split = row.split(factor=0.8, align=True) col_s = split.row(align=True) col_s.prop(self.scene_props, "rot_step") col_s = split.row(align=True) col_s.menu(UVP2_MT_SetRotStep.bl_idname, text='Set') # Pre validate pre_validate_name = UvpLabels.PRE_VALIDATE_NAME if self.prefs.FEATURE_demo: pre_validate_name += ' (DEMO)' box = col.box() box.enabled = self.prefs.FEATURE_validation row = box.row() self.handle_prop("pre_validate", self.prefs.FEATURE_validation, UvpLabels.FEATURE_NOT_SUPPORTED_MSG, row) class UVP2_PT_IslandRotStepBase(UVP2_PT_Generic): bl_label = 'Island Rotation Step' bl_context = '' bl_options = {'DEFAULT_CLOSED'} def draw_specific(self, context): layout = self.layout panel_enabled = True if not self.prefs.FEATURE_island_rotation_step: layout.label(text=UvpLabels.FEATURE_NOT_SUPPORTED_MSG) panel_enabled = False elif not self.scene_props.rot_enable: layout.label(text='Island rotations must be enabled to activate this panel', icon='ERROR') panel_enabled = False col = layout.column(align=True) col.enabled = panel_enabled box = col.box() row = box.row() row.prop(self.scene_props, "island_rot_step_enable") row.operator(UVP2_OT_IslandRotStepHelp.bl_idname, icon='HELP', text='') col2 = col.column(align=True) col2.enabled = self.scene_props.island_rot_step_enable row = col2.row(align=True) row.prop(self.scene_props, "island_rot_step") row = col2.row(align=True) row.operator(UVP2_OT_SetRotStepIslandParam.bl_idname) col2.separator() row = col2.row(align=True) row.operator(UVP2_OT_ResetRotStepIslandParam.bl_idname) row = col2.row(align=True) row.operator(UVP2_OT_ShowRotStepIslandParam.bl_idname) class UVP2_PT_ManualGroupingBase(UVP2_PT_Generic): bl_label = 'Manual Grouping' bl_context = '' bl_options = {'DEFAULT_CLOSED'} def draw_specific(self, context): layout = self.layout panel_enabled = True if not self.prefs.FEATURE_grouping: layout.label(text=UvpLabels.FEATURE_NOT_SUPPORTED_MSG) panel_enabled = False col = layout.column(align=True) col.enabled = panel_enabled container = col row = container.row(align=True) row.prop(self.scene_props, "manual_group_num") row.operator(UVP2_OT_ManualGroupingHelp.bl_idname, icon='HELP', text='') row = container.row(align=True) row.operator(UVP2_OT_SetManualGroupIslandParam.bl_idname) container.separator() container.label(text="Select islands assigned to a group:") row = container.row(align=True) op = row.operator(UVP2_OT_SelectManualGroupIslandParam.bl_idname, text="Select") op.select = True op = row.operator(UVP2_OT_SelectManualGroupIslandParam.bl_idname, text="Deselect") op.select = False # container.separator() # row = container.row(align=True) # row.operator(UVP2_OT_ResetManualGroupIslandParam.bl_idname) row = container.row(align=True) row.operator(UVP2_OT_ShowManualGroupIslandParam.bl_idname) class UVP2_PT_LockGroupsBase(UVP2_PT_Generic): bl_label = 'Lock Groups' bl_context = '' bl_options = {'DEFAULT_CLOSED'} def draw_specific(self, context): layout = self.layout panel_enabled = True if not self.prefs.FEATURE_lock_overlapping: layout.label(text=UvpLabels.FEATURE_NOT_SUPPORTED_MSG) panel_enabled = False col = layout.column(align=True) col.enabled = panel_enabled box = col.box() row = box.row() row.prop(self.scene_props, "lock_groups_enable") container = col.column(align=True) container.enabled = self.scene_props.lock_groups_enable row = container.row(align=True) row.prop(self.scene_props, "lock_group_num") row = container.row(align=True) row.operator(UVP2_OT_SetLockGroupIslandParam.bl_idname) row = container.row(align=True) row.operator(UVP2_OT_SetFreeLockGroupIslandParam.bl_idname) container.separator() container.label(text="Select islands assigned to a lock group:") row = container.row(align=True) op = row.operator(UVP2_OT_SelectLockGroupIslandParam.bl_idname, text="Select") op.select = True op = row.operator(UVP2_OT_SelectLockGroupIslandParam.bl_idname, text="Deselect") op.select = False # row = container.row(align=True) # row.operator(UVP2_OT_SelectNonDefaultLockGroupIslandParam.bl_idname) row = container.row(align=True) row.operator(UVP2_OT_ResetLockGroupIslandParam.bl_idname) row = container.row(align=True) row.operator(UVP2_OT_ShowLockGroupIslandParam.bl_idname) class UVP2_PT_HeuristicBase(UVP2_PT_Generic): bl_label = 'Heuristic' bl_context = '' bl_options = {'DEFAULT_CLOSED'} def draw_specific(self, context): layout = self.layout heurstic_supported, not_supported_msg = self.prefs.heuristic_supported(self.scene_props) col = layout.column(align=True) col.enabled = heurstic_supported # Heuristic search box = col.box() box.enabled = self.prefs.FEATURE_heuristic_search row = box.row() self.handle_prop("heuristic_enable", heurstic_supported, not_supported_msg, row) row.operator(UVP2_OT_HeuristicSearchHelp.bl_idname, icon='HELP', text='') col2 = col.column(align=True) col2.enabled = self.prefs.heuristic_enabled(self.scene_props) row = col2.row(align=True) row.prop(self.scene_props, "heuristic_search_time") row = col2.row(align=True) row.prop(self.scene_props, "heuristic_max_wait_time") # Advanced Heuristic box = col2.box() box.enabled = self.prefs.advanced_heuristic_available(self.scene_props) row = box.row() self.handle_prop("advanced_heuristic", self.prefs.FEATURE_advanced_heuristic, UvpLabels.FEATURE_NOT_SUPPORTED_MSG, row) class UVP2_PT_NonSquarePackingBase(UVP2_PT_Generic): bl_label = 'Non-Square Packing' bl_context = '' bl_options = {'DEFAULT_CLOSED'} def draw_specific(self, context): layout = self.layout col = layout.column(align=True) # Tex ratio box = col.box() row = box.row() self.handle_prop("tex_ratio", self.prefs.pack_ratio_supported(), UvpLabels.FEATURE_NOT_SUPPORTED_MSG, row) col.separator() row = col.row(align=True) row.operator(UVP2_OT_AdjustIslandsToTexture.bl_idname) row.operator(UVP2_OT_NonSquarePackingHelp.bl_idname, icon='HELP', text='') row = col.row(align=True) row.operator(UVP2_OT_UndoIslandsAdjustemntToTexture.bl_idname) class UVP2_PT_AdvancedOptionsBase(UVP2_PT_Generic): bl_label = 'Advanced Options' bl_context = '' bl_options = {'DEFAULT_CLOSED'} def draw_specific(self, context): layout = self.layout demo_suffix = " (DEMO)" if self.prefs.FEATURE_demo else '' col = layout.column(align=True) # Grouped pack box = col.box() col2 = box.column(align=True) col2.label(text=UvpLabels.PACK_MODE_NAME + ':') row = col2.row(align=True) row.prop(self.scene_props, "pack_mode", text='') if self.prefs.tiles_enabled(self.scene_props): row.operator(UVP2_OT_UdimSupportHelp.bl_idname, icon='HELP', text='') box = col2.box() box.prop(self.scene_props, "use_blender_tile_grid") tile_col = col2.column(align=True) tile_col.enabled = not self.scene_props.use_blender_tile_grid if self.prefs.pack_to_tiles(self.scene_props): tile_col.prop(self.scene_props, "tile_count") tile_col.prop(self.scene_props, "tiles_in_row") if self.prefs.pack_groups_together(self.scene_props): row = col2.row(align=True) row.prop(self.scene_props, "group_compactness") if self.prefs.grouping_enabled(self.scene_props): box = col.box() col3 = box.column() col3.label(text=UvpLabels.GROUP_METHOD_NAME + ':') row = col3.row(align=True) row.prop(self.scene_props, "group_method", text='') if self.scene_props.group_method == UvGroupingMethod.MANUAL.code: row.operator(UVP2_OT_ManualGroupingHelp.bl_idname, icon='HELP', text='') # col2.separator() # Pack to others box = col.box() pto_supported, pto_not_supported_msg = self.prefs.pack_to_others_supported(self.scene_props) row = box.row() self.handle_prop("pack_to_others", pto_supported, pto_not_supported_msg, row) # Fixed Scale box = col.box() fs_supported, fs_not_supported_msg = self.prefs.fixed_scale_supported(self.scene_props) row = box.row() self.handle_prop("fixed_scale", fs_supported, fs_not_supported_msg, row) # box = col.box() col2 = box.column() col2.enabled = self.prefs.fixed_scale_enabled(self.scene_props) col2.label(text=UvpLabels.FIXED_SCALE_STRATEGY_NAME + ':') row = col2.row(align=True) row.prop(self.scene_props, "fixed_scale_strategy", text='') # Normalize islands box = col.box() norm_supported, norm_not_supported_msg = self.prefs.normalize_islands_supported(self.scene_props) row = box.row() self.handle_prop("normalize_islands", norm_supported, norm_not_supported_msg,
from collections import namedtuple from itertools import islice import numpy as np import pandas as pd from dataclasses import dataclass @dataclass class BinningInfo(object): """Docstring for BinningInfo.""" variable_extents: tuple step: float num_bins: int bin_indicies: np.ndarray def build_spanning_grid_matrix(x_values, y_values, debug_print=False): """ builds a 2D matrix with entries spanning x_values across axis 0 and spanning y_values across axis 1. For example, used to build a grid of position points from xbins and ybins. Usage: all_positions_matrix, flat_all_positions_matrix, original_data_shape = build_all_positions_matrix(active_one_step_decoder.xbin_centers, active_one_step_decoder.ybin_centers) """ num_rows = len(y_values) num_cols = len(x_values) original_data_shape = (num_cols, num_rows) # original_position_data_shape: (64, 29) if debug_print: print(f'original_position_data_shape: {original_data_shape}') x_only_matrix = np.repeat(np.expand_dims(x_values, 1).T, num_rows, axis=0).T # np.shape(x_only_matrix) # (29, 64) flat_x_only_matrix = np.reshape(x_only_matrix, (-1, 1)) if debug_print: print(f'np.shape(x_only_matrix): {np.shape(x_only_matrix)}, np.shape(flat_x_only_matrix): {np.shape(flat_x_only_matrix)}') # np.shape(x_only_matrix): (64, 29), np.shape(flat_x_only_matrix): (1856, 1) y_only_matrix = np.repeat(np.expand_dims(y_values, 1), num_cols, axis=1).T # np.shape(y_only_matrix) # (29, 64) flat_y_only_matrix = np.reshape(y_only_matrix, (-1, 1)) # flat_all_positions_matrix = np.array([np.append(an_x, a_y) for (an_x, a_y) in zip(flat_x_only_matrix, flat_y_only_matrix)]) flat_all_entries_matrix = [tuple(np.append(an_x, a_y)) for (an_x, a_y) in zip(flat_x_only_matrix, flat_y_only_matrix)] # a list of position tuples (containing two elements) # reconsitute its shape: all_entries_matrix = np.reshape(flat_all_entries_matrix, (original_data_shape[0], original_data_shape[1], 2)) if debug_print: print(f'np.shape(all_positions_matrix): {np.shape(all_entries_matrix)}') # np.shape(all_positions_matrix): (1856, 2) # np.shape(all_positions_matrix): (64, 29, 2) print(f'flat_all_positions_matrix[0]: {flat_all_entries_matrix[0]}\nall_positions_matrix[0,0,:]: {all_entries_matrix[0,0,:]}') return all_entries_matrix, flat_all_entries_matrix, original_data_shape def safe_get(list, index, fallback_value): """Similar to dict's .get(key, fallback) function but for lists. Returns a fallback/default value if the index is not valid for the list, otherwise returns the value at that index. Args: list (_type_): a list-like object index (_type_): an index into the list fallback_value (_type_): any value to be returned when the indexing fails Returns: _type_: the value in the list, or the fallback_value is the index is not valid for the list. """ try: return list[index] except IndexError: return fallback_value def safe_pandas_get_group(dataframe_group, key): """ returns an empty dataframe if the key isn't found in the group.""" if key in dataframe_group.groups.keys(): return dataframe_group.get_group(key) else: original_df = dataframe_group.obj return original_df.drop(original_df.index) # class MatrixFlattenTransformer(object): # """ Supposed to allow easy transformation of data from a flattened representation to the original. # Usage: # trans = MatrixFlattenTransformer(original_data_shape) # test_all_positions_matrix = trans.unflatten(flat_all_positions_matrix) # print(f'np.shape(test_all_positions_matrix): {np.shape(test_all_positions_matrix)}') # """ # """ TODO: does not yet work. for MatrixFlattenTransformer.""" # def __init__(self, original_data_shape): # super(MatrixFlattenTransformer, self).__init__() # self.original_data_shape = original_data_shape # def flatten(self, data): # data_shape = np.shape(data) # original_flat_shape = np.prod(self.original_data_shape) # # assert np.shape(data) == self.original_data_shape, f"data passed in to flatten (with shape {np.shape(data)}) is not equal to the original data shape: {self.original_data_shape}" # assert data_shape == original_flat_shape, f"data passed in to flatten (with shape {data_shape}) is not equal to the original shape's number of items (shape: {self.original_data_shape}, original_flat_shape: {original_flat_shape}" # return np.reshape(data, (-1, 1)) # def unflatten(self, flat_data): # flat_data_shape = np.shape(flat_data) # original_data_shape_ndim = len(self.original_data_shape) # # assert (flat_data_shape[:original_data_shape_ndim] == self.original_data_shape), f"data passed in to unflatten (with shape {flat_data_shape}) must match the original data shape ({self.original_data_shape}), at least up to the number of dimensions in the original" # additional_dimensions = flat_data_shape[original_data_shape_ndim:] # return np.reshape(flat_data, (self.original_data_shape[0], self.original_data_shape[1], additional_dimensions)) def build_spanning_bins(variable_values, max_bin_size:float, debug_print=False): """ DEPRICATED! out_digitized_variable_bins include both endpoints (bin edges) Args: variable_values ([type]): [description] max_bin_size (float): [description] debug_print (bool, optional): [description]. Defaults to False. Returns: out_digitized_variable_bins [type]: [description] out_binning_info [BinningInfo]: contains info about how the binning was conducted """ raise DeprecationWarning # compute extents: curr_variable_extents = (np.nanmin(variable_values), np.nanmax(variable_values)) num_subdivisions = int(np.ceil((curr_variable_extents[1] - curr_variable_extents[0])/max_bin_size)) # get the next integer size above float_bin_size actual_subdivision_step_size = (curr_variable_extents[1] - curr_variable_extents[0]) / float(num_subdivisions) # the actual exact size of the bin if debug_print: print(f'for max_bin_size: {max_bin_size} -> num_subdivisions: {num_subdivisions}, actual_subdivision_step_size: {actual_subdivision_step_size}') # out_bin_indicies = np.arange(num_subdivisions) out_binning_info = BinningInfo(curr_variable_extents, actual_subdivision_step_size, num_subdivisions, np.arange(num_subdivisions)) out_digitized_variable_bins = np.linspace(curr_variable_extents[0], curr_variable_extents[1], num_subdivisions, dtype=float)#.astype(float) assert out_digitized_variable_bins[-1] == out_binning_info.variable_extents[1], "out_digitized_variable_bins[-1] should be the maximum variable extent!" assert out_digitized_variable_bins[0] == out_binning_info.variable_extents[0], "out_digitized_variable_bins[0] should be the minimum variable extent!" # All above arge the bin_edges return out_digitized_variable_bins, out_binning_info def compute_spanning_bins(variable_values, num_bins:int=None, bin_size:float=None): """[summary] Args: variable_values ([type]): [description] num_bins (int, optional): [description]. Defaults to None. bin_size (float, optional): [description]. Defaults to None. debug_print (bool, optional): [description]. Defaults to False. Raises: ValueError: [description] Returns: [type]: [description] Usage: ## Binning with Fixed Number of Bins: xbin, ybin, bin_info = compute_spanning_bins(pos_df.x.to_numpy(), bin_size=active_config.computation_config.grid_bin[0]) # bin_size mode print(bin_info) ## Binning with Fixed Bin Sizes: xbin, ybin, bin_info = compute_spanning_bins(pos_df.x.to_numpy(), num_bins=num_bins) # num_bins mode print(bin_info) """ assert (num_bins is None) or (bin_size is None), 'You cannot constrain both num_bins AND bin_size. Specify only one or the other.' assert (num_bins is not None) or (bin_size is not None), 'You must specify either the num_bins XOR the bin_size.' curr_variable_extents = (np.nanmin(variable_values), np.nanmax(variable_values)) if num_bins is not None: ## Binning with Fixed Number of Bins: mode = 'num_bins' xnum_bins = num_bins xbin, xstep = np.linspace(curr_variable_extents[0], curr_variable_extents[1], num=num_bins, retstep=True) # binning of x position elif bin_size is not None: ## Binning with Fixed Bin Sizes: mode = 'bin_size' xstep = bin_size xbin = np.arange(curr_variable_extents[0], (curr_variable_extents[1] + xstep), xstep, ) # binning of x position # the interval does not include this value, except in some cases where step is not an integer and floating point round-off affects the length of out. xnum_bins = len(xbin) else: raise ValueError return xbin, BinningInfo(curr_variable_extents, xstep, xnum_bins, np.arange(xnum_bins)) def compute_position_grid_size(any_1d_series, num_bins:tuple): """ Computes the required bin_sizes from the required num_bins (for each dimension independently) Usage: out_grid_bin_size, out_bins, out_bins_infos = compute_position_grid_size(curr_kdiba_pipeline.sess.position.x, curr_kdiba_pipeline.sess.position.y, num_bins=(64, 64)) active_grid_bin = tuple(out_grid_bin_size) print(f'active_grid_bin: {active_grid_bin}') # (3.776841861770752, 1.043326930905373) """ assert (len(any_1d_series)) == len(num_bins), f'(len(other_1d_series)) must be the same length as the num_bins tuple! But (len(other_1d_series)): {(len(any_1d_series))} and len(num_bins): {len(num_bins)}!' num_series = len(num_bins) out_bins = [] out_bins_info = [] out_bin_grid_step_size = np.zeros((num_series,)) for i in np.arange(num_series): xbins, xbin_info = compute_spanning_bins(any_1d_series[i], num_bins=num_bins[i]) out_bins.append(xbins) out_bins_info.append(xbin_info) out_bin_grid_step_size[i] = xbin_info.step return out_bin_grid_step_size, out_bins, out_bins_info def get_bin_centers(bin_edges): """ For a series of 1D bin edges given by bin_edges, returns the center of the bins. Output will have one less element than bin_edges. """ return (bin_edges[:-1] + np.diff(bin_edges) / 2.0) def get_bin_edges(bin_centers): """ TODO: CHECK For a series of 1D bin centers given by bin_centers, returns the edges of the bins. Reciprocal of get_bin_centers(bin_edges) """ half_bin_width = float((bin_centers[1] - bin_centers[0])) / 2.0 # TODO: assumes fixed bin width bin_starts = bin_centers - half_bin_width bin_ends = bin_centers + half_bin_width return interleave_elements(bin_starts, bin_ends) def build_pairwise_indicies(target_indicies, debug_print=False): """ Builds pairs of indicies from a simple list of indicies, for use in computing pairwise operations. Example: target_indicies = np.arange(5) # [0, 1, 2, 3, 4] out_pair_indicies = build_pairwise_indicies(target_indicies) > out_pair_indicies: [(0, 1), (1, 2), (2, 3), (3, 4)] Args: target_indicies ([type]): [description] debug_print (bool, optional): [description]. Defaults to False. Returns: [type]: [description] Usage: target_indicies = np.arange(5) out_pair_indicies = build_pairwise_indicies(target_indicies) # out_pair_indicies = list(out_pair_indicies) # print(f'out_pair_indicies: {list(out_pair_indicies)}') print(f'out_pair_indicies: {list(out_pair_indicies)}') for i, pair in enumerate(list(out_pair_indicies)): # first_item_lap_idx, next_item_lap_idx print(f'i: {i}, pair: {pair}') """ start_pairs = target_indicies[0:-1] # all but the last index end_pairs = target_indicies[1:] # from the second to the last index out_pair_indicies = list(zip(start_pairs, end_pairs)) # want to wrap in list so it isn't consumed if debug_print: print(f'target_indicies: {target_indicies}\nstart_pairs: {start_pairs}\nend_pairs: {end_pairs}') return out_pair_indicies def interleave_elements(start_points, end_points): """ Given two equal sized arrays, produces an output array of double that size that contains elements of start_points interleaved with elements of end_points Example: a_starts = ['A','B','C','D'] a_ends = ['a','b','c','d'] a_interleaved = interleave_elements(a_starts, a_ends) >> a_interleaved: ['A','a','B','b','C','c','D','d'] """ assert np.shape(start_points) == np.shape(end_points), f"start_points and end_points must be the same shape. np.shape(start_points): {np.shape(start_points)}, np.shape(end_points): {np.shape(end_points)}" start_points = np.atleast_2d(start_points) end_points = np.atleast_2d(end_points) all_points_shape = (np.shape(start_points)[0] * 2, np.shape(start_points)[1]) # it's double the length of the start_points all_points = np.zeros(all_points_shape) all_points[np.arange(0, all_points_shape[0], 2), :] = start_points # fill the even elements all_points[np.arange(1, all_points_shape[0], 2), :] = end_points # fill the odd elements assert np.shape(all_points)[0] == (np.shape(start_points)[0] * 2), f"newly created all_points is not of corrrect size! np.shape(all_points): {np.shape(all_points)}" return all_points def get_dict_subset(a_dict, included_keys=None, require_all_keys=False): """Gets a subset of a dictionary from a list of keys (included_keys) Args: a_dict ([type]): [description] included_keys ([type], optional): [description]. Defaults to None. require_all_keys: Bool, if True, requires all keys in included_keys to be in the dictionary (a_dict) Returns: [type]: [description]
import random import pygame import constants import utils from graphics_environment import Environment, Triggers import os, sys import time from graphics_fauna import Player, Npcs from dialogs import DialogFight, DialogText, DialogPlayerInventory, \ DialogInput, DialogPlayerInfo, DialogText, DialogGoodbye, \ DialogUseItemInInventory, DialogShowQuests from NEW_inventory import Conversation # ----------------------------------------------------------- # class Game # ----------------------------------------------------------- class Game: def __init__(self): user_info = utils.get_user_data() self.player_name = user_info["character_name"] self.zone_name = "" self.map_name = "" # ---- self.environment = None self.npcs = None self.player = None self.screen = None self.init_pygame() # ---- self.all_sprites = pygame.sprite.Group() self.keep_looping = True self.current_monster = None # ------------------------------------- # self.quest_histories = None def read_data(self): user_data = utils.get_user_data() self.zone_name = user_data["zone_name"] self.map_name = user_data["map_name"] if self._exception01() == True: self._change01() pygame.display.set_caption("Enter {} \| ({})".format(constants.TITLE, self.map_name)) # print(user_data) # ---- self.environment = Environment(self.zone_name, self.map_name) self.environment.read_data() self.npcs = Npcs(self.zone_name, self.map_name) self.npcs.read_data() self.player = Player(self.player_name, self.zone_name, self.map_name) self.player.read_data() # ---- if self.player.is_dead() == True: myresult = self.player.resurrect_player() # ---- if myresult == "y": utils.copy_original_player_files(self.player.profession, self.player.name) self.player.read_data() self.init_pygame() elif myresult == "n": self.keep_looping = False elif len(myresult) == 0: # esc was pressed; I'm going to take it that the player wishes to quit. self.keep_looping = False else: raise ValueError("Error!") def init_pygame(self): pygame.init() self.BG_COLOR = constants.BG_COLOR self.clock = pygame.time.Clock() pygame.display.set_caption("Enter {}".format(constants.TITLE)) self.screen = pygame.display.set_mode((constants.SCREEN_WIDTH, constants.SCREEN_HEIGHT)) self.font = pygame.font.Font(None, 40) # self.font = pygame.font.SysFont(constants.FONT_NAME, constants.FONT_SIZE) def player_died(self): s = "You're dead! Game over." mydialog = DialogText(s) mydialog.main() self.player.image = self.player.image_dead self.init_pygame() self.keep_looping = False def there_is_a_monster_on_this_tile(self, x, y): this_monster = self.npcs.get_npc_if_monster(x, y) if this_monster is None: return False return True # def there_is_an_angel_on_this_tile(self, x, y): # raise NotImplemented # this_angel = self.npcs.get_npc_if_angel(x, y) # if this_angel is None: return False # return True # def this_npc_is_a_questgiver(self, x, y): # current_npc = self.npcs.get_npc(self.player.x, self.player.y) # if current_npc is None: # s = "Error! There should be an NPC here, but there isn't." # raise ValueError(s) # if current_npc.is_a_questgiver(self.zone_name, self.map_name) == True: # return True # return False def there_is_an_npc_on_this_tile(self, x, y): this_npc = self.npcs.get_npc(x, y) if this_npc is None: return False return True def there_is_an_action_on_this_tile(self, x, y): print("Testing to see whether there is an action on tile x,y: ({},{})".format(x, y)) this_action = self.environment.actions.get_action(x, y) if this_action is None: return False return True def there_is_a_persistent_object_on_this_tile(self, x, y): if len(self.environment.persistents) == 0: return False this_persistent = self.environment.persistents.get_persistent_object(x, y) if this_persistent is None: return False return True def debugging_info(self): # ---- Debugging (after) (top)---- mylist = [] mylist.append("character_name (from player): {}".format(self.player.name)) mylist.append("x,y: ({},{})".format(self.player.x, self.player.y)) mylist.append("zone_name: {}".format(self.zone_name)) mylist.append("map_name: {}".format(self.map_name)) mylist.append("------------") mylist.append("From file:") mydict = utils.get_user_data() mylist.append("character_name: {}".format(mydict["character_name"])) mylist.append("zone_name: {}".format(mydict["zone_name"])) mylist.append("map_name: {}".format(mydict["map_name"])) mylist.append("------------") mylist.append("From Player:") mylist.append("x,y: ({},{})".format(self.player.x, self.player.y)) mylist.append("zone_name: {}".format(self.player.zone_name)) mylist.append("map_name: {}".format(self.player.map_name)) print("****************** Debugging (begin) ****************") print("-----------------------------------------------------------") print(mylist) print("---------------------------------------------------------") print("**************** Debugging (end) ****************") # ---- Debugging (after) (bottom) ---- def npc_encounter(self): # The user wants an encounter with the NPC they clicked on. current_npc = self.npcs.get_npc(self.player.x, self.player.y) if current_npc is None: s = "Error! There should be an NPC here, but there isn't." raise ValueError(s) print("This is the amount of gold the player has before he has the INTERACTION: {}".format(self.player.gold)) result, myinventory, player_gold = current_npc.have_interaction(self.environment.events, self.player) # ---- # The following line is executed when the user exists out of an NPC encounter. if result is None and myinventory is None: return False if result is None or myinventory is None: raise ValueError("Error") print("This is the amount of gold the player has BEFORE: {}".format(self.player.gold)) self.player.gold = player_gold print("This is the amount of gold the player has AFTER: {}".format(self.player.gold)) self.player.inventory = myinventory # ---- if result == "end game": self.player_died() elif result == "load next map": self.map_name = utils.get_next_map_name(self.map_name) utils.set_user_data(self.player.name, self.zone_name, self.map_name, self.player.profession) self.read_data() elif result in ["end conversation", "continue", "completed"]: # player goes on about their day pass else: s = "I don't understand this: {}".format(result) raise ValueError(s) def handle_events(self): # catch all events here for event in pygame.event.get(): if event.type == pygame.QUIT: self.keep_looping = False self.save_data() return True if event.type == pygame.KEYDOWN: print("self.player coords: x,y: {},{}: ".format(self.player.x, self.player.y)) if event.key == pygame.K_ESCAPE: self.keep_looping = False self.save_data() return True if self.player.is_dead() == True: return False if event.key == pygame.K_LEFT or event.key == pygame.K_a: if self.player.move(dx=-1, dy=0, obstacles=self.environment.obstacles) == True: # self.player.image = self.player.image_left self.player.direction = constants.LEFT self.player.my_update_image() elif event.key == pygame.K_RIGHT or event.key == pygame.K_d: if self.player.move(dx=1, obstacles=self.environment.obstacles) == True: self.player.direction = constants.RIGHT self.player.my_update_image() elif event.key == pygame.K_DOWN or event.key == pygame.K_s: if self.player.move(dy=1, obstacles=self.environment.obstacles) == True: self.player.direction = constants.DOWN self.player.my_update_image() elif event.key == pygame.K_UP or event.key == pygame.K_w: if self.player.move(dy=-1, obstacles=self.environment.obstacles) == True: self.player.direction = constants.UP self.player.my_update_image() # =============================================== elif event.key == pygame.K_h: if self.there_is_an_npc_on_this_tile(self.player.x, self.player.y) == True: message, self.player.inventory = self.npcs.do_something(self.player) if message == "load next map": print("current map name: {}".format(self.map_name)) self.map_name = utils.get_next_map_name(self.map_name) print("moving to map name: {}".format(self.map_name)) self.load_map(zone_name=self.zone_name, map_name=self.map_name, dialog_text="") elif self.there_is_an_action_on_this_tile(self.player.x, self.player.y) == True: # After an action is used, the tile is removed. print("There is an action on this tile.") if self.environment.actions.conditions_passed(self.player) == False: return False current_action = self.environment.actions.get_action(self.player.x, self.player.y) self.do_action(current_action) self.environment.actions.remove_tile(self.player.x, self.player.y) self.init_pygame() self.all_sprites = pygame.sprite.Group() return "" elif self.there_is_a_persistent_object_on_this_tile(self.player.x, self.player.y) == True: # After a persistent object is used, it remains. # (I could probably just make 'persistent' and 'temporary' values of a # field for an Actions object rather than make a separate class.) print("This is a persistent object on this tile.") # if self.environment.persistents.conditions_passed(self.player, self.environment.events) == False: # return False if self.environment.persistents.conditions_passed(self.player) == False: return False persistent_object = self.environment.persistents.get_persistent_object(self.player.x, self.player.y) if persistent_object is None: raise ValueError("Error") self.do_persistent(persistent_object) self.init_pygame() return "" # =============================================== elif event.key == pygame.K_i: # Inventory if len(self.player.inventory) == 0: raise ValueError("The player has lost their inventory!") mydialog = DialogPlayerInventory(self.player) mydialog.main() self.init_pygame() elif event.key == pygame.K_p: mydialog = DialogPlayerInfo(self.player) mydialog.main() self.init_pygame() # elif event.key == pygame.K_u: # mydialog = DialogUseItemInInventory(self.player) # mydialog.main() # self.init_pygame() elif event.key == pygame.K_q: mydialog = DialogShowQuests() mydialog.read_data() mydialog.main() else: print("I don't recognize this event.key in handle_events: {}".format(event.key)) # ------------------------------------------------------ self.check_for_trigger() def check_for_trigger(self): if self.environment.triggers is None: print("There is no TRIGGER on this tile.") return False current_trigger = self.environment.triggers.get_trigger(self.player.x, self.player.y) if not current_trigger is None: self.do_trigger(current_trigger) # self.npcs.debug_print() # raise NotImplemented def show_text(self, current_trigger): # print("debugging: in def load_map(self, current_trigger") filename = "{}.txt".format(current_trigger.data) filepath = os.path.join("data", "zones", self.zone_name, self.map_name, "texts", filename) mydict = utils.read_file(filepath)[0] for key, value in mydict.items(): # print(key, value) mytextdialog = DialogText(value) mytextdialog.main() self.init_pygame() def fire_attack(self, attack_strength): self.player.hit_points -= attack_strength if self.player.hit_points <= 0: self.player_died() def do_trigger(self, current_trigger): # print("There is a current trigger") # print("current_trigger.command = {}".format(current_trigger.command)) # current_trigger.debug_print() if current_trigger.command == "load_map": if current_trigger.conditions_fulfilled(self.zone_name, self.map_name, self.player.inventory) == False: return False # ---- self.save_data() # print("*********** dkdkkd") zone_name, map_name = current_trigger.parse_data() self.zone_name = zone_name self.map_name = map_name utils.set_user_data(self.player.name, self.zone_name, self.map_name, self.player.profession) # ---- self.read_data() elif current_trigger.command == "show_text": self.show_text(current_trigger) elif current_trigger.command == "fire_attack": if not utils.is_int(current_trigger.data): raise ValueError("Error") self.fire_attack(int(current_trigger.data)) elif current_trigger.command == "fire_attack_big": if not utils.is_int(current_trigger.data): raise ValueError("Error") self.fire_attack(int(current_trigger.data)) elif current_trigger.command == "change_npc_passive": # check to make sure that the name given in data is that of an npc # so check the npc_name_lookup.txt file in the zone directory. # if this goes through then turn that npc passive. if utils.npc_exists_in_zone(npc_name=current_trigger.data, zone_name=self.zone_name, map_name=self.map_name) == False: s = "Error! That npc ({}) does not exist in this zone ({})." s = s.format(current_trigger.data.replace(".txt", ""), self.zone_name) raise ValueError(s) raise NotImplemented elif current_trigger.command == "change_npc_agro": # check to make sure that the name given in data is that of an npc # so check the npc_name_lookup.txt file in the zone directory. # if this goes through then turn that npc agro. if utils.npc_exists_in_zone(npc_name=current_trigger.data, zone_name=self.zone_name, map_name=self.map_name) == False: s = "Error! That npc ({}) does not exist in this zone ({}).".format(current_trigger.data, self.zone_name) raise ValueError(s) the_npc = self.npcs.get_npc_by_name(current_trigger.data) if the_npc is None: s = "This name ({}) is not the name of a current npc.".format(current_trigger.data.replace(" ", "_")) print(s) return False # raise ValueError(s) the_npc.agro_level = "agro" else: current_trigger.debug_print() s = "I couldn't find that: {}".format(current_trigger.command) raise ValueError(s) return True def load_map(self, zone_name, map_name, dialog_text=""): """This loads both a zone and a map.""" if not zone_name in constants.ZONE_NAMES: s
<filename>leiaapi/generated/api/application_admin_api.py<gh_stars>0 # coding: utf-8 """ LEIA RESTful API for AI Leia API # noqa: E501 OpenAPI spec version: 1.0.0 Contact: <EMAIL> Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import import re # noqa: F401 # python 2 and python 3 compatibility library import six from leiaapi.generated.api_client import ApiClient class ApplicationAdminApi(object): """NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. Ref: https://github.com/swagger-api/swagger-codegen """ def __init__(self, api_client=None): if api_client is None: api_client = ApiClient() self.api_client = api_client def admin_create_application(self, token, kwargs): # noqa: E501 """Adds a new application to the system (admin only) # noqa: E501 Adds a new application to the system. This method is only accessible to admins. An API key will be generated for the new application when calling this method. Note or store it carefully, it will not be recoverable after this call. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.admin_create_application(token, async_req=True) >>> result = thread.get() :param async_req bool :param str token: The login token obtained via GET /login/{api_key} (required) :param Application body: :return: Application If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.admin_create_application_with_http_info(token, kwargs) # noqa: E501 else: (data) = self.admin_create_application_with_http_info(token, kwargs) # noqa: E501 return data def admin_create_application_with_http_info(self, token, kwargs): # noqa: E501 """Adds a new application to the system (admin only) # noqa: E501 Adds a new application to the system. This method is only accessible to admins. An API key will be generated for the new application when calling this method. Note or store it carefully, it will not be recoverable after this call. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.admin_create_application_with_http_info(token, async_req=True) >>> result = thread.get() :param async_req bool :param str token: The login token obtained via GET /login/{api_key} (required) :param Application body: :return: Application If the method is called asynchronously, returns the request thread. """ all_params = ['token', 'body'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method admin_create_application" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'token' is set if ('token' not in params or params['token'] is None): raise ValueError("Missing the required parameter `token` when calling `admin_create_application`") # noqa: E501 collection_formats = {} path_params = {} query_params = [] header_params = {} if 'token' in params: header_params['token'] = params['token'] # noqa: E501 form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/admin/application', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='Application', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def admin_delete_always_on_schedule(self, token, application_id, always_on_schedule_id, kwargs): # noqa: E501 """Removes a schedule from an application # noqa: E501 Removes a schedule from an application # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.admin_delete_always_on_schedule(token, application_id, always_on_schedule_id, async_req=True) >>> result = thread.get() :param async_req bool :param str token: The login token obtained via GET /login/{api_key} (required) :param str application_id: The id of the application (required) :param str always_on_schedule_id: The id of the schedule to delete (required) :return: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.admin_delete_always_on_schedule_with_http_info(token, application_id, always_on_schedule_id, kwargs) # noqa: E501 else: (data) = self.admin_delete_always_on_schedule_with_http_info(token, application_id, always_on_schedule_id, kwargs) # noqa: E501 return data def admin_delete_always_on_schedule_with_http_info(self, token, application_id, always_on_schedule_id, kwargs): # noqa: E501 """Removes a schedule from an application # noqa: E501 Removes a schedule from an application # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.admin_delete_always_on_schedule_with_http_info(token, application_id, always_on_schedule_id, async_req=True) >>> result = thread.get() :param async_req bool :param str token: The login token obtained via GET /login/{api_key} (required) :param str application_id: The id of the application (required) :param str always_on_schedule_id: The id of the schedule to delete (required) :return: None If the method is called asynchronously, returns the request thread. """ all_params = ['token', 'application_id', 'always_on_schedule_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method admin_delete_always_on_schedule" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'token' is set if ('token' not in params or params['token'] is None): raise ValueError("Missing the required parameter `token` when calling `admin_delete_always_on_schedule`") # noqa: E501 # verify the required parameter 'application_id' is set if ('application_id' not in params or params['application_id'] is None): raise ValueError("Missing the required parameter `application_id` when calling `admin_delete_always_on_schedule`") # noqa: E501 # verify the required parameter 'always_on_schedule_id' is set if ('always_on_schedule_id' not in params or params['always_on_schedule_id'] is None): raise ValueError("Missing the required parameter `always_on_schedule_id` when calling `admin_delete_always_on_schedule`") # noqa: E501 collection_formats = {} path_params = {} if 'application_id' in params: path_params['application_id'] = params['application_id'] # noqa: E501 if 'always_on_schedule_id' in params: path_params['always_on_schedule_id'] = params['always_on_schedule_id'] # noqa: E501 query_params = [] header_params = {} if 'token' in params: header_params['token'] = params['token'] # noqa: E501 form_params = [] local_var_files = {} body_params = None # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/admin/application/{application_id}/always_on_schedules/{always_on_schedule_id}', 'DELETE', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def admin_delete_application(self, token, application_id, kwargs): # noqa: E501 """Deletes an application (admin only) # noqa: E501 Retrieves a new application from the system. This method is only accessible to admins # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.admin_delete_application(token, application_id, async_req=True) >>> result = thread.get() :param async_req bool :param str token: The login token obtained via GET /login/{api_key} (required) :param str application_id: The id of the application to delete (required) :return: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.admin_delete_application_with_http_info(token, application_id, kwargs) # noqa: E501 else: (data) = self.admin_delete_application_with_http_info(token, application_id, kwargs) # noqa: E501 return data def admin_delete_application_with_http_info(self, token, application_id, kwargs): # noqa: E501 """Deletes an application (admin only) # noqa: E501 Retrieves a new application from the system. This method is only accessible to admins # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.admin_delete_application_with_http_info(token, application_id, async_req=True) >>> result = thread.get() :param async_req bool :param str token: The login token obtained via GET /login/{api_key} (required) :param str application_id: The id of the application to delete (required) :return: None If the method is called asynchronously, returns the request thread. """ all_params = ['token', 'application_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method admin_delete_application" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'token' is set if ('token' not in params or params['token'] is None): raise ValueError("Missing the required parameter `token` when calling `admin_delete_application`") # noqa: E501 # verify the required parameter 'application_id' is set if ('application_id' not in params or params['application_id'] is None): raise ValueError("Missing the required parameter `application_id` when calling `admin_delete_application`") # noqa: E501 collection_formats = {} path_params = {} if 'application_id' in params: path_params['application_id']
"""Library implementing convolutional neural networks. Authors * <NAME> 2020 * <NAME> 2020 * <NAME> 2021 * <NAME> 2021 """ import math import torch import logging import numpy as np import torch.nn as nn import torch.nn.functional as F from typing import Tuple logger = logging.getLogger(__name__) class SincConv(nn.Module): """This function implements SincConv (SincNet). <NAME>, <NAME>, "Speaker Recognition from raw waveform with SincNet", in Proc. of SLT 2018 (https://arxiv.org/abs/1808.00158) Arguments --------- input_shape : tuple The shape of the input. Alternatively use ``in_channels``. in_channels : int The number of input channels. Alternatively use ``input_shape``. out_channels : int It is the number of output channels. kernel_size: int Kernel size of the convolutional filters. stride : int Stride factor of the convolutional filters. When the stride factor > 1, a decimation in time is performed. dilation : int Dilation factor of the convolutional filters. padding : str (same, valid, causal). If "valid", no padding is performed. If "same" and stride is 1, output shape is the same as the input shape. "causal" results in causal (dilated) convolutions. padding_mode : str This flag specifies the type of padding. See torch.nn documentation for more information. groups : int This option specifies the convolutional groups. See torch.nn documentation for more information. bias : bool If True, the additive bias b is adopted. sample_rate : int, Sampling rate of the input signals. It is only used for sinc_conv. min_low_hz : float Lowest possible frequency (in Hz) for a filter. It is only used for sinc_conv. min_low_hz : float Lowest possible value (in Hz) for a filter bandwidth. Example ------- >>> inp_tensor = torch.rand([10, 16000]) >>> conv = SincConv(input_shape=inp_tensor.shape, out_channels=25, kernel_size=11) >>> out_tensor = conv(inp_tensor) >>> out_tensor.shape torch.Size([10, 16000, 25]) """ def __init__( self, out_channels, kernel_size, input_shape=None, in_channels=None, stride=1, dilation=1, padding="same", padding_mode="reflect", sample_rate=16000, min_low_hz=50, min_band_hz=50, ): super().__init__() self.out_channels = out_channels self.kernel_size = kernel_size self.stride = stride self.dilation = dilation self.padding = padding self.padding_mode = padding_mode self.sample_rate = sample_rate self.min_low_hz = min_low_hz self.min_band_hz = min_band_hz # input shape inference if input_shape is None and in_channels is None: raise ValueError("Must provide one of input_shape or in_channels") if in_channels is None: in_channels = self._check_input_shape(input_shape) # Initialize Sinc filters self._init_sinc_conv() def forward(self, x): """Returns the output of the convolution. Arguments --------- x : torch.Tensor (batch, time, channel) input to convolve. 2d or 4d tensors are expected. """ x = x.transpose(1, -1) self.device = x.device unsqueeze = x.ndim == 2 if unsqueeze: x = x.unsqueeze(1) if self.padding == "same": x = self._manage_padding( x, self.kernel_size, self.dilation, self.stride ) elif self.padding == "causal": num_pad = (self.kernel_size - 1) * self.dilation x = F.pad(x, (num_pad, 0)) elif self.padding == "valid": pass else: raise ValueError( "Padding must be 'same', 'valid' or 'causal'. Got %s." % (self.padding) ) sinc_filters = self._get_sinc_filters() wx = F.conv1d( x, sinc_filters, stride=self.stride, padding=0, dilation=self.dilation, ) if unsqueeze: wx = wx.squeeze(1) wx = wx.transpose(1, -1) return wx def _check_input_shape(self, shape): """Checks the input shape and returns the number of input channels. """ if len(shape) == 2: in_channels = 1 elif len(shape) == 3: in_channels = 1 else: raise ValueError( "sincconv expects 2d or 3d inputs. Got " + str(len(shape)) ) # Kernel size must be odd if self.kernel_size % 2 == 0: raise ValueError( "The field kernel size must be an odd number. Got %s." % (self.kernel_size) ) return in_channels def _get_sinc_filters(self,): """This functions creates the sinc-filters to used for sinc-conv. """ # Computing the low frequencies of the filters low = self.min_low_hz + torch.abs(self.low_hz_) # Setting minimum band and minimum freq high = torch.clamp( low + self.min_band_hz + torch.abs(self.band_hz_), self.min_low_hz, self.sample_rate / 2, ) band = (high - low)[:, 0] # Passing from n_ to the corresponding f_times_t domain self.n_ = self.n_.to(self.device) self.window_ = self.window_.to(self.device) f_times_t_low = torch.matmul(low, self.n_) f_times_t_high = torch.matmul(high, self.n_) # Left part of the filters. band_pass_left = ( (torch.sin(f_times_t_high) - torch.sin(f_times_t_low)) / (self.n_ / 2) ) * self.window_ # Central element of the filter band_pass_center = 2 * band.view(-1, 1) # Right part of the filter (sinc filters are symmetric) band_pass_right = torch.flip(band_pass_left, dims=[1]) # Combining left, central, and right part of the filter band_pass = torch.cat( [band_pass_left, band_pass_center, band_pass_right], dim=1 ) # Amplitude normalization band_pass = band_pass / (2 * band[:, None]) # Setting up the filter coefficients filters = band_pass.view(self.out_channels, 1, self.kernel_size) return filters def _init_sinc_conv(self): """Initializes the parameters of the sinc_conv layer.""" # Initialize filterbanks such that they are equally spaced in Mel scale high_hz = self.sample_rate / 2 - (self.min_low_hz + self.min_band_hz) mel = torch.linspace( self._to_mel(self.min_low_hz), self._to_mel(high_hz), self.out_channels + 1, ) hz = self._to_hz(mel) # Filter lower frequency and bands self.low_hz_ = hz[:-1].unsqueeze(1) self.band_hz_ = (hz[1:] - hz[:-1]).unsqueeze(1) # Maiking freq and bands learnable self.low_hz_ = nn.Parameter(self.low_hz_) self.band_hz_ = nn.Parameter(self.band_hz_) # Hamming window n_lin = torch.linspace( 0, (self.kernel_size / 2) - 1, steps=int((self.kernel_size / 2)) ) self.window_ = 0.54 - 0.46 * torch.cos( 2 * math.pi * n_lin / self.kernel_size ) # Time axis (only half is needed due to symmetry) n = (self.kernel_size - 1) / 2.0 self.n_ = ( 2 * math.pi * torch.arange(-n, 0).view(1, -1) / self.sample_rate ) def _to_mel(self, hz): """Converts frequency in Hz to the mel scale. """ return 2595 * np.log10(1 + hz / 700) def _to_hz(self, mel): """Converts frequency in the mel scale to Hz. """ return 700 * (10 ** (mel / 2595) - 1) def _manage_padding( self, x, kernel_size: int, dilation: int, stride: int, ): """This function performs zero-padding on the time axis such that their lengths is unchanged after the convolution. Arguments --------- x : torch.Tensor Input tensor. kernel_size : int Size of kernel. dilation : int Dilation used. stride : int Stride. """ # Detecting input shape L_in = x.shape[-1] # Time padding padding = get_padding_elem(L_in, stride, kernel_size, dilation) # Applying padding x = F.pad(x, padding, mode=self.padding_mode) return x class Conv1d(nn.Module): """This function implements 1d convolution. Arguments --------- out_channels : int It is the number of output channels. kernel_size : int Kernel size of the convolutional filters. input_shape : tuple The shape of the input. Alternatively use ``in_channels``. in_channels : int The number of input channels. Alternatively use ``input_shape``. stride : int Stride factor of the convolutional filters. When the stride factor > 1, a decimation in time is performed. dilation : int Dilation factor of the convolutional filters. padding : str (same, valid, causal). If "valid", no padding is performed. If "same" and stride is 1, output shape is the same as the input shape. "causal" results in causal (dilated) convolutions. groups: int Number of blocked connections from input channels to output channels. padding_mode : str This flag specifies the type of padding. See torch.nn documentation for more information. skip_transpose : bool If False, uses batch x time x channel convention of speechbrain. If True, uses batch x channel x time convention. Example ------- >>> inp_tensor = torch.rand([10, 40, 16]) >>> cnn_1d = Conv1d( ... input_shape=inp_tensor.shape, out_channels=8, kernel_size=5 ... ) >>> out_tensor = cnn_1d(inp_tensor) >>> out_tensor.shape torch.Size([10, 40, 8]) """ def __init__( self, out_channels, kernel_size, input_shape=None, in_channels=None, stride=1, dilation=1, padding="same", groups=1, bias=True, padding_mode="reflect", skip_transpose=False, ): super().__init__() self.kernel_size = kernel_size self.stride = stride self.dilation = dilation self.padding = padding self.padding_mode = padding_mode self.unsqueeze = False self.skip_transpose = skip_transpose if input_shape is None and in_channels is None: raise ValueError("Must provide one of input_shape or in_channels") if in_channels is None: in_channels = self._check_input_shape(input_shape) self.conv = nn.Conv1d( in_channels, out_channels, self.kernel_size, stride=self.stride, dilation=self.dilation, padding=0, groups=groups, bias=bias, ) def forward(self, x): """Returns the output of the convolution. Arguments --------- x : torch.Tensor (batch, time, channel) input to convolve. 2d or 4d tensors are expected. """ if not self.skip_transpose: x = x.transpose(1, -1) if self.unsqueeze: x = x.unsqueeze(1) if self.padding == "same": x = self._manage_padding( x, self.kernel_size, self.dilation, self.stride ) elif self.padding == "causal": num_pad = (self.kernel_size - 1) * self.dilation x = F.pad(x, (num_pad, 0)) elif self.padding == "valid": pass else: raise ValueError( "Padding
#!/usr/bin/env python # coding: utf-8 # In[1]: import torch import numpy as np import matplotlib.pyplot as plt import csv from PIL import Image import matplotlib as mpl from tqdm import tqdm from sklearn.manifold import TSNE import umap from sklearn.metrics import silhouette_score , silhouette_samples from sklearn.discriminant_analysis import LinearDiscriminantAnalysis # # Visualizing the Disregarding classes # # ### Load data # In[2]: def access_data(letter,shot): feat = torch.load('features'+letter+str(shot),map_location=torch.device('cpu')) classifier= torch.load('classifier'+letter,map_location=torch.device('cpu')) accuracy = torch.load('complete_class_accuracy'+letter+str(shot)+'shots',map_location=torch.device('cpu')) idx = torch.load('complete_class_accuracy'+letter+'idx'+str(shot)+'shots',map_location=torch.device('cpu')) return feat,classifier,accuracy,idx # In[3]: shot=5 letter='A' feat,classifier,acc,idx = access_data(letter,shot) print(acc.shape) print(feat.shape) print(classifier.shape) print(idx.shape) # In[4]: shot=5 letter='B' featB,classifierB,accB,idxB = access_data(letter,shot) print(accB.shape) print(featB.shape) print(classifierB.shape) print(idxB.shape) # In[5]: base_mean = feat[:64].mean(-2) base_meanB = featB[:64].mean(-2) print(base_mean.shape) # In[6]: def sphering(features): return features / torch.norm(features, p = 2, dim = 2, keepdim = True) def centering(train_features, features): return features - train_features.reshape(-1, train_features.shape[2]).mean(dim = 0).unsqueeze(0).unsqueeze(0) feat_processed = sphering(centering(sphering(feat)[:64],sphering(feat) )) # In[7]: def proj_class(i,test_features,letter='A'): if letter=='A': #one projection per 64 clesses on miniimagenet w=base_mean[i] #select weights of the i-th class else: w=base_meanB[i] proj = torch.matmul(test_features,w)/ torch.norm(w)*2 #get coef of projection and normalize try: projection_ortho = proj.unsqueeze(-1).repeat(1,640) except: projection_ortho = proj.unsqueeze(-1).repeat(1,1,640) projection_ortho = projection_ortho w #vector of projection along w projection_ortho = test_features - projection_ortho #projection on the orthogonal space of w return projection_ortho # In[8]: filenametrain = '/home/r21lafar/Documents/dataset/miniimagenetimages/train.csv' filenametest = '/home/r21lafar/Documents/dataset/miniimagenetimages/test.csv' directory = '/home/r21lafar/Documents/dataset/miniimagenetimages/images/' def opencsv(filename): file = open(filename) csvreader = csv.reader(file) header = [] header = next(csvreader) print(header) rowstrain = [] rows = [] for row in csvreader: rows.append(row) return rows test = opencsv(filenametest) train = opencsv(filenametrain) def openimg(cl,title): if cl<64: src=train if cl>=80: src=test cl-=80 if type(cl)==int: plt.figure(figsize=(5,5)) idx=int((cl+0.5)600)+np.random.randint(-100,100) filename=src[idx][0] im = Image.open(directory +filename) plt.title(title) plt.imshow(np.array(im)) # In[9]: def distance_from_base(proj,run,plot=False,letter='A'): if letter=='A': fs_run = feat[acc[0,0,run].long()] else: fs_run = featB[acc[0,0,run].long()] if proj==-1 and run ==-1: if letter=='A': proto_fs = feat[-20:].mean(1) else: proto_fs = featB[-20:].mean(1) else: fs_run = torch.gather(fs_run,dim=1,index=idx[0,run].unsqueeze(-1).repeat(1,1,640).long()) proto_fs = fs_run[:,:shot].mean(1) if proj!=0: proto_fs=proj_class(proj-1,proto_fs,letter=letter) if letter=='A': D = torch.cdist(proto_fs,base_mean) else: D = torch.cdist(proto_fs,base_meanB) if plot: plt.figure() plt.imshow(D.detach().numpy(),aspect='auto') plt.colorbar() plt.title('distance between FS class mean and base class '+letter+' mean \n (whole base dataset) projection ' +str(proj) + ' (0 is no projection)') plt.xlabel('64 base class mean') plt.ylabel('FS prototype of class') return D # ## Create FS scenarii or runs # ### 2 ways # In[10]: n_runs, batch_few_shot_runs = 20,10 n_ways=2 def ncm(train_features, features, run_classes, run_indices, n_shots,i_proj): with torch.no_grad(): dim = features.shape[2] targets = torch.arange(n_ways).unsqueeze(1).unsqueeze(0) #features = preprocess(train_features, features) scores = [] score=0 for batch_idx in range(n_runs // batch_few_shot_runs): runs = generate_runs(features, run_classes, run_indices, batch_idx) means = torch.mean(runs[:,:,:n_shots], dim = 2) var_intra = runs[:,:,:n_shots].var(2).mean(-1) var_inter = runs[:,:,:n_shots].mean(2).var(1).mean(-1).unsqueeze(1) var = torch.cat((var_intra,var_inter),dim=1) distances = torch.norm(runs[:,:,n_shots:].reshape(batch_few_shot_runs, n_ways, 1, -1, dim) - means.reshape(batch_few_shot_runs, 1, n_ways, 1, dim), dim = 4, p = 2) winners = torch.min(distances, dim = 2)[1] accuracy = (winners == targets) if batch_idx==0: full_accuracy=accuracy full_mean=means full_var = var else: full_accuracy=torch.cat((full_accuracy,accuracy),dim=0) full_mean=torch.cat((full_mean,means),dim=0) full_var=torch.cat((full_var,var),dim=0) return full_accuracy,full_mean,full_var def generate_runs(data, run_classes, run_indices, batch_idx): n_runs, n_ways, n_samples = run_classes.shape[0], run_classes.shape[1], run_indices.shape[2] run_classes = run_classes[batch_idx batch_few_shot_runs : (batch_idx + 1) * batch_few_shot_runs] run_indices = run_indices[batch_idx * batch_few_shot_runs : (batch_idx + 1) * batch_few_shot_runs] run_classes = run_classes.unsqueeze(2).unsqueeze(3).repeat(1,1,data.shape[1], data.shape[2]) run_indices = run_indices.unsqueeze(3).repeat(1, 1, 1, data.shape[2]) datas = data.unsqueeze(0).repeat(batch_few_shot_runs, 1, 1, 1) cclasses = torch.gather(datas, 1, run_classes.to(torch.int64)) res = torch.gather(cclasses, 2, run_indices) return res def define_runs(n_ways, n_shots, n_queries, num_classes, elements_per_class): shuffle_classes = torch.LongTensor(np.arange(num_classes)) run_classes = torch.LongTensor(n_runs, n_ways) run_indices = torch.LongTensor(n_runs, n_ways, n_shots + n_queries) for i in range(n_runs): run_classes[i] = torch.randperm(num_classes)[:n_ways] for j in range(n_ways): run_indices[i,j] = torch.randperm(elements_per_class[run_classes[i, j]])[:n_shots + n_queries] return run_classes, run_indices run_classes, run_indices = define_runs(n_ways, 5, 500,20, [600 for i in range(20)]) # In[11]: A,_,full_var = ncm(feat[:64], feat[-20:], run_classes, run_indices, 5,0) B,_,full_var = ncm(featB[:64], featB[-20:],run_classes, run_indices, 5,0) plt.plot(A.float().mean(-1).mean(-1),label='backbone A') plt.plot(B.float().mean(-1).mean(-1),label='backbone B') plt.legend() plt.xlabel('run') plt.ylabel('accuracy') plt.title('no projection') # In[12]: for i in tqdm(range(65)): if i!=0: feature=proj_class(i-1,feat,'A') featureB=proj_class(i-1,featB,'B') else: feature =feat featureB =featB A,meanA,varA = ncm(feature[:64], feature[-20:], run_classes, run_indices, 5,0) B,meanB,varB = ncm(featureB[:64], featureB[-20:],run_classes, run_indices, 5,0) if i==0: fullA = A.unsqueeze(0) fullB = B.unsqueeze(0) fullmeanA = meanA.unsqueeze(0) fullmeanB = meanB.unsqueeze(0) fullvarA = varA.unsqueeze(0) fullvarB = varB.unsqueeze(0) else: fullA = torch.cat((fullA, A.unsqueeze(0)) ,dim = 0) fullB = torch.cat((fullB, B.unsqueeze(0)) ,dim = 0) fullmeanA = torch.cat((fullmeanA, meanA.unsqueeze(0)) ,dim = 0) fullmeanB = torch.cat((fullmeanB, meanB.unsqueeze(0)) ,dim = 0) fullvarA = torch.cat((fullvarA, varA.unsqueeze(0)) ,dim = 0) fullvarB = torch.cat((fullvarB, varB.unsqueeze(0)) ,dim = 0) # In[12]: def what_proj(run): return fullA[:,run].float().mean(-1).mean(-1).argsort()-1 # In[13]: fullA[0,2,0].float().mean(-1) # In[14]: run=0 fullvarA[0,run,:2].mean(-1)-fullvarA[0,run,2] # In[15]: for prj in [0,1,2,3]: plt.plot(fullvarA[prj,:,:2].mean(-1)-fullvarA[prj,:,2],fullA[prj,:,:].float().mean(-1).mean(-1),'.',label='projection '+ str(prj)) plt.xlabel('intraclass var -(minus)- interclass var') plt.ylabel('accuracy of run') plt.legend() plt.title('20 runs') # In[16]: best_boost =fullA.float().mean(-1).mean(-1).max(0)[0] - fullA[0,:,:].float().mean(-1).mean(-1) worst_boost =fullA.float().mean(-1).mean(-1).min(0)[0] - fullA[0,:,:].float().mean(-1).mean(-1) # In[17]: best_boost_id = fullA[:,:,:].float().mean(-1).mean(-1).max(0)[1] worst_boost_id = fullA[:,:,:].float().mean(-1).mean(-1).min(0)[1] # In[18]: intrater = fullvarA[:,:,:2].mean(-1)-fullvarA[:,:,2] intrater_min = intrater.min(0)[1] intrater_max = intrater.max(0)[1] # In[19]: boost = torch.zeros(intrater_min.shape) for i in range(intrater_min.shape[0]): boost[i] = fullA[intrater_min[i],i].float().mean(-1).mean(-1)-fullA[0,i].float().mean(-1).mean(-1) # In[20]: boost_max = torch.zeros(intrater_min.shape) for i in range(intrater_min.shape[0]): boost_max[i] = fullA[intrater_max[i],i].float().mean(-1).mean(-1)-fullA[0,i].float().mean(-1).mean(-1) # In[21]: fullA.shape # In[ ]: # In[22]: plt.hlines(y=0 ,xmin=0,xmax = 20) plt.plot(boost,'.',label='proj with min intra - inter') plt.plot(boost_max,'.',label='proj with max intra - inter') plt.plot(best_boost,'.',label='best boost') plt.xlabel('run') plt.ylabel('boost') plt.legend() # In[23]: intrater_best_boost = torch.zeros(intrater_min.shape) for i in range(intrater_min.shape[0]): intrater_best_boost[i] = intrater[best_boost_id[i],i] # In[24]: plt.plot(intrater_best_boost,'.', label = 'best boost') plt.plot(intrater.mean(0),'.', label = 'mean intra -inter') plt.plot(intrater.min(0)[0],'.', label = 'minimum intra -inter') plt.plot(intrater.max(0)[0],'.', label = 'maximum intra -inter') plt.ylabel('intra-class - interclass variance') plt.xlabel('run') plt.legend() # In[25]: intrater.min(dim=0)[0] # In[ ]: # In[26]: get_ipython().run_line_magic('matplotlib', 'inline') run = 12 nb_sample=30 mk_size=4 plt.figure() plt.plot(fullA[:,run].float().mean(-1).mean(-1)) plt.figure() plt.plot(fullvarA[:,run].float().mean(-1).mean(-1)) FULLumap = torch.cat((base_mean,fullmeanA[0,run],feat[80+run_classes[run],:nb_sample].reshape(n_waysnb_sample,640) )) umapA=umap.UMAP().fit_transform(FULLumap) plt.figure() plt.plot(umapA[:64,0],umapA[:64,1],'o',label='base', c='b') plt.plot(umapA[64,0],umapA[64,1],'',label='proto 0', c='purple',markersize=20) plt.plot(umapA[65,0],umapA[65,1],'',label='proto 1', c='k',markersize=20) plt.plot(umapA[69:69+nb_sample,0],umapA[64+5:69+nb_sample,1],'.',label='samples 0',markersize=mk_size, c='purple') plt.plot(umapA[64+5+nb_sample:69+nb_sample2,0],umapA[64+5+nb_sample:69+nb_sample2,1],'.',label='samples 1',markersize=mk_size, c='k') plt.legend() boost = fullA[:,run].float().mean(-1).mean(-1)-fullA[0,run].float().mean(-1).mean(-1) example = what_proj(run) signboost = boost>=0. label = [str(i) for i in range(65)] couleur = ['red','green'] for i in range(len(label)): plt.annotate(label[i], (umapA[example[i],0], umapA[example[i],1]), color = couleur[signboost[example[i]]1]) # In[27]: get_ipython().run_line_magic('matplotlib', 'inline') run = 0 plt.plot(fullA[:,run].float().mean(-1).mean(-1),label='backbone A') plt.plot(fullB[:,run].float().mean(-1).mean(-1),label='backbone B') plt.legend() plt.xlabel('projection') plt.ylabel('accuracy') print(fullA[:,run].shape) # In[28]: feat.shape # In[29]: nb_samples = 100 feat_sil = feat[:,:nb_samples].reshape(-1,640) # In[30]: labels = torch.arange(0,100).unsqueeze(1).repeat(1,nb_samples).reshape(-1) # In[31]: sil = silhouette_samples(feat_sil,labels) # In[32]: sil_r = sil.reshape(100,nb_samples) # In[33]: plt.plot(sil_r.mean(1),'.') plt.xlabel('class') plt.ylabel('silhouette') plt.vlines(x=64,ymin=sil_r.mean(1).min(),ymax = sil_r.mean(1).max()) plt.vlines(x=64+20,ymin=sil_r.mean(1).min(),ymax = sil_r.mean(1).max()) # In[34]: feat.shape # In[35]: plt.plot(feat.var(1).mean(1),'.',label='intra class variance') plt.hlines(y=feat.mean(1).var(0).mean(),xmin=0,xmax=100,label='interclass variance') plt.legend() plt.xlabel('class') plt.ylabel('mean variance over features') plt.title('whole dataset') # ## Project on aligning vector # In[11]: def proj_vec(v_proj): proj = torch.matmul(features,v_proj)/ torch.norm(v_proj)2 #get coef of projection and normalize return proj # In[12]: run_classes, run_indices = define_runs(n_ways, 5, 500,20, [600 for i in range(20)]) A,full_meanA,full_varA = ncm(feat[:64], feat[-20:], run_classes, run_indices, 5,0) B,full_meanB,full_var = ncm(featB[:64], featB[-20:],run_classes, run_indices, 5,0) # In[13]: def ncm_proj(train_features, features, run_classes, run_indices, n_shots): with torch.no_grad(): dim = features.shape[2] targets = torch.arange(n_ways).unsqueeze(1).unsqueeze(0) #features = preprocess(train_features, features) scores = [] score=0 for batch_idx in range(n_runs // batch_few_shot_runs): runs = generate_runs(features, run_classes, run_indices, batch_idx) means = torch.mean(runs[:,:,:n_shots], dim = 2) v_diff = (means[:,0]-means[:,1]) #v_diff = torch.randn(means[:,0].shape) var_intra = runs[:,:,:n_shots].var(2).mean(-1) var_inter = runs[:,:,:n_shots].mean(2).var(1).mean(-1).unsqueeze(1) var = torch.cat((var_intra,var_inter),dim=1) proj_means =torch.zeros(means[:,:,0].shape) proj_runs =torch.zeros(runs[:,:,:,0].shape) for i in range(batch_few_shot_runs): proj_runs[i] = torch.matmul(v_diff[i], torch.swapaxes(runs[i],-1,-2)) proj_means[i] = torch.matmul(v_diff[i], torch.swapaxes(means[i],-1,-2)) distances = torch.norm(proj_runs[:,:,n_shots:].reshape(batch_few_shot_runs, n_ways, 1, -1, 1) - proj_means.reshape(batch_few_shot_runs, 1, n_ways, 1, 1), dim = 4, p = 2) winners = torch.min(distances, dim = 2)[1] accuracy = (winners == targets) if batch_idx==0: full_accuracy=accuracy full_mean=means full_var = var else: full_accuracy=torch.cat((full_accuracy,accuracy),dim=0) full_mean=torch.cat((full_mean,means),dim=0) full_var=torch.cat((full_var,var),dim=0) return full_accuracy,full_mean,full_var # In[ ]: n_runs = 1000 run_classes, run_indices = define_runs(n_ways, 5, 500,20, [600 for i in range(20)]) n_shots=5 # In[105]: a,b,c = ncm_proj(feat[:64], feat[-20:], run_classes, run_indices, n_shots) print(a.float().mean()) # In[104]: a,b,c = ncm(feat[:64], feat[-20:], run_classes, run_indices, n_shots,0) print(a.float().mean()) # Avec et sans projection sur l'axe reliant les deux protopypes ou templates. La performance reste la même # ## test suppression de classe orthogonale à v_diff # In[101]: def ncm_del_otho_vdif(train_features, features, run_classes, run_indices, n_shots): with torch.no_grad(): dim = features.shape[2] targets = torch.arange(n_ways).unsqueeze(1).unsqueeze(0) #features = preprocess(train_features, features) scores = [] score=0 for batch_idx in tqdm(range(n_runs // batch_few_shot_runs)): runs = generate_runs(features, run_classes, run_indices, batch_idx) for i in range(3): runs,means = remove_the_class(runs) var_intra = runs[:,:,:n_shots].var(2).mean(-1) var_inter = runs[:,:,:n_shots].mean(2).var(1).mean(-1).unsqueeze(1) var = torch.cat((var_intra,var_inter),dim=1) distances = torch.norm(runs[:,:,n_shots:].reshape(batch_few_shot_runs, n_ways, 1, -1, dim) - means.reshape(batch_few_shot_runs, 1, n_ways, 1, dim), dim = 4, p = 2) winners = torch.min(distances, dim = 2)[1] accuracy = (winners == targets) if batch_idx==0: full_accuracy=accuracy full_mean=means full_var = var else: full_accuracy=torch.cat((full_accuracy,accuracy),dim=0) full_mean=torch.cat((full_mean,means),dim=0) full_var=torch.cat((full_var,var),dim=0) return full_accuracy,full_mean,full_var def remove_the_class(runs): means = torch.mean(runs[:,:,:n_shots], dim = 2) v_diff = (means[:,0]-means[:,1]) #axis between proto 0 and proto 1 proj_base = torch.zeros(batch_few_shot_runs,base_mean.shape[0]) for j in range(batch_few_shot_runs): for i in range(base_mean.shape[0]): w = base_mean[i] proj_base[j,i] = torch.torch.matmul(v_diff[j], w)/torch.norm(w) id_proj = abs(proj_base).min(1)[1] for j in range(batch_few_shot_runs): runs[j] = proj_class(id_proj[j],runs[j]) means = torch.mean(runs[:,:,:n_shots], dim = 2) return runs,means # In[ ]: # In[99]: a,b,c = ncm_del_otho_vdif(feat[:64], feat[-20:], run_classes, run_indices, n_shots) print(a.float().mean().item()) # In[100]: a,b,c = ncm(feat_processed[:64], feat_processed[-20:], run_classes, run_indices, n_shots,0) print(a.float().mean()) a,b,c = ncm_del_otho_vdif(feat_processed[:64], feat_processed[-20:], run_classes, run_indices, n_shots) print(a.float().mean()) # ## Test du LDA / shrinkage # In[15]: def LDA(run,kwargs): s = run.shape s_shots = run[:,:n_shots].shape run_reshaped = run.reshape(s[0]s[1],-1) run_reshaped_shots = run[:,:n_shots].reshape(s_shots[0]s_shots[1],-1) target = torch.cat((torch.zeros(n_shots),torch.ones(n_shots))) clf = LinearDiscriminantAnalysis(kwargs) clf.fit(run_reshaped_shots, target) out = torch.tensor(clf.transform(run_reshaped)) out = out.reshape((s[0],s[1],-1)) return out # In[16]: def ncm_lda(train_features, features, run_classes, run_indices, n_shots,dictlda): with torch.no_grad(): dim = features.shape[2] targets = torch.arange(n_ways).unsqueeze(1).unsqueeze(0) #features = preprocess(train_features, features) scores = [] score=0 for batch_idx in tqdm(range(n_runs // batch_few_shot_runs)): runs = generate_runs(features, run_classes, run_indices, batch_idx) runs_reduced = torch.zeros((runs.shape[0],runs.shape[1],runs.shape[2],n_components)) for i,run in enumerate(runs): runs_reduced[i] = LDA(run,dictlda) means = torch.mean(runs_reduced[:,:,:n_shots], dim = 2) distances = torch.norm(runs_reduced[:,:,n_shots:].reshape(batch_few_shot_runs, n_ways, 1, -1, n_components)
# -- coding: utf-8 -- """ This module """ import attr import typing from ..core.model import ( Property, Resource, Tag, GetAtt, TypeHint, TypeCheck, ) from ..core.constant import AttrMeta #--- Property declaration --- @attr.s class PropDeliveryStreamAmazonopensearchserviceRetryOptions(Property): """ AWS Object Type = "AWS::KinesisFirehose::DeliveryStream.AmazonopensearchserviceRetryOptions" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-amazonopensearchserviceretryoptions.html Property Document: - ``p_DurationInSeconds``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-amazonopensearchserviceretryoptions.html#cfn-kinesisfirehose-deliverystream-amazonopensearchserviceretryoptions-durationinseconds """ AWS_OBJECT_TYPE = "AWS::KinesisFirehose::DeliveryStream.AmazonopensearchserviceRetryOptions" p_DurationInSeconds: int = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(int)), metadata={AttrMeta.PROPERTY_NAME: "DurationInSeconds"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-amazonopensearchserviceretryoptions.html#cfn-kinesisfirehose-deliverystream-amazonopensearchserviceretryoptions-durationinseconds""" @attr.s class PropDeliveryStreamHiveJsonSerDe(Property): """ AWS Object Type = "AWS::KinesisFirehose::DeliveryStream.HiveJsonSerDe" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-hivejsonserde.html Property Document: - ``p_TimestampFormats``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-hivejsonserde.html#cfn-kinesisfirehose-deliverystream-hivejsonserde-timestampformats """ AWS_OBJECT_TYPE = "AWS::KinesisFirehose::DeliveryStream.HiveJsonSerDe" p_TimestampFormats: typing.List[TypeHint.intrinsic_str] = attr.ib( default=None, validator=attr.validators.optional(attr.validators.deep_iterable(member_validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), iterable_validator=attr.validators.instance_of(list))), metadata={AttrMeta.PROPERTY_NAME: "TimestampFormats"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-hivejsonserde.html#cfn-kinesisfirehose-deliverystream-hivejsonserde-timestampformats""" @attr.s class PropDeliveryStreamSchemaConfiguration(Property): """ AWS Object Type = "AWS::KinesisFirehose::DeliveryStream.SchemaConfiguration" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-schemaconfiguration.html Property Document: - ``p_CatalogId``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-schemaconfiguration.html#cfn-kinesisfirehose-deliverystream-schemaconfiguration-catalogid - ``p_DatabaseName``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-schemaconfiguration.html#cfn-kinesisfirehose-deliverystream-schemaconfiguration-databasename - ``p_Region``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-schemaconfiguration.html#cfn-kinesisfirehose-deliverystream-schemaconfiguration-region - ``p_RoleARN``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-schemaconfiguration.html#cfn-kinesisfirehose-deliverystream-schemaconfiguration-rolearn - ``p_TableName``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-schemaconfiguration.html#cfn-kinesisfirehose-deliverystream-schemaconfiguration-tablename - ``p_VersionId``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-schemaconfiguration.html#cfn-kinesisfirehose-deliverystream-schemaconfiguration-versionid """ AWS_OBJECT_TYPE = "AWS::KinesisFirehose::DeliveryStream.SchemaConfiguration" p_CatalogId: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "CatalogId"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-schemaconfiguration.html#cfn-kinesisfirehose-deliverystream-schemaconfiguration-catalogid""" p_DatabaseName: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "DatabaseName"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-schemaconfiguration.html#cfn-kinesisfirehose-deliverystream-schemaconfiguration-databasename""" p_Region: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "Region"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-schemaconfiguration.html#cfn-kinesisfirehose-deliverystream-schemaconfiguration-region""" p_RoleARN: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "RoleARN"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-schemaconfiguration.html#cfn-kinesisfirehose-deliverystream-schemaconfiguration-rolearn""" p_TableName: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "TableName"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-schemaconfiguration.html#cfn-kinesisfirehose-deliverystream-schemaconfiguration-tablename""" p_VersionId: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "VersionId"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-schemaconfiguration.html#cfn-kinesisfirehose-deliverystream-schemaconfiguration-versionid""" @attr.s class PropDeliveryStreamSplunkRetryOptions(Property): """ AWS Object Type = "AWS::KinesisFirehose::DeliveryStream.SplunkRetryOptions" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-splunkretryoptions.html Property Document: - ``p_DurationInSeconds``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-splunkretryoptions.html#cfn-kinesisfirehose-deliverystream-splunkretryoptions-durationinseconds """ AWS_OBJECT_TYPE = "AWS::KinesisFirehose::DeliveryStream.SplunkRetryOptions" p_DurationInSeconds: int = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(int)), metadata={AttrMeta.PROPERTY_NAME: "DurationInSeconds"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-splunkretryoptions.html#cfn-kinesisfirehose-deliverystream-splunkretryoptions-durationinseconds""" @attr.s class PropDeliveryStreamHttpEndpointConfiguration(Property): """ AWS Object Type = "AWS::KinesisFirehose::DeliveryStream.HttpEndpointConfiguration" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-httpendpointconfiguration.html Property Document: - ``rp_Url``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-httpendpointconfiguration.html#cfn-kinesisfirehose-deliverystream-httpendpointconfiguration-url - ``p_AccessKey``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-httpendpointconfiguration.html#cfn-kinesisfirehose-deliverystream-httpendpointconfiguration-accesskey - ``p_Name``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-httpendpointconfiguration.html#cfn-kinesisfirehose-deliverystream-httpendpointconfiguration-name """ AWS_OBJECT_TYPE = "AWS::KinesisFirehose::DeliveryStream.HttpEndpointConfiguration" rp_Url: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), metadata={AttrMeta.PROPERTY_NAME: "Url"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-httpendpointconfiguration.html#cfn-kinesisfirehose-deliverystream-httpendpointconfiguration-url""" p_AccessKey: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "AccessKey"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-httpendpointconfiguration.html#cfn-kinesisfirehose-deliverystream-httpendpointconfiguration-accesskey""" p_Name: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "Name"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-httpendpointconfiguration.html#cfn-kinesisfirehose-deliverystream-httpendpointconfiguration-name""" @attr.s class PropDeliveryStreamCopyCommand(Property): """ AWS Object Type = "AWS::KinesisFirehose::DeliveryStream.CopyCommand" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-copycommand.html Property Document: - ``rp_DataTableName``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-copycommand.html#cfn-kinesisfirehose-deliverystream-copycommand-datatablename - ``p_CopyOptions``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-copycommand.html#cfn-kinesisfirehose-deliverystream-copycommand-copyoptions - ``p_DataTableColumns``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-copycommand.html#cfn-kinesisfirehose-deliverystream-copycommand-datatablecolumns """ AWS_OBJECT_TYPE = "AWS::KinesisFirehose::DeliveryStream.CopyCommand" rp_DataTableName: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), metadata={AttrMeta.PROPERTY_NAME: "DataTableName"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-copycommand.html#cfn-kinesisfirehose-deliverystream-copycommand-datatablename""" p_CopyOptions: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "CopyOptions"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-copycommand.html#cfn-kinesisfirehose-deliverystream-copycommand-copyoptions""" p_DataTableColumns: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "DataTableColumns"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-copycommand.html#cfn-kinesisfirehose-deliverystream-copycommand-datatablecolumns""" @attr.s class PropDeliveryStreamOpenXJsonSerDe(Property): """ AWS Object Type = "AWS::KinesisFirehose::DeliveryStream.OpenXJsonSerDe" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-openxjsonserde.html Property Document: - ``p_CaseInsensitive``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-openxjsonserde.html#cfn-kinesisfirehose-deliverystream-openxjsonserde-caseinsensitive - ``p_ColumnToJsonKeyMappings``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-openxjsonserde.html#cfn-kinesisfirehose-deliverystream-openxjsonserde-columntojsonkeymappings - ``p_ConvertDotsInJsonKeysToUnderscores``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-openxjsonserde.html#cfn-kinesisfirehose-deliverystream-openxjsonserde-convertdotsinjsonkeystounderscores """ AWS_OBJECT_TYPE = "AWS::KinesisFirehose::DeliveryStream.OpenXJsonSerDe" p_CaseInsensitive: bool = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(bool)), metadata={AttrMeta.PROPERTY_NAME: "CaseInsensitive"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-openxjsonserde.html#cfn-kinesisfirehose-deliverystream-openxjsonserde-caseinsensitive""" p_ColumnToJsonKeyMappings: typing.Dict[str, TypeHint.intrinsic_str] = attr.ib( default=None, validator=attr.validators.optional(attr.validators.deep_mapping(key_validator=attr.validators.instance_of(str), value_validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type))), metadata={AttrMeta.PROPERTY_NAME: "ColumnToJsonKeyMappings"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-openxjsonserde.html#cfn-kinesisfirehose-deliverystream-openxjsonserde-columntojsonkeymappings""" p_ConvertDotsInJsonKeysToUnderscores: bool = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(bool)), metadata={AttrMeta.PROPERTY_NAME: "ConvertDotsInJsonKeysToUnderscores"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-openxjsonserde.html#cfn-kinesisfirehose-deliverystream-openxjsonserde-convertdotsinjsonkeystounderscores""" @attr.s class PropDeliveryStreamOrcSerDe(Property): """ AWS Object Type = "AWS::KinesisFirehose::DeliveryStream.OrcSerDe" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-orcserde.html Property Document: - ``p_BlockSizeBytes``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-orcserde.html#cfn-kinesisfirehose-deliverystream-orcserde-blocksizebytes - ``p_BloomFilterColumns``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-orcserde.html#cfn-kinesisfirehose-deliverystream-orcserde-bloomfiltercolumns - ``p_BloomFilterFalsePositiveProbability``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-orcserde.html#cfn-kinesisfirehose-deliverystream-orcserde-bloomfilterfalsepositiveprobability - ``p_Compression``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-orcserde.html#cfn-kinesisfirehose-deliverystream-orcserde-compression - ``p_DictionaryKeyThreshold``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-orcserde.html#cfn-kinesisfirehose-deliverystream-orcserde-dictionarykeythreshold - ``p_EnablePadding``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-orcserde.html#cfn-kinesisfirehose-deliverystream-orcserde-enablepadding - ``p_FormatVersion``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-orcserde.html#cfn-kinesisfirehose-deliverystream-orcserde-formatversion - ``p_PaddingTolerance``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-orcserde.html#cfn-kinesisfirehose-deliverystream-orcserde-paddingtolerance - ``p_RowIndexStride``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-orcserde.html#cfn-kinesisfirehose-deliverystream-orcserde-rowindexstride - ``p_StripeSizeBytes``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-orcserde.html#cfn-kinesisfirehose-deliverystream-orcserde-stripesizebytes """ AWS_OBJECT_TYPE = "AWS::KinesisFirehose::DeliveryStream.OrcSerDe" p_BlockSizeBytes: int = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(int)), metadata={AttrMeta.PROPERTY_NAME: "BlockSizeBytes"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-orcserde.html#cfn-kinesisfirehose-deliverystream-orcserde-blocksizebytes""" p_BloomFilterColumns: typing.List[TypeHint.intrinsic_str] = attr.ib( default=None, validator=attr.validators.optional(attr.validators.deep_iterable(member_validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), iterable_validator=attr.validators.instance_of(list))), metadata={AttrMeta.PROPERTY_NAME: "BloomFilterColumns"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-orcserde.html#cfn-kinesisfirehose-deliverystream-orcserde-bloomfiltercolumns""" p_BloomFilterFalsePositiveProbability: float = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(float)), metadata={AttrMeta.PROPERTY_NAME: "BloomFilterFalsePositiveProbability"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-orcserde.html#cfn-kinesisfirehose-deliverystream-orcserde-bloomfilterfalsepositiveprobability""" p_Compression: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "Compression"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-orcserde.html#cfn-kinesisfirehose-deliverystream-orcserde-compression""" p_DictionaryKeyThreshold: float = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(float)), metadata={AttrMeta.PROPERTY_NAME: "DictionaryKeyThreshold"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-orcserde.html#cfn-kinesisfirehose-deliverystream-orcserde-dictionarykeythreshold""" p_EnablePadding: bool = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(bool)), metadata={AttrMeta.PROPERTY_NAME: "EnablePadding"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-orcserde.html#cfn-kinesisfirehose-deliverystream-orcserde-enablepadding""" p_FormatVersion: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "FormatVersion"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-orcserde.html#cfn-kinesisfirehose-deliverystream-orcserde-formatversion""" p_PaddingTolerance: float = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(float)), metadata={AttrMeta.PROPERTY_NAME: "PaddingTolerance"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-orcserde.html#cfn-kinesisfirehose-deliverystream-orcserde-paddingtolerance""" p_RowIndexStride: int = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(int)), metadata={AttrMeta.PROPERTY_NAME: "RowIndexStride"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-orcserde.html#cfn-kinesisfirehose-deliverystream-orcserde-rowindexstride""" p_StripeSizeBytes: int = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(int)), metadata={AttrMeta.PROPERTY_NAME: "StripeSizeBytes"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-orcserde.html#cfn-kinesisfirehose-deliverystream-orcserde-stripesizebytes""" @attr.s class PropDeliveryStreamElasticsearchBufferingHints(Property): """ AWS Object Type = "AWS::KinesisFirehose::DeliveryStream.ElasticsearchBufferingHints" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-elasticsearchbufferinghints.html Property Document: - ``p_IntervalInSeconds``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-elasticsearchbufferinghints.html#cfn-kinesisfirehose-deliverystream-elasticsearchbufferinghints-intervalinseconds - ``p_SizeInMBs``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-elasticsearchbufferinghints.html#cfn-kinesisfirehose-deliverystream-elasticsearchbufferinghints-sizeinmbs """ AWS_OBJECT_TYPE = "AWS::KinesisFirehose::DeliveryStream.ElasticsearchBufferingHints" p_IntervalInSeconds: int = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(int)), metadata={AttrMeta.PROPERTY_NAME: "IntervalInSeconds"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-elasticsearchbufferinghints.html#cfn-kinesisfirehose-deliverystream-elasticsearchbufferinghints-intervalinseconds""" p_SizeInMBs: int = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(int)), metadata={AttrMeta.PROPERTY_NAME: "SizeInMBs"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-elasticsearchbufferinghints.html#cfn-kinesisfirehose-deliverystream-elasticsearchbufferinghints-sizeinmbs""" @attr.s class PropDeliveryStreamCloudWatchLoggingOptions(Property): """ AWS Object Type = "AWS::KinesisFirehose::DeliveryStream.CloudWatchLoggingOptions" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-cloudwatchloggingoptions.html Property Document: - ``p_Enabled``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-cloudwatchloggingoptions.html#cfn-kinesisfirehose-deliverystream-cloudwatchloggingoptions-enabled - ``p_LogGroupName``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-cloudwatchloggingoptions.html#cfn-kinesisfirehose-deliverystream-cloudwatchloggingoptions-loggroupname - ``p_LogStreamName``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-cloudwatchloggingoptions.html#cfn-kinesisfirehose-deliverystream-cloudwatchloggingoptions-logstreamname """ AWS_OBJECT_TYPE = "AWS::KinesisFirehose::DeliveryStream.CloudWatchLoggingOptions" p_Enabled: bool = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(bool)), metadata={AttrMeta.PROPERTY_NAME: "Enabled"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-cloudwatchloggingoptions.html#cfn-kinesisfirehose-deliverystream-cloudwatchloggingoptions-enabled""" p_LogGroupName: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "LogGroupName"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-cloudwatchloggingoptions.html#cfn-kinesisfirehose-deliverystream-cloudwatchloggingoptions-loggroupname""" p_LogStreamName: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "LogStreamName"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-cloudwatchloggingoptions.html#cfn-kinesisfirehose-deliverystream-cloudwatchloggingoptions-logstreamname""" @attr.s class PropDeliveryStreamBufferingHints(Property): """ AWS Object Type = "AWS::KinesisFirehose::DeliveryStream.BufferingHints" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-bufferinghints.html Property Document: - ``p_IntervalInSeconds``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-bufferinghints.html#cfn-kinesisfirehose-deliverystream-bufferinghints-intervalinseconds - ``p_SizeInMBs``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-bufferinghints.html#cfn-kinesisfirehose-deliverystream-bufferinghints-sizeinmbs """ AWS_OBJECT_TYPE = "AWS::KinesisFirehose::DeliveryStream.BufferingHints" p_IntervalInSeconds: int = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(int)), metadata={AttrMeta.PROPERTY_NAME: "IntervalInSeconds"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-bufferinghints.html#cfn-kinesisfirehose-deliverystream-bufferinghints-intervalinseconds""" p_SizeInMBs: int = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(int)), metadata={AttrMeta.PROPERTY_NAME: "SizeInMBs"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-bufferinghints.html#cfn-kinesisfirehose-deliverystream-bufferinghints-sizeinmbs""" @attr.s class PropDeliveryStreamProcessorParameter(Property): """ AWS Object Type = "AWS::KinesisFirehose::DeliveryStream.ProcessorParameter" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-processorparameter.html Property Document: - ``rp_ParameterName``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-processorparameter.html#cfn-kinesisfirehose-deliverystream-processorparameter-parametername - ``rp_ParameterValue``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-processorparameter.html#cfn-kinesisfirehose-deliverystream-processorparameter-parametervalue """ AWS_OBJECT_TYPE = "AWS::KinesisFirehose::DeliveryStream.ProcessorParameter" rp_ParameterName: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), metadata={AttrMeta.PROPERTY_NAME: "ParameterName"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-processorparameter.html#cfn-kinesisfirehose-deliverystream-processorparameter-parametername""" rp_ParameterValue: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), metadata={AttrMeta.PROPERTY_NAME: "ParameterValue"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-processorparameter.html#cfn-kinesisfirehose-deliverystream-processorparameter-parametervalue""" @attr.s class PropDeliveryStreamAmazonopensearchserviceBufferingHints(Property): """ AWS Object Type = "AWS::KinesisFirehose::DeliveryStream.AmazonopensearchserviceBufferingHints" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-amazonopensearchservicebufferinghints.html Property Document: - ``p_IntervalInSeconds``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-amazonopensearchservicebufferinghints.html#cfn-kinesisfirehose-deliverystream-amazonopensearchservicebufferinghints-intervalinseconds - ``p_SizeInMBs``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-amazonopensearchservicebufferinghints.html#cfn-kinesisfirehose-deliverystream-amazonopensearchservicebufferinghints-sizeinmbs """ AWS_OBJECT_TYPE = "AWS::KinesisFirehose::DeliveryStream.AmazonopensearchserviceBufferingHints" p_IntervalInSeconds: int = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(int)), metadata={AttrMeta.PROPERTY_NAME: "IntervalInSeconds"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-amazonopensearchservicebufferinghints.html#cfn-kinesisfirehose-deliverystream-amazonopensearchservicebufferinghints-intervalinseconds""" p_SizeInMBs: int = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(int)), metadata={AttrMeta.PROPERTY_NAME: "SizeInMBs"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-amazonopensearchservicebufferinghints.html#cfn-kinesisfirehose-deliverystream-amazonopensearchservicebufferinghints-sizeinmbs""" @attr.s class PropDeliveryStreamDeliveryStreamEncryptionConfigurationInput(Property): """ AWS Object Type = "AWS::KinesisFirehose::DeliveryStream.DeliveryStreamEncryptionConfigurationInput" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-deliverystreamencryptionconfigurationinput.html Property Document: - ``rp_KeyType``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-deliverystreamencryptionconfigurationinput.html#cfn-kinesisfirehose-deliverystream-deliverystreamencryptionconfigurationinput-keytype - ``p_KeyARN``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-deliverystreamencryptionconfigurationinput.html#cfn-kinesisfirehose-deliverystream-deliverystreamencryptionconfigurationinput-keyarn """ AWS_OBJECT_TYPE = "AWS::KinesisFirehose::DeliveryStream.DeliveryStreamEncryptionConfigurationInput" rp_KeyType: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), metadata={AttrMeta.PROPERTY_NAME: "KeyType"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-deliverystreamencryptionconfigurationinput.html#cfn-kinesisfirehose-deliverystream-deliverystreamencryptionconfigurationinput-keytype""" p_KeyARN: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "KeyARN"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-deliverystreamencryptionconfigurationinput.html#cfn-kinesisfirehose-deliverystream-deliverystreamencryptionconfigurationinput-keyarn""" @attr.s class PropDeliveryStreamElasticsearchRetryOptions(Property): """ AWS Object Type = "AWS::KinesisFirehose::DeliveryStream.ElasticsearchRetryOptions" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-elasticsearchretryoptions.html Property Document: - ``p_DurationInSeconds``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-elasticsearchretryoptions.html#cfn-kinesisfirehose-deliverystream-elasticsearchretryoptions-durationinseconds """ AWS_OBJECT_TYPE = "AWS::KinesisFirehose::DeliveryStream.ElasticsearchRetryOptions" p_DurationInSeconds: int = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(int)), metadata={AttrMeta.PROPERTY_NAME: "DurationInSeconds"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-elasticsearchretryoptions.html#cfn-kinesisfirehose-deliverystream-elasticsearchretryoptions-durationinseconds""" @attr.s class PropDeliveryStreamKMSEncryptionConfig(Property): """ AWS Object Type = "AWS::KinesisFirehose::DeliveryStream.KMSEncryptionConfig" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-kmsencryptionconfig.html Property Document: - ``rp_AWSKMSKeyARN``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-kmsencryptionconfig.html#cfn-kinesisfirehose-deliverystream-kmsencryptionconfig-awskmskeyarn """ AWS_OBJECT_TYPE = "AWS::KinesisFirehose::DeliveryStream.KMSEncryptionConfig" rp_AWSKMSKeyARN: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), metadata={AttrMeta.PROPERTY_NAME: "AWSKMSKeyARN"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-kmsencryptionconfig.html#cfn-kinesisfirehose-deliverystream-kmsencryptionconfig-awskmskeyarn""" @attr.s class PropDeliveryStreamDeserializer(Property): """ AWS Object Type = "AWS::KinesisFirehose::DeliveryStream.Deserializer" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-deserializer.html Property Document: - ``p_HiveJsonSerDe``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-deserializer.html#cfn-kinesisfirehose-deliverystream-deserializer-hivejsonserde - ``p_OpenXJsonSerDe``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-deserializer.html#cfn-kinesisfirehose-deliverystream-deserializer-openxjsonserde """ AWS_OBJECT_TYPE = "AWS::KinesisFirehose::DeliveryStream.Deserializer" p_HiveJsonSerDe: typing.Union['PropDeliveryStreamHiveJsonSerDe', dict] = attr.ib( default=None, converter=PropDeliveryStreamHiveJsonSerDe.from_dict, validator=attr.validators.optional(attr.validators.instance_of(PropDeliveryStreamHiveJsonSerDe)), metadata={AttrMeta.PROPERTY_NAME: "HiveJsonSerDe"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-deserializer.html#cfn-kinesisfirehose-deliverystream-deserializer-hivejsonserde""" p_OpenXJsonSerDe: typing.Union['PropDeliveryStreamOpenXJsonSerDe', dict] = attr.ib( default=None, converter=PropDeliveryStreamOpenXJsonSerDe.from_dict, validator=attr.validators.optional(attr.validators.instance_of(PropDeliveryStreamOpenXJsonSerDe)), metadata={AttrMeta.PROPERTY_NAME: "OpenXJsonSerDe"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-deserializer.html#cfn-kinesisfirehose-deliverystream-deserializer-openxjsonserde""" @attr.s class PropDeliveryStreamKinesisStreamSourceConfiguration(Property): """ AWS Object Type = "AWS::KinesisFirehose::DeliveryStream.KinesisStreamSourceConfiguration" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-kinesisstreamsourceconfiguration.html Property Document: - ``rp_KinesisStreamARN``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-kinesisstreamsourceconfiguration.html#cfn-kinesisfirehose-deliverystream-kinesisstreamsourceconfiguration-kinesisstreamarn - ``rp_RoleARN``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-kinesisstreamsourceconfiguration.html#cfn-kinesisfirehose-deliverystream-kinesisstreamsourceconfiguration-rolearn """ AWS_OBJECT_TYPE = "AWS::KinesisFirehose::DeliveryStream.KinesisStreamSourceConfiguration" rp_KinesisStreamARN: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), metadata={AttrMeta.PROPERTY_NAME: "KinesisStreamARN"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-kinesisstreamsourceconfiguration.html#cfn-kinesisfirehose-deliverystream-kinesisstreamsourceconfiguration-kinesisstreamarn""" rp_RoleARN: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), metadata={AttrMeta.PROPERTY_NAME: "RoleARN"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-kinesisstreamsourceconfiguration.html#cfn-kinesisfirehose-deliverystream-kinesisstreamsourceconfiguration-rolearn""" @attr.s class PropDeliveryStreamRedshiftRetryOptions(Property): """ AWS Object Type = "AWS::KinesisFirehose::DeliveryStream.RedshiftRetryOptions" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-redshiftretryoptions.html Property Document: - ``p_DurationInSeconds``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-redshiftretryoptions.html#cfn-kinesisfirehose-deliverystream-redshiftretryoptions-durationinseconds """ AWS_OBJECT_TYPE = "AWS::KinesisFirehose::DeliveryStream.RedshiftRetryOptions" p_DurationInSeconds: int = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(int)), metadata={AttrMeta.PROPERTY_NAME: "DurationInSeconds"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-redshiftretryoptions.html#cfn-kinesisfirehose-deliverystream-redshiftretryoptions-durationinseconds""" @attr.s class PropDeliveryStreamRetryOptions(Property): """ AWS Object Type = "AWS::KinesisFirehose::DeliveryStream.RetryOptions" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-retryoptions.html Property Document: - ``p_DurationInSeconds``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-retryoptions.html#cfn-kinesisfirehose-deliverystream-retryoptions-durationinseconds """ AWS_OBJECT_TYPE = "AWS::KinesisFirehose::DeliveryStream.RetryOptions" p_DurationInSeconds: int = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(int)), metadata={AttrMeta.PROPERTY_NAME: "DurationInSeconds"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-retryoptions.html#cfn-kinesisfirehose-deliverystream-retryoptions-durationinseconds""" @attr.s class PropDeliveryStreamParquetSerDe(Property): """ AWS Object Type = "AWS::KinesisFirehose::DeliveryStream.ParquetSerDe" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-parquetserde.html Property Document: - ``p_BlockSizeBytes``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-parquetserde.html#cfn-kinesisfirehose-deliverystream-parquetserde-blocksizebytes - ``p_Compression``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-parquetserde.html#cfn-kinesisfirehose-deliverystream-parquetserde-compression - ``p_EnableDictionaryCompression``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-parquetserde.html#cfn-kinesisfirehose-deliverystream-parquetserde-enabledictionarycompression - ``p_MaxPaddingBytes``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-parquetserde.html#cfn-kinesisfirehose-deliverystream-parquetserde-maxpaddingbytes - ``p_PageSizeBytes``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-parquetserde.html#cfn-kinesisfirehose-deliverystream-parquetserde-pagesizebytes - ``p_WriterVersion``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-parquetserde.html#cfn-kinesisfirehose-deliverystream-parquetserde-writerversion """ AWS_OBJECT_TYPE = "AWS::KinesisFirehose::DeliveryStream.ParquetSerDe" p_BlockSizeBytes: int = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(int)), metadata={AttrMeta.PROPERTY_NAME: "BlockSizeBytes"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-parquetserde.html#cfn-kinesisfirehose-deliverystream-parquetserde-blocksizebytes""" p_Compression: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "Compression"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-parquetserde.html#cfn-kinesisfirehose-deliverystream-parquetserde-compression""" p_EnableDictionaryCompression: bool = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(bool)), metadata={AttrMeta.PROPERTY_NAME: "EnableDictionaryCompression"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-parquetserde.html#cfn-kinesisfirehose-deliverystream-parquetserde-enabledictionarycompression""" p_MaxPaddingBytes: int = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(int)), metadata={AttrMeta.PROPERTY_NAME: "MaxPaddingBytes"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-parquetserde.html#cfn-kinesisfirehose-deliverystream-parquetserde-maxpaddingbytes""" p_PageSizeBytes: int = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(int)), metadata={AttrMeta.PROPERTY_NAME: "PageSizeBytes"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-parquetserde.html#cfn-kinesisfirehose-deliverystream-parquetserde-pagesizebytes""" p_WriterVersion: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "WriterVersion"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-parquetserde.html#cfn-kinesisfirehose-deliverystream-parquetserde-writerversion""" @attr.s class PropDeliveryStreamVpcConfiguration(Property): """ AWS Object Type = "AWS::KinesisFirehose::DeliveryStream.VpcConfiguration" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-vpcconfiguration.html Property Document: - ``rp_RoleARN``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-vpcconfiguration.html#cfn-kinesisfirehose-deliverystream-vpcconfiguration-rolearn - ``rp_SecurityGroupIds``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-vpcconfiguration.html#cfn-kinesisfirehose-deliverystream-vpcconfiguration-securitygroupids - ``rp_SubnetIds``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-vpcconfiguration.html#cfn-kinesisfirehose-deliverystream-vpcconfiguration-subnetids """ AWS_OBJECT_TYPE = "AWS::KinesisFirehose::DeliveryStream.VpcConfiguration" rp_RoleARN: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), metadata={AttrMeta.PROPERTY_NAME: "RoleARN"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-vpcconfiguration.html#cfn-kinesisfirehose-deliverystream-vpcconfiguration-rolearn""" rp_SecurityGroupIds: typing.List[TypeHint.intrinsic_str] = attr.ib( default=None, validator=attr.validators.deep_iterable(member_validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), iterable_validator=attr.validators.instance_of(list)), metadata={AttrMeta.PROPERTY_NAME: "SecurityGroupIds"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-vpcconfiguration.html#cfn-kinesisfirehose-deliverystream-vpcconfiguration-securitygroupids""" rp_SubnetIds: typing.List[TypeHint.intrinsic_str] = attr.ib( default=None, validator=attr.validators.deep_iterable(member_validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), iterable_validator=attr.validators.instance_of(list)), metadata={AttrMeta.PROPERTY_NAME: "SubnetIds"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-vpcconfiguration.html#cfn-kinesisfirehose-deliverystream-vpcconfiguration-subnetids""" @attr.s class PropDeliveryStreamHttpEndpointCommonAttribute(Property): """ AWS Object Type = "AWS::KinesisFirehose::DeliveryStream.HttpEndpointCommonAttribute" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-httpendpointcommonattribute.html Property Document: - ``rp_AttributeName``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-httpendpointcommonattribute.html#cfn-kinesisfirehose-deliverystream-httpendpointcommonattribute-attributename - ``rp_AttributeValue``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-httpendpointcommonattribute.html#cfn-kinesisfirehose-deliverystream-httpendpointcommonattribute-attributevalue """ AWS_OBJECT_TYPE = "AWS::KinesisFirehose::DeliveryStream.HttpEndpointCommonAttribute" rp_AttributeName: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), metadata={AttrMeta.PROPERTY_NAME: "AttributeName"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-httpendpointcommonattribute.html#cfn-kinesisfirehose-deliverystream-httpendpointcommonattribute-attributename""" rp_AttributeValue: TypeHint.intrinsic_str = attr.ib(
#!/usr/bin/env python3 # -- coding: utf-8 -- """ Created on Thu Jun 10 15:51:15 2021 @author: rosariouceda-sosa """ ########################################### # Extraction of Propbank, Verbnet and mappings # It requires verbnet3.4, verbnet3.3 and verbnet3.2 in nltk_data directory, # as well as the latest version of propbank # # In particular, it does require the last ########################################### import json import re from nltk.corpus import treebank from nltk.corpus.util import LazyCorpusLoader from VerbnetCorpusReaderEx import VerbnetCorpusReaderEx from nltk.corpus import PropbankCorpusReader from semlinkEx import query_pb_vn_mapping, query_pb_vn_mapping_1_2 from xml.etree import ElementTree from propbank_readerEx import PropbankCorpusReaderEx #from nltk.corpus import propbank propbank = LazyCorpusLoader( "propbank-latest", PropbankCorpusReaderEx, "prop.txt", r"frames/.\.xml", "verbs.txt", lambda filename: re.sub(r"^wsj/\d\d/", "", filename), treebank, ) # Must be defined after* treebank corpus. vn_dict = { "verbnet3.2": LazyCorpusLoader("verbnet3.2", VerbnetCorpusReaderEx, r"(?!\.).\.xml"), "verbnet3.3": LazyCorpusLoader("verbnet3.3", VerbnetCorpusReaderEx, r"(?!\.).\.xml"), "verbnet3.4": LazyCorpusLoader("verbnet3.4", VerbnetCorpusReaderEx, r"(?!\.).*\.xml") } #The default is 3.4 current_vn = vn_dict["verbnet3.4"] VN_FILES = "/Users/rosariouceda-sosa/Documents/usr/SemanticsSvces/verbnet/verbnet-master/verbnet3.4" VN_DIR = "/Users/rosariouceda-sosa/Documents/usr/SemanticsSvces/verbnet/" PB_DIR = "/Users/rosariouceda-sosa/Documents/usr/SemanticsSvces/propbank/" outputFile = "" logFile = "" processedGroupingsVN = {} processedMaps = [] #key is entity and the list of mappings they have. Keys for Verbnet, Propbank and WN are their id's memberToMap = {} #inverse of memberToMap mapToMember = {} #Each propbank has: [roleSet] : name, arguments, lemmas, provenance pb_index = {} #Each verbnet has: [CODE] : name, [arguments] variableName, variableType, lemmas, provenance, vn_index = {} #{roleset} admire-31.2': {'provenance': 'verbnet3.4', 'arguments' : {'ARG0' : {'description' : "XXX" , 'vnArg' : Agent}}] map_index = {} extended_semnlink_index = [] # #vnCodeToLemma = {} ###### LOG outLog = open("/Users/rosariouceda-sosa/Downloads/OutLog_ULKB_Clean.txt", "w") ########################################################### # AUXILIARY FUNCTIONS ########################################################### #IMPORTANT: To standardize both verbnet names and codes, def vn_standard(_verb: str) -> str: # return _verb.replace(".", "-") #do nothing return _verb def count_dict() -> int : highest = 0 for thisMap in mapToMember : if len(mapToMember[thisMap]) > highest: highest = len(mapToMember[thisMap]) return highest def compare_strs(_first : str, _second : str) -> bool : if (_first.lower() == _second.lower()): return 1 return 0 def checkKeyStr(_dict : {}, _key: str) -> str : if _key in _dict.keys(): return _dict[_key] else: return "" def toRDFStr(_in :str) -> str : #somewhat sloppy, but gets the data _in = _in.replace("/", "_") _in = _in.replace(":", "-") _in = _in.replace(" ", "") _in = _in.replace("(", "_") _in = _in.replace(")", "_") _in = _in.replace("'", "") _in = _in.replace(".", "-") _in = _in.replace(",", "_") _in = _in.replace("__", "_") _in = _in.replace(">", "") _in = _in.replace("<", "") _in = _in.replace("#", "-") _in = _in.replace("%", "_") _in = _in.replace("?", "_") #ADD THIS FOR THE INCONSISTENT VERBNET NAMING IN SEMLINK AND NLTK _in = _in.replace("-", "_") return _in def checkArray(_dict : {}, _name : str) -> [] : if (_name) in _dict.keys() : return _dict.get(_name) else: return [] # Whether the mapping points to 'nothing' def wrong_mapping(_term :str) -> bool : _term = _term.strip() if len(_term) == 0 : return True if _term == 'NP' or _term == 'np' or _term == 'NP>' or _term == 'np>': return True if _term == 'NM' or _term == 'nm' or _term == 'NM>' or _term == 'nm>': return True return False def clean_text(_text :str, _oneLine : bool) -> str : _text = _text.replace("\"", "\\\"") _text = _text.replace("'", "\\'") _text = _text.replace("\/", "-") _text = _text.replace("`", " ") if _oneLine : _text = _text.replace("\n", "") return _text def chunk(mappings: str) -> []: rList = mappings.split(',') for item in rList : item = item.strip() return rList # from admire-31.2 to admire def get_vn_lemma(_verb : str) -> str : return _verb.split('-', 1)[0] # from admire-31.2 to 31.2 -- The first hyphen is the one that counts def get_vn_code(_verb: str) -> str : stVerb = vn_standard(_verb) return stVerb.split('-',1)[1] #from admire.01 to admire def get_pb_lemma(_verb : str) -> str : return _verb.split('.', 1)[0] def get_vn_varName(_var : str) -> str : if _var.startswith('?') : _var = _var[1:] return _var def match_vn_codes(_first:str , _second: str) -> bool : if toRDFStr(_first) == toRDFStr(_second): return True return False def matchRDF(_item, _dict: {}) -> str : toMatch = toRDFStr(_item) for keyItem in _dict: if toMatch == toRDFStr(keyItem): return keyItem return "" # Also consider one start with another def vn_in_dict(_item:str, _dict: {}, _name: str) -> bool : for keyItem in _dict: if len (_name ) == 0 : compareTo = keyItem else : compareTo = _dict[keyItem][_name] if compareTo == _item : return True if compareTo.startswith(_item) or compareTo.startswith(_name) : return True return False def vn_to_swap(_item:str, _dict: {}, _name:str) -> str : _itemCode = get_vn_code(_item) if _itemCode not in vn_index : return "" _itemProvenance = vn_index[_itemCode]['provenance'] _itemVersion = _itemProvenance.split('.',1)[1] for keyItem in _dict: if len(_name) == 0 : compareTo = keyItem else : compareTo = _dict[keyItem][_name] compareToCode = get_vn_code(compareTo) if _itemCode == compareToCode or compareToCode.startswith(_itemCode) or _itemCode.startswith(compareToCode) : if compareToCode in vn_index : compareToProvenance = vn_index[compareToCode]['provenance'] compareToVersion = compareToProvenance.split('.', 1)[1] if compareToVersion < _itemVersion : return compareTo return "" def unmatched_roles(_have : [], _want: []) -> [] : result = [] for haveEntry in _have : haveEntry = haveEntry.lower() found = False for wantEntry in _want : if wantEntry.lower() == haveEntry : found = True if not found : result.append(haveEntry) return result def wrongly_matched_roles(_have : [], _want: []) -> [] : result = [] for haveEntry in _have : haveEntry = haveEntry.lower() found = False for wantEntry in _want : if wantEntry.lower() == haveEntry : found = True if not found : result.append(haveEntry) return result def getRoleStrFrom(_list : []) -> str : resultStr = "" noDupList = [] for item in _list : if item.startswith("?") : item = item[1:] if item not in noDupList : noDupList.append(item) for item in noDupList : if len(resultStr) == 0 : resultStr += item else : resultStr += ", " + item return resultStr def getRoleListFrom(_list : []) -> [[str]] : resultStr = "" noDupList = [] for item in _list : if item.startswith("?") : item = item[1:] if item not in noDupList : noDupList.append(item) return noDupList ###################################################################### # SEMLINK INGESTION ###################################################################### #maps from verbnet class + argument a URL #Check the variables that have been already mapped through Verbnet def map_to_url(_class: str, _param : str) -> []: global vnClassToVars, framesToVars resultList = [] if _class not in vnClassToVars : return resultList argList = vnClassToVars[_class] for argKey in argList : if argKey.lower() == _param.lower(): resultList.append(argList[argKey]) # elif _class in framesToVars : #try the frames # argList = framesToVars[_class] # for frameKey in argList : # for argKey in argList[frameKey] : # if argKey.lower() == _param.lower() : # resultList.append(argList[frameKey][argKey]) return resultList def process_semlink_1_2() : global provenance, pbToMap_params, pbToMap, semLinkFromPB # from {'mapping': '51.2', 'source': 'verbnet3.4', 'arguments': {'ARG0': 'Theme'}} # TO map_index #[{'vnVerb': 'admire-31.2', 'provenance': 'verbnet3.4', 'arguments' : {'ARG0' : {'description' : "XXX" , 'vnArg' : Agent}}] oldProvenance = provenance provenance = "semlink 1.2.2" for roleset in pb_index : if "abound.01" in roleset: print("DEBUG " + roleset) semLinkmappingList = query_pb_vn_mapping_1_2(roleset) #If there is no mapping, ignore. if not semLinkmappingList or len(semLinkmappingList) == 0 : # if outLog is not None : # outLog.write("PROPBANK NO_SEMLINK_1_2," + roleset + "\n") # outLog.flush() continue #If there is a mapping BUT we don't have the roleset, it's an issue. if roleset not in map_index : if outLog is not None : outLog.write("NO_PROPBANK SEMLINK_1_2," + roleset + "\n") outLog.flush() map_index[roleset] = {} #Grab the current map_index entry. We know it's there ourMappings = map_index[roleset] for mapping in semLinkmappingList : vnRawCode = mapping['mapping'] vnRawCode = vn_standard(vnRawCode) vnRawName = "" if vnRawCode in vn_index : vnRawName = vn_index[vnRawCode]['name'] else : # use a hack to substitute the first hyphen by a dot. Oh brother... if outLog is not None : outLog.write("NO VERBNET SEMLINK_1_2," + vnRawName + "," + vnRawCode + "\n") outLog.flush() continue #go to the next mapping #If the verbnet
# """handle input Text for Larch -- inclides translation to Python text """ from __future__ import print_function from utils import isValidName, isNumber, isLiteralStr, strip_comments, find_delims def get_DefVar(text): """ looks for defined variable statement, of the form >> def varname = exression returns (varname, expression) if this is a valid defvar statement or None, None if not a valid defvar statement """ if text.find('=') > 0 and text.startswith('def '): t = text[4:].replace('=',' = ').strip() words = t.split() if len(words) > 2 and words[1] == '=': iequal = t.find('=') iparen = t.find('(') icolon = t.find(':') if iparen < 0 : iparen = len(t)+1 if icolon < 0 : icolon = len(t)+1 # print iequal, iparen, icolon, words[0], isValidName(words[0]) if (iequal < iparen and iequal < icolon and isValidName(words[0])): return words[0], t[iequal+1:].strip() return None, None class InputText: """Input Larch Code: handles loading and reading code text, and providing blocks of compile-able python code to be converted to AST. InputText accepts and stores single or multiple lines of input text, including as from an interactive prompt, watching for blocks of code, and keepin track of whether a block are complete. When asked for the next block of code, it emits blocks of valid (hopefully!) python code ready to parsed by 'ast.parse' Uses a FIFO (First In, First Out) buffer to allow mixing of input/output. Can read from a specified input source for 'interactive' mode usage: >>> text = InputText() >>> s = 'a statement' >>> text.put(s) # add lines of text >>> text.get(s) # get complete code block, ready for Compiler.eval the following translations are made from valid Larch to valid Python: 1. Block Indentation: larch blocks may end with one of the tokens: ('end', 'endXXX', '#end', '#endXXX') for an 'XXX' block (one of 'if', 'for', 'while', 'try', and 'def') where the token starts a line of text, followed by whitespace or a comment starting with a '#'. 2. Defined Variables: larch uses 'def VarName = Expression' for a Defined Variable (the expression is stored and accessing the VarName causes the expression to be re-evaluated) The tokens are found with "def" "varname" "=" "text of expression" and then translated into _builtin._definevar_("varname", "text of expression") 3. Command Syntax: larch allows lines of code which execute a function without return value to be viewed as "commands" and written without parentheses, so that the function call function(x, y) can be written as function x, y 4. Print: as a special case of rule 3, and because Python is going through changes in the syntax of "print", print statements are translated from either "print(list)" or "print list" to _builtin._print(list) """ indent = ' '4 ps1 = ' >' ps2 = '....>' block_friends = {'if': ('else', 'elif'), 'for': ('else'), 'def': (), 'try': ('else', 'except', 'finally'), 'while': ('else') } parens = {'{':'}', '(':')', '[':']'} fcn_defvar = "_builtin.definevar" fcn_print = "_builtin._print_" nonkey = 'NONKEY' empty_frame = (None, None, -1) def __init__(self, prompt=None, interactive=True, input=None, filename=None, _larch=None): self.prompt = prompt or self.ps1 self.input = None self._larch = _larch self.interactive = interactive self.lineno = 0 self.filename = filename or '<stdin>' if interactive: self.input = input or self.__defaultInput self._fifo = [[], []] self.block = [] self.keys = [] self.current = None self.endkeys = () self.friends = () self.delims = [] self.eos = '' self.in_string = False self.input_buff = [] self.input_complete = True def readfile(self, fname): fh = open(fname, 'r') self.put(fh.read(), filename=fname, lineno=0) fh.close() def put(self, text, filename=None, lineno=None ): """add line of input code text""" fname = filename or self.filename or '<stdin>' if lineno is not None: self.lineno = lineno def addTextInput(thisline, fname): self.input_complete = self.__isComplete(thisline) self.input_buff.append((thisline, self.input_complete, self.eos, fname, self.lineno)) self.lineno += 1 text = text.split('\n') text.reverse() while len(text) > 0: addTextInput(text.pop(), fname) if self.interactive: self.prompt = self.ps2 while not self.input_complete: t = self.input() t0 = t.strip() if len(t0) > 0: addTextInput(t, fname) if self.input_complete: self.prompt = self.ps1 nkeys, nblock = self.convert() return self.input_complete def get(self): """get compile-able block of python code""" if len(self) > 0: if not self._fifo[0]: self._fifo.reverse() self._fifo[0].reverse() try: return self._fifo[0].pop() except IndexError: msg = 'InputText out of complete text' if self._larch is None: raise IndexError(msg) else: self._larch.raise_exception(None, exc=IndexError, msg=msg) return self.empty_frame def convert(self): """ Convert input buff (in self.input_buff) to valid python code and stores this (text, filename, lineno) into _fifo buffer """ indent_level = 0 oneliner = False startkeys = self.block_friends.keys() self.input_buff.reverse() while self.input_buff: text, complete, eos, fname, lineno = self.input_buff.pop() long_text = eos in '"\'' sindent = self.indent(indent_level+1) while not complete: tnext, complete, xeos, fname, lineno2 = self.input_buff.pop() if long_text: text = "%s\n%s" % (text, tnext) else: text = "%s\n %s%s" % (text, sindent, tnext) text = text.strip().rstrip() txt = text.replace('(', ' (').replace(')', ' )') if text.startswith('"') or text.startswith("'"): delim = text[0] if text[0:3] == text[0]3: delim = text[0:3] while not find_delims(text, delim=delim)[0]: tnext, complete, eos, fname, lineno2 = self.input_buff.pop() text = "%s\n %s%s" % (text, sindent, tnext) # note here the trick of replacing '#end' with '&end' so # that it is not removed by strip_comments. then below, # we look for '&end' as an end-of-block token. if txt.startswith('#end'): txt = '&end%s' % txt[4:] txt = strip_comments(txt) # thiskey, word2 = (txt.split() + [''])[0:2] words = txt.split(' ', 1) thiskey = words.pop(0).strip() word2 = '' if len(words) > 0: word2 = words[0].replace(',', ' ').split()[0] if thiskey.endswith(':'): thiskey = thiskey[:-1] prefix, oneliner = '', False if thiskey in startkeys: # check for defined variables if thiskey == 'def': dname, dexpr = get_DefVar(text) if dname is not None and dexpr is not None: if "'" in dexpr: dexpr.replace("'", "\'") text = "%s('%s', '%s')" % (self.fcn_defvar, dname, dexpr) thiskey = self.nonkey # note that we re-test* here, # as thiskey may have changed above for defined variables if thiskey in startkeys: if text.find(':') < 1: msg = "%s statement needs a ':' at\n %s" % (thiskey, text) if self._larch is None: raise SyntaxError(msg) else: self._larch.raise_exception(None, exc=SyntaxError, msg=msg, expr=text) elif text.endswith(':'): self.current = thiskey self.keys.append(thiskey) self.friends = self.block_friends[thiskey] self.endkeys = ('end', 'end%s'% thiskey, '&end', '&end%s'% thiskey) else: # one-liner form oneliner = True elif thiskey in self.endkeys: # end of block if not thiskey.startswith('&'): prefix = '#' if len(self.keys) != 0: self.current = None self.friends = () self.keys.pop() if len(self.keys)>0: self.current = self.keys[-1] self.friends = self.block_friends[self.current] self.endkeys = ('end', 'end%s'%self.current, '&end', '&end%s'%self.current) elif not text.endswith(')') and self.__isCommand(thiskey, word2): # handle 'command format', including 'print' text = '%s(%s)' % (thiskey, text[len(thiskey):].strip()) indent_level = len(self.keys) if (not oneliner and len(thiskey)>0 and (thiskey == self.current or thiskey in self.friends)): indent_level = indent_level - 1 if indent_level < 0: msg = 'impossible indent level!' if self._larch is None: raise SyntaxError(msg) else: self._larch.raise_exception(None, exc=SyntaxError, msg=msg) self.block.append('%s%s%s' % (self.indent*indent_level, prefix, text)) if len(self.keys) == 0: outtext = '\n'.join(self.block) if '\n' in outtext: outtext = outtext + '\n' self._fifo[1].append((outtext, fname, 1+lineno-len(self.block))) self.block = [] return len(self.keys), len(self.block) def clear(self): "clear the input" self._fifo = [[], []] def __isCommand(self, key, word2): """ decide if a keyword and next word are of the form 'command arg, ...' which will get translated to 'command(arg, ...)' to allow 'command syntax' """ # this could be in one long test, but we simplify: # first test key: if (not isValidName(key) or key in self.friends or key.startswith('#') or len(key) < 1 or len(word2) < 1): return False # next test word2 return (isValidName(word2) or isNumber(word2) or isLiteralStr(word2) ) def __isComplete(self, text): """returns whether input text is a complete: that is: does not contains unclosed parens or quotes and does not end with a backslash stores state information from previous textline in self.eos = char(s) to look for 'end of string' ("" == string complete) self.delims = current list of closing delims being waited for """ parens = self.parens opens = ''.join(parens.keys()) closes = ''.join(parens.values()) quotes, bslash = '\'"', '\\' prev_char = '' # txt = strip_comments(text) txt = text
""" This is the core file in the `gradio` package, and defines the Interface class, including methods for constructing the interface using the input and output types. """ import copy import csv import getpass import inspect import markdown2 import numpy as np import os import pkg_resources import requests import random import sys import time import warnings import webbrowser import weakref from gradio import networking, strings, utils, encryptor, queue from gradio.inputs import get_input_instance from gradio.outputs import get_output_instance from gradio.interpretation import quantify_difference_in_label, get_regression_or_classification_value from gradio.external import load_interface, load_from_pipeline class Interface: """ Interfaces are created with Gradio by constructing a `gradio.Interface()` object or by calling `gradio.Interface.load()`. """ instances = weakref.WeakSet() # stores references to all currently existing Interface instances @classmethod def get_instances(cls): """ :return: list of all current instances. """ return list(Interface.instances) @classmethod def load(cls, name, src=None, api_key=None, alias=None, kwargs): """ Class method to construct an Interface from an external source repository, such as huggingface. Parameters: name (str): the name of the model (e.g. "gpt2"), can include the `src` as prefix (e.g. "huggingface/gpt2") src (str): the source of the model: `huggingface` or `gradio` (or empty if source is provided as a prefix in `name`) api_key (str): optional api key for use with Hugging Face Model Hub alias (str): optional, used as the name of the loaded model instead of the default name Returns: (gradio.Interface): a Gradio Interface object for the given model """ interface_info = load_interface(name, src, api_key, alias) # create a dictionary of kwargs without overwriting the original interface_info dict because it is mutable # and that can cause some issues since the internal prediction function may rely on the original interface_info dict kwargs = dict(interface_info, kwargs) interface = cls(kwargs) interface.api_mode = True # set api mode to true so that the interface will not preprocess/postprocess return interface @classmethod def from_pipeline(cls, pipeline, kwargs): """ Class method to construct an Interface from a Hugging Face transformers.Pipeline. pipeline (transformers.Pipeline): Returns: (gradio.Interface): a Gradio Interface object from the given Pipeline """ interface_info = load_from_pipeline(pipeline) kwargs = dict(interface_info, kwargs) interface = cls(kwargs) return interface def __init__(self, fn, inputs=None, outputs=None, verbose=None, examples=None, examples_per_page=10, live=False, layout="unaligned", show_input=True, show_output=True, capture_session=None, interpretation=None, num_shap=2.0, theme=None, repeat_outputs_per_model=True, title=None, description=None, article=None, thumbnail=None, css=None, height=500, width=900, allow_screenshot=True, allow_flagging=None, flagging_options=None, encrypt=False, show_tips=None, flagging_dir="flagged", analytics_enabled=None, enable_queue=None, api_mode=None): """ Parameters: fn (Callable): the function to wrap an interface around. inputs (Union[str, List[Union[str, InputComponent]]]): a single Gradio input component, or list of Gradio input components. Components can either be passed as instantiated objects, or referred to by their string shortcuts. The number of input components should match the number of parameters in fn. outputs (Union[str, List[Union[str, OutputComponent]]]): a single Gradio output component, or list of Gradio output components. Components can either be passed as instantiated objects, or referred to by their string shortcuts. The number of output components should match the number of values returned by fn. verbose (bool): DEPRECATED. Whether to print detailed information during launch. examples (Union[List[List[Any]], str]): sample inputs for the function; if provided, appears below the UI components and can be used to populate the interface. Should be nested list, in which the outer list consists of samples and each inner list consists of an input corresponding to each input component. A string path to a directory of examples can also be provided. If there are multiple input components and a directory is provided, a log.csv file must be present in the directory to link corresponding inputs. examples_per_page (int): If examples are provided, how many to display per page. live (bool): whether the interface should automatically reload on change. layout (str): Layout of input and output panels. "horizontal" arranges them as two columns of equal height, "unaligned" arranges them as two columns of unequal height, and "vertical" arranges them vertically. capture_session (bool): DEPRECATED. If True, captures the default graph and session (needed for Tensorflow 1.x) interpretation (Union[Callable, str]): function that provides interpretation explaining prediction output. Pass "default" to use simple built-in interpreter, "shap" to use a built-in shapley-based interpreter, or your own custom interpretation function. num_shap (float): a multiplier that determines how many examples are computed for shap-based interpretation. Increasing this value will increase shap runtime, but improve results. Only applies if interpretation is "shap". title (str): a title for the interface; if provided, appears above the input and output components. description (str): a description for the interface; if provided, appears above the input and output components. article (str): an expanded article explaining the interface; if provided, appears below the input and output components. Accepts Markdown and HTML content. thumbnail (str): path to image or src to use as display picture for models listed in gradio.app/hub theme (str): Theme to use - one of "default", "huggingface", "grass", "peach". Add "dark" prefix, e.g. "darkpeach" or "darkdefault" for darktheme. css (str): custom css or path to custom css file to use with interface. allow_screenshot (bool): if False, users will not see a button to take a screenshot of the interface. allow_flagging (bool): if False, users will not see a button to flag an input and output. flagging_options (List[str]): if not None, provides options a user must select when flagging. encrypt (bool): If True, flagged data will be encrypted by key provided by creator at launch flagging_dir (str): what to name the dir where flagged data is stored. show_tips (bool): DEPRECATED. if True, will occasionally show tips about new Gradio features enable_queue (bool): DEPRECATED. if True, inference requests will be served through a queue instead of with parallel threads. Required for longer inference times (> 1min) to prevent timeout. api_mode (bool): DEPRECATED. If True, will skip preprocessing steps when the Interface is called() as a function (should remain False unless the Interface is loaded from an external repo) """ if not isinstance(fn, list): fn = [fn] if not isinstance(inputs, list): inputs = [inputs] if not isinstance(outputs, list): outputs = [outputs] self.input_components = [get_input_instance(i) for i in inputs] self.output_components = [get_output_instance(o) for o in outputs] if repeat_outputs_per_model: self.output_components = len(fn) if interpretation is None or isinstance(interpretation, list) or callable(interpretation): self.interpretation = interpretation elif isinstance(interpretation, str): self.interpretation = [interpretation.lower() for _ in self.input_components] else: raise ValueError("Invalid value for parameter: interpretation") self.predict = fn self.predict_durations = [[0, 0]] len(fn) self.function_names = [func.__name__ for func in fn] self.__name__ = ", ".join(self.function_names) if verbose is not None: warnings.warn("The `verbose` parameter in the `Interface` is deprecated and has no effect.") self.status = "OFF" self.live = live self.layout = layout self.show_input = show_input self.show_output = show_output self.flag_hash = random.getrandbits(32) self.capture_session = capture_session if capture_session is not None: warnings.warn("The `capture_session` parameter in the `Interface` will be deprecated in the near future.") self.session = None self.title = title self.description = description if article is not None: article = utils.readme_to_html(article) article = markdown2.markdown( article, extras=["fenced-code-blocks"]) self.article = article self.thumbnail = thumbnail theme = theme if theme is not None else os.getenv("GRADIO_THEME", "default") if theme not in ("default", "huggingface", "grass", "peach", "darkdefault", "darkhuggingface", "darkgrass", "darkpeach"): raise ValueError("Invalid theme name.") self.theme = theme self.height = height self.width = width if css is not None and os.path.exists(css): with open(css) as css_file: self.css = css_file.read() else: self.css = css if examples is None or isinstance(examples, str) or (isinstance(examples, list) and (len(examples) == 0 or isinstance(examples[0], list))): self.examples = examples elif isinstance(examples, list) and len(self.input_components) == 1: # If there is only one input component, examples can be provided as a regular list instead of a list of lists self.examples = [[e] for e in examples] else: raise ValueError( "Examples argument must either be a directory or a nested list, where each sublist represents a set of inputs.") self.num_shap = num_shap self.examples_per_page = examples_per_page self.simple_server = None self.allow_screenshot = allow_screenshot # For allow_flagging and analytics_enabled: (1) first check for parameter, (2) check for environment variable, (3) default to True self.allow_flagging = allow_flagging if allow_flagging is not None else os.getenv("GRADIO_ALLOW_FLAGGING", "True")=="True" self.analytics_enabled = analytics_enabled
<filename>src/app/services/plot_service.py # MIT License # # Copyright (c) 2020 OdinLabs IO # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. import itertools from collections import defaultdict from math import pi from typing import Dict from bokeh.layouts import column from bokeh.models import ColumnDataSource, FactorRange, Range1d, LinearAxis, PreText from bokeh.palettes import Spectral11 from bokeh.plotting import figure from bokeh.transform import cumsum from app.model import is_valid_aggregate, is_valid_dimension from app.model.model import CHART_PARAMETERS_AXIS, is_valid_filter TOOLS = "hover,crosshair,pan,wheel_zoom,zoom_in,zoom_out,box_zoom,undo,redo,reset,tap,save,box_select,poly_select,lasso_select," PALETTE = Spectral11 ALPHA = 0.8 class PlotService: def __init__(self): self._analytics_service = None self._dashboard_service = None self._axis_order = defaultdict(lambda: 0, {}) def init_app(self, analytics_service, dashboard_service): self._analytics_service = analytics_service self._dashboard_service = dashboard_service self._axis_order = defaultdict(lambda: len(CHART_PARAMETERS_AXIS), {axe: i for i, axe in enumerate(CHART_PARAMETERS_AXIS, 0)}) def _axis_index(self, axis, sort_by): axis_order = self._axis_order axis = sorted([(axe, axis_order[axe[sort_by]]) for axe in axis], key=lambda x: x[1]) return [axe[0] for axe in axis] def _generate_value_tooltips(self, fields, fields_legend): return [(legend, "@" + field) for field, legend in zip(fields, fields_legend)] def _color(self, values): return self._color_def(values, color_dict=dict()) def _color_def(self, values, color_dict): color_palette = itertools.cycle(PALETTE) colors = [] for c in values: color = color_dict.get(c) if not color: color_dict[c] = next(color_palette) colors.append(color_dict[c]) return colors def _bar_chart(self, labels: Dict[str, str], df): dimensions = df.index.names aggregates = df.columns if len(dimensions) == 1: aggr_0 = aggregates[0] # normal vbar and line for other aggregates factors = [factor for factor in df.index.values] data = dict({'x': factors, 'aggr_0': [v for v in df[aggr_0].values]}) data['color'] = self._color(factors) tooltips_var = [] tooltips_label = [] extra_y_ranges = {} if len(aggregates) > 1: for i in range(1, len(aggregates)): aggr_n = aggregates[i] aggr_n_name = 'aggr_' + str(i) tooltips_var.append(aggr_n) tooltips_label.append(aggr_n_name) data[aggr_n_name] = df[aggr_n] extra_y_ranges[aggr_n_name] = Range1d(start=df[aggr_n].min() - 10, end=df[aggr_n].max() + 10) p = figure(x_range=factors, tools=TOOLS, tooltips=[('Value', "@x: @aggr_0")] + self._generate_value_tooltips(tooltips_label, tooltips_var)) source = ColumnDataSource(data=data) p.vbar(x='x', top='aggr_0', source=source, width=0.9, alpha=ALPHA, legend_field='x', fill_color='color') if len(aggregates) > 1: color_palette = itertools.cycle(PALETTE) p.extra_y_ranges = extra_y_ranges for i in range(1, len(aggregates)): aggr_n = aggregates[i] aggr_n_name = 'aggr_' + str(i) color = next(color_palette) p.line(x='x', y=aggr_n_name, color=color, y_range_name=aggr_n_name, source=source) p.add_layout(LinearAxis(y_range_name=aggr_n_name, axis_label=aggr_n), 'left') p.yaxis[i].major_label_text_color = color p.yaxis[0].axis_label = aggr_0 p.legend.title = labels.get(dimensions[0], dimensions[0]) p.add_layout(p.legend[0], 'right') p.xaxis.major_label_orientation = 1 p.xgrid.grid_line_color = None return p elif len(dimensions) == 2: # stacked bar chart aggr_0 = aggregates[0] unstacked = df.unstack(-1) factors = [factor for factor in unstacked.index] data = dict({'x': factors}) aggr_column = unstacked[aggr_0] unstacked_values = [v for v in aggr_column.columns] color = [] colors = itertools.cycle(PALETTE) for memb in unstacked_values: data[memb] = [v for v in aggr_column[memb].fillna(0).values] color.append(next(colors)) p = figure(x_range=factors, tools=TOOLS, tooltips="$name @x: @$name") source = ColumnDataSource(data=data) p.vbar_stack(unstacked_values, x='x', source=source, color=color, width=0.9, alpha=ALPHA, legend_label=unstacked_values) p.legend.title = labels.get(dimensions[-1], dimensions[-1]) p.add_layout(p.legend[0], 'right') p.yaxis.axis_label = aggr_0 p.xaxis.major_label_orientation = 1 p.xgrid.grid_line_color = None return p elif len(dimensions) == 3 or len(dimensions) == 4: # group by n-1 first and stack by last dimension aggr_0 = aggregates[0] unstacked = df.unstack(-1) factors = [factor for factor in unstacked.index] data = dict({'x': factors}) aggr_column = unstacked[aggr_0] unstacked_values = [v for v in aggr_column.columns] color = [] colors = itertools.cycle(PALETTE) for memb in unstacked_values: data[memb] = [v for v in aggr_column[memb].fillna(0).values] color.append(next(colors)) source = ColumnDataSource(data=data) p = figure(x_range=FactorRange(factors), tools=TOOLS, tooltips="$name @x: @$name") p.vbar_stack(unstacked_values, x='x', source=source, color=color, width=0.9, alpha=ALPHA, legend_label=unstacked_values) p.legend.title = labels.get(dimensions[-1], dimensions[-1]) p.add_layout(p.legend[0], 'right') p.yaxis.axis_label = aggr_0 p.xaxis.major_label_orientation = 1 p.xgrid.grid_line_color = None return p else: p = PreText(text="""Bar Chart accepts 1 to 4 dimensions.""", width=500, height=100) return p def _pie_chart(self, labels: Dict[str, str], df): dimensions = df.index.names aggregates = df.columns if len(dimensions) == 1 and len(aggregates) != 0: aggr_0 = aggregates[0] category = dimensions[0] data = dict() df = df.reset_index() aggr_0_sum = df[aggr_0].sum() data['value'] = df[aggr_0] data['angle'] = df[aggr_0] / aggr_0_sum 2 * pi data['percentage'] = df[aggr_0] / aggr_0_sum * 100 data['color'] = self._color(df[category]) data['category'] = df[category] data['legend'] = [l + ": {:.2f} %".format(v) for l, v in zip(data['category'], data['percentage'])] source = ColumnDataSource(data=data) p = figure(tools=TOOLS, tooltips="@category:@value / @percentage") p.wedge(x=0, y=0, radius=0.8, start_angle=cumsum('angle', include_zero=True), end_angle=cumsum('angle'), line_color="white", fill_color='color', legend_field='legend', source=source) p.legend.title = labels.get(category, category) p.add_layout(p.legend[0], 'right') p.xaxis.major_label_orientation = 1 p.xgrid.grid_line_color = None p.toolbar.autohide = True return p else: p = PreText(text="""Pie chart accepts at most one aggregate and a dimension.""", width=500, height=100) return p def _scatter_chart(self, labels: Dict[str, str], df): dimensions = df.index.names aggregates = df.columns if len(aggregates) == 1: aggr_0 = aggregates[0] if len(dimensions) == 1: factors = [factor for factor in df.index] p = figure(x_range=factors, tools=TOOLS, tooltips="@x: @aggr_0") category = dimensions[0] data = dict({'x': factors, 'aggr_0': [v for v in df[aggr_0].values]}) p.line(x='x', y='aggr_0', source=ColumnDataSource(data=data)) p.xaxis.axis_label = labels.get(category, category) p.yaxis.axis_label = aggr_0 p.xaxis.major_label_orientation = 1 p.xgrid.grid_line_color = None p.toolbar.autohide = True return p elif len(dimensions) == 2 or len(dimensions) == 3 or len(dimensions) == 4: # unstack first unstacked = df.stack().unstack(-2).unstack(-1) p = figure(x_range=FactorRange(unstacked.index), tools=TOOLS, tooltips=self._generate_value_tooltips(['x', 'aggr_0', 'category'], ["", aggr_0, labels.get(dimensions[-1])])) color_dict = dict() stacked_columns = list(set([i[0] for i in unstacked.columns])) self._color_def(stacked_columns, color_dict=color_dict) aggr_0_values = [] factors = [] categories = [] for cat in stacked_columns: cat_column = unstacked[cat].dropna() aggr_0_values += cat_column[aggr_0].values.tolist() factors += [i for i in cat_column.index] categories += [cat for i in range(len(cat_column))] colors = [color_dict[cat_val] for cat_val in categories] data = dict({'x': factors, 'aggr_0': aggr_0_values, 'category': categories, 'color': colors}) p.scatter(x='x', y='aggr_0', size=10, fill_color='color', fill_alpha=ALPHA, source=ColumnDataSource(data), legend_group='category') p.legend.title = labels.get(dimensions[-1], dimensions[-1]) p.add_layout(p.legend[0], 'right') p.yaxis.axis_label = aggr_0 p.xaxis.axis_label = "/".join([labels.get(dim, dim) for dim in dimensions[:-1]]) p.xaxis.major_label_orientation = 1 p.xgrid.grid_line_color = None p.toolbar.autohide = True return p else: p = PreText(text="""Scatter Plot 1 aggregates accepts at most 4 dimensions.""", width=500, height=100) return p elif len(aggregates) == 2: aggr_0 = aggregates[0] aggr_1 = aggregates[1] if len(dimensions) == 1: df = df.reset_index() category = dimensions[0] data = dict() data['aggr_0'] = df[aggr_0] data['aggr_1'] = df[aggr_1] data['category'] = df[category] data['color'] = self._color(df[category]) p = figure(tools=TOOLS, tooltips=self._generate_value_tooltips(['aggr_0', 'aggr_1', 'category'], [aggr_0, aggr_1, labels.get(category, category)])) p.scatter(x='aggr_0', y='aggr_1', source=ColumnDataSource(data=data), fill_color='color', fill_alpha=ALPHA, legend_group='category') p.legend.title = labels.get(category, category) p.add_layout(p.legend[0], 'right') p.xaxis.axis_label = aggr_0 p.yaxis.axis_label = aggr_1 p.add_layout(p.legend[0], 'right') p.xaxis.major_label_orientation = 1 p.xgrid.grid_line_color = None p.toolbar.autohide = True return p elif len(dimensions) == 2 or len(dimensions) == 3 or len(dimensions) == 4: unstacked = df.stack().unstack(-2).unstack(-1) p = figure(x_range=FactorRange(unstacked.index), tools=TOOLS, tooltips=self._generate_value_tooltips(['x', 'aggr_0', 'aggr_1', 'category'], ["", aggr_0, aggr_1, labels.get(dimensions[-1])])) color_dict = dict() stacked_columns = list(set([i[0] for i in unstacked.columns])) self._color_def(stacked_columns, color_dict=color_dict) aggr_0_values = [] aggr_1_values = [] factors = [] categories = [] for cat in stacked_columns: cat_column = unstacked[cat].dropna() aggr_0_values += cat_column[aggr_0].values.tolist() aggr_1_values += cat_column[aggr_1].values.tolist() factors += [i for i in cat_column.index] categories += [cat for i in range(len(cat_column))] colors = [color_dict[cat_val] for cat_val in categories] data = dict({'x': factors, 'aggr_0': aggr_0_values, 'aggr_1': aggr_1_values, 'category': categories, 'color': colors}) p.scatter(x='x', y='aggr_0', size='aggr_1', fill_color='color', fill_alpha=ALPHA, source=ColumnDataSource(data), legend_group='category') p.legend.title = labels.get(dimensions[-1], dimensions[-1]) p.add_layout(p.legend[0], 'right') p.yaxis.axis_label = aggr_0 p.xaxis.axis_label = "/".join([labels.get(dim, dim) for dim in dimensions[:-1]]) p.xaxis.major_label_orientation = 1 p.xgrid.grid_line_color = None p.toolbar.autohide = True return p else: # get first dimension (more than one dimension => duplicates in plot) df = df.reset_index() category = dimensions[0] data = dict() data['aggr_0'] = df[aggr_0] data['aggr_1'] = df[aggr_1] data['category'] = df[category] data['color'] = self._color(df[category]) p = figure(tools=TOOLS, tooltips=self._generate_value_tooltips(['aggr_0', 'aggr_1', 'category'], [aggr_0, aggr_1, labels.get(category, category)])) p.scatter(x='aggr_0', y='aggr_1', source=ColumnDataSource(data=data), fill_color='color', fill_alpha=ALPHA, legend_group='category') p.legend.title = labels.get(category, category) p.xaxis.axis_label = aggr_0
<filename>catkit/hardware/boston/BostonDmController.py import os import sys import threading import numpy as np from catkit.interfaces.DeformableMirrorController import DeformableMirrorController from catkit.hardware.boston.DmCommand import DmCommand, convert_dm_image_to_command from catkit.multiprocessing import SharedMemoryManager # BMC is Boston's library and it only works on windows. try: sdk_path = os.environ.get('CATKIT_BOSTON_SDK_PATH') if sdk_path is not None: sys.path.append(sdk_path) import bmc else: bmc = None except ImportError: bmc = None """Interface for Boston Micro-machines deformable mirror controller that can control 2 DMs. It does so by interpreting the first half of the command for DM1, and the second for DM2. This controller cannot control the two DMs independently, it will always send a command to both.""" class BostonDmController(DeformableMirrorController): instrument_lib = bmc def _clear_state(self): self.dm1_command = None self.dm2_command = None self.dm1_command_object = None self.dm2_command_object = None self.channels = {} def initialize(self, serial_number, command_length, dac_bit_width): """ Initialize dm manufacturer specific object - this does not, nor should it, open a connection.""" self.log.info("Opening DM connection") # Create class attributes for storing individual DM commands. self._clear_state() self.serial_num = serial_number self.command_length = command_length self.dac_bit_width = dac_bit_width self.lock = threading.Lock() def send_data(self, data): # The DM controller expects the command to be unitless (normalized Volts): 0.0 - 1.0, where 1.0 := max_volts data_min = np.min(data) data_max = np.max(data) if data_min < 0 or data_max > 1: self.log.warning(f"DM command out of range and will be clipped by hardware. min:{data_min}, max:{data_max}") status = self.instrument.send_data(data) if status != self.instrument_lib.NO_ERR: raise Exception("{}: Failed to send data - {}".format(self.config_id, self.instrument.error_string(status))) def _open(self): self._clear_state() dm = self.instrument_lib.BmcDm() status = dm.open_dm(self.serial_num) if status != self.instrument_lib.NO_ERR: raise Exception("{}: Failed to connect - {}.".format(self.config_id, dm.error_string(status))) # If we get this far, a connection has been successfully opened. # Set self.instrument so that we can close if anything here subsequently fails. self.instrument = dm hardware_command_length = dm.num_actuators() if self.command_length != hardware_command_length: raise ValueError("config.ini error - '{}':'command_length' = {} but hardware gives {}.".format(self.config_id, self.command_length, hardware_command_length)) # Initialize the DM to zeros. zeros = np.zeros(self.command_length, dtype=float) try: self.send_data(zeros) # TODO: call self.apply_shape_to_both() except Exception: self._clear_state() raise else: # Store the current dm_command values in class attributes. self.dm1_command = zeros self.dm2_command = zeros.copy() # dm 1 & 2 should NOT be using the same memory self.dm_controller = self.instrument # For legacy API purposes return self.instrument def _close(self): """Close dm connection safely.""" try: try: self.log.info("Closing DM connection") # FIXME: I'm pretty sure the new SDK does this under the hood. # Set the DM to zeros. zeros = np.zeros(self.command_length, dtype=float) self.send_data(zeros) finally: self.instrument.close_dm() finally: self.instrument = None self._clear_state() def apply_shape_to_both(self, dm1_shape, dm2_shape, flat_map=True, bias=False, as_voltage_percentage=False, as_volts=False, sin_specification=None, output_path=None, channel=None, do_logging=True): """ Combines both commands and sends to the controller to produce a shape on each DM. The concept of channels is optional. If no channel is supplied, the DM shapes are applied directly onto the DM. However, if channels are used, each channel acts as an independent contribution to the total shape that is on the DM. Each contribution will be updated (= replaced) by calling apply_shape_to_both() with the name of that channel. In this way, the current contribution from each channel can be read out using the BOSTON_DM.channels[channel_name] attribute. While individual contributions can be added as delta contributions to a running total, this approach was not taken for code clarity. This comes at the cost of a few microseconds of runtime for each sent DM command. Note: if channels are used, the dm shapes are required to be numpy arrays. In this case, DmCommand objects are not allowed. A TypeError will be thrown is this is the case. :param dm<1\|2>_shape: catkit.hardware.boston.DmCommand.DmCommand or numpy array of the following shapes: 34x34, 1x952, 1x2048, 1x4096. Interpreted by default as the desired DM surface height in units of meters, but see parameters as_volts and as_voltage_percentage. When using channels, this should be a numpy array, and they should have the same shape for each channel. :param flat_map: If true, add flat map correction to the data before outputting commands :param bias: If true, add bias to the data before outputting commands :param as_voltage_percentage: Interpret the data as a voltage percentage instead of meters; Deprecated. :param as_volts: If true, interpret the data as volts instead of meters :param sin_specification: Add this sine to the data :param output_path: str, Path to save commands to if provided. Default `None` := don't save. :param channel: str or None, the DM channel on which to write this shape. Default `None` := set the entire shape. :param do_logging: boolean. Whether to emit a logging message. In fast (>100Hz) loops, the logs can be overwhelmed by log messages from the DM. Setting this to False doesn't emit a log message. Default: True. """ with self.lock: if do_logging: if channel is None: self.log.info("Applying shape to both DMs") else: self.log.info(f'Applying shape to both DMs in channel {channel}.') if channel is None: if self.channels: self.log.warn('A channel was not supplied while channels were used previously. ' + 'All channels will be reset. This may not be what you want.') self.channels = {} else: if isinstance(dm1_shape, DmCommand) or isinstance(dm2_shape, DmCommand): # DmCommand objects cannot be added together, yet. raise TypeError('DM shapes cannot be DmCommands when using channels.') # Check if dm{1,2}_shape is 2D, then convert to 1D. # This standardizes the shape stored in the channels attribute. if dm1_shape.ndim == 2: dm1_shape = convert_dm_image_to_command(dm1_shape) if dm2_shape.ndim == 2: dm2_shape = convert_dm_image_to_command(dm2_shape) self.channels[channel] = (dm1_shape, dm2_shape) # Add contributions for each channel, and use that as the dm command. dm1_shape = 0 dm2_shape = 0 for dm1, dm2 in self.channels.values(): dm1_shape += dm1 dm2_shape += dm2 if not isinstance(dm1_shape, DmCommand): dm1_shape = DmCommand(data=dm1_shape, dm_num=1, flat_map=flat_map, bias=bias, as_voltage_percentage=as_voltage_percentage, as_volts=as_volts, sin_specification=sin_specification) if not isinstance(dm2_shape, DmCommand): dm2_shape = DmCommand(data=dm2_shape, dm_num=2, flat_map=flat_map, bias=bias, as_voltage_percentage=as_voltage_percentage, as_volts=as_volts, sin_specification=sin_specification) # Ensure that the correct dm_num is set. dm1_shape.dm_num = 1 dm2_shape.dm_num = 2 if output_path is not None: dm1_shape.export_fits(output_path) dm2_shape.export_fits(output_path) # Use DmCommand class to format the commands correctly (with zeros for other DM). dm1_command = dm1_shape.to_dm_command() dm2_command = dm2_shape.to_dm_command() # Add both arrays together (first half and second half) and send to DM. full_command = dm1_command + dm2_command try: self.send_data(full_command) except Exception: # We shouldn't guarantee the state of the DM. self._clear_state() raise else: # Update both dm_command class attributes. self.dm1_command = dm1_command self.dm2_command = dm2_command self.dm1_command_object = dm1_shape self.dm2_command_object = dm2_shape def apply_shape(self, dm_shape, dm_num, flat_map=True, bias=False, as_voltage_percentage=False, as_volts=False, sin_specification=None, output_path=None): """ Forms a command for a single DM, and re-sends the existing shape to other DM. :param dm_shape: catkit.hardware.boston.DmCommand.DmCommand or numpy array of the following shapes: 34x34, 1x952, 1x2048, 1x4096. Interpreted by default as the desired DM surface height in units of meters, but see parameters as_volts and as_voltage_percentage. :param dm_num: Which DM to apply the shape to. Valid values are 1, 2. :param flat_map: If true, add flat map correction to the data before outputting commands :param bias: If true, add bias to the data before outputting commands :param as_voltage_percentage: Interpret the data as a voltage percentage instead of meters; Deprecated. :param as_volts: If true, interpret the data as volts instead of meters :param sin_specification: Add this sine to the data :param output_path: str, Path to save commands to if provided. Default `None` := don't save. """ self.log.info("Applying shape to DM " + str(dm_num)) if not isinstance(dm_shape, DmCommand): dm_shape = DmCommand(data=dm_shape, dm_num=dm_num, flat_map=flat_map, bias=bias, as_voltage_percentage=as_voltage_percentage, as_volts=as_volts, sin_specification=sin_specification) # Ensure the dm_num is correct. dm_shape.dm_num = dm_num if output_path is not None: dm_shape.export_fits(output_path) other_dm_command_object = self.dm2_command_object if dm_num == 1 else self.dm1_command_object other_dm_command_object.export_fits(output_path) # Use DmCommand class to format the single command correctly (with zeros for other DM). dm_command = dm_shape.to_dm_command() # Grab the other DM's currently applied shape. other_dm_command = self.dm2_command if dm_num == 1 else self.dm1_command # Add both arrays together (first half and second half) and send to DM. full_command = dm_command + other_dm_command try: self.send_data(full_command) except Exception: # We shouldn't guarantee the state of the DM. self._clear_state() raise else: # Update the dm_command
<reponame>wenting-zhao/sgnmt # -- coding: utf-8 -- # coding=utf-8 # Copyright 2019 The SGNMT Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """This module contains predictors that deal wit the length of the target sentence. The ``NBLengthPredictor`` assumes a negative binomial distribution on the target sentence lengths, where the parameters r and p are linear combinations of features extracted from the source sentence. The ``WordCountPredictor`` adds the number of words as cost, which can be used to prevent hypotheses from getting to short when using a language model. """ import logging import math from scipy.special import logsumexp from scipy.special import gammaln from cam.sgnmt import utils from cam.sgnmt.misc.trie import SimpleTrie from cam.sgnmt.predictors.core import Predictor, UnboundedVocabularyPredictor import numpy as np NUM_FEATURES = 5 EPS_R = 0.1; def load_external_lengths(path): """Loads a length distribution from a plain text file. The file must contain blank separated <length>:<score> pairs in each line. Args: path (string): Path to the length file. Returns: list of dicts mapping a length to its scores, one dict for each sentence. """ lengths = [] with open(path) as f: for line in f: scores = {} for pair in line.strip().split(): if ':' in pair: length, score = pair.split(':') scores[int(length)] = float(score) else: scores[int(pair)] = 0.0 lengths.append(scores) return lengths def load_external_ids(path): """ load file of ids to list """ with open(path) as f: return [int(line.strip()) for line in f] class NBLengthPredictor(Predictor): """This predictor assumes that target sentence lengths are distributed according a negative binomial distribution with parameters r,p. r is linear in features, p is the logistic of a linear function over the features. Weights can be trained using the Matlab script ``estimate_length_model.m`` Let w be the model_weights. All features are extracted from the src sentence:: r = w0 * #char + w1 * #words + w2 * #punctuation + w3 * #char/#words + w4 * #punct/#words + w10 p = logistic(w5 * #char + w6 * #words + w7 * #punctuation + w8 * #char/#words + w9 * #punct/#words + w11) target_length ~ NB(r,p) The biases w10 and w11 are optional. The predictor predicts EOS with NB(#consumed_words,r,p) """ def __init__(self, text_file, model_weights, use_point_probs, offset = 0): """Creates a new target sentence length model predictor. Args: text_file (string): Path to the text file with the unindexed source sentences, i.e. not using word ids model_weights (list): Weights w0 to w11 of the length model. See class docstring for more information use_point_probs (bool): Use point estimates for EOS token, 0.0 otherwise offset (int): Subtract this from hypothesis length before applying the NB model """ super(NBLengthPredictor, self).__init__() self.use_point_probs = use_point_probs self.offset = offset if len(model_weights) == 2NUM_FEATURES: # add biases model_weights.append(0.0) model_weights.append(0.0) if len(model_weights) != 2NUM_FEATURES+2: logging.fatal("Number of length model weights has to be %d or %d" % (2NUM_FEATURES, 2NUM_FEATURES+2)) self.r_weights = model_weights[0:NUM_FEATURES] + [model_weights[-2]] self.p_weights = model_weights[NUM_FEATURES:2NUM_FEATURES] + [model_weights[-1]] self.src_features = self._extract_features(text_file) self.n_consumed = 0 def _extract_features(self, file_name): """Extract all features from the source sentences. """ feats = [] with open(file_name) as f: for line in f: feats.append(self._analyse_sentence(line.strip())) return feats def _analyse_sentence(self, sentence): """Extract features for a single source sentence. Args: sentence (string): Source sentence string Returns: 5-tuple of features as described in the class docstring """ n_char = len(sentence) + 0.0 n_words = len(sentence.split()) + 0.0 n_punct = sum([sentence.count(s) for s in ",.:;-"]) + 0.0 return [n_char, n_words, n_punct, n_char/n_words, n_punct/n_words] def get_unk_probability(self, posterior): """If we use point estimates, return 0 (=1). Otherwise, return the 1-p(EOS), with p(EOS) fetched from ``posterior`` """ if self.use_point_probs: if self.n_consumed == 0: return self.max_eos_prob return 0.0 if self.n_consumed == 0: return 0.0 return np.log(1.0 - np.exp(posterior[utils.EOS_ID])) def predict_next(self): """Returns a dictionary with single entry for EOS. """ if self.n_consumed == 0: return {utils.EOS_ID : utils.NEG_INF} return {utils.EOS_ID : self._get_eos_prob()} def _get_eos_prob(self): """Get loglikelihood according cur_p, cur_r, and n_consumed """ eos_point_prob = self._get_eos_point_prob(max( 1, self.n_consumed - self.offset)) if self.use_point_probs: return eos_point_prob - self.max_eos_prob if not self.prev_eos_probs: self.prev_eos_probs.append(eos_point_prob) return eos_point_prob # bypass utils.log_sum because we always want to use logsumexp here prev_sum = logsumexp(np.asarray([p for p in self.prev_eos_probs])) self.prev_eos_probs.append(eos_point_prob) # Desired prob is eos_point_prob / (1-last_eos_probs_sum) return eos_point_prob - np.log(1.0-np.exp(prev_sum)) def _get_eos_point_prob(self, n): return gammaln(n + self.cur_r) \ - gammaln(n + 1) \ - gammaln(self.cur_r) \ + n np.log(self.cur_p) \ + self.cur_r * np.log(1.0-self.cur_p) def _get_max_eos_prob(self): """Get the maximum loglikelihood according cur_p, cur_r TODO: replace this brute force impl. with something smarter """ max_prob = utils.NEG_INF n_prob = max_prob n = 0 while n_prob == max_prob: n += 1 n_prob = self._get_eos_point_prob(n) max_prob = max(max_prob, n_prob) return max_prob def initialize(self, src_sentence): """Extract features for the source sentence. Note that this method does not use ``src_sentence`` as we need the string representation of the source sentence to extract features. Args: src_sentence (list): Not used """ feat = self.src_features[self.current_sen_id] + [1.0] self.cur_r = max(EPS_R, np.dot(feat, self.r_weights)); p = np.dot(feat, self.p_weights) p = 1.0 / (1.0 + math.exp(-p)) self.cur_p = max(utils.EPS_P, min(1.0 - utils.EPS_P, p)) self.n_consumed = 0 self.prev_eos_probs = [] if self.use_point_probs: self.max_eos_prob = self._get_max_eos_prob() def consume(self, word): """Increases the current history length Args: word (int): Not used """ self.n_consumed = self.n_consumed + 1 def get_state(self): """State consists of the number of consumed words, and the accumulator for previous EOS probability estimates if we don't use point estimates. """ return self.n_consumed,self.prev_eos_probs def set_state(self, state): """Set the predictor state """ self.n_consumed,self.prev_eos_probs = state def is_equal(self, state1, state2): """Returns true if the number of consumed words is the same """ n1,_ = state1 n2,_ = state2 return n1 == n2 class WordCountPredictor(Predictor): """This predictor adds the (negative) number of words as feature. This means that this predictor encourages shorter hypotheses when used with a positive weight. """ def __init__(self, word=-1, nonterminal_penalty=False, nonterminal_ids=None, min_terminal_id=0, max_terminal_id=30003, negative_wc=True, vocab_size=30003): """Creates a new word count predictor instance. Args: word (int): If this is non-negative we count only the number of the specified word. If its negative, count all words nonterminal_penalty (bool): If true, apply penalty only to tokens in a range (the range outside min/max terminal id) nonterminal_ids: file containing ids of nonterminal tokens min_terminal_id: lower bound of tokens not to penalize, if nonterminal_penalty selected max_terminal_id: upper bound of tokens not to penalize, if nonterminal_penalty selected negative_wc: If true, the score of this predictor is the negative word count. vocab_size: upper bound of tokens, used to find nonterminal range """ super(WordCountPredictor, self).__init__() val = 1.0 if negative_wc: val = -1.0 if nonterminal_penalty: if nonterminal_ids: nts = load_external_ids(nonterminal_ids) else: min_nt_range = range(0, min_terminal_id) max_nt_range = range(max_terminal_id + 1, vocab_size) nts = list(min_nt_range) + list(max_nt_range) self.posterior = {nt: val for nt in nts} self.posterior[utils.EOS_ID] = 0.0 self.posterior[utils.UNK_ID] = 0.0 self.unk_prob = 0.0 elif word < 0: self.posterior = {utils.EOS_ID : 0.0} self.unk_prob = val else: self.posterior = {word : val} self.unk_prob = 0.0 def get_unk_probability(self, posterior): return self.unk_prob def predict_next(self): return self.posterior def initialize(self, src_sentence): """Empty""" pass def consume(self, word): """Empty""" pass def get_state(self): """Returns true """ return True def set_state(self, state): """Empty""" pass def is_equal(self, state1, state2): """Returns true """ return True class WeightNonTerminalPredictor(Predictor): """This wrapper multiplies the weight of given tokens (those outside the min/max terminal range) by a factor.""" def __init__(self, slave_predictor, penalty_factor=1.0, nonterminal_ids=None, min_terminal_id=0, max_terminal_id=30003, vocab_size=30003): """Creates a new id-weighting wrapper for a predictor Args: slave_predictor: predictor to
""" Authors: <NAME>, <NAME> TUM, 2020 In order to guarantee transferability of models, Network models should follow the following conventions. Classes should be called Node Edge Network in order to guarantee correct import in other modules. """ # -------------------------------------------------------------------------------------------------------------------- # # standard distribution imports # ----------------------------- import os import logging # additional module imports (> requirements) # ------------------------------------------ import pandas as pd import numpy as np from pyproj import Transformer # src imports # ----------- from src.routing.NetworkBase import NetworkBase from src.routing.routing_imports.Router import Router # -------------------------------------------------------------------------------------------------------------------- # # global variables # ---------------- from src.misc.globals import * LOG = logging.getLogger(__name__) # import os # import pandas as pd # import imports.Router as Router def read_node_line(columns): return Node(int(columns["node_index"]), int(columns["is_stop_only"]), float(columns["pos_x"]), float(columns["pos_y"])) class Node(): def __init__(self, node_index, is_stop_only, pos_x, pos_y, node_order=None): self.node_index = node_index self.is_stop_only = is_stop_only self.pos_x = pos_x self.pos_y = pos_y # self.edges_to = {} #node_obj -> edge self.edges_from = {} #node_obj -> edge # self.travel_infos_from = {} #node_index -> (tt, dis) self.travel_infos_to = {} #node_index -> (tt, dis) # # attributes set during path calculations self.is_target_node = False # is set and reset in computeFromNodes #attributes for forwards dijkstra self.prev = None self.settled = 1 self.cost_index = -1 self.cost = None # attributes for backwards dijkstra (for bidirectional dijkstra) self.next = None self.settled_back = 1 self.cost_index_back = -1 self.cost_back = None def __str__(self): return str(self.node_index) def must_stop(self): return self.is_stop_only def get_position(self): return (self.pos_x, self.pos_y) def get_next_node_edge_pairs(self, ch_flag = False): """ :return: list of (node, edge) tuples [references to objects] in forward direction """ return self.edges_to.items() def get_prev_node_edge_pairs(self, ch_flag = False): """ :return: list of (node, edge) tuples [references to objects] in backward direction """ return self.edges_from.items() def add_next_edge_to(self, other_node, edge): #print("add next edge to: {} -> {}".format(self.node_index, other_node.node_index)) self.edges_to[other_node] = edge self.travel_infos_to[other_node.node_index] = edge.get_tt_distance() def add_prev_edge_from(self, other_node, edge): self.edges_from[other_node] = edge self.travel_infos_from[other_node.node_index] = edge.get_tt_distance() def get_travel_infos_to(self, other_node_index): return self.travel_infos_to[other_node_index] def get_travel_infos_from(self, other_node_index): return self.travel_infos_from[other_node_index] class Edge(): def __init__(self, edge_index, distance, travel_time): self.edge_index = edge_index self.distance = distance self.travel_time = travel_time # def __str__(self): return "-".join(self.edge_index) def set_tt(self, travel_time): self.travel_time = travel_time def get_tt(self): """ :return: (current) travel time on edge """ return self.travel_time def get_distance(self): """ :return: distance of edge """ return self.distance def get_tt_distance(self): """ :return: (travel time, distance) tuple """ return (self.travel_time, self.distance) # Position: (start_node_id, end_node_id, relative_pos) # -> (node_id, None, None) in case vehicle is on a node # -> relative_pos in [0.0, 1.0] # A Route is defined as list of node-indices (int) # while all given start-and end-position nodes are included class NetworkBasic(NetworkBase): def __init__(self, network_name_dir, network_dynamics_file_name=None, scenario_time=None): """ The network will be initialized. This network only uses basic routing algorithms (dijkstra and bidirectional dijkstra) :param network_name_dir: name of the network_directory to be loaded :param type: determining whether the base or a pre-processed network will be used :param scenario_time: applying travel times for a certain scenario at a given time in the scenario :param network_dynamics_file_name: file-name of the network dynamics file :type network_dynamics_file_name: str """ self.nodes = [] #list of all nodes in network (index == node.node_index) self.network_name_dir = network_name_dir self.travel_time_file_folders = self._load_tt_folder_path(network_dynamics_file_name=network_dynamics_file_name) self.loadNetwork(network_name_dir, network_dynamics_file_name=network_dynamics_file_name, scenario_time=scenario_time) self.current_dijkstra_number = 1 #used in dijkstra-class self.sim_time = 0 # TODO # self.zones = None # TODO # with open(os.sep.join([self.network_name_dir, "base","crs.info"]), "r") as f: self.crs = f.read() def loadNetwork(self, network_name_dir, network_dynamics_file_name=None, scenario_time=None): nodes_f = os.path.join(network_name_dir, "base", "nodes.csv") print(f"Loading nodes from {nodes_f} ...") nodes_df = pd.read_csv(nodes_f) self.nodes = nodes_df.apply(read_node_line, axis=1) # edges_f = os.path.join(network_name_dir, "base", "edges.csv") print(f"Loading edges from {edges_f} ...") with open(edges_f) as fhin: header = fhin.readline() for line in fhin: lc = line.strip().split(",") o_node = self.nodes[int(lc[0])] d_node = self.nodes[int(lc[1])] # for the table approach, int values are used (to avoid rounding mistakes!) tmp_edge = Edge((o_node, d_node), float(lc[2]), float(lc[3])) o_node.add_next_edge_to(d_node, tmp_edge) d_node.add_prev_edge_from(o_node, tmp_edge) print("... {} nodes loaded!".format(len(self.nodes))) if scenario_time is not None: latest_tt = None if len(self.travel_time_file_folders.keys()) > 0: tts = sorted(list(self.travel_time_file_folders.keys())) for tt in tts: if tt > scenario_time: break latest_tt = tt self.load_tt_file(latest_tt) def _load_tt_folder_path(self, network_dynamics_file_name=None): """ this method searches in the network-folder for travel_times folder. the name of the folder is defined by the simulation time from which these travel times are valid stores the corresponding time to trigger loading of new travel times ones the simulation time is reached. """ tt_folders = {} if network_dynamics_file_name is None: LOG.info("... no network dynamics file given -> read folder structure") for f in os.listdir(self.network_name_dir): time = None try: time = int(f) except: continue tt_folders[time] = os.path.join(self.network_name_dir, f) else: LOG.info("... load network dynamics file: {}".format(os.path.join(self.network_name_dir, network_dynamics_file_name))) nw_dynamics_df = pd.read_csv(os.path.join(self.network_name_dir, network_dynamics_file_name)) nw_dynamics_df.set_index("simulation_time", inplace=True) for sim_time, tt_folder_name in nw_dynamics_df["travel_time_folder"].items(): tt_folders[int(sim_time)] = os.path.join(self.network_name_dir, str(tt_folder_name)) return tt_folders def update_network(self, simulation_time, update_state = True): """This method can be called during simulations to update travel times (dynamic networks). :param simulation_time: time of simulation :type simulation_time: float :return: new_tt_flag True, if new travel times found; False if not :rtype: bool """ LOG.debug(f"update network {simulation_time}") self.sim_time = simulation_time if update_state: if self.travel_time_file_folders.get(simulation_time, None) is not None: self.load_tt_file(simulation_time) return True return False def load_tt_file(self, scenario_time): """ loads new travel time files for scenario_time """ self._reset_internal_attributes_after_travel_time_update() f = self.travel_time_file_folders[scenario_time] tt_file = os.path.join(f, "edges_td_att.csv") tmp_df = pd.read_csv(tt_file, index_col=[0,1]) for edge_index_tuple, new_tt in tmp_df["edge_tt"].iteritems(): self._set_edge_tt(edge_index_tuple[0], edge_index_tuple[1], new_tt) def _set_edge_tt(self, o_node_index, d_node_index, new_travel_time): o_node = self.nodes[o_node_index] d_node = self.nodes[d_node_index] edge_obj = o_node.edges_to[d_node] edge_obj.set_tt(new_travel_time) new_tt, dis = edge_obj.get_tt_distance() o_node.travel_infos_to[d_node_index] = (new_tt, dis) d_node.travel_infos_from[o_node_index] = (new_tt, dis) def get_node_list(self): """ :return: list of node objects. """ return self.nodes def get_number_network_nodes(self): return len(self.nodes) def get_must_stop_nodes(self): """ returns a list of node-indices with all nodes with a stop_only attribute """ return [n.node_index for n in self.nodes if n.must_stop()] def return_position_from_str(self, position_str): a, b, c = position_str.split(";") if b == "-1": return (int(a), None, None) else: return (int(a), int(b), float(c)) def return_node_coordinates(self, node_index): return self.nodes[node_index].get_position() def return_position_coordinates(self, position_tuple): """Returns the spatial coordinates of a position. :param position_tuple: (o_node, d_node, rel_pos) \| (o_node, None, None) :return: (x,y) for metric systems """ if position_tuple[1] is None: return self.return_node_coordinates(position_tuple[0]) else: c0 = np.array(self.return_node_coordinates(position_tuple[0])) c1 = np.array(self.return_node_coordinates(position_tuple[1])) c_rel = position_tuple[2] * c1 + (1 - position_tuple[2]) * c0 return c_rel[0], c_rel[1] def return_network_bounding_box(self): min_x = min([node.pos_x for node in self.nodes]) max_x = max([node.pos_x for node in self.nodes]) min_y = min([node.pos_y for node in self.nodes]) max_y = max([node.pos_y for node in self.nodes]) proj_transformer = Transformer.from_proj(self.crs, 'epsg:4326') lats, lons = proj_transformer.transform([min_x, max_x], [min_y, max_y]) return list(zip(lons, lats)) def return_positions_lon_lat(self, position_tuple_list: list) -> list: pos_list = [self.return_position_coordinates(pos) for pos in position_tuple_list] x, y = list(zip(*pos_list)) proj_transformer = Transformer.from_proj(self.crs, 'epsg:4326') lats, lons = proj_transformer.transform(x, y) return list(zip(lons, lats)) def get_section_infos(self, start_node_index, end_node_index): """ :param start_node_index_index: index of start_node of section :param end_node_index: index of end_node of section :return: (travel time, distance); if no section between nodes (None, None) """ return self.nodes[start_node_index].get_travel_infos_to(end_node_index) def return_route_infos(self, route, rel_start_edge_position, start_time): """ This method returns the information travel information along a route. The start position is given by a relative value on the first edge [0,1], where 0 means that the vehicle is at the first node. :param route: list of nodes :param rel_start_edge_position: float [0,1] determining the start position :param start_time: can be used as an offset in case the route is planned for a future time :return: (arrival time, distance to travel) """ arrival_time = start_time distance = 0 _, start_tt, start_dis = self.get_section_overhead( (route[0], route[1], rel_start_edge_position), from_start=False) arrival_time += start_tt distance += start_dis if len(route) > 2: for i in range(2, len(route)): tt, dis = self.get_section_infos(route[i-1], route[i]) arrival_time += tt distance += dis return (arrival_time, distance) def assign_route_to_network(self, route, start_time): """This method can be used for dynamic network models in which the travel times will be derived from the number of vehicles/routes assigned to the network. :param route: list of nodes :param start_time: can be used as an offset in case the route is planned for a future time :return: TODO """ pass def get_section_overhead(self, position, from_start=True, customized_section_cost_function=None): """This method computes the section overhead for a certain position. :param position: (current_edge_origin_node_index, current_edge_destination_node_index, relative_position) :param from_start: computes already traveled travel_time and distance, if False: computes rest travel time (relative_position -> 1.0-relative_position) :param customized_section_cost_function: customized routing objective function
<filename>embiggen/pipelines/compute_node_embedding.py """Sub-module with methods to compute node-embedding with a one-liner.""" import inspect import warnings from typing import Dict, List, Tuple, Union import pandas as pd import tensorflow as tf from cache_decorator import Cache from ensmallen import Graph from ..embedders import (Embedder, GraphCBOW, GraphGloVe, GraphSkipGram, Siamese, SimplE, TransE, TransH, TransR) from ..utils import has_gpus, has_nvidia_drivers, has_rocm_drivers SUPPORTED_NODE_EMBEDDING_METHODS = { "CBOW": GraphCBOW, "GloVe": GraphGloVe, "SkipGram": GraphSkipGram, "Siamese": Siamese, "TransE": TransE, "SimplE": SimplE, "TransH": TransH, "TransR": TransR, } REQUIRE_ZIPFIAN = [ "CBOW", "SkipGram" ] RANDOM_WALK_BASED_MODELS = [ "CBOW", "GloVe", "SkipGram" ] LINK_PREDICTION_BASED_MODELS = [ "Siamese", "TransR", "TransE", "TransH", "SimplE" ] assert set(RANDOM_WALK_BASED_MODELS + LINK_PREDICTION_BASED_MODELS) == set(SUPPORTED_NODE_EMBEDDING_METHODS) def get_available_node_embedding_methods() -> List[str]: """Return list of supported node embedding methods.""" return list(SUPPORTED_NODE_EMBEDDING_METHODS.keys()) def get_node_embedding_method(node_embedding_method_name: str) -> Embedder: """Return node embedding method curresponding to given name.""" return SUPPORTED_NODE_EMBEDDING_METHODS[node_embedding_method_name] def is_node_embedding_method_supported(node_embedding_method_name: str) -> bool: """Return boolean value representing if given node embedding method is supported. Parameters -------------------- node_embedding_method_name: str, Name of the node embedding method. Returns -------------------- Whether the given node embedding method is supported. """ return node_embedding_method_name in get_available_node_embedding_methods() def _train_model( graph: Graph, node_embedding_method_name: str, fit_kwargs: Dict, verbose: bool, support_mirrored_strategy: bool, kwargs: Dict ) -> Tuple[Union[pd.DataFrame, Tuple[pd.DataFrame]], pd.DataFrame]: """Return embedding computed with required node embedding method. Parameters -------------------------- graph: Graph, The graph to embed. node_embedding_method_name: str, The name of the node embedding method to use. fit_kwargs: Dict, Arguments to pass to the fit call. verbose: bool = True, Whether to show loading bars. use_mirrored_strategy: bool = True, Whether to use mirrored strategy. kwargs: Dict, Arguments to pass to the node embedding method constructor. Read the documentation of the selected method. Returns -------------------------- Tuple with node embedding and training history. """ # Creating the node embedding model model = get_node_embedding_method(node_embedding_method_name)( graph, support_mirrored_strategy=support_mirrored_strategy, kwargs ) # Fitting the node embedding model history = model.fit( verbose=verbose, fit_kwargs ) # Extracting computed embedding node_embedding = model.get_embedding_dataframe() return node_embedding, history @Cache( cache_path=[ "node_embeddings/{node_embedding_method_name}/{graph_name}/{_hash}_embedding.pkl.gz", "node_embeddings/{node_embedding_method_name}/{graph_name}/{_hash}_training_history.csv.xz", ], args_to_ignore=["devices", "use_mirrored_strategy", "verbose"] ) def _compute_node_embedding( graph: Graph, graph_name: str, # pylint: disable=unused-argument node_embedding_method_name: str, fit_kwargs: Dict, verbose: bool = True, use_mirrored_strategy: bool = True, devices: Union[List[str], str] = None, kwargs: Dict ) -> Tuple[Union[pd.DataFrame, Tuple[pd.DataFrame]], pd.DataFrame]: """Return embedding computed with required node embedding method. Specifically, this method also caches the embedding automatically. Parameters -------------------------- graph: Graph, The graph to embed. graph_name: str, The name of the graph. node_embedding_method_name: str, The name of the node embedding method to use. fit_kwargs: Dict, Arguments to pass to the fit call. verbose: bool = True, Whether to show loading bars. use_mirrored_strategy: bool = True, Whether to use mirrored strategy. devices: Union[List[str], str] = None, The devices to use. If None, all GPU devices available are used. kwargs: Dict, Arguments to pass to the node embedding method constructor. Read the documentation of the selected method. Returns -------------------------- Tuple with node embedding and training history. """ # Since the verbose kwarg may be provided also on the fit_kwargs # we normalize the parameter to avoid collisions. verbose = fit_kwargs.pop("verbose", verbose) kwargs = dict( graph=graph, node_embedding_method_name=node_embedding_method_name, fit_kwargs=fit_kwargs, verbose=verbose, support_mirrored_strategy=use_mirrored_strategy, kwargs ) if use_mirrored_strategy: strategy = tf.distribute.MirroredStrategy(devices=devices) with strategy.scope(): return _train_model(kwargs) return _train_model(kwargs) def compute_node_embedding( graph: Graph, node_embedding_method_name: str, use_mirrored_strategy: bool = True, devices: Union[List[str], str] = None, fit_kwargs: Dict = None, verbose: Union[bool, int] = True, automatically_drop_unsupported_parameters: bool = False, automatically_enable_time_memory_tradeoffs: bool = True, automatically_sort_by_decreasing_outbound_node_degree: bool = True, kwargs: Dict ) -> Tuple[pd.DataFrame, pd.DataFrame]: """Return embedding computed using SkipGram on given graph. Parameters -------------------------- graph: Graph, Graph to embed. node_embedding_method_name: str, The name of the node embedding method to use. use_mirrored_strategy: bool = True, Whether to use mirror strategy to distribute the computation across multiple devices. Note that this will be automatically disabled if the set of devices detected is only composed of one, since using MirroredStrategy adds a significant overhead and may endup limiting the device usage. devices: Union[List[str], str] = None, The devices to use. If None, all GPU devices available are used. fit_kwargs: Dict = None, Arguments to pass to the fit call. verbose: bool = True, Whether to show loading bars. automatically_drop_unsupported_parameters: bool = False, If required, we filter out the unsupported parameters. This may be useful when running a suite of experiments with a set of parameters and you do not want to bother in dropping the parameters that are only supported in a subset of methods. automatically_enable_time_memory_tradeoffs: bool = True, Whether to activate the time memory tradeoffs automatically. Often case, this is something you want enabled on your graph object. Since, generally, it is a good idea to enable these while computing a node embedding we enable these by default. automatically_sort_by_decreasing_outbound_node_degree: bool = True, Whether to automatically sort the nodes by the outbound node degree. This is necessary in order to run SkipGram efficiently with the NCE loss. It will ONLY be executed if the requested model is SkipGram. **kwargs: Dict, Arguments to pass to the node embedding method constructor. Read the documentation of the selected method to learn which methods are supported by the selected constructor. Returns -------------------------- Tuple with node embedding and training history. """ if not is_node_embedding_method_supported(node_embedding_method_name): raise ValueError( ( "The given node embedding method `{}` is not supported. " "The supported node embedding methods are `{}`." ).format( node_embedding_method_name, get_available_node_embedding_methods() ) ) # To avoid some nighmares we check availability of GPUs. if not has_gpus(): # If there are no GPUs, mirrored strategy makes no sense. if use_mirrored_strategy: use_mirrored_strategy = False warnings.warn( "It does not make sense to use mirrored strategy " "when GPUs are not available.\n" "The parameter has been disabled." ) # We check for drivers to try and give a more explainatory # warning about the absence of GPUs. if has_nvidia_drivers(): warnings.warn( "It was not possible to detect GPUs but the system " "has NVIDIA drivers installed.\n" "It is very likely there is some mis-configuration " "with your TensorFlow instance.\n" "The model will train a LOT faster if you figure " "out what may be the cause of this issue on your " "system: sometimes a simple reboot will do a lot of good.\n" "If you are currently on COLAB, remember to enable require " "a GPU instance from the menu!" ) elif has_rocm_drivers(): warnings.warn( "It was not possible to detect GPUs but the system " "has ROCM drivers installed.\n" "It is very likely there is some mis-configuration " "with your TensorFlow instance.\n" "The model will train a LOT faster if you figure " "out what may be the cause of this issue on your " "system: sometimes a simple reboot will do a lot of good." ) else: warnings.warn( "It was neither possible to detect GPUs nor GPU drivers " "of any kind on your system (neither CUDA or ROCM).\n" "The model will proceed with trainining, but it will be " "significantly slower than what would be possible " "with GPU acceleration." ) # If the fit kwargs are not given we normalize them to an empty dictionary. if fit_kwargs is None: fit_kwargs = {} # If the model requested is SkipGram and the given graph does not have sorted # node IDs according to decreasing outbound node degrees, we create the new graph # that has the node IDs sorted. if automatically_sort_by_decreasing_outbound_node_degree and node_embedding_method_name in REQUIRE_ZIPFIAN and not graph.has_nodes_sorted_by_decreasing_outbound_node_degree(): graph = graph.sort_by_decreasing_outbound_node_degree() # If required, we filter out the unsupported parameters. # This may be useful when running a suite of experiments with a set of # parameters and you do not want to bother in dropping the parameters # that are only supported in a subset of methods. if automatically_drop_unsupported_parameters and kwargs: # Get the list of supported parameters supported_parameter = inspect.signature( get_node_embedding_method(node_embedding_method_name).__init__ ).parameters # Filter out the unsupported parameters kwargs = { key: value for key, value in kwargs.items() if key in supported_parameter } # If required we enable the time memory tradeoffs. if automatically_enable_time_memory_tradeoffs: if node_embedding_method_name in RANDOM_WALK_BASED_MODELS: graph.enable( vector_sources=False, vector_destinations=True, vector_cumulative_node_degrees=True ) if node_embedding_method_name in LINK_PREDICTION_BASED_MODELS: graph.enable( vector_sources=True, vector_destinations=True, vector_cumulative_node_degrees=False ) # If devices are
<reponame>joaopbicalho/CodingInPython def get_cur_hedons(): global cur_hedons return cur_hedons def get_cur_health(): global cur_health return cur_health def offer_star(activity): global cur_star global star_counter global time_since_curstar global time_since_star global star_break global time_since_star1 global time_since_star2 global star_span time_since_curstar = 0 star_counter += 1 time_since_star = time_since_star1 + time_since_star2 if time_since_star >= 120: star_counter += -1 time_since_star1 = time_since_star2 time_since_star2 = 0 star_span = 1 if star_counter > 2: cur_star = "none" star_break = "activated" elif activity == "running": cur_star = "running" elif activity == "textbooks": cur_star = "textbooks" elif time_since_curstar < 120: if star_counter > 2: cur_star = "none" star_break = "activated" elif activity == "running": cur_star = "running" star_span = star_counter elif activity == "textbooks": cur_star = "textbooks" star_span = star_counter def perform_activity(activity, duration): global cur_health global cur_hedons global running_duration global running_counter global resting_duration global user_state global textbooks_duration global textbooks_counter global running_hed_counter global star_time global cur_star global star_counter global time_since_star global time_since_curstar global star_break global time_since_star1 global time_since_star2 global star_span global textbook_hed_counter if activity == "running": running_duration += duration resting_duration = 0 textbook_hed_counter = 0 textbooks_duration = 0 textbooks_counter = 0 if user_state == "tired" and cur_star != "running": cur_hedons += duration * (-2) cur_star = "none" time_since_curstar = "not zero" if running_duration <= 180: cur_health += duration3 user_state = "tired" if star_span == 1: time_since_star1 += duration elif star_span == 2: time_since_star2 += duration elif running_duration >180: running_counter += 1 if running_counter == 1: cur_health += (running_duration - 180) + 540 - (running_duration - duration) 3 user_state = "tired" if star_span == 1: time_since_star1 += duration elif star_span == 2: time_since_star2 += duration else: cur_health += (duration) user_state = "tired" if star_span == 1: time_since_star1 += duration elif star_span == 2: time_since_star2 += duration elif user_state == "not tired" and cur_star != "running": running_hed_counter += 1 user_state = "tired" cur_star = "none" time_since_curstar = "not zero" if running_duration <= 10: cur_hedons += running_duration * 2 user_state = "tired" cur_health += duration * 3 if star_span == 1: time_since_star1 += duration elif star_span == 2: time_since_star2 += duration elif running_duration > 10 and running_hed_counter == 1: cur_hedons += (running_duration - 10) * (-2) + 20 if running_duration <= 180: cur_health += duration * 3 if star_span == 1: time_since_star1 += duration elif star_span == 2: time_since_star2 += duration elif running_duration > 180 and running_counter == 1: running_counter += 1 cur_health += (running_duration - 180) + 540 - (running_duration - duration) * 3 if star_span == 1: time_since_star1 += duration elif star_span == 2: time_since_star2 += duration else: cur_health += duration if star_span == 1: time_since_star1 += duration elif star_span == 2: time_since_star2 += duration else: cur_hedons += duration * (-1) user_state = "tired" if running_duration <= 180: cur_health += duration * 3 if star_span == 1: time_since_star1 += duration elif star_span == 2: time_since_star2 += duration elif running_duration > 180 and running_counter == 1: running_counter += 1 cur_health += (running_duration - 180) + 540 if star_span == 1: time_since_star1 += duration elif star_span == 2: time_since_star2 += duration elif running_counter != 1: cur_health += (running_duration - 180) + 540 - (running_duration - duration) * 3 if star_span == 1: time_since_star1 += duration elif star_span == 2: time_since_star2 += duration elif cur_star == "running" and user_state == "not tired" and star_break != "activated" and time_since_curstar == 0: user_state = "tired" cur_star = "none" time_since_curstar = "not zero" if duration <= 10: cur_hedons += 5 * duration if running_duration <= 180: cur_health += duration * 3 if star_span == 1: time_since_star1 += duration elif star_span == 2: time_since_star2 += duration elif running_duration > 180 and running_counter == 1: running_counter += 1 cur_health += (running_duration - 180) + 540 - (running_duration - duration) * 3 if star_span == 1: time_since_star1 += duration elif star_span == 2: time_since_star2 += duration else: cur_health += duration if star_span == 1: time_since_star1 += duration elif star_span == 2: time_since_star2 += duration elif duration > 10: cur_hedons += (duration - 10) * (-2) + 50 if running_duration <= 180: cur_health += duration * 3 if star_span == 1: time_since_star1 += duration elif star_span == 2: time_since_star2 += duration elif running_duration > 180 and running_counter == 1: running_counter += 1 cur_health += (running_duration - 180) + 540 - (running_duration - duration) * 3 if star_span == 1: time_since_star1 += duration elif star_span == 2: time_since_star2 += duration else: cur_health += duration if star_span == 1: time_since_star1 += duration elif star_span == 2: time_since_star2 += duration elif cur_star == "running" and user_state == "tired" and star_break != "activated" and time_since_curstar == 0: user_state = "tired" cur_star = "none" time_since_curstar = "not zero" if duration <= 10: cur_hedons += duration if running_duration <= 180: cur_health += duration * 3 if star_span == 1: time_since_star1 += duration elif star_span == 2: time_since_star2 += duration elif running_duration > 180 and running_counter == 1: running_counter += 1 cur_health += (running_duration - 180) + 540 if star_span == 1: time_since_star1 += duration elif star_span == 2: time_since_star2 += duration else: cur_health += duration if star_span == 1: time_since_star1 += duration elif star_span == 2: time_since_star2 += duration elif duration > 10: cur_hedons += (duration - 10) * (-2) + 10 if running_duration <= 180: cur_health += duration * 3 if star_span == 1: time_since_star1 += duration elif star_span == 2: time_since_star2 += duration elif running_duration > 180 and running_counter == 1: running_counter += 1 cur_health += (running_duration - 180) + 540 if star_span == 1: time_since_star1 += duration elif star_span == 2: time_since_star2 += duration else: cur_health += duration if star_span == 1: time_since_star1 += duration elif star_span == 2: time_since_star2 += duration elif activity == "textbooks": resting_duration = 0 cur_health = cur_health + 2 * duration running_duration = 0 running_counter = 0 textbooks_counter += 1 textbooks_duration += duration if user_state == "tired" and cur_star != "textbooks": cur_star = "none" cur_hedons += duration * (-2) time_since_curstar = "not zero" user_state = "tired" if star_span == 1: time_since_star1 += duration elif star_span == 2: time_since_star2 += duration elif user_state == "not tired" and cur_star == "textbooks" and star_break != "activated" and time_since_curstar == 0: cur_star = "none" time_since_curstar = "not zero" user_state = "tired" if duration <= 10: cur_hedons += 4 * duration if star_span == 1: time_since_star1 += duration elif star_span == 2: time_since_star2 += duration elif duration <= 20: cur_hedons += (duration - 10) + 40 if star_span == 1: time_since_star1 += duration elif star_span == 2: time_since_star2 += duration elif duration > 20: cur_hedons += ((duration - 20) * (-1)) + 50 if star_span == 1: time_since_star1 += duration elif star_span == 2: time_since_star2 += duration elif user_state == "tired" and cur_star == "textbooks" and star_break != "activated" and time_since_curstar == 0: cur_star = "none" user_state = "tired" time_since_curstar = "not zero" if duration <= 10: cur_hedons += duration if star_span == 1: time_since_star1 += duration elif star_span == 2: time_since_star2 += duration elif duration > 10: cur_hedons += (duration - 10) * (-2) + 10 if star_span == 1: time_since_star1 += duration elif star_span == 2: time_since_star2 += duration elif user_state == "not tired" and cur_star != "textbooks": cur_star = "none" user_state = "tired" time_since_curstar = "not zero" if duration <= 20: cur_hedons += duration if star_span == 1: time_since_star1 += duration elif star_span == 2: time_since_star2 += duration elif duration > 20 and textbook_hed_counter == 0: textbook_hed_counter += 1 cur_hedons += (duration - 20) * (-1) + 20 if star_span == 1: time_since_star1 += duration elif star_span == 2: time_since_star2 += duration elif duration > 20 and textbook_hed_counter != 0: textbook_hed_counter += 1 cur_hedons += (duration) * (-1) if star_span == 1: time_since_star1 += duration elif star_span == 2: time_since_star2 += duration elif activity == "resting": running_duration = 0 textbook_hed_counter = 0 running_counter = 0 textbooks_duration = 0 textbooks_counter
memory if it is a valid point if np.isnan(y0).any(): self.mem_ban.add(x0, y0) else: self.mem_med.add(x0, y0) if self.verbose: print(" y = %s" % np.array_str(y0)) return y0 def feasible_moves(self, x0, dx): """Starting from x0, all moves within constraints and not tabu.""" # Generate candidate moves X = hj_move(x0, dx) # Remove duplicate moves (can arise if an element of dx is zero) X = np.unique(X, axis=0) # Filter by input constraints X = X[self.constraint(X)] # Filter against short term memory X = X[~self.mem_short.contains(X)] # Filter against permanent ban list # (we put points where CFD results indicate constraint violated here) X = X[~self.mem_ban.contains(X)] return X def evaluate_moves(self, x0, dx): """From a given start point, evaluate permissible candidate moves.""" X = self.feasible_moves(x0, dx) # Check which points we have seen before log_seen = self.mem_long.contains(X) X_seen = X[log_seen] X_unseen = X[~log_seen] # Re-use previous objectives from long-term mem if possible Y_seen = self.mem_long.lookup(X_seen) # Only go as far as evaluating unseen if there are actually points if X_unseen.shape[0] > 0: # Shuffle the unseen points to remove selection bias np.random.shuffle(X_unseen) # Limit the maximum parallel evaluations if self.max_parallel: # Evaluate in batches isplit = range( self.max_parallel, len(X_unseen), self.max_parallel ) X_batch = np.split(X_unseen, isplit) Y_batch = [self.objective(Xi) for Xi in X_batch] # Join results Y_unseen = np.concatenate(Y_batch) else: # Evaluate objective for unseen points Y_unseen = self.objective(X_unseen) # Increment function evaluation counter self.fevals += len(X_unseen) # Join the results together X = np.vstack((X_seen, X_unseen)) Y = np.vstack((Y_seen, Y_unseen)) # If there are no unseen points else: X = X_seen Y = Y_seen return X, Y def select_move(self, x0, y0, X, Y): """Choose next move given starting point and list of candidate moves.""" j = self.j_objective try: # Categorise the candidates for next move with respect to current with np.errstate(invalid="ignore"): b_dom = (Y[:, j] < y0[:, j]).all(axis=1) b_non_dom = (Y[:, j] > y0[:, j]).all(axis=1) b_equiv = ~np.logical_and(b_dom, b_non_dom) except IndexError: print("ERROR! in select_move") print("Y=%s", str(Y)) print("y0=%s", str(y0)) print("shape Y", Y.shape) print("shape y0", y0.shape) quit() # Convert to indices i_dom = np.where(b_dom)[0] i_non_dom = np.where(b_non_dom)[0] i_equiv = np.where(b_equiv)[0] # Choose the next point if len(i_dom) > 0: # If we have dominating points, randomly choose from them np.random.shuffle(i_dom) x1, y1 = X[i_dom[0]], Y[i_dom[0]] elif len(i_equiv) > 0: # Randomly choose from equivalent points np.random.shuffle(i_equiv) x1, y1 = X[i_equiv[0]], Y[i_equiv[0]] elif len(i_non_dom) > 0: # Randomly choose from non-dominating points np.random.shuffle(i_non_dom) x1, y1 = X[i_non_dom[0]], Y[i_non_dom[0]] else: raise Exception("No valid points to pick next move from") # Keep in matrix form x1 = np.atleast_2d(x1) y1 = np.atleast_2d(y1) return x1, y1 def pattern_move(self, x0, y0, x1, y1, dx): """If this move is in a good direction, increase move length.""" x1a = x0 + self.fac_pattern * (x1 - x0) # # If we are running objectives in parallel, do not waste the spare cores # if self.max_parallel: # # Pick (n_parallel - 1) feasible moves from the pattern move point # X1a = self.feasible_moves(x1a, dx) # X1a_unseen = X1a[~self.mem_long.contains(X1a)] # np.random.shuffle(X1a_unseen) # X1a_unseen = X1a_unseen[: (self.max_parallel -1)] # X = np.vstack(x1,X1a_unseen) # Y = self.objective(X) # else: y1a = self.objective(x1a) if (y1a < y1).all(): return x1a else: return x1 def update_med(self, X, Y): """Update the near-optimal points in medium term memory.""" if X.shape[0] == 0: flag = False else: if len(self.j_objective) == 1: flag = self.mem_med.update_best(X, Y) else: flag = self.mem_med.update_front(X, Y) return flag def search(self, x0, dx, callback=None): """Perform a search with given intial point and step size.""" # Evaluate the objective at given initial guess point, update memories y0 = self.initial_guess(x0) max_step = dx * self.fac_restart ** 2.0 # Main loop, until max evaluations reached or step size below tolerance self.i_search = 0 while self.fevals < self.max_fevals and np.any(dx > self.tol): # Save in case we want to resume later self.dx = dx.reshape(-1).tolist() self.x0 = x0.reshape(-1).tolist() self.y0 = y0.reshape(-1).tolist() # Record our progress in a memory file, if specified if self.mem_file: self.save_memories(self.mem_file) # Plot stuff if self.verbose: self.plot_long("long.pdf") self.plot_opt("opt.pdf") # If we are given a callback, evaluate it now if callback: callback(self) # Evaluate objective for permissible candidate moves X, Y = self.evaluate_moves(x0, dx) # If any objectives are NaN, add to permanent ban list inan = np.isnan(Y).any(-1) Xnan = X[inan] self.mem_ban.add(Xnan) # Delete NaN from results X, Y = X[~inan], Y[~inan] # Put new results into long-term memory self.mem_long.add(X, Y) # Put Pareto-equivalent results into medium-term memory # Flag true if we sucessfully added a point flag = self.update_med(X, Y) if self.verbose and flag: print( "NEW OPT\n x = %s\n y = %s" % tuple([np.array_str(xy) for xy in self.mem_med.get(0)]) ) # Reset counter if we added to medium memory, otherwise increment self.i_search = 0 if flag else self.i_search + 1 # Choose next point based on local search counter if self.i_search == self.i_restart: if self.verbose: print("RESTART") # RESTART: reduce step sizes and randomly select from # medium-term dx = self.fac_restart if len(self.j_objective) == 1: # Pick the current optimum if scalar objective x1, y1 = self.mem_med.get(0) else: # Pick from sparse region of Pareto from if multi-objective x1, y1 = self.mem_med.sample_sparse(self.x_regions) self.i_search = 0 elif self.i_search in self.i_intensify or X.shape[0] == 0: # INTENSIFY: Select a near-optimal point if the medium memory # is populated if self.mem_med.npts > 0: if self.verbose: print("INTENSIFY") x1, y1 = self.mem_med.sample_random() else: # If nothing in the medium-term memory, we have not found # any valid points yet, so increase step size and try again if np.all(dx <= max_step): if self.verbose: print("INCREASE STEP") dx /= self.fac_restart x1, y1 = x0, y0 else: print( "Could not find a point satisfying constraints near initial guess, quitting." ) elif self.i_search == self.i_diversify: if self.verbose: print("DIVERSIFY") # DIVERSIFY: Generate a new point in sparse design region x1 = self.mem_long.generate_sparse(self.x_regions) y1 = self.objective(x1) else: if self.verbose: print("i=%d, fevals=%d" % (self.i_search, self.fevals)) # Normally, choose the best candidate move x1, y1 = self.select_move(x0, y0, X, Y) # Check for a pattern move every i_pattern steps if not self.i_pattern is None: if np.mod(self.i_search, self.i_pattern): x1 = self.pattern_move(x0, y0, x1, y1, dx) if self.verbose: print( " x = %s\n y = %s" % tuple([np.array_str(xy) for xy in (x1, y1)]) ) # Add chosen point to short-term list (tabu) self.mem_short.add(x1) # Update current point before next iteration x0, y0 = x1, y1 # After the loop return current point return x0, y0 def resume(self, fname): self.load_memories(fname) self.mem_file = fname self.search(self.x0, self.dx) def save_memories(self, fname): """Dump the memories to a json file.""" # Assemble a dict for each memory d = {k: m.to_dict() for k, m in zip(self.MEM_KEYS, self.mem_all)} for a in ["i_search", "x0", "y0", "dx"]: d[a] = getattr(self, a) # Write the file with open(fname, "w") as f: json.dump(d, f) def load_memories(self, fname): """Populate memories from a json file.""" if self.verbose: print("READ memories from %s" % fname) # Load the file with open(fname, "r") as f: d = json.load(f) # Populate the memories for k, m in zip(self.MEM_KEYS, self.mem_all): if self.verbose: print(" %s: %d points" % (k, d[k]["npts"])) m.from_dict(d[k]) if "i_search" in d: self.i_search = d["i_search"] self.x0 = np.atleast_2d(d["x0"]) self.y0 = np.atleast_2d(d["y0"]) self.dx = np.atleast_2d(d["dx"]) def plot_long(self, fname): """Generate a graph of long-term memory.""" import matplotlib.pyplot as plt fig, ax = plt.subplots() Yl = np.flip(self.mem_long.Y, axis=0) 100.0 pts = np.arange(len(Yl)) Ym = self.mem_med.Y _, ind = find_rows(Ym, Yl) ax.plot(pts, Yl[:, 0], "k-") ax.plot(pts[ind], Yl[ind, 0], "r") ax.set_ylabel("Lost Efficiency, $\Delta \eta/\%$") ax.set_xlabel("Design Evaluations") plt.tight_layout() plt.savefig(fname) plt.close() def plot_opt(self, fname): """Generate a graph of optimisation progress.""" import matplotlib.pyplot as plt fig, ax = plt.subplots() Yl = np.flip(self.mem_long.Y, axis=0) pts = np.arange(len(Yl)) Ymin = np.empty_like(pts) for i, p in enumerate(pts): if np.all(np.isnan(Yl[: (p + 1), 0])): Ymin[i] = np.nan else: Ymin[i] = np.nanmin(Yl[: (p + 1), 0]) 100.0 ax.plot(pts, Ymin - Ymin[-1], "k-") ax.set_ylabel("Optimum Lost Efficiency Error, $\Delta \eta/\%$") ax.set_xlabel("Design Evaluations") plt.tight_layout() plt.savefig(fname)
__version__) models = MODELS_MAP[g_param[OptionsDefine.Version]] model = models.ModifyLivePlayAuthKeyRequest() model.from_json_string(json.dumps(args)) rsp = client.ModifyLivePlayAuthKey(model) result = rsp.to_json_string() try: json_obj = json.loads(result) except TypeError as e: json_obj = json.loads(result.decode('utf-8')) # python3.3 FormatOutput.output("action", json_obj, g_param[OptionsDefine.Output], g_param[OptionsDefine.Filter]) def doDescribeLiveTranscodeTemplate(args, parsed_globals): g_param = parse_global_arg(parsed_globals) cred = credential.Credential( g_param[OptionsDefine.SecretId], g_param[OptionsDefine.SecretKey], g_param[OptionsDefine.Token] ) http_profile = HttpProfile( reqTimeout=60 if g_param[OptionsDefine.Timeout] is None else int(g_param[OptionsDefine.Timeout]), reqMethod="POST", endpoint=g_param[OptionsDefine.Endpoint], proxy=g_param[OptionsDefine.HttpsProxy] ) profile = ClientProfile(httpProfile=http_profile, signMethod="HmacSHA256") mod = CLIENT_MAP[g_param[OptionsDefine.Version]] client = mod.LiveClient(cred, g_param[OptionsDefine.Region], profile) client._sdkVersion += ("_CLI_" + __version__) models = MODELS_MAP[g_param[OptionsDefine.Version]] model = models.DescribeLiveTranscodeTemplateRequest() model.from_json_string(json.dumps(args)) rsp = client.DescribeLiveTranscodeTemplate(model) result = rsp.to_json_string() try: json_obj = json.loads(result) except TypeError as e: json_obj = json.loads(result.decode('utf-8')) # python3.3 FormatOutput.output("action", json_obj, g_param[OptionsDefine.Output], g_param[OptionsDefine.Filter]) def doDescribeScreenShotSheetNumList(args, parsed_globals): g_param = parse_global_arg(parsed_globals) cred = credential.Credential( g_param[OptionsDefine.SecretId], g_param[OptionsDefine.SecretKey], g_param[OptionsDefine.Token] ) http_profile = HttpProfile( reqTimeout=60 if g_param[OptionsDefine.Timeout] is None else int(g_param[OptionsDefine.Timeout]), reqMethod="POST", endpoint=g_param[OptionsDefine.Endpoint], proxy=g_param[OptionsDefine.HttpsProxy] ) profile = ClientProfile(httpProfile=http_profile, signMethod="HmacSHA256") mod = CLIENT_MAP[g_param[OptionsDefine.Version]] client = mod.LiveClient(cred, g_param[OptionsDefine.Region], profile) client._sdkVersion += ("_CLI_" + __version__) models = MODELS_MAP[g_param[OptionsDefine.Version]] model = models.DescribeScreenShotSheetNumListRequest() model.from_json_string(json.dumps(args)) rsp = client.DescribeScreenShotSheetNumList(model) result = rsp.to_json_string() try: json_obj = json.loads(result) except TypeError as e: json_obj = json.loads(result.decode('utf-8')) # python3.3 FormatOutput.output("action", json_obj, g_param[OptionsDefine.Output], g_param[OptionsDefine.Filter]) def doUnBindLiveDomainCert(args, parsed_globals): g_param = parse_global_arg(parsed_globals) cred = credential.Credential( g_param[OptionsDefine.SecretId], g_param[OptionsDefine.SecretKey], g_param[OptionsDefine.Token] ) http_profile = HttpProfile( reqTimeout=60 if g_param[OptionsDefine.Timeout] is None else int(g_param[OptionsDefine.Timeout]), reqMethod="POST", endpoint=g_param[OptionsDefine.Endpoint], proxy=g_param[OptionsDefine.HttpsProxy] ) profile = ClientProfile(httpProfile=http_profile, signMethod="HmacSHA256") mod = CLIENT_MAP[g_param[OptionsDefine.Version]] client = mod.LiveClient(cred, g_param[OptionsDefine.Region], profile) client._sdkVersion += ("_CLI_" + __version__) models = MODELS_MAP[g_param[OptionsDefine.Version]] model = models.UnBindLiveDomainCertRequest() model.from_json_string(json.dumps(args)) rsp = client.UnBindLiveDomainCert(model) result = rsp.to_json_string() try: json_obj = json.loads(result) except TypeError as e: json_obj = json.loads(result.decode('utf-8')) # python3.3 FormatOutput.output("action", json_obj, g_param[OptionsDefine.Output], g_param[OptionsDefine.Filter]) def doDeleteRecordTask(args, parsed_globals): g_param = parse_global_arg(parsed_globals) cred = credential.Credential( g_param[OptionsDefine.SecretId], g_param[OptionsDefine.SecretKey], g_param[OptionsDefine.Token] ) http_profile = HttpProfile( reqTimeout=60 if g_param[OptionsDefine.Timeout] is None else int(g_param[OptionsDefine.Timeout]), reqMethod="POST", endpoint=g_param[OptionsDefine.Endpoint], proxy=g_param[OptionsDefine.HttpsProxy] ) profile = ClientProfile(httpProfile=http_profile, signMethod="HmacSHA256") mod = CLIENT_MAP[g_param[OptionsDefine.Version]] client = mod.LiveClient(cred, g_param[OptionsDefine.Region], profile) client._sdkVersion += ("_CLI_" + __version__) models = MODELS_MAP[g_param[OptionsDefine.Version]] model = models.DeleteRecordTaskRequest() model.from_json_string(json.dumps(args)) rsp = client.DeleteRecordTask(model) result = rsp.to_json_string() try: json_obj = json.loads(result) except TypeError as e: json_obj = json.loads(result.decode('utf-8')) # python3.3 FormatOutput.output("action", json_obj, g_param[OptionsDefine.Output], g_param[OptionsDefine.Filter]) def doDescribeLiveTranscodeDetailInfo(args, parsed_globals): g_param = parse_global_arg(parsed_globals) cred = credential.Credential( g_param[OptionsDefine.SecretId], g_param[OptionsDefine.SecretKey], g_param[OptionsDefine.Token] ) http_profile = HttpProfile( reqTimeout=60 if g_param[OptionsDefine.Timeout] is None else int(g_param[OptionsDefine.Timeout]), reqMethod="POST", endpoint=g_param[OptionsDefine.Endpoint], proxy=g_param[OptionsDefine.HttpsProxy] ) profile = ClientProfile(httpProfile=http_profile, signMethod="HmacSHA256") mod = CLIENT_MAP[g_param[OptionsDefine.Version]] client = mod.LiveClient(cred, g_param[OptionsDefine.Region], profile) client._sdkVersion += ("_CLI_" + __version__) models = MODELS_MAP[g_param[OptionsDefine.Version]] model = models.DescribeLiveTranscodeDetailInfoRequest() model.from_json_string(json.dumps(args)) rsp = client.DescribeLiveTranscodeDetailInfo(model) result = rsp.to_json_string() try: json_obj = json.loads(result) except TypeError as e: json_obj = json.loads(result.decode('utf-8')) # python3.3 FormatOutput.output("action", json_obj, g_param[OptionsDefine.Output], g_param[OptionsDefine.Filter]) def doDescribeLogDownloadList(args, parsed_globals): g_param = parse_global_arg(parsed_globals) cred = credential.Credential( g_param[OptionsDefine.SecretId], g_param[OptionsDefine.SecretKey], g_param[OptionsDefine.Token] ) http_profile = HttpProfile( reqTimeout=60 if g_param[OptionsDefine.Timeout] is None else int(g_param[OptionsDefine.Timeout]), reqMethod="POST", endpoint=g_param[OptionsDefine.Endpoint], proxy=g_param[OptionsDefine.HttpsProxy] ) profile = ClientProfile(httpProfile=http_profile, signMethod="HmacSHA256") mod = CLIENT_MAP[g_param[OptionsDefine.Version]] client = mod.LiveClient(cred, g_param[OptionsDefine.Region], profile) client._sdkVersion += ("_CLI_" + __version__) models = MODELS_MAP[g_param[OptionsDefine.Version]] model = models.DescribeLogDownloadListRequest() model.from_json_string(json.dumps(args)) rsp = client.DescribeLogDownloadList(model) result = rsp.to_json_string() try: json_obj = json.loads(result) except TypeError as e: json_obj = json.loads(result.decode('utf-8')) # python3.3 FormatOutput.output("action", json_obj, g_param[OptionsDefine.Output], g_param[OptionsDefine.Filter]) def doDescribeLiveRecordRules(args, parsed_globals): g_param = parse_global_arg(parsed_globals) cred = credential.Credential( g_param[OptionsDefine.SecretId], g_param[OptionsDefine.SecretKey], g_param[OptionsDefine.Token] ) http_profile = HttpProfile( reqTimeout=60 if g_param[OptionsDefine.Timeout] is None else int(g_param[OptionsDefine.Timeout]), reqMethod="POST", endpoint=g_param[OptionsDefine.Endpoint], proxy=g_param[OptionsDefine.HttpsProxy] ) profile = ClientProfile(httpProfile=http_profile, signMethod="HmacSHA256") mod = CLIENT_MAP[g_param[OptionsDefine.Version]] client = mod.LiveClient(cred, g_param[OptionsDefine.Region], profile) client._sdkVersion += ("_CLI_" + __version__) models = MODELS_MAP[g_param[OptionsDefine.Version]] model = models.DescribeLiveRecordRulesRequest() model.from_json_string(json.dumps(args)) rsp = client.DescribeLiveRecordRules(model) result = rsp.to_json_string() try: json_obj = json.loads(result) except TypeError as e: json_obj = json.loads(result.decode('utf-8')) # python3.3 FormatOutput.output("action", json_obj, g_param[OptionsDefine.Output], g_param[OptionsDefine.Filter]) def doDescribeLiveDelayInfoList(args, parsed_globals): g_param = parse_global_arg(parsed_globals) cred = credential.Credential( g_param[OptionsDefine.SecretId], g_param[OptionsDefine.SecretKey], g_param[OptionsDefine.Token] ) http_profile = HttpProfile( reqTimeout=60 if g_param[OptionsDefine.Timeout] is None else int(g_param[OptionsDefine.Timeout]), reqMethod="POST", endpoint=g_param[OptionsDefine.Endpoint], proxy=g_param[OptionsDefine.HttpsProxy] ) profile = ClientProfile(httpProfile=http_profile, signMethod="HmacSHA256") mod = CLIENT_MAP[g_param[OptionsDefine.Version]] client = mod.LiveClient(cred, g_param[OptionsDefine.Region], profile) client._sdkVersion += ("_CLI_" + __version__) models = MODELS_MAP[g_param[OptionsDefine.Version]] model = models.DescribeLiveDelayInfoListRequest() model.from_json_string(json.dumps(args)) rsp = client.DescribeLiveDelayInfoList(model) result = rsp.to_json_string() try: json_obj = json.loads(result) except TypeError as e: json_obj = json.loads(result.decode('utf-8')) # python3.3 FormatOutput.output("action", json_obj, g_param[OptionsDefine.Output], g_param[OptionsDefine.Filter]) def doDescribeLiveStreamPublishedList(args, parsed_globals): g_param = parse_global_arg(parsed_globals) cred = credential.Credential( g_param[OptionsDefine.SecretId], g_param[OptionsDefine.SecretKey], g_param[OptionsDefine.Token] ) http_profile = HttpProfile( reqTimeout=60 if g_param[OptionsDefine.Timeout] is None else int(g_param[OptionsDefine.Timeout]), reqMethod="POST", endpoint=g_param[OptionsDefine.Endpoint], proxy=g_param[OptionsDefine.HttpsProxy] ) profile = ClientProfile(httpProfile=http_profile, signMethod="HmacSHA256") mod = CLIENT_MAP[g_param[OptionsDefine.Version]] client = mod.LiveClient(cred, g_param[OptionsDefine.Region], profile) client._sdkVersion += ("_CLI_" + __version__) models = MODELS_MAP[g_param[OptionsDefine.Version]] model = models.DescribeLiveStreamPublishedListRequest() model.from_json_string(json.dumps(args)) rsp = client.DescribeLiveStreamPublishedList(model) result = rsp.to_json_string() try: json_obj = json.loads(result) except TypeError as e: json_obj = json.loads(result.decode('utf-8')) # python3.3 FormatOutput.output("action", json_obj, g_param[OptionsDefine.Output], g_param[OptionsDefine.Filter]) def doDescribeLiveDomain(args, parsed_globals): g_param = parse_global_arg(parsed_globals) cred = credential.Credential( g_param[OptionsDefine.SecretId], g_param[OptionsDefine.SecretKey], g_param[OptionsDefine.Token] ) http_profile = HttpProfile( reqTimeout=60 if g_param[OptionsDefine.Timeout] is None else int(g_param[OptionsDefine.Timeout]), reqMethod="POST", endpoint=g_param[OptionsDefine.Endpoint], proxy=g_param[OptionsDefine.HttpsProxy] ) profile = ClientProfile(httpProfile=http_profile, signMethod="HmacSHA256") mod = CLIENT_MAP[g_param[OptionsDefine.Version]] client = mod.LiveClient(cred, g_param[OptionsDefine.Region], profile) client._sdkVersion += ("_CLI_" + __version__) models = MODELS_MAP[g_param[OptionsDefine.Version]] model = models.DescribeLiveDomainRequest() model.from_json_string(json.dumps(args)) rsp = client.DescribeLiveDomain(model) result = rsp.to_json_string() try: json_obj = json.loads(result) except TypeError as e: json_obj = json.loads(result.decode('utf-8')) # python3.3 FormatOutput.output("action", json_obj, g_param[OptionsDefine.Output], g_param[OptionsDefine.Filter]) def doCreateLiveCallbackRule(args, parsed_globals): g_param = parse_global_arg(parsed_globals) cred = credential.Credential( g_param[OptionsDefine.SecretId], g_param[OptionsDefine.SecretKey], g_param[OptionsDefine.Token] ) http_profile = HttpProfile( reqTimeout=60 if g_param[OptionsDefine.Timeout] is None else int(g_param[OptionsDefine.Timeout]), reqMethod="POST", endpoint=g_param[OptionsDefine.Endpoint], proxy=g_param[OptionsDefine.HttpsProxy] ) profile = ClientProfile(httpProfile=http_profile, signMethod="HmacSHA256") mod = CLIENT_MAP[g_param[OptionsDefine.Version]] client = mod.LiveClient(cred, g_param[OptionsDefine.Region], profile) client._sdkVersion += ("_CLI_" + __version__) models = MODELS_MAP[g_param[OptionsDefine.Version]] model = models.CreateLiveCallbackRuleRequest() model.from_json_string(json.dumps(args)) rsp = client.CreateLiveCallbackRule(model) result = rsp.to_json_string() try: json_obj = json.loads(result) except TypeError as e: json_obj = json.loads(result.decode('utf-8')) # python3.3 FormatOutput.output("action", json_obj, g_param[OptionsDefine.Output], g_param[OptionsDefine.Filter]) def doBindLiveDomainCert(args, parsed_globals): g_param = parse_global_arg(parsed_globals) cred = credential.Credential( g_param[OptionsDefine.SecretId], g_param[OptionsDefine.SecretKey], g_param[OptionsDefine.Token] ) http_profile = HttpProfile( reqTimeout=60 if g_param[OptionsDefine.Timeout] is None else int(g_param[OptionsDefine.Timeout]), reqMethod="POST", endpoint=g_param[OptionsDefine.Endpoint], proxy=g_param[OptionsDefine.HttpsProxy] ) profile = ClientProfile(httpProfile=http_profile, signMethod="HmacSHA256") mod = CLIENT_MAP[g_param[OptionsDefine.Version]] client = mod.LiveClient(cred, g_param[OptionsDefine.Region], profile) client._sdkVersion += ("_CLI_" + __version__) models = MODELS_MAP[g_param[OptionsDefine.Version]] model = models.BindLiveDomainCertRequest() model.from_json_string(json.dumps(args)) rsp = client.BindLiveDomainCert(model) result = rsp.to_json_string() try: json_obj = json.loads(result) except TypeError as e: json_obj = json.loads(result.decode('utf-8')) # python3.3 FormatOutput.output("action", json_obj, g_param[OptionsDefine.Output], g_param[OptionsDefine.Filter]) def doDescribeLiveCallbackRules(args, parsed_globals): g_param = parse_global_arg(parsed_globals) cred = credential.Credential( g_param[OptionsDefine.SecretId], g_param[OptionsDefine.SecretKey], g_param[OptionsDefine.Token] ) http_profile = HttpProfile( reqTimeout=60 if g_param[OptionsDefine.Timeout] is None else int(g_param[OptionsDefine.Timeout]), reqMethod="POST", endpoint=g_param[OptionsDefine.Endpoint], proxy=g_param[OptionsDefine.HttpsProxy] ) profile = ClientProfile(httpProfile=http_profile, signMethod="HmacSHA256") mod = CLIENT_MAP[g_param[OptionsDefine.Version]] client = mod.LiveClient(cred, g_param[OptionsDefine.Region], profile) client._sdkVersion += ("_CLI_" + __version__) models = MODELS_MAP[g_param[OptionsDefine.Version]] model = models.DescribeLiveCallbackRulesRequest() model.from_json_string(json.dumps(args)) rsp = client.DescribeLiveCallbackRules(model) result = rsp.to_json_string() try: json_obj = json.loads(result) except TypeError as e: json_obj = json.loads(result.decode('utf-8')) # python3.3 FormatOutput.output("action", json_obj, g_param[OptionsDefine.Output], g_param[OptionsDefine.Filter]) def doDescribePlayErrorCodeDetailInfoList(args, parsed_globals): g_param = parse_global_arg(parsed_globals) cred = credential.Credential( g_param[OptionsDefine.SecretId], g_param[OptionsDefine.SecretKey], g_param[OptionsDefine.Token] ) http_profile = HttpProfile( reqTimeout=60 if g_param[OptionsDefine.Timeout] is None else int(g_param[OptionsDefine.Timeout]), reqMethod="POST", endpoint=g_param[OptionsDefine.Endpoint], proxy=g_param[OptionsDefine.HttpsProxy] ) profile = ClientProfile(httpProfile=http_profile, signMethod="HmacSHA256") mod = CLIENT_MAP[g_param[OptionsDefine.Version]] client = mod.LiveClient(cred, g_param[OptionsDefine.Region], profile) client._sdkVersion += ("_CLI_" + __version__) models = MODELS_MAP[g_param[OptionsDefine.Version]] model = models.DescribePlayErrorCodeDetailInfoListRequest() model.from_json_string(json.dumps(args)) rsp = client.DescribePlayErrorCodeDetailInfoList(model) result = rsp.to_json_string() try: json_obj = json.loads(result) except TypeError as e: json_obj = json.loads(result.decode('utf-8')) # python3.3 FormatOutput.output("action", json_obj, g_param[OptionsDefine.Output], g_param[OptionsDefine.Filter]) def doDeleteLiveRecordRule(args, parsed_globals): g_param = parse_global_arg(parsed_globals) cred = credential.Credential( g_param[OptionsDefine.SecretId], g_param[OptionsDefine.SecretKey], g_param[OptionsDefine.Token] ) http_profile = HttpProfile( reqTimeout=60 if g_param[OptionsDefine.Timeout] is None else int(g_param[OptionsDefine.Timeout]), reqMethod="POST", endpoint=g_param[OptionsDefine.Endpoint], proxy=g_param[OptionsDefine.HttpsProxy] ) profile = ClientProfile(httpProfile=http_profile, signMethod="HmacSHA256") mod = CLIENT_MAP[g_param[OptionsDefine.Version]] client = mod.LiveClient(cred, g_param[OptionsDefine.Region], profile) client._sdkVersion += ("_CLI_" + __version__) models = MODELS_MAP[g_param[OptionsDefine.Version]] model = models.DeleteLiveRecordRuleRequest() model.from_json_string(json.dumps(args)) rsp = client.DeleteLiveRecordRule(model) result = rsp.to_json_string() try: json_obj = json.loads(result) except TypeError as e: json_obj = json.loads(result.decode('utf-8')) # python3.3 FormatOutput.output("action", json_obj, g_param[OptionsDefine.Output], g_param[OptionsDefine.Filter]) def doDescribePushBandwidthAndFluxList(args, parsed_globals): g_param = parse_global_arg(parsed_globals) cred = credential.Credential( g_param[OptionsDefine.SecretId], g_param[OptionsDefine.SecretKey], g_param[OptionsDefine.Token] ) http_profile = HttpProfile( reqTimeout=60 if g_param[OptionsDefine.Timeout] is None else int(g_param[OptionsDefine.Timeout]), reqMethod="POST", endpoint=g_param[OptionsDefine.Endpoint], proxy=g_param[OptionsDefine.HttpsProxy] ) profile = ClientProfile(httpProfile=http_profile, signMethod="HmacSHA256") mod = CLIENT_MAP[g_param[OptionsDefine.Version]] client = mod.LiveClient(cred, g_param[OptionsDefine.Region], profile) client._sdkVersion += ("_CLI_" + __version__) models = MODELS_MAP[g_param[OptionsDefine.Version]] model = models.DescribePushBandwidthAndFluxListRequest() model.from_json_string(json.dumps(args)) rsp = client.DescribePushBandwidthAndFluxList(model) result = rsp.to_json_string() try: json_obj = json.loads(result) except TypeError as e: json_obj = json.loads(result.decode('utf-8')) # python3.3 FormatOutput.output("action", json_obj, g_param[OptionsDefine.Output], g_param[OptionsDefine.Filter]) def doForbidLiveStream(args, parsed_globals): g_param = parse_global_arg(parsed_globals) cred = credential.Credential( g_param[OptionsDefine.SecretId], g_param[OptionsDefine.SecretKey], g_param[OptionsDefine.Token] ) http_profile = HttpProfile( reqTimeout=60 if g_param[OptionsDefine.Timeout] is None else int(g_param[OptionsDefine.Timeout]), reqMethod="POST", endpoint=g_param[OptionsDefine.Endpoint], proxy=g_param[OptionsDefine.HttpsProxy] ) profile = ClientProfile(httpProfile=http_profile, signMethod="HmacSHA256") mod = CLIENT_MAP[g_param[OptionsDefine.Version]] client = mod.LiveClient(cred, g_param[OptionsDefine.Region], profile) client._sdkVersion += ("_CLI_" + __version__) models = MODELS_MAP[g_param[OptionsDefine.Version]] model = models.ForbidLiveStreamRequest() model.from_json_string(json.dumps(args)) rsp = client.ForbidLiveStream(model) result = rsp.to_json_string() try: json_obj = json.loads(result) except TypeError as e: json_obj = json.loads(result.decode('utf-8')) # python3.3 FormatOutput.output("action", json_obj, g_param[OptionsDefine.Output], g_param[OptionsDefine.Filter]) def doAddLiveDomain(args, parsed_globals): g_param = parse_global_arg(parsed_globals) cred = credential.Credential(
if jBase.createDatabase(val) == 0: bd = TS.SimboloBase(val, None, nodo.mode) tablaSimbolos.put(val, bd) consola += "Base de datos " + val + " creada. \n" else: consola += "Error al crear la base de datos \n" elif nodo.owner != False and nodo.mode == False: if jBase.createDatabase(val) == 0: bd = TS.SimboloBase(val, nodo.owner, None) tablaSimbolos.put(val, bd) consola += "Base de datos " + val + " creada. \n" else: consola += "Error al crear la base de datos \n" elif nodo.owner != False and nodo.mode != False: if jBase.createDatabase(val) == 0: bd = TS.SimboloBase(val, nodo.owner, nodo.mode) tablaSimbolos.put(val, bd) consola += "Base de datos " + val + " creada. \n" else: consola += "Error al crear la base de datos \n" elif nodo.replace == False and nodo.exists != False: if nodo.owner == False and nodo.mode == False: if jBase.createDatabase(val) == 0: bd = TS.SimboloBase(val, None, None) tablaSimbolos.put(val, bd) consola += "Base de datos " + val + " creada. \n" elif jBase.createDatabase(val) == 2: consola += "La base de datos " + val + " ya existe. \n" else: consola += "Error al crear la base de datos \n" elif nodo.owner == False and nodo.mode != False: if jBase.createDatabase(val) == 0: bd = TS.SimboloBase(val, None, nodo.mode) tablaSimbolos.put(val, bd) consola += "Base de datos " + val + " creada. \n" elif jBase.createDatabase(val) == 2: consola += "La base de datos " + val + " ya existe. \n" else: consola += "Error al crear la base de datos \n" elif nodo.owner != False and nodo.mode == False: if jBase.createDatabase(val) == 0: bd = TS.SimboloBase(val, nodo.owner, None) tablaSimbolos.put(val, bd) consola += "Base de datos " + val + " creada. \n" elif jBase.createDatabase(val) == 2: consola += "La base de datos " + val + " ya existe. \n" else: consola += "Error al crear la base de datos \n" elif nodo.owner != False and nodo.mode != False: if jBase.createDatabase(val) == 0: bd = TS.SimboloBase(val, nodo.owner, nodo.mode) tablaSimbolos.put(val, bd) consola += "Base de datos " + val + " creada. \n" elif jBase.createDatabase(val) == 2: consola += "La base de datos " + val + " ya existe. \n" else: consola += "Error al crear la base de datos \n" def crearTabla(nodo, tablaSimbolos): val = nodo.id global useActual global consola primarykeys = [] if nodo.herencia == False: contador = 0 nueva = TS.SimboloTabla(val, None) for col in nodo.columnas: pk = False default_ = None check = None null = True unique = False if isinstance(col, SColumna): if col.opcionales != None: for opc in col.opcionales: if isinstance(opc, SOpcionales): if opc.tipo == TipoOpcionales.PRIMARYKEY: pk = True elif opc.tipo == TipoOpcionales.DEFAULT: default_ = opc.valor elif opc.tipo == TipoOpcionales.CHECK: if opc.id == None: check = {"id": col.id + "_check", "condicion": opc.valor} listaConstraint.append( TS.Constraints(useActual, val, col.id + "_check", col.id, "check")) else: check = {"id": opc.id, "condicion": opc.valor} listaConstraint.append( TS.Constraints(useActual, val, opc.id, col.id, "check")) elif opc.tipo == TipoOpcionales.NULL: null = True elif opc.tipo == TipoOpcionales.NOTNULL: null = False elif opc.tipo == TipoOpcionales.UNIQUE: if opc.id == None: unique = col.id + "_unique" listaConstraint.append( TS.Constraints(useActual, val, col.id + "_unique", col.id, "unique")) else: unique = opc.id listaConstraint.append( TS.Constraints(useActual, val, opc.id, col.id, "unique")) colnueva = TS.SimboloColumna(col.id, col.tipo, pk, None, unique, default_, null, check, len(nueva.columnas)) if pk: primarykeys.append(colnueva.index) nueva.crearColumna(col.id, colnueva) if colnueva == None: listaSemanticos.append( Error.ErrorS("Error Semantico", "Ya existe una columna con el nombre " + col.id)) else: auxc = TS.SimboloColumna(col.id, col.tipo, pk, None, unique, default_, null, check, len(nueva.columnas)) nueva.crearColumna(col.id, auxc) contador += 1 elif isinstance(col, SColumnaUnique): for id in col.id: if nueva.modificarUnique(id.valor, True, id.valor + "_unique") == None: listaSemanticos.append( Error.ErrorS("Error Semantico", "No se encontró la columna con id " + id.valor)) else: listaConstraint.append(TS.Constraints( useActual, val, id.valor + "_unique", id.valor, "unique")) elif isinstance(col, SColumnaCheck): print("Entró al constraint") condicion = col.condicion opIzq = condicion.opIzq idcol = opIzq.valor result = False if col.id == None: result = nueva.modificarCheck( idcol, col.condicion, idcol + "_check") listaConstraint.append(TS.Constraints( useActual, val, idcol + "_check", idcol, "check")) else: result = nueva.modificarCheck(idcol, condicion, col.id) listaConstraint.append(TS.Constraints( useActual, val, col.id, idcol, "check")) if result != True: listaSemanticos.append(Error.ErrorS( "Error Semantico", "No se encontró la columna con id " + idcol)) elif isinstance(col, SColumnaFk): for i in range(len(col.idlocal)): idlocal = col.idlocal[i].valor idfk = col.idfk[i].valor columnafk = tablaSimbolos.getColumna( useActual, col.id, idfk) columnalocal = nueva.getColumna(idlocal) if columnafk != None and columnalocal != None: if columnafk.tipo.tipo == columnalocal.tipo.tipo: nueva.modificarFk(idlocal, col.id, idfk) if col.idconstraint != None: listaConstraint.append( TS.Constraints(useActual, val, col.idconstraint, columnalocal, "FK")) listaFK.append(TS.llaveForanea( useActual, val, col.id, idlocal, idfk)) else: listaSemanticos.append(Error.ErrorS("Error Semantico", "La columna %s y la columna %s no tienen el mismo tipo" % ( idlocal, idfk))) else: listaSemanticos.append( Error.ErrorS("Error Semantico", "No se encontró la columna")) elif isinstance(col, SColumnaPk): for id in col.id: if nueva.modificarPk(id.valor) == None: listaSemanticos.append( Error.ErrorS("Error Semantico", "No se encontró la columna " + id.valor)) else: primarykeys.append(nueva.getColumna(id.valor).index) base = tablaSimbolos.get(useActual) base.crearTabla(val, nueva) tt = jBase.createTable(useActual, nodo.id, contador) if len(primarykeys) > 0: jBase.alterAddPK(useActual, val, primarykeys) if tt == 0: consola += "La tabla " + nodo.id + " se creó con éxito. \n" elif tt == 1: consola += "Error en la operación al crear la tabla " + nodo.id + "\n" elif tt == 2: consola += "La base de datos " + useActual + " no existe. \n" else: consola += "La tabla " + nodo.id + " ya existe. \n" def AlterDatabase(nodo, tablaSimbolos): global consola if nodo.rename: b = jBase.alterDatabase(nodo.id.valor, nodo.idnuevo) if b == 0: base = tablaSimbolos.renameBase(nodo.id.valor, nodo.idnuevo) if base: for fk in listaFK: if fk.idbase == nodo.id.valor: fk.idbase = nodo.idnuevo for cons in listaConstraint: if cons.idbase == nodo.id.valor: cons.idbase = nodo.idnuevo consola += "La base se renombró con éxito " + nodo.idnuevo + " \n" else: consola += "Error no se pudo renombrar la base " + \ nodo.id.valor + " en la tabla de simbolos \n" elif b == 2: listaSemanticos.append(Error.ErrorS( "Error Semantico", "La base de datos " + nodo.id.valor + " no existe")) elif b == 3: listaSemanticos.append(Error.ErrorS( "Error Semantico", "La base de datos ya existe " + nodo.idnuevo)) elif b == 1: listaSemanticos.append(Error.ErrorS( "Error Semantico", "Error en la operacion.")) def AlterAddColumn(nodo, tablaSimbolos): global consola global useActual base = tablaSimbolos.get(useActual) tabla = base.getTabla(nodo.idtabla) for col in nodo.listaColumnas: auxcol = TS.SimboloColumna( col.idcolumna, col.tipo, False, None, None, None, True, None, len(tabla.columnas)) if tabla.crearColumna(col.idcolumna, auxcol): b = jBase.alterAddColumn(useActual, nodo.idtabla, col.idcolumna) if b == 0: consola += "La columna " + col.idcolumna + \ " se agregó a la tabla " + nodo.idtabla + " \n" elif b == 1: listaSemanticos.append(Error.ErrorS( "Error Semantico", "Error en la operacion.")) elif b == 2: listaSemanticos.append(Error.ErrorS( "Error Semantico", "Error la base " + useActual + "no existe")) elif b == 3: listaSemanticos.append(Error.ErrorS( "Error Semantico", "Error la tabla " + nodo.idtabla + "no existe")) else: consola += "Error al crear la columna " + col.idcolumna + " \n" def AlterRenameColumn(nodo, tablaSimbolos): base = tablaSimbolos.get(useActual) tabla = base.getTabla(nodo.idtabla) global consola op = tabla.renameColumna(nodo.idcolumna, nodo.idnuevo) if op == 0: for fk in listaFK: if fk.idcfk == nodo.idcolumna: fk.idcfk = nodo.idnuevo tablaRF = base.getTabla(fk.idtlocal) columnaRF = tablaRF.getColumna(fk.idclocal) columnaRF.foreign_key["columna"] = nodo.idnuevo elif fk.idclocal == nodo.idcolumna: fk.idclocal = nodo.idnuevo for cons in listaConstraint: if cons.idcol == nodo.idcolumna: cons.idcol = nodo.idnuevo consola += "Se cambio el nombre de la columna " + \ nodo.idcolumna + " a " + nodo.idnuevo + " con exito \n" elif op == 1: listaSemanticos.append(Error.ErrorS( "Error Semantico", "La columna con nombre " + nodo.idnuevo + " ya existe")) elif op == 2: listaSemanticos.append(Error.ErrorS( "Error Semantico", "La columna con nombre " + nodo.idactual + " no existe")) def AlterRenameTable(nodo, tablaSimbolos): global useActual global consola base = tablaSimbolos.get(useActual) op = base.renameTable(nodo.idactual, nodo.idnuevo) if op == 0: lib = jBase.alterTable(useActual, nodo.idactual, nodo.idnuevo) if lib == 0: for fk in listaFK: if fk.idtfk == nodo.idactual: fk.idtfk = nodo.idnuevo tablaRF = base.getTabla(fk.idtlocal) columnaRF = tablaRF.getColumna(fk.idclocal) columnaRF.foreign_key["tabla"] = nodo.idnuevo elif fk.idtlocal == nodo.idactual: fk.idtlocal = nodo.idnuevo for cons in listaConstraint: if cons.idtabla == nodo.idactual:
<reponame>FiskFan1999/ergochat_irctest # # (C) Copyright 2011 <NAME> <<EMAIL>> # # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License Version # 2.1 as published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with this program; if not, write to the Free Software # Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. # """SCRAM authentication mechanisms for PyXMPP SASL implementation. Normative reference: - :RFC:`5802` """ from __future__ import absolute_import, division, unicode_literals __docformat__ = "restructuredtext en" import sys import re import logging import hashlib import hmac from binascii import a2b_base64 from base64 import standard_b64encode from .core import default_nonce_factory from .exceptions import BadChallengeException, \ ExtraChallengeException, ServerScramError, BadSuccessException, \ NotAuthorizedException logger = logging.getLogger("pyxmpp2_scram") HASH_FACTORIES = { "SHA-1": hashlib.sha1, # pylint: disable=E1101 "SHA-224": hashlib.sha224, # pylint: disable=E1101 "SHA-256": hashlib.sha256, # pylint: disable=E1101 "SHA-384": hashlib.sha384, # pylint: disable=E1101 "SHA-512": hashlib.sha512, # pylint: disable=E1101 "MD-5": hashlib.md5, # pylint: disable=E1101 } VALUE_CHARS_RE = re.compile(br"^[\x21-\x2B\x2D-\x7E]+$") _QUOTED_VALUE_RE = br"(?:[\x21-\x2B\x2D-\x7E]\|=2C\|=3D)+" CLIENT_FIRST_MESSAGE_RE = re.compile( br"^(?P<gs2_header>(?:y\|n\|p=(?P<cb_name>[a-zA-z0-9.-]+))," br"(?:a=(?P<authzid>" + _QUOTED_VALUE_RE + br"))?,)" br"(?P<client_first_bare>(?P<mext>m=[^\000=]+,)?" br"n=(?P<username>" + _QUOTED_VALUE_RE + br")," br"r=(?P<nonce>[\x21-\x2B\x2D-\x7E]+)" br"(?:,.)?)$" ) SERVER_FIRST_MESSAGE_RE = re.compile( br"^(?P<mext>m=[^\000=]+,)?" br"r=(?P<nonce>[\x21-\x2B\x2D-\x7E]+)," br"s=(?P<salt>[a-zA-Z0-9/+=]+)," br"i=(?P<iteration_count>\d+)" br"(?:,.)?$" ) CLIENT_FINAL_MESSAGE_RE = re.compile( br"(?P<without_proof>c=(?P<cb>[a-zA-Z0-9/+=]+)," br"(?:r=(?P<nonce>[\x21-\x2B\x2D-\x7E]+))" br"(?:,.)?)" br",p=(?P<proof>[a-zA-Z0-9/+=]+)$" ) SERVER_FINAL_MESSAGE_RE = re.compile( br"^(?:e=(?P<error>[^,]+)\|v=(?P<verifier>[a-zA-Z0-9/+=]+)(?:,.)?)$") class SCRAMOperations(object): """Functions used during SCRAM authentication and defined in the RFC. """ def __init__(self, hash_function_name): self.hash_function_name = hash_function_name self.hash_factory = HASH_FACTORIES[hash_function_name] self.digest_size = self.hash_factory().digest_size @staticmethod def Normalize(str_): """The Normalize(str) function. This one also accepts Unicode string input (in the RFC only UTF-8 strings are used). """ # pylint: disable=C0103 if isinstance(str_, bytes): str_ = str_.decode("utf-8") return str_.encode("utf-8") def HMAC(self, key, str_): """The HMAC(key, str) function.""" # pylint: disable=C0103 return hmac.new(key, str_, self.hash_factory).digest() def H(self, str_): """The H(str) function.""" # pylint: disable=C0103 return self.hash_factory(str_).digest() if sys.version_info.major >= 3: @staticmethod # pylint: disable=C0103 def XOR(str1, str2): """The XOR operator for two byte strings.""" return bytes(a ^ b for a, b in zip(str1, str2)) else: @staticmethod # pylint: disable=C0103 def XOR(str1, str2): """The XOR operator for two byte strings.""" return "".join(chr(ord(a) ^ ord(b)) for a, b in zip(str1, str2)) def Hi(self, str_, salt, i): """The Hi(str, salt, i) function.""" # pylint: disable=C0103 Uj = self.HMAC(str_, salt + b"\000\000\000\001") # U1 result = Uj for _ in range(2, i + 1): Uj = self.HMAC(str_, Uj) # Uj = HMAC(str, Uj-1) result = self.XOR(result, Uj) # ... XOR Uj-1 XOR Uj return result @staticmethod def escape(data): """Escape the ',' and '=' characters for 'a=' and 'n=' attributes. Replaces '=' with '=3D' and ',' with '=2C'. :Parameters: - `data`: string to escape :Types: - `data`: `bytes` """ return data.replace(b'=', b'=3D').replace(b',', b'=2C') @staticmethod def unescape(data): """Unescape the ',' and '=' characters for 'a=' and 'n=' attributes. Reverse of `escape`. :Parameters: - `data`: string to unescape :Types: - `data`: `bytes` """ return data.replace(b'=2C', b',').replace(b'=3D', b'=') class SCRAMClientAuthenticator(SCRAMOperations): """Provides SCRAM SASL authentication for a client. :Ivariables: - `password`: current authentication password - `pformat`: current authentication password format - `realm`: current authentication realm """ # pylint: disable-msg=R0902 def __init__(self, hash_name, channel_binding): """Initialize a `SCRAMClientAuthenticator` object. :Parameters: - `hash_function_name`: hash function name, e.g. ``"SHA-1"`` - `channel_binding`: `True` to enable channel binding :Types: - `hash_function_name`: `unicode` - `channel_binding`: `bool` """ SCRAMOperations.__init__(self, hash_name) self.name = "SCRAM-{0}".format(hash_name) if channel_binding: self.name += "-PLUS" self.channel_binding = channel_binding self.username = None self.password = <PASSWORD> self.authzid = None self._c_nonce = None self._server_first_message = False self._client_first_message_bare = False self._gs2_header = None self._finished = False self._auth_message = None self._salted_password = <PASSWORD> self._cb_data = None @classmethod def are_properties_sufficient(cls, properties): return "username" in properties and "password" in properties def start(self, properties): self.username = properties["username"] self.password = properties["password"] self.authzid = properties.get("authzid", "") c_nonce = properties.get("nonce_factory", default_nonce_factory)() if not VALUE_CHARS_RE.match(c_nonce): c_nonce = standard_b64encode(c_nonce) self._c_nonce = c_nonce if self.channel_binding: cb_data = properties.get("channel-binding") if not cb_data: raise ValueError("No channel binding data provided") if "tls-unique" in cb_data: cb_type = "tls-unique" elif "tls-server-end-point" in cb_data: cb_type = "tls-server-end-point" elif cb_data: cb_type = cb_data.keys()[0] self._cb_data = cb_data[cb_type] cb_flag = b"p=" + cb_type.encode("utf-8") else: plus_name = self.name + "-PLUS" if plus_name in properties.get("enabled_mechanisms", []): # -PLUS is enabled (supported) on our side, # but was not selected - that means it was not included # in the server features cb_flag = b"y" else: cb_flag = b"n" if self.authzid: authzid = b"a=" + self.escape(self.authzid.encode("utf-8")) else: authzid = b"" gs2_header = cb_flag + b"," + authzid + b"," self._gs2_header = gs2_header nonce = b"r=" + c_nonce client_first_message_bare = (b"n=" + self.escape(self.username.encode("utf-8")) + b"," + nonce) self._client_first_message_bare = client_first_message_bare client_first_message = gs2_header + client_first_message_bare return client_first_message def challenge(self, challenge): """Process a challenge and return the response. :Parameters: - `challenge`: the challenge from server. :Types: - `challenge`: `bytes` :return: the response :returntype: bytes :raises: `BadChallengeException` """ # pylint: disable=R0911 if not challenge: raise BadChallengeException('Empty challenge') if self._server_first_message: return self._final_challenge(challenge) match = SERVER_FIRST_MESSAGE_RE.match(challenge) if not match: raise BadChallengeException("Bad challenge syntax: {0!r}".format(challenge)) self._server_first_message = challenge mext = match.group("mext") if mext: raise BadChallengeException("Unsupported extension received: {0!r}".format(mext)) nonce = match.group("nonce") if not nonce.startswith(self._c_nonce): raise BadChallengeException("Nonce does not start with our nonce") salt = match.group("salt") try: salt = a2b_base64(salt) except ValueError: raise BadChallengeException("Bad base64 encoding for salt: {0!r}".format(salt)) iteration_count = match.group("iteration_count") try: iteration_count = int(iteration_count) except ValueError: raise BadChallengeException("Bad iteration_count: {0!r}".format(iteration_count)) return self._make_response(nonce, salt, iteration_count) def _make_response(self, nonce, salt, iteration_count): """Make a response for the first challenge from the server. :return: the response :returntype: bytes """ self._salted_password = self.Hi(self.Normalize(self.password), salt, iteration_count) self.password = None # not needed any more if self.channel_binding: channel_binding = b"c=" + standard_b64encode(self._gs2_header + self._cb_data) else: channel_binding = b"c=" + standard_b64encode(self._gs2_header) # pylint: disable=C0103 client_final_message_without_proof = (channel_binding + b",r=" + nonce) client_key = self.HMAC(self._salted_password, b"Client Key") stored_key = self.H(client_key) auth_message = ( self._client_first_message_bare + b"," + self._server_first_message + b"," + client_final_message_without_proof ) self._auth_message = auth_message client_signature = self.HMAC(stored_key, auth_message) client_proof = self.XOR(client_key, client_signature) proof = b"p=" + standard_b64encode(client_proof) client_final_message = (client_final_message_without_proof + b"," + proof) return client_final_message def _final_challenge(self, challenge): """Process the second challenge from the server and return the response. :Parameters: - `challenge`: the challenge from server. :Types: - `challenge`: `bytes` :raises: `ExtraChallengeException`, `BadChallengeException`, `ServerScramError`, or `BadSuccessException` """ if self._finished: return ExtraChallengeException() match = SERVER_FINAL_MESSAGE_RE.match(challenge) if not match: raise BadChallengeException("Bad final message syntax: {0!r}".format(challenge)) error = match.group("error") if error: raise ServerScramError("{0!r}".format(error)) verifier = match.group("verifier") if not verifier: raise BadSuccessException("No verifier value in the final message") server_key = self.HMAC(self._salted_password, b"Server Key") server_signature = self.HMAC(server_key, self._auth_message) if server_signature != a2b_base64(verifier): raise BadSuccessException("Server verifier does not match") self._finished = True def finish(self, data): """Process success indicator from the server. Process any addiitional data passed with the success. Fail if the server was not authenticated. :Parameters: - `data`: an optional additional data with success. :Types: - `data`: `bytes` :return: username and authzid :returntype: `dict` :raises: `BadSuccessException`""" if not self._server_first_message: raise BadSuccessException("Got success too early") if self._finished: return {"username": self.username, "authzid": self.authzid} else: self._final_challenge(data) if self._finished: return {"username": self.username, "authzid": self.authzid} else: raise BadSuccessException("Something went wrong when processing additional" " data with success?") class SCRAMServerAuthenticator(SCRAMOperations): """Provides SCRAM SASL authentication for a server. """ def __init__(self, hash_name, channel_binding, password_database): """Initialize a `SCRAMClientAuthenticator` object. :Parameters: - `hash_function_name`: hash function name, e.g. ``"SHA-1"`` - `channel_binding`: `True` to enable channel binding :Types: - `hash_function_name`: `unicode` - `channel_binding`: `bool` """ SCRAMOperations.__init__(self, hash_name) self.name = "SCRAM-{0}".format(hash_name) if channel_binding: self.name += "-PLUS" self.channel_binding = channel_binding self.properties = None self.out_properties = None self.password_database = password_database self._client_first_message_bare = None self._stored_key = None self._server_key = None def start(self, properties, initial_response): self.properties = properties self._client_first_message_bare = None self.out_properties = {} if not initial_response: return b"" return self.response(initial_response) def response(self, response): if self._client_first_message_bare: logger.debug("Client final message: {0!r}".format(response)) return self._handle_final_response(response) else: logger.debug("Client first message: {0!r}".format(response)) return self._handle_first_response(response) def _handle_first_response(self, response): match = CLIENT_FIRST_MESSAGE_RE.match(response) if not match: raise NotAuthorizedException("Bad response syntax: {0!r}".format(response)) mext = match.group("mext") if mext: raise NotAuthorizedException("Unsupported extension received: {0!r}".format(mext)) gs2_header
<filename>gpsTime.py<gh_stars>0 import numpy as np from math import modf import datetime as dt import calendar def cal2jd(yr,mn,dy) : """ CAL2JD Converts calendar date to Julian date using algorithm from "Practical Ephemeris Calculations" by <NAME> (Springer-Verlag, 1989). Uses astronomical year for B.C. dates (2 BC = -1 yr). Input: yr : YYYY (int) mn : MM 01 to 12 (int) day : DD 01 to 31 (int) Output: jd : julian date (float) """ if mn > 2: y = yr m = mn else: y = yr - 1 m = mn + 12 date1=4.5+31.(10.+12.1582.) # Last day of Julian calendar (1582.10.04 Noon) date2=15.5+31.(10.+12.1582.) # First day of Gregorian calendar (1582.10.15 Noon) date=dy+31.(mn+12.yr) if date <= date1: b = -2 elif date >= date2 : b = np.fix(y/400.) - np.fix(y/100.) else: #warning('Dates between October 5 & 15, 1582 do not exist'); return if y > 0: jd = np.fix(365.25y) + np.fix(30.6001(m+1)) + b + 1720996.5 + dy else: jd = np.fix(365.25y-0.75) + np.fix(30.6001(m+1)) + b + 1720996.5 + dy return jd def yyyydoy2jd(year,doy,hh=0,mm=0,ss=0.0): """ yyyydoy2jd Take a year, day-of-year, etc and convert it into a julian day Usage: jd = yyyydoy2jd(year,doy,hh,mm,ss) Input: year - 4 digit integer doy - 3 digit, or less integer, (1 <= doy <= 366) hh - 2 digit, or less int, (0 <= hh < 24) (not required) mm - 2 digit, or less int,(0 <= ss < 60) (not required) ss - float (not required) Output: 'jd' (float) """ # # need to split seconds into two components # sec => 2 digit, or less int, (0 <= ss < 60) # ms => int 0 <= ms < 1,000,000 # ms,sec = modf(float(ss)) ms = ms * 10e5 dto = dt.datetime(int(year),01,01,int(hh),int(mm),int(sec),int(ms)) dto = dto + dt.timedelta(days=(int(doy) - 1)) mn = dto.month dy = dto.day jd = cal2jd(int(year),int(mn),int(dy)) jd = jd + float(hh)/24. + float(mm)/60./24. + float(sec)/3600./24. return jd - 2400000.5 def jd2gps(jd): """ JD2GPS Converts Julian date to GPS week number (since 1980.01.06) and seconds of week. Usage: [gpsweek,sow,rollover]=jd2gps(jd) Input: jd - Julian date Output: gpsweek - GPS week number sow - seconds of week since 0 hr, Sun. rollover - number of GPS week rollovers (modulus 1024) """ jdgps = cal2jd(1980,1,6); # beginning of GPS week numbering nweek = int(np.fix((jd-jdgps)/7.)) sow = (jd - (jdgps+nweek7)) 360024 rollover = np.fix(nweek/1024) # rollover every 1024 weeks gpsweek = int(nweek) # rollover is being returned as an array? # should just be an int return gpsweek,sow,rollover def jd2cal(jd): """ JD2CAL Converts Julian date to calendar date using algorithm from "Practical Ephemeris Calculations" by <NAME> (Springer-Verlag, 1989). Must use astronomical year for B.C. dates (2 BC = -1 yr). Non-vectorized version. See also CAL2JD, DOY2JD, GPS2JD, JD2DOW, JD2DOY, JD2GPS, JD2YR, YR2JD. Usage: [yr, mn, dy]=jd2cal(jd) Input: jd - Julian date Output: yr - year of calendar date mn - month of calendar date dy - day of calendar date (including decimal) """ a = np.fix(jd+0.5) if a < 2299161. : c = a + 1524. else: b = np.fix( (a-1867216.25) / 36524.25 ) c = a + b - np.fix(b/4.) + 1525. d = np.fix( (c-122.1)/365.25 ) e = np.fix(365.25d) f = np.fix( (c-e) / 30.6001 ) dy = c - e - np.fix(30.6001f) + np.remainder((jd+0.5),a) mn = f - 1. - 12. np.fix(f/14.) yr = d - 4715. - np.fix( (7.+mn)/10. ) return yr,mn,dy def jd2doy(jd): """ JD2DOY Converts Julian date to year and day of year. Usage: [doy,yr]=jd2doy(jd) Input: jd - Julian date Output: doy - day of year yr - year """ [yr,mn,dy] = jd2cal(jd) doy = jd - cal2jd(yr,1,0) # MM ensure the doy is 0 padded doy = "%03d" % doy return yr, doy def yyyy2yy(year): """ yy = yyyy2yy(YYYY) return the yy form of YYYY yy - last two digits of YYYY - returned as an int very messy hack """ yy = int( str(int(year))[-2] + str(int(year))[-1] ) return(yy) def dateTime2gpssow(dt): """ dateTime2gpssow Converts a datetime object into gps week, and gps seconds of week Usage: week,sow = dateTime2gpssow(dateTime) Input: dt - python datetime object Output: week - gps week (int) sow - seconds into gpsweek since 0 hr, Sunday (float) """ day = dt.day + dt.hour/24. + dt.minute/1440. + dt.second/86400. jd = cal2jd(dt.year,dt.month,day) week, sow, rollover = jd2gps(jd) return week, sow def ydhms2dt(year,doy,hh,mm,ss): """ ydhms2dt Take a year, day-of-year, etc and convert it into a date time object Usage: dto = ydhms2dt(year,day,hh,mm,ss) Input: year - 4 digit integer doy - 3 digit, or less integer, (1 <= doy <= 366) hh - 2 digit, or less int, (0 <= hh < 24) mm - 2 digit, or less int,(0 <= ss < 60) ss - float Output: 'dto' a date time object """ # # need to split seconds into two components # sec => 2 digit, or less int, (0 <= ss < 60) # ms => int 0 <= ms < 1,000,000 ms,sec = modf(float(ss)) ms = ms * 10e5 dto = dt.datetime(int(year),01,01,int(hh),int(mm),int(sec),int(ms)) dto = dto + dt.timedelta(days=(int(doy) - 1)) return dto def ymdhms2dt(year,month,day,hh,mm,ss): """ ymhms2dt Take a year, day-of-year, etc and convert it into a date time object Usage: dto = ymdhms2dt(year,month,day,hh,mm,ss) Input: year - 4 digit integer month - integer, (1 => January) day - integer hh - 2 digit, or less int, (0 <= hh < 24) mm - 2 digit, or less int,(0 <= ss < 60) ss - float Output: 'dto' a date time object """ # # need to split seconds into two components # sec => 2 digit, or less int, (0 <= ss < 60) # ms => int 0 <= ms < 1,000,000 ms,sec = modf(float(ss)) ms = ms * 10e5 dto = dt.datetime(int(year),int(month),int(day),int(hh),int(mm),int(sec),0) #dto = dt.datetime(int(year),int(month),int(day),int(hh),int(mm),int(sec),int(ms)) #dt.date(int(year),int(month),int(day)) #01,01,int(hh),int(mm),int(sec),int(ms)) #dto = dto + dt.timedelta(hours= int(hh),minutes=int(mm),seconds=int(sec)) return dto def jd2mdt(jd): """ jd2mdt Take a julian date and convert it into a matplotlib date time stamp All matplotlib date plotting is done by converting date instances into days since the 0001-01-01 UTC Usage: mp_ts = jd2mdt(jd) Input: jd julian date Output: 'mp_ts' (float) a matplot lib time stamp which is days from 0001-01-01 """ #ms,sec = modf(float(ss)) #ms = ms * 10e5 year,mon,d = jd2cal(jd) day = int(np.fix(d)) h = (d - float(day)) * 24. hh = int(np.fix(h)) m = (h - float(hh)) * 60. mm = int(np.fix(m)) s = (m - float(mm)) * 60. sec = int(np.fix(s)) ms = 0 dto = dt.datetime(int(year),int(mon),int(day),int(hh),int(mm),int(sec),int(ms)) mp_epoch = dt.datetime(1, 1, 1) DAY = 86400 td = dto - mp_epoch mp_ts = td.days + 1 + (1000000 * td.seconds + td.microseconds) / 1e6 / DAY return mp_ts def ydhms2mdt(year,doy,hh,mm,ss): """ ydhms2mdt Take a year, day-of-year, etc and convert it into a matplotlib date All matplotlib date plotting is done by converting date instances into days since the 0001-01-01 UTC Usage: mp_ts = ydhms2dt(year,day,hh,mm,ss) Input: year - 4 digit integer doy - 3 digit, or less integer, (1 <= doy <= 366) hh - 2 digit, or less int, (0 <= hh < 24) mm - 2 digit, or less int,(0 <= ss < 60) ss - float Output: 'mp_ts' (float) a matplot lib time stamp which is days from 0001-01-01 """ # # need to split seconds into two components # sec => 2 digit, or less int, (0 <= ss < 60) # ms => int 0 <= ms < 1,000,000 ms,sec = modf(float(ss)) ms = ms * 10e5 dto = dt.datetime(int(year),01,01,int(hh),int(mm),int(sec),int(ms)) dto = dto + dt.timedelta(days=(int(doy) - 1)) mp_epoch = dt.datetime(1, 1, 1) DAY = 86400 td = dto - mp_epoch mp_ts = td.days + 1 + (1000000 * td.seconds + td.microseconds) / 1e6 / DAY return mp_ts def ydhms2decyr(year,doy,hh=0,mm=0,ss=0.0): """ ydhms2decyr(year,doy,hh,mm,ss) Convert
import numpy as np import pandas as pd import re import warnings import scipy.optimize as opt from scipy.stats import norm, f, chi2, ncf, ncx2, binom from scipy.special import ncfdtrinc, chndtrinc import matplotlib.pyplot as plt import seaborn as sns from poibin import PoiBin warnings.filterwarnings("ignore") def adjust_spines(ax, spines): for loc, spine in ax.spines.items(): if loc in spines: spine.set_position(('outward', 10)) # outward by 10 points else: spine.set_color('none') # don't draw spine # turn off ticks where there is no spine if 'left' in spines: ax.yaxis.set_ticks_position('left') else: # no yaxis ticks ax.yaxis.set_ticks([]) if 'bottom' in spines: ax.xaxis.set_ticks_position('bottom') else: # no xaxis ticks ax.xaxis.set_ticks([]) return None # noinspection PyProtectedMember,PyProtectedMember,PyProtectedMember,PyProtectedMember,PyProtectedMember,PyProtectedMember,PyProtectedMember,PyProtectedMember class PCurve(object): __version__ = "0.1.0" __pcurve_app_version__ = "4.06" _REGEX_STAT_TEST = re.compile( """ ^ # Beginning of string (?P<testtype>chi2\|F\|t\|r\|z\|p) # Matches the type of test statistic ($(?P<df1>\d+)(,)?(?P<df2>\d+)?$)? #Matches the degrees of freedom =(-?) #Matches an equal sign with a potential minus sign (?P<stat>(\d)\.(\d+)) # Matches the test statistics """, re.IGNORECASE \| re.VERBOSE) _POWER_LEVELS = [0.051, 0.06, 0.07, 0.08, 0.09, 0.1, 0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.7, 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.8, 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.9, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99] @staticmethod def _bound_pvals(pvals): """ Bound a p-value to avoid precision error :param pvals: The p-values :return: The bounded p-values 2.2e-16 < p < 1-2.2e-16 """ return np.where(pvals < 2.2e-16, 2.2e-16, np.where(pvals > 1 - 2.2e-16, 1 - 2.2e-16, pvals)) @staticmethod def _format_pval(p): if p < .0001: return "< .0001" elif p < .9999: pstring = f"= {p:.4f}".strip("0") return pstring else: return "> .9999" @staticmethod def _compute_prop_lower_33(pcrit, family, df1, df2, p, ncp33): """ Compute the proportion of p-values that is expected to be smaller than `pcrit` under 33% power. :param pcrit: The p-values :return: The proportion """ # Transform the critical p-value `pcrit` into the corresponding critical value for the dist. critval_f = f.ppf(1 - pcrit, df1, df2) critval_chi = chi2.ppf(1 - pcrit, df1) # Compute the proportion of values that are expected to be larger than this under 33% power exp_smaller_f = ncf._sf(critval_f, df1, df2, ncp33) exp_smaller_chi = ncx2._sf(critval_chi, df1, ncp33) # Return the appropriate stats for the family return np.where(p > .05, np.nan, np.where(family == "F", exp_smaller_f, exp_smaller_chi)) @staticmethod def _compute_stouffer_z(pp): isnotnan = ~np.isnan(pp) pp_notnan = pp[isnotnan] return np.sum(norm._ppf(pp_notnan)) / np.sqrt(isnotnan.sum()) @staticmethod def _compute_ncp_f(df1, df2, power=1 / 3): """ Uses the inverse function of the non-central F distribution with regard to NCP to recover the NCP corresponding to a given level of power for a given F test. :param df1: Numerator degrees of freedom :param df2: Denominator degrees of freedom :param power: Desired level of power :return: """ critval = f._ppf(.95, df1, df2) return ncfdtrinc(df1, df2, 1 - power, critval) @staticmethod def _compute_ncp_chi(df, power=1 / 3): """ Uses the inverse function of the non-central Chi2 distribution with regard to NCP to recover the NCP corresponding to a given level of power for a given Chi2 test. :param df: Degrees of freedom :param power: Desired level of power :return: """ critval = chi2._ppf(.95, df) return chndtrinc(critval, df, 1 - power) def _compute_ncp_all(self, power=1 / 3): family = self._df_results["family"].values df1, df2 = self._df_results[["df1", "df2"]].to_numpy().T return np.where(family == "F", self._compute_ncp_f(df1, df2, power), self._compute_ncp_chi(df1, power)) def _parse_result(self, result_str): """ Parse a statistical result, entered as a string :param result_str: A statistical result (e.g., F(1, 234) = 12.32) :return: """ result_str_replaced = ( result_str .replace(" ", "") # Whitespaces .replace("\u2009", "") # Thin whitespaces .replace("\u2212", "-") # All possible symbols for 'minus' .replace("\u2013", "-") .replace("\uFE63", "-") .replace("\u002D", "-") .replace("\uFF0D", "-") ) match = self._REGEX_STAT_TEST.match(result_str_replaced) # See regex above if match is None: # Statistical test not recognized raise ValueError(f"The input {result_str} is not recognized. Please correct it") test_props = match.groupdict() # Test recognized, accessing properties test_type = test_props["testtype"] df1_raw = test_props["df1"] df2_raw = test_props["df2"] stat_raw = test_props["stat"] # Testing that degrees of freedom are correctly entered if (test_type == "F") and ((test_props["df1"] is None) or (test_props["df2"] is None)): raise ValueError( f"Error in {result_str}: The test statistics {test_type} requires you to specify the numerator \ and denominator degrees of freedom.") if (test_type not in ["z", "p"]) and (test_props["df1"] is None): raise ValueError( f"Error in {result_str}: The test statistics {test_type} requires you to specify the degrees of \ freedom.") stat_raw = float(stat_raw) if test_type == "F": family = "F" df1 = float(df1_raw) df2 = float(df2_raw) stat = stat_raw elif test_type == "t": family = "F" df1 = 1 df2 = float(df1_raw) stat = stat_raw * 2 elif test_type == "r": family = "F" df1 = 1 df2 = float(df1_raw) stat = (stat_raw / (np.sqrt((1 - stat_raw 2) / df2))) 2 elif test_type == "chi2": family = "Chi2" df1 = float(df1_raw) df2 = None stat = stat_raw elif test_type == "z": family = "Chi2" df1 = 1 df2 = None stat = stat_raw 2 else: family = "Chi2" df1 = 1 df2 = None stat = norm.ppf(1 - stat_raw / 2) 2 return result_str_replaced, family, df1, df2, stat def _compute_pvals(self): family = self._df_results["family"].values df1, df2, stat = self._df_results[["df1", "df2", "stat"]].to_numpy().T pval = np.where(family == "F", f._sf(stat, df1, df2), chi2._sf(stat, df1)) return self._bound_pvals(pval) def _compute_stouffer_z_at_power(self, power): # NCP and pp-values of F tests df1_f, df2_f, stat_f = self._sig_f_tests ncp_f_est = self._compute_ncp_f(df1_f, df2_f, power) pp_f_est = (ncf._cdf(stat_f, df1_f, df2_f, ncp_f_est) - (1 - power)) / power # NCP and pp-values of Chi2 tests df1_chi, stat_chi = self._sig_chi_tests ncp_chi_est = self._compute_ncp_chi(df1_chi, power) pp_chi_est = (ncx2._cdf(stat_chi, df1_chi, ncp_chi_est) - (1 - power)) / power # pp-values for all tests pp_est = self._bound_pvals(np.hstack([pp_f_est, pp_chi_est])) stouffer_at_power = self._compute_stouffer_z(pp_est) return stouffer_at_power def _solve_power_for_pct(self, pct): z = norm._ppf(pct) error = lambda est: self._compute_stouffer_z_at_power(est) - z return opt.brentq(error, .0501, .9999) def _compute_ppvals_null(self, pcurvetype="full"): """ Compute the pp-value of the p-values under the null. It simply stretches the p-value over the interval [0, 1] :param pcurvetype: The type of p-curve (full or half) :return: """ p = self._df_results["p"].values if pcurvetype == "full": return np.array([self._bound_pvals(x * 20) if x < .05 else np.nan for x in p]) else: return np.array([self._bound_pvals(x * 40) if x < .025 else np.nan for x in p]) def _compute_ppvals_33(self, pcurvetype="full"): family = self._df_results["family"].values df1, df2, stat, pvals, ncp33 = self._df_results[["df1", "df2", "stat", "p", "ncp33"]].to_numpy().T if pcurvetype == "full": pthresh = .05 # Only keep p-values smaller than .05 propsig = 1 / 3 # Under 33% power, 1/3 of p-values should be lower than .05 else: pthresh = .025 # Only keep p-values smaller than .025 # We don't know which prop of p-values should be smaller than .025 under 33% power, so compute it propsig = 3 * self._compute_prop_lower_33(.025, family, df1, df2, pvals, ncp33) # We then stretch the ppval on the [0, 1] interval. pp_33_f = (1 / propsig) * (ncf.cdf(stat, df1, df2, ncp33) - (1 - propsig)) pp_33_chi = (1 / propsig) * (ncx2.cdf(stat, df1, ncp33) - (1 - propsig)) pp_33 = np.where(family == "F", pp_33_f, pp_33_chi) return np.array([self._bound_pvals(pp) if p < pthresh else np.nan for (p, pp) in zip(pvals, pp_33)]) def _get_33_power_curve(self): family = self._df_results["family"].values df1, df2, p, ncp33 = self._df_results[["df1", "df2", "p", "ncp33"]].to_numpy().T cprop = lambda x: self._compute_prop_lower_33(x, family, df1, df2, p, ncp33) propsig = np.array([cprop(c) for c in [.01, .02, .03, .04, .05]]) diffs = np.diff(propsig, axis=0, prepend=0) # Difference of CDFs: Likelihood of p-values falling between each value props = np.nanmean(3 * diffs, axis=1) return props def _run_binom_test(self, alternative="null"): family = self._df_results["family"].values df1, df2, p, ncp33 = self._df_results[["df1", "df2", "p", "ncp33"]].to_numpy().T k_below_25 = self._n_tests['p025'] if alternative == "null": return binom(n=self._n_tests['p05'], p=.5).sf(k_below_25 - 1)
self.cmdForms['loadMacro'].descr.entryByName ebn['loadMacro']['widget'].configure(state='disabled') def __call__(self, macroName, macroFile, menuBar='menuRoot', menuButton='Macros', menuEntry=None, cascade=None, kw): """None<---loadMacro(macroName, macroFile, menuBar='menuRoot', menuButton='Macros', menuEntry=None, cascade=None, kw) """ self.doitWrapper(macroName, macroFile, menuBar=menuBar, menuButton=menuButton, menuEntry=menuEntry, cascade=cascade) def doit(self, macroName, macroFile, menuBar='menuRoot', menuButton='Macros', menuEntry=None, cascade=None): if not hasattr(self, 'macroFile') or macroFile != self.macroFile: names, macros, docs = self.getMacros(macroFile) else: names = self.macNames macros = self.macMacros docs = self.macDoc if len(names) == 0 or len(macros)==0 or len(docs)==0: return macIndex = names.index(macroName) macro = macros[macIndex] from VFCommand import Command, CommandGUI c = Command(func=macro) g = CommandGUI() if cascade: g.addMenuCommand(menuBar, menuButton, menuEntry, cascadeName=cascade) else: g.addMenuCommand(menuBar, menuButton, menuEntry) self.vf.addCommand(c, macro.__name__, g) ## class loadMacroCommand(Command): ## """ ## Command to load dynamically macro commands. ## Using the Gui the user can open a macro file. The macros available in ## that file are then displayed in a list chooser. When a macro is selected ## in the listchooser, its documentation string is deisplayed and a default ## name for the macro in the viewer is suggested. The user can also specify ## a menuBar, a menuButton as well as an optional cascade name. ## """ ## active = 0 ## def getMacros(self, file): ## """load all macro functions from file""" ## self.file = file ## _dir, file = os.path.split(file) ## if file[-3:]=='.py': file = file[:-3] ## import sys ## sys.path.insert(0, _dir) ## m = __import__(file, globals(), locals(), []) ## sys.path = sys.path[1:] ## m.__dict__['self'] = self.vf ## import types ## names = [] ## macros = [] ## doc = [] ## for key,value in m.__dict__.items(): ## if type(value)==types.FunctionType: ## names.append(key) ## macros.append(value) ## doc.append(value.__doc__) ## return names, macros, doc ## def loadMacLib_cb(self, filename): ## """Call back function for 'Open Macro library' button""" ## # self.ifd[0]['widget'] is the 'Open Macro Library' button ## self.ifd[0]['widget'].configure(relief='sunken') ## #file = os.path.split(filename)[1][:-3] ## names, macros, docs = self.getMacros(filename) ## self.macNames = names ## self.macMacros = macros ## self.macDoc = docs ## # Get a handle to the listchooser widget ## lc = self.ifd[1]['widget'] ## lc.clear() ## if len(names) == len(docs): ## entries = map(lambda x, y: (x, y), names, docs) ## else: ## entries = map(lambda x: (x, None), names) ## map(lc.add, entries) ## self.ifd[0]['widget'].configure(relief='raised') ## # set cascade name to libary Name - "mac" ## w = self.ifd[5]['widget'] ## w.delete(0, 'end') ## w.insert(0, os.path.split(filename)[1][:-3]) ## def setDefaultEntryName(self, event=None): ## """Call back function for the listchooser showing macros. ## gets the name of the currently selected macro and puts it in the entry ## type in""" ## # enable add button ## self.ifd.entryByName['Load Macro']['widget'].configure(state='normal') ## # put default name into name entry ## val = self.ifd[1]['widget'].get() ## w = self.ifd[4]['widget'] ## w.delete(0, 'end') ## w.insert(0, val[0]) ## self.selectedMac = val[0] ## def addMacro(self, macro, menuBar, menuButton, name, cascade=None): ## from VFCommand import Command, CommandGUI ## c = Command(func=macro) ## g = CommandGUI() ## if cascade: ## g.addMenuCommand(menuBar, menuButton, name, cascadeName=cascade) ## else: ## g.addMenuCommand(menuBar, menuButton, name) ## self.vf.addCommand(c, macro.__name__, g) ## ## g.register(self.vf) ## self.log(file=self.file, macroName=macro.__name__, menuBar=menuBar, ## menuButton=menuButton, name=name, cascade=cascade) ## def loadMacro_cb(self, event=None): ## bar = self.ifd[2]['widget'].get() ## menub = self.ifd[3]['widget'].get() ## name = self.ifd[4]['widget'].get() ## cascade = self.ifd[5]['widget'].get() ## if cascade=='': cascade=None ## macIndex = self.macNames.index(self.selectedMac) ## macFunc = self.macMacros[macIndex] ## self.addMacro(macFunc, bar, menub, name, cascade) ## self.ifd[0]['widget'].configure(relief='raised') ## def customizeGUI(self): ## """create the cascade menu for selecting modules to be loaded""" ## self.selectedMac = '' ## # create the for descriptor ## ifd = self.ifd = InputFormDescr(title='Load macro commands') ## if len(self.vf.libraries) is None: ## modu = __import__('ViewerFramework') ## else: ## modu = __import__(self.vf.libraries[0]) ## idir = os.path.split(modu.__file__)[0] + '/Macros' ## if not os.path.exists(idir): ## idir = None ## ifd.append( {'widgetType':'OpenButton', 'text':'Open Macro library ...', ## 'types':[('Macro Module Library', 'Mac.py'), ## ('Any Python Function', '.py')], ## 'idir':idir, ## 'title':'Open Macro File', ## 'callback': self.loadMacLib_cb } ) ## ifd.append({'title':'Choose a macro', ## 'widgetType':ListChooser, ## 'wcfg':{ ## 'command':self.setDefaultEntryName, ## 'title':'Choose a macro'}, ## 'gridcfg':{'sticky':Tkinter.E+Tkinter.W}} ) ## ifd.append({'widgetType':Tkinter.Entry, ## 'defaultValue':'menuRoot', ## 'wcfg':{'label':'menu bar'}, ## 'gridcfg':{'sticky':Tkinter.E} ## }) ## ifd.append({'widgetType':Tkinter.Entry, ## 'defaultValue':'Macros', ## 'wcfg':{'label':'menu button'}, ## 'gridcfg':{'sticky':Tkinter.E} ## }) ## ifd.append({'widgetType':Tkinter.Entry, ## 'defaultValue':'', ## 'wcfg':{'label':'menu entry'}, ## 'gridcfg':{'sticky':Tkinter.E} ## }) ## ifd.append({'widgetType':Tkinter.Entry, ## 'defaultValue':'', ## 'wcfg':{'label':'cascade'}, ## 'gridcfg':{'sticky':Tkinter.E} ## }) ## ifd.append({'name': 'Load Macro', ## 'widgetType':Tkinter.Button, ## 'text':'Load Macro', ## 'wcfg':{'bd':6}, ## 'gridcfg':{'sticky':Tkinter.E+Tkinter.W}, ## 'command': self.loadMacro_cb}) ## ifd.append({'widgetType':Tkinter.Button, ## 'text':'Dismiss', ## 'command': self.Dismiss_cb}) ## def Dismiss_cb(self): ## #self.cmdForms['loadMacro'].withdraw() ## self.ifd.form.destroy() ## self.active = 0 ## def guiCallback(self, event=None, file=None): ## if self.active: return ## self.active = 1 ## self.customizeGUI() ## self.form = self.vf.getUserInput(self.ifd, modal=0, blocking=0) ## self.ifd.entryByName['Load Macro']['widget'].configure(state='disabled') ## if file: self.loadMacLib_cb(file) ## def __call__(self, file=None, macroName=None, menuBar='menuRoot', ## menuButton='Macros', name=None, cascade=None): ## """file=None, macroName=None, menuBar='menuRoot', menuButton='Macros', ## name=None, cascade=None""" ## if not macroName: self.guiCallback(file=file) ## else: ## if file[-3:]=='.py': file = file[:-3] ## names, macros, docs = self.getMacros(file) ## i = names.index(macroName) ## if name==None: name=macroName ## self.addMacro(macros[i], menuBar, menuButton, name, cascade) class ShellCommand(Command): """Command to show/Hide the Python shell. \nPackage : ViewerFramework \nModule : basicCommand.py \nClass : ShellCommand \nCommand : Shell \nSynopsis:\n None<---Shell() """ def onAddCmdToViewer(self): if self.vf.hasGui: self.vf.GUI.pyshell.top.protocol('WM_DELETE_WINDOW', self.vf.Shell.onDestroy) def show(self): self.vf.GUI.pyshell.top.deiconify() def hide(self): self.vf.GUI.pyshell.top.withdraw() def __call__(self, args): """None<---Shell() """ if args[0]: self.show() self.vf.GUI.toolbarCheckbuttons['Shell']['Variable'].set(1) else: self.hide() self.vf.GUI.toolbarCheckbuttons['Shell']['Variable'].set(0) def guiCallback(self): on = self.vf.GUI.toolbarCheckbuttons['Shell']['Variable'].get() if on: self.show() else: self.hide() def onDestroy(self): self.vf.GUI.toolbarCheckbuttons['Shell']['Variable'].set(0) self.hide() ShellCommandGUI = CommandGUI() ShellCommandGUI.addToolBar('Shell', icon1='PyShell.gif', balloonhelp='Python IDLE Shell', index=1) class SaveSessionCommand(Command): """Command to allow the user to save the session as it is in a file. It logs all the transformation. \nPackage : Pmv \nModule : customizationCommands.py \nClass : SaveSessionCommand """ def logString(self, args, *kw): """return None as log string as we don't want to log this """ pass def guiCallback(self, event=None): ### FIXME all the logs should be in a stack and not in a file. if self.vf.logMode == 'no': self.vf.warningMsg("No log information because logMode was set to no.") return newfile = self.vf.askFileSave(types = [ ('Pmv sesion files', '.psf'), ('all files', '*.py')], defaultextension=".psf", title = 'Save Session in File:') if not newfile is None: self.doitWrapper(newfile, redraw=0) def doit(self, filename): #print "SaveSessionCommand.doit" ext = os.path.splitext(filename)[1].lower() if ext=='.psf': self.vf.saveFullSession(filename) else: import shutil # get the current log. if hasattr(self.vf, 'logAllFile'): logFileName = self.vf.logAllFile.name self.vf.logAllFile.close() if filename!=logFileName: shutil.copy(logFileName, filename) self.vf.logAllFile = open(logFileName,'a') # Add to it the transformation log. logFile = open(filename,'a') vi = self.vf.GUI.VIEWER code = vi.getViewerStateDefinitionCode('self.GUI.VIEWER') code.extend( vi.getObjectsStateDefinitionCode('self.GUI.VIEWER') ) if code: for line in code: logFile.write(line) if vi.GUI.contourTk.get(): controlpoints=vi.GUI.curvetool.getControlPoints() sensitivity=vi.GUI.d1scalewheel.get() logFile.write("self.GUI.VIEWER.GUI.curvetool.setControlPoints(%s)" %controlpoints) logFile.write("\n") logFile.write("self.GUI.VIEWER.GUI.curvetool.setSensitivity(%s)" %sensitivity) #sceneLog = self.vf.Exit.logScene() #for l in sceneLog: # l1 = l+'\n' # logFile.write(l1) logFile.close() if hasattr(self.vf, 'recentFiles'): self.vf.recentFiles.add(filename, 'readSourceMolecule') # SaveSessionCommand Command GUI SaveSessionCommandGUI = CommandGUI() SaveSessionCommandGUI.addMenuCommand( 'menuRoot', 'File', 'Current Session', index=2, cascadeName='Save', cascadeIndex=2, separatorAboveCascade=1) SaveSessionCommandGUI.addToolBar('Save', icon1='filesave.gif', type='ToolBarButton', balloonhelp='Save Session', index=1) class ExitCommand(Command): """Command to destroy application \nPackage : ViewerFramework \nModule : basicCommand.py \nClass : ExitCommand \nCommand : Exit \nSynopsis:\n None<---Exit(ask) \nask = Flag when set to 1 a form asking you if you really want to quit will popup, it will quit directly if set to 0 """ def onAddCmdToViewer(self): #print "ExitComand.onAddCmdToViewer" import warnings if self.vf.hasGui: self.vf.GUI.ROOT.protocol('WM_DELETE_WINDOW',self.askquit) def logObjectTransformations(self, object): warnings.warn( "logObjectTransformations is deprecated", DeprecationWarning, stacklevel=2) log = [] # FIXME won't work with instance matrices mat = object.GetMatrix(object) import numpy.oldnumeric as Numeric log.append("self.transformObject('rotation', '%s', matrix=%s,log=0)"%(object.fullName,tuple(object.rotation))) log.append("self.transformObject('translation', '%s', matrix=%s, log=0 )"%(object.fullName, tuple(object.translation))) log.append("self.transformObject('scale', '%s', matrix=%s, log=0 )"%(object.fullName,tuple(object.scale))) log.append("self.transformObject('pivot', '%s', matrix=%s, log=0 )"%(object.fullName,tuple(object.pivot))) return log def logObjectMaterial(self, object): warnings.warn("logObjectMaterial is deprecated", DeprecationWarning, stacklevel=2) log = [] from opengltk.OpenGL import GL log.append("from opengltk.OpenGL import GL") mat = object.materials[GL.GL_FRONT] log.append("self.setObject('%s', materials=%s, propName='ambi', matBind=%d)" % (object.fullName, repr(mat.prop[0])[6:-5],mat.binding[0])) log.append("self.setObject('%s', materials=%s, propName='diff', matBind=%d)" % (object.fullName, repr(mat.prop[1])[6:-5],mat.binding[1])) log.append("self.setObject('%s', materials=%s, propName='emis', matBind=%d)" % (object.fullName, repr(mat.prop[2])[6:-5],mat.binding[2])) log.append("self.setObject('%s', materials=%s, propName='spec', matBind=%d)" % (object.fullName, repr(mat.prop[3])[6:-5],mat.binding[3])) log.append("self.setObject('%s', materials=%s, propName='shini', matBind=%d)" % (object.fullName, repr(mat.prop[4])[6:-5],mat.binding[4])) mat = object.materials[GL.GL_BACK] log.append("self.setObject('%s', materials=%s, polyFace=GL.GL_BACK,propName='ambi', matBind=%d)" % (object.fullName, repr(mat.prop[0])[6:-5],mat.binding[0])) log.append("self.setObject('%s', materials=%s, polyFace=GL.GL_BACK,propName='diff', matBind=%d)" % (object.fullName, repr(mat.prop[1])[6:-5],mat.binding[1])) log.append("self.setObject('%s', materials=%s, polyFace=GL.GL_BACK,propName='spec', matBind=%d)" % (object.fullName, repr(mat.prop[2])[6:-5],mat.binding[2])) log.append("self.setObject('%s', materials=%s, polyFace=GL.GL_BACK,propName='emis', matBind=%d)" % (object.fullName, repr(mat.prop[3])[6:-5],mat.binding[3])) log.append("self.setObject('%s', materials=%s, polyFace=GL.GL_BACK,propName='shini', matBind=%d)" % (object.fullName, repr(mat.prop[4])[6:-5],mat.binding[4])) return log def logCameraTransformations(self, camera): warnings.warn("logCameraTransformations is deprecated", DeprecationWarning, stacklevel=2) logStr = "self.setCamera('%s', \n"%camera.name logStr = logStr + "rotation=(%9.3f, %9.3f, %9.3f, %9.3f, %9.3f, %9.3f, %9.3f, %9.3f, %9.3f, %9.3f, %9.3f, %9.3f, %9.3f, %9.3f, %9.3f, %9.3f),\n"%tuple(camera.rotation) logStr=logStr + "translation=(%9.3f, %9.3f, %9.3f),\n"%tuple(camera.translation) logStr = logStr + "scale=(%9.3f, %9.3f, %9.3f),\n"%tuple(camera.scale) logStr = logStr + "pivot=(%9.3f, %9.3f, %9.3f),\n"%tuple(camera.pivot) logStr = logStr + "lookAt=(%9.3f, %9.3f, %9.3f),\n"%tuple(camera.lookAt) logStr = logStr + "lookFrom=(%9.3f, %9.3f, %9.3f),\n"%tuple(camera.lookFrom) logStr = logStr + "direction=(%9.3f, %9.3f, %9.3f))"%tuple(camera.direction) return logStr+'\n' def logCameraProp(self, camera): warnings.warn("logCameraProp is deprecated", DeprecationWarning, stacklevel=2) logStr = "self.setCamera('%s', \n"%camera.name logStr = logStr + "width=%d, height=%d, rootx=%d, rooty=%d,"%\ (camera.width, camera.height, camera.rootx, camera.rooty) logStr = logStr + "fov=%f, near=%f, far=%f,"%\ (camera.fovy, camera.near, camera.far) logStr = logStr + "color=(%6.3f,%6.3f,%6.3f,%6.3f))"%\ tuple(camera.backgroundColor) return logStr+'\n' def logLightTransformations(self, light): warnings.warn("logLightTransformations is deprecated", DeprecationWarning, stacklevel=2) logStr = "self.setLight('%s', \n"%light.name logStr = logStr + "rotation=(%9.3f, %9.3f, %9.3f, %9.3f, %9.3f, %9.3f, %9.3f, %9.3f, %9.3f, %9.3f, %9.3f, %9.3f,
<reponame>rahul2393/python-spanner<filename>samples/samples/snippets_test.py # Copyright 2016 Google, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import time import uuid from google.api_core import exceptions from google.cloud import spanner import pytest from test_utils.retry import RetryErrors import snippets CREATE_TABLE_SINGERS = """\ CREATE TABLE Singers ( SingerId INT64 NOT NULL, FirstName STRING(1024), LastName STRING(1024), SingerInfo BYTES(MAX) ) PRIMARY KEY (SingerId) """ CREATE_TABLE_ALBUMS = """\ CREATE TABLE Albums ( SingerId INT64 NOT NULL, AlbumId INT64 NOT NULL, AlbumTitle STRING(MAX) ) PRIMARY KEY (SingerId, AlbumId), INTERLEAVE IN PARENT Singers ON DELETE CASCADE """ retry_429 = RetryErrors(exceptions.ResourceExhausted, delay=15) @pytest.fixture(scope="module") def sample_name(): return "snippets" @pytest.fixture(scope="module") def create_instance_id(): """Id for the low-cost instance.""" return f"create-instance-{uuid.uuid4().hex[:10]}" @pytest.fixture(scope="module") def lci_instance_id(): """Id for the low-cost instance.""" return f"lci-instance-{uuid.uuid4().hex[:10]}" @pytest.fixture(scope="module") def database_id(): return f"test-db-{uuid.uuid4().hex[:10]}" @pytest.fixture(scope="module") def create_database_id(): return f"create-db-{uuid.uuid4().hex[:10]}" @pytest.fixture(scope="module") def cmek_database_id(): return f"cmek-db-{uuid.uuid4().hex[:10]}" @pytest.fixture(scope="module") def default_leader_database_id(): return f"leader_db_{uuid.uuid4().hex[:10]}" @pytest.fixture(scope="module") def database_ddl(): """Sequence of DDL statements used to set up the database. Sample testcase modules can override as needed. """ return [CREATE_TABLE_SINGERS, CREATE_TABLE_ALBUMS] @pytest.fixture(scope="module") def default_leader(): """Default leader for multi-region instances.""" return "us-east4" def test_create_instance_explicit(spanner_client, create_instance_id): # Rather than re-use 'sample_isntance', we create a new instance, to # ensure that the 'create_instance' snippet is tested. retry_429(snippets.create_instance)(create_instance_id) instance = spanner_client.instance(create_instance_id) retry_429(instance.delete)() def test_create_database_explicit(sample_instance, create_database_id): # Rather than re-use 'sample_database', we create a new database, to # ensure that the 'create_database' snippet is tested. snippets.create_database(sample_instance.instance_id, create_database_id) database = sample_instance.database(create_database_id) database.drop() def test_create_instance_with_processing_units(capsys, lci_instance_id): processing_units = 500 retry_429(snippets.create_instance_with_processing_units)( lci_instance_id, processing_units, ) out, _ = capsys.readouterr() assert lci_instance_id in out assert "{} processing units".format(processing_units) in out spanner_client = spanner.Client() instance = spanner_client.instance(lci_instance_id) retry_429(instance.delete)() def test_create_database_with_encryption_config(capsys, instance_id, cmek_database_id, kms_key_name): snippets.create_database_with_encryption_key(instance_id, cmek_database_id, kms_key_name) out, _ = capsys.readouterr() assert cmek_database_id in out assert kms_key_name in out def test_get_instance_config(capsys): instance_config = "nam6" snippets.get_instance_config(instance_config) out, _ = capsys.readouterr() assert instance_config in out def test_list_instance_config(capsys): snippets.list_instance_config() out, _ = capsys.readouterr() assert "regional-us-central1" in out def test_list_databases(capsys, instance_id): snippets.list_databases(instance_id) out, _ = capsys.readouterr() assert "has default leader" in out def test_create_database_with_default_leader(capsys, multi_region_instance, multi_region_instance_id, default_leader_database_id, default_leader): retry_429 = RetryErrors(exceptions.ResourceExhausted, delay=15) retry_429(snippets.create_database_with_default_leader)( multi_region_instance_id, default_leader_database_id, default_leader ) out, _ = capsys.readouterr() assert default_leader_database_id in out assert default_leader in out def test_update_database_with_default_leader(capsys, multi_region_instance, multi_region_instance_id, default_leader_database_id, default_leader): retry_429 = RetryErrors(exceptions.ResourceExhausted, delay=15) retry_429(snippets.update_database_with_default_leader)( multi_region_instance_id, default_leader_database_id, default_leader ) out, _ = capsys.readouterr() assert default_leader_database_id in out assert default_leader in out def test_get_database_ddl(capsys, instance_id, sample_database): snippets.get_database_ddl(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert sample_database.database_id in out def test_query_information_schema_database_options(capsys, multi_region_instance, multi_region_instance_id, default_leader_database_id, default_leader): snippets.query_information_schema_database_options( multi_region_instance_id, default_leader_database_id ) out, _ = capsys.readouterr() assert default_leader in out @pytest.mark.dependency(name="insert_data") def test_insert_data(capsys, instance_id, sample_database): snippets.insert_data(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "Inserted data" in out @pytest.mark.dependency(depends=["insert_data"]) def test_delete_data(capsys, instance_id, sample_database): snippets.delete_data(instance_id, sample_database.database_id) # put it back for other tests snippets.insert_data(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "Deleted data" in out @pytest.mark.dependency(depends=["insert_data"]) def test_query_data(capsys, instance_id, sample_database): snippets.query_data(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "SingerId: 1, AlbumId: 1, AlbumTitle: Total Junk" in out @pytest.mark.dependency(name="add_column", depends=["insert_data"]) def test_add_column(capsys, instance_id, sample_database): snippets.add_column(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "Added the MarketingBudget column." in out @pytest.mark.dependency(depends=["insert_data"]) def test_read_data(capsys, instance_id, sample_database): snippets.read_data(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "SingerId: 1, AlbumId: 1, AlbumTitle: Total Junk" in out @pytest.mark.dependency(name="update_data", depends=["add_column"]) def test_update_data(capsys, instance_id, sample_database): # Sleep for 15 seconds to ensure previous inserts will be # 'stale' by the time test_read_stale_data is run. time.sleep(15) snippets.update_data(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "Updated data." in out @pytest.mark.dependency(depends=["update_data"]) def test_read_stale_data(capsys, instance_id, sample_database): # This snippet relies on test_update_data inserting data # at least 15 seconds after the previous insert snippets.read_stale_data(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "SingerId: 1, AlbumId: 1, MarketingBudget: None" in out @pytest.mark.dependency(depends=["add_column"]) def test_read_write_transaction(capsys, instance_id, sample_database): snippets.read_write_transaction(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "Transaction complete" in out @pytest.mark.dependency(depends=["add_column"]) def test_query_data_with_new_column(capsys, instance_id, sample_database): snippets.query_data_with_new_column(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "SingerId: 1, AlbumId: 1, MarketingBudget: 300000" in out assert "SingerId: 2, AlbumId: 2, MarketingBudget: 300000" in out @pytest.mark.dependency(name="add_index", depends=["insert_data"]) def test_add_index(capsys, instance_id, sample_database): snippets.add_index(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "Added the AlbumsByAlbumTitle index" in out @pytest.mark.dependency(depends=["add_index"]) def test_query_data_with_index(capsys, instance_id, sample_database): snippets.query_data_with_index(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "Go, Go, Go" in out assert "Forever Hold Your Peace" in out assert "Green" not in out @pytest.mark.dependency(depends=["add_index"]) def test_read_data_with_index(capsys, instance_id, sample_database): snippets.read_data_with_index(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "Go, Go, Go" in out assert "Forever Hold Your Peace" in out assert "Green" in out @pytest.mark.dependency(name="add_storing_index", depends=["insert_data"]) def test_add_storing_index(capsys, instance_id, sample_database): snippets.add_storing_index(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "Added the AlbumsByAlbumTitle2 index." in out @pytest.mark.dependency(depends=["add_storing_index"]) def test_read_data_with_storing_index(capsys, instance_id, sample_database): snippets.read_data_with_storing_index(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "300000" in out @pytest.mark.dependency(depends=["insert_data"]) def test_read_only_transaction(capsys, instance_id, sample_database): snippets.read_only_transaction(instance_id, sample_database.database_id) out, _ = capsys.readouterr() # Snippet does two reads, so entry should be listed twice assert out.count("SingerId: 1, AlbumId: 1, AlbumTitle: Total Junk") == 2 @pytest.mark.dependency(name="add_timestamp_column", depends=["insert_data"]) def test_add_timestamp_column(capsys, instance_id, sample_database): snippets.add_timestamp_column(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert 'Altered table "Albums" on database ' in out @pytest.mark.dependency(depends=["add_timestamp_column"]) def test_update_data_with_timestamp(capsys, instance_id, sample_database): snippets.update_data_with_timestamp(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "Updated data" in out @pytest.mark.dependency(depends=["add_timestamp_column"]) def test_query_data_with_timestamp(capsys, instance_id, sample_database): snippets.query_data_with_timestamp(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "SingerId: 1, AlbumId: 1, MarketingBudget: 1000000" in out assert "SingerId: 2, AlbumId: 2, MarketingBudget: 750000" in out @pytest.mark.dependency(name="create_table_with_timestamp") def test_create_table_with_timestamp(capsys, instance_id, sample_database): snippets.create_table_with_timestamp(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "Created Performances table on database" in out @pytest.mark.dependency(depends=["create_table_with_datatypes"]) def test_insert_data_with_timestamp(capsys, instance_id, sample_database): snippets.insert_data_with_timestamp(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "Inserted data." in out @pytest.mark.dependency(name="write_struct_data") def test_write_struct_data(capsys, instance_id, sample_database): snippets.write_struct_data(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "Inserted sample data for STRUCT queries" in out @pytest.mark.dependency(depends=["write_struct_data"]) def test_query_with_struct(capsys, instance_id, sample_database): snippets.query_with_struct(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "SingerId: 6" in out @pytest.mark.dependency(depends=["write_struct_data"]) def test_query_with_array_of_struct(capsys, instance_id, sample_database): snippets.query_with_array_of_struct(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "SingerId: 8" in out assert "SingerId: 7" in out assert "SingerId: 6" in out @pytest.mark.dependency(depends=["write_struct_data"]) def test_query_struct_field(capsys, instance_id, sample_database): snippets.query_struct_field(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "SingerId: 6" in out @pytest.mark.dependency(depends=["write_struct_data"]) def test_query_nested_struct_field(capsys, instance_id, sample_database): snippets.query_nested_struct_field(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "SingerId: 6 SongName: Imagination" in out assert "SingerId: 9 SongName: Imagination" in out @pytest.mark.dependency(name="insert_data_with_dml") def test_insert_data_with_dml(capsys, instance_id, sample_database): snippets.insert_data_with_dml(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "1 record(s) inserted." in out @pytest.mark.dependency(name="log_commit_stats") def test_log_commit_stats(capsys, instance_id, sample_database): snippets.log_commit_stats(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "1 record(s) inserted." in out assert "3 mutation(s) in transaction." in out @pytest.mark.dependency(depends=["insert_data"]) def test_update_data_with_dml(capsys, instance_id, sample_database): snippets.update_data_with_dml(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "1 record(s) updated." in out @pytest.mark.dependency(depends=["insert_data"]) def test_delete_data_with_dml(capsys, instance_id, sample_database): snippets.delete_data_with_dml(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "1 record(s) deleted." in out @pytest.mark.dependency(depends=["add_timestamp_column"]) def test_update_data_with_dml_timestamp(capsys, instance_id, sample_database): snippets.update_data_with_dml_timestamp(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "2 record(s) updated." in out @pytest.mark.dependency(name="dml_write_read_transaction") def test_dml_write_read_transaction(capsys, instance_id, sample_database): snippets.dml_write_read_transaction(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "1 record(s) inserted." in out assert "FirstName: Timothy, LastName: Campbell" in out @pytest.mark.dependency(depends=["dml_write_read_transaction"]) def test_update_data_with_dml_struct(capsys, instance_id, sample_database): snippets.update_data_with_dml_struct(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "1 record(s) updated" in out @pytest.mark.dependency(name="insert_with_dml") def test_insert_with_dml(capsys, instance_id, sample_database): snippets.insert_with_dml(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "4 record(s) inserted" in out @pytest.mark.dependency(depends=["insert_with_dml"]) def test_query_data_with_parameter(capsys, instance_id, sample_database): snippets.query_data_with_parameter(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "SingerId: 12, FirstName: Melissa, LastName: Garcia" in out @pytest.mark.dependency(depends=["add_column"]) def test_write_with_dml_transaction(capsys, instance_id, sample_database): snippets.write_with_dml_transaction(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "Transferred 200000 from Album2's budget to Album1's" in out @pytest.mark.dependency(depends=["add_column"]) def update_data_with_partitioned_dml(capsys, instance_id, sample_database): snippets.update_data_with_partitioned_dml(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "3 record(s) updated" in out @pytest.mark.dependency(depends=["insert_with_dml"]) def test_delete_data_with_partitioned_dml(capsys, instance_id, sample_database): snippets.delete_data_with_partitioned_dml(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "6 record(s) deleted" in out @pytest.mark.dependency(depends=["add_column"]) def test_update_with_batch_dml(capsys, instance_id, sample_database): snippets.update_with_batch_dml(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "Executed 2 SQL statements using Batch DML" in out @pytest.mark.dependency(name="create_table_with_datatypes") def test_create_table_with_datatypes(capsys, instance_id, sample_database): snippets.create_table_with_datatypes(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "Created Venues table on database" in out @pytest.mark.dependency( name="insert_datatypes_data", depends=["create_table_with_datatypes"], ) def test_insert_datatypes_data(capsys, instance_id, sample_database): snippets.insert_datatypes_data(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "Inserted data." in out @pytest.mark.dependency(depends=["insert_datatypes_data"]) def test_query_data_with_array(capsys, instance_id, sample_database): snippets.query_data_with_array(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "VenueId: 19, VenueName: Venue 19, AvailableDate: 2020-11-01" in out assert "VenueId: 42, VenueName: Venue 42, AvailableDate: 2020-10-01" in out @pytest.mark.dependency(depends=["insert_datatypes_data"]) def test_query_data_with_bool(capsys, instance_id, sample_database): snippets.query_data_with_bool(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "VenueId: 19, VenueName: Venue 19, OutdoorVenue: True" in out @pytest.mark.dependency(depends=["insert_datatypes_data"]) def test_query_data_with_bytes(capsys, instance_id, sample_database): snippets.query_data_with_bytes(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "VenueId: 4, VenueName: Venue 4" in out @pytest.mark.dependency(depends=["insert_datatypes_data"]) def test_query_data_with_date(capsys, instance_id, sample_database): snippets.query_data_with_date(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "VenueId: 4, VenueName: Venue 4, LastContactDate: 2018-09-02" in out assert "VenueId: 42,
from dg_db.db_write import write_countries, write_skews, write_platforms def populate_accounts(): countries = [ ("Germany", "DE", "Central", "Gat"), ("Austria", "AT", "Central", "Gat"), ("Switzerland", "CH", "Central", "None"), ("France", "FR", "South", "None"), ("Italy", "IT", "South", "None"), ("Spain", "ES", "South", "Iberia"), ("Portugal", "PT", "South", "Iberia"), ("Denmark", "DK", "North", "None"), ("Norway", "NO", "North", "None"), ("Sweden", "SE", "North", "None"), ("Finland", "FI", "North", "None"), ("Netherlands", "NL", "North", "Benelux"), ("Belgium", "BE", "North", "Benelux"), ("UK", "UK", "UKI", "None"), ("Ireland", "IE", "UKI", "None"), ] write_countries(countries) def populate_platforms(): platforms = [ (1, "Google", "8005495881", "2059894093"), (2, "Google", "4825989316", "4302291843"), (3, "Google", "1345515457", "5635690780"), (4, "Google", "3136320411", "7447885588"), (5, "Google", "3456991375", "2037891425"), (6, "Google", "6033808683", "9021596480"), (7, "Google", "1795086707", "4753585796"), (8, "Google", "9503587615", "7310537721"), (9, "Google", "1739364726", "8212508088"), (10, "Google", "7245755087", "7575293197"), (11, "Google", "1332379677", "1904670595"), (12, "Google", "7966843210", "9271529992"), (13, "Google", "3704484034", "6338903272"), (14, "Google", "8457701491", "1150412027"), (15, "Google", "3377816067", "6186544301"), (1, "Microsoft", "X7356537", "X7356537"), (2, "Microsoft", "X7004130", "X7004130"), (3, "Microsoft", "F1071RW6", "F1071RW6"), (4, "Microsoft", "X8349003", "X8349003"), (5, "Microsoft", "B004ETLE", "B004ETLE"), (6, "Microsoft", "B004V7BM", "B004V7BM"), (7, "Microsoft", "NOTAVAIL", "NOTAVAIL"), (8, "Microsoft", "X000XGAU", "X000XGAU"), (9, "Microsoft", "B004SUHL", "B004SUHL"), (10, "Microsoft", "F107DTZT", "F107DTZT"), (11, "Microsoft", "F1074ELB", "F1074ELB"), (12, "Microsoft", "X0006AXW", "X0006AXW"), (13, "Microsoft", "B004295N", "B004295N"), (14, "Microsoft", "F107P4UT", "F107P4UT"), (15, "Microsoft", "F107MVC9", "F107MVC9") ] write_platforms(platforms) def populate_skews(): # Q2 22 records_to_insert = [ ("UK", 910534.84, 4660585.24, 0.1954, 0.0173, 0.0419, 0.0440, 0.0692, 0.0533, 0.0533, 0.0551, 0.0589, 0.2653, 0.0677, 0.0799, 0.0810, 0.0810, 0.0320), ("IE", 102951.89, 727779.08, 0.1415, 0.0180, 0.0437, 0.0459, 0.0722, 0.0556, 0.0556, 0.0574, 0.0614, 0.2339, 0.0706, 0.0833, 0.0844, 0.0844, 0.0334), ("DE", 1109019.05, 5427544.86, 0.2043, 0.0153, 0.0400, 0.0438, 0.0762, 0.0514, 0.0533, 0.0552, 0.0590, 0.2649, 0.0670, 0.0789, 0.0812, 0.0812, 0.0325), ("AT", 105074.22, 745541.88, 0.1409, 0.0160, 0.0417, 0.0457, 0.0794, 0.0536, 0.0556, 0.0576, 0.0616, 0.2335, 0.0700, 0.0826, 0.0850, 0.0842, 0.0335), ("CH", 240387.78, 1437894.37, 0.1672, 0.0218, 0.0496, 0.0595, 0.0811, 0.0811, 0.0811, 0.0991, 0.0631, 0.1295, 0.0673, 0.0737, 0.0737, 0.0804, 0.0389), ("FR", 529057.49, 2634799.26, 0.2008, 0.0170, 0.0348, 0.0382, 0.0531, 0.0448, 0.0481, 0.0498, 0.0514, 0.2308, 0.0638, 0.0705, 0.1072, 0.1440, 0.0466), ("ES", 153548.47, 807181.61, 0.1902, 0.0170, 0.0348, 0.0382, 0.0531, 0.0448, 0.0481, 0.0498, 0.0514, 0.2308, 0.0638, 0.0705, 0.1072, 0.1440, 0.0466), ("IT", 165266.60, 898683.98, 0.1839, 0.0270, 0.0505, 0.0586, 0.0631, 0.0622, 0.0712, 0.0667, 0.0703, 0.1558, 0.0930, 0.0842, 0.0842, 0.0797, 0.0337), ("PT", 39063.01, 148691.35, 0.2627, 0.0270, 0.0631, 0.0631, 0.0721, 0.0721, 0.0721, 0.0811, 0.0631, 0.0945, 0.0821, 0.0885, 0.0885, 0.0885, 0.0442), ("NO", 49170.23, 213901.64, 0.2299, 0.0270, 0.0505, 0.0586, 0.0631, 0.0622, 0.0712, 0.0667, 0.0703, 0.1558, 0.0930, 0.0842, 0.0842, 0.0797, 0.0337), ("SE", 68291.98, 315653.46, 0.2164, 0.0361, 0.0541, 0.0586, 0.0811, 0.0631, 0.0856, 0.0631, 0.0631, 0.1170, 0.0748, 0.0807, 0.0807, 0.0807, 0.0612), ("FI", 32780.15, 151513.66, 0.2164, 0.0361, 0.0541, 0.0586, 0.0811, 0.0631, 0.0856, 0.0631, 0.0631, 0.1170, 0.0748, 0.0807, 0.0807, 0.0807, 0.0613), ("DK", 122925.57, 635020.50, 0.1936, 0.0361, 0.0541, 0.0586, 0.0811, 0.0631, 0.0856, 0.0631, 0.0631, 0.1170, 0.0748, 0.0807, 0.0807, 0.0807, 0.0612), ("NL", 170456.79, 834216.40, 0.2043, 0.0406, 0.0628, 0.0628, 0.0679, 0.0679, 0.0787, 0.0763, 0.0652, 0.1213, 0.0732, 0.0848, 0.0821, 0.0795, 0.0369), ("BE", 113637.86, 617938.08, 0.1839, 0.0406, 0.0628, 0.0628, 0.0679, 0.0679, 0.0787, 0.0763, 0.0652, 0.1213, 0.0732, 0.0848, 0.0821, 0.0795, 0.0369) ] # Q1 Final (Reduced Budget) # records_to_insert = [ # ("UK", 850737, 3511354, 0.24, 0.02, 0.04, 0.04, 0.07, 0.05, 0.05, 0.06, 0.06, 0.27, 0.07, 0.08, 0.08, 0.08, 0.03), # ("IE", 92370, 564691, 0.16, 0.02, 0.04, 0.05, 0.07, 0.06, 0.06, 0.06, 0.06, 0.23, 0.07, 0.08, 0.08, 0.08, 0.03), # ("DE", 1085957, 3956971, 0.27, 0.02, 0.04, 0.04, 0.08, 0.05, 0.05, 0.06, 0.06, 0.26, 0.07, 0.08, 0.08, 0.08, 0.03), # ("AT", 98265, 508753, 0.19, 0.02, 0.04, 0.05, 0.08, 0.05, 0.06, 0.06, 0.06, 0.23, 0.07, 0.08, 0.09, 0.08, 0.03), # ("CH", 195306, 996198, 0.20, 0.02, 0.05, 0.06, 0.08, 0.08, 0.08, 0.10, 0.06, 0.13, 0.07, 0.07, 0.07, 0.08, 0.04), # ("FR", 531644, 1648781, 0.32, 0.02, 0.03, 0.04, 0.05, 0.04, 0.05, 0.05, 0.05, 0.23, 0.06, 0.07, 0.11, 0.14, 0.05), # ("ES", 126968, 590136, 0.22, 0.02, 0.03, 0.04, 0.05, 0.04, 0.05, 0.05, 0.05, 0.23, 0.06, 0.07, 0.11, 0.14, 0.05), # ("IT", 137325, 567718, 0.24, 0.03, 0.05, 0.06, 0.06, 0.06, 0.07, 0.07, 0.07, 0.16, 0.09, 0.08, 0.08, 0.08, 0.03), # ("PT", 32134, 101955, 0.32, 0.03, 0.06, 0.06, 0.07, 0.07, 0.07, 0.08, 0.06, 0.09, 0.08, 0.09, 0.09, 0.09, 0.04), # ("NO", 39934, 168357, 0.24, 0.03, 0.05, 0.06, 0.06, 0.06, 0.07, 0.07, 0.07, 0.16, 0.09, 0.08, 0.08, 0.08, 0.03), # ("SE", 55464, 167020, 0.33, 0.04, 0.05, 0.06, 0.08, 0.06, 0.09, 0.06, 0.06, 0.12, 0.07, 0.08, 0.08, 0.08, 0.06), # ("FI", 26623, 101548, 0.26, 0.04, 0.05, 0.06, 0.08, 0.06, 0.09, 0.06, 0.06, 0.12, 0.07, 0.08, 0.08, 0.08, 0.06), # ("DK", 99835, 440934, 0.23, 0.04, 0.05, 0.06, 0.08, 0.06, 0.09, 0.06, 0.06, 0.12, 0.07, 0.08, 0.08, 0.08, 0.06), # ("NL", 136775, 542948, 0.25, 0.04, 0.06, 0.06, 0.07, 0.07, 0.08, 0.08, 0.07, 0.12, 0.07, 0.08, 0.08, 0.08, 0.04), # ("BE", 91184, 398162, 0.23, 0.04, 0.06, 0.06, 0.07, 0.07, 0.08, 0.08, 0.07, 0.12, 0.07, 0.08, 0.08, 0.08, 0.04) # ] # Q1 22 # records_to_insert = [ # ("UK", 659636, 3970904, 0.1661, 0.0227, 0.0550, 0.0577, 0.0909, 0.0700, 0.0700, 0.0723, 0.0773, 0.1104, 0.0784, 0.0861, 0.0873, 0.0873, 0.0345), # ("IE", 73293, 638596, 0.1148, 0.0227, 0.0550, 0.0577, 0.0909, 0.0700, 0.0700, 0.0723, 0.0773, 0.1104, 0.0784, 0.0861, 0.0873, 0.0873, 0.0345), # ("DE", 826140, 4474841, 0.1846, 0.0201, 0.0525, 0.0575, 0.1000, 0.0675, 0.0700, 0.0725, 0.0775, 0.1100, 0.0775, 0.0850, 0.0875, 0.0875, 0.0350), # ("AT", 81706, 575337, 0.1420, 0.0201, 0.0525, 0.0575, 0.1000, 0.0675, 0.0700, 0.0725, 0.0775, 0.1100, 0.0775, 0.0850, 0.0875, 0.0875, 0.0350), # ("CH", 175908, 1126576, 0.1561, 0.0201, 0.0525, 0.0575, 0.1000, 0.0675, 0.0700, 0.0725, 0.0775, 0.1100, 0.0775, 0.0850, 0.0875, 0.0875, 0.0350), # ("FR", 359010, 1864565, 0.1925, 0.0255, 0.0525, 0.0575, 0.0800, 0.0675, 0.0725, 0.0750, 0.0775, 0.1099, 0.0850, 0.0875, 0.0875, 0.0875, 0.0350), # ("ES", 116539, 667371, 0.1746, 0.0255, 0.0525, 0.0575, 0.0800, 0.0675, 0.0725, 0.0750, 0.0775, 0.1099, 0.0850, 0.0875, 0.0875, 0.0875, 0.0350), # ("IT", 123686, 642018, 0.1927, 0.0255, 0.0525, 0.0575, 0.0800, 0.0675, 0.0725, 0.0750, 0.0775, 0.1099, 0.0850, 0.0875, 0.0875, 0.0875, 0.0350), # ("PT", 28942, 115298, 0.2510, 0.0255, 0.0525, 0.0575, 0.0800, 0.0675, 0.0725, 0.0750, 0.0775, 0.1099, 0.0850, 0.0875, 0.0875, 0.0875, 0.0350), # ("NO", 35968, 190390, 0.1889, 0.0400, 0.0600, 0.0650, 0.0900, 0.0700, 0.0950, 0.0700, 0.0700, 0.0950, 0.0725, 0.0725, 0.0725, 0.0725, 0.0550), # ("SE", 49955, 188879, 0.2645, 0.0400, 0.0600, 0.0650, 0.0900, 0.0700, 0.0950, 0.0700, 0.0700, 0.0950, 0.0725, 0.0725, 0.0725, 0.0725, 0.0550), # ("FI", 23979, 114839, 0.2088, 0.0400, 0.0600, 0.0650, 0.0900, 0.0700, 0.0950, 0.0700, 0.0700, 0.0950, 0.0725, 0.0725, 0.0725, 0.0725, 0.0550), # ("DK", 89919, 498641, 0.1803, 0.0400, 0.0600, 0.0650, 0.0900, 0.0700, 0.0950, 0.0700, 0.0700, 0.0950, 0.0725, 0.0725, 0.0725, 0.0725, 0.0550), # ("NL", 123191, 614006, 0.2006, 0.0450, 0.0697, 0.0697, 0.0753, 0.0753, 0.0873, 0.0847, 0.0723, 0.0997, 0.0703, 0.0750, 0.0727, 0.0703, 0.0327), # ("BE", 82127, 450271, 0.1824, 0.0450, 0.0697, 0.0697, 0.0753, 0.0753, 0.0873, 0.0847, 0.0723, 0.0997, 0.0703, 0.0750, 0.0727, 0.0703, 0.0327) # ] # // Q4 # records_to_insert = [ # ("UK", 492693, 2458665, 0.20, 0.02, 0.1, 0.08, 0.08, 0.08, 0.07, 0.07, 0.07, 0.07, 0.07, 0.1, 0.08, 0.08, 0.03), # ("IE", 51965, 409777, 0.13, 0.02, 0.1, 0.08, 0.08, 0.08, 0.07, 0.07, 0.07, 0.07, 0.07, 0.1, 0.08, 0.08, 0.03), # ("DE", 729221, 3698601, 0.20, 0.03, 0.07, 0.07, 0.07, 0.08, 0.08, 0.07, 0.08, 0.08, 0.12, 0.08, 0.08, 0.08, 0.04), # ("AT", 111815, 821338, 0.14, 0.03, 0.07, 0.07, 0.07, 0.08, 0.08, 0.07, 0.08, 0.08, 0.12, 0.08, 0.08, 0.08, 0.04), # ("CH", 157896, 1044068, 0.15, 0.03, 0.07, 0.07, 0.07, 0.08, 0.08, 0.07, 0.08, 0.08, 0.12, 0.08, 0.08, 0.08, 0.04), # ("FR", 230684, 1120651, 0.20, 0.02, 0.06, 0.07, 0.09, 0.09, 0.09, 0.11, 0.07, 0.11, 0.06, 0.06, 0.06, 0.07, 0.03), # ("ES", 95035, 545993, 0.17, 0.06, 0.06, 0.06, 0.07, 0.08, 0.08, 0.08, 0.08, 0.08, 0.09, 0.08, 0.08, 0.08, 0.04), # ("IT", 127896, 816425, 0.16, 0.03, 0.07, 0.07, 0.08, 0.08, 0.08, 0.09, 0.07, 0.07, 0.08, 0.08, 0.08, 0.08, 0.04), # ("PT", 16885, 60524, 0.27, 0.05, 0.06, 0.06, 0.07, 0.08, 0.08, 0.09, 0.07, 0.09, 0.08, 0.08, 0.08, 0.08, 0.03), # ("NO", 26584, 150431, 0.18, 0.02, 0.05, 0.07, 0.08, 0.07, 0.08, 0.13, 0.08, 0.1, 0.08, 0.08, 0.08, 0.08, 0.04), # ("SE", 44339, 243477, 0.18, 0.02, 0.05, 0.07, 0.08, 0.07, 0.08, 0.13, 0.08, 0.1, 0.08, 0.08, 0.08,
<reponame>mydevice/python-openstackclient # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. # from unittest import mock from unittest.mock import call from cinderclient import api_versions from osc_lib import exceptions from osc_lib import utils from openstackclient.tests.unit.volume.v2 import fakes as volume_fakes from openstackclient.volume.v2 import volume_backup class TestBackup(volume_fakes.TestVolume): def setUp(self): super(TestBackup, self).setUp() self.backups_mock = self.app.client_manager.volume.backups self.backups_mock.reset_mock() self.volumes_mock = self.app.client_manager.volume.volumes self.volumes_mock.reset_mock() self.snapshots_mock = self.app.client_manager.volume.volume_snapshots self.snapshots_mock.reset_mock() self.restores_mock = self.app.client_manager.volume.restores self.restores_mock.reset_mock() class TestBackupCreate(TestBackup): volume = volume_fakes.FakeVolume.create_one_volume() snapshot = volume_fakes.FakeSnapshot.create_one_snapshot() new_backup = volume_fakes.FakeBackup.create_one_backup( attrs={'volume_id': volume.id, 'snapshot_id': snapshot.id}) columns = ( 'availability_zone', 'container', 'description', 'id', 'name', 'object_count', 'size', 'snapshot_id', 'status', 'volume_id', ) data = ( new_backup.availability_zone, new_backup.container, new_backup.description, new_backup.id, new_backup.name, new_backup.object_count, new_backup.size, new_backup.snapshot_id, new_backup.status, new_backup.volume_id, ) def setUp(self): super(TestBackupCreate, self).setUp() self.volumes_mock.get.return_value = self.volume self.snapshots_mock.get.return_value = self.snapshot self.backups_mock.create.return_value = self.new_backup # Get the command object to test self.cmd = volume_backup.CreateVolumeBackup(self.app, None) def test_backup_create(self): arglist = [ "--name", self.new_backup.name, "--description", self.new_backup.description, "--container", self.new_backup.container, "--force", "--incremental", "--snapshot", self.new_backup.snapshot_id, self.new_backup.volume_id, ] verifylist = [ ("name", self.new_backup.name), ("description", self.new_backup.description), ("container", self.new_backup.container), ("force", True), ("incremental", True), ("snapshot", self.new_backup.snapshot_id), ("volume", self.new_backup.volume_id), ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) columns, data = self.cmd.take_action(parsed_args) self.backups_mock.create.assert_called_with( self.new_backup.volume_id, container=self.new_backup.container, name=self.new_backup.name, description=self.new_backup.description, force=True, incremental=True, snapshot_id=self.new_backup.snapshot_id, ) self.assertEqual(self.columns, columns) self.assertEqual(self.data, data) def test_backup_create_with_properties(self): self.app.client_manager.volume.api_version = \ api_versions.APIVersion('3.43') arglist = [ "--property", "foo=bar", "--property", "wow=much-cool", self.new_backup.volume_id, ] verifylist = [ ("properties", {"foo": "bar", "wow": "much-cool"}), ("volume", self.new_backup.volume_id), ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) columns, data = self.cmd.take_action(parsed_args) self.backups_mock.create.assert_called_with( self.new_backup.volume_id, container=None, name=None, description=None, force=False, incremental=False, metadata={"foo": "bar", "wow": "much-cool"}, ) self.assertEqual(self.columns, columns) self.assertEqual(self.data, data) def test_backup_create_with_properties_pre_v343(self): self.app.client_manager.volume.api_version = \ api_versions.APIVersion('3.42') arglist = [ "--property", "foo=bar", "--property", "wow=much-cool", self.new_backup.volume_id, ] verifylist = [ ("properties", {"foo": "bar", "wow": "much-cool"}), ("volume", self.new_backup.volume_id), ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) exc = self.assertRaises( exceptions.CommandError, self.cmd.take_action, parsed_args) self.assertIn("--os-volume-api-version 3.43 or greater", str(exc)) def test_backup_create_with_availability_zone(self): self.app.client_manager.volume.api_version = \ api_versions.APIVersion('3.51') arglist = [ "--availability-zone", "my-az", self.new_backup.volume_id, ] verifylist = [ ("availability_zone", "my-az"), ("volume", self.new_backup.volume_id), ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) columns, data = self.cmd.take_action(parsed_args) self.backups_mock.create.assert_called_with( self.new_backup.volume_id, container=None, name=None, description=None, force=False, incremental=False, availability_zone="my-az", ) self.assertEqual(self.columns, columns) self.assertEqual(self.data, data) def test_backup_create_with_availability_zone_pre_v351(self): self.app.client_manager.volume.api_version = \ api_versions.APIVersion('3.50') arglist = [ "--availability-zone", "my-az", self.new_backup.volume_id, ] verifylist = [ ("availability_zone", "my-az"), ("volume", self.new_backup.volume_id), ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) exc = self.assertRaises( exceptions.CommandError, self.cmd.take_action, parsed_args) self.assertIn("--os-volume-api-version 3.51 or greater", str(exc)) def test_backup_create_without_name(self): arglist = [ "--description", self.new_backup.description, "--container", self.new_backup.container, self.new_backup.volume_id, ] verifylist = [ ("description", self.new_backup.description), ("container", self.new_backup.container), ("volume", self.new_backup.volume_id), ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) columns, data = self.cmd.take_action(parsed_args) self.backups_mock.create.assert_called_with( self.new_backup.volume_id, container=self.new_backup.container, name=None, description=self.new_backup.description, force=False, incremental=False, ) self.assertEqual(self.columns, columns) self.assertEqual(self.data, data) class TestBackupDelete(TestBackup): backups = volume_fakes.FakeBackup.create_backups(count=2) def setUp(self): super(TestBackupDelete, self).setUp() self.backups_mock.get = ( volume_fakes.FakeBackup.get_backups(self.backups)) self.backups_mock.delete.return_value = None # Get the command object to mock self.cmd = volume_backup.DeleteVolumeBackup(self.app, None) def test_backup_delete(self): arglist = [ self.backups[0].id ] verifylist = [ ("backups", [self.backups[0].id]) ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) result = self.cmd.take_action(parsed_args) self.backups_mock.delete.assert_called_with( self.backups[0].id, False) self.assertIsNone(result) def test_backup_delete_with_force(self): arglist = [ '--force', self.backups[0].id, ] verifylist = [ ('force', True), ("backups", [self.backups[0].id]) ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) result = self.cmd.take_action(parsed_args) self.backups_mock.delete.assert_called_with(self.backups[0].id, True) self.assertIsNone(result) def test_delete_multiple_backups(self): arglist = [] for b in self.backups: arglist.append(b.id) verifylist = [ ('backups', arglist), ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) result = self.cmd.take_action(parsed_args) calls = [] for b in self.backups: calls.append(call(b.id, False)) self.backups_mock.delete.assert_has_calls(calls) self.assertIsNone(result) def test_delete_multiple_backups_with_exception(self): arglist = [ self.backups[0].id, 'unexist_backup', ] verifylist = [ ('backups', arglist), ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) find_mock_result = [self.backups[0], exceptions.CommandError] with mock.patch.object(utils, 'find_resource', side_effect=find_mock_result) as find_mock: try: self.cmd.take_action(parsed_args) self.fail('CommandError should be raised.') except exceptions.CommandError as e: self.assertEqual('1 of 2 backups failed to delete.', str(e)) find_mock.assert_any_call(self.backups_mock, self.backups[0].id) find_mock.assert_any_call(self.backups_mock, 'unexist_backup') self.assertEqual(2, find_mock.call_count) self.backups_mock.delete.assert_called_once_with( self.backups[0].id, False ) class TestBackupList(TestBackup): volume = volume_fakes.FakeVolume.create_one_volume() backups = volume_fakes.FakeBackup.create_backups( attrs={'volume_id': volume.name}, count=3) columns = ( 'ID', 'Name', 'Description', 'Status', 'Size', ) columns_long = columns + ( 'Availability Zone', 'Volume', 'Container', ) data = [] for b in backups: data.append(( b.id, b.name, b.description, b.status, b.size, )) data_long = [] for b in backups: data_long.append(( b.id, b.name, b.description, b.status, b.size, b.availability_zone, volume_backup.VolumeIdColumn(b.volume_id), b.container, )) def setUp(self): super(TestBackupList, self).setUp() self.volumes_mock.list.return_value = [self.volume] self.backups_mock.list.return_value = self.backups self.volumes_mock.get.return_value = self.volume self.backups_mock.get.return_value = self.backups[0] # Get the command to test self.cmd = volume_backup.ListVolumeBackup(self.app, None) def test_backup_list_without_options(self): arglist = [] verifylist = [ ("long", False), ("name", None), ("status", None), ("volume", None), ("marker", None), ("limit", None), ('all_projects', False), ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) columns, data = self.cmd.take_action(parsed_args) search_opts = { "name": None, "status": None, "volume_id": None, 'all_tenants': False, } self.volumes_mock.get.assert_not_called() self.backups_mock.get.assert_not_called() self.backups_mock.list.assert_called_with( search_opts=search_opts, marker=None, limit=None, ) self.assertEqual(self.columns, columns) self.assertItemsEqual(self.data, list(data)) def test_backup_list_with_options(self): arglist = [ "--long", "--name", self.backups[0].name, "--status", "error", "--volume", self.volume.id, "--marker", self.backups[0].id, "--all-projects", "--limit", "3", ] verifylist = [ ("long", True), ("name", self.backups[0].name), ("status", "error"), ("volume", self.volume.id), ("marker", self.backups[0].id), ('all_projects', True), ("limit", 3), ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) columns, data = self.cmd.take_action(parsed_args) search_opts = { "name": self.backups[0].name, "status": "error", "volume_id": self.volume.id, 'all_tenants': True, } self.volumes_mock.get.assert_called_once_with(self.volume.id) self.backups_mock.get.assert_called_once_with(self.backups[0].id) self.backups_mock.list.assert_called_with( search_opts=search_opts, marker=self.backups[0].id, limit=3, ) self.assertEqual(self.columns_long, columns) self.assertItemsEqual(self.data_long, list(data)) class TestBackupRestore(TestBackup): volume = volume_fakes.FakeVolume.create_one_volume() backup = volume_fakes.FakeBackup.create_one_backup( attrs={'volume_id': volume.id}) def setUp(self): super(TestBackupRestore, self).setUp() self.backups_mock.get.return_value = self.backup self.volumes_mock.get.return_value = self.volume self.restores_mock.restore.return_value = ( volume_fakes.FakeVolume.create_one_volume( {'id': self.volume['id']})) # Get the command object to mock self.cmd = volume_backup.RestoreVolumeBackup(self.app, None) def test_backup_restore(self): arglist = [ self.backup.id, self.backup.volume_id ] verifylist = [ ("backup", self.backup.id), ("volume", self.backup.volume_id) ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) result = self.cmd.take_action(parsed_args) self.restores_mock.restore.assert_called_with(self.backup.id, self.backup.volume_id) self.assertIsNotNone(result) class TestBackupSet(TestBackup): backup = volume_fakes.FakeBackup.create_one_backup( attrs={'metadata': {'wow': 'cool'}}, ) def setUp(self): super(TestBackupSet, self).setUp() self.backups_mock.get.return_value = self.backup # Get the command object to test self.cmd = volume_backup.SetVolumeBackup(self.app, None) def test_backup_set_name(self): self.app.client_manager.volume.api_version = \ api_versions.APIVersion('3.9') arglist = [ '--name', 'new_name', self.backup.id, ] verifylist = [ ('name', 'new_name'), ('backup', self.backup.id), ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) # In base command class ShowOne in cliff, abstract method take_action() # returns nothing result = self.cmd.take_action(parsed_args) self.backups_mock.update.assert_called_once_with( self.backup.id, {'name': 'new_name'}) self.assertIsNone(result) def test_backup_set_name_pre_v39(self): self.app.client_manager.volume.api_version = \ api_versions.APIVersion('3.8') arglist = [ '--name', 'new_name', self.backup.id, ] verifylist = [ ('name', 'new_name'), ('backup', self.backup.id), ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) exc = self.assertRaises( exceptions.CommandError, self.cmd.take_action, parsed_args) self.assertIn("--os-volume-api-version 3.9 or greater", str(exc)) def test_backup_set_description(self): self.app.client_manager.volume.api_version = \ api_versions.APIVersion('3.9') arglist = [ '--description', 'new_description', self.backup.id, ] verifylist = [ ('name', None), ('description', 'new_description'), ('backup', self.backup.id), ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) result = self.cmd.take_action(parsed_args) # Set expected values kwargs = { 'description': 'new_description' } self.backups_mock.update.assert_called_once_with( self.backup.id, kwargs ) self.assertIsNone(result) def test_backup_set_description_pre_v39(self): self.app.client_manager.volume.api_version = \ api_versions.APIVersion('3.8') arglist = [ '--description', 'new_description', self.backup.id, ] verifylist = [ ('name', None), ('description', 'new_description'), ('backup', self.backup.id), ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) exc = self.assertRaises( exceptions.CommandError, self.cmd.take_action, parsed_args) self.assertIn("--os-volume-api-version 3.9 or greater", str(exc)) def test_backup_set_state(self): arglist = [ '--state', 'error', self.backup.id ] verifylist = [ ('state', 'error'), ('backup', self.backup.id) ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) result = self.cmd.take_action(parsed_args) self.backups_mock.reset_state.assert_called_once_with( self.backup.id, 'error') self.assertIsNone(result) def test_backup_set_state_failed(self): self.backups_mock.reset_state.side_effect = exceptions.CommandError() arglist = [ '--state', 'error', self.backup.id ] verifylist = [ ('state', 'error'), ('backup', self.backup.id) ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) try: self.cmd.take_action(parsed_args) self.fail('CommandError should be raised.') except exceptions.CommandError as e: self.assertEqual('One or more of the set operations failed', str(e)) self.backups_mock.reset_state.assert_called_with( self.backup.id, 'error') def test_backup_set_no_property(self): self.app.client_manager.volume.api_version = \ api_versions.APIVersion('3.43') arglist = [ '--no-property', self.backup.id, ] verifylist = [ ('no_property', True), ('backup', self.backup.id), ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) result = self.cmd.take_action(parsed_args) # Set expected values kwargs = { 'metadata': {}, } self.backups_mock.update.assert_called_once_with( self.backup.id, **kwargs ) self.assertIsNone(result) def test_backup_set_no_property_pre_v343(self): self.app.client_manager.volume.api_version = \ api_versions.APIVersion('3.42') arglist = [ '--no-property', self.backup.id, ] verifylist = [ ('no_property', True), ('backup', self.backup.id), ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) exc = self.assertRaises( exceptions.CommandError, self.cmd.take_action, parsed_args) self.assertIn("--os-volume-api-version 3.43 or greater", str(exc)) def test_backup_set_property(self): self.app.client_manager.volume.api_version = \ api_versions.APIVersion('3.43') arglist = [ '--property', 'foo=bar', self.backup.id, ] verifylist = [ ('properties', {'foo': 'bar'}), ('backup', self.backup.id), ] parsed_args = self.check_parser(self.cmd, arglist,
in range(maxmajorticks): if axis == xaxis: self.majorticks[axis][axissign].append(label(display=self.display, yoffset=-tmajor, font=graphfont, height=fontheight, border=0, linecolor=grey, visible=False, box=False, opacity=0)) else: self.majorticks[axis][axissign].append(label(display=self.display, xoffset=-tmajor, font=graphfont, height=fontheight, border=2, linecolor=grey, visible=False, box=False, opacity=0)) currentdisplay.select() def __del__(self): self.visible = False try: gdisplays.remove(self) except: pass def mouse(self, evt): m = evt if m.press == 'left': self.mousepos = self.display.mouse.pos+vector(10,20,30) # so mousepos != newpos self.horline.visible = True self.vertline.visible = True self.showxy.visible = True self.display.cursor.visible = False elif m.release == 'left': self.mousepos = None self.showxy.visible = False self.horline.visible = False self.vertline.visible = False self.display.cursor.visible = True newpos = self.display.mouse.pos if newpos != self.mousepos: self.mousepos = newpos xmax = self.display.range.x ymax = self.display.range.y xcenter = self.display.center.x ycenter = self.display.center.y self.horline.pos = [(xcenter-xmax,self.mousepos.y,.01), (xcenter+xmax,self.mousepos.y,.01)] self.vertline.pos = [(self.mousepos.x,ycenter-ymax,.01), (self.mousepos.x,ycenter+ymax,.01)] v = self.showxy.pos = self.mousepos if self.logx: x = 10v.x if self.logy: y = 10v.y if v.x > xcenter: self.showxy.xoffset = -10 else: self.showxy.xoffset = 10 self.showxy.text = '({0:0.4g}, {1:0.4g})'.format(v.x,v.y) def setcenter(self): x0, y0 = self.getorigin() xright = self.minmax[xaxis][posaxis] xleft = self.minmax[xaxis][negaxis] ytop = self.minmax[yaxis][posaxis] ybottom = self.minmax[yaxis][negaxis] rightpixels = self.xtitlewidth leftpixels = 0 if xleft == x0: leftpixels = 3tmajor toppixels = bottompixels = 0 if self.ytitlewidth: toppixels = 2fontheight if ybottom == y0: bottompixels = tmajor+fontheight xrange = 0.55(xright-xleft)self.width/(self.width-(rightpixels+leftpixels)) yrange = 0.55(ytop-ybottom)self.height/(self.height-(toppixels+bottompixels)) xscale = xrange/(.5self.width) yscale = yrange/(.5self.height) x1 = xright+rightpixelsxscale x2 = x0+self.ytitlewidthxscale if x2 > x1: # ytitle extends farther to the right than xaxis + xtitle xrange = 0.55(x2-xleft)self.width/(self.width-leftpixels) xscale = xrange/(.5self.width) xright = x2 rightpixels = 0 if xrange == 0: xrange = 1e-300 if yrange == 0: yrange = 1e-300 self.display.range = (xrange,yrange,0.1) self.display.center = ((xright+xleft+(rightpixels-leftpixels)xscale)/2.0, (ytop+ybottom+(toppixels-bottompixels)yscale)/2.0,0) def getorigin(self): return (self.zero[0].pos[0], self.zero[1].pos[1]) def setminorticks(self, axis, axissign, loglabel, dmajor, dminor): ## For log axis labels, show the standard uneven log tick marks if dmajor == 1, ## but for dmajor > 1, show minor tick marks at the decade locations. ## Since we have only minorticks-1 = 4 minor tick marks between major tick marks, ## if dmajor > minorticks (=5), don't show any minor tick marks. if loglabel and (dmajor > minorticks): return 0 x0,y0 = self.getorigin() limit = self.minmax[axis][axissign] if axis == xaxis: limit -= x0 else: limit -= y0 if axissign == negaxis: dminor = -dminor ntick = nmajor = nminor = 0 exclude = minorticks if loglabel and (dmajor > 1): exclude = dmajor if dminor > 0: dminor = 1 else: dminor = -1 while True: ntick += 1 tickpos = ntickdminor if (ntick % exclude) == 0: nmajor += 1 continue # no minor tick where there is a major one if loglabel: # have already excluded dmajor > minorticks (=5) if dmajor == 1: if dminor > 0: tickpos = dmajor(nmajor+logticks[(ntick-1)%exclude]) else: tickpos = dmajor(-(nmajor+1)+logticks[3-((ntick-1)%exclude)]) if dminor > 0: if tickpos > limit: break else: if tickpos < limit: break obj = self.minorticks[axis][axissign][nminor] if axis == xaxis: obj.pos = (x0+tickpos,y0,0) else: obj.pos = (x0,y0+tickpos,0) obj.visible = True nminor += 1 return nminor def axisdisplay(self, axis, axissign): # axis = 0 for x axis, 1 for y axis # axissign = 0 for negative half-axis, 1 for positive half-axis if not self.makeaxis[axis][axissign]: return sign = 1 if axissign == negaxis: sign = -1 x0,y0 = self.getorigin() if axis == xaxis: loglabel = self.logx else: loglabel = self.logy if axis == xaxis: origin = x0 else: origin = y0 if self.axis[axis][axissign] is None: # new; no axis displayed up till now if self.minmax[axis][axissign] == origin: return # zero-length axis # Display axis and axis title if axis == xaxis: axispos = ([(x0,y0,0), (self.minmax[axis][axissign],y0,0)]) titlepos = ([self.minmax[axis][posaxis],y0,0]) else: axispos = ([(x0,y0,0), (x0,self.minmax[axis][axissign],0)]) titlepos = ([x0,self.minmax[axis][posaxis]+2fontheightself.display.range.y/(.5self.width),0]) self.axis[axis][axissign] = curve(pos=axispos, color=grey, display=self.display) if axis == xaxis and self.Lxtitle.text != "": self.Lxtitle.pos = titlepos self.Lxtitle.visible = True if axis == yaxis and self.Lytitle.text != "": self.Lytitle.pos = titlepos self.Lytitle.visible = True # Determine major tick marks and labels if origin != 0: newmajormarks, form = labelnum(self.minmax[axis][posaxis] - self.minmax[axis][negaxis], loglabel) dmajor = newmajormarks[0] for n, mark in enumerate(newmajormarks): if origin > 0: newmajormarks[n] += self.minmax[axis][negaxis] else: newmajormarks[n] -= self.minmax[axis][posaxis] newmajormarks[n] = newmajormarks[n] else: if self.minmax[axis][posaxis] >= -self.minmax[axis][negaxis]: newmajormarks, form = labelnum(self.minmax[axis][posaxis], loglabel) else: if loglabel: newmajormarks, form = labelnum(self.minmax[axis][negaxis], loglabel) else: newmajormarks, form = labelnum(-self.minmax[axis][negaxis], loglabel) dmajor = newmajormarks[0] self.format[axis] = form # Display major tick marks and labels nmajor = 0 marks = [] for x1 in newmajormarks: if x1 > abs(self.minmax[axis][axissign]): break # newmajormarks can refer to opposite half-axis if axissign == posaxis and x1 < origin: continue elif axissign == negaxis and x1 < abs(origin): continue marks.append(x1) obj = self.majorticks[axis][axissign][nmajor] if loglabel: obj.text = self.format[axis].format(int(signx1)) else: obj.text = cleaneformat(self.format[axis].format(signx1)) obj.color = self.foreground obj.visible = True if axis == xaxis: obj.pos = [signx1,y0,0] obj.yoffset = -tmajor else: obj.pos = [x0,signx1,0] obj.xoffset = -tmajor nmajor = nmajor+1 # Display minor tick marks self.setminorticks(axis, axissign, loglabel, dmajor, dmajor/minorticks) if marks != []: self.majormarks[axis][axissign] = marks self.lastlabel[axis][axissign] = self.majormarks[axis][axissign][-1] else: self.lastlabel[axis][axissign] = 0 else: # Extend axis, which has grown if axis == xaxis: self.axis[axis][axissign].pos = [[x0,y0,0],[self.minmax[axis][axissign], y0, 0]] else: self.axis[axis][axissign].pos = [[x0,y0,0],[x0,self.minmax[axis][axissign],0]] # Reposition xtitle (at right) or ytitle (at top) if axis == xaxis and axissign == posaxis: self.Lxtitle.pos = (self.minmax[axis][posaxis],y0,0) if axis == yaxis and axissign == posaxis: self.Lytitle.pos = ([x0,self.minmax[axis][posaxis]+2fontheightself.display.range.y/(.5self.width),0]) # See how many majormarks are now needed, and in what format if self.minmax[axis][posaxis] >= -self.minmax[axis][negaxis]: newmajormarks, form = labelnum(self.minmax[axis][posaxis], loglabel) else: newmajormarks, form = labelnum(-self.minmax[axis][negaxis], loglabel) if (self.majormarks[axis][axissign] is not None) and (len(self.majormarks[axis][axissign]) > 0): # this axis already has major tick marks/labels olddmajor = self.majormarks[axis][axissign][0] else: olddmajor = 0. olddminor = olddmajor/minorticks dmajor = newmajormarks[0] dminor = dmajor/minorticks newformat = (form != self.format[axis]) self.format[axis] = form check = (self.minmax[axis][axissign] >= self.lastlabel[axis][axissign]+dminor) if axissign == negaxis: check = (self.minmax[axis][axissign] <= self.lastlabel[axis][axissign]-dminor) needminor = check or (dminor != olddminor) needmajor = ((self.majormarks[axis][axissign] is None) or (newmajormarks[-1] != self.majormarks[axis][axissign][-1]) or newformat) if needmajor: # need new labels start = 0 if (self.majormarks[axis][axissign] is None) or newformat or (dmajor != olddmajor): marks = [] else: for num in newmajormarks: if num > self.majormarks[axis][axissign][-1]: start = num break marks = self.majormarks[axis][axissign] for nmajor in range(maxmajorticks): obj = self.majorticks[axis][axissign][nmajor] if nmajor < len(newmajormarks): x1 = newmajormarks[nmajor] if abs(self.minmax[axis][axissign]) >= x1: if x1 < start: continue else: obj.visible = False continue else: obj.visible = False continue marks.append(x1) if loglabel: obj.text = self.format[axis].format(int(signx1)) else: obj.text = cleaneformat(self.format[axis].format(signx1)) obj.color = self.foreground obj.visible = True if axis == xaxis: obj.pos = [signx1,y0,0] else: obj.pos = [x0,signx1,0] if marks != []: self.majormarks[axis][axissign] = marks if needminor: # adjust minor tick marks nminor = self.setminorticks(axis, axissign, loglabel, dmajor, dminor) while nminor < maxminorticks: self.minorticks[axis][axissign][nminor].visible = False nminor = nminor+1 self.lastlabel[axis][axissign] = dminorint(self.minmax[axis][axissign]/dminor) def resize(self, x, y): redox = redoy = False if self.autoscale[xaxis]: if x > self.lastminmax[xaxis][posaxis]: self.minmax[xaxis][posaxis] = x+fracself.display.range[0] if (self.lastminmax[xaxis][posaxis] == 0 or (self.minmax[xaxis][posaxis] >= self.lastminmax[xaxis][posaxis])): redox = True elif x < self.lastminmax[xaxis][negaxis]: self.minmax[xaxis][negaxis] = x-fracself.display.range[0] if (self.lastminmax[xaxis][negaxis] == 0 or (self.minmax[xaxis][negaxis] <= self.lastminmax[xaxis][negaxis])): redox = True elif not self.active: redox = redoy = True if self.autoscale[yaxis]: if y > self.lastminmax[yaxis][posaxis]: self.minmax[yaxis][posaxis] = y+fracself.display.range[1] if (self.lastminmax[yaxis][posaxis] == 0 or (self.minmax[yaxis][posaxis] >= self.lastminmax[yaxis][posaxis])): redoy = True elif y < self.lastminmax[yaxis][negaxis]: self.minmax[yaxis][negaxis] = y-frac*self.display.range[1] if (self.lastminmax[yaxis][negaxis] == 0 or (self.minmax[yaxis][negaxis] <= self.lastminmax[yaxis][negaxis])): redoy = True elif not self.active: redox = redoy = True if (redox or redoy ): self.setcenter() # approximate if redox: self.axisdisplay(xaxis,posaxis) self.lastminmax[xaxis][posaxis] = self.minmax[xaxis][posaxis] self.axisdisplay(xaxis,negaxis) self.lastminmax[xaxis][negaxis] = self.minmax[xaxis][negaxis] if redoy: self.axisdisplay(yaxis,posaxis) self.lastminmax[yaxis][posaxis] = self.minmax[yaxis][posaxis] self.axisdisplay(yaxis,negaxis) self.lastminmax[yaxis][negaxis] = self.minmax[yaxis][negaxis] self.setcenter() # revised if not self.active: self.active = True gdisplays.append(self) self.display.bind("mousedown mousemove mouseup", checkGraphMouse, self) self.zero[xaxis].visible = True self.zero[yaxis].visible = True def getgdisplay(): return gdisplay() def constructorargs(obj,arguments): if 'gdisplay' in arguments: obj.gdisplay = arguments['gdisplay'] else: if lastgdisplay is None: obj.gdisplay = getgdisplay() else: obj.gdisplay = lastgdisplay if 'color' in
from pypipe import formats class Bcftools: @staticmethod def view(): return { 'cmd': 'bcftools view', 'type': None, 'log': 'log', 'out': { 'redirect': True, 'return': [ {'arg': 'out', 'type': {'b': formats.Bcf, '': formats.Vcf}, 'suffix': ''}, ] }, 'args': { 'named': { '-A': bool, '-b': bool, '-D': formats.TextFile, '-F': bool, '-G': bool, '-l': formats.TextFile, '-N': bool, '-Q': bool, '-s': formats.TextFile, '-S': bool, '-u': bool, '-c': bool, '-d': float, '-e': bool, '-g': bool, '-i': float, '-p': float, '-P': str, '-t': float, '-T': str, '-v': bool, '-1': int, '-U': int, '-X': float, }, 'unnamed': [ ('in_', {'-S': formats.Vcf, '': formats.Bcf}), ('out', str), ], }, } @staticmethod def cat(): return { 'cmd': 'bcftools cat', 'type': None, 'log': 'log', 'out': { 'redirect': True, 'return': [ {'arg': 'out', 'type': formats.Bcf, 'suffix': ''}, ], }, 'args': { 'named': { }, 'unnamed': [ ('in_', formats.Bcf), ('out', str), ], } } class Bowtie2: @staticmethod def bowtie2(): return { 'cmd': 'bowtie2', 'type': None, 'log': 'log', 'out': { 'redirect': False, 'return': [ {'arg': '-S', 'type': formats.Sam, 'suffix': ''}, ] }, 'args': { 'named': { '-x': formats.Bowtie2Index, '-S': str, '-U': { '--qseq': [formats.Qseq, 1, ','], '-f': [formats.Fasta, 1, ','], '-r': [formats.TextFile, 1, ','], '-c': str, '': [formats.Fastq, 1, ','] }, '-1': { '--qseq': [formats.Qseq, 1, ','], '-f': [formats.Fasta, 1, ','], '-r': [formats.TextFile, 1, ','], '-c': str, '': [formats.Fastq, 1, ','] }, '-2': { '--qseq': [formats.Qseq, 1, ','], '-f': [formats.Fasta, 1, ','], '-r': [formats.TextFile, 1, ','], '-c': str, '': [formats.Fastq, 1, ','] }, '-q': bool, '--qseq': bool, '-f': bool, '-r': bool, '-c': bool, '-s': int, '-u': int, '-5': int, '-3': int, '--phred33': bool, '--phred64': bool, '--solexa-quals': bool, '--int-quals': bool, '--very-fast': bool, '--fast': bool, '--sensitive': bool, '--very-sensitive': bool, '--very-fast-local': bool, '--fast-local': bool, '--sensitive-local': bool, '--very-sensitive-local': bool, '-N': int, '-L': int, '-i': str, '--n-ceil': str, '--dpad': int, '--gbar': int, '--ignore-quals': bool, '--nofw': bool, '--norc': bool, '--no-1mm-upfront': bool, '--end-to-end': bool, '--local': bool, '-k': int, '-a': bool, '-D': int, '-R': int, '--ma': int, '--mp': [int, 2, ','], '--np': int, '--rdg': [int, 2, ','], '--rfg': [int, 2, ','], '--score-min': str, '-I': int, '-X': int, '--fr': bool, '--rf': bool, '--ff': bool, '--no-mixed': bool, '--no-discordant': bool, '--dovetail': bool, '--no-contain': bool, '--no-overlap': bool, '--no-unal': bool, '--no-hd': bool, '--no-sq': bool, '--rg-id': str, '--rg': str, '--omit-sec-seq': bool, '-o': int, '-p': int, '--reorder': bool, '--mm': bool, '--qc-filter': bool, '--seed': int, '--non-deterministic': bool, '-t': bool, '--un': str, '--un-gz': str, '--un-bz2': str, '--al': str, '--al-gz': str, '--al-bz2': str, '--un-conc': str, '--un-conc-gz': str, '--un-conc-bz2': str, '--al-conc': str, '--al-conc-gz': str, '--al-conc-bz2': str, '--quiet': bool, '--met-file': str, '--met-stderr': str, '--met': int, }, 'unnamed': [ ], }, } class Bwa: @staticmethod def mem(): return { 'cmd': 'bwa mem', 'type': None, 'log': 'log', 'out': { 'redirect': True, 'return': [ {'arg': 'out', 'type': formats.Sam, 'suffix': ''}, ], }, 'args': { 'named': { '-t': int, '-k': int, '-w': int, '-d': int, '-r': float, '-c': int, '-P': bool, '-a': int, '-B': int, '-O': int, '-E': int, '-L': int, '-U': int, '-p': bool, '-R': str, '-T': int, '-C': bool, '-H': bool, '-M': bool, '-v': int, }, 'unnamed': [ ('ref', formats.BwaIndex), ('in1', formats.Fastq), ('in2', formats.Fastq), ('out', str), ], }, } @staticmethod def aln(): return { 'cmd': 'bwa aln', 'type': None, 'log': 'log', 'out': { 'redirect': True, 'return': [ {'arg': 'out', 'type': formats.Sai, 'suffix': ''}, ], }, 'args': { 'named': { '-n': int, '-o': int, '-e': int, '-d': int, '-i': int, '-l': int, '-k': int, '-t': int, '-M': int, '-O': int, '-E': int, '-R': int, '-c': bool, '-N': bool, '-q': int, '-I': bool, '-B': int, '-b': bool, '-0': bool, '-1': bool, '-2': bool, }, 'unnamed': [ ('ref', formats.BwaIndex), ('in_', formats.Fastq), ('out', str), ], }, } @staticmethod def samse(): return { 'cmd': 'bwa samse', 'type': None, 'log': 'log', 'out': { 'redirect': True, 'return': [ {'arg': 'out', 'type': formats.Sam, 'suffix': ''}, ], }, 'args': { 'named': { '-n': int, '-r': str, }, 'unnamed': [ ('ref', formats.BwaIndex), ('sai', formats.Sai), ('in_', formats.Fastq), ('out', str), ], }, } @staticmethod def sampe(): return { 'cmd': 'bwa sampe', 'type': None, 'log': 'log', 'out': { 'redirect': True, 'return': [ {'arg': 'out', 'type': formats.Sam, 'suffix': ''}, ], }, 'args': { 'named': { '-a': int, '-o': int, '-P': bool, '-n': int, '-N': int, '-r': str, }, 'unnamed': [ ('ref', formats.BwaIndex), ('sai1', formats.Sai), ('sai2', formats.Sai), ('in1', formats.Fastq), ('in2', formats.Fastq), ('out', str), ], }, } @staticmethod def bwasw(): return { 'cmd': 'bwa bwasw', 'type': None, 'log': 'log', 'out': { 'redirect': True, 'return': [ {'arg': 'out', 'type': formats.Sam, 'suffix': ''}, ] }, 'args': { 'named': { '-a': int, '-b': int, '-q': int, '-r': int, '-t': int, '-w': int, '-T': int, '-c': float, '-z': int, '-s': int, '-N': int, }, 'unnamed': [ ('ref', formats.BwaIndex), ('in1', formats.Fastq), ('in2', formats.Fastq), ('out', str), ], }, } class Freebayes: @staticmethod def freebayes(): return { 'cmd': 'freebayes', 'type': None, 'log': 'log', 'out': { 'redirect': False, 'return': [ {'arg': 'v', 'type': formats.Vcf, 'suffix': ''}, ] }, 'args': { 'named': { '-v': str, '-f': formats.Fasta, '-b': formats.Bam, '-t': formats.Bed, '-r': str, '-s': formats.TextFile, '--populations': formats.TextFile, '-A': formats.Bed, '--trace': str, '--failed-alleles': formats.Bed, '--variant-input': formats.Vcf, '-l': bool, '--haplotype-basis-alleles': bool, '--report-all-haplotype-alleles': bool, '--report-monorphic': bool, '-P': float, '-T': float, '-p': int, '-J': bool, '-Z': bool, '--reference_quality': [int, 2, ','], '-I': bool, '-i': bool, '-X': bool, '-u': bool, '-n': int, '-E': int, '--max-complex-gap': int, '--haplotype-length': int, '--min-repeat-length': int, '--min-repeat-entropy': int, '--no-partial-observation': bool, '-O': bool, '-4': bool, '-m': int, '-q': int, '-R': int, '-Y': int, '-Q': int, '-U': int, '-z': int, '--read-snp-limit': int, '-e': int, '-0': int, '-F': int, '-C': int, '-3': int, '-G': int, '--min-coverage': int, '-w': bool, '-V': bool, '-a': bool, '--observation-bias': formats.TextFile, '--base-quality-cap': int, '--experimental-gls': bool, '--prob-contamination': float, '--contamination-estimates': formats.TextFile, '--report-genotype-likelihood-max': bool, '-B': int, '--genotype-max-iterations': int, '-W': [int, 2, ','], '-S': int, '-j': bool, '-H': bool, '-D': int, '--genotype-qualities': bool, }, 'unnamed': [ ('in_', [formats.Bam, 1, ' ']), ] } } class Samtools: @staticmethod def view(): return { 'cmd': 'samtools view', 'type': None, 'log': 'log', 'out': { 'redirect': False, 'return': [ {'arg': '-o', 'type': {'-b': formats.Bam, '': formats.Sam}, 'suffix': ''}, ] }, 'args': { 'named': { '-o': str, '-b': bool, '-f': int, '-F': int, '-h': bool, '-H': bool, '-l': str, '-q': int, '-r': str, '-R': formats.Bed, '-S': bool, '-c': bool, '-t': formats.TextFile, '-u': bool, }, 'unnamed': [ ('in_', {'-S': formats.Sam, '': formats.Bam}), ], }, } @staticmethod def mpileup(): return { 'cmd': 'samtools mpileup', 'type': None, 'log': 'log', 'out': { 'redirect': True, 'return': [ {'arg': 'out', 'type': {'-u': formats.Bcf, '-g': formats.Bcf, '': formats.Pileup}, 'suffix': ''}, ], }, 'args': { 'named': { '-6': bool, '-A': bool, '-B': bool, '-b': formats.TextFile, '-C': int, '-E': bool, '-f': formats.Fasta, '-l': formats.Bed, '-q': int, '-Q': int, '-r': str, '-D': bool, '-g': bool, '-S': bool, '-u': bool, '-e': int, '-h': int, '-I': bool, '-L': bool, '-o': int, '-P': str, }, 'unnamed': [ ('in_', [formats.Bam, 1, ' ']), ('out', str), ] } } @staticmethod def cat(): return { 'cmd': 'samtools cat', 'type': None, 'log': 'log', 'out': { 'redirect': False, 'return': [ {'arg': 'o', 'type': formats.Bam, 'suffix': ''}, ], }, 'args': { 'named': { '-h': formats.Sam, }, 'unnamed': [ ('in_', formats.Bam), ('o', str) ], }, } @staticmethod def sort(): return { 'cmd': 'samtools sort', 'type': None, 'log': 'log', 'out': { 'redirect': False, 'return': [ {'arg': 'out', 'type': formats.Bam, 'suffix': '.bam'}, ], }, 'args': { 'named': { '-n': bool, '-m': int, }, 'unnamed': [ ('in_', formats.Bam), ('out', str) ], }, } @staticmethod def merge(): return { 'cmd': 'samtools merge', 'type': None, 'log': 'log', 'out': { 'redirect': False, 'return': [ {'arg': 'out', 'type': formats.Bam, 'suffix': ''}, ], }, 'args': { 'named': { '-1': bool, '-f': bool, '-h': formats.Sam, '-n': bool, '-R': str, '-r': bool, '-u': bool, }, 'unnamed': [ ('out', str), ('in_', [formats.Bam, 2, ' ']), ], }, } @staticmethod def rmdup(): return { 'cmd': 'samtools rmdup', 'type': None, 'log': 'log', 'out': { 'redirect': False, 'return': [ {'arg': 'out', 'type': formats.Bam, 'suffix': ''}, ], }, 'args': { 'named': { '-s': bool, '-S': bool, }, 'unnamed': [ ('in_', formats.Bam), ('out',
hmn_dhcp_bootstrap = self.sls_networks["HMN"].subnets()["bootstrap_dhcp"] for name, reservation in hmn_dhcp_bootstrap.reservations().items(): if str(bmc_ip) == str(reservation.ipv4_address()): reservation_found = True action_log(action, f'Removing existing IP Reservation for {self.bmc_alias} in the bootstrap_dhcp subnet of the HMN network: {reservation.name()} {reservation.ipv4_address()} {reservation.aliases()} {reservation.comment()}') del hmn_dhcp_bootstrap.reservations()[name] break if not reservation_found: action_log(action, f"Error BMC IP Reservation for {self.bmc_alias} missing from the HMN bootstrap_dhcp subnet") failed_to_find_ip = True if failed_to_find_ip: print_action(action) sys.exit(1) # Update State self.ncn_ips = ncn_ips self.bmc_ip = bmc_ip def allocate_ncn_ips(self, action: dict,): # # Allocate new NCN BMC # action_log(action, "Allocating NCN BMC IP address") bmc_ip = allocate_ip_address_in_subnet(action, self.sls_networks, "HMN", "bootstrap_dhcp") # Add BMC IP reservation to the HMN network. # Example: {"Aliases":["ncn-s001-mgmt"],"Comment":"x3000c0s13b0","IPAddress":"10.254.1.31","Name":"x3000c0s13b0"} bmc_ip_reservation = IPReservation(self.bmc_xname, bmc_ip, comment=self.bmc_xname, aliases=[self.bmc_alias]) action_log(action, f"Temporally adding NCN BMC IP reservation to bootstrap_dhcp subnet in the HMN network: {bmc_ip_reservation.to_sls()}") self.sls_networks["HMN"].subnets()["bootstrap_dhcp"].reservations().update( { bmc_ip_reservation.name(): bmc_ip_reservation } ) # # Allocate new NCN IPs in SLS # action_log(action, "") action_log(action, "Allocating NCN IP addresses") ncn_ips = {} for network_name in ["CAN", "CHN", "CMN", "HMN", "MTL", "NMN"]: if network_name not in self.sls_networks: continue ncn_ips[network_name] = allocate_ip_address_in_subnet(action, self.sls_networks, network_name, "bootstrap_dhcp", self.networks_allowed_in_dhcp_range) action_log(action, "Removing temporary NCN BMC IP reservation in the bootstrap_dhcp subnet for the HMN network") del self.sls_networks["HMN"].subnets()["bootstrap_dhcp"].reservations()[bmc_ip_reservation.name()] # Update State self.ncn_ips = ncn_ips self.bmc_ip = bmc_ip self.action_log_ncn_ips(action) # Only for new IP addresses that have been allocated: # Validate the NCN and its BMC to be added does not have an IP reservation already defined for it # Also validate that none of the IP addresses we have allocated are currently in use in SLS. fail_sls_network_check = False for network_name, sls_network in self.sls_networks.items(): for subnet in sls_network.subnets().values(): for ip_reservation in subnet.reservations().values(): # Verify no IP Reservations exist for the NCN if ip_reservation.name() == self.ncn_alias: fail_sls_network_check = True action_log(action, f'Error found existing NCN IP Reservation in subnet {subnet.name()} network {network_name} in SLS: {ip_reservation.to_sls()}') # Verify no IP Reservations exist for the NCN BMC if ip_reservation.name() == self.bmc_xname: fail_sls_network_check = True action_log(action, f'Error found existing NCN BMC IP Reservation in subnet {subnet.name()} network {network_name} in SLS: {ip_reservation.to_sls()}') # Verify no IP Reservations exist with any NCN IP if sls_network.name() in ncn_ips: allocated_ip = ncn_ips[network_name] if ip_reservation.ipv4_address() == allocated_ip: fail_sls_network_check = True action_log(action, f'Error found allocated NCN IP {allocated_ip} in subnet {subnet.name()} network {network_name} in SLS: {ip_reservation.to_sls()}') # Verify no IP Reservations exist with the NCN BMC IP if sls_network.name() == "HMN" and ip_reservation.ipv4_address() == bmc_ip: fail_sls_network_check = True action_log(action, f'Error found allocated NCN BMC IP {allocated_ip} in subnet {subnet.name()} network {network_name} in SLS: {ip_reservation.to_sls()}') if fail_sls_network_check: print_action(action) sys.exit(1) action_log(action, f'Pass {self.ncn_xname} ({self.ncn_alias}) does not currently exist in SLS Networks') action_log(action, f'Pass {self.bmc_xname} ({self.bmc_alias}) does not currently exist in SLS Networks') action_log(action, f'Pass allocated IPs for NCN {self.ncn_xname} ({self.ncn_alias}) are not currently in use in SLS Networks') action_log(action, f'Pass allocated IP for NCN BMC {self.bmc_xname} ({self.bmc_alias}) are not currently in use in SLS Networks') def action_log_ncn_ips(self, action: dict): action_log(action, "") action_log(action, "=================================") action_log(action, "Management NCN IP Allocation") action_log(action, "=================================") action_log(action, "Network \| IP Address") action_log(action, "--------\|-----------") for network in sorted(self.ncn_ips): ip = self.ncn_ips[network] action_log(action, f'{network:<8}\| {ip}') action_log(action, "") action_log(action, "=================================") action_log(action, "Management NCN BMC IP Allocation") action_log(action, "=================================") action_log(action, "Network \| IP Address") action_log(action, "--------\|-----------") action_log(action, f'HMN \| {self.bmc_ip}') action_log(action, "") def print_ncn_ips(self): print("") print(" =================================") print(" Management NCN IP Allocation") print(" =================================") print(" Network \| IP Address") print(" --------\|-----------") for network in sorted(self.ncn_ips): ip = self.ncn_ips[network] print(f' {network:<8}\| {ip}') print("") print(" =================================") print(" Management NCN BMC IP Allocation") print(" =================================") print(" Network \| IP Address") print(" --------\|-----------") print(f' HMN \| {self.bmc_ip}') print("") def validate_global_bss_bootparameters(self, action: dict): if not self.use_existing_ip_addresses: # Validate the NCN is not referenced in the Global boot parameters fail_host_records = False for host_record in self.global_bootparameters["cloud-init"]["meta-data"]["host_records"]: # Check for NCN and NCN BMC for alias in host_record["aliases"]: if alias.startswith(self.ncn_alias): action_log(action, f'Error found NCN alias in Global host_records in BSS: {host_record}') fail_host_records = True # Check for if this IP is one of our allocated IPs for network, ip in self.ncn_ips.items(): if host_record["ip"] == ip: action_log(action, f'Error found {network} IP Address {ip} in Global host_records in BSS: {host_record}') fail_host_records = True if host_record["ip"] == self.bmc_ip: action_log(action, f'Error found NCN BMC IP Address {self.bmc_ip} in Global host_records in BSS: {host_record}') fail_host_records = True if fail_host_records: print_action(action) sys.exit(1) action_log(action, f"Pass {self.ncn_xname} does not currently exist in BSS Global host_records") print_action(action) else: # Validate the NCN has the expected data in the BSS Global boot parameters fail_host_records = False for host_record in self.global_bootparameters["cloud-init"]["meta-data"]["host_records"]: for network_name, ip in self.ncn_ips.items(): # Verify each NCN IP is associated with correct NCN expected_alias = f'{self.ncn_alias}.{network_name.lower()}' if str(ip) == host_record["ip"]: expected_aliases = [expected_alias] alternate_aliases = [] # ncn-m001 on the NMN can have an alternate host record for the the PIT if network_name == "NMN": expected_aliases.append(self.ncn_alias) alternate_aliases = ["pit", "pit.nmn"] if expected_aliases == host_record["aliases"] or alternate_aliases == host_record["aliases"]: action_log(action, f"Pass found existing host_record with the IP address {ip} which contains the expected aliases of {expected_aliases}") else: fail_host_records = True action_log(action, f'Error existing host_record with IP address {ip} with aliases {host_record["aliases"]}, instead of {expected_aliases}') # Verify each NCN alias is associated with the correct IP if expected_alias in host_record["aliases"]: if str(ip) == host_record["ip"]: action_log(action, f"Pass found existing host_record for alias {expected_alias} which has the expected IP address of {ip}") else: fail_host_records = True action_log(action, f'Error existing host_record for alias {expected_alias} has the IP address of {host_record["ip"]}, instead of the expected {ip}') # Verify the NCN BMC IP is associated with correct BMC if str(self.bmc_ip) == host_record["ip"]: expected_aliases = [self.bmc_alias] if expected_aliases == host_record["aliases"]: action_log(action, f"Pass found existing BMC host_record with the IP address {self.bmc_ip} which contains the expected aliases of {expected_aliases}") else: fail_host_records = True action_log(action, f'Error existing BMC host_record with IP address {self.bmc_ip} with aliases {host_record["aliases"]}, instead of {expected_aliases}') if self.bmc_alias in host_record["aliases"]: if str(self.bmc_ip) == host_record["ip"]: action_log(action, f"Pass found existing BMC host_record for alias {self.bmc_alias} which has the expected IP address of {self.bmc_ip}") else: fail_host_records = True action_log(action, f'Error existing BMC host_record for alias {expected_alias} has the IP address of {host_record["ip"]}, instead of the expected {self.bmc_ip}') if fail_host_records: print_action(action) sys.exit(1) # Validate the NCN being added is not configured as the 'first-master-hostname' first_master_hostname = self.global_bootparameters["cloud-init"]["meta-data"]["first-master-hostname"] if first_master_hostname == self.ncn_alias: action_log(action, f'Error the NCN being added {self.ncn_alias} is currently configured as the "first-master-hostname" in the Global BSS Bootparameters') print_action(action) sys.exit(1) else: action_log(action, f'Pass the NCN being added {self.ncn_alias} is not configured as the "first-master-hostname", currently {first_master_hostname} is in the Global BSS Bootparameters.') print_action(action) def update_sls_networking(self, session: requests.Session): # Add IP Reservations for all of the networks that make sense for network_name, ip in self.ncn_ips.items(): sls_network = self.sls_networks[network_name] # CAN # Master: {"Aliases":["ncn-m002-can","time-can","time-can.local"],"Comment":"x3000c0s3b0n0","IPAddress":"10.101.5.134","Name":"ncn-m002"} # Worker: {"Aliases":["ncn-w001-can","time-can","time-can.local"],"Comment":"x3000c0s7b0n0","IPAddress":"10.101.5.136","Name":"ncn-w001"} # Storage: {"Aliases":["ncn-s001-can","time-can","time-can.local"],"Comment":"x3000c0s13b0n0","IPAddress":"10.101.5.147","Name":"ncn-s001"} # CHN # Master: {"Aliases":["ncn-m002-chn","time-chn","time-chn.local"],"Comment":"x3000c0s3b0n0","IPAddress":"10.101.5.198","Name":"ncn-m002"} # Worker: {"Aliases":["ncn-w001-chn","time-chn","time-chn.local"],"Comment":"x3000c0s7b0n0","IPAddress":"10.101.5.200","Name":"ncn-w001"} # Storage: {"Aliases":["ncn-s001-chn","time-chn","time-chn.local"],"Comment":"x3000c0s13b0n0","IPAddress":"10.101.5.211","Name":"ncn-s001"} # CMN # Master: {"Aliases":["ncn-m002-cmn","time-cmn","time-cmn.local"],"Comment":"x3000c0s3b0n0","IPAddress":"10.101.5.20","Name":"ncn-m002"} # Worker: {"Aliases":["ncn-w001-cmn","time-cmn","time-cmn.local"],"Comment":"x3000c0s7b0n0","IPAddress":"10.101.5.22","Name":"ncn-w001"} # Storage: {"Aliases":["ncn-s001-cmn","time-cmn","time-cmn.local"],"Comment":"x3000c0s13b0n0","IPAddress":"10.101.5.33","Name":"ncn-s001"} # HMN # Master: {"Aliases":["ncn-m002-hmn","time-hmn","time-hmn.local"],"Comment":"x3000c0s3b0n0","IPAddress":"10.254.1.6","Name":"ncn-m002"} # Worker: {"Aliases":["ncn-w001-hmn","time-hmn","time-hmn.local"],"Comment":"x3000c0s7b0n0","IPAddress":"10.254.1.10","Name":"ncn-w001"} # Storage: {"Aliases":["ncn-s001-hmn","time-hmn","time-hmn.local","rgw-vip.hmn"],"Comment":"x3000c0s13b0n0","IPAddress":"10.254.1.32","Name":"ncn-s001"} # MTL # Master: {"Aliases":["ncn-m002-mtl","time-mtl","time-mtl.local"],"Comment":"x3000c0s3b0n0","IPAddress":"10.1.1.3","Name":"ncn-m002"} # Worker: {"Aliases":["ncn-w001-mtl","time-mtl","time-mtl.local"],"Comment":"x3000c0s7b0n0","IPAddress":"10.1.1.5","Name":"ncn-w001"} # Storage: {"Aliases":["ncn-s001-mtl","time-mtl","time-mtl.local"],"Comment":"x3000c0s13b0n0","IPAddress":"10.1.1.16","Name":"ncn-s001"} # NMN # Master: {"Aliases":["ncn-m002-nmn","time-nmn","time-nmn.local","x3000c0s3b0n0","ncn-m002.local"],"Comment":"x3000c0s3b0n0","IPAddress":"10.252.1.5","Name":"ncn-m002"} # Worker: {"Aliases":["ncn-w001-nmn","time-nmn","time-nmn.local","x3000c0s7b0n0","ncn-w001.local"],"Comment":"x3000c0s7b0n0","IPAddress":"10.252.1.7","Name":"ncn-w001"} # Storage: {"Aliases":["ncn-s001-nmn","time-nmn","time-nmn.local","x3000c0s13b0n0","ncn-s001.local"],"Comment":"x3000c0s13b0n0","IPAddress":"10.252.1.18","Name":"ncn-s001"} # Generalizations # - All IP reservations have the NCN xname for the comment # - Following rules apply to all but CHN # - NCN Alias is the IP reservation name # - Each master/worker/storage have the following aliases # - ncn-{}-{network} # - time-{network} # - time-{network}.local # - Storage nodes on the HMN have additional alias rgw-vip.hmn # - All NCNs on the NMN have the additional aliases: # - xname # - ncn-{}.local # - The CHN # - No reservations # - have IP reservations with the node xname for the reservation name # All networks except for the CHN have the NCNs alias as the name for the reservation. The CHN has the node xname. name = self.ncn_alias # All NCN types have thier xname as the comment for their IP reservation comment = self.ncn_xname # For all
2; # break; # BIT instructions if instruction == 0x24: # $24/36 BIT zp self.BIT(OperandRef(LOC_VAL, self.zeropage())) self.pc += 1 return 1 if instruction == 0x2c: # $2C/44 BIT abs self.BIT(OperandRef(LOC_VAL, self.absolute())) self.pc += 2 return 1 # case 0x30: # if (flags & FN) BRANCH() # else pc++; # break; # BMI instruction if instruction == 0x30: # $30/48 BMI rel if (self.flags & FN): self.branch() else: self.pc += 1 return 1 # case 0xd0: # if (!(flags & FZ)) BRANCH() # else pc++; # break; # BNE instruction if instruction == 0xd0: # $D0/208 BNE rel if not (self.flags & FZ): self.branch() else: self.pc += 1 return 1 # case 0x10: # if (!(flags & FN)) BRANCH() # else pc++; # break; # BPL instruction if instruction == 0x10: # $10/16 BPL rel if not (self.flags & FN): self.branch() else: self.pc += 1 return 1 # case 0x50: # if (!(flags & FV)) BRANCH() # else pc++; # break; # BVC instruction if instruction == 0x50: # $50/80 BVC rel if not (self.flags & FV): self.branch() else: self.pc += 1 return 1 # case 0x70: # if (flags & FV) BRANCH() # else pc++; # break; # BVS instruction if instruction == 0x70: # $70/112 BVS rel if (self.flags & FV): self.branch() else: self.pc += 1 return 1 # case 0x18: # flags &= ~FC; # break; # CLC instruction if instruction == 0x18: # $18/24 CLC self.flags &= (~FC & 0xff) return 1 # case 0xd8: # flags &= ~FD; # break; # CLD instruction if instruction == 0xd8: # $D8/216 CLD self.flags &= (~FD & 0xff) return 1 # case 0x58: # flags &= ~FI; # break; # CLI instruction if instruction == 0x58: # $58/88 CLI self.flags &= (~FI & 0xff) return 1 # case 0xb8: # flags &= ~FV; # break; # CLV instruction if instruction == 0xb8: # $B8/184 CLV self.flags &= (~FV & 0xff) return 1 # case 0xc9: # CMP(a, IMMEDIATE()); # pc++; # break; # # case 0xc5: # CMP(a, MEM(ZEROPAGE())); # pc++; # break; # # case 0xd5: # CMP(a, MEM(ZEROPAGEX())); # pc++; # break; # # case 0xcd: # CMP(a, MEM(ABSOLUTE())); # pc += 2; # break; # # case 0xdd: # cpucycles += EVALPAGECROSSING_ABSOLUTEX(); # CMP(a, MEM(ABSOLUTEX())); # pc += 2; # break; # # case 0xd9: # cpucycles += EVALPAGECROSSING_ABSOLUTEY(); # CMP(a, MEM(ABSOLUTEY())); # pc += 2; # break; # # case 0xc1: # CMP(a, MEM(INDIRECTX())); # pc++; # break; # # case 0xd1: # cpucycles += EVALPAGECROSSING_INDIRECTY(); # CMP(a, MEM(INDIRECTY())); # pc++; # break; # CMP instructions if instruction == 0xc9: # $C9/201 CMP #n self.CMP(A_OPREF, OperandRef(BYTE_VAL, self.immediate())) self.pc += 1 return 1 if instruction == 0xc5: # $C5/197 CMP zp self.CMP(A_OPREF, OperandRef(LOC_VAL, self.zeropage())) self.pc += 1 return 1 if instruction == 0xd5: # $D5/213 CMP zp,X self.CMP(A_OPREF, OperandRef(LOC_VAL, self.zeropage_x())) self.pc += 1 return 1 if instruction == 0xcd: # $CD/205 CMP abs self.CMP(A_OPREF, OperandRef(LOC_VAL, self.absolute())) self.pc += 2 return 1 if instruction == 0xdd: # $DD/221 CMP abs,X self.cpucycles += self.eval_page_crossing_absolute_x() self.CMP(A_OPREF, OperandRef(LOC_VAL, self.absolute_x())) self.pc += 2 return 1 if instruction == 0xd9: # $D9/217 CMP abs,Y self.cpucycles += self.eval_page_crossing_absolute_y() self.CMP(A_OPREF, OperandRef(LOC_VAL, self.absolute_y())) self.pc += 2 return 1 if instruction == 0xc1: # $C1/193 CMP (zp,X) self.CMP(A_OPREF, OperandRef(LOC_VAL, self.indirect_x())) self.pc += 1 return 1 if instruction == 0xd1: # $D1/209 CMP (zp),Y self.cpucycles += self.eval_page_crossing_indirect_y() self.CMP(A_OPREF, OperandRef(LOC_VAL, self.indirect_y())) self.pc += 1 return 1 # case 0xe0: # CMP(x, IMMEDIATE()); # pc++; # break; # # case 0xe4: # CMP(x, MEM(ZEROPAGE())); # pc++; # break; # # case 0xec: # CMP(x, MEM(ABSOLUTE())); # pc += 2; # break; # CPX instructions if instruction == 0xe0: # $E0/224 CPX #n self.CMP(X_OPREF, OperandRef(BYTE_VAL, self.immediate())) self.pc += 1 return 1 if instruction == 0xe4: # $E4/228 CPX zp self.CMP(X_OPREF, OperandRef(LOC_VAL, self.zeropage())) self.pc += 1 return 1 if instruction == 0xec: # $EC/236 CPX abs self.CMP(X_OPREF, OperandRef(LOC_VAL, self.absolute())) self.pc += 2 return 1 # case 0xc0: # CMP(y, IMMEDIATE()); # pc++; # break; # # case 0xc4: # CMP(y, MEM(ZEROPAGE())); # pc++; # break; # # case 0xcc: # CMP(y, MEM(ABSOLUTE())); # pc += 2; # break; # CPY instructions if instruction == 0xc0: # $C0/192 CPY #n self.CMP(Y_OPREF, OperandRef(BYTE_VAL, self.immediate())) self.pc += 1 return 1 if instruction == 0xc4: # $C4/196 CPY zp self.CMP(Y_OPREF, OperandRef(LOC_VAL, self.zeropage())) self.pc += 1 return 1 if instruction == 0xcc: # $CC/204 CPY abs self.CMP(Y_OPREF, OperandRef(LOC_VAL, self.absolute())) self.pc += 2 return 1 # case 0xc6: # DEC(MEM(ZEROPAGE())); # WRITE(ZEROPAGE()); # pc++; # break; # # case 0xd6: # DEC(MEM(ZEROPAGEX())); # WRITE(ZEROPAGEX()); # pc++; # break; # # case 0xce: # DEC(MEM(ABSOLUTE())); # WRITE(ABSOLUTE()); # pc += 2; # break; # # case 0xde: # DEC(MEM(ABSOLUTEX())); # WRITE(ABSOLUTEX()); # pc += 2; # break; # DEC instructions if instruction == 0xc6: # $C6/198 DEC zp self.DEC(OperandRef(LOC_VAL, self.zeropage())) self.pc += 1 return 1 if instruction == 0xd6: # $D6/214 DEC zp,X self.DEC(OperandRef(LOC_VAL, self.zeropage_x())) self.pc += 1 return 1 if instruction == 0xce: # $CE/206 DEC abs self.DEC(OperandRef(LOC_VAL, self.absolute())) self.pc += 2 return 1 if instruction == 0xde: # $DE/222 DEC abs,X self.DEC(OperandRef(LOC_VAL, self.absolute_x())) self.pc += 2 return 1 # case 0xca: # x--; # SETFLAGS(x); # break; # DEX instruction if instruction == 0xca: # $CA/202 DEX self.x -= 1 self.x &= 0xff self.set_flags(self.x) return 1 # case 0x88: # y--; # SETFLAGS(y); # break; # DEY instruction if instruction == 0x88: # $88/136 DEY self.y -= 1 self.y &= 0xff self.set_flags(self.y) return 1 # case 0x49: # EOR(IMMEDIATE()); # pc++; # break; # # case 0x45: # EOR(MEM(ZEROPAGE())); # pc++; # break; # # case 0x55: # EOR(MEM(ZEROPAGEX())); # pc++; # break; # # case 0x4d: # EOR(MEM(ABSOLUTE())); # pc += 2; # break; # # case 0x5d: # cpucycles += EVALPAGECROSSING_ABSOLUTEX(); # EOR(MEM(ABSOLUTEX())); # pc += 2; # break; # # case 0x59: # cpucycles += EVALPAGECROSSING_ABSOLUTEY(); # EOR(MEM(ABSOLUTEY())); # pc += 2; # break; # # case 0x41: # EOR(MEM(INDIRECTX())); # pc++; # break; # # case 0x51: # cpucycles += EVALPAGECROSSING_INDIRECTY(); # EOR(MEM(INDIRECTY())); # pc++; # break; # EOR instructions if instruction == 0x49: # $49/73 EOR #n self.EOR(OperandRef(BYTE_VAL, self.immediate())) self.pc += 1 return 1 if instruction == 0x45: # $45/69 EOR zp self.EOR(OperandRef(LOC_VAL, self.zeropage())) self.pc += 1 return 1 if instruction == 0x55: # $55/85 EOR zp,X self.EOR(OperandRef(LOC_VAL, self.zeropage_x())) self.pc += 1 return 1 if instruction == 0x4d: # $4D/77 EOR abs self.EOR(OperandRef(LOC_VAL, self.absolute())) self.pc += 2 return 1 if instruction == 0x5d: # $5D/93 EOR abs,X self.cpucycles += self.eval_page_crossing_absolute_x() self.EOR(OperandRef(LOC_VAL, self.absolute_x())) self.pc += 2 return 1 if instruction == 0x59: # $59/89 EOR abs,Y self.cpucycles += self.eval_page_crossing_absolute_y() self.EOR(OperandRef(LOC_VAL, self.absolute_y())) self.pc += 2 return 1 if instruction == 0x41: # $41/65 EOR (zp,X) self.EOR(OperandRef(LOC_VAL, self.indirect_x())) self.pc += 1 return 1 if instruction == 0x51: # $51/81 EOR (zp),Y self.cpucycles += self.eval_page_crossing_indirect_y() self.EOR(OperandRef(LOC_VAL, self.indirect_y())) self.pc += 1 return 1 # case 0xe6: # INC(MEM(ZEROPAGE())); # WRITE(ZEROPAGE()); # pc++; # break; # # case 0xf6: # INC(MEM(ZEROPAGEX())); # WRITE(ZEROPAGEX()); # pc++; # break; # # case 0xee: # INC(MEM(ABSOLUTE())); # WRITE(ABSOLUTE()); # pc += 2; # break; # # case 0xfe: # INC(MEM(ABSOLUTEX())); # WRITE(ABSOLUTEX()); # pc += 2; # break; # INC instructions if instruction == 0xe6: # $E6/230 INC zp self.INC(OperandRef(LOC_VAL, self.zeropage())) self.pc += 1 return 1 if instruction == 0xf6: # $F6/246 INC zp,X self.INC(OperandRef(LOC_VAL, self.zeropage_x())) self.pc += 1 return 1 if instruction == 0xee: # $EE/238 INC abs self.INC(OperandRef(LOC_VAL, self.absolute())) self.pc += 2 return 1 if instruction == 0xfe: # $FE/254 INC abs,X self.INC(OperandRef(LOC_VAL, self.absolute_x())) self.pc += 2 return 1 # case 0xe8: # x++; # SETFLAGS(x); # break; # INX instruction if instruction == 0xe8: # $E8/232 INX self.x += 1 self.x &= 0xff self.set_flags(self.x) return 1 # case 0xc8: # y++; # SETFLAGS(y); # break; # INY instruction if instruction == 0xc8: # $C8/200 INY self.y += 1 self.y &= 0xff self.set_flags(self.y) return 1 # case 0x20: # PUSH((pc+1) >> 8); # PUSH((pc+1) &
#If gate.qubits is None, gate is assumed to be single-qubit gate #acting in parallel on all qubits. If the gate is a global idle, then #Pragma blocks are inserted (for tests like idle tomography) even #if block_between_layers==False. Set block_idles=False to disable this as well. if gate.qubits is None: if quil_for_gate == 'I': if block_idles: quil += 'PRAGMA PRESERVE_BLOCK\n' for q in gate_qubits: quil += quil_for_gate + ' ' + str(qubit_conversion[q]) + '\n' if block_idles: quil += 'PRAGMA END_PRESERVE_BLOCK\n' else: for q in gate_qubits: quil += quil_for_gate + ' ' + str(qubit_conversion[q]) + '\n' #If gate.qubits is not None, then apply the one- or multi-qubit gate to #the explicitly specified qubits. else: for q in gate_qubits: quil_for_gate += ' ' + str(qubit_conversion[q]) quil_for_gate += '\n' # Add the quil for the gate to the quil string. quil += quil_for_gate # Keeps track of the qubits that have been accounted for, and checks that hadn't been used # although that should already be checked in the .get_layer_label(), which checks for its a valid # circuit layer. assert(not set(gate_qubits).issubset(set(qubits_used))) qubits_used.extend(gate_qubits) # All gates that don't have a non-idle gate acting on them get an idle in the layer. for q in self.line_labels: if q not in qubits_used: quil += 'I' + ' ' + str(qubit_conversion[q]) + '\n' # Add in a barrier after every circuit layer if block_between_layers==True. # Including pragma blocks are critical for QCVV testing, as circuits should usually # experience minimal "behind-the-scenes" compilation (beyond necessary # conversion to native instructions) # To do: Add "barrier" as native pygsti circuit instruction, and use for indicating # where pragma blocks should be. if block_between_layers: quil += 'PRAGMA PRESERVE_BLOCK\nPRAGMA END_PRESERVE_BLOCK\n' # Add in a measurement at the end. if readout_conversion is None: for q in self.line_labels: # quil += "MEASURE {0} [{1}]\n".format(str(qubit_conversion[q]),str(qubit_conversion[q])) quil += "MEASURE {0} ro[{1}]\n".format(str(qubit_conversion[q]), str(qubit_conversion[q])) else: for q in self.line_labels: quil += "MEASURE {0} ro[{1}]\n".format(str(qubit_conversion[q]), str(readout_conversion[q])) return quil def convert_to_openqasm(self, num_qubits=None, gatename_conversion=None, qubit_conversion=None, block_between_layers=True): # TODO """ Converts this circuit to an openqasm string. Parameters ---------- gatename_conversion : dict, optional If not None, a dictionary that converts the gatenames in the circuit to the gatenames that will appear in the openqasm output. If only standard pyGSTi names are used (e.g., 'Gh', 'Gp', 'Gcnot', 'Gcphase', etc) this dictionary need not be specified, and an automatic conversion to the standard openqasm names will be implemented. qubit_conversion : dict, optional If not None, a dictionary converting the qubit labels in the circuit to the desired qubit labels in the openqasm output. Can be left as None if the qubit labels are either (1) integers, or (2) of the form 'Qi' for integer i. In this case they are converted to integers (i.e., for (1) the mapping is trivial, for (2) the mapping strips the 'Q'). Returns ------- str An openqasm string. """ # create standard conversations. if gatename_conversion is None: gatename_conversion = _itgs.get_standard_gatenames_openqasm_conversions() if qubit_conversion is None: # To tell us whether we have found a standard qubit labelling type. standardtype = False # Must first check they are strings, because cannot query q[0] for int q. if all([isinstance(q, str) for q in self.line_labels]): if all([q[0] == 'Q' for q in self.line_labels]): standardtype = True qubit_conversion = {llabel: int(llabel[1:]) for llabel in self.line_labels} if all([isinstance(q, int) for q in self.line_labels]): qubit_conversion = {q: q for q in self.line_labels} standardtype = True if not standardtype: raise ValueError( "No standard qubit labelling conversion is available! Please provide `qubit_conversion`.") if num_qubits is None: num_qubits = len(self.line_labels) #Currently only using 'Iz' as valid intermediate measurement ('IM') label. #Todo: Expand to all intermediate measurements. if 'Iz' in self.str: # using_IMs = True num_IMs = self.str.count('Iz') else: # using_IMs = False num_IMs = 0 num_IMs_used = 0 # Init the openqasm string. openqasm = 'OPENQASM 2.0;\ninclude "qelib1.inc";\n\n' openqasm += 'qreg q[{0}];\n'.format(str(num_qubits)) # openqasm += 'creg cr[{0}];\n'.format(str(num_qubits)) openqasm += 'creg cr[{0}];\n'.format(str(num_qubits + num_IMs)) openqasm += '\n' depth = self.num_layers() # Go through the layers, and add the openqasm for each layer in turn. for l in range(depth): # Get the layer, without identity gates and containing each gate only once. layer = self.get_layer_label(l) # For keeping track of which qubits have a gate on them in the layer. qubits_used = [] # Go through the (non-self.identity) gates in the layer and convert them to openqasm for gate in layer.components: gate_qubits = gate.qubits if (gate.qubits is not None) else self.line_labels assert(len(gate_qubits) <= 2), 'Gates on more than 2 qubits given; this is currently not supported!' # Find the openqasm for the gate. if gate.name.__str__() != 'Iz': openqasm_for_gate = gatename_conversion[gate.name] #If gate.qubits is None, gate is assumed to be single-qubit gate #acting in parallel on all qubits. if gate.qubits is None: for q in gate_qubits: openqasm += openqasm_for_gate + ' q[' + str(qubit_conversion[q]) + '];\n' else: for q in gate_qubits: openqasm_for_gate += ' q[' + str(qubit_conversion[q]) + ']' if q != gate_qubits[-1]: openqasm_for_gate += ', ' openqasm_for_gate += ';\n' else: assert len(gate.qubits) == 1 q = gate.qubits[0] # classical_bit = num_IMs_used openqasm_for_gate = "measure q[{0}] -> cr[{1}];\n".format(str(qubit_conversion[q]), num_IMs_used) num_IMs_used += 1 # Add the openqasm for the gate to the openqasm string. openqasm += openqasm_for_gate # Keeps track of the qubits that have been accounted for, and checks that hadn't been used # although that should already be checked in the .get_layer_label(), which checks for its a valid # circuit layer. assert(not set(gate_qubits).issubset(set(qubits_used))) qubits_used.extend(gate_qubits) # All gates that don't have a non-idle gate acting on them get an idle in the layer. for q in self.line_labels: if q not in qubits_used: openqasm += 'id' + ' q[' + str(qubit_conversion[q]) + '];\n' # Add in a barrier after every circuit layer if block_between_layers==True. # Including barriers is critical for QCVV testing, circuits should usually # experience minimal "behind-the-scenes" compilation (beyond necessary # conversion to native instructions). # To do: Add "barrier" as native pygsti circuit instruction, and use for indicating # where pragma blocks should be. if block_between_layers: openqasm += 'barrier ' for q in self.line_labels[:-1]: openqasm += 'q[{0}], '.format(str(qubit_conversion[q])) openqasm += 'q[{0}];\n'.format(str(qubit_conversion[self.line_labels[-1]])) # openqasm += ';' # Add in a measurement at the end. for q in self.line_labels: # openqasm += "measure q[{0}] -> cr[{1}];\n".format(str(qubit_conversion[q]), str(qubit_conversion[q])) openqasm += "measure q[{0}] -> cr[{1}];\n".format(str(qubit_conversion[q]), str(num_IMs_used + qubit_conversion[q])) return openqasm def simulate(self, model, return_all_outcomes=False): """ Compute the outcome probabilities of this Circuit using `model` as a model for the gates. The order of the outcome strings (e.g., '0100') is w.r.t. to the ordering of the qubits in the circuit. That is, the ith element of the outcome string corresponds to the qubit with label `self.qubit_labels[i]`. Parameters ---------- model : Model A description of the gate and SPAM operations corresponding to the labels stored in this Circuit. If this model is over more qubits than the circuit, the output will be the probabilities for the qubits in the circuit marginalized over the other qubits. But, the simulation is over the full set of qubits in the model, and so the time taken for the simulation scales with the number of qubits in the model. For models whereby "spectator" qubits do not affect the qubits in this circuit (such as with perfect gates), more efficient simulations will be obtained by first creating a model only over the qubits in this circuit. return_all_outcomes: bool, optional Whether to include outcomes in the returned dictionary that have zero probability. When False, the threshold for discarding an outcome as z ero probability is 10^-12. Returns ------- probs : dictionary A dictionary with keys equal to all (`return_all_outcomes` is True) or possibly only some (`return_all_outcomes` is False) of the possible outcomes, and values that are float probabilities. """ # These results is a dict with strings of outcomes (normally bits) ordered according
:type DnsQueryType: str :param UserName: 登录服务器的账号 :type UserName: str :param PassWord: 登录服务器的密码 :type PassWord: str :param UseSecConn: 是否使用安全链接SSL， 0 不使用，1 使用 :type UseSecConn: int :param NeedAuth: FTP登录验证方式 0 不验证 1 匿名登录 2 需要身份验证 :type NeedAuth: int :param ReqDataType: 请求数据类型。0 表示请求为字符串类型。1表示为二进制类型 :type ReqDataType: int :param ReqData: 发起TCP, UDP请求的协议请求数据 :type ReqData: str :param RespDataType: 响应数据类型。0 表示响应为字符串类型。1表示为二进制类型 :type RespDataType: int :param RespData: 预期的UDP请求的回应数据 :type RespData: str :param RedirectFollowNum: 跟随跳转次数 :type RedirectFollowNum: int """ self.TaskId = None self.TaskName = None self.Period = None self.CatTypeName = None self.CgiUrl = None self.AgentGroupId = None self.PolicyGroupId = None self.Status = None self.AddTime = None self.Type = None self.TopicId = None self.AlarmStatus = None self.Host = None self.Port = None self.CheckStr = None self.CheckType = None self.UserAgent = None self.Cookie = None self.PostData = None self.SslVer = None self.IsHeader = None self.DnsSvr = None self.DnsCheckIp = None self.DnsQueryType = None self.UserName = None self.PassWord = None self.UseSecConn = None self.NeedAuth = None self.ReqDataType = None self.ReqData = None self.RespDataType = None self.RespData = None self.RedirectFollowNum = None def _deserialize(self, params): self.TaskId = params.get("TaskId") self.TaskName = params.get("TaskName") self.Period = params.get("Period") self.CatTypeName = params.get("CatTypeName") self.CgiUrl = params.get("CgiUrl") self.AgentGroupId = params.get("AgentGroupId") self.PolicyGroupId = params.get("PolicyGroupId") self.Status = params.get("Status") self.AddTime = params.get("AddTime") self.Type = params.get("Type") self.TopicId = params.get("TopicId") self.AlarmStatus = params.get("AlarmStatus") self.Host = params.get("Host") self.Port = params.get("Port") self.CheckStr = params.get("CheckStr") self.CheckType = params.get("CheckType") self.UserAgent = params.get("UserAgent") self.Cookie = params.get("Cookie") self.PostData = params.get("PostData") self.SslVer = params.get("SslVer") self.IsHeader = params.get("IsHeader") self.DnsSvr = params.get("DnsSvr") self.DnsCheckIp = params.get("DnsCheckIp") self.DnsQueryType = params.get("DnsQueryType") self.UserName = params.get("UserName") self.PassWord = params.get("PassWord") self.UseSecConn = params.get("UseSecConn") self.NeedAuth = params.get("NeedAuth") self.ReqDataType = params.get("ReqDataType") self.ReqData = params.get("ReqData") self.RespDataType = params.get("RespDataType") self.RespData = params.get("RespData") self.RedirectFollowNum = params.get("RedirectFollowNum") memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class CreateAgentGroupRequest(AbstractModel): """CreateAgentGroup请求参数结构体 """ def __init__(self): r""" :param GroupName: 拨测分组名称，不超过32个字符 :type GroupName: str :param IsDefault: 是否为默认分组，取值可为 0 或 1。取 1 时表示设置为默认分组 :type IsDefault: int :param Agents: Province, Isp 需要成对地进行选择。参数对的取值范围。参见：DescribeAgents 的返回结果。 :type Agents: list of CatAgent """ self.GroupName = None self.IsDefault = None self.Agents = None def _deserialize(self, params): self.GroupName = params.get("GroupName") self.IsDefault = params.get("IsDefault") if params.get("Agents") is not None: self.Agents = [] for item in params.get("Agents"): obj = CatAgent() obj._deserialize(item) self.Agents.append(obj) memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class CreateAgentGroupResponse(AbstractModel): """CreateAgentGroup返回参数结构体 """ def __init__(self): r""" :param GroupId: 拨测分组Id :type GroupId: int :param RequestId: 唯一请求 ID，每次请求都会返回。定位问题时需要提供该次请求的 RequestId。 :type RequestId: str """ self.GroupId = None self.RequestId = None def _deserialize(self, params): self.GroupId = params.get("GroupId") self.RequestId = params.get("RequestId") class CreateProbeTasksRequest(AbstractModel): """CreateProbeTasks请求参数结构体 """ def __init__(self): r""" :param BatchTasks: 批量任务名-地址 :type BatchTasks: list of ProbeTaskBasicConfiguration :param TaskType: 任务类型 :type TaskType: int :param Nodes: 拨测节点 :type Nodes: list of str :param Interval: 拨测间隔 :type Interval: int :param Parameters: 拨测参数 :type Parameters: str :param TaskCategory: 任务分类 <li>1 = PC</li> <li> 2 = Mobile </li> :type TaskCategory: int :param Cron: 定时任务cron表达式 :type Cron: str :param Tag: 资源标签值 :type Tag: list of Tag """ self.BatchTasks = None self.TaskType = None self.Nodes = None self.Interval = None self.Parameters = None self.TaskCategory = None self.Cron = None self.Tag = None def _deserialize(self, params): if params.get("BatchTasks") is not None: self.BatchTasks = [] for item in params.get("BatchTasks"): obj = ProbeTaskBasicConfiguration() obj._deserialize(item) self.BatchTasks.append(obj) self.TaskType = params.get("TaskType") self.Nodes = params.get("Nodes") self.Interval = params.get("Interval") self.Parameters = params.get("Parameters") self.TaskCategory = params.get("TaskCategory") self.Cron = params.get("Cron") if params.get("Tag") is not None: self.Tag = [] for item in params.get("Tag"): obj = Tag() obj._deserialize(item) self.Tag.append(obj) memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class CreateProbeTasksResponse(AbstractModel): """CreateProbeTasks返回参数结构体 """ def __init__(self): r""" :param RequestId: 唯一请求 ID，每次请求都会返回。定位问题时需要提供该次请求的 RequestId。 :type RequestId: str """ self.RequestId = None def _deserialize(self, params): self.RequestId = params.get("RequestId") class CreateTaskExRequest(AbstractModel): """CreateTaskEx请求参数结构体 """ def __init__(self): r""" :param CatTypeName: http, https, ping, tcp, ftp, smtp, udp, dns 之一 :type CatTypeName: str :param Url: 拨测的URL，例如：www.qq.com (URL域名解析需要能解析出具体的IP) :type Url: str :param Period: 拨测周期。取值可为1,5,15,30之一, 单位：分钟。精度不能低于用户等级规定的最小精度 :type Period: int :param TaskName: 拨测任务名称不能超过32个字符。同一个用户创建的任务名不可重复 :type TaskName: str :param AgentGroupId: 拨测分组ID，体现本拨测任务要采用哪些运营商作为拨测源。一般可直接填写本用户的默认拨测分组。参见：DescribeAgentGroups 接口，本参数使用返回结果里的GroupId的值。注意： Type为0时，AgentGroupId为必填 :type AgentGroupId: int :param Host: 指定域名(如需要) :type Host: str :param IsHeader: 是否为Header请求（非0 发起Header 请求。为0，且PostData 非空，发起POST请求。为0，PostData 为空，发起GET请求） :type IsHeader: int :param SslVer: URL中含有"https"时有用。缺省为SSLv23。需要为 TLSv1_2, TLSv1_1, TLSv1, SSLv2, SSLv23, SSLv3 之一 :type SslVer: str :param PostData: POST请求数据。空字符串表示非POST请求 :type PostData: str :param UserAgent: 用户Agent信息 :type UserAgent: str :param CheckStr: 要在结果中进行匹配的字符串 :type CheckStr: str :param CheckType: 1 表示通过检查结果是否包含CheckStr 进行校验 :type CheckType: int :param Cookie: 需要设置的Cookie信息 :type Cookie: str :param TaskId: 任务ID，用于验证且修改任务时传入原任务ID :type TaskId: int :param UserName: 登录服务器的账号。如果为空字符串，表示不用校验用户密码。只做简单连接服务器的拨测 :type UserName: str :param PassWord: 登录服务器的密码 :type PassWord: str :param ReqDataType: 缺省为0。0 表示请求为字符串类型。1表示为二进制类型 :type ReqDataType: int :param ReqData: 发起TCP, UDP请求的协议请求数据 :type ReqData: str :param RespDataType: 缺省为0。0 表示响应为字符串类型。1表示为二进制类型 :type RespDataType: int :param RespData: 预期的UDP请求的回应数据。字符串型，只需要返回的结果里包含本字符串算校验通过。二进制型，则需要严格等于才算通过 :type RespData: str :param DnsSvr: 目的DNS服务器可以为空字符串 :type DnsSvr: str :param DnsCheckIp: 需要检验是否在DNS IP列表的IP。可以为空字符串，表示不校验 :type DnsCheckIp: str :param DnsQueryType: 需要为下列值之一。缺省为A。A, MX, NS, CNAME, TXT, ANY :type DnsQueryType: str :param UseSecConn: 是否使用安全链接SSL， 0 不使用，1 使用 :type UseSecConn: int :param NeedAuth: FTP登录验证方式， 0 不验证， 1 匿名登录， 2 需要身份验证 :type NeedAuth: int :param Port: 拨测目标的端口号 :type Port: int :param Type: Type=0 默认（站点监控）Type=2 可用率监控 :type Type: int :param IsVerify: IsVerify=0 非验证任务 IsVerify=1 验证任务，不传则默认为0 :type IsVerify: int :param RedirectFollowNum: 跟随跳转次数，取值范围0-5，不传则表示不跟随 :type RedirectFollowNum: int """ self.CatTypeName = None self.Url = None self.Period = None self.TaskName = None self.AgentGroupId = None self.Host = None self.IsHeader = None self.SslVer = None self.PostData = None self.UserAgent = None self.CheckStr = None self.CheckType = None self.Cookie = None self.TaskId = None self.UserName = None self.PassWord = <PASSWORD> self.ReqDataType = None self.ReqData = None self.RespDataType = None self.RespData = None self.DnsSvr = None self.DnsCheckIp = None self.DnsQueryType = None self.UseSecConn = None self.NeedAuth = None self.Port = None self.Type = None self.IsVerify = None self.RedirectFollowNum = None def _deserialize(self, params): self.CatTypeName = params.get("CatTypeName") self.Url = params.get("Url") self.Period = params.get("Period") self.TaskName = params.get("TaskName") self.AgentGroupId = params.get("AgentGroupId") self.Host = params.get("Host") self.IsHeader = params.get("IsHeader") self.SslVer = params.get("SslVer") self.PostData = params.get("PostData") self.UserAgent = params.get("UserAgent") self.CheckStr = params.get("CheckStr") self.CheckType = params.get("CheckType") self.Cookie = params.get("Cookie") self.TaskId = params.get("TaskId") self.UserName = params.get("UserName") self.PassWord = params.get("PassWord") self.ReqDataType = params.get("ReqDataType") self.ReqData = params.get("ReqData") self.RespDataType = params.get("RespDataType") self.RespData = params.get("RespData") self.DnsSvr = params.get("DnsSvr") self.DnsCheckIp = params.get("DnsCheckIp") self.DnsQueryType = params.get("DnsQueryType") self.UseSecConn = params.get("UseSecConn") self.NeedAuth = params.get("NeedAuth") self.Port = params.get("Port") self.Type = params.get("Type") self.IsVerify = params.get("IsVerify") self.RedirectFollowNum = params.get("RedirectFollowNum") memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class CreateTaskExResponse(AbstractModel): """CreateTaskEx返回参数结构体 """ def __init__(self): r""" :param ResultId: 拨测结果查询ID。接下来可以使用查询拨测是否能够成功，验证能否通过。 :type ResultId: int :param TaskId: 拨测任务ID。验证通过后，创建任务时使用，传递给CreateTask 接口。 :type TaskId: int :param RequestId: 唯一请求 ID，每次请求都会返回。定位问题时需要提供该次请求的 RequestId。 :type RequestId: str """ self.ResultId = None self.TaskId = None self.RequestId = None def _deserialize(self, params): self.ResultId = params.get("ResultId") self.TaskId = params.get("TaskId") self.RequestId = params.get("RequestId") class DataPoint(AbstractModel): """时延等数据，数据点 """ def __init__(self): r""" :param LogTime: 数据点的时间 :type LogTime: str :param MetricValue: 数据值 :type MetricValue: float """ self.LogTime = None self.MetricValue = None def _deserialize(self, params): self.LogTime = params.get("LogTime") self.MetricValue = params.get("MetricValue") memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class DataPointMetric(AbstractModel): """包含MetricName的DataPoint数据 """ def __init__(self): r""" :param MetricName: 数据项 :type MetricName: str :param Points: 数据点的时间和值 :type Points: list of DataPoint """ self.MetricName = None self.Points = None def _deserialize(self, params): self.MetricName = params.get("MetricName") if params.get("Points") is not None: self.Points = [] for item in params.get("Points"): obj = DataPoint() obj._deserialize(item) self.Points.append(obj) memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class DeleteAgentGroupRequest(AbstractModel): """DeleteAgentGroup请求参数结构体 """ def __init__(self): r""" :param GroupId: 拨测分组id :type GroupId: int """ self.GroupId = None def _deserialize(self, params): self.GroupId = params.get("GroupId") memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class
<filename>likeyoubot_kaiser.py import likeyoubot_game as lybgame import likeyoubot_kaiser_scene as lybscene from likeyoubot_configure import LYBConstant as lybconstant import time import sys import tkinter from tkinter import ttk from tkinter import font import copy class LYBKaiser(lybgame.LYBGame): work_list = [ '게임 시작', '로그인', '자동 사냥', '메인 퀘스트', '지역 퀘스트', '퀵슬롯 등록', '퀘스트', '우편', '일괄 분해', '알림', '[반복 시작]', '[반복 종료]', '[작업 대기]', '[작업 예약]', '' ] nox_kaiser_icon_list = [ 'nox_kaiser_icon' ] momo_kaiser_icon_list = [ 'momo_kaiser_icon' ] character_move_list = [ "↑", "↗", "→", "↘", "↓", "↙", "←", "↖" ] slot_item_list = [ '없음', '소형 체력 물약', '중형 체력 물약', '속도의 물약', '전투의 물약', '증폭 마법석', '펫 소환 주문서', ] def __init__(self, game_name, game_data_name, window): lybgame.LYBGame.__init__(self, lybconstant.LYB_GAME_KAISER, lybconstant.LYB_GAME_DATA_KAISER, window) def process(self, window_image): rc = super(LYBKaiser, self).process(window_image) if rc < 0: return rc return rc def custom_check(self, window_image, window_pixel): pb_name = 'skip' (loc_x, loc_y), match_rate = self.locationOnWindowPart( self.window_image, self.resource_manager.pixel_box_dic[pb_name], custom_below_level=(130, 130, 130), custom_top_level=(255, 255, 255), custom_threshold=0.9, custom_flag=1, custom_rect=(560, 240, 600, 280) ) if loc_x != -1: self.logger.warn('건너뛰기: ' + str(match_rate)) self.mouse_click(pb_name) # 패배! # (loc_x, loc_y), match_rate = self.locationResourceOnWindowPart( # self.window_image, # 'defeat_press_key_loc', # custom_below_level=(250, 250, 250), # custom_top_level=(255, 255, 255), # custom_threshold=0.7, # custom_flag=1, # custom_rect=(280, 190, 360, 230) # ) # if loc_x != -1: # self.logger.warn('전투 패배: ' + str(match_rate)) # self.mouse_click('defeat_press_key_0') return '' def get_screen_by_location(self, window_image): scene_name = self.scene_init_screen(window_image) if len(scene_name) > 0: return scene_name scene_name = self.popup_scene(window_image) if len(scene_name) > 0: return scene_name # scene_name = self.jeontoo_scene(window_image) # if len(scene_name) > 0: # return scene_name # scene_name = self.scene_google_play_account_select(window_image) # if len(scene_name) > 0: # return scene_name return '' def popup_scene(self, window_image): loc_name = 'popup_scene_loc' match_rate = self.rateMatchedResource(self.window_pixels, loc_name, custom_below_level=100, custom_top_level=255) self.logger.debug(loc_name + ' ' + str(match_rate)) if match_rate > 0.7: return 'popup_scene' return '' # def jeontoo_scene(self, window_image): # (loc_x, loc_y), match_rate = self.locationResourceOnWindowPart( # self.window_image, # 'jeontoo_scene_loc', # custom_below_level=(100, 100, 100), # custom_top_level=(255, 255, 255), # custom_threshold=0.7, # custom_flag=1, # custom_rect=(5, 90, 80, 130) # ) # if match_rate > 0.7: # return 'jeontoo_scene' # return '' def scene_init_screen(self, window_image): loc_x = -1 loc_y = -1 if self.player_type == 'nox': for each_icon in LYBKaiser.nox_kaiser_icon_list: (loc_x, loc_y), match_rate = self.locationOnWindowPart( window_image, self.resource_manager.pixel_box_dic[each_icon], custom_threshold=0.8, custom_flag=1, custom_rect=(80, 110, 570, 300) ) # print('[DEBUG] nox yh icon:', (loc_x, loc_y), match_rate) if loc_x != -1: break elif self.player_type == 'momo': for each_icon in LYBKaiser.momo_kaiser_icon_list: (loc_x, loc_y), match_rate = self.locationOnWindowPart( window_image, self.resource_manager.pixel_box_dic[each_icon], custom_threshold=0.8, custom_flag=1, custom_rect=(30, 10, 610, 300) ) # print('[DEBUG] momo yh icon:', (loc_x, loc_y), match_rate) if loc_x != -1: break if loc_x == -1: return '' return 'init_screen_scene' def scene_google_play_account_select(self, window_image): loc_x_list = [] loc_y_list = [] (loc_x, loc_y) = lybgame.LYBGame.locationOnWindow( window_image, self.resource_manager.pixel_box_dic['google_play_letter'] ) loc_x_list.append(loc_x) loc_y_list.append(loc_y) for i in range(6): (loc_x, loc_y) = lybgame.LYBGame.locationOnWindow( window_image, self.resource_manager.pixel_box_dic['google_play_letter_' + str(i)] ) loc_x_list.append(loc_x) loc_y_list.append(loc_y) for each_loc in loc_x_list: if each_loc == -1: return '' else: continue return 'google_play_account_select_scene' def clear_scene(self): last_scene = self.scene_dic self.scene_dic = {} for scene_name, scene in last_scene.items(): if ( 'google_play_account_select_scene' in scene_name or 'logo_screen_scene' in scene_name or 'connect_account_scene' in scene_name ): self.scene_dic[scene_name] = last_scene[scene_name] def add_scene(self, scene_name): self.scene_dic[scene_name] = lybscene.LYBKaiserScene(scene_name) self.scene_dic[scene_name].setLoggingQueue(self.logging_queue) self.scene_dic[scene_name].setGameObject(self) class LYBKaiserTab(lybgame.LYBGameTab): def __init__(self, root_frame, configure, game_options, inner_frame_dics, width, height, game_name=lybconstant.LYB_GAME_KAISER): lybgame.LYBGameTab.__init__(self, root_frame, configure, game_options, inner_frame_dics, width, height, game_name) def set_work_list(self): lybgame.LYBGameTab.set_work_list(self) for each_work in LYBKaiser.work_list: self.option_dic['work_list_listbox'].insert('end', each_work) self.configure.common_config[self.game_name]['work_list'].append(each_work) def set_option(self): ############################################### # 메인 퀘스트 진행 # ############################################### # frame = ttk.Frame(self.inner_frame_dic['frame_top'], relief=self.frame_relief) # label = tkinter.Label( # master = frame, # text = "메인 퀘스트를 ", # anchor = tkinter.W, # justify = tkinter.LEFT, # font = lybconstant.LYB_FONT # # fg='White' if brightness < 120 else 'Black', # # bg=bg_colour # ) # # countif.place( # # x=lybconstant.LYB_PADDING, # # y=lybconstant.LYB_PADDING, # # width=lybconstant.LYB_LABEL_WIDTH, height=lybconstant.LYB_LABEL_HEIGHT # # ) # label.pack(side=tkinter.LEFT) # option_name_mq = lybconstant.LYB_DO_STRING_DURATION_MAIN_QUEST # self.option_dic[option_name_mq] = tkinter.StringVar(frame) # self.option_dic[option_name_mq].trace('w', lambda args: self.callback_main_quest_stringvar(args, option_name=option_name_mq)) # if not option_name_mq in self.configure.common_config[self.game_name]: # self.configure.common_config[self.game_name][option_name_mq] = 20 # entry = tkinter.Entry( # master = frame, # relief = 'sunken', # textvariable = self.option_dic[option_name_mq], # justify = tkinter.RIGHT, # width = 5, # font = lybconstant.LYB_FONT # ) # entry.pack(side=tkinter.LEFT) # label = tkinter.Label( # master = frame, # text = "분 동안 진행합니다.", # justify = tkinter.LEFT, # font = lybconstant.LYB_FONT # # fg='White' if brightness < 120 else 'Black', # # bg=bg_colour # ) # label.pack(side=tkinter.LEFT) # frame.pack(anchor=tkinter.W) # PADDING frame = ttk.Frame( master = self.master, relief = self.frame_relief ) frame.pack(pady=5) self.inner_frame_dic['options'] = ttk.Frame( master = self.master, relief = self.frame_relief ) self.option_dic['option_note'] = ttk.Notebook( master = self.inner_frame_dic['options'] ) self.inner_frame_dic['common_tab_frame'] = ttk.Frame( master = self.option_dic['option_note'], relief = self.frame_relief ) self.inner_frame_dic['common_tab_frame'].pack(anchor=tkinter.NW, fill=tkinter.BOTH, expand=True) self.option_dic['option_note'].add(self.inner_frame_dic['common_tab_frame'], text='일반') self.inner_frame_dic['work_tab_frame'] = ttk.Frame( master = self.option_dic['option_note'], relief = self.frame_relief ) self.inner_frame_dic['work_tab_frame'].pack(anchor=tkinter.NW, fill=tkinter.BOTH, expand=True) self.option_dic['option_note'].add(self.inner_frame_dic['work_tab_frame'], text='작업') self.inner_frame_dic['notify_tab_frame'] = ttk.Frame( master = self.option_dic['option_note'], relief = self.frame_relief ) self.inner_frame_dic['notify_tab_frame'].pack(anchor=tkinter.NW, fill=tkinter.BOTH, expand=True) self.option_dic['option_note'].add(self.inner_frame_dic['notify_tab_frame'], text='알림') # ------ # 일반 탭 좌측 frame_l = ttk.Frame(self.inner_frame_dic['common_tab_frame']) frame_label = ttk.LabelFrame(frame_l, text='설정') frame_label_inner = ttk.LabelFrame(frame_label, text='소형 체력 물약') frame = ttk.Frame(frame_label_inner) self.option_dic[lybconstant.LYB_DO_STRING_KAISER_CONFIG + 'auto_potion_set'] = tkinter.BooleanVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_KAISER_CONFIG + 'auto_potion_set'].trace( 'w', lambda args: self.callback_auto_potion_set(args, lybconstant.LYB_DO_STRING_KAISER_CONFIG + 'auto_potion_set') ) if not lybconstant.LYB_DO_STRING_KAISER_CONFIG + 'auto_potion_set' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_KAISER_CONFIG + 'auto_potion_set'] = False check_box = ttk.Checkbutton( master = frame, text = '물약 소진시 현재 작업 종료', variable = self.option_dic[lybconstant.LYB_DO_STRING_KAISER_CONFIG + 'auto_potion_set'], onvalue = True, offvalue = False ) check_box.pack(anchor=tkinter.W, side=tkinter.LEFT) frame.pack(anchor=tkinter.NW) frame = ttk.Frame(frame_label_inner) label = ttk.Label( master = frame, text = self.get_option_text("물약 슬롯 번호") ) label.pack(side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_KAISER_CONFIG + 'auto_potion_number'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_KAISER_CONFIG + 'auto_potion_number'].trace( 'w', lambda args: self.callback_auto_potion_number(args, lybconstant.LYB_DO_STRING_KAISER_CONFIG + 'auto_potion_number') ) combobox_list = [] for i in range(1, 5): combobox_list.append(str(i)) if not lybconstant.LYB_DO_STRING_KAISER_CONFIG + 'auto_potion_number' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_KAISER_CONFIG + 'auto_potion_number'] = 1 combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_KAISER_CONFIG + 'auto_potion_number'], state = "readonly", height = 10, width = 5, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_KAISER_CONFIG + 'auto_potion_number']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) frame.pack(anchor=tkinter.NW) frame_label_inner.pack(anchor=tkinter.NW, padx=5, pady=5) frame_label_inner = ttk.LabelFrame(frame_label, text='수동 체력 물약') frame = ttk.Frame(frame_label_inner) self.option_dic[lybconstant.LYB_DO_STRING_KAISER_CONFIG + 'potion_set'] = tkinter.BooleanVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_KAISER_CONFIG + 'potion_set'].trace( 'w', lambda args: self.callback_potion_set(args, lybconstant.LYB_DO_STRING_KAISER_CONFIG + 'potion_set') ) if not lybconstant.LYB_DO_STRING_KAISER_CONFIG + 'potion_set' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_KAISER_CONFIG + 'potion_set'] = False check_box = ttk.Checkbutton( master = frame, text = '물약 소진시 현재 작업 종료', variable = self.option_dic[lybconstant.LYB_DO_STRING_KAISER_CONFIG + 'potion_set'], onvalue = True, offvalue = False ) check_box.pack(anchor=tkinter.W, side=tkinter.LEFT) frame.pack(anchor=tkinter.NW) frame = ttk.Frame(frame_label_inner) label = ttk.Label( master = frame, text = self.get_option_text("수동 회복 물약 사용(HP %)") ) label.pack(side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_KAISER_CONFIG + 'potion_hp'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_KAISER_CONFIG + 'potion_hp'].trace( 'w', lambda args: self.callback_potion_hp(args, lybconstant.LYB_DO_STRING_KAISER_CONFIG + 'potion_hp') ) combobox_list = [] for i in range(50, 91): combobox_list.append(str(i)) if not lybconstant.LYB_DO_STRING_KAISER_CONFIG + 'potion_hp' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_KAISER_CONFIG + 'potion_hp'] = 70 combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_KAISER_CONFIG + 'potion_hp'], state = "readonly", height = 10, width = 5, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_KAISER_CONFIG + 'potion_hp']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) frame.pack(anchor=tkinter.NW) frame = ttk.Frame(frame_label_inner) label = ttk.Label( master = frame, text = self.get_option_text("수동 회복 물약 슬롯 번호") ) label.pack(side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_KAISER_CONFIG + 'potion_number'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_KAISER_CONFIG + 'potion_number'].trace( 'w', lambda args: self.callback_potion_number(args, lybconstant.LYB_DO_STRING_KAISER_CONFIG + 'potion_number') ) combobox_list = [] for i in range(1, 5): combobox_list.append(str(i)) if not lybconstant.LYB_DO_STRING_KAISER_CONFIG + 'potion_number' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_KAISER_CONFIG + 'potion_number'] = 2 combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_KAISER_CONFIG + 'potion_number'], state = "readonly", height = 10, width = 5, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_KAISER_CONFIG + 'potion_number']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) frame.pack(anchor=tkinter.NW) frame_label_inner.pack(anchor=tkinter.NW, padx=5, pady=5) frame_label.pack(anchor=tkinter.NW, padx=5, pady=5) frame_l.pack(side=tkinter.LEFT, anchor=tkinter.NW) # 일반 탭 중간 frame_m = ttk.Frame(self.inner_frame_dic['common_tab_frame']) frame_m.pack(side=tkinter.LEFT, anchor=tkinter.NW) # 일반 탭 우측 frame_r = ttk.Frame(self.inner_frame_dic['common_tab_frame']) frame_r.pack(side=tkinter.LEFT, anchor=tkinter.NW) # 작업 탭 좌측 frame_l = ttk.Frame(self.inner_frame_dic['work_tab_frame']) frame_label = ttk.LabelFrame(frame_l, text='자동 사냥') frame = ttk.Frame(frame_label) label = ttk.Label( master = frame, text = self.get_option_text("진행 시간(초)") ) label.pack(side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_KAISER_WORK + 'auto_play_duration'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_KAISER_WORK + 'auto_play_duration'].trace( 'w', lambda args: self.callback_auto_play_duration(args, lybconstant.LYB_DO_STRING_KAISER_WORK + 'auto_play_duration') ) combobox_list = [] for i in range(0, 86401, 60): combobox_list.append(str(i)) if not lybconstant.LYB_DO_STRING_KAISER_WORK + 'auto_play_duration' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_KAISER_WORK + 'auto_play_duration'] = 1800 combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_KAISER_WORK + 'auto_play_duration'], state = "readonly", height = 10, width = 5, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_KAISER_WORK + 'auto_play_duration']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) frame.pack(anchor=tkinter.NW) frame = ttk.Frame(frame_label) label = ttk.Label( master = frame, text = self.get_option_text("자동 전환 감지 횟수") ) label.pack(side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_KAISER_WORK + 'auto_limit_count'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_KAISER_WORK + 'auto_limit_count'].trace( 'w', lambda args: self.callback_auto_limit_count(args, lybconstant.LYB_DO_STRING_KAISER_WORK + 'auto_limit_count') ) combobox_list = [] for i in range(2, 101): combobox_list.append(str(i)) if not lybconstant.LYB_DO_STRING_KAISER_WORK + 'auto_limit_count' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_KAISER_WORK + 'auto_limit_count'] = 5 combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_KAISER_WORK + 'auto_limit_count'], state = "readonly", height = 10, width = 5, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_KAISER_WORK + 'auto_limit_count']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) frame.pack(anchor=tkinter.NW) frame_label.pack(anchor=tkinter.NW, padx=5, pady=5) frame_label = ttk.LabelFrame(frame_l, text='메인 퀘스트') frame = ttk.Frame(frame_label) label = ttk.Label( master = frame, text = self.get_option_text("진행 시간(초)") ) label.pack(side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_KAISER_WORK + 'main_quest_duration'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_KAISER_WORK + 'main_quest_duration'].trace( 'w', lambda args: self.callback_main_quest_duration(args, lybconstant.LYB_DO_STRING_KAISER_WORK + 'main_quest_duration') ) combobox_list = [] for i in range(0, 86401, 60): combobox_list.append(str(i)) if not lybconstant.LYB_DO_STRING_KAISER_WORK + 'main_quest_duration' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_KAISER_WORK + 'main_quest_duration'] = 1800 combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_KAISER_WORK + 'main_quest_duration'], state = "readonly", height = 10, width = 5, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_KAISER_WORK + 'main_quest_duration']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) frame.pack(anchor=tkinter.NW) frame = ttk.Frame(frame_label) label = ttk.Label( master = frame, text = self.get_option_text("퀘스트 지역 이탈 판정 횟수") ) label.pack(side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_KAISER_WORK + 'main_quest_distance'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_KAISER_WORK + 'main_quest_distance'].trace( 'w', lambda args: self.callback_main_quest_distance(args, lybconstant.LYB_DO_STRING_KAISER_WORK + 'main_quest_distance') ) combobox_list = [] for i in range(1, 101): combobox_list.append(str(i)) if not lybconstant.LYB_DO_STRING_KAISER_WORK + 'main_quest_distance' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_KAISER_WORK + 'main_quest_distance'] = 3 combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_KAISER_WORK + 'main_quest_distance'], state = "readonly", height = 10, width = 5, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_KAISER_WORK + 'main_quest_distance']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) frame.pack(anchor=tkinter.NW) frame = ttk.Frame(frame_label) label = ttk.Label( master = frame, text = self.get_option_text("자동 전환 감지 횟수") ) label.pack(side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_KAISER_WORK + 'main_quest_auto'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_KAISER_WORK + 'main_quest_auto'].trace( 'w', lambda args: self.callback_main_quest_auto(args, lybconstant.LYB_DO_STRING_KAISER_WORK + 'main_quest_auto') ) combobox_list = [] for i in range(2, 101): combobox_list.append(str(i)) if not lybconstant.LYB_DO_STRING_KAISER_WORK + 'main_quest_auto' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_KAISER_WORK + 'main_quest_auto'] = 5 combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_KAISER_WORK + 'main_quest_auto'], state = "readonly", height = 10, width = 5, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_KAISER_WORK + 'main_quest_auto']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) frame.pack(anchor=tkinter.NW) frame_label.pack(anchor=tkinter.NW, padx=5, pady=5) frame_label = ttk.LabelFrame(frame_l, text='지역 퀘스트') frame = ttk.Frame(frame_label) label = ttk.Label( master = frame, text = self.get_option_text("진행 시간(초)") ) label.pack(side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_KAISER_WORK + 'local_quest_duration'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_KAISER_WORK + 'local_quest_duration'].trace( 'w', lambda args: self.callback_local_quest_duration(args, lybconstant.LYB_DO_STRING_KAISER_WORK + 'local_quest_duration') ) combobox_list = [] for i in range(0, 86401, 60): combobox_list.append(str(i)) if not lybconstant.LYB_DO_STRING_KAISER_WORK + 'local_quest_duration' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_KAISER_WORK + 'local_quest_duration'] = 1800 combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_KAISER_WORK + 'local_quest_duration'], state = "readonly", height = 10, width = 5, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_KAISER_WORK + 'local_quest_duration']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) frame.pack(anchor=tkinter.NW) frame = ttk.Frame(frame_label) label = ttk.Label( master = frame, text = self.get_option_text("퀘스트 지역 이탈 판정 거리(m)") ) label.pack(side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_KAISER_WORK + 'local_quest_distance_limit'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_KAISER_WORK + 'local_quest_distance_limit'].trace( 'w', lambda args: self.callback_local_quest_distance_limit(args, lybconstant.LYB_DO_STRING_KAISER_WORK + 'local_quest_distance_limit') ) combobox_list = [] for i in range(1, 11): combobox_list.append(str(i 10)) if not lybconstant.LYB_DO_STRING_KAISER_WORK + 'local_quest_distance_limit' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_KAISER_WORK + 'local_quest_distance_limit'] = 40 combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_KAISER_WORK + 'local_quest_distance_limit'], state = "readonly", height = 10, width = 5, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_KAISER_WORK + 'local_quest_distance_limit']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) frame.pack(anchor=tkinter.NW) frame = ttk.Frame(frame_label) label = ttk.Label( master
<filename>Bucket 1.0/Bucket Interpreter.py<gh_stars>0 #_Bucket Compiler by Pixet Bits {Version : 1.0.0.0}_---------------------------# import random as rand #Libraries LibCtrls = {"[B]" : False, "[F]" : False, "[C]" : False} #Objects SfSystVr = {"FLCnt" : 0} ActivInt = {} ActivStr = {} ActivBol = {} ActivFlt = {} IfElseSy = {"Result" : "", "inCent" : "", "isIf" : "", "isEl" : False, "Enable" : False} ActivMth = {"inMain" : False, "inTask" : False, "CallIt" : False} ActivTsk = {} #_Principal_-------------------------------------------------------------------# def Compiler(FileName) : # Name = FileName.replace(".bk", "") try : File = open(FileName) Lines = File.readlines() File.close() except FileNotFoundError : Error("File ["+Name+"] not found", "") #Tab and Enter Lines = [l.replace(" ", "") for l in Lines] Lines = [l.replace("\n", "") for l in Lines] Lines = [l.replace("\t", "") for l in Lines] #Blank lines and Coments Lines = [l for l in Lines if l != ""] Lines = [l for l in Lines if not l.startswith("//")] if Lines[0] != "[to Basic]" : Error("All Bucket classes need [to Baisc] library", Lines[0]) for y in range(0, len(Lines)) : #Tab error if Lines[y].startswith(" ") : Error("Tab error", Lines[y]) #Class Error if Lines[y].endswith("in Bucket :") : if Lines[y].startswith(Name) : continue else : Error("Namespace of class is wrong", Lines[y]) #Vars if Lines[y].startswith("int ") : Int(Lines[y]) if Lines[y].startswith("str ") : Str(Lines[y]) if Lines[y].startswith("bol ") : Bol(Lines[y]) if Lines[y].startswith("flt ") : Flt(Lines[y]) #Methods if "task :" in Lines[y] : NewTask(Lines, y) # Runner(Lines) # def Runner(lines) : # for x in range(0, len(lines)) : # #bucket open if lines[x] == "bucket open :" : ActivMth["inMain"] = True #any task if "task :" in lines[x] : ActivMth["inTask"] = True #Output if lines[x].startswith("show ") : if ActivMth["inMain"] == True : Show(lines[x]) elif ActivMth["inTask"] == True : continue else : Error("Funtion out of an method", lines[x]) #Call an Task if lines[x].startswith("call ") : if ActivMth["inMain"] == True : TaskCtrl(lines, lines[x]) else : Error("Only 'bucket open' can call tasks", lines[x]) #Set if lines[x].startswith("set ") : if ActivMth["inMain"] == True : Set(lines[x]) elif ActivMth["inTask"] == True : continue else : Error("Funtion out of an method", lines[x]) #Make if lines[x].startswith("make ") : if ActivMth["inMain"] == True : Make(lines[x]) elif ActivMth["inTask"] == True : continue else : Error("Funtion out of an method", lines[x]) #Up if lines[x].startswith("up ") : if ActivMth["inMain"] == True : Up(lines[x]) elif ActivMth["inTask"] == True : continue else : Error("Funtion out of an method", lines[x]) #Convert if lines[x].startswith("convert ") : if ActivMth["inMain"] == True : Convert(lines[x]) elif ActivMth["inTask"] == True : continue else : Error("Funtion out of an method", lines[x]) #If-Else system #If if lines[x].startswith("if ") : if ActivMth["inMain"] == True : IfElseSy["inCent"] = True IfElseSy["isIf"] = True IfElseSy["Result"] = If(lines[x]) elif ActivMth["inTask"] == True : continue else : Error("Funtion out of an method", lines[x]) #Else elif lines[x].startswith("else ") and IfElseSy["Result"] == False : if ActivMth["inMain"] == True : IfElseSy["inCent"] = True IfElseSy["isEl"] = True elif ActivMth["inTask"] == True : continue else : Error("Funtion out of an method", lines[x]) #End of statlement #Do System if lines[x].startswith("do : ") and IfElseSy["inCent"] == True : # line = lines[x].replace("do : ", "") #Is if? if IfElseSy["isIf"] == True and IfElseSy["Result"] == True : IfElseSy["Enable"] = True #Is Else? if IfElseSy["isEl"] == True and IfElseSy["Result"] == False : IfElseSy["Enable"] = True #Then if IfElseSy["Enable"] == True : #Functions if line.startswith("show ") : Show(line) if line.startswith("call ") : ActivMth["CallIt"] = True if line.startswith("set ") : Set(line) if line.startswith("up ") : Up(line) #Errors if line.startswith("int ") : Error("If-else systems cannot declare vars", line) if line.startswith("str ") : Error("If-else systems cannot declare vars", line) if line.startswith("bol ") : Error("If-else systems cannot declare vars", line) if line.startswith("flt ") : Error("If-else systems cannot declare vars", line) # #Compiler errors elif lines[x].startswith("do : ") and not IfElseSy["inCent"] == False : Error("Do out o if statlement", lines[x]) #For loop if lines[x].startswith("for ") : lLine = lines[x].replace("for ", "") limit = lLine.find("times ") count = lLine[:limit] #N° Times try : count = int(count) except ValueError : if count in ActivInt : count = ActivInt[count] else : Error("For loop needs an int", lines[x]) #Loop ForLoop(lines, x, count, lLine.replace(lLine[:limit + 6], "")) #Enders if lines[x] == "finish" : IfElseSy["inCent"] = False IfElseSy["Enable"] = False IfElseSy["isIf"] = False IfElseSy["isEl"] = False if lines[x] == "end l." : ActivMth["inMain"] = False if lines[x] == "end t." : ActivMth["inTask"] = False # return 0 # def Error(ErrorType, Line) : # print("\nCompiler error {"+ErrorType+"}\nin line : \""+str(Line)+"\"") return 0 # #_If-Else System_--------------------------------------------------------------# def If(string) : # String = (string.replace("if ", "")).replace("then :", "") String = String.replace(" ", "") limit = "" isG = False isL = False isE = False isGoE = False isLoE = False #Common if ">" in String : isG = True limit = String.find(">") if "<" in string : isL = True limit = String.find("<") #Equal if "==" in String : isE = True limit = String.find("==") #Or Equal's if "<=" in String : isGoE = True limit = String.find("<=") if ">=" in String : isLoE = True limit = String.find(">=") #------------------------------------------------------------------------------# #This is greather than? if isG == True : FstArg = String[:limit] SstArg = String[limit + 1:] #1st Arg #Declareted Vars if FstArg in ActivInt : FstArg = ActivInt[FstArg] elif FstArg in ActivFlt : FstArg = ActivFlt[FstArg] #Int elif not FstArg.endswith(".f") : try : FstArg = int(FstArg) except ValueError : Error("Unknow value", string) #Float else : FstArg = FstArg.replace(".f", "") try : FstArg = float(FstArg) except ValueError : Error("Unknow value", string) #------------------------------------------------------------------------------# #2st Arg #Declareted Vars if SstArg in ActivInt : SstArg = ActivInt[SstArg] elif SstArg in ActivFlt : SstArg = ActivFlt[SstArg] #Int elif not SstArg.endswith(".f") : try : SstArg = int(SstArg) except ValueError : Error("Unknow value", string) #Float else : SstArg = SstArg.replace(".f", "") try : SstArg = float(SstArg) except ValueError : Error("Unknow value", string) #Finally if FstArg > SstArg : return True else :return False #------------------------------------------------------------------------------# #This is less than? elif isL == True : FstArg = String[:limit] SstArg = String[limit + 1:] #1st Arg #Declareted Vars if FstArg in ActivInt : FstArg = ActivInt[FstArg] elif FstArg in ActivFlt : FstArg = ActivFlt[FstArg] #Int elif not FstArg.endswith(".f") : try : FstArg = int(FstArg) except ValueError : Error("Unknow value", string) #Float else : FstArg = FstArg.replace(".f", "") try : FstArg = float(FstArg) except ValueError : Error("Unknow value", string) #------------------------------------------------------------------------------# #2st Arg #Declareted Vars if SstArg in ActivInt : SstArg = ActivInt[SstArg] elif SstArg in ActivFlt : SstArg = ActivFlt[SstArg] #Int elif not SstArg.endswith(".f") : try : SstArg = int(SstArg) except ValueError : Error("Unknow value", string) #Float else : SstArg = SstArg.replace(".f", "") try : SstArg = float(SstArg) except ValueError : Error("Unknow value", string) #Finally if FstArg < SstArg : return True else : return False #------------------------------------------------------------------------------# elif isE == True : FstArg = String[:limit] SstArg = String[limit + 2:] #1st Arg #Declareted Vars if FstArg in ActivInt : FstArg = ActivInt[FstArg] elif FstArg in ActivStr : FstArg = ActivStr[FstArg] elif FstArg in ActivFlt : FstArg = ActivFlt[FstArg] #String elif FstArg.startswith("'") : FstArg = FstArg.replace("'", "") elif FstArg.startswith("\"") : Error("Strings must be in ''", string) #Int elif not FstArg.endswith(".f") : try : FstArg = int(FstArg) except ValueError : Error("Unknow value", string) #Float else : FstArg = FstArg.replace(".f", "") try : FstArg = float(FstArg) except ValueError : Error("Unknow value", string) #------------------------------------------------------------------------------# #2st Arg #Declareted Vars if SstArg in ActivInt : SstArg = ActivInt[SstArg] elif SstArg in ActivStr : SstArg = ActivStr[SstArg] elif SstArg in ActivFlt : SstArg = ActivFlt[SstArg] #String elif SstArg.startswith("'") : SstArg = SstArg.replace("'", "") elif SstArg.startswith("\"") : Error("Strings must be in ''", string) #Int elif not SstArg.endswith(".f") : try : SstArg = int(SstArg) except ValueError : Error("Unknow value", string) #Float else : SstArg = SstArg.replace(".f", "") try : SstArg = float(SstArg) except ValueError : Error("Unknow
in self.interface_stp_cfg: self.cur_cfg["bpdu_filter"] = "enable" self.existing["bpdu_filter"] = "enable" else: self.cur_cfg["bpdu_filter"] = "disable" self.existing["bpdu_filter"] = "disable" if self.bpdu_protection: if "stp bpdu-protection" in self.stp_cfg: self.cur_cfg["bpdu_protection"] = "enable" self.existing["bpdu_protection"] = "enable" else: self.cur_cfg["bpdu_protection"] = "disable" self.existing["bpdu_protection"] = "disable" if self.tc_protection: if "stp tc-protection" in self.stp_cfg: self.cur_cfg["tc_protection"] = "enable" self.existing["tc_protection"] = "enable" else: self.cur_cfg["tc_protection"] = "disable" self.existing["tc_protection"] = "disable" if self.tc_protection_interval: if "stp tc-protection interval" in self.stp_cfg: tmp_value = re.findall(r'stp tc-protection interval (.)', self.stp_cfg) if not tmp_value: self.module.fail_json( msg='Error: Can not find tc-protection interval on the device.') self.cur_cfg["tc_protection_interval"] = tmp_value[0] self.existing["tc_protection_interval"] = tmp_value[0] else: self.cur_cfg["tc_protection_interval"] = "null" self.existing["tc_protection_interval"] = "null" if self.tc_protection_threshold: if "stp tc-protection threshold" in self.stp_cfg: tmp_value = re.findall(r'stp tc-protection threshold (.)', self.stp_cfg) if not tmp_value: self.module.fail_json( msg='Error: Can not find tc-protection threshold on the device.') self.cur_cfg["tc_protection_threshold"] = tmp_value[0] self.existing["tc_protection_threshold"] = tmp_value[0] else: self.cur_cfg["tc_protection_threshold"] = "1" self.existing["tc_protection_threshold"] = "1" if self.cost: tmp_value = re.findall(r'stp instance (.) cost (.)', self.interface_stp_cfg) if not tmp_value: self.cur_cfg["cost"] = "null" self.existing["cost"] = "null" else: self.cur_cfg["cost"] = tmp_value[0][1] self.existing["cost"] = tmp_value[0][1] # root_protection and loop_protection should get configuration at the same time if self.root_protection or self.loop_protection: if "stp root-protection" in self.interface_stp_cfg: self.cur_cfg["root_protection"] = "enable" self.existing["root_protection"] = "enable" else: self.cur_cfg["root_protection"] = "disable" self.existing["root_protection"] = "disable" if "stp loop-protection" in self.interface_stp_cfg: self.cur_cfg["loop_protection"] = "enable" self.existing["loop_protection"] = "enable" else: self.cur_cfg["loop_protection"] = "disable" self.existing["loop_protection"] = "disable" def get_end_state(self): """ Get end state """ self.cli_get_stp_config() if self.interface and self.interface != "all": self.cli_get_interface_stp_config() if self.stp_mode: if "stp mode stp" in self.stp_cfg: self.end_state["stp_mode"] = "stp" elif "stp mode rstp" in self.stp_cfg: self.end_state["stp_mode"] = "rstp" else: self.end_state["stp_mode"] = "mstp" if self.stp_enable: if "stp disable" in self.stp_cfg: self.end_state["stp_enable"] = "disable" else: self.end_state["stp_enable"] = "enable" if self.stp_converge: if "stp converge fast" in self.stp_cfg: self.end_state["stp_converge"] = "fast" else: self.end_state["stp_converge"] = "normal" if self.edged_port: if self.interface == "all": if "stp edged-port default" in self.stp_cfg: self.end_state["edged_port"] = "enable" else: self.end_state["edged_port"] = "disable" else: if "stp edged-port enable" in self.interface_stp_cfg: self.end_state["edged_port"] = "enable" else: self.end_state["edged_port"] = "disable" if self.bpdu_filter: if self.interface == "all": if "stp bpdu-filter default" in self.stp_cfg: self.end_state["bpdu_filter"] = "enable" else: self.end_state["bpdu_filter"] = "disable" else: if "stp bpdu-filter enable" in self.interface_stp_cfg: self.end_state["bpdu_filter"] = "enable" else: self.end_state["bpdu_filter"] = "disable" if self.bpdu_protection: if "stp bpdu-protection" in self.stp_cfg: self.end_state["bpdu_protection"] = "enable" else: self.end_state["bpdu_protection"] = "disable" if self.tc_protection: if "stp tc-protection" in self.stp_cfg: self.end_state["tc_protection"] = "enable" else: self.end_state["tc_protection"] = "disable" if self.tc_protection_interval: if "stp tc-protection interval" in self.stp_cfg: tmp_value = re.findall(r'stp tc-protection interval (.)', self.stp_cfg) if not tmp_value: self.module.fail_json( msg='Error: Can not find tc-protection interval on the device.') self.end_state["tc_protection_interval"] = tmp_value[0] else: self.end_state["tc_protection_interval"] = "null" if self.tc_protection_threshold: if "stp tc-protection threshold" in self.stp_cfg: tmp_value = re.findall(r'stp tc-protection threshold (.)', self.stp_cfg) if not tmp_value: self.module.fail_json( msg='Error: Can not find tc-protection threshold on the device.') self.end_state["tc_protection_threshold"] = tmp_value[0] else: self.end_state["tc_protection_threshold"] = "1" if self.cost: tmp_value = re.findall(r'stp instance (.) cost (.)', self.interface_stp_cfg) if not tmp_value: self.end_state["cost"] = "null" else: self.end_state["cost"] = tmp_value[0][1] if self.root_protection: if "stp root-protection" in self.interface_stp_cfg: self.end_state["root_protection"] = "enable" else: self.end_state["root_protection"] = "disable" if self.loop_protection: if "stp loop-protection" in self.interface_stp_cfg: self.end_state["loop_protection"] = "enable" else: self.end_state["loop_protection"] = "disable" def present_stp(self): """ Present stp configuration """ cmds = list() # cofig stp global if self.stp_mode: if self.stp_mode != self.cur_cfg["stp_mode"]: cmd = "stp mode %s" % self.stp_mode cmds.append(cmd) self.updates_cmd.append(cmd) if self.stp_enable: if self.stp_enable != self.cur_cfg["stp_enable"]: cmd = "stp %s" % self.stp_enable cmds.append(cmd) self.updates_cmd.append(cmd) if self.stp_converge: if self.stp_converge != self.cur_cfg["stp_converge"]: cmd = "stp converge %s" % self.stp_converge cmds.append(cmd) self.updates_cmd.append(cmd) if self.edged_port: if self.interface == "all": if self.edged_port != self.cur_cfg["edged_port"]: if self.edged_port == "enable": cmd = "stp edged-port default" cmds.append(cmd) self.updates_cmd.append(cmd) else: cmd = "undo stp edged-port default" cmds.append(cmd) self.updates_cmd.append(cmd) if self.bpdu_filter: if self.interface == "all": if self.bpdu_filter != self.cur_cfg["bpdu_filter"]: if self.bpdu_filter == "enable": cmd = "stp bpdu-filter default" cmds.append(cmd) self.updates_cmd.append(cmd) else: cmd = "undo stp bpdu-filter default" cmds.append(cmd) self.updates_cmd.append(cmd) if self.bpdu_protection: if self.bpdu_protection != self.cur_cfg["bpdu_protection"]: if self.bpdu_protection == "enable": cmd = "stp bpdu-protection" cmds.append(cmd) self.updates_cmd.append(cmd) else: cmd = "undo stp bpdu-protection" cmds.append(cmd) self.updates_cmd.append(cmd) if self.tc_protection: if self.tc_protection != self.cur_cfg["tc_protection"]: if self.tc_protection == "enable": cmd = "stp tc-protection" cmds.append(cmd) self.updates_cmd.append(cmd) else: cmd = "undo stp tc-protection" cmds.append(cmd) self.updates_cmd.append(cmd) if self.tc_protection_interval: if self.tc_protection_interval != self.cur_cfg["tc_protection_interval"]: cmd = "stp tc-protection interval %s" % self.tc_protection_interval cmds.append(cmd) self.updates_cmd.append(cmd) if self.tc_protection_threshold: if self.tc_protection_threshold != self.cur_cfg["tc_protection_threshold"]: cmd = "stp tc-protection threshold %s" % self.tc_protection_threshold cmds.append(cmd) self.updates_cmd.append(cmd) # config interface stp if self.interface and self.interface != "all": tmp_changed = False cmd = "interface %s" % self.interface cmds.append(cmd) self.updates_cmd.append(cmd) if self.edged_port: if self.edged_port != self.cur_cfg["edged_port"]: if self.edged_port == "enable": cmd = "stp edged-port enable" cmds.append(cmd) self.updates_cmd.append(cmd) tmp_changed = True else: cmd = "undo stp edged-port" cmds.append(cmd) self.updates_cmd.append(cmd) tmp_changed = True if self.bpdu_filter: if self.bpdu_filter != self.cur_cfg["bpdu_filter"]: if self.bpdu_filter == "enable": cmd = "stp bpdu-filter enable" cmds.append(cmd) self.updates_cmd.append(cmd) tmp_changed = True else: cmd = "undo stp bpdu-filter" cmds.append(cmd) self.updates_cmd.append(cmd) tmp_changed = True if self.root_protection: if self.root_protection == "enable" and self.cur_cfg["loop_protection"] == "enable": self.module.fail_json( msg='Error: The interface has enable loop_protection, can not enable root_protection.') if self.root_protection != self.cur_cfg["root_protection"]: if self.root_protection == "enable": cmd = "stp root-protection" cmds.append(cmd) self.updates_cmd.append(cmd) tmp_changed = True else: cmd = "undo stp root-protection" cmds.append(cmd) self.updates_cmd.append(cmd) tmp_changed = True if self.loop_protection: if self.loop_protection == "enable" and self.cur_cfg["root_protection"] == "enable": self.module.fail_json( msg='Error: The interface has enable root_protection, can not enable loop_protection.') if self.loop_protection != self.cur_cfg["loop_protection"]: if self.loop_protection == "enable": cmd = "stp loop-protection" cmds.append(cmd) self.updates_cmd.append(cmd) tmp_changed = True else: cmd = "undo stp loop-protection" cmds.append(cmd) self.updates_cmd.append(cmd) tmp_changed = True if self.cost: if self.cost != self.cur_cfg["cost"]: cmd = "stp cost %s" % self.cost cmds.append(cmd) self.updates_cmd.append(cmd) tmp_changed = True if not tmp_changed: cmd = "interface %s" % self.interface self.updates_cmd.remove(cmd) cmds.remove(cmd) if cmds: self.cli_load_config(cmds) self.changed = True def absent_stp(self): """ Absent stp configuration """ cmds = list() if self.stp_mode: if self.stp_mode == self.cur_cfg["stp_mode"]: if self.stp_mode != "mstp": cmd = "undo stp mode" cmds.append(cmd) self.updates_cmd.append(cmd) self.changed = True if self.stp_enable: if self.stp_enable != self.cur_cfg["stp_enable"]: cmd = "stp %s" % self.stp_enable cmds.append(cmd) self.updates_cmd.append(cmd) if self.stp_converge: if self.stp_converge == self.cur_cfg["stp_converge"]: cmd = "undo stp converge" cmds.append(cmd) self.updates_cmd.append(cmd) self.changed = True if self.edged_port: if self.interface == "all": if self.edged_port != self.cur_cfg["edged_port"]: if self.edged_port == "enable": cmd = "stp edged-port default" cmds.append(cmd) self.updates_cmd.append(cmd) else: cmd = "undo stp edged-port default" cmds.append(cmd) self.updates_cmd.append(cmd) if self.bpdu_filter: if self.interface == "all": if self.bpdu_filter != self.cur_cfg["bpdu_filter"]: if self.bpdu_filter == "enable": cmd = "stp bpdu-filter default" cmds.append(cmd) self.updates_cmd.append(cmd) else: cmd = "undo stp bpdu-filter default" cmds.append(cmd) self.updates_cmd.append(cmd) if self.bpdu_protection: if self.bpdu_protection != self.cur_cfg["bpdu_protection"]: if self.bpdu_protection == "enable": cmd = "stp bpdu-protection" cmds.append(cmd) self.updates_cmd.append(cmd) else: cmd = "undo stp bpdu-protection" cmds.append(cmd) self.updates_cmd.append(cmd) if self.tc_protection: if self.tc_protection != self.cur_cfg["tc_protection"]: if self.tc_protection == "enable": cmd = "stp tc-protection" cmds.append(cmd) self.updates_cmd.append(cmd) else: cmd = "undo stp tc-protection" cmds.append(cmd) self.updates_cmd.append(cmd) if self.tc_protection_interval: if self.tc_protection_interval == self.cur_cfg["tc_protection_interval"]: cmd = "undo stp tc-protection interval" cmds.append(cmd) self.updates_cmd.append(cmd) self.changed = True if self.tc_protection_threshold: if self.tc_protection_threshold == self.cur_cfg["tc_protection_threshold"]: if self.tc_protection_threshold != "1": cmd = "undo stp tc-protection threshold" cmds.append(cmd) self.updates_cmd.append(cmd) self.changed = True # undo interface stp if self.interface and self.interface != "all": tmp_changed = False cmd = "interface %s" % self.interface cmds.append(cmd) self.updates_cmd.append(cmd) if self.edged_port: if self.edged_port != self.cur_cfg["edged_port"]: if self.edged_port == "enable": cmd = "stp edged-port enable" cmds.append(cmd) self.updates_cmd.append(cmd) tmp_changed = True else: cmd = "undo stp edged-port" cmds.append(cmd) self.updates_cmd.append(cmd) tmp_changed = True if self.bpdu_filter: if self.bpdu_filter != self.cur_cfg["bpdu_filter"]: if self.bpdu_filter == "enable": cmd = "stp bpdu-filter enable" cmds.append(cmd) self.updates_cmd.append(cmd) tmp_changed = True else: cmd = "undo stp bpdu-filter" cmds.append(cmd) self.updates_cmd.append(cmd) tmp_changed = True if self.root_protection: if self.root_protection == "enable" and self.cur_cfg["loop_protection"] == "enable": self.module.fail_json( msg='Error: The interface has enable loop_protection, can not enable root_protection.') if self.root_protection != self.cur_cfg["root_protection"]: if self.root_protection == "enable": cmd = "stp root-protection" cmds.append(cmd) self.updates_cmd.append(cmd) tmp_changed = True else: cmd = "undo stp root-protection" cmds.append(cmd) self.updates_cmd.append(cmd) tmp_changed = True if self.loop_protection: if self.loop_protection == "enable" and self.cur_cfg["root_protection"] == "enable": self.module.fail_json( msg='Error: The interface has enable root_protection, can not enable loop_protection.') if self.loop_protection != self.cur_cfg["loop_protection"]: if self.loop_protection == "enable": cmd = "stp loop-protection" cmds.append(cmd)
inverse=False, init=init, hparams=self._fparams, disable_dropout=disable_dropout, kwargs) if self.is_evaluating and check_invertibility: z_inv_inv, _, _, _ = glow.glow( "glow", z_inv, targets_mask, decoder_self_attention_bias, inverse=True, split_zs=zs, init=False, hparams=self._fparams, disable_dropout=True, kwargs) z_diff = z_q - z_inv_inv tf.summary.scalar("flow_recon_forward", tf.reduce_max(tf.abs(z_diff))) return log_p_z_base, log_abs_det def sample_p( self, targets_length, temp, check_invertibility=False, targets_mask=None, *kwargs): hparams = self._hparams if targets_mask is None: targets_mask = ops.sequence_mask(targets_length, hparams) decoder_self_attention_bias = ( common_attention.attention_bias_ignore_padding(1.0 - targets_mask)) batch_size, targets_max_length = ( common_layers.shape_list(targets_mask)[:2]) prior_shape = [batch_size, targets_max_length, hparams.latent_size] noise = tf.random.normal(prior_shape, stddev=temp) p_dist = None if hparams.prior_type == "standard_normal": z_p = noise elif hparams.prior_type == "diagonal_normal": diag_prior_params = ops.cond_prior( "diag_prior", hparams, tf.zeros(prior_shape), targets_mask, hparams.latent_size2, decoder_self_attention_bias, *kwargs) p_dist = gops.diagonal_normal(diag_prior_params, "diag_prior") z_p = p_dist.loc + p_dist.scale noise elif hparams.prior_type in ["affine", "additive", "rq"]: n_levels = len(hparams.depths.split("/")) divi = max(1, hparams.factor(n_levels-1)) flow_prior_shape = [ batch_size, targets_max_length//divi, hparams.latent_size] noise = tf.random_normal(flow_prior_shape, stddev=temp) z_p, _, _, _ = glow.glow( "glow", noise, targets_mask, decoder_self_attention_bias, inverse=True, init=False, hparams=self._fparams, disable_dropout=True, temp=temp, kwargs) if self.is_evaluating and check_invertibility: noise_inv, _, _, _ = glow.glow( "glow", z_p, targets_mask, decoder_self_attention_bias, inverse=False, init=False, hparams=self._fparams, disable_dropout=True, *kwargs) z_diff = noise - noise_inv tf.summary.scalar("flow_recon_inverse", tf.reduce_max(tf.abs(z_diff))) return z_p, p_dist def optimize(self, loss, num_async_replicas=1, use_tpu=False, variables=None): """Return a training op minimizing loss.""" lr = ops.learning_rate_schedule(self.hparams) if num_async_replicas > 1: t2t_model.log_info("Dividing learning rate by num_async_replicas: %d", num_async_replicas) lr /= math.sqrt(float(num_async_replicas)) train_op = optimize.optimize( loss, lr, self.hparams, use_tpu=use_tpu, variables=variables) return train_op def body(self, features, real_features): return self.internal(features, real_features) def infer(self, features, args, *kwargs): """Produce predictions from the model.""" del args, kwargs inputs_old = None if "inputs" in features and len(features["inputs"].shape) < 4: inputs_old = features["inputs"] features["inputs"] = tf.expand_dims(features["inputs"], 2) features["targets"] = tf.identity(features["inputs"]) # logits, _ = self(features) t2t_model.set_custom_getter_compose(self._custom_getter) tf.get_variable_scope().set_initializer( optimize.get_variable_initializer(self.hparams)) with self._eager_var_store.as_default(): self._fill_problem_hparams_features(features) # intentionally disable sharding during inference (in multi GPU) with tf.variable_scope(self.name): logits, _, _, targets_mask = self.model_fn(features) samples = tf.argmax(logits, axis=-1) samples = tf.where( tf.cast(targets_mask[..., tf.newaxis, tf.newaxis], tf.bool), samples, tf.ones_like(samples)) if inputs_old is not None: # Restore to not confuse Estimator. features["inputs"] = inputs_old return samples def model_fn(self, features): with tf.variable_scope( tf.get_variable_scope(), use_resource=True, reuse=tf.AUTO_REUSE): transformed_features = self.bottom(features) if self.hparams.activation_dtype == "bfloat16": for k, v in sorted(six.iteritems(transformed_features)): if v.dtype == tf.float32: transformed_features[k] = tf.cast(v, tf.bfloat16) t2t_model.log_info("Building model body") output, losses, monitor, targets_mask = self.body( transformed_features, features) output, losses = self._normalize_body_output((output, losses)) if "training" in losses: t2t_model.log_info( "Skipping T2TModel top and loss because training loss " "returned from body") logits = output else: logits = self.top(output, features) losses["training"] = 0.0 if (self._hparams.mode != tf.estimator.ModeKeys.PREDICT and self._hparams.mode != "attack"): losses["training"] = self.loss(logits, features) return logits, losses, monitor, targets_mask def model_fn_sharded(self, sharded_features): """Estimator model_fn sharded along batch dimension. Args: sharded_features: {str: [Tensor]}. Features sharded along batch dimension. Each list is the same length (== number of shards). Returns: sharded_logits: [Tensor]. Logits for each shard of examples. losses: {str: 0-D Tensor}. Loss averaged across shards. """ dp = self._data_parallelism # [{str: Tensor}]. Transpose of 'sharded_features'. datashard_to_features = self._to_features_per_datashard(sharded_features) sharded_logits, sharded_losses, sharded_monitors, _ = ( dp(self.model_fn, datashard_to_features)) sharded_logits, sharded_losses = dp( self.maybe_scheduled_sampling, datashard_to_features, sharded_logits, sharded_losses) if isinstance(sharded_logits[0], dict): temp_dict = {k: [] for k, _ in six.iteritems(sharded_logits[0])} for k, _ in six.iteritems(sharded_logits[0]): for l in sharded_logits: temp_dict[k].append(l[k]) sharded_logits = temp_dict losses = t2t_model.average_sharded_losses(sharded_losses) monitor = {} for key in list(sharded_monitors[0].keys()): monitor[key] = ( tf.add_n([m[key] for m in sharded_monitors]) / len(sharded_monitors)) ops.save_summary(monitor, "monitor") return sharded_logits, losses @registry.register_hparams def wmt_enro_tpu(): """HParams for Transformer model on TPU.""" hparams = transformer.transformer_base() hparams = transformer.update_hparams_for_tpu(hparams) hparams.batch_size = 512 return hparams @registry.register_hparams def iwslt_baseline_gpu(): """HParams for Transformer model on TPU.""" hparams = transformer.transformer_base() hparams.hidden_size = 256 hparams.filter_size = 1024 hparams.num_hidden_layers = 5 hparams.num_heads = 2 hparams.layer_prepostprocess_dropout = 0.1 hparams.attention_dropout = 0.1 hparams.relu_dropout = 0.1 hparams.dropout = 0.1 return hparams @registry.register_hparams def iwslt_baseline_single_gpu(): """HParams for Transformer model on TPU.""" hparams = iwslt_baseline_gpu() hparams.batch_size = 1024 hparams.learning_rate_schedule = "constantlinear_warmuprsqrt_decay" hparams.learning_rate_constant = 0.1 hparams.learning_rate_warmup_steps = 16000 return hparams @registry.register_hparams def iwslt_baseline_tpu(): """HParams for Transformer model on TPU.""" hparams = transformer.transformer_base() transformer.update_hparams_for_tpu(hparams) hparams.hidden_size = 256 hparams.filter_size = 1024 hparams.num_hidden_layers = 5 hparams.num_heads = 2 hparams.layer_prepostprocess_dropout = 0.1 hparams.attention_dropout = 0.1 hparams.relu_dropout = 0.1 hparams.dropout = 0.1 hparams.add_hparam("pos_attn", False) return hparams @registry.register_hparams def iwslt_base(): """Set of hyperparameters.""" # Model architecture flags. hparams = transformer.transformer_base() hparams.num_hidden_layers = 5 hparams.hidden_size = 256 hparams.filter_size = 1024 hparams.num_heads = 4 # Other flags. hparams.summarize_grads = False hparams.summarize_vars = False # Optimization-related flags. hparams.clip_grad_norm = 1.0 hparams.learning_rate_decay_scheme = "noam" hparams.learning_rate_warmup_steps = 8000 hparams.learning_rate = 0.2 hparams.learning_rate_schedule = ( "constantlinear_warmuprsqrt_decayrsqrt_hidden_size") hparams.learning_rate_constant = 2.0 hparams.add_hparam("predict_target_length", True) hparams.add_hparam("lendiff_bound", 30) hparams = update_hparams_for_tpu(hparams) hparams.add_hparam("pos_attn", False) return hparams @registry.register_hparams def iwslt_diag(): """Set of hyperparameters.""" hparams = iwslt_base() hparams.batch_size = 4096 # Other flags. hparams.force_full_predict = True hparams.causal_decoder_self_attention = False # VAE-related flags. hparams.add_hparam("latent_size", 256) hparams.add_hparam("anneal_min_value", 0.0) hparams.add_hparam("kl_startup_steps", 5000) hparams.add_hparam("kl_anneal_steps", 20000) hparams.add_hparam("n_posterior_layers", 3) hparams.add_hparam("n_decoder_layers", 3) hparams.add_hparam("posterior_2d_dropout", 0.20) # diagonal_normal / affine / additive / rq hparams.add_hparam("posterior_type", "diagonal_normal") # standard_normal / diagonal_normal hparams.add_hparam("prior_type", "diagonal_normal") hparams.add_hparam("decoder_2d_dropout", 0.00) # Optimization-related flags. hparams.learning_rate_warmup_steps = 8000 hparams.learning_rate_constant = 2.0 hparams.layer_prepostprocess_dropout = 0.2 hparams.attention_dropout = 0.2 hparams.relu_dropout = 0.2 hparams.dropout = 0.2 # Optimization-related flags. hparams.add_hparam("kl_reg", 0.0) hparams.add_hparam("n_gibbs_steps", 0) hparams.add_hparam("compute_kl_refinement", False) hparams.add_hparam("compute_iw_marginal", False) hparams.add_hparam("n_samples", 1) return hparams @registry.register_hparams def wmt_diag_base(): """Set of hyperparameters.""" hparams = iwslt_diag() hparams.batch_size = 4096 hparams.num_hidden_layers = 6 hparams.hidden_size = 512 hparams.filter_size = 2048 hparams.num_heads = 8 # VAE-related flags. hparams.latent_size = 512 hparams.n_posterior_layers = 4 hparams.n_decoder_layers = 6 hparams.dropout = 0.1 hparams.layer_prepostprocess_dropout = 0.1 hparams.attention_dropout = 0.1 hparams.relu_dropout = 0.1 return hparams @registry.register_hparams def wmt_diag_small(): """Set of hyperparameters.""" hparams = wmt_diag_base() hparams.n_posterior_layers = 3 hparams.n_decoder_layers = 3 hparams.kl_reg = 1e-4 return hparams @registry.register_hparams def wmt_diag_small_trueadam(): """Set of hyperparameters.""" hparams = wmt_diag_small() hparams.optimizer = "true_adam" return hparams @registry.register_hparams def wmt_diag_small_trueadam_longer(): """Set of hyperparameters.""" hparams = wmt_diag_small_trueadam() hparams.learning_rate_constant = 4.0 hparams.learning_rate_warmup_steps = 20000 return hparams @registry.register_hparams def wmt_diag_small_trueadam_shorter(): """Set of hyperparameters.""" hparams = wmt_diag_small_trueadam() hparams.learning_rate_constant = 2.0 hparams.learning_rate_warmup_steps = 4000 return hparams @registry.register_hparams def wmt_diag_base_trueadam_1e4(): """Set of hyperparameters.""" hparams = wmt_diag_base() hparams.kl_reg = 1e-4 hparams.optimizer = "true_adam" hparams.learning_rate_constant = 2.0 hparams.learning_rate_warmup_steps = 8000 return hparams @registry.register_hparams def wmt_diag_base_trueadam_longer_1e4(): """Set of hyperparameters.""" hparams = wmt_diag_base_trueadam_1e4() hparams.learning_rate_constant = 4.0 hparams.learning_rate_warmup_steps = 20000 return hparams @registry.register_hparams def wmt_diag_base_trueadam_shorter_1e4(): """Set of hyperparameters.""" hparams = wmt_diag_base_trueadam_1e4() hparams.learning_rate_constant = 2.0 hparams.learning_rate_warmup_steps = 4000 return hparams @registry.register_hparams def wmt_diag_base_1e4_trueadam(): """Set of hyperparameters.""" hparams = wmt_diag_base() hparams.kl_reg = 1e-4 hparams.optimizer = "true_adam" return hparams @registry.register_hparams def wmt_diag_base_1e4_trueadam_longer(): """Set of hyperparameters.""" hparams = wmt_diag_base_1e4_trueadam() hparams.learning_rate_constant = 4.0 hparams.learning_rate_warmup_steps = 20000 return hparams @registry.register_hparams def wmt_diag_base_1e4_trueadam_shorter(): """Set of hyperparameters.""" hparams = wmt_diag_base_1e4_trueadam() hparams.learning_rate_constant = 2.0 hparams.learning_rate_warmup_steps = 4000 return hparams @registry.register_hparams def wmt_diag_base_1e4(): """Set of hyperparameters.""" hparams = wmt_diag_base() hparams.kl_reg = 1e-4 return hparams @registry.register_hparams def wmt_diag_base_longer_1e4(): """Set of hyperparameters.""" hparams = wmt_diag_base_1e4() hparams.learning_rate_constant = 4.0 hparams.learning_rate_warmup_steps = 20000 return hparams @registry.register_hparams def wmt_diag_base_shorter_1e4(): """Set of hyperparameters.""" hparams = wmt_diag_base_1e4() hparams.learning_rate_constant = 2.0 hparams.learning_rate_warmup_steps = 4000 return hparams @registry.register_hparams def iwslt_diag_1e5(): """Set of hyperparameters.""" hparams = iwslt_diag() hparams.kl_reg = 1e-5 return hparams @registry.register_hparams def iwslt_diag_1e4(): """Set of hyperparameters.""" hparams = iwslt_diag() hparams.kl_reg = 1e-4 return hparams @registry.register_hparams def iwslt_affine(): """Set of hyperparameters.""" hparams = iwslt_diag() hparams.prior_type = "affine" hparams.batch_size = 2048 hparams.latent_size = 256 # Glow-related flags. hparams.add_hparam("depths", "4/8/8") # infer n_levels from depths hparams.add_hparam("step_fn", "glow") # glow / chunting hparams.add_hparam("affine_scale", "glow") # glow / jason hparams.add_hparam("conv_fn", "np") # np / tf hparams.add_hparam("split_plans", "cat/cat/ca") hparams.add_hparam("factor", 2) # squeezing factor hparams.add_hparam("n_layers_transform_params", 1) hparams.add_hparam("n_1x1_heads", 4) hparams.add_hparam("flow_num_heads", 4) hparams.add_hparam("flow_hidden_size", 256) hparams.add_hparam("flow_filter_size", 512) # Control max scale change. hparams.add_hparam("scale_width", 0.999) # Optimization-related flags. # hparams.learning_rate_warmup_steps = 20000 hparams.add_hparam("flow_layer_prepostprocess_dropout", 0.0) hparams.add_hparam("flow_attention_dropout", 0.0) hparams.add_hparam("flow_relu_dropout", 0.0) # hparams.optimizer_adam_beta1 = 0.9 # hparams.optimizer_adam_beta2 = 0.999 # hparams.optimizer_adam_epsilon = 1e-8 # Precision-related flags. hparams.activation_dtype = "float32" hparams.weight_dtype = "float32" return hparams @registry.register_hparams def wmt_affine(): """Set of hyperparameters.""" hparams = iwslt_affine() hparams.batch_size = 2048 # TODO(jason) : address this later. hparams.num_hidden_layers = 6 hparams.hidden_size = 256 hparams.filter_size = 1024 hparams.num_heads = 8 # VAE-related flags. hparams.latent_size = 256 hparams.n_posterior_layers = 4 hparams.n_decoder_layers = 4 hparams.layer_prepostprocess_dropout = 0.1 hparams.attention_dropout = 0.1 hparams.relu_dropout = 0.1 # Glow-related flags. hparams.flow_num_heads = 8 hparams.flow_filter_size = 512 return hparams @registry.register_hparams def wmt_affine_base(): """Set of hyperparameters.""" hparams = wmt_affine() hparams.batch_size = 2048 hparams.hidden_size = 320 hparams.latent_size = 320 hparams.flow_filter_size = 640 return hparams @registry.register_hparams def wmt_affine_base_small(): """Set of hyperparameters.""" hparams = wmt_affine_base() hparams.depths = "4/4/4" hparams.kl_reg = 1e-4 hparams.learning_rate_constant = 2.0 hparams.learning_rate_warmup_steps = 8000 return hparams @registry.register_hparams def wmt_affine_base_trueadam_small(): """Set of hyperparameters.""" hparams
<gh_stars>0 import os import re import ast import sys import json import uuid import MySQLdb import functools import threading import subprocess import unicodedata import flask, flask.views app = flask.Flask(__name__) # Don't do this! app.secret_key = "bacon" #get app directory loc = os.getcwd()+"/" #variables for registry registry = "" regmail = "" reguser = "" regpas = "" #variables for databse access dbhost = "" dbuser = "" dbpasswd = "" #read config.txt file with open(loc+"static/configs/config.txt") as f: details = f.read() f.close() for line in details.splitlines(): line = line.split() if line == []: pass elif line[0] == "registry": registry = line[2] elif line[0] == "regmail": regmail = line[2] elif line[0] == "reguser": reguser = line[2] elif line[0] == "regmail": regmail = line[2] elif line[0] == "regpas": regpas = line[2] elif line[0] == "dbhost": dbhost = line[2] elif line[0] == "dbuser": dbuser = line[2] elif line[0] == "dbpasswd": dbpasswd = line[2] grps = [] radio1 = '' radio2 = '' search1 = '' search2 = '' dld = [] dld_lsn = [] output = [] def executer(cmd): p = subprocess.Popen(cmd, stdin=subprocess.PIPE,stdout=subprocess.PIPE,stderr=subprocess.PIPE) out, err = p.communicate() return out, err def thread_executer(cmd): global dld print "in thread",cmd p = subprocess.Popen(cmd, stdout=subprocess.PIPE,stderr=subprocess.PIPE) out, err = p.communicate() temp=out.splitlines()[-2].split()[0] if temp=='Digest:': dld.remove(cmd[-1]) def downloader(cmd,image,info): global dld,loc dld.append(image) print "Downloading ",image out,err = executer(cmd) print "finished dld ",image if 'Digest' in out: try: cmd = ['docker','tag',cmd[2],image] out,err = executer(cmd) try: print loc+"static/lessons/"+image.replace('/','-') with open(loc+"static/lessons/"+image.replace('/','-'),'w') as f: f.write(info[0]+"\n"+info[1]) f.close() except: print "error writing file -",image except: print "error renaming ",image else: print "failed downloading",image while image in dld : dld.remove(image) print "exiting ",image def add_lesson(old_lesn,lesn,index,line,info): global dld,loc global dld_lsn flag = 1 print "enter loop - add_lesson" while flag: flag = 0 for item in index: if item in dld: flag = 1 print "exit loop - add_lesson" dld_lsn.remove(old_lesn) target = loc+'static/configs/lesson.txt' try: cmd=['grep','^'+lesn+' ',target] val,err = executer(cmd) #add or replace line in the configs/lesson.txt file if val: print "Replacing line" cmd=['sed','-i','/^'+lesn+' /c '+line,target] val = executer(cmd) else: print "Adding line" with open(target, 'a') as f: f.write(line) f.close() #add description about lesson in the static/lessons/ folder with open(loc+'static/lessons/'+lesn,'w') as f: f.write(info[0]+'\n'+info[1]) f.close() except: print "error writing file",lesn def thread_executer_2(cmd,args): global dld print "in thread",cmd if args[0] == 'play': try: f = open(cmd[2],'w') f.write(args[1]) f.close() f = open(cmd[3],'w') f.write(args[2]) f.close() except: print "Error creating playbook ",cmd p = subprocess.Popen(cmd,shell=False,stdin=None,stdout=None,stderr=None,close_fds=True) print "out of process",cmd def reader(fname): index=[] try: with open(fname) as f: index = f.read().splitlines() f.close() except: pass return index def db_ops(cmds,arg): global dbuser, dbpasswd, dbhost db = MySQLdb.connect(host=dbhost, user=dbuser, passwd=<PASSWORD>, db="lense") cur = db.cursor() for cmd in cmds: cur.execute(cmd) result = cur.fetchall() #commit if arg = 1 if arg == 1: db.commit() #return the results return result db.close() def filechecker(): #check and create lessons.txt if doesnot exist already path="static/configs/lesson.txt" if not os.path.exists(path): print "asdad" fh = open(path, "w") fh.write(' ') fh.close() class Main(flask.views.MethodView): def get(self): return flask.render_template('index.html') def post(self): flag = [] if 'logout' in flask.request.form: flask.session.pop('username', None) return flask.redirect(flask.url_for('index')) required = ['username', 'passwd'] for r in required: if r not in flask.request.form: flask.flash("Error: {0} is required.".format(r)) return flask.redirect(flask.url_for('index')) username = flask.request.form['username'] passwd = flask.request.form['passwd'] cmd = "SELECT * FROM users WHERE passwd='"+passwd+"' AND uname='"+username+"'" flag=db_ops([cmd],0) #flag = 1 #check if all files are available filechecker() #if username in users and users[username] == passwd: if flag: flask.session['username'] = username with open('/tmp/.esnel','w') as f: f.write(username) f.close() else: flask.flash("Username doesn't exist or incorrect password") return flask.redirect(flask.url_for('home')) def login_required(method): @functools.wraps(method) def wrapper(args, kwargs): if 'username' in flask.session: return method(args, **kwargs) else: flask.flash("A login is required to proceed!") return flask.redirect(flask.url_for('index')) return wrapper class Repo(flask.views.MethodView): @login_required def get(self): global dld_lsn global dld global registry,regmail,reguser,regpas #dld=['lesson3'] cmd=['curl','https://'+reguser+':'+regpas+'@'+registry+'/v2/_catalog'] out1, out2 = executer(cmd) temp = {'index':{},'lesns':{},'comps':{},'dld':dld,'dld_lsn':dld_lsn} try: images= ast.literal_eval(out1.splitlines()[0])['repositories'] for image in images: #check if description for component exist and add it to temp #cmd=['curl','http://test:user@registry.cs.uno.edu/'+image.replace('/','-')] cmd=['curl','http://'+reguser+':'+regpas+'@'+registry+'/'+image.replace('/','-')] out1, out2 = executer(cmd) desc=out1.splitlines() if desc[0]!='<html>' and desc[0]!='': temp['comps'][image]=[desc[0],'\n'.join(desc[1:])] #check if description for lesson exist and add it to temp, if absent image=image.split('/')[0] try: if temp['lesns'][image]: pass except: #cmd=['curl','http://test:<EMAIL>@<EMAIL>/'+image] cmd=['curl','http://'+reguser+':'+regpas+'@'+registry+'/'+image] out1, out2 = executer(cmd) desc=out1.splitlines() if desc[0]!='<html>' and desc[0]!='': temp['lesns'][image]=[desc[0],'\n'.join(desc[1:])] #check if index for lesson exist and add to temp, if absent try: if temp['index'][image]: pass except: #cmd=['curl','http://test:user@registry.cs.uno.edu/'+image+'_index'] cmd=['curl','http://'+reguser+':'+regpas+'@'+registry+'/'+image+'_index'] out1, out2 = executer(cmd) desc=out1.splitlines()[0] if desc!='<html>' and desc!='': temp['index'][image]=desc else: temp['lesns'][image]=['n/a','n/a'] else: temp['comps'][image]=['n/a','n/a'] except: print "some error in getting repo data" result = temp print result flask.flash(result) return flask.render_template('repo.html') @login_required def post(self): global dld_lsn global loc global registry,regmail,reguser,regpas flag = 0 #login to the registry server #cmd = ['docker','login','-u','test','-p','user','--email="<EMAIL>"','https://registry.cs.uno.edu'] cmd = ['docker','login','-u',reguser,'-p',regpas,'--email="'+regmail+'"','https://'+registry] out1,out2=executer(cmd) try: request = flask.request.form['lesn'] request = ast.literal_eval(request) lesn = request[0] cont = request[1] #info = cont['comps'][image] flag = 1 except: request = flask.request.form['comp'] request = ast.literal_eval(request) image = request[0] cont = request[1] info = cont['comps'][image] #download just the component image from the registry server in a thread cmd = ['docker','pull',registry+'/'+image] t = threading.Thread(name='child procs', target=downloader, args=[cmd,image,info]) t.daemon = True t.start() #return to back to web page return flask.redirect(flask.url_for('repo')) #add code if lesson is to be saved under a new name new_lsn = lesn #add lesson to the download list for lessons dld_lsn.append(lesn) #print lesn,'\n', cont new_cont = [] for comp in cont['index'][lesn].split()[1:]: print "loop main",comp image1 = comp.replace(lesn,new_lsn) image = image1.replace('-','/') new_cont.append(image1) #download image from the registry server in a thread cmd = ['docker','pull',registry+'/'+image] info = cont['comps'][image] t = threading.Thread(name='child procs', target=downloader, args=[cmd,image,info]) t.daemon = True t.start() #get description from POST and other attributes required for the lesson desc = cont['lesns'][lesn] line = new_lsn+' '+' '.join(new_cont) index = new_cont t = threading.Thread(name='child procs', target=add_lesson, args=[lesn,new_lsn,index,line,desc]) t.daemon = True t.start() return flask.redirect(flask.url_for('repo')) class Home(flask.views.MethodView): @login_required def get(self): global loc #index2 {'lesson1': {'status': 'Y', 'comps': {'lesson1/comp1': {'status': ['Y'], 'index': ['lesson1/comp1', 'latest', '252f198a8beb', 'ago 380MB', 'Y', []], 'desc': ['Web Server', 'LAMP server hosting a PHP webpage.']}}, 'desc': ['SQL Injection to Shell II', 'This exercise explains how you can, from a blind SQL injection, gain access to the administration console. Then once in the administration console, how you can run commands on the system. ']}} #check if all files are available filechecker() #--------------------------------- #check for status of containers cmd = ['docker', 'ps', '-a'] out1, out2 = executer(cmd) index3={} index4=[] tag = "" if out1: temp2=[] temp3=[] flag=0 for line in out1.splitlines(): if 'lesson' in line: var1=line.split() if var1[var1.index('ago')+1] == 'Up': index3[var1[-1]]=[var1[1],'Y'] else: index3[var1[-1]]=[var1[1],'S'] index4.append(var1[-1]) print "Home",index3,index4 index1={} temp2=[] #check downloaded images cmd = ['docker', 'images'] out1, out2 = executer(cmd) for line in out1.splitlines(): temp3 = [] flags = [] temp = line.split() if line.startswith('lesson'): status='' #555 history command no longer gives you image id of intermediate containers cmd = ["docker","history","--no-trunc",temp[0]] temp2=executer(cmd) image = [] flags = 0 for step in temp2[0].splitlines(): if '"@STEP@' in step: step = step.split() image = step[0][0:3] temp1=[] try: temp1=index3[temp[0].replace('/','-')] if image == temp1[0] : #print temp1 flags=temp1[1] else: temp1=['',''] except: temp1=['',''] temp3.append([image,temp1[1],' '.join(step[step.index('"@STEP@')+1:-2])[:-1]]) if image: temp[2]=image if not flags: try: flags=index3[temp[0].replace('/','-')][1] except: flags='N' index1[temp[0]]=[temp[0],temp[1],temp[2],' '.join(temp[-2:]),flags,temp3[::-1]] print "index",index1 temp=[] index2={} fname=loc+'static/configs/lesson.txt' with open(fname) as f: temp=f.read().splitlines() for item in temp: count1 = count2 = 0 item = item.split() index2[item[0]]={} if True: #check files and add the lesson title and description try: fbuf=[] fname=loc+'static/lessons/'+item[0] with open(fname) as f: fbuf=f.read().splitlines() index2[item[0]]['desc']=[fbuf[0],''.join(fbuf[1:])] except: index2[item[0]]['desc']=['',''] index2[item[0]]['comps']={} index2[item[0]]['status']='' #print item,index2 for key in item[1:]: #check files and add the component title and description print "--",key try: fbuf=[] fname='static/lessons/'+key with open(fname) as f: fbuf=f.read().splitlines() comp_desc = [fbuf[0],''.join(fbuf[1:])] except: comp_desc = ['',''] ip = 'n/a' try: temp3=index1[key.replace('-','/')] if temp3[4]=='Y': cmd = ['docker','inspect','--format','{{ .NetworkSettings.IPAddress}}',key] ip,err = executer(cmd) ip = ip.rstrip() count1+=1 elif temp3[4]=='N': count2+=1 except: temp3=[] index2[item[0]]['comps'][key.replace('-','/')]={'index':temp3,'desc':comp_desc,'status':[temp3[4]],'network':[ip]} #print key,comp_desc,temp3 #print index2 print item[1:],count1,count2 if count1 == len(item[1:]): index2[item[0]]['status']='Y' elif count2 == len(item[1:]) : index2[item[0]]['status']='N' else: index2[item[0]]['status']='S' #print "new" #print index3 #print index1 print "index2",index2 flask.flash(index2,'lesson') return flask.render_template('home.html') @login_required def post(self): request = flask.request.form result = {} temp1 = [] temp2 = [] print request try: if request['start-all']: print request['start-all'] try: temp=ast.literal_eval(request['start-all']) targets=temp.keys() except: pass print targets for cont in targets: image = temp[cont]['index'][2] print "starting container ",cont,image cmd = ['docker', 'run', '-Pitd', '--name='+cont.replace('/','-'), image] out1, out2 = executer(cmd) print "out-",cont,out2 except: try: if request['stop-all']: try: temp=ast.literal_eval(request['stop-all']) request=temp.keys() except: request=[request['stop-all']] print "stop all containers ",request for cont in request: cont = cont.replace('/','-') print "stopping container "+cont cmd = ['docker', 'stop', cont] out1, out2 = executer(cmd) except: try: if request['reset-all']: try: conts = ast.literal_eval(request['reset-all']) targets = conts.keys() except: targets = [request['reset-all']] for cont in targets: print
<gh_stars>0 import sys import time import stat from typing import Any import random import subprocess import glob import os import pandas as pd # type: ignore from pathlib import Path from typing import List from sys import platform import pathlib import shutil import traceback from pylpg.lpgdata import * from pylpg.lpgpythonbindings import * def execute_lpg_tsib( year: int, number_of_households: int, number_of_people_per_household: int, startdate: str = None, enddate: str = None, transportation: bool = False, energy_intensity: str = "Random", ) -> pd.DataFrame: lpe: LPGExecutor = LPGExecutor(1, False) if number_of_households < 1: print("too few households") raise Exception("Need at least one household") # basic default spec request = lpe.make_default_lpg_settings(year) # create random households for idx in range(number_of_households): hhd: HouseholdData = HouseholdData() hhd.HouseholdDataSpecification = HouseholdDataSpecificationType.ByPersons hhd.HouseholdDataPersonSpec = HouseholdDataPersonSpecification() hhd.HouseholdDataPersonSpec.Persons = [] hhd.ChargingStationSet = ( ChargingStationSets.Charging_At_Home_with_03_7_kW_output_results_to_Car_Electricity ) hhd.TravelRouteSet = ( TravelRouteSets.Travel_Route_Set_for_30km_Commuting_Distance ) hhd.TransportationDeviceSet = TransportationDeviceSets.Bus_and_two_30_km_h_Cars hhd.HouseholdDataPersonSpec.Persons = make_reasonable_family( number_of_people_per_household ) request.House.Households.append(hhd) # set more parameters if request.CalcSpec is None: raise Exception("Failed to initialize the calculation spec") if startdate is not None: request.CalcSpec.set_StartDate(startdate) if enddate is not None: request.CalcSpec.set_EndDate(enddate) request.CalcSpec.EnergyIntensityType = energy_intensity calcspecfilename = Path(lpe.calculation_directory, "calcspec.json") if transportation: request.CalcSpec.EnableTransportation = True request.CalcSpec.CalcOptions.append(CalcOption.TansportationDeviceJsons) # write to json with open(calcspecfilename, "w") as calcspecfile: jsonrequest = request.to_json(indent=4) # type: ignore calcspecfile.write(jsonrequest) # execute lpe.execute_lpg_binaries() # read the results and return as dataframe return lpe.read_all_json_results_in_directory() def make_reasonable_family(person_count: int): previousage = 0 persons = [] g = 0 for person_idx in range(person_count): if person_idx == 0: # first is an adult age = random.randint(18, 100) previousage = age g = random.randint(0, 1) elif person_idx == 1: # 2nd adult should be roughly similar age diffage = random.randint(0, 10) age = previousage - 5 + diffage if g == 0: g = 1 else: g = 0 else: # other people are children age = random.randint(0, 20) if g == 0: g = 1 else: g = 0 if g == 0: gender = Gender.Male else: gender = Gender.Female pd = PersonData(age, gender) persons.append(pd) return persons def execute_lpg_single_household( year: int, householdref: JsonReference, housetype: str, startdate: str = None, enddate: str = None, geographic_location: JsonReference = None, simulate_transportation: bool = False, chargingset: JsonReference = None, transportation_device_set: JsonReference = None, travel_route_set: JsonReference = None, random_seed: int = None, energy_intensity: str = "Random", ) -> pd.DataFrame: lpe: LPGExecutor = LPGExecutor(1, False) # basic request request = lpe.make_default_lpg_settings(year) if random_seed is not None: request.CalcSpec.RandomSeed = random_seed request.House.HouseTypeCode = housetype hhnamespec = HouseholdNameSpecification(householdref) hhn = HouseholdData( None, None, hhnamespec, "hhid", "hhname", chargingset, transportation_device_set, travel_route_set, None, HouseholdDataSpecificationType.ByHouseholdName, ) request.House.Households.append(hhn) if request.CalcSpec is None: raise Exception("Failed to initialize the calculation spec") if startdate is not None: request.CalcSpec.set_StartDate(startdate) if enddate is not None: request.CalcSpec.set_EndDate(enddate) request.CalcSpec.EnergyIntensityType = energy_intensity calcspecfilename = Path(lpe.calculation_directory, "calcspec.json") request.CalcSpec.GeographicLocation = geographic_location if simulate_transportation: request.CalcSpec.EnableTransportation = True request.CalcSpec.CalcOptions.append(CalcOption.TansportationDeviceJsons) with open(calcspecfilename, "w") as calcspecfile: jsonrequest = request.to_json(indent=4) # type: ignore calcspecfile.write(jsonrequest) lpe.execute_lpg_binaries() return lpe.read_all_json_results_in_directory() def execute_lpg_with_householdata( year: int, householddata: HouseholdData, housetype: str, startdate: str = None, enddate: str = None, simulate_transportation: bool = False, target_heating_demand: Optional[float] = None, target_cooling_demand: Optional[float] = None, calculation_index: int = 1, clear_previous_calc: bool = False, random_seed: int = None, energy_intensity: str = "Random", ): try: print( "Starting calc with " + str(calculation_index) + " for " + householddata.Name ) lpe: LPGExecutor = LPGExecutor(calculation_index, clear_previous_calc) # basic request request = lpe.make_default_lpg_settings(year) request.House.HouseTypeCode = housetype if random_seed is not None: request.CalcSpec.RandomSeed = random_seed if target_heating_demand is not None: request.House.TargetHeatDemand = target_heating_demand if target_cooling_demand is not None: request.House.TargetCoolingDemand = target_cooling_demand request.House.Households.append(householddata) if request.CalcSpec is None: raise Exception("Failed to initialize the calculation spec") if startdate is not None: request.CalcSpec.set_StartDate(startdate) if enddate is not None: request.CalcSpec.set_EndDate(enddate) request.CalcSpec.EnergyIntensityType = energy_intensity calcspecfilename = Path(lpe.calculation_directory, "calcspec.json") if simulate_transportation: request.CalcSpec.EnableTransportation = True request.CalcSpec.CalcOptions.append(CalcOption.TansportationDeviceJsons) with open(calcspecfilename, "w") as calcspecfile: jsonrequest = request.to_json(indent=4) # type: ignore calcspecfile.write(jsonrequest) lpe.execute_lpg_binaries() df = lpe.read_all_json_results_in_directory() return df except OSError as why: print("Exception: " + str(why)) traceback.print_stack() raise except: # catch all exceptions e = sys.exc_info()[0] print("Exception: " + str(e)) traceback.print_stack() raise def execute_lpg_with_many_householdata( year: int, householddata: List[HouseholdData], housetype: str, startdate: str = None, enddate: str = None, simulate_transportation: bool = False, target_heating_demand: Optional[float] = None, target_cooling_demand: Optional[float] = None, calculation_index: int = 1, clear_previous_calc: bool = False, random_seed: int = None, energy_intensity: str = "Random", ): try: print( "Starting calc with " + str(calculation_index) + " for " + str(len(householddata)) + " households" ) lpe: LPGExecutor = LPGExecutor(calculation_index, clear_previous_calc) # basic request request = lpe.make_default_lpg_settings(year) request.House.HouseTypeCode = housetype if random_seed is not None: request.CalcSpec.RandomSeed = random_seed if target_heating_demand is not None: request.House.TargetHeatDemand = target_heating_demand if target_cooling_demand is not None: request.House.TargetCoolingDemand = target_cooling_demand request.House.Households = request.House.Households + householddata if request.CalcSpec is None: raise Exception("Failed to initialize the calculation spec") if startdate is not None: request.CalcSpec.set_StartDate(startdate) if enddate is not None: request.CalcSpec.set_EndDate(enddate) request.CalcSpec.EnergyIntensityType = energy_intensity calcspecfilename = Path(lpe.calculation_directory, "calcspec.json") if simulate_transportation: request.CalcSpec.EnableTransportation = True request.CalcSpec.CalcOptions.append(CalcOption.TansportationDeviceJsons) with open(calcspecfilename, "w") as calcspecfile: jsonrequest = request.to_json(indent=4) # type: ignore calcspecfile.write(jsonrequest) lpe.execute_lpg_binaries() df = lpe.read_all_json_results_in_directory() return df except OSError as why: print("Exception: " + str(why)) traceback.print_stack() raise except: # catch all exceptions e = sys.exc_info()[0] print("Exception: " + str(e)) traceback.print_stack() raise def execute_lpg_with_householdata_with_csv_save( year: int, householddata: HouseholdData, housetype: str, startdate: str = None, enddate: str = None, simulate_transportation: bool = False, target_heating_demand: Optional[float] = None, target_cooling_demand: Optional[float] = None, calculation_index: int = 1, ): try: df = execute_lpg_with_householdata( year, householddata, housetype, startdate, enddate, simulate_transportation, target_heating_demand, target_cooling_demand, calculation_index, True, ) df_electricity = df["Electricity_HH1"] df_electricity.to_csv("R" + str(calculation_index) + ".csv") calcdir = "C" + str(calculation_index) if os.path.exists(calcdir): print("cleaning up " + calcdir) shutil.rmtree(calcdir) time.sleep(1) except OSError as why: print("Exception: " + str(why)) traceback.print_stack() raise except: # catch all exceptions e = sys.exc_info()[0] print("Exception: " + str(e)) traceback.print_stack() raise def execute_grid_calc( year: int, household_size_list: List[int], housetype: str, startdate: str = None, enddate: str = None, simulate_transportation: bool = False, chargingset: JsonReference = None, transportation_device_set: JsonReference = None, travel_route_set: JsonReference = None, ) -> pd.DataFrame: lpe: LPGExecutor = LPGExecutor(1, True) # basic request request = lpe.make_default_lpg_settings(year) request.CalcSpec.CalcOptions.clear() request.CalcSpec.CalcOptions.append(CalcOption.JsonHouseSumFiles) if len(household_size_list) < 1: raise Exception("need at least one household.") request.House.HouseTypeCode = housetype count = 1 for hs in household_size_list: hhdps: HouseholdDataPersonSpecification = HouseholdDataPersonSpecification( make_reasonable_family(hs) ) hhn = HouseholdData( hhdps, None, None, "hhid", "hhname" + str(count), chargingset, transportation_device_set, travel_route_set, None, HouseholdDataSpecificationType.ByPersons, ) request.House.Households.append(hhn) count = count + 1 if request.CalcSpec is None: raise Exception("Failed to initialize the calculation spec") if startdate is not None: request.CalcSpec.set_StartDate(startdate) if enddate is not None: request.CalcSpec.set_EndDate(enddate) calcspecfilename = Path(lpe.calculation_directory, "calcspec.json") if simulate_transportation: request.CalcSpec.EnableTransportation = True request.CalcSpec.CalcOptions.append(CalcOption.TansportationDeviceJsons) with open(calcspecfilename, "w") as calcspecfile: jsonrequest = request.to_json(indent=4) # type: ignore calcspecfile.write(jsonrequest) lpe.execute_lpg_binaries() return lpe.read_all_json_results_in_directory() class LPGExecutor: def __init__(self, calcidx: int, clear_previous_calc: bool): version = "_" self.working_directory = pathlib.Path(__file__).parent.absolute() if platform == "linux" or platform == "linux2": self.calculation_src_directory = Path( self.working_directory, "LPG" + version + "linux" ) self.simengine_src_filename = "simengine2" fullname = Path(self.calculation_src_directory, self.simengine_src_filename) print("starting to execute " + str(fullname)) os.chmod(str(fullname), stat.S_IRWXU \| stat.S_IRWXG \| stat.S_IRWXO) print("Permissions:" + str(oct(os.stat(str(fullname))[stat.ST_MODE])[-3:])) elif platform == "win32": self.calculation_src_directory = Path( self.working_directory, "LPG" + version + "win" ) self.simengine_src_filename = "simulationengine.exe" else: raise Exception("unknown operating system detected: " + platform) self.calculation_directory = Path(self.working_directory, "C" + str(calcidx)) print("Working in directory: " + str(self.calculation_directory)) if clear_previous_calc and os.path.exists(self.calculation_directory): self.error_tolerating_directory_clean(self.calculation_directory) print("Removing " + str(self.calculation_directory)) shutil.rmtree(self.calculation_directory) time.sleep(1) if not os.path.exists(self.calculation_directory): print( "copying from " + str(self.calculation_src_directory) + " to " + str(self.calculation_directory) ) shutil.copytree(self.calculation_src_directory, self.calculation_directory) print("copied to: " + str(self.calculation_directory)) def error_tolerating_directory_clean(self, path: str): mypath = str(path) if len(str(mypath)) < 10: raise Exception( "Path too short. This is suspicious. Trying to delete more than you meant to?" ) print("cleaning " + mypath) files = glob.glob(mypath + "/*", recursive=True) for file in files: if os.path.isfile(file): print("Removing " + file) os.remove(file) def execute_lpg_binaries(self) -> Any: # execute LPG pathname = Path(self.calculation_directory, self.simengine_src_filename) print("executing in " + str(self.calculation_directory)) subprocess.run( [str(pathname), "processhousejob", "-j", "calcspec.json"], cwd=str(self.calculation_directory), ) def make_default_lpg_settings(self, year: int) -> HouseCreationAndCalculationJob: print("Creating") hj = HouseCreationAndCalculationJob() hj.set_Scenario("S1").set_Year(str(year)).set_DistrictName("district") hd = HouseData() hj.House = hd hd.Name = "House" hd.HouseGuid = StrGuid("houseguid") hd.HouseTypeCode = ( HouseTypes.HT01_House_with_a_10kWh_Battery_and_a_fuel_cell_battery_charger_5_MWh_yearly_space_heating_gas_heating ) hd.TargetCoolingDemand = 10000 hd.TargetHeatDemand = 0 hd.Households = [] cs: JsonCalcSpecification = JsonCalcSpecification() hj.CalcSpec = cs cs.IgnorePreviousActivitiesWhenNeeded = True cs.LoadTypePriority = LoadTypePriority.All cs.DefaultForOutputFiles
# 1972 article n = int(n) d = len(A) if len(set(a%d for a in A)) == d: return [id for i in range(n//d)] # next, we consider an exhaustive search from sage.combinat.dlx import DLXMatrix rows = [] for i in range(n): rows.append([i+1, [(i+a)%n+1 for a in A]]) M = DLXMatrix(rows) for c in M: return [i-1 for i in c] def one_radical_difference_family(K, k): r""" Search for a radical difference family on ``K`` using dancing links algorithm. For the definition of radical difference family, see :func:`radical_difference_family`. Here, we consider only radical difference family with `\lambda = 1`. INPUT: - ``K`` -- a finite field of cardinality `q`. - ``k`` -- a positive integer so that `k(k-1)` divides `q-1`. OUTPUT: Either a difference family or ``None`` if it does not exist. ALGORITHM: The existence of a radical difference family is equivalent to a one dimensional tiling (or packing) problem in a cyclic group. This subsequent problem is solved by a call to the function :func:`one_cyclic_tiling`. Let `K^` be the multiplicative group of the finite field `K`. A radical family has the form `\mathcal B = \{x_1 B, \ldots, x_k B\}`, where `B=\{x:x^{k}=1\}` (for `k` odd) or `B=\{x:x^{k-1}=1\}\cup \{0\}` (for `k` even). Equivalently, `K^` decomposes as: .. MATH:: K^ = \Delta (x_1 B) \cup ... \cup \Delta (x_k B) = x_1 \Delta B \cup ... \cup x_k \Delta B We observe that `C=B\backslash 0` is a subgroup of the (cyclic) group `K^`, that can thus be generated by some element `r`. Furthermore, we observe that `\Delta B` is always a union of cosets of `\pm C` (which is twice larger than `C`). .. MATH:: \begin{array}{llll} (k\text{ odd} ) & \Delta B &= \{r^i-r^j:r^i\neq r^j\} &= \pm C\cdot \{r^i-1: 0 < i \leq m\}\\ (k\text{ even}) & \Delta B &= \{r^i-r^j:r^i\neq r^j\}\cup C &= \pm C\cdot \{r^i-1: 0 < i < m\}\cup \pm C \end{array} where .. MATH:: (k\text{ odd})\ m = (k-1)/2 \quad \text{and} \quad (k\text{ even})\ m = k/2. Consequently, `\mathcal B = \{x_1 B, \ldots, x_k B\}` is a radical difference family if and only if `\{x_1 (\Delta B/(\pm C)), \ldots, x_k (\Delta B/(\pm C))\}` is a partition of the cyclic group `K^/(\pm C)`. EXAMPLES:: sage: from sage.combinat.designs.difference_family import ( ....: one_radical_difference_family, ....: is_difference_family) sage: one_radical_difference_family(GF(13),4) [[0, 1, 3, 9]] The parameters that appear in [Bu95]_:: sage: df = one_radical_difference_family(GF(449), 8); df [[0, 1, 18, 25, 176, 324, 359, 444], [0, 9, 88, 162, 222, 225, 237, 404], [0, 11, 140, 198, 275, 357, 394, 421], [0, 40, 102, 249, 271, 305, 388, 441], [0, 49, 80, 93, 161, 204, 327, 433], [0, 70, 99, 197, 230, 362, 403, 435], [0, 121, 141, 193, 293, 331, 335, 382], [0, 191, 285, 295, 321, 371, 390, 392]] sage: is_difference_family(GF(449), df, 449, 8, 1) True """ q = K.cardinality() x = K.multiplicative_generator() e = k(k-1) if q%e != 1: raise ValueError("q%e is not 1") # We define A by (see the function's documentation): # ΔB = C.A if k%2 == 1: m = (k-1) // 2 r = x * ((q-1) // k) # k-th root of unity A = [ri - 1 for i in range(1,m+1)] else: m = k // 2 r = x ((q-1) // (k-1)) # (k-1)-th root of unity A = [r*i - 1 for i in range(1,m)] A.append(K.one()) # instead of the complicated multiplicative group K^/(±C) we use the # discrete logarithm to convert everything into the additive group Z/cZ c = m * (q-1) // e # cardinal of ±C from sage.groups.generic import discrete_log logA = [discrete_log(a,x)%c for a in A] # if two elements of A are equal modulo c then no tiling is possible if len(set(logA)) != m: return None # brute force tiling = one_cyclic_tiling(logA, c) if tiling is None: return None D = K.cyclotomic_cosets(r, [x*i for i in tiling]) if k%2 == 0: for d in D: d.insert(K.zero(),0) return D def radical_difference_family(K, k, l=1, existence=False, check=True): r""" Return a ``(v,k,l)``-radical difference family. Let fix an integer `k` and a prime power `q = t k(k-1) + 1`. Let `K` be a field of cardinality `q`. A `(q,k,1)`-difference family is radical* if its base blocks are either: a coset of the `k`-th root of unity for `k` odd or a coset of `k-1`-th root of unity and `0` if `k` is even (the number `t` is the number of blocks of that difference family). The terminology comes from M. Buratti article [Bu95]_ but the first constructions go back to <NAME> [Wi72]_. INPUT: - ``K`` - a finite field - ``k`` -- positive integer, the size of the blocks - ``l`` -- the `\lambda` parameter (default to `1`) - ``existence`` -- if ``True``, then return either ``True`` if Sage knows how to build such design, ``Unknown`` if it does not and ``False`` if it knows that the design does not exist. - ``check`` -- boolean (default: ``True``). If ``True`` then the result of the computation is checked before being returned. This should not be needed but ensures that the output is correct. EXAMPLES:: sage: from sage.combinat.designs.difference_family import radical_difference_family sage: radical_difference_family(GF(73),9) [[1, 2, 4, 8, 16, 32, 37, 55, 64]] sage: radical_difference_family(GF(281),5) [[1, 86, 90, 153, 232], [4, 50, 63, 79, 85], [5, 36, 149, 169, 203], [7, 40, 68, 219, 228], [9, 121, 212, 248, 253], [29, 81, 222, 246, 265], [31, 137, 167, 247, 261], [32, 70, 118, 119, 223], [39, 56, 66, 138, 263], [43, 45, 116, 141, 217], [98, 101, 109, 256, 279], [106, 124, 145, 201, 267], [111, 123, 155, 181, 273], [156, 209, 224, 264, 271]] sage: for k in range(5,10): ....: print("k = {}".format(k)) ....: list_q = [] ....: for q in range(k(k-1)+1, 2000, k(k-1)): ....: if is_prime_power(q): ....: K = GF(q,'a') ....: if radical_difference_family(K, k, existence=True): ....: list_q.append(q) ....: _ = radical_difference_family(K,k) ....: print(" ".join([str(p) for p in list_q])) k = 5 41 61 81 241 281 401 421 601 641 661 701 761 821 881 1181 1201 1301 1321 1361 1381 1481 1601 1681 1801 1901 k = 6 181 211 241 631 691 1531 1831 1861 k = 7 337 421 463 883 1723 k = 8 449 1009 k = 9 73 1153 1873 """ v = K.cardinality() x = K.multiplicative_generator() one = K.one() e = k(k-1) if (l(v-1)) % e: raise ValueError("k (k-1) = {} should be a multiple of l (v-1) ={}".format( k(k-1), l(v-1))) t = l(v-1) // e # number of blocks if t == 1: return radical_difference_set(K, k, l, existence=existence, check=check) elif l == (k-1): if existence: return True else: return K.cyclotomic_cosets(x*((v-1)//k))[1:] # all the other cases below concern the case l == 1 elif l != 1: if existence: return Unknown raise NotImplementedError("No radical families implemented for l > 2") else: D = one_radical_difference_family(K,k) if D is None: if existence: return False raise EmptySetError("No such difference family") elif existence: return True if check and not is_difference_family(K, D, v, k, l): raise RuntimeError("radical_difference_family produced a wrong " "difference family with parameters v={}, " "k={}, l={}. Please contact " "<EMAIL>".format(v,k,l)) return D def twin_prime_powers_difference_set(p, check=True): r""" Return a difference set on `GF(p) \times GF(p+2)`. The difference set is built from the following element of the Cartesian product of finite fields `GF(p) \times GF(p+2)`: - `(x,0)` with any `x` - `(x,y)` with `x` and `y` squares - `(x,y)` with `x` and `y` non-squares For more information see :wikipedia:`Difference_set`. INPUT: - ``check`` -- boolean (default: ``True``). If ``True`` then the result of the computation is checked before being returned. This should not be needed but ensures that the output is correct. EXAMPLES:: sage: from sage.combinat.designs.difference_family import twin_prime_powers_difference_set sage: G,D = twin_prime_powers_difference_set(3) sage: G The Cartesian product of (Finite Field of size 3, Finite Field of
isinstance(o.rvalue, CallExpr): call_expr = o.rvalue if self._IsInstantiation(call_expr): temp_name = 'gobj%d' % self.unique_id self.unique_id += 1 self.log('INSTANCE lval %s rval %s', lval, call_expr) self.write('%s %s', call_expr.callee.name, temp_name) # C c;, not C c(); which is most vexing parse if call_expr.args: self._WriteArgList(call_expr) self.write(';\n') self.write('%s %s = &%s;', get_c_type(lval_type), lval.name, temp_name) return # src = cast(source__SourcedFile, src) # -> source__SourcedFile* src = static_cast<source__SourcedFile>(src) if isinstance(o.rvalue, CallExpr) and o.rvalue.callee.name == 'cast': assert isinstance(lval, NameExpr) call = o.rvalue type_expr = call.args[0] subtype_name = _GetCTypeForCast(type_expr) cast_kind = _GetCastKind(self.module_path, subtype_name) # HACK: Distinguish between UP cast and DOWN cast. # osh/cmd_parse.py _MakeAssignPair does an UP cast within branches. # _t is the base type, so that means it's an upcast. if isinstance(type_expr, NameExpr) and type_expr.name.endswith('_t'): if self.decl: self.local_var_list.append((lval.name, subtype_name)) self.write_ind( '%s = %s<%s>(', lval.name, cast_kind, subtype_name) else: self.write_ind( '%s %s = %s<%s>(', subtype_name, lval.name, cast_kind, subtype_name) self.accept(call.args[1]) # variable being casted self.write(');\n') return if isinstance(lval, NameExpr): if lval.name == '_': # Skip _ = log return lval_type = self.types[lval] #c_type = get_c_type(lval_type, local=self.indent != 0) c_type = get_c_type(lval_type) # for "hoisting" to the top of the function if self.in_func_body: self.write_ind('%s = ', lval.name) if self.decl: self.local_var_list.append((lval.name, c_type)) else: # globals always get a type -- they're not mutated self.write_ind('%s %s = ', c_type, lval.name) # Special case for list comprehensions. Note that a variable has to # be on the LHS, so we can append to it. # # y = [i+1 for i in x[1:] if i] # => # y = [] # for i in x[1:]: # if i: # y.append(i+1) # (but in C++) if isinstance(o.rvalue, ListComprehension): gen = o.rvalue.generator # GeneratorExpr left_expr = gen.left_expr index_expr = gen.indices[0] seq = gen.sequences[0] cond = gen.condlists[0] # TODO: not used! # Write empty container as initialization. assert c_type.endswith(''), c_type # Hack self.write('Alloc<%s>();\n' % c_type[:-1]) over_type = self.types[seq] #self.log(' iterating over type %s', over_type) if over_type.type.fullname() == 'builtins.list': c_type = get_c_type(over_type) assert c_type.endswith(''), c_type c_iter_type = c_type.replace('List', 'ListIter', 1)[:-1] # remove * else: # Example: assoc == Optional[Dict[str, str]] c_iter_type = 'TODO_ASSOC' self.write_ind('for (%s it(', c_iter_type) self.accept(seq) self.write('); !it.Done(); it.Next()) {\n') seq_type = self.types[seq] item_type = seq_type.args[0] # get 'int' from 'List<int>' if isinstance(item_type, Instance): self.write_ind(' %s ', get_c_type(item_type)) self.accept(index_expr) self.write(' = it.Value();\n') elif isinstance(item_type, TupleType): # for x, y in pairs c_item_type = get_c_type(item_type) if isinstance(index_expr, TupleExpr): temp_name = 'tup%d' % self.unique_id self.unique_id += 1 self.write_ind(' %s %s = it.Value();\n', c_item_type, temp_name) self.indent += 1 self._write_tuple_unpacking( temp_name, index_expr.items, item_type.items) self.indent -= 1 else: raise AssertionError() else: raise AssertionError('Unexpected type %s' % item_type) if cond: self.indent += 1 self.write_ind('if (') self.accept(cond[0]) # Just the first one self.write(') {\n') self.write_ind(' %s->append(', lval.name) self.accept(left_expr) self.write(');\n') if cond: self.write_ind('}\n') self.indent -= 1 self.write_ind('}\n') return self.accept(o.rvalue) self.write(';\n') elif isinstance(lval, MemberExpr): self.write_ind('') self.accept(lval) self.write(' = ') self.accept(o.rvalue) self.write(';\n') # Collect statements that look like self.foo = 1 if isinstance(lval.expr, NameExpr) and lval.expr.name == 'self': #log(' lval.name %s', lval.name) lval_type = self.types[lval] self.member_vars[lval.name] = lval_type elif isinstance(lval, IndexExpr): # a[x] = 1 # d->set(x, 1) for both List and Dict self.write_ind('') self.accept(lval.base) self.write('->set(') self.accept(lval.index) self.write(', ') self.accept(o.rvalue) self.write(');\n') elif isinstance(lval, TupleExpr): # An assignment to an n-tuple turns into n+1 statements. Example: # # x, y = mytuple # # Tuple2<int, Str> tup1 = mytuple # int x = tup1->at0() # Str y = tup1->at1() rvalue_type = self.types[o.rvalue] c_type = get_c_type(rvalue_type) is_return = isinstance(o.rvalue, CallExpr) if is_return: assert c_type.endswith('') c_type = c_type[:-1] temp_name = 'tup%d' % self.unique_id self.unique_id += 1 self.write_ind('%s %s = ', c_type, temp_name) self.accept(o.rvalue) self.write(';\n') self._write_tuple_unpacking(temp_name, lval.items, rvalue_type.items, is_return=is_return) else: raise AssertionError(lval) def _write_body(self, body): """Write a block without the { }.""" for stmt in body: # Ignore things that look like docstrings if isinstance(stmt, ExpressionStmt) and isinstance(stmt.expr, StrExpr): continue #log('-- %d', self.indent) self.accept(stmt) def visit_for_stmt(self, o: 'mypy.nodes.ForStmt') -> T: if 0: self.log('ForStmt') self.log(' index_type %s', o.index_type) self.log(' inferred_item_type %s', o.inferred_item_type) self.log(' inferred_iterator_type %s', o.inferred_iterator_type) func_name = None # does the loop look like 'for x in func():' ? if isinstance(o.expr, CallExpr) and isinstance(o.expr.callee, NameExpr): func_name = o.expr.callee.name # special case: 'for i in xrange(3)' if func_name == 'xrange': index_name = o.index.name args = o.expr.args num_args = len(args) if num_args == 1: # xrange(end) self.write_ind('for (int %s = 0; %s < ', index_name, index_name) self.accept(args[0]) self.write('; ++%s) ', index_name) elif num_args == 2: # xrange(being, end) self.write_ind('for (int %s = ', index_name) self.accept(args[0]) self.write('; %s < ', index_name) self.accept(args[1]) self.write('; ++%s) ', index_name) elif num_args == 3: # xrange(being, end, step) # Special case to detect a constant -1. This is a static # heuristic, because it could be negative dynamically. TODO: # mylib.reverse_xrange() or something? step = args[2] if isinstance(step, UnaryExpr) and step.op == '-': comparison_op = '>' else: comparison_op = '<' self.write_ind('for (int %s = ', index_name) self.accept(args[0]) self.write('; %s %s ', index_name, comparison_op) self.accept(args[1]) self.write('; %s += ', index_name) self.accept(step) self.write(') ') else: raise AssertionError() self.accept(o.body) return reverse = False # for i, x in enumerate(...): index0_name = None if func_name == 'enumerate': assert isinstance(o.index, TupleExpr), o.index index0 = o.index.items[0] assert isinstance(index0, NameExpr), index0 index0_name = index0.name # generate int i = 0; ; ++i # type of 'x' in 'for i, x in enumerate(...)' item_type = o.inferred_item_type.items[1] index_expr = o.index.items[1] # enumerate(mylist) turns into iteration over mylist with variable i assert len(o.expr.args) == 1, o.expr.args iterated_over = o.expr.args[0] elif func_name == 'reversed': # NOTE: enumerate() and reversed() can't be mixed yet. But you CAN # reverse iter over tuples. item_type = o.inferred_item_type index_expr = o.index args = o.expr.args assert len(args) == 1, args iterated_over = args[0] reverse = True # use different iterate elif func_name == 'iteritems': item_type = o.inferred_item_type index_expr = o.index args = o.expr.args assert len(args) == 1, args # This should be a dict iterated_over = args[0] log('------------ ITERITEMS OVER %s', iterated_over) else: item_type = o.inferred_item_type index_expr = o.index iterated_over = o.expr over_type = self.types[iterated_over] #self.log(' iterating over type %s', over_type) #self.log(' iterating over type %s', over_type.type.fullname()) over_dict = False if over_type.type.fullname() == 'builtins.list': c_type = get_c_type(over_type) assert c_type.endswith(''), c_type c_iter_type = c_type.replace('List', 'ListIter', 1)[:-1] # remove * # ReverseListIter! if reverse: c_iter_type = 'Reverse' + c_iter_type elif over_type.type.fullname() == 'builtins.dict': # Iterator c_type = get_c_type(over_type) assert c_type.endswith(''), c_type c_iter_type = c_type.replace('Dict', 'DictIter', 1)[:-1] # remove over_dict = True assert not reverse elif over_type.type.fullname() == 'builtins.str': c_iter_type = 'StrIter' assert not reverse # can't reverse iterate over string yet else: # assume it's like d.iteritems()? Iterator type assert False, over_type if index0_name: # can't initialize two things in a for loop, so do it on a separate line if self.decl: self.local_var_list.append((index0_name, 'int')) self.write_ind('%s = 0;\n', index0_name) index_update = ', ++%s' % index0_name else: index_update = '' self.write_ind('for (%s it(', c_iter_type) self.accept(iterated_over) # the thing being iterated over self.write('); !it.Done(); it.Next()%s) {\n', index_update) # for x in it: ... # for i, x in enumerate(pairs): ... if isinstance(item_type, Instance) or index0_name: c_item_type = get_c_type(item_type) self.write_ind(' %s ', c_item_type) self.accept(index_expr) if over_dict: self.write(' = it.Key();\n') else: self.write(' = it.Value();\n') elif isinstance(item_type, TupleType): # for x, y in pairs if over_dict: assert isinstance(o.index, TupleExpr), o.index index_items = o.index.items assert len(index_items) == 2, index_items assert len(item_type.items) == 2, item_type.items key_type = get_c_type(item_type.items[0]) val_type = get_c_type(item_type.items[1]) self.write_ind(' %s %s = it.Key();\n', key_type, index_items[0].name) self.write_ind(' %s %s = it.Value();\n', val_type, index_items[1].name) else: # Example: # for (ListIter it(mylist); !it.Done(); it.Next()) { # Tuple2<int, Str> tup1 = it.Value(); # int i = tup1->at0(); # Str s = tup1->at1(); # log("%d %s", i, s); # } c_item_type = get_c_type(item_type) if isinstance(o.index, TupleExpr): temp_name = 'tup%d' % self.unique_id self.unique_id += 1 self.write_ind(' %s %s = it.Value();\n', c_item_type, temp_name) self.indent += 1 self._write_tuple_unpacking( temp_name, o.index.items, item_type.items) self.indent -= 1 else: self.write_ind(' %s %s = it.Value();\n', c_item_type, o.index.name) else: raise AssertionError('Unexpected type %s' % item_type) # Copy
= Var(within=Reals,bounds=(0,1),initialize=0) m.x598 = Var(within=Reals,bounds=(0,1),initialize=0) m.x599 = Var(within=Reals,bounds=(0,1),initialize=0) m.x600 = Var(within=Reals,bounds=(0,1),initialize=0) m.x601 = Var(within=Reals,bounds=(0,1),initialize=0) m.x602 = Var(within=Reals,bounds=(0,1),initialize=0) m.x603 = Var(within=Reals,bounds=(0,1),initialize=0) m.x604 = Var(within=Reals,bounds=(0,1),initialize=0) m.x605 = Var(within=Reals,bounds=(0,1),initialize=0) m.x606 = Var(within=Reals,bounds=(0,1),initialize=0) m.x607 = Var(within=Reals,bounds=(0,1),initialize=0) m.x608 = Var(within=Reals,bounds=(0,1),initialize=0) m.x609 = Var(within=Reals,bounds=(0,1),initialize=0) m.x610 = Var(within=Reals,bounds=(0,1),initialize=0) m.x611 = Var(within=Reals,bounds=(0,1),initialize=0) m.x612 = Var(within=Reals,bounds=(0,1),initialize=0) m.x613 = Var(within=Reals,bounds=(0,1),initialize=0) m.x614 = Var(within=Reals,bounds=(0,1),initialize=0) m.x615 = Var(within=Reals,bounds=(0,1),initialize=0) m.x616 = Var(within=Reals,bounds=(0,1),initialize=0) m.x617 = Var(within=Reals,bounds=(0,1),initialize=0) m.x618 = Var(within=Reals,bounds=(0,1),initialize=0) m.x619 = Var(within=Reals,bounds=(0,1),initialize=0) m.x620 = Var(within=Reals,bounds=(0,1),initialize=0) m.x621 = Var(within=Reals,bounds=(0,1),initialize=0) m.x622 = Var(within=Reals,bounds=(0,1),initialize=0) m.x623 = Var(within=Reals,bounds=(0,1),initialize=0) m.x624 = Var(within=Reals,bounds=(0,1),initialize=0) m.x625 = Var(within=Reals,bounds=(0,1),initialize=0) m.x626 = Var(within=Reals,bounds=(0,1),initialize=0) m.x627 = Var(within=Reals,bounds=(0,1),initialize=0) m.x628 = Var(within=Reals,bounds=(0,1),initialize=0) m.x629 = Var(within=Reals,bounds=(0,1),initialize=0) m.x630 = Var(within=Reals,bounds=(0,1),initialize=0) m.x631 = Var(within=Reals,bounds=(0,1),initialize=0) m.x632 = Var(within=Reals,bounds=(0,1),initialize=0) m.x633 = Var(within=Reals,bounds=(0,1),initialize=0) m.x634 = Var(within=Reals,bounds=(0,1),initialize=0) m.x635 = Var(within=Reals,bounds=(0,1),initialize=0) m.x636 = Var(within=Reals,bounds=(0,1),initialize=0) m.x637 = Var(within=Reals,bounds=(0,1),initialize=0) m.x638 = Var(within=Reals,bounds=(0,1),initialize=0) m.x639 = Var(within=Reals,bounds=(0,1),initialize=0) m.x640 = Var(within=Reals,bounds=(0,1),initialize=0) m.x641 = Var(within=Reals,bounds=(0,1),initialize=0) m.x642 = Var(within=Reals,bounds=(0,1),initialize=0) m.x643 = Var(within=Reals,bounds=(0,1),initialize=0) m.x644 = Var(within=Reals,bounds=(0,1),initialize=0) m.x645 = Var(within=Reals,bounds=(0,1),initialize=0) m.x646 = Var(within=Reals,bounds=(0,1),initialize=0) m.x647 = Var(within=Reals,bounds=(0,1),initialize=0) m.x648 = Var(within=Reals,bounds=(0,1),initialize=0) m.x649 = Var(within=Reals,bounds=(0,1),initialize=0) m.x650 = Var(within=Reals,bounds=(0,1),initialize=0) m.x651 = Var(within=Reals,bounds=(0,1),initialize=0) m.x652 = Var(within=Reals,bounds=(0,1),initialize=0) m.x653 = Var(within=Reals,bounds=(0,1),initialize=0) m.x654 = Var(within=Reals,bounds=(0,1),initialize=0) m.x655 = Var(within=Reals,bounds=(0,1),initialize=0) m.x656 = Var(within=Reals,bounds=(0,1),initialize=0) m.x657 = Var(within=Reals,bounds=(0,1),initialize=0) m.x658 = Var(within=Reals,bounds=(0,1),initialize=0) m.x659 = Var(within=Reals,bounds=(0,1),initialize=0) m.x660 = Var(within=Reals,bounds=(0,1),initialize=0) m.x661 = Var(within=Reals,bounds=(0,1),initialize=0) m.x662 = Var(within=Reals,bounds=(0,1),initialize=0) m.x663 = Var(within=Reals,bounds=(0,1),initialize=0) m.x664 = Var(within=Reals,bounds=(0,1),initialize=0) m.x665 = Var(within=Reals,bounds=(0,1),initialize=0) m.x666 = Var(within=Reals,bounds=(0,1),initialize=0) m.x667 = Var(within=Reals,bounds=(0,1),initialize=0) m.x668 = Var(within=Reals,bounds=(0,1),initialize=0) m.x669 = Var(within=Reals,bounds=(0,1),initialize=0) m.x670 = Var(within=Reals,bounds=(0,1),initialize=0) m.x671 = Var(within=Reals,bounds=(0,1),initialize=0) m.x672 = Var(within=Reals,bounds=(0,1),initialize=0) m.x673 = Var(within=Reals,bounds=(0,1),initialize=0) m.x674 = Var(within=Reals,bounds=(0,1),initialize=0) m.x675 = Var(within=Reals,bounds=(0,1),initialize=0) m.x676 = Var(within=Reals,bounds=(0,1),initialize=0) m.x677 = Var(within=Reals,bounds=(0,1),initialize=0) m.x678 = Var(within=Reals,bounds=(0,1),initialize=0) m.x679 = Var(within=Reals,bounds=(0,1),initialize=0) m.x680 = Var(within=Reals,bounds=(0,1),initialize=0) m.x681 = Var(within=Reals,bounds=(0,1),initialize=0) m.x682 = Var(within=Reals,bounds=(0,1),initialize=0) m.x683 = Var(within=Reals,bounds=(0,1),initialize=0) m.x684 = Var(within=Reals,bounds=(0,1),initialize=0) m.x685 = Var(within=Reals,bounds=(0,1),initialize=0) m.x686 = Var(within=Reals,bounds=(0,1),initialize=0) m.x687 = Var(within=Reals,bounds=(0,1),initialize=0) m.x688 = Var(within=Reals,bounds=(0,1),initialize=0) m.x689 = Var(within=Reals,bounds=(0,1),initialize=0) m.x690 = Var(within=Reals,bounds=(0,1),initialize=0) m.x691 = Var(within=Reals,bounds=(0,1),initialize=0) m.x692 = Var(within=Reals,bounds=(0,1),initialize=0) m.x693 = Var(within=Reals,bounds=(0,1),initialize=0) m.x694 = Var(within=Reals,bounds=(0,1),initialize=0) m.x695 = Var(within=Reals,bounds=(0,1),initialize=0) m.x696 = Var(within=Reals,bounds=(0,1),initialize=0) m.x697 = Var(within=Reals,bounds=(0,1),initialize=0) m.x698 = Var(within=Reals,bounds=(0,1),initialize=0) m.x699 = Var(within=Reals,bounds=(0,1),initialize=0) m.x700 = Var(within=Reals,bounds=(0,1),initialize=0) m.x701 = Var(within=Reals,bounds=(0,1),initialize=0) m.x702 = Var(within=Reals,bounds=(0,1),initialize=0) m.x703 = Var(within=Reals,bounds=(0,1),initialize=0) m.x704 = Var(within=Reals,bounds=(0,1),initialize=0) m.x705 = Var(within=Reals,bounds=(0,1),initialize=0) m.x706 = Var(within=Reals,bounds=(0,1),initialize=0) m.x707 = Var(within=Reals,bounds=(0,1),initialize=0) m.x708 = Var(within=Reals,bounds=(0,1),initialize=0) m.x709 = Var(within=Reals,bounds=(0,1),initialize=0) m.x710 = Var(within=Reals,bounds=(0,1),initialize=0) m.x711 = Var(within=Reals,bounds=(0,1),initialize=0) m.x712 = Var(within=Reals,bounds=(0,1),initialize=0) m.x713 = Var(within=Reals,bounds=(0,1),initialize=0) m.x714 = Var(within=Reals,bounds=(0,1),initialize=0) m.x715 = Var(within=Reals,bounds=(0,1),initialize=0) m.x716 = Var(within=Reals,bounds=(0,1),initialize=0) m.x717 = Var(within=Reals,bounds=(0,1),initialize=0) m.x718 = Var(within=Reals,bounds=(0,1),initialize=0) m.x719 = Var(within=Reals,bounds=(0,1),initialize=0) m.x720 = Var(within=Reals,bounds=(0,1),initialize=0) m.x721 = Var(within=Reals,bounds=(0,1),initialize=0) m.x722 = Var(within=Reals,bounds=(0,1),initialize=0) m.x723 = Var(within=Reals,bounds=(0,1),initialize=0) m.x724 = Var(within=Reals,bounds=(0,1),initialize=0) m.x725 = Var(within=Reals,bounds=(0,1),initialize=0) m.x726 = Var(within=Reals,bounds=(0,1),initialize=0) m.x727 = Var(within=Reals,bounds=(0,1),initialize=0) m.x728 = Var(within=Reals,bounds=(0,1),initialize=0) m.x729 = Var(within=Reals,bounds=(0,1),initialize=0) m.x730 = Var(within=Reals,bounds=(0,1),initialize=0) m.x731 = Var(within=Reals,bounds=(0,1),initialize=0) m.x732 = Var(within=Reals,bounds=(0,1),initialize=0) m.x733 = Var(within=Reals,bounds=(0,1),initialize=0) m.x734 = Var(within=Reals,bounds=(0,1),initialize=0) m.x735 = Var(within=Reals,bounds=(0,1),initialize=0) m.x736 = Var(within=Reals,bounds=(0,1),initialize=0) m.x737 = Var(within=Reals,bounds=(0,1),initialize=0) m.x738 = Var(within=Reals,bounds=(0,1),initialize=0) m.x739 = Var(within=Reals,bounds=(0,1),initialize=0) m.x740 = Var(within=Reals,bounds=(0,1),initialize=0) m.x741 = Var(within=Reals,bounds=(0,1),initialize=0) m.x742 = Var(within=Reals,bounds=(0,1),initialize=0) m.x743 = Var(within=Reals,bounds=(0,1),initialize=0) m.x744 = Var(within=Reals,bounds=(0,1),initialize=0) m.x745 = Var(within=Reals,bounds=(0,1),initialize=0) m.x746 = Var(within=Reals,bounds=(0,1),initialize=0) m.x747 = Var(within=Reals,bounds=(0,1),initialize=0) m.x748 = Var(within=Reals,bounds=(0,1),initialize=0) m.x749 = Var(within=Reals,bounds=(0,1),initialize=0) m.x750 = Var(within=Reals,bounds=(0,1),initialize=0) m.x751 = Var(within=Reals,bounds=(0,1),initialize=0) m.x752 = Var(within=Reals,bounds=(0,1),initialize=0) m.x753 = Var(within=Reals,bounds=(0,1),initialize=0) m.x754 = Var(within=Reals,bounds=(0,1),initialize=0) m.x755 = Var(within=Reals,bounds=(0,1),initialize=0) m.x756 = Var(within=Reals,bounds=(0,1),initialize=0) m.x757 = Var(within=Reals,bounds=(0,1),initialize=0) m.x758 = Var(within=Reals,bounds=(0,1),initialize=0) m.x759 = Var(within=Reals,bounds=(0,1),initialize=0) m.x760 = Var(within=Reals,bounds=(0,1),initialize=0) m.x761 = Var(within=Reals,bounds=(0,1),initialize=0) m.x762 = Var(within=Reals,bounds=(0,1),initialize=0) m.x763 = Var(within=Reals,bounds=(0,1),initialize=0) m.x764 = Var(within=Reals,bounds=(0,1),initialize=0) m.x765 = Var(within=Reals,bounds=(0,1),initialize=0) m.x766 = Var(within=Reals,bounds=(0,1),initialize=0) m.x767 = Var(within=Reals,bounds=(0,1),initialize=0) m.x768 = Var(within=Reals,bounds=(0,1),initialize=0) m.x769 = Var(within=Reals,bounds=(0,1),initialize=0) m.x770 = Var(within=Reals,bounds=(0,1),initialize=0) m.x771 = Var(within=Reals,bounds=(0,1),initialize=0) m.x772 = Var(within=Reals,bounds=(0,1),initialize=0) m.x773 = Var(within=Reals,bounds=(0,1),initialize=0) m.x774 = Var(within=Reals,bounds=(0,1),initialize=0) m.x775 = Var(within=Reals,bounds=(0,1),initialize=0) m.x776 = Var(within=Reals,bounds=(0,1),initialize=0) m.x777 = Var(within=Reals,bounds=(0,1),initialize=0) m.x778 = Var(within=Reals,bounds=(0,1),initialize=0) m.x779 = Var(within=Reals,bounds=(0,1),initialize=0) m.x780 = Var(within=Reals,bounds=(0,1),initialize=0) m.x781 = Var(within=Reals,bounds=(0,1),initialize=0) m.x782 = Var(within=Reals,bounds=(0,1),initialize=0) m.x783 = Var(within=Reals,bounds=(0,1),initialize=0) m.x784 = Var(within=Reals,bounds=(0,1),initialize=0) m.x785 = Var(within=Reals,bounds=(0,1),initialize=0) m.x786 = Var(within=Reals,bounds=(0,1),initialize=0) m.x787 = Var(within=Reals,bounds=(0,1),initialize=0) m.x788 = Var(within=Reals,bounds=(0,1),initialize=0) m.x789 = Var(within=Reals,bounds=(0,1),initialize=0) m.x790 = Var(within=Reals,bounds=(0,1),initialize=0) m.x791 = Var(within=Reals,bounds=(0,1),initialize=0) m.x792 = Var(within=Reals,bounds=(0,1),initialize=0) m.x793 = Var(within=Reals,bounds=(0,1),initialize=0) m.x794 = Var(within=Reals,bounds=(0,1),initialize=0) m.x795 = Var(within=Reals,bounds=(0,1),initialize=0) m.x796 = Var(within=Reals,bounds=(0,1),initialize=0) m.x797 = Var(within=Reals,bounds=(0,1),initialize=0) m.x798 = Var(within=Reals,bounds=(0,1),initialize=0) m.x799 = Var(within=Reals,bounds=(0,1),initialize=0) m.x800 = Var(within=Reals,bounds=(0,1),initialize=0) m.x801 = Var(within=Reals,bounds=(0,1),initialize=0) m.x802 = Var(within=Reals,bounds=(0,1),initialize=0) m.x803 = Var(within=Reals,bounds=(0,1),initialize=0) m.x804 = Var(within=Reals,bounds=(0,1),initialize=0) m.x805 = Var(within=Reals,bounds=(0,1),initialize=0) m.x806 = Var(within=Reals,bounds=(0,1),initialize=0) m.x807 = Var(within=Reals,bounds=(0,1),initialize=0) m.x808 = Var(within=Reals,bounds=(0,1),initialize=0) m.x809 = Var(within=Reals,bounds=(0,1),initialize=0) m.x810 = Var(within=Reals,bounds=(0,1),initialize=0) m.x811 = Var(within=Reals,bounds=(0,1),initialize=0) m.x812 = Var(within=Reals,bounds=(0,1),initialize=0) m.x813 = Var(within=Reals,bounds=(0,1),initialize=0) m.x814 = Var(within=Reals,bounds=(0,1),initialize=0) m.x815 = Var(within=Reals,bounds=(0,1),initialize=0) m.x816 = Var(within=Reals,bounds=(0,1),initialize=0) m.x817 = Var(within=Reals,bounds=(0,1),initialize=0) m.x818 = Var(within=Reals,bounds=(0,1),initialize=0) m.x819 = Var(within=Reals,bounds=(0,1),initialize=0) m.x820 = Var(within=Reals,bounds=(0,1),initialize=0) m.x821 = Var(within=Reals,bounds=(0,1),initialize=0) m.x822 = Var(within=Reals,bounds=(0,1),initialize=0) m.x823 = Var(within=Reals,bounds=(0,1),initialize=0) m.x824 = Var(within=Reals,bounds=(0,1),initialize=0) m.x825 = Var(within=Reals,bounds=(0,1),initialize=0) m.x826 = Var(within=Reals,bounds=(0,1),initialize=0) m.x827 = Var(within=Reals,bounds=(0,1),initialize=0) m.x828 = Var(within=Reals,bounds=(0,1),initialize=0) m.x829 = Var(within=Reals,bounds=(0,1),initialize=0) m.x830 = Var(within=Reals,bounds=(0,1),initialize=0) m.x831 = Var(within=Reals,bounds=(0,1),initialize=0) m.x832 = Var(within=Reals,bounds=(0,1),initialize=0) m.x833 = Var(within=Reals,bounds=(0,1),initialize=0) m.x834 = Var(within=Reals,bounds=(0,1),initialize=0) m.x835 = Var(within=Reals,bounds=(0,1),initialize=0) m.x836 = Var(within=Reals,bounds=(0,1),initialize=0) m.x837 = Var(within=Reals,bounds=(0,1),initialize=0) m.x838 = Var(within=Reals,bounds=(0,1),initialize=0) m.x839 = Var(within=Reals,bounds=(0,1),initialize=0) m.x840 = Var(within=Reals,bounds=(0,1),initialize=0) m.x841 = Var(within=Reals,bounds=(0,1),initialize=0) m.x842 = Var(within=Reals,bounds=(0,1),initialize=0) m.x843 = Var(within=Reals,bounds=(0,1),initialize=0) m.x844 = Var(within=Reals,bounds=(0,1),initialize=0) m.x845 = Var(within=Reals,bounds=(0,1),initialize=0) m.x846 = Var(within=Reals,bounds=(0,1),initialize=0) m.x847 = Var(within=Reals,bounds=(0,1),initialize=0) m.x848 = Var(within=Reals,bounds=(0,1),initialize=0) m.x849 = Var(within=Reals,bounds=(0,1),initialize=0) m.x850 = Var(within=Reals,bounds=(0,1),initialize=0) m.x851 = Var(within=Reals,bounds=(0,1),initialize=0) m.x852 = Var(within=Reals,bounds=(0,1),initialize=0) m.x853 = Var(within=Reals,bounds=(0,1),initialize=0) m.x854 = Var(within=Reals,bounds=(0,1),initialize=0) m.x855 = Var(within=Reals,bounds=(0,1),initialize=0) m.x856 = Var(within=Reals,bounds=(0,1),initialize=0) m.x857 = Var(within=Reals,bounds=(0,1),initialize=0) m.x858 = Var(within=Reals,bounds=(0,1),initialize=0) m.x859 = Var(within=Reals,bounds=(0,1),initialize=0) m.x860 = Var(within=Reals,bounds=(0,1),initialize=0) m.x861 = Var(within=Reals,bounds=(0,1),initialize=0) m.x862 = Var(within=Reals,bounds=(0,1),initialize=0) m.x863 = Var(within=Reals,bounds=(0,1),initialize=0) m.x864 = Var(within=Reals,bounds=(0,1),initialize=0) m.x865 = Var(within=Reals,bounds=(0,1),initialize=0) m.x866 = Var(within=Reals,bounds=(0,1),initialize=0) m.x867 = Var(within=Reals,bounds=(0,1),initialize=0) m.x868 = Var(within=Reals,bounds=(0,1),initialize=0) m.x869 = Var(within=Reals,bounds=(0,1),initialize=0) m.x870 = Var(within=Reals,bounds=(0,1),initialize=0) m.x871 = Var(within=Reals,bounds=(0,1),initialize=0) m.x872 = Var(within=Reals,bounds=(0,1),initialize=0) m.x873 = Var(within=Reals,bounds=(0,1),initialize=0) m.x874 = Var(within=Reals,bounds=(0,1),initialize=0) m.x875 = Var(within=Reals,bounds=(0,1),initialize=0) m.x876 = Var(within=Reals,bounds=(0,1),initialize=0) m.x877 = Var(within=Reals,bounds=(0,1),initialize=0) m.x878 = Var(within=Reals,bounds=(0,1),initialize=0) m.x879 = Var(within=Reals,bounds=(0,1),initialize=0) m.x880 = Var(within=Reals,bounds=(0,1),initialize=0) m.x881 = Var(within=Reals,bounds=(0,1),initialize=0) m.x882 = Var(within=Reals,bounds=(0,1),initialize=0) m.x883 = Var(within=Reals,bounds=(0,1),initialize=0) m.x884 = Var(within=Reals,bounds=(0,1),initialize=0) m.x885 = Var(within=Reals,bounds=(0,1),initialize=0) m.x886 = Var(within=Reals,bounds=(0,1),initialize=0) m.x887 = Var(within=Reals,bounds=(0,1),initialize=0) m.x888 = Var(within=Reals,bounds=(0,1),initialize=0) m.x889 = Var(within=Reals,bounds=(0,1),initialize=0) m.x890 = Var(within=Reals,bounds=(0,1),initialize=0) m.x891 = Var(within=Reals,bounds=(0,1),initialize=0) m.x892 = Var(within=Reals,bounds=(0,1),initialize=0) m.x893 = Var(within=Reals,bounds=(0,1),initialize=0) m.x894 = Var(within=Reals,bounds=(0,1),initialize=0) m.x895 = Var(within=Reals,bounds=(0,1),initialize=0) m.x896 = Var(within=Reals,bounds=(0,1),initialize=0) m.x897 = Var(within=Reals,bounds=(0,1),initialize=0) m.x898 = Var(within=Reals,bounds=(0,1),initialize=0) m.x899 = Var(within=Reals,bounds=(0,1),initialize=0) m.x900 = Var(within=Reals,bounds=(0,1),initialize=0) m.x901 = Var(within=Reals,bounds=(0,1),initialize=0) m.x902 = Var(within=Reals,bounds=(0,1),initialize=0) m.x903 = Var(within=Reals,bounds=(0,1),initialize=0) m.x904 = Var(within=Reals,bounds=(0,1),initialize=0) m.x905 = Var(within=Reals,bounds=(0,1),initialize=0) m.x906 = Var(within=Reals,bounds=(0,1),initialize=0) m.x907 = Var(within=Reals,bounds=(0,1),initialize=0) m.x908 = Var(within=Reals,bounds=(0,1),initialize=0) m.x909 = Var(within=Reals,bounds=(0,1),initialize=0) m.x910 = Var(within=Reals,bounds=(0,1),initialize=0) m.x911 = Var(within=Reals,bounds=(0,1),initialize=0) m.x912 = Var(within=Reals,bounds=(0,1),initialize=0) m.x913 = Var(within=Reals,bounds=(0,1),initialize=0) m.x914 = Var(within=Reals,bounds=(0,1),initialize=0) m.x915 = Var(within=Reals,bounds=(0,1),initialize=0) m.x916 = Var(within=Reals,bounds=(0,1),initialize=0) m.x917 = Var(within=Reals,bounds=(0,1),initialize=0) m.x918 = Var(within=Reals,bounds=(0,1),initialize=0) m.x919 = Var(within=Reals,bounds=(0,1),initialize=0) m.x920 = Var(within=Reals,bounds=(0,1),initialize=0) m.x921 = Var(within=Reals,bounds=(0,1),initialize=0) m.x922 = Var(within=Reals,bounds=(0,1),initialize=0) m.x923 = Var(within=Reals,bounds=(0,1),initialize=0) m.x924 = Var(within=Reals,bounds=(0,1),initialize=0) m.x925 = Var(within=Reals,bounds=(0,1),initialize=0) m.x926 = Var(within=Reals,bounds=(0,1),initialize=0) m.x927 = Var(within=Reals,bounds=(0,1),initialize=0) m.x928 = Var(within=Reals,bounds=(0,1),initialize=0) m.x929 = Var(within=Reals,bounds=(0,1),initialize=0) m.x930 = Var(within=Reals,bounds=(0,1),initialize=0) m.x931 = Var(within=Reals,bounds=(0,1),initialize=0) m.x932 = Var(within=Reals,bounds=(0,1),initialize=0) m.x933 = Var(within=Reals,bounds=(0,1),initialize=0) m.x934 = Var(within=Reals,bounds=(0,1),initialize=0) m.x935 = Var(within=Reals,bounds=(0,1),initialize=0) m.x936 = Var(within=Reals,bounds=(0,1),initialize=0) m.x937 = Var(within=Reals,bounds=(0,1),initialize=0) m.x938 = Var(within=Reals,bounds=(0,1),initialize=0) m.x939 = Var(within=Reals,bounds=(0,1),initialize=0) m.x940 = Var(within=Reals,bounds=(0,1),initialize=0) m.x941 = Var(within=Reals,bounds=(0,1),initialize=0) m.x942 = Var(within=Reals,bounds=(0,1),initialize=0) m.x943 = Var(within=Reals,bounds=(0,1),initialize=0) m.x944 = Var(within=Reals,bounds=(0,1),initialize=0) m.x945 = Var(within=Reals,bounds=(0,1),initialize=0) m.x946 = Var(within=Reals,bounds=(0,1),initialize=0) m.x947 = Var(within=Reals,bounds=(0,1),initialize=0) m.x948 = Var(within=Reals,bounds=(0,1),initialize=0) m.x949 = Var(within=Reals,bounds=(0,1),initialize=0) m.x950 = Var(within=Reals,bounds=(0,1),initialize=0) m.x951 = Var(within=Reals,bounds=(0,1),initialize=0) m.x952 = Var(within=Reals,bounds=(0,1),initialize=0) m.x953 = Var(within=Reals,bounds=(0,1),initialize=0) m.x954 = Var(within=Reals,bounds=(0,1),initialize=0) m.x955 = Var(within=Reals,bounds=(0,1),initialize=0) m.x956 = Var(within=Reals,bounds=(0,1),initialize=0) m.x957 = Var(within=Reals,bounds=(0,1),initialize=0) m.x958 = Var(within=Reals,bounds=(0,1),initialize=0) m.x959 = Var(within=Reals,bounds=(0,1),initialize=0) m.x960 = Var(within=Reals,bounds=(0,1),initialize=0) m.x961 = Var(within=Reals,bounds=(0,1),initialize=0) m.x962 = Var(within=Reals,bounds=(0,1),initialize=0) m.x963 = Var(within=Reals,bounds=(0,1),initialize=0) m.x964 = Var(within=Reals,bounds=(0,1),initialize=0) m.x965 = Var(within=Reals,bounds=(0,1),initialize=0) m.x966 = Var(within=Reals,bounds=(0,1),initialize=0) m.x967 = Var(within=Reals,bounds=(0,1),initialize=0) m.x968 = Var(within=Reals,bounds=(0,1),initialize=0) m.x969 = Var(within=Reals,bounds=(0,1),initialize=0) m.x970 = Var(within=Reals,bounds=(0,1),initialize=0) m.x971 = Var(within=Reals,bounds=(0,1),initialize=0) m.x972 = Var(within=Reals,bounds=(0,1),initialize=0) m.x973 = Var(within=Reals,bounds=(0,1),initialize=0) m.x974 = Var(within=Reals,bounds=(0,1),initialize=0) m.x975 = Var(within=Reals,bounds=(0,1),initialize=0) m.x976 = Var(within=Reals,bounds=(0,1),initialize=0) m.x977 = Var(within=Reals,bounds=(0,1),initialize=0) m.x978 = Var(within=Reals,bounds=(0,1),initialize=0) m.x979 = Var(within=Reals,bounds=(0,1),initialize=0) m.x980 = Var(within=Reals,bounds=(0,1),initialize=0) m.x981 = Var(within=Reals,bounds=(0,1),initialize=0) m.x982 = Var(within=Reals,bounds=(0,1),initialize=0) m.x983 = Var(within=Reals,bounds=(0,1),initialize=0) m.x984 = Var(within=Reals,bounds=(0,1),initialize=0) m.x985 = Var(within=Reals,bounds=(0,1),initialize=0) m.x986 = Var(within=Reals,bounds=(0,1),initialize=0) m.x987 = Var(within=Reals,bounds=(0,1),initialize=0) m.x988 = Var(within=Reals,bounds=(0,1),initialize=0) m.x989 = Var(within=Reals,bounds=(0,1),initialize=0) m.x990 = Var(within=Reals,bounds=(0,1),initialize=0) m.x991 = Var(within=Reals,bounds=(0,1),initialize=0) m.x992 = Var(within=Reals,bounds=(0,1),initialize=0) m.x993 = Var(within=Reals,bounds=(0,1),initialize=0) m.x994 = Var(within=Reals,bounds=(0,1),initialize=0) m.x995 = Var(within=Reals,bounds=(0,1),initialize=0) m.x996 = Var(within=Reals,bounds=(0,1),initialize=0) m.x997 = Var(within=Reals,bounds=(0,1),initialize=0) m.x998 = Var(within=Reals,bounds=(0,1),initialize=0) m.x999 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1000 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1001 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1002 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1003 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1004 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1005 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1006 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1007 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1008 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1009 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1010 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1011 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1012 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1013 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1014 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1015 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1016 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1017 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1018 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1019 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1020 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1021 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1022 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1023 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1024 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1025 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1026 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1027 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1028 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1029 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1030 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1031 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1032 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1033 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1034 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1035 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1036 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1037 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1038 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1039 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1040 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1041 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1042 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1043 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1044 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1045 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1046 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1047 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1048 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1049 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1050 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1051 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1052 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1053 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1054 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1055 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1056 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1057 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1058 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1059 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1060 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1061 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1062 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1063 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1064 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1065 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1066 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1067 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1068 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1069 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1070 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1071 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1072 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1073 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1074 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1075 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1076 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1077 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1078 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1079 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1080 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1081 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1082 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1083 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1084 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1085 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1086 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1087 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1088 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1089 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1090 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1091 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1092 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1093 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1094 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1095 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1096 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1097 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1098 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1099 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1100 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1101 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1102 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1103 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1104 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1105 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1106 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1107 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1108 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1109 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1110 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1111 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1112 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1113 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1114 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1115 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1116 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1117 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1118 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1119 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1120 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1121 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1122 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1123 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1124 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1125 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1126 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1127 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1128 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1129 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1130 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1131 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1132 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1133 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1134 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1135 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1136 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1137 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1138 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1139 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1140 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1141 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1142 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1143 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1144 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1145 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1146 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1147 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1148 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1149 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1150 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1151 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1152 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1153 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1154 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1155 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1156 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1157 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1158 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1159 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1160 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1161 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1162 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1163 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1164 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1165 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1166 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1167 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1168 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1169 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1170 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1171 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1172 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1173 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1174 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1175 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1176 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1177 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1178 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1179 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1180 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1181 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1182 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1183 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1184 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1185 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1186 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1187 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1188 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1189 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1190 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1191 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1192 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1193 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1194 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1195 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1196 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1197 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1198 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1199 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1200 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1201 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1202 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1203 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1204 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1205 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1206 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1207 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1208 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1209 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1210 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1211 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1212 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1213 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1214 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1215 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1216 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1217 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1218 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1219 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1220 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1221 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1222 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1223 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1224 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1225 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1226 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1227 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1228 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1229 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1230 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1231 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1232 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1233 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1234 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1235 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1236 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1237
from django.contrib import admin from parler.admin import TranslatableAdmin from django.utils.html import format_html from django.forms import BaseInlineFormSet from django.shortcuts import redirect from django import forms import data_wizard # Solution to data import madness that had refused to go from django.conf import settings # allow import of projects settings at the root from django.forms import TextInput,Textarea #customize textarea row and column size from import_export.formats import base_formats from .models import (StgFacilityType,StgFacilityServiceMeasureUnits, StgFacilityOwnership,StgHealthFacility,StgServiceDomain,StgLocationCodes, FacilityServiceAvailability,FacilityServiceAvailabilityProxy, FacilityServiceProvision,StgFacilityServiceIntervention, FacilityServiceReadiness,StgFacilityServiceAreas, FacilityServiceProvisionProxy,FacilityServiceReadinesProxy) from commoninfo.admin import OverideImportExport,OverideExport,OverideImport # from publications.serializers import StgKnowledgeProductSerializer from .resources import (StgFacilityResourceExport,FacilityTypeResourceExport, FacilityServiceDomainResourceExport,StgFacilityServiceAvailabilityExport, StgFacilityServiceCapacityExport,StgFacilityServiceReadinessExport,) from regions.models import StgLocation,StgLocationCodes from django_admin_listfilter_dropdown.filters import ( DropdownFilter, RelatedDropdownFilter, ChoiceDropdownFilter, RelatedOnlyDropdownFilter) #custom from import_export.admin import (ImportExportModelAdmin, ExportMixin, ImportExportActionModelAdmin,ExportActionModelAdmin,) from authentication.models import CustomUser, CustomGroup from bootstrap_datepicker_plus import DatePickerInput # Nice date picker 06/03 from .filters import TranslatedFieldFilter #Danile solution to duplicate filters #Methods used to register global actions performed on data. See actions listbox def transition_to_pending (modeladmin, request, queryset): queryset.update(comment = 'pending') transition_to_pending.short_description = "Mark selected as Pending" def transition_to_approved (modeladmin, request, queryset): queryset.update (comment = 'approved') transition_to_approved.short_description = "Mark selected as Approved" def transition_to_rejected (modeladmin, request, queryset): queryset.update (comment = 'rejected') transition_to_rejected.short_description = "Mark selected as Rejected" @admin.register(StgFacilityType) class FacilityTypeAdmin(TranslatableAdmin,OverideExport): from django.db import models formfield_overrides = { models.CharField: {'widget': TextInput(attrs={'size':'100'})}, models.TextField: {'widget': Textarea(attrs={'rows':3, 'cols':100})}, } """ Serge requested that a user does not see other users or groups data. This method filters logged in users depending on group roles and permissions. Only the superuser can see all users and locations data while a users can only see data from registered location within his/her group/system role. If a user is not assigned to a group, he/she can only own data - 01/02/2021 """ def get_queryset(self, request): language = request.LANGUAGE_CODE qs = super().get_queryset(request).filter( translations__language_code=language).order_by( 'translations__name').distinct() # Get a query of groups the user belongs and flatten it to list object groups = list(request.user.groups.values_list('user', flat=True)) user = request.user.id user_location = request.user.location.location_id db_locations = StgLocation.objects.all().order_by('location_id') # Returns data for all the locations to the lowest location level if request.user.is_superuser: qs # returns data for AFRO and member countries elif user in groups and user_location==1: qs_admin=db_locations.filter( locationlevel__locationlevel_id__gte=1, locationlevel__locationlevel_id__lte=2) return qs def get_export_resource_class(self): return FacilityTypeResourceExport fieldsets = ( ('Health Facility Type', { 'fields':('name','shortname','description',) #afrocode may be null }), ) list_display=['name','code','shortname','description'] list_display_links =('code', 'name',) search_fields = ('code','translations__name',) #display search field list_per_page = 30 #limit records displayed on admin site to 15 exclude = ('date_created','date_lastupdated','code',) @admin.register(StgFacilityOwnership) class FacilityOwnership (TranslatableAdmin): from django.db import models formfield_overrides = { models.CharField: {'widget': TextInput(attrs={'size':'100'})}, models.TextField: {'widget': Textarea(attrs={'rows':3, 'cols':100})}, } """ Serge requested that a user does not see other users or groups data. This method filters logged in users depending on group roles and permissions. Only the superuser can see all users and locations data while a users can only see data from registered location within his/her group/system role. If a user is not assigned to a group, he/she can only own data - 01/02/2021 """ def get_queryset(self, request): language = request.LANGUAGE_CODE qs = super().get_queryset(request).filter( translations__language_code=language).order_by( 'translations__name').distinct() groups = list(request.user.groups.values_list('user', flat=True)) user = request.user.id user_location = request.user.location.location_id db_locations = StgLocation.objects.all().order_by('location_id') # Returns data for all the locations to the lowest location level if request.user.is_superuser: qs # returns data for AFRO and member countries elif user in groups and user_location==1: qs_admin=db_locations.filter( locationlevel__locationlevel_id__gte=1, locationlevel__locationlevel_id__lte=2) return qs def formfield_for_foreignkey(self, db_field, request =None, kwargs): qs = super().get_queryset(request) groups = list(request.user.groups.values_list('user', flat=True)) user = request.user.username countrycodes=StgLocationCodes.objects.values_list( 'country_code',flat=True) # This queryset is used to load specific phone code for logged in user if db_field.name == "location": if request.user.is_superuser: kwargs["queryset"] = StgLocationCodes.objects.all().order_by( 'location_id') # Looks up for the location level upto the country level else: kwargs["queryset"] = StgLocationCodes.objects.filter( location_id=request.user.location_id).order_by( 'location_id') if db_field.name == "user": kwargs["queryset"] = CustomUser.objects.filter( username=user) return super().formfield_for_foreignkey(db_field, request,kwargs) fieldsets = ( ('Facility Ownership Details', { 'fields':('name','shortname','description','location',) #afrocode may be null }), ('Logged Admin/Staff', { 'fields': ('user',) }), ) list_display=['name','code','shortname','description','location',] list_select_related = ('location','user',) list_display_links =('code', 'name',) search_fields = ('code','translations__name','translations__shortname',) #display search field list_per_page = 30 #limit records displayed on admin site to 15 exclude = ('date_created','date_lastupdated','code',) class FacilityServiceAvailabilityProxyForm(forms.ModelForm): class Meta: model = FacilityServiceAvailability fields = ('facility','domain','intervention','service','provided', 'specialunit','staff','infrastructure','supplies','date_assessed',) widgets = { 'date_assessed': DatePickerInput(), # # default date-format %m/%d/%Y will be used } class FacilityServiceAvailabilityInline(admin.TabularInline): """ Serge requested that a user does not see other users or groups data. This method filters logged in users depending on group roles and permissions. Only the superuser can see all users and locations data while a users can only see data from registered location within his/her group/system role. If a user is not assigned to a group, he/she can only own data - 01/02/2021 """ def get_queryset(self, request): qs = super().get_queryset(request) # Get a query of groups the user belongs and flatten it to list object groups = list(request.user.groups.values_list('user', flat=True)) user = request.user.username user_location = request.user.location.location_id db_locations = StgLocation.objects.all().order_by('location_id') # Returns data for all the locations to the lowest location level if request.user.is_superuser: qs # returns data for AFRO and member countries elif user in groups and user_location==1: qs_admin=db_locations.filter( locationlevel__locationlevel_id__gte=1, locationlevel__locationlevel_id__lte=2) # return data based on the location of the user logged/request location elif user in groups and user_location>1: qs=qs.filter(username=user) return qs """ Serge requested that the form for data input be restricted to user's country. Thus, this function is for filtering location to display country level. The location is used to filter the dropdownlist based on the request object's USER, If the user has superuser privileges or is a member of AFRO-DataAdmins, he/she can enter data for all the AFRO member countries otherwise, can only enter data for his/her country.=== modified 02/02/2021 """ def formfield_for_foreignkey(self, db_field, request =None, kwargs): qs = super().get_queryset(request) db_sevicedomains = StgServiceDomain.objects.all() db_sevicesubdomains=db_sevicedomains.exclude( parent_id__isnull=True).filter(category=1) if db_field.name == "domain": kwargs["queryset"]=db_sevicesubdomains return super().formfield_for_foreignkey(db_field, request,kwargs) # form = FacilityServiceAvailabilityProxyForm #overrides the default model form model = FacilityServiceAvailability # formset = LimitModelFormset extra = 1 # Used to control number of empty rows displayed. list_select_related = ('facility','domain','intervention','service',) fields = ('facility','domain','intervention','service','provided', 'specialunit','staff','infrastructure','supplies','date_assessed',) class FacilityServiceCapacityInline(admin.TabularInline): """ Serge requested that a user does not see other users or groups data. This method filters logged in users depending on group roles and permissions. Only the superuser can see all users and locations data while a users can only see data from registered location within his/her group/system role. If a user is not assigned to a group, he/she can only own data - 01/02/2021 """ def get_queryset(self, request): qs = super().get_queryset(request) # Get a query of groups the user belongs and flatten it to list object groups = list(request.user.groups.values_list('user', flat=True)) user = request.user.username user_location = request.user.location.location_id db_locations = StgLocation.objects.all().order_by('location_id') # Returns data for all the locations to the lowest location level if request.user.is_superuser: qs # returns data for AFRO and member countries elif user in groups and user_location==1: qs_admin=db_locations.filter( locationlevel__locationlevel_id__gte=1, locationlevel__locationlevel_id__lte=2) # return data based on the location of the user logged/request location elif user in groups and user_location>1: qs=qs.filter(username=user) return qs def formfield_for_foreignkey(self, db_field, request =None, kwargs): db_sevicedomains = StgServiceDomain.objects.all() db_sevicesubdomains=db_sevicedomains.exclude( parent_id__isnull=True).filter(category=2).filter( level='Level 2') db_provisionunits=StgFacilityServiceMeasureUnits.objects.select_related( 'domain') #good if db_field.name == "domain": kwargs["queryset"]=db_sevicesubdomains return super().formfield_for_foreignkey(db_field, request,kwargs) model = FacilityServiceProvision # formset = LimitModelFormset extra = 1 # Used to control number of empty rows displayed. list_select_related = ('facility','domain','units') fields = ('facility','domain','units','available','functional', 'date_assessed',) class FacilityServiceReadinessInline(admin.TabularInline): def get_queryset(self, request): qs = super().get_queryset(request) groups = list(request.user.groups.values_list('user', flat=True)) user = request.user.username user_location = request.user.location.location_id db_locations = StgLocation.objects.all().order_by('location_id') # Returns data for all the locations to the lowest location level if request.user.is_superuser: qs # returns data for AFRO and member countries elif user in groups and user_location<=2: qs_admin=db_locations.filter( locationlevel__locationlevel_id__gt=2, locationlevel__locationlevel_id__lte=3) # return data based on the location of the user logged/request location elif user in groups and user_location>1: qs=qs.filter(username=user) return qs """ Serge requested that the form for data input be restricted to user's country. Thus, this function is for filtering location to display country level. The location is used to filter the dropdownlist based on the request object's USER, If the user has superuser privileges or is a member of AFRO-DataAdmins, he/she can enter data for all the AFRO member countries otherwise, can only enter data for his/her country.=== modified 02/02/2021 """ def formfield_for_foreignkey(self, db_field, request =None,
<gh_stars>1-10 """Core async functions.""" import asyncio from pathlib import Path from ssl import SSLContext from typing import Any, Awaitable, Dict, List, Optional, Sequence, Tuple, Union import cytoolz as tlz import ujson as json from aiohttp import TCPConnector from aiohttp.typedefs import StrOrURL from aiohttp_client_cache import CachedSession, SQLiteBackend from . import utils from .exceptions import InvalidInputValue from .utils import EXPIRE, BaseRetriever __all__ = ["retrieve", "delete_url_cache", "retrieve_text", "retrieve_json", "retrieve_binary"] async def async_session( url_kwds: Tuple[Tuple[int, StrOrURL, Dict[StrOrURL, Any]], ...], read: str, r_kwds: Dict[str, Any], request_method: str, cache_name: Path, family: int, timeout: float = 5.0, expire_after: float = EXPIRE, ssl: Union[SSLContext, bool, None] = None, disable: bool = False, ) -> Awaitable[Union[str, bytes, Dict[str, Any]]]: """Create an async session for sending requests. Parameters ---------- url_kwds : list of tuples of urls and payloads A list of URLs or URLs with their payloads to be retrieved. read : str The method for returning the request; ``binary`` (bytes), ``json``, and ``text``. r_kwds : dict Keywords to pass to the response read function. ``{"content_type": None}`` if read is ``json`` else it's empty. request_method : str The request type; GET or POST. cache_name : str Path to a file for caching the session, defaults to ``./cache/aiohttp_cache.sqlite``. family : int TCP socket family timeout : float, optional Timeout for the request, defaults to 5.0. expire_after : int, optional Expiration time for the cache in seconds, defaults to -1 (never expire). ssl : bool or SSLContext, optional SSLContext to use for the connection, defaults to None. Set to False to disable SSL cetification verification. disable : bool, optional If ``True`` temporarily disable caching requests and get new responses from the server, defaults to False. Returns ------- asyncio.gather An async gather function """ cache = SQLiteBackend( cache_name=cache_name, expire_after=expire_after, allowed_methods=("GET", "POST"), timeout=timeout, ) connector = TCPConnector(family=family, ssl=ssl) async with CachedSession( json_serialize=json.dumps, cache=cache, connector=connector, trust_env=True, ) as session: _session = session.disabled() if disable else session async with _session: request_func = getattr(session, request_method.lower()) tasks = ( utils.retriever(uid, url, kwds, request_func, read, r_kwds) for uid, url, kwds in url_kwds ) return await asyncio.gather(tasks) # type: ignore def delete_url_cache( url: StrOrURL, request_method: str = "GET", cache_name: Optional[Union[Path, str]] = None, kwargs: Dict[str, Any], ) -> None: """Delete cached response associated with ``url``, along with its history (if applicable). Parameters ---------- url : str URL to be deleted from the cache request_method : str, optional HTTP request method to be deleted from the cache, defaults to ``GET``. cache_name : str, optional Path to a file for caching the session, defaults to ``./cache/aiohttp_cache.sqlite``. kwargs : dict, optional Keywords to pass to the ``cache.delete_url()``. """ loop, new_loop = utils.get_event_loop() asyncio.set_event_loop(loop) request_method = request_method.upper() valid_methods = ["GET", "POST"] if request_method not in valid_methods: raise InvalidInputValue("method", valid_methods) loop.run_until_complete( utils.delete_url(url, request_method, utils.create_cachefile(cache_name), kwargs) ) if new_loop: loop.close() def retrieve( urls: Sequence[StrOrURL], read: str, request_kwds: Optional[Sequence[Dict[str, Any]]] = None, request_method: str = "GET", max_workers: int = 8, cache_name: Optional[Union[Path, str]] = None, family: str = "both", timeout: float = 5.0, expire_after: float = EXPIRE, ssl: Union[SSLContext, bool, None] = None, disable: bool = False, ) -> List[Union[str, Dict[str, Any], bytes]]: r"""Send async requests. Parameters ---------- urls : list of str List of URLs. read : str Method for returning the request; ``binary``, ``json``, and ``text``. request_kwds : list of dict, optional List of requests keywords corresponding to input URLs (1 on 1 mapping), defaults to ``None``. For example, ``[{"params": {...}, "headers": {...}}, ...]``. request_method : str, optional Request type; ``GET`` (``get``) or ``POST`` (``post``). Defaults to ``GET``. max_workers : int, optional Maximum number of async processes, defaults to 8. cache_name : str, optional Path to a file for caching the session, defaults to ``./cache/aiohttp_cache.sqlite``. family : str, optional TCP socket family, defaults to both, i.e., IPv4 and IPv6. For IPv4 or IPv6 only pass ``ipv4`` or ``ipv6``, respectively. timeout : float, optional Timeout for the request, defaults to 5.0. expire_after : int, optional Expiration time for response caching in seconds, defaults to -1 (never expire). ssl : bool or SSLContext, optional SSLContext to use for the connection, defaults to None. Set to False to disable SSL cetification verification. disable : bool, optional If ``True`` temporarily disable caching requests and get new responses from the server, defaults to False. Returns ------- list List of responses in the order of input URLs. Examples -------- >>> import async_retriever as ar >>> stations = ["01646500", "08072300", "11073495"] >>> url = "https://waterservices.usgs.gov/nwis/site" >>> urls, kwds = zip( ... [ ... (url, {"params": {"format": "rdb", "sites": s, "siteStatus": "all"}}) ... for s in stations ... ] ... ) >>> resp = ar.retrieve(urls, "text", request_kwds=kwds) >>> resp[0].split('\n')[-2].split('\t')[1] '01646500' """ inp = BaseRetriever(urls, read, request_kwds, request_method, cache_name, family) loop, new_loop = utils.get_event_loop() asyncio.set_event_loop(loop) session = tlz.partial( async_session, read=inp.read, r_kwds=inp.r_kwds, request_method=inp.request_method, cache_name=inp.cache_name, family=inp.family, timeout=timeout, expire_after=expire_after, ssl=ssl, disable=disable, ) chunked_reqs = tlz.partition_all(max_workers, inp.url_kwds) results = (loop.run_until_complete(session(url_kwds=c)) for c in chunked_reqs) resp = [r for _, r in sorted(tlz.concat(results))] if new_loop: loop.close() return resp def retrieve_text( urls: Sequence[StrOrURL], request_kwds: Optional[Sequence[Dict[str, Any]]] = None, request_method: str = "GET", max_workers: int = 8, cache_name: Optional[Union[Path, str]] = None, family: str = "both", timeout: float = 5.0, expire_after: float = EXPIRE, ssl: Union[SSLContext, bool, None] = None, disable: bool = False, ) -> List[str]: r"""Send async requests and get the response as ``text``. Parameters ---------- urls : list of str List of URLs. request_kwds : list of dict, optional List of requests keywords corresponding to input URLs (1 on 1 mapping), defaults to ``None``. For example, ``[{"params": {...}, "headers": {...}}, ...]``. request_method : str, optional Request type; ``GET`` (``get``) or ``POST`` (``post``). Defaults to ``GET``. max_workers : int, optional Maximum number of async processes, defaults to 8. cache_name : str, optional Path to a file for caching the session, defaults to ``./cache/aiohttp_cache.sqlite``. family : str, optional TCP socket family, defaults to both, i.e., IPv4 and IPv6. For IPv4 or IPv6 only pass ``ipv4`` or ``ipv6``, respectively. timeout : float, optional Timeout for the request in seconds, defaults to 5.0. expire_after : int, optional Expiration time for response caching in seconds, defaults to -1 (never expire). ssl : bool or SSLContext, optional SSLContext to use for the connection, defaults to None. Set to False to disable SSL cetification verification. disable : bool, optional If ``True`` temporarily disable caching requests and get new responses from the server, defaults to False. Returns ------- list List of responses in the order of input URLs. Examples -------- >>> import async_retriever as ar >>> stations = ["01646500", "08072300", "11073495"] >>> url = "https://waterservices.usgs.gov/nwis/site" >>> urls, kwds = zip( ... *[ ... (url, {"params": {"format": "rdb", "sites": s, "siteStatus": "all"}}) ... for s in stations ... ] ... ) >>> resp = ar.retrieve_text(urls, kwds) >>> resp[0].split('\n')[-2].split('\t')[1] '01646500' """ resp: List[str] = retrieve( # type: ignore urls, "text", request_kwds, request_method, max_workers, cache_name, family, timeout, expire_after, ssl, disable, ) return resp def retrieve_json( urls: Sequence[StrOrURL], request_kwds: Optional[Sequence[Dict[str, Any]]] = None, request_method: str = "GET", max_workers: int = 8, cache_name: Optional[Union[Path, str]] = None, family: str = "both", timeout: float = 5.0, expire_after: float = EXPIRE, ssl: Union[SSLContext, bool, None] = None, disable: bool = False, ) -> List[Dict[str, Any]]: r"""Send async requests and get the response as ``json``. Parameters ---------- urls : list of str List of URLs. request_kwds : list of dict, optional List of requests keywords corresponding to input URLs (1 on 1 mapping), defaults to ``None``. For example, ``[{"params": {...}, "headers": {...}}, ...]``. request_method : str, optional Request type; ``GET`` (``get``) or ``POST`` (``post``). Defaults to ``GET``. max_workers : int, optional Maximum number of async processes, defaults to 8. cache_name : str, optional Path to a file for caching the session, defaults to ``./cache/aiohttp_cache.sqlite``. family : str, optional TCP socket family, defaults to both, i.e., IPv4 and IPv6. For IPv4 or IPv6 only pass ``ipv4`` or ``ipv6``, respectively. timeout : float, optional Timeout for the request, defaults to 5.0. expire_after : int, optional Expiration time for response caching
<reponame>ssalonen/pandas<filename>pandas/io/html.py<gh_stars>0 """:mod:`pandas.io.html` is a module containing functionality for dealing with HTML IO. """ import os import re import numbers import collections from distutils.version import LooseVersion import numpy as np from pandas import DataFrame, MultiIndex, isnull from pandas.io.common import _is_url, urlopen, parse_url from pandas.compat import range, lrange, lmap, u, map from pandas import compat try: import bs4 except ImportError: _HAS_BS4 = False else: _HAS_BS4 = True try: import lxml except ImportError: _HAS_LXML = False else: _HAS_LXML = True try: import html5lib except ImportError: _HAS_HTML5LIB = False else: _HAS_HTML5LIB = True ############# # READ HTML # ############# _RE_WHITESPACE = re.compile(r'([\r\n]+\|\s{2,})') def _remove_whitespace(s, regex=_RE_WHITESPACE): """Replace extra whitespace inside of a string with a single space. Parameters ---------- s : str or unicode The string from which to remove extra whitespace. regex : regex The regular expression to use to remove extra whitespace. Returns ------- subd : str or unicode `s` with all extra whitespace replaced with a single space. """ return regex.sub(' ', s.strip()) def _get_skiprows_iter(skiprows): """Get an iterator given an integer, slice or container. Parameters ---------- skiprows : int, slice, container The iterator to use to skip rows; can also be a slice. Raises ------ TypeError * If `skiprows` is not a slice, integer, or Container Raises ------ TypeError * If `skiprows` is not a slice, integer, or Container Returns ------- it : iterable A proper iterator to use to skip rows of a DataFrame. """ if isinstance(skiprows, slice): return lrange(skiprows.start or 0, skiprows.stop, skiprows.step or 1) elif isinstance(skiprows, numbers.Integral): return lrange(skiprows) elif isinstance(skiprows, collections.Container): return skiprows else: raise TypeError('{0} is not a valid type for skipping' ' rows'.format(type(skiprows))) def _read(io): """Try to read from a url, file or string. Parameters ---------- io : str, unicode, or file-like Returns ------- raw_text : str """ if _is_url(io): with urlopen(io) as url: raw_text = url.read() elif hasattr(io, 'read'): raw_text = io.read() elif os.path.isfile(io): with open(io) as f: raw_text = f.read() elif isinstance(io, compat.string_types): raw_text = io else: raise TypeError("Cannot read object of type " "'{0.__class__.__name__!r}'".format(io)) return raw_text class _HtmlFrameParser(object): """Base class for parsers that parse HTML into DataFrames. Parameters ---------- io : str or file-like This can be either a string of raw HTML, a valid URL using the HTTP, FTP, or FILE protocols or a file-like object. match : str or regex The text to match in the document. attrs : dict List of HTML <table> element attributes to match. Attributes ---------- io : str or file-like raw HTML, URL, or file-like object match : regex The text to match in the raw HTML attrs : dict-like A dictionary of valid table attributes to use to search for table elements. Notes ----- To subclass this class effectively you must override the following methods: * :func:`_build_doc` * :func:`_text_getter` * :func:`_parse_td` * :func:`_parse_tables` * :func:`_parse_tr` * :func:`_parse_thead` * :func:`_parse_tbody` * :func:`_parse_tfoot` See each method's respective documentation for details on their functionality. """ def __init__(self, io, match, attrs): self.io = io self.match = match self.attrs = attrs def parse_tables(self): tables = self._parse_tables(self._build_doc(), self.match, self.attrs) return (self._build_table(table) for table in tables) def _parse_raw_data(self, rows): """Parse the raw data into a list of lists. Parameters ---------- rows : iterable of node-like A list of row elements. text_getter : callable A callable that gets the text from an individual node. This must be defined by subclasses. column_finder : callable A callable that takes a row node as input and returns a list of the column node in that row. This must be defined by subclasses. Raises ------ AssertionError * If `text_getter` is not callable * If `column_finder` is not callable Returns ------- data : list of list of strings """ data = [[_remove_whitespace(self._text_getter(col)) for col in self._parse_td(row)] for row in rows] return data def _text_getter(self, obj): """Return the text of an individual DOM node. Parameters ---------- obj : node-like A DOM node. Returns ------- text : str or unicode The text from an individual DOM node. """ raise NotImplementedError def _parse_td(self, obj): """Return the td elements from a row element. Parameters ---------- obj : node-like Returns ------- columns : list of node-like These are the elements of each row, i.e., the columns. """ raise NotImplementedError def _parse_tables(self, doc, match, attrs): """Return all tables from the parsed DOM. Parameters ---------- doc : tree-like The DOM from which to parse the table element. match : str or regular expression The text to search for in the DOM tree. attrs : dict A dictionary of table attributes that can be used to disambiguate mutliple tables on a page. Raises ------ AssertionError * If `match` does not match any text in the document. Returns ------- tables : list of node-like A list of <table> elements to be parsed into raw data. """ raise NotImplementedError def _parse_tr(self, table): """Return the list of row elements from the parsed table element. Parameters ---------- table : node-like A table element that contains row elements. Returns ------- rows : list of node-like A list row elements of a table, usually <tr> or <th> elements. """ raise NotImplementedError def _parse_thead(self, table): """Return the header of a table. Parameters ---------- table : node-like A table element that contains row elements. Returns ------- thead : node-like A <thead>...</thead> element. """ raise NotImplementedError def _parse_tbody(self, table): """Return the body of the table. Parameters ---------- table : node-like A table element that contains row elements. Returns ------- tbody : node-like A <tbody>...</tbody> element. """ raise NotImplementedError def _parse_tfoot(self, table): """Return the footer of the table if any. Parameters ---------- table : node-like A table element that contains row elements. Returns ------- tfoot : node-like A <tfoot>...</tfoot> element. """ raise NotImplementedError def _build_doc(self): """Return a tree-like object that can be used to iterate over the DOM. Returns ------- obj : tree-like """ raise NotImplementedError def _build_table(self, table): header = self._parse_raw_thead(table) body = self._parse_raw_tbody(table) footer = self._parse_raw_tfoot(table) return header, body, footer def _parse_raw_thead(self, table): thead = self._parse_thead(table) res = [] if thead: res = lmap(self._text_getter, self._parse_th(thead[0])) return np.array(res).squeeze() if res and len(res) == 1 else res def _parse_raw_tfoot(self, table): tfoot = self._parse_tfoot(table) res = [] if tfoot: res = lmap(self._text_getter, self._parse_td(tfoot[0])) return np.array(res).squeeze() if res and len(res) == 1 else res def _parse_raw_tbody(self, table): tbody = self._parse_tbody(table) try: res = self._parse_tr(tbody[0]) except IndexError: res = self._parse_tr(table) return self._parse_raw_data(res) class _BeautifulSoupHtml5LibFrameParser(_HtmlFrameParser): """HTML to DataFrame parser that uses BeautifulSoup under the hood. See Also -------- pandas.io.html._HtmlFrameParser pandas.io.html._LxmlFrameParser Notes ----- Documentation strings for this class are in the base class :class:`pandas.io.html._HtmlFrameParser`. """ def __init__(self, args, kwargs): super(_BeautifulSoupHtml5LibFrameParser, self).__init__(args, **kwargs) from bs4 import SoupStrainer self._strainer = SoupStrainer('table') def _text_getter(self, obj): return obj.text def _parse_td(self, row): return row.find_all(('td', 'th')) def _parse_tr(self, element): return element.find_all('tr') def _parse_th(self, element): return element.find_all('th') def _parse_thead(self, table): return table.find_all('thead') def _parse_tbody(self, table): return table.find_all('tbody') def _parse_tfoot(self, table): return table.find_all('tfoot') def _parse_tables(self, doc, match, attrs): element_name = self._strainer.name tables = doc.find_all(element_name, attrs=attrs) if not tables: # known sporadically working release raise AssertionError('No tables found') mts = [table.find(text=match) for table in tables] matched_tables = [mt for mt in mts if mt is not None] tables = list(set(mt.find_parent(element_name) for mt in matched_tables)) if not tables: raise AssertionError("No tables found matching " "'{0}'".format(match.pattern)) return tables def _setup_build_doc(self): raw_text = _read(self.io) if not raw_text: raise AssertionError('No text parsed from document: ' '{0}'.format(self.io)) return raw_text def _build_doc(self): from bs4 import BeautifulSoup return BeautifulSoup(self._setup_build_doc(), features='html5lib') def _build_node_xpath_expr(attrs): """Build an xpath expression to simulate bs4's ability to pass in kwargs to search for attributes when using the lxml parser. Parameters ---------- attrs : dict A dict of HTML attributes. These are NOT checked for validity. Returns ------- expr : unicode An XPath expression that checks for the given HTML attributes. """ # give class attribute as class_ because class is a python keyword if 'class_' in attrs: attrs['class'] = attrs.pop('class_') s = (u("@{k}='{v}'").format(k=k, v=v) for k, v in compat.iteritems(attrs)) return u('[{0}]').format(' and '.join(s)) _re_namespace = {'re': 'http://exslt.org/regular-expressions'} _valid_schemes = 'http', 'file', 'ftp' class _LxmlFrameParser(_HtmlFrameParser): """HTML to DataFrame parser that uses lxml under the hood. Warning ------- This parser
#----------------------------------------------------------------------------- # Copyright (c) 2012 - 2017, Anaconda, Inc. All rights reserved. # # Powered by the Bokeh Development Team. # # The full license is in the file LICENSE.txt, distributed with this software. #----------------------------------------------------------------------------- #----------------------------------------------------------------------------- # Boilerplate #----------------------------------------------------------------------------- import pytest ; pytest #----------------------------------------------------------------------------- # Imports #----------------------------------------------------------------------------- # External imports from flaky import flaky # Bokeh imports from bokeh._testing.util.selenium import ( RECORD, alt_click, enter_text_in_cell, get_table_cell, get_table_column_cells, get_table_row, get_table_selected_rows, shift_click, sort_table_column, ) from bokeh.layouts import column from bokeh.models import ( Button, ColumnDataSource, CustomAction, CustomJS, DataTable, NumberEditor, Plot, Range1d, Rect, TableColumn, TapTool, ) #----------------------------------------------------------------------------- # Tests #----------------------------------------------------------------------------- pytest_plugins = ( "bokeh._testing.plugins.project", ) def _is_cds_data_patch(evt): return evt['kind'] == 'ModelChanged' and evt['attr'] == 'data' def has_cds_data_patches(msgs): for msg in msgs: if msg.msgtype == "PATCH-DOC": if any(_is_cds_data_patch(evt) for evt in msg.content.get('events', [])): return True return False @pytest.mark.selenium class Test_DataTableSource(object): def test_server_source_patch_does_not_duplicate_data_update_event(self, bokeh_server_page) -> None: def modify_doc(doc): data = {'x': [1,2,3,4], 'y': [10,20,30,40]} source = ColumnDataSource(data) plot = Plot(plot_height=400, plot_width=400, x_range=Range1d(0, 1), y_range=Range1d(0, 1), min_border=0) plot.add_tools(CustomAction(callback=CustomJS(args=dict(s=source), code=RECORD("data", "s.data")))) table = DataTable(columns=[ TableColumn(field="x"), TableColumn(field="y") ], source=source, editable=False) btn = Button(label="Click Me!", css_classes=["foo"]) @btn.on_click def btn_click(): source.patch({"x": [(0, 42)]}) doc.add_root(column(plot, table, btn)) page = bokeh_server_page(modify_doc) page.click_custom_action() results = page.results assert results == {'data': {'x': [1,2,3,4], 'y': [10,20,30,40]}} button = page.driver.find_element_by_class_name('foo') button.click() page.click_custom_action() results = page.results assert results == {'data': {'x': [42,2,3,4], 'y': [10,20,30,40]}} # if the server receives something back like: # # Message 'PATCH-DOC' (revision 1) content: { # 'events': [{ # 'kind': 'ModelChanged', # 'model': {'id': '1001'}, # 'attr': 'data', 'new': {'x': [42, 2, 3, 4], 'y': [10, 20, 30, 40]} # }], # 'references': [] # } # # Then that means the client got our patch message and erroneously ping # ponged a full data update back to us assert not has_cds_data_patches(page.message_test_port.received) # XXX (bev) disabled until https://github.com/bokeh/bokeh/issues/7970 is resolved #assert page.has_no_console_errors() def test_server_source_stream_does_not_duplicate_data_update_event(self, bokeh_server_page) -> None: def modify_doc(doc): data = {'x': [1,2,3,4], 'y': [10,20,30,40]} source = ColumnDataSource(data) plot = Plot(plot_height=400, plot_width=400, x_range=Range1d(0, 1), y_range=Range1d(0, 1), min_border=0) plot.add_tools(CustomAction(callback=CustomJS(args=dict(s=source), code=RECORD("data", "s.data")))) table = DataTable(columns=[ TableColumn(field="x"), TableColumn(field="y") ], source=source, editable=False) btn = Button(label="Click Me!", css_classes=["foo"]) @btn.on_click def btn_click(): source.stream({"x": [5], "y": [50]}) doc.add_root(column(plot, table, btn)) page = bokeh_server_page(modify_doc) page.click_custom_action() results = page.results assert results == {'data': {'x': [1,2,3,4], 'y': [10,20,30,40]}} button = page.driver.find_element_by_class_name('foo') button.click() page.click_custom_action() results = page.results assert results == {'data': {'x': [1,2,3,4,5], 'y': [10,20,30,40,50]}} # if the server receives something back like: # # Message 'PATCH-DOC' (revision 1) content: { # 'events': [{ # 'kind': 'ModelChanged', # 'model': {'id': '1001'}, # 'attr': 'data', 'new': {'x': [1, 2, 3, 4, 5], 'y': [10, 20, 30, 40, 50]} # }], # 'references': [] # } # # Then that means the client got our stream message and erroneously ping # ponged a full data update back to us assert not has_cds_data_patches(page.message_test_port.received) # XXX (bev) disabled until https://github.com/bokeh/bokeh/issues/7970 is resolved #assert page.has_no_console_errors() def test_server_source_update_does_not_duplicate_data_update_event(self, bokeh_server_page) -> None: def modify_doc(doc): data = {'x': [1,2,3,4], 'y': [10,20,30,40]} source = ColumnDataSource(data) plot = Plot(plot_height=400, plot_width=400, x_range=Range1d(0, 1), y_range=Range1d(0, 1), min_border=0) plot.add_tools(CustomAction(callback=CustomJS(args=dict(s=source), code=RECORD("data", "s.data")))) table = DataTable(columns=[ TableColumn(field="x"), TableColumn(field="y") ], source=source, editable=False) btn = Button(label="Click Me!", css_classes=["foo"]) @btn.on_click def btn_click(): source.data = {'x': [5,6,7,8], 'y': [50,60,70,80]} doc.add_root(column(plot, table, btn)) page = bokeh_server_page(modify_doc) page.click_custom_action() results = page.results assert results == {'data': {'x': [1,2,3,4], 'y': [10,20,30,40]}} button = page.driver.find_element_by_class_name('foo') button.click() page.click_custom_action() results = page.results assert results == {'data': {'x': [5,6,7,8], 'y': [50,60,70,80]}} # if the server receives something back like: # # Message 'PATCH-DOC' (revision 1) content: { # 'events': [{ # 'kind': 'ModelChanged', # 'model': {'id': '1001'}, # 'attr': 'data', 'new': {'x': [1, 2, 3, 4, 5], 'y': [10, 20, 30, 40, 50]} # }], # 'references': [] # } # # Then that means the client got our stream message and erroneously ping # ponged a full data update back to us assert not has_cds_data_patches(page.message_test_port.received) # XXX (bev) disabled until https://github.com/bokeh/bokeh/issues/7970 is resolved #assert page.has_no_console_errors() def test_server_edit_does_not_duplicate_data_update_event(self, bokeh_server_page) -> None: def modify_doc(doc): data = {'x': [1,2,3,4], 'y': [10,20,30,40]} source = ColumnDataSource(data) plot = Plot(plot_height=400, plot_width=400, x_range=Range1d(0, 1), y_range=Range1d(0, 1), min_border=0) plot.add_tools(CustomAction(callback=CustomJS(args=dict(s=source), code=RECORD("data", "s.data")))) table = DataTable(columns=[ TableColumn(field="x"), TableColumn(field="y", editor=NumberEditor()) ], source=source, editable=True) doc.add_root(column(plot, table)) page = bokeh_server_page(modify_doc) page.click_custom_action() results = page.results assert results == {'data': {'x': [1,2,3,4], 'y': [10,20,30,40]}} cell = get_table_cell(page.driver, 3, 2) assert cell.text == '30' enter_text_in_cell(page.driver, cell, '100') page.click_custom_action() results = page.results assert results == {'data': {'x': [1,2,3,4], 'y': [10, 20, 100, 40]}} # if the server receives something back like: # # Message 'PATCH-DOC' (revision 1) content: { # 'events': [{ # 'kind': 'ModelChanged', # 'model': {'id': '1001'}, # 'attr': 'data', 'new': {'x': [1,2,3,4], 'y': [10, 20, 100, 40]} # }], # 'references': [] # } # # Then that means the client got our stream message and erroneously ping # ponged a full data update back to us assert not has_cds_data_patches(page.message_test_port.received) # XXX (bev) disabled until https://github.com/bokeh/bokeh/issues/7970 is resolved #assert page.has_no_console_errors() def test_server_basic_selection(self, bokeh_server_page) -> None: data = {'x': [1,2,3,4,5,6], 'y': [60,50,40,30,20,10]} source = ColumnDataSource(data) def modify_doc(doc): plot = Plot(plot_height=400, plot_width=400, x_range=Range1d(0, 1), y_range=Range1d(0, 1), min_border=0) plot.add_tools(CustomAction(callback=CustomJS(args=dict(s=source), code=RECORD("indices", "s.selected.indices")))) table = DataTable(columns=[ TableColumn(field="x"), TableColumn(field="y") ], source=source, editable=False) doc.add_root(column(plot, table)) page = bokeh_server_page(modify_doc) page.click_custom_action() results = page.results assert results == {'indices': []} assert set(source.selected.indices) == set([]) assert get_table_selected_rows(page.driver) == set([]) # select the third row row = get_table_row(page.driver, 3) row.click() page.click_custom_action() results = page.results assert results == {'indices': [2]} assert source.selected.indices == [2] assert get_table_selected_rows(page.driver) == set([2]) # select the first row row = get_table_row(page.driver, 1) row.click() page.click_custom_action() results = page.results assert results == {'indices': [0]} assert source.selected.indices == [0] assert get_table_selected_rows(page.driver) == set([0]) # XXX (bev) disabled until https://github.com/bokeh/bokeh/issues/7970 is resolved #assert page.has_no_console_errors() def test_server_basic_mulitselection(self, bokeh_server_page) -> None: data = {'x': [1,2,3,4,5,6], 'y': [60,50,40,30,20,10]} source = ColumnDataSource(data) def modify_doc(doc): plot = Plot(plot_height=400, plot_width=400, x_range=Range1d(0, 1), y_range=Range1d(0, 1), min_border=0) plot.add_tools(CustomAction(callback=CustomJS(args=dict(s=source), code=RECORD("indices", "s.selected.indices")))) table = DataTable(columns=[ TableColumn(field="x"), TableColumn(field="y") ], source=source, editable=False) doc.add_root(column(plot, table)) page = bokeh_server_page(modify_doc) page.click_custom_action() results = page.results assert results == {'indices': []} assert set(source.selected.indices) == set([]) assert get_table_selected_rows(page.driver) == set([]) # select the third row row = get_table_row(page.driver, 2) row.click() row = get_table_row(page.driver, 4) shift_click(page.driver, row) page.click_custom_action() results = page.results assert set(results['indices']) == set([1, 2, 3]) assert set(source.selected.indices) == set([1, 2, 3]) assert get_table_selected_rows(page.driver) == set([1, 2, 3]) row = get_table_row(page.driver, 6) alt_click(page.driver, row) page.click_custom_action() results = page.results assert set(results['indices']) == set([1, 2, 3, 5]) assert set(source.selected.indices) == set([1, 2, 3, 5]) assert get_table_selected_rows(page.driver) == set([1, 2, 3, 5]) # XXX (bev) disabled until https://github.com/bokeh/bokeh/issues/7970 is resolved #assert page.has_no_console_errors() @flaky(max_runs=5) def test_server_sorted_after_data_update(self, bokeh_server_page) -> None: data = {'x': [1,2,5,6], 'y': [60,50,20,10]} source = ColumnDataSource(data) def modify_doc(doc): plot = Plot(plot_height=400, plot_width=400, x_range=Range1d(0, 1), y_range=Range1d(0, 1), min_border=0) plot.add_tools(CustomAction(callback=CustomJS(args=dict(s=source), code=RECORD("data", "s.data")))) table = DataTable(columns=[ TableColumn(field="x", title="x", sortable=True), TableColumn(field="y", title="y", sortable=True) ], source=source, editable=False) button = Button(css_classes=["foo"]) def cb(): source.data = {'x': [0,1,2,3,4,5,6,7], 'y': [70,60,50,40,30,20,10,0]} button.on_click(cb) doc.add_root(column(plot, table, button)) page = bokeh_server_page(modify_doc) page.click_custom_action() results = page.results assert results == {'data': {'x': [1,2,5,6], 'y': [60,50,20,10]}} assert get_table_column_cells(page.driver, 1) == ['1', '2', '5', '6'] assert get_table_column_cells(page.driver, 2) == ['60', '50', '20', '10'] sort_table_column(page.driver, 1) assert get_table_column_cells(page.driver, 1) == ['1', '2', '5', '6'] assert get_table_column_cells(page.driver, 2) == ['60', '50', '20', '10'] sort_table_column(page.driver, 2, True) assert get_table_column_cells(page.driver, 1) == ['6', '5', '2', '1'] assert get_table_column_cells(page.driver, 2) == ['10', '20', '50', '60'] button = page.driver.find_element_by_class_name('foo') button.click() page.click_custom_action() results = page.results assert results == {'data': {'x': [0,1,2,3,4,5,6,7], 'y': [70,60,50,40,30,20,10,0]}} assert source.data == {'x': [0,1,2,3,4,5,6,7], 'y': [70,60,50,40,30,20,10,0]} assert get_table_column_cells(page.driver, 1) == ['7', '6', '5', '4', '3', '2', '1', '0'] assert get_table_column_cells(page.driver, 2) == ['0', '10', '20', '30', '40', '50', '60', '70'] # XXX (bev) disabled until https://github.com/bokeh/bokeh/issues/7970 is resolved #assert page.has_no_console_errors() @pytest.mark.skip def test_server_sorted_after_patch(self, bokeh_server_page) -> None: data = {'x': [1,2,5,6], 'y': [60,50,20,10]} source = ColumnDataSource(data) def modify_doc(doc): plot = Plot(plot_height=400, plot_width=400, x_range=Range1d(0, 1), y_range=Range1d(0, 1), min_border=0) plot.add_tools(CustomAction(callback=CustomJS(args=dict(s=source), code=RECORD("data", "s.data")))) table = DataTable(columns=[ TableColumn(field="x", title="x", sortable=True), TableColumn(field="y", title="y", sortable=True) ], source=source, editable=False) button = Button(css_classes=["foo"]) def cb(): source.patch({'y': [[2, 100]]}) button.on_click(cb) doc.add_root(column(plot, table, button)) page = bokeh_server_page(modify_doc) page.click_custom_action() results = page.results assert results == {'data': {'x': [1,2,5,6], 'y': [60,50,20,10]}} assert get_table_column_cells(page.driver, 1) == ['1', '2', '5',
import pandas as pd import sys import numpy as np import scipy as sp import json import os from decimal import Decimal import scipy.optimize as opt from scipy.optimize import minimize, curve_fit from scipy.special import erfc from scipy.stats import crystalball from scipy.signal import medfilt, find_peaks import pygama.analysis.histograms as pgh import pygama.utils as pgu import pygama.analysis.peak_fitting as pga import matplotlib.pyplot as plt from matplotlib.lines import Line2D plt.style.use('style.mplstyle') def main(): ## this code takes the peaks from thorium's first-pass calibration and fits them. the values from these fits are used to then do a non-linear, second-pass calibration. peak_2615() #peak_1765() #peak_1460() #peak_609() #peak_352() def peak_2615(): if(len(sys.argv) != 2): print('Usage: fit_bkg_peaks.py [run number]') sys.exit() with open("runDB.json") as f: runDB = json.load(f) meta_dir = os.path.expandvars(runDB["meta_dir"]) #df = pd.read_hdf("{}/Spectrum_280-329.hdf5".format(meta_dir), key="df") df = pd.read_hdf("{}/Spectrum_{}.hdf5".format(meta_dir,sys.argv[1]), key="df") def gauss(x, mu, sigma, A=1): """ define a gaussian distribution, w/ args: mu, sigma, area (optional). """ return A * (1. / sigma / np.sqrt(2 * np.pi)) * np.exp(-(x - mu)*2 / (2. sigma*2)) def radford_peak(x, mu, sigma, hstep, htail, tau, bg0, a=1): """ David Radford's HPGe peak shape function """ # make sure the fractional amplitude parameters stay reasonable... if htail < 0 or htail > 1: return np.zeros_like(x) if hstep < 0 or hstep > 1: return np.zeros_like(x) bg_term = bg0 #+ xbg1 if np.any(bg_term < 0): return np.zeros_like(x) # compute the step and the low energy tail step = a * hstep * erfc((x - mu) / (sigma * np.sqrt(2))) le_tail = a * htail le_tail = erfc((x - mu) / (sigma np.sqrt(2)) + sigma / (tau * np.sqrt(2))) le_tail = np.exp((x - mu) / tau) le_tail /= (2 tau * np.exp(-(sigma / (np.sqrt(2) * tau))*2)) # add up all the peak shape components return (1 - htail) gauss(x, mu, sigma, a) + bg_term + step + le_tail hist, bins, var = pgh.get_hist(df['e_cal'], range=(2540,2680), dx=0.5) pgh.plot_hist(hist, bins, var=hist, label="data") pars, cov = pga.fit_hist(radford_peak, hist, bins, var=hist, guess=[2608.5, 1.05, 0.001, 0.02, 5, 1, 4000]) pgu.print_fit_results(pars, cov, radford_peak) pgu.plot_func(radford_peak, pars, label="chi2 fit", color='red') #x_vals = np.arange(2540,2680,0.5) #plt.plot(x_vals, radford_peak(x_vals, 2608.5, 1.05, .001, 0.02, 5, 1, 4000)) FWHM = '%.2f' % Decimal(pars[1]2) FWHM_uncertainty = '%.2f' % Decimal(np.sqrt(cov[1][1])2) peak = '%.2f' % Decimal(pars[0]) peak_uncertainty = '%.2f' % Decimal(np.sqrt(cov[0][0])) residual = '%.2f' % (2614.51 - float(peak)) chi_2_element_list = [] for i in range(len(hist)): chi_2_element = abs((radford_peak(bins[i], pars) - hist[i])2/radford_peak(bins[i], pars)) chi_2_element_list.append(chi_2_element) chi_2 = sum(chi_2_element_list) reduced_chi_2 = '%.2f' % Decimal(chi_2/len(hist)) print(reduced_chi_2) label_01 = '2614.51 keV peak fit' label_02 = 'FWHM = '+str(FWHM)+r' $\pm$ '+str(FWHM_uncertainty) label_03 = 'Peak = '+str(peak)+r' $\pm$ '+str(peak_uncertainty) label_04 = 'Residual = '+str(residual)+r' $\pm$ '+str(peak_uncertainty) colors = ['red', 'red','red', 'red'] lines = [Line2D([0], [0], color=c, lw=2) for c in colors] labels = [label_01, label_02, label_03, label_04] plt.xlim(2540,2680) plt.ylim(0,plt.ylim()[1]) plt.xlabel('Energy (keV)', ha='right', x=1.0) plt.ylabel('Counts', ha='right', y=1.0) plt.title('Fit of First-Pass Kr83m Calibration Peak') plt.tight_layout() #plt.semilogy() plt.legend(lines, labels, frameon=False, loc='upper right', fontsize='small') plt.show() def peak_1765(): if(len(sys.argv) != 2): print('Usage: fit_bkg_peaks.py [run number]') sys.exit() with open("runDB.json") as f: runDB = json.load(f) meta_dir = os.path.expandvars(runDB["meta_dir"]) #df = pd.read_hdf("{}/Spectrum_280-329.hdf5".format(meta_dir), key="df") df = pd.read_hdf("{}/Spectrum_{}.hdf5".format(meta_dir,sys.argv[1]), key="df") def gauss(x, mu, sigma, A=1): """ define a gaussian distribution, w/ args: mu, sigma, area (optional). """ return A * (1. / sigma / np.sqrt(2 * np.pi)) * np.exp(-(x - mu)*2 / (2. sigma*2)) def radford_peak(x, mu, sigma, hstep, htail, tau, bg0, a=1): """ <NAME>'s HPGe peak shape function """ # make sure the fractional amplitude parameters stay reasonable... if htail < 0 or htail > 1: return np.zeros_like(x) if hstep < 0 or hstep > 1: return np.zeros_like(x) bg_term = bg0 #+ xbg1 if np.any(bg_term < 0): return np.zeros_like(x) # compute the step and the low energy tail step = a * hstep * erfc((x - mu) / (sigma * np.sqrt(2))) le_tail = a * htail le_tail = erfc((x - mu) / (sigma np.sqrt(2)) + sigma / (tau * np.sqrt(2))) le_tail = np.exp((x - mu) / tau) le_tail /= (2 tau * np.exp(-(sigma / (np.sqrt(2) * tau))*2)) # add up all the peak shape components return (1 - htail) gauss(x, mu, sigma, a) + bg_term + step + le_tail hist, bins, var = pgh.get_hist(df['e_cal'], range=(1740,1780), dx=0.5) pgh.plot_hist(hist, bins, var=hist, label="data") pars, cov = pga.fit_hist(radford_peak, hist, bins, var=hist, guess=[1761, 1.85, 0.001, 0.02, 5, 1, 4000]) pgu.print_fit_results(pars, cov, radford_peak) pgu.plot_func(radford_peak, pars, label="chi2 fit", color='red') #x_vals = np.arange(1740,1780,0.5) #plt.plot(x_vals, radford_peak(x_vals, 1761, 1.85, .001, 0.02, 5, 1, 4000)) FWHM = '%.2f' % Decimal(pars[1]2) FWHM_uncertainty = '%.2f' % Decimal(np.sqrt(cov[1][1])2) peak = '%.2f' % Decimal(pars[0]) peak_uncertainty = '%.2f' % Decimal(np.sqrt(cov[0][0])) residual = '%.2f' % (1764.49 - float(peak)) #chi_2_element_list = [] #for i in range(len(hist)): #chi_2_element = abs((radford_peak(bins[i], pars) - hist[i])2/radford_peak(bins[i], pars)) #chi_2_element_list.append(chi_2_element) #chi_2 = sum(chi_2_element_list) #reduced_chi_2 = '%.2f' % Decimal(chi_2/len(hist)) label_01 = '1764.49 keV peak fit' label_02 = 'FWHM = '+str(FWHM)+r' $\pm$ '+str(FWHM_uncertainty) label_03 = 'Peak = '+str(peak)+r' $\pm$ '+str(peak_uncertainty) label_04 = 'Residual = '+str(residual)+r' $\pm$ '+str(peak_uncertainty) colors = ['red', 'red','red', 'red'] lines = [Line2D([0], [0], color=c, lw=2) for c in colors] labels = [label_01, label_02, label_03, label_04] plt.xlim(1740,1780) plt.ylim(0,plt.ylim()[1]) plt.xlabel('Energy (keV)', ha='right', x=1.0) plt.ylabel('Counts', ha='right', y=1.0) plt.tight_layout() #plt.semilogy() plt.legend(lines, labels, frameon=False, loc='upper right', fontsize='small') plt.show() def peak_1460(): if(len(sys.argv) != 2): print('Usage: fit_bkg_peaks.py [run number]') sys.exit() with open("runDB.json") as f: runDB = json.load(f) meta_dir = os.path.expandvars(runDB["meta_dir"]) tier_dir = os.path.expandvars(runDB["tier_dir"]) #df = pd.read_hdf("{}/Spectrum_280-329.hdf5".format(meta_dir), key="df") df = pd.read_hdf("{}/Spectrum_{}.hdf5".format(meta_dir,sys.argv[1]), key="df") #df = pd.read_hdf("{}/t2_run{}.h5".format(tier_dir,sys.argv[1])) #df['e_cal'] = 0.4054761904761905 * df['e_ftp'] + 3.113095238095184 def gauss(x, mu, sigma, A=1): """ define a gaussian distribution, w/ args: mu, sigma, area (optional). """ return A * (1. / sigma / np.sqrt(2 * np.pi)) * np.exp(-(x - mu)*2 / (2. sigma*2)) def radford_peak(x, mu, sigma, hstep, htail, tau, bg0, a=1): """ <NAME>'s HPGe peak shape function """ # make sure the fractional amplitude parameters stay reasonable... if htail < 0 or htail > 1: return np.zeros_like(x) if hstep < 0 or hstep > 1: return np.zeros_like(x) bg_term = bg0 #+ xbg1 if np.any(bg_term < 0): return np.zeros_like(x) # compute the step and the low energy tail step = a * hstep * erfc((x - mu) / (sigma * np.sqrt(2))) le_tail = a * htail le_tail = erfc((x - mu) / (sigma np.sqrt(2)) + sigma / (tau * np.sqrt(2))) le_tail = np.exp((x - mu) / tau) le_tail /= (2 tau * np.exp(-(sigma / (np.sqrt(2) * tau))*2)) # add up all the peak shape components return (1 - htail) gauss(x, mu, sigma, a) + bg_term + step + le_tail hist, bins, var = pgh.get_hist(df['e_cal'], range=(1420,1500), dx=0.5) pgh.plot_hist(hist, bins, var=hist, label="data") pars, cov = pga.fit_hist(radford_peak, hist, bins, var=hist, guess=[1460.8, 1.95, 0.001, 0.03, 4, 1, 100000]) pgu.print_fit_results(pars, cov, radford_peak) pgu.plot_func(radford_peak, pars, label="chi2 fit", color='red') #x_vals = np.arange(1420,1500,0.5) #plt.plot(x_vals, radford_peak(x_vals, 1460.8, 2.95, .001, 0.03, 5, 1, 100000)) FWHM = '%.2f' % Decimal(pars[1]2) FWHM_uncertainty = '%.2f' % Decimal(np.sqrt(cov[1][1])2) peak = '%.2f' % Decimal(pars[0]) peak_uncertainty = '%.2f' % Decimal(np.sqrt(cov[0][0])) residual = '%.2f' % (1460.82 - float(peak)) #chi_2_element_list = [] #for i in range(len(hist)): #chi_2_element = abs((radford_peak(bins[i], pars) - hist[i])2/radford_peak(bins[i], pars)) #chi_2_element_list.append(chi_2_element) #chi_2 = sum(chi_2_element_list) #reduced_chi_2 = '%.2f' % Decimal(chi_2/len(hist)) label_01 = '1460.82 keV peak fit' label_02 = 'FWHM = '+str(FWHM)+r' $\pm$ '+str(FWHM_uncertainty) label_03 = 'Peak = '+str(peak)+r' $\pm$ '+str(peak_uncertainty) label_04 = 'Residual = '+str(residual)+r' $\pm$ '+str(peak_uncertainty) colors = ['red', 'red','red', 'red'] lines = [Line2D([0], [0], color=c, lw=2) for c in colors] labels = [label_01, label_02, label_03, label_04] plt.xlim(1420,1500) plt.ylim(0,plt.ylim()[1]) plt.xlabel('Energy (keV)', ha='right', x=1.0) plt.ylabel('Counts', ha='right', y=1.0) plt.tight_layout() plt.legend(lines, labels, frameon=False, loc='upper right', fontsize='small') #plt.semilogy() plt.show() def peak_609(): if(len(sys.argv) != 2): print('Usage: fit_bkg_peaks.py [run number]') sys.exit() with open("runDB.json") as f: runDB = json.load(f) meta_dir = os.path.expandvars(runDB["meta_dir"]) #df = pd.read_hdf("{}/Spectrum_280-329.hdf5".format(meta_dir), key="df") df = pd.read_hdf("{}/Spectrum_{}.hdf5".format(meta_dir,sys.argv[1]), key="df") def gauss(x, mu, sigma, A=1): """ define a gaussian distribution, w/ args: mu, sigma, area (optional). """ return A * (1. / sigma / np.sqrt(2 * np.pi)) * np.exp(-(x - mu)*2 / (2. sigma**2)) def radford_peak(x, mu, sigma, hstep, htail, tau, bg0, a=1): """ <NAME>'s HPGe peak shape function """ # make sure the fractional amplitude
condition(self): """ Return a non-numerical health status """ status = "unknown" if self.getHitPoints() <= 0: status = "dead" elif self.getHitPoints() < self.getMaxHP() * 0.10: # Less than 10% of health remains status = "desperate" elif self.getHitPoints() < self.getMaxHP() * 0.25: # 11-25% of health remains status = "injured" elif self.getHitPoints() < self.getMaxHP() * 0.50: # 26-50% of health remains status = "drained" elif self.getHitPoints() < self.getMaxHP() * 0.75: # 51-75% of health remains status = "fatigued" elif self.getHitPoints() < self.getMaxHP() * 0.99: # 76-99% of health remains status = "healthy" elif self.getHitPoints() == self.getMaxHP(): # totally healthy status = "fresh" return status def dodge(self, basePercent=100, dodgeTxt=""): """ Return true if dodged * If basePercent is increased, chance of dodging goes down. * chances improved by dex, class, dodge skill, and dodgeBonus """ result = "dodge failed" randX = random.randint(1, 100) classMult = 2 if self.getClassName().lower() == "rogue" else 1 skillMult = self._dodge + self._dodgeBonus dodgeAdv = self.getDexterity() * (classMult + skillMult) / 10 dodgeCalc = (randX + dodgeAdv) * 2 if dodgeCalc > basePercent: result = "dodged" if dodgeTxt != "": dodgeTxt += " " dLog( "{0}{1} - character dodge calc ({2}) >? {0}odds ({3})".format( dodgeTxt, result, dodgeCalc, basePercent ), self._instanceDebug, ) if result == "dodged": return True return False def acDamageReduction(self, damage): """ reduce damage based on AC """ ac = self.getAc() # reduce AC if protection is broken for obj in self.getEquippedProtection(): if obj.isBroken(): ac -= obj.getAc() # reduce damage based on percentage: acReduction = int(damage * (0.05 * ac)) damage -= acReduction return max(0, damage) def getCircleSecs(self): """ Returns the number seconds a creature will wait given a successful circle - based on character level/stats""" secsToWait = random.randint(self.getLevel(), 20 + self.getDexterity()) return secsToWait def damageIsLethal(self, num=0): if num >= self.getHitPoints(): return True return False def takeDamage(self, damage=0, nokill=False): """ Take damage and check for death """ self.subtractHP(damage) if nokill and self.getHitPoints() <= 0: self.setNearDeathExperience() condition = self.condition() dLog(self.getName() + " takes " + str(damage) + " damage", self._instanceDebug) self.save() if self.getHitPoints() <= 0: if self.isDm(): self._spoolOut( "You would be dead if you weren't a dm." + " Resetting hp to maxhp.\n" ) self.setHitPoints(self._maxhp) else: self.processDeath() return condition def obituary(self): """ Notify/record death """ deathMsg = self.describe() + " has died" self.client.getGameObj().gameMsg(self.client.txtBanner(deathMsg) + "\n") logger.info("obituary: " + deathMsg) def processDeath(self, calculateLevelsToLose=True, silent=False): """ Do all the things related to dying """ levelsToLose = 1 if calculateLevelsToLose: levelsToLose = self.levelsToLose() for numlvl in range(1, levelsToLose + 1): self.levelDownStats() if self.getLevel() > 1: self.subtractlevel() self.setHitPoints(self.getMaxHP()) self.setPoisoned(False) self.setPlagued(False) self.save() if not silent: # primarily used for testing hundreds of deaths self._spoolOut("You are dead!\n") self.obituary() # return to starting room or guild self.client.gameObj.joinRoom(58, self) self._spoolOut(self.getRoom().display(self)) return True def searchSucceeds(self, obj, basePercent=30): """ Returns True if search succeeds * chance of success based on dex, level, and luck """ logPrefix = __class__.__name__ + " searchSucceeds: " if self.canSeeHidden(): dLog(logPrefix + "Pass - Character can see hidden", self._instanceDebug) return True percentChance = ( basePercent + self.getDexterity() + self.getLevel() + self.getLuck() ) if obj.getType() == "Creature" or obj.getType() == "Character": # +/- 10% per level difference percentChance += (self.getLevel() - obj.getLevel()) * 10 if random.randint(1, 20) == 1: # Always a 5 percent chance of success dLog(logPrefix + "Pass - Always 5% Chance", self._instanceDebug) return True randX = random.randint(1, 100) if randX <= percentChance: dLog( logPrefix + "Pass - Roll - " + str(randX) + " < " + str(percentChance), self._instanceDebug, ) return True dLog(logPrefix + "Failed", self._instanceDebug) return False def equipFist(self): """ equip fist, the default weapon - fist is a special weapon that is not in any inventory """ obj = Weapon() obj.setName("fist") obj._article = "a" obj._singledesc = "fist" obj.setMaximumDamage(self.getFistDamage()) self.equip(obj) def getFistDamage(self): """ calculate damage for the fist, the default weapon """ damage = int((self.getStrength() / 5) + (self.getLevel() / 2)) damage += self.classDict[self.getClassKey()]["baseDamage"] damage -= random.randint(0, 3) return max(0, damage) def equip(self, obj): # Deal with currently equipped item equippedObj = getattr(self, obj.getEquippedSlotName()) if equippedObj is None: # Nothing is currently equipped pass elif equippedObj == obj: # desired object is already in use return True elif obj is not None: # wearing some other item self.unEquip(obj) # Pass object so we know which slot to vacate slotName = obj.getEquippedSlotName() if slotName: setattr(self, slotName, obj) self.setAc() return True return False def unEquip(self, obj=None, slotName=""): if obj and slotName == "": # Use the current object to determine slot name if obj.isEquippable(): slotName = obj.getEquippedSlotName() if slotName == "": return False setattr(self, slotName, None) self.setAc() if self.getEquippedWeapon() is None: self.equipFist() return True def attemptToHide(self): randX = random.randint(0, 99) hidechance = self.getLevel() * 20 + self.dexterity if self.getClassName().lower() == "rogue": hidechance = 2 # double the chance of success for rogues # consider additional bonus for guild status # half the chance of success if there are already creatures in the room if len(self._roomObj.getCreatureList()) > 0: hidechance /= 2 hidechance = max(66, hidechance) # Chance to hide tops out at 66% if hidechance > randX: self.setHidden() return True return False def hearsWhispers(self): """ calculate whether a character can hear whispers in a room todo: make this more random and skill/sluck based """ if self.getClassName().lower() == "ranger": return True return False def adjustPrice(self, price): """ Adjust the price of goods depending on character attributes non-character price changes occur elsewhere """ # consider adjustments for charisma, alignment, luck return price def setMaxWeightForCharacter(self): """ Maxweight varies depending on attributes """ weight = 10 * max(7, int(self.strength)) self.setInventoryMaxWeight(weight) def fumbles(self, basePercent=20): """ Return true if player fumbles. * Fumble is a trip while attacking which causes player to unequip weapon and shield and wait 30 seconds before attacking again * random chance, partially based on dex. * if fumble, player's weapon is unequipped """ logPrefix = "char.fumbles: " fumbles = False if self.isAttackingWithFist(): return False fumbleRoll = random.randint(1, 100) percentage = basePercent - self.getDexterity() if fumbleRoll == 1: # always a 1% change of fumbling dLog(logPrefix + "Bad luck - 1% fumble triggered", self._instanceDebug) fumbles = True elif fumbleRoll < percentage: dLog( logPrefix + "Standard Roll: " + str(fumbleRoll) + " < " + str(percentage), self._instanceDebug, ) fumbles = True if fumbles: self.unEquip(slotName="_equippedWeapon") self.unEquip(slotName="_equippedShield") self.setSecondsUntilNextAttack(30) return fumbles def discardsEquippedWeapon(self): """ drop currently equipped weapon """ if self.isAttackingWithFist(): return True weaponObj = self.getEquippedWeapon() self.unEquip(slotName="_equippedWeapon") self.removeFromInventory(weaponObj) roomObj = self.getRoom() if roomObj: roomObj.addToInventory(weaponObj) return True def possibilyLoseHiddenWhenMoving(self): """ set hidden to false if you fail the roll. * when moving, there is a chance that you will not remain hidden * base chance of remaining hidden is 50% + dex * rangers and theives get improved chance = dex a ranger/thief with 20 dex has 99% chance of staying hidden """ if not self.isHidden: return False oddsOfStayingHidden = 60 + self.getDexterity() if self.getClassName() in ["rogue", "ranger"]: oddsOfStayingHidden += self.getDexterity() if random.randint(1, 100) >= oddsOfStayingHidden: self.setHidden(False) return True def processPoisonAndRegen(self, regenInterval=90, poisonInterval=60): """ At certain intervals, poison and hp regeneration kick in * poison should be faster and/or stronger than regen """ conAdj = self.getConstitution() - 12 intAdj = self.getIntelligence() - 12 regenHp = max(1, int(self.getMaxHP() / 10) + conAdj) regenMana = max(1, int(self.getMaxMana() / 8) + intAdj) poisonHp = max(1, int(self.getLevel() - conAdj)) if not self.isPlagued(): # no regen if plagued # Check the time if self.getLastRegenDate() == getNeverDate(): regenSecsRemaining = 0 else: regenSecsRemaining = regenInterval - secsSinceDate( self.getLastRegenDate() ) dLog( "regen counter: " + str(regenSecsRemaining) + " secs - " + str(self.getLastRegenDate()) + " - " + str(secsSinceDate(self.getLastRegenDate())), False, ) if regenSecsRemaining <= 0: self.addHP(regenHp) self.addMana(regenMana) self.setLastRegen() if self.isPoisoned(): # take damage if poisoned # Check the time if self.getLastPoisonDate() == getNeverDate(): poisonSecsRemaining = 0 else: poisonSecsRemaining = poisonInterval - secsSinceDate( self.getLastPoisonDate() ) dLog("poison cntr: " + str(regenSecsRemaining) + " secs", False) if poisonSecsRemaining <= 0: self._spoolOut( "As
:class:`int` :param invite_link: If user has joined the chat using an invite link, the invite link; may be null, defaults to None :type invite_link: :class:`ChatInviteLink`, optional :param old_chat_member: Previous chat member :type old_chat_member: :class:`ChatMember` :param new_chat_member: New chat member :type new_chat_member: :class:`ChatMember` """ ID: str = Field("updateChatMember", alias="@type") chat_id: int actor_user_id: int date: int invite_link: typing.Optional[ChatInviteLink] = None old_chat_member: ChatMember new_chat_member: ChatMember @staticmethod def read(q: dict) -> UpdateChatMember: return UpdateChatMember.construct(q) class UpdateChatMessageSender(Update): """ The message sender that is selected to send messages in a chat has changed :param chat_id: Chat identifier :type chat_id: :class:`int` :param message_sender_id: New value of message_sender_id; may be null if the user can't change message sender, defaults to None :type message_sender_id: :class:`MessageSender`, optional """ ID: str = Field("updateChatMessageSender", alias="@type") chat_id: int message_sender_id: typing.Optional[MessageSender] = None @staticmethod def read(q: dict) -> UpdateChatMessageSender: return UpdateChatMessageSender.construct(q) class UpdateChatMessageTtl(Update): """ The message Time To Live setting for a chat was changed :param chat_id: Chat identifier :type chat_id: :class:`int` :param message_ttl: New value of message_ttl :type message_ttl: :class:`int` """ ID: str = Field("updateChatMessageTtl", alias="@type") chat_id: int message_ttl: int @staticmethod def read(q: dict) -> UpdateChatMessageTtl: return UpdateChatMessageTtl.construct(q) class UpdateChatNotificationSettings(Update): """ Notification settings for a chat were changed :param chat_id: Chat identifier :type chat_id: :class:`int` :param notification_settings: The new notification settings :type notification_settings: :class:`ChatNotificationSettings` """ ID: str = Field("updateChatNotificationSettings", alias="@type") chat_id: int notification_settings: ChatNotificationSettings @staticmethod def read(q: dict) -> UpdateChatNotificationSettings: return UpdateChatNotificationSettings.construct(q) class UpdateChatOnlineMemberCount(Update): """ The number of online group members has changed. This update with non-zero count is sent only for currently opened chats. There is no guarantee that it will be sent just after the count has changed :param chat_id: Identifier of the chat :type chat_id: :class:`int` :param online_member_count: New number of online members in the chat, or 0 if unknown :type online_member_count: :class:`int` """ ID: str = Field("updateChatOnlineMemberCount", alias="@type") chat_id: int online_member_count: int @staticmethod def read(q: dict) -> UpdateChatOnlineMemberCount: return UpdateChatOnlineMemberCount.construct(q) class UpdateChatPendingJoinRequests(Update): """ The chat pending join requests were changed :param chat_id: Chat identifier :type chat_id: :class:`int` :param pending_join_requests: The new data about pending join requests; may be null, defaults to None :type pending_join_requests: :class:`ChatJoinRequestsInfo`, optional """ ID: str = Field("updateChatPendingJoinRequests", alias="@type") chat_id: int pending_join_requests: typing.Optional[ChatJoinRequestsInfo] = None @staticmethod def read(q: dict) -> UpdateChatPendingJoinRequests: return UpdateChatPendingJoinRequests.construct(q) class UpdateChatPermissions(Update): """ Chat permissions was changed :param chat_id: Chat identifier :type chat_id: :class:`int` :param permissions: The new chat permissions :type permissions: :class:`ChatPermissions` """ ID: str = Field("updateChatPermissions", alias="@type") chat_id: int permissions: ChatPermissions @staticmethod def read(q: dict) -> UpdateChatPermissions: return UpdateChatPermissions.construct(q) class UpdateChatPhoto(Update): """ A chat photo was changed :param chat_id: Chat identifier :type chat_id: :class:`int` :param photo: The new chat photo; may be null, defaults to None :type photo: :class:`ChatPhotoInfo`, optional """ ID: str = Field("updateChatPhoto", alias="@type") chat_id: int photo: typing.Optional[ChatPhotoInfo] = None @staticmethod def read(q: dict) -> UpdateChatPhoto: return UpdateChatPhoto.construct(q) class UpdateChatPosition(Update): """ The position of a chat in a chat list has changed. Instead of this update updateChatLastMessage or updateChatDraftMessage might be sent :param chat_id: Chat identifier :type chat_id: :class:`int` :param position: New chat position. If new order is 0, then the chat needs to be removed from the list :type position: :class:`ChatPosition` """ ID: str = Field("updateChatPosition", alias="@type") chat_id: int position: ChatPosition @staticmethod def read(q: dict) -> UpdateChatPosition: return UpdateChatPosition.construct(q) class UpdateChatReadInbox(Update): """ Incoming messages were read or the number of unread messages has been changed :param chat_id: Chat identifier :type chat_id: :class:`int` :param last_read_inbox_message_id: Identifier of the last read incoming message :type last_read_inbox_message_id: :class:`int` :param unread_count: The number of unread messages left in the chat :type unread_count: :class:`int` """ ID: str = Field("updateChatReadInbox", alias="@type") chat_id: int last_read_inbox_message_id: int unread_count: int @staticmethod def read(q: dict) -> UpdateChatReadInbox: return UpdateChatReadInbox.construct(q) class UpdateChatReadOutbox(Update): """ Outgoing messages were read :param chat_id: Chat identifier :type chat_id: :class:`int` :param last_read_outbox_message_id: Identifier of last read outgoing message :type last_read_outbox_message_id: :class:`int` """ ID: str = Field("updateChatReadOutbox", alias="@type") chat_id: int last_read_outbox_message_id: int @staticmethod def read(q: dict) -> UpdateChatReadOutbox: return UpdateChatReadOutbox.construct(q) class UpdateChatReplyMarkup(Update): """ The default chat reply markup was changed. Can occur because new messages with reply markup were received or because an old reply markup was hidden by the user :param chat_id: Chat identifier :type chat_id: :class:`int` :param reply_markup_message_id: Identifier of the message from which reply markup needs to be used; 0 if there is no default custom reply markup in the chat :type reply_markup_message_id: :class:`int` """ ID: str = Field("updateChatReplyMarkup", alias="@type") chat_id: int reply_markup_message_id: int @staticmethod def read(q: dict) -> UpdateChatReplyMarkup: return UpdateChatReplyMarkup.construct(q) class UpdateChatTheme(Update): """ The chat theme was changed :param chat_id: Chat identifier :type chat_id: :class:`int` :param theme_name: The new name of the chat theme; may be empty if theme was reset to default :type theme_name: :class:`str` """ ID: str = Field("updateChatTheme", alias="@type") chat_id: int theme_name: str @staticmethod def read(q: dict) -> UpdateChatTheme: return UpdateChatTheme.construct(q) class UpdateChatThemes(Update): """ The list of available chat themes has changed :param chat_themes: The new list of chat themes :type chat_themes: :class:`list[ChatTheme]` """ ID: str = Field("updateChatThemes", alias="@type") chat_themes: list[ChatTheme] @staticmethod def read(q: dict) -> UpdateChatThemes: return UpdateChatThemes.construct(q) class UpdateChatTitle(Update): """ The title of a chat was changed :param chat_id: Chat identifier :type chat_id: :class:`int` :param title: The new chat title :type title: :class:`str` """ ID: str = Field("updateChatTitle", alias="@type") chat_id: int title: str @staticmethod def read(q: dict) -> UpdateChatTitle: return UpdateChatTitle.construct(q) class UpdateChatUnreadMentionCount(Update): """ The chat unread_mention_count has changed :param chat_id: Chat identifier :type chat_id: :class:`int` :param unread_mention_count: The number of unread mention messages left in the chat :type unread_mention_count: :class:`int` """ ID: str = Field("updateChatUnreadMentionCount", alias="@type") chat_id: int unread_mention_count: int @staticmethod def read(q: dict) -> UpdateChatUnreadMentionCount: return UpdateChatUnreadMentionCount.construct(q) class UpdateChatVideoChat(Update): """ A chat video chat state has changed :param chat_id: Chat identifier :type chat_id: :class:`int` :param video_chat: New value of video_chat :type video_chat: :class:`VideoChat` """ ID: str = Field("updateChatVideoChat", alias="@type") chat_id: int video_chat: VideoChat @staticmethod def read(q: dict) -> UpdateChatVideoChat: return UpdateChatVideoChat.construct(q) class UpdateConnectionState(Update): """ The connection state has changed. This update must be used only to show a human-readable description of the connection state :param state: The new connection state :type state: :class:`ConnectionState` """ ID: str = Field("updateConnectionState", alias="@type") state: ConnectionState @staticmethod def read(q: dict) -> UpdateConnectionState: return UpdateConnectionState.construct(q) class UpdateDeleteMessages(Update): """ Some messages were deleted :param chat_id: Chat identifier :type chat_id: :class:`int` :param message_ids: Identifiers of the deleted messages :type message_ids: :class:`list[int]` :param is_permanent: True, if the messages are permanently deleted by a user (as opposed to just becoming inaccessible) :type is_permanent: :class:`bool` :param from_cache: True, if the messages are deleted only from the cache and can possibly be retrieved again in the future :type from_cache: :class:`bool` """ ID: str = Field("updateDeleteMessages", alias="@type") chat_id: int message_ids: list[int] is_permanent: bool from_cache: bool @staticmethod def read(q: dict) -> UpdateDeleteMessages: return UpdateDeleteMessages.construct(q) class UpdateDiceEmojis(Update): """ The list of supported dice emojis has changed :param emojis: The new list of supported dice emojis :type emojis: :class:`list[str]` """ ID: str = Field("updateDiceEmojis", alias="@type") emojis: list[str] @staticmethod def read(q: dict) -> UpdateDiceEmojis: return UpdateDiceEmojis.construct(q) class UpdateFavoriteStickers(Update): """ The list of favorite stickers was updated :param sticker_ids: The new list of file identifiers of favorite stickers :type sticker_ids: :class:`list[int]` """ ID: str = Field("updateFavoriteStickers", alias="@type") sticker_ids: list[int] @staticmethod def read(q: dict) -> UpdateFavoriteStickers: return UpdateFavoriteStickers.construct(q) class UpdateFile(Update): """ Information about a file was updated :param file: New data about the file :type file: :class:`File` """ ID: str = Field("updateFile", alias="@type") file: File @staticmethod def read(q: dict) -> UpdateFile: return UpdateFile.construct(q) class UpdateFileGenerationStart(Update): """ The file generation process needs to be started by the application :param generation_id: Unique identifier for the generation process :type generation_id: :class:`int` :param
nav_only["NAV_fx"][0]) * 100 nav_only["NAV_ret"] = nav_only["NAV_norm"].pct_change() table = {} table["meta"] = {} table["meta"]["start_date"] = (nav_only.index[0]).strftime("%m-%d-%Y") table["meta"]["end_date"] = nav_only.index[-1].strftime("%m-%d-%Y") table["meta"]["number_of_days"] = ( (nav_only.index[-1] - nav_only.index[0])).days table["meta"]["count_of_points"] = nav_only["NAV_fx"].count().astype(float) table["NAV"] = {} table["NAV"]["start"] = nav_only["NAV_fx"][0] table["NAV"]["end"] = nav_only["NAV_fx"][-1] table["NAV"]["return"] = (nav_only["NAV_fx"][-1] / nav_only["NAV_fx"][0]) - 1 table["NAV"]["avg_return"] = nav_only["NAV_ret"].mean() table["NAV"]["ann_std_dev"] = nav_only["NAV_ret"].std() * math.sqrt(365) # Include avg return + and - for ticker in tickers: if messages[ticker] == "ok": # Include new columns for return and normalized data nav_only[ticker + "_norm"] = ( nav_only[ticker + "_price"] / nav_only[ticker + "_price"][0] ) * 100 nav_only[ticker + "_ret"] = nav_only[ticker + "_norm"].pct_change() # Create Metadata table[ticker] = {} table[ticker]["start"] = nav_only[ticker + "_price"][0] table[ticker]["end"] = nav_only[ticker + "_price"][-1] table[ticker]["return"] = ( nav_only[ticker + "_price"][-1] / nav_only[ticker + "_price"][0] ) - 1 table[ticker]["comp2nav"] = table[ticker]["return"] - \ table["NAV"]["return"] table[ticker]["avg_return"] = nav_only[ticker + "_ret"].mean() table[ticker]["ann_std_dev"] = nav_only[ticker + "_ret"].std() * math.sqrt( 365 ) logging.info("[scatter_json] Success") # Create Correlation Matrix filter_col = [col for col in nav_only if col.endswith("_ret")] nav_matrix = nav_only[filter_col] corr_matrix = nav_matrix.corr(method="pearson").round(2) corr_html = corr_matrix.to_html( classes="table small text-center", border=0, justify="center" ) # Create series data for HighCharts in scatter plot format # series : [{ # name: 'NAV / BTC', # color: '[blue]', # data: [[-0.01,-0.02], [0.02, 0.04]] # },{ # name: .....}] series_hc = [] # Append NAV ticker to list of tickers, remove duplicates tickers.append("NAV") tickers = list(set(tickers)) for ticker in tickers: tmp_dict = {} if ticker == market: continue tmp_dict["name"] = "x: " + market + ", y: " + ticker tmp_dict["regression"] = 1 tmp_df = nav_matrix[[market + "_ret", ticker + "_ret"]] tmp_df.fillna(0, inplace=True) tmp_dict["data"] = list( zip(tmp_df[market + "_ret"], tmp_df[ticker + "_ret"])) series_hc.append(tmp_dict) # Now, let's return the data in the correct format as requested return jsonify( { "chart_data": series_hc, "messages": messages, "meta_data": meta_data, "table": table, "corr_html": corr_html, } ) @api.route("/transactionsandcost_json", methods=["GET"]) @login_required # Return daily data on transactions and cost for a single ticker # Takes arguments: # ticker - single ticker for filter # start - start date in the format YYMMDD (defaults to 1st transaction on ticker) # end - end date in the format YYMMDD (defaults to today) def transactionsandcost_json(): # Get arguments and assign values if needed if request.method == "GET": start_date = request.args.get("start") ticker = request.args.get("ticker") # Check if start and end dates exist, if not assign values try: start_date = datetime.strptime(start_date, "%Y-%m-%d") except (ValueError, TypeError) as e: logging.info( f"[transactionsandcost_json] Warning: {e}, " + "setting start_date to zero" ) start_date = datetime(2000, 1, 1) end_date = request.args.get("end") try: end_date = datetime.strptime(end_date, "%Y-%m-%d") except (ValueError, TypeError) as e: logging.info( f"[transactionsandcost_json] Warning: {e}, " + "setting end_date to now" ) end_date = datetime.now() # Get Transaction List df = transactions_fx() # Filter only to requested ticker # if no ticker, use BTC as default, if not BTC then the 1st in list tickers = df.trade_asset_ticker.unique().tolist() try: tickers.remove(current_user.fx()) except ValueError: pass if not ticker: if "BTC" in tickers: ticker = "BTC" else: ticker = tickers[0] # Filter only the trades for current user df = df[(df.trade_asset_ticker == ticker)] # Filter only buy and sells, ignore deposit / withdraw # For now, including Deposits and Withdrawns as well but # may consider only B and S as line below. df = df[(df.trade_operation == "B") \| (df.trade_operation == "S")] df.drop("user_id", axis=1, inplace=True) # Create a cash_flow column - so we can calculate # average price for days with multiple buys and/or sells df["cash_flow"] = df["trade_quantity"] * \ df["trade_price_fx"] + df["trade_fees_fx"] # Consolidate all transactions from a single day by grouping df = df.groupby(["date"])[["cash_value", "trade_fees", "trade_quantity", "cash_value_fx"]].agg([ "sum", "count"]) # Remove the double index for column and consolidate under one row df.columns = ["_".join(col).strip() for col in df.columns.values] # Filter to Start and End Dates passed as arguments mask = (df.index >= start_date) & (df.index <= end_date) df = df.loc[mask] # --------------------------------------------------------- # Get price of ticker passed as argument and merge into df message = {} data = price_data_fx(ticker) # If notification is an error, skip this ticker if data is None: messages = data.errors return jsonify(messages) data = data.rename(columns={'close_converted': ticker}) data = data.astype(float) # Create a DF, fill with dates and fill with operation and prices start_date = df.index.min() daily_df = pd.DataFrame(columns=["date"]) daily_df["date"] = pd.date_range(start=start_date, end=end_date) daily_df = daily_df.set_index("date") # Fill dailyNAV with prices for each ticker daily_df = pd.merge(daily_df, df, on="date", how="left") daily_df.fillna(0, inplace=True) if type(daily_df) != type(data): data = data.to_frame() daily_df = pd.merge(daily_df, data, on="date", how="left") daily_df[ticker].fillna(method="ffill", inplace=True) message = "ok" logging.info(f"[transactionandcost_json] {ticker}: Success - Merged OK") # --------------------------------------------------------- # Create additional columns on df # --------------------------------------------------------- daily_df.loc[daily_df.trade_quantity_sum > 0, "traded"] = 1 daily_df.loc[daily_df.trade_quantity_sum <= 0, "traded"] = 0 daily_df["q_cum_sum"] = daily_df["trade_quantity_sum"].cumsum() daily_df["cv_cum_sum"] = daily_df["cash_value_sum"].cumsum() daily_df["cv_fx_cum_sum"] = daily_df["cash_value_fx_sum"].cumsum() daily_df["avg_cost"] = daily_df["cv_fx_cum_sum"] / daily_df["q_cum_sum"] daily_df["price_over_cost_usd"] = daily_df[ticker] - daily_df["avg_cost"] daily_df["price_over_cost_perc"] = ( daily_df[ticker] / daily_df["avg_cost"]) - 1 daily_df["impact_on_cost_usd"] = daily_df["avg_cost"].diff() daily_df["impact_on_cost_per"] = daily_df["impact_on_cost_usd"] / \ daily_df[ticker] # Remove cost if position is too small - this avoids large numbers # Also, remove cost calculation if positions are open (from zero) daily_df.loc[daily_df.q_cum_sum <= 0.009, "price_over_cost_usd"] = np.NaN daily_df.loc[daily_df.q_cum_sum <= 0.009, "avg_cost"] = np.NaN daily_df.loc[daily_df.q_cum_sum.shift( 1) <= 0.009, "impact_on_cost_usd"] = np.NaN daily_df.loc[daily_df.q_cum_sum <= 0.009, "impact_on_cost_usd"] = np.NaN daily_df.loc[daily_df.q_cum_sum <= 0.009, "impact_on_cost_per"] = np.NaN return_dict = {} return_dict["data"] = daily_df.to_json() return_dict["message"] = message return_dict["fx"] = fxsymbol(current_user.fx()) logging.info(f"[transactionandcost_json] Success generating data") return jsonify(return_dict) @api.route("/heatmapbenchmark_json", methods=["GET"]) @login_required # Return Monthly returns for Benchmark and Benchmark difference from NAV # Takes arguments: # ticker - single ticker for filter def heatmapbenchmark_json(): # Get portfolio data first heatmap_gen, heatmap_stats, years, cols = heatmap_generator() # Now get the ticker information and run comparison if request.method == "GET": ticker = request.args.get("ticker") # Defaults to king BTC if not ticker: ticker = "BTC" # Gather the first trade date in portfolio and store # used to match the matrixes later # Panda dataframe with transactions df = pd.read_sql_table("trades", db.engine) df = df[(df.user_id == current_user.username)] # Filter the df acccoring to filter passed as arguments df["trade_date"] = pd.to_datetime(df["trade_date"]) start_date = df["trade_date"].min() start_date -= timedelta(days=1) # start on t-1 of first trade # Generate price Table now for the ticker and trim to match portfolio data = price_data_fx(ticker) mask = data.index >= start_date data = data.loc[mask] # If notification is an error, skip this ticker if data is None: messages = data.errors return jsonify(messages) data = data.rename(columns={'close_converted': ticker+'_price'}) data = data[[ticker+'_price']] data.sort_index(ascending=True, inplace=True) data["pchange"] = (data / data.shift(1)) - 1 # Run the mrh function to generate heapmap table heatmap = mrh.get(data["pchange"], eoy=True) heatmap_stats = heatmap cols = [ "Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec", "eoy", ] cols_months = [ "Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec", ] years = heatmap.index.tolist() # Create summary stats for the Ticker heatmap_stats["MAX"] = heatmap_stats[heatmap_stats[cols_months] != 0].max( axis=1) heatmap_stats["MIN"] = heatmap_stats[heatmap_stats[cols_months] != 0].min( axis=1) heatmap_stats["POSITIVES"] = heatmap_stats[heatmap_stats[cols_months] > 0].count( axis=1 ) heatmap_stats["NEGATIVES"] = heatmap_stats[heatmap_stats[cols_months] < 0].count( axis=1 ) heatmap_stats["POS_MEAN"] = heatmap_stats[heatmap_stats[cols_months] > 0].mean( axis=1 ) heatmap_stats["NEG_MEAN"] = heatmap_stats[heatmap_stats[cols_months] < 0].mean( axis=1 ) heatmap_stats["MEAN"] = heatmap_stats[heatmap_stats[cols_months] != 0].mean( axis=1) # Create the difference between the 2 df - Pandas is cool! heatmap_difference = heatmap_gen - heatmap # return (heatmap, heatmap_stats, years, cols, ticker, heatmap_diff) return simplejson.dumps( { "heatmap": heatmap.to_dict(), "heatmap_stats": heatmap_stats.to_dict(), "cols": cols, "years": years, "ticker": ticker, "heatmap_diff": heatmap_difference.to_dict(), }, ignore_nan=True, default=datetime.isoformat, ) @api.route("/drawdown_json", methods=["GET"]) @login_required # Return the largest drawdowns in a time period # Takes arguments: # ticker: Single ticker for filter (default = NAV) # start_date: If none, defaults to all available # end_date: If none, defaults to today # n_dd: Top n drawdowns to be calculated # chart: Boolean - return data for chart def drawdown_json(): # Get the arguments and store if request.method == "GET": start_date = request.args.get("start") ticker = request.args.get("ticker") n_dd = request.args.get("n_dd") chart = request.args.get("chart") if not ticker: ticker = "NAV" ticker = ticker.upper() if n_dd: try: n_dd = int(n_dd) except TypeError: n_dd = 2 if not n_dd: n_dd = 2
return not (self == other) class EndMaintenanceResult: """ Attributes: - statuses """ thrift_spec = ( None, # 0 (1, TType.SET, 'statuses', (TType.STRUCT,(HostStatus, HostStatus.thrift_spec)), None, ), # 1 ) def __init__(self, statuses=None,): self.statuses = statuses def read(self, iprot): if iprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None and fastbinary is not None: fastbinary.decode_binary(self, iprot.trans, (self.__class__, self.thrift_spec)) return iprot.readStructBegin() while True: (fname, ftype, fid) = iprot.readFieldBegin() if ftype == TType.STOP: break if fid == 1: if ftype == TType.SET: self.statuses = set() (_etype231, _size228) = iprot.readSetBegin() for _i232 in range(_size228): _elem233 = HostStatus() _elem233.read(iprot) self.statuses.add(_elem233) iprot.readSetEnd() else: iprot.skip(ftype) else: iprot.skip(ftype) iprot.readFieldEnd() iprot.readStructEnd() def write(self, oprot): if oprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and self.thrift_spec is not None and fastbinary is not None: oprot.trans.write(fastbinary.encode_binary(self, (self.__class__, self.thrift_spec))) return oprot.writeStructBegin('EndMaintenanceResult') if self.statuses is not None: oprot.writeFieldBegin('statuses', TType.SET, 1) oprot.writeSetBegin(TType.STRUCT, len(self.statuses)) for iter234 in self.statuses: iter234.write(oprot) oprot.writeSetEnd() oprot.writeFieldEnd() oprot.writeFieldStop() oprot.writeStructEnd() def validate(self): return def __hash__(self): value = 17 value = (value * 31) ^ hash(self.statuses) return value def __repr__(self): L = ['%s=%r' % (key, value) for key, value in self.__dict__.items()] return '%s(%s)' % (self.__class__.__name__, ', '.join(L)) def __eq__(self, other): return isinstance(other, self.__class__) and self.__dict__ == other.__dict__ def __ne__(self, other): return not (self == other) class RoleSummaryResult: """ Attributes: - summaries """ thrift_spec = ( None, # 0 (1, TType.SET, 'summaries', (TType.STRUCT,(RoleSummary, RoleSummary.thrift_spec)), None, ), # 1 ) def __init__(self, summaries=None,): self.summaries = summaries def read(self, iprot): if iprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None and fastbinary is not None: fastbinary.decode_binary(self, iprot.trans, (self.__class__, self.thrift_spec)) return iprot.readStructBegin() while True: (fname, ftype, fid) = iprot.readFieldBegin() if ftype == TType.STOP: break if fid == 1: if ftype == TType.SET: self.summaries = set() (_etype238, _size235) = iprot.readSetBegin() for _i239 in range(_size235): _elem240 = RoleSummary() _elem240.read(iprot) self.summaries.add(_elem240) iprot.readSetEnd() else: iprot.skip(ftype) else: iprot.skip(ftype) iprot.readFieldEnd() iprot.readStructEnd() def write(self, oprot): if oprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and self.thrift_spec is not None and fastbinary is not None: oprot.trans.write(fastbinary.encode_binary(self, (self.__class__, self.thrift_spec))) return oprot.writeStructBegin('RoleSummaryResult') if self.summaries is not None: oprot.writeFieldBegin('summaries', TType.SET, 1) oprot.writeSetBegin(TType.STRUCT, len(self.summaries)) for iter241 in self.summaries: iter241.write(oprot) oprot.writeSetEnd() oprot.writeFieldEnd() oprot.writeFieldStop() oprot.writeStructEnd() def validate(self): return def __hash__(self): value = 17 value = (value * 31) ^ hash(self.summaries) return value def __repr__(self): L = ['%s=%r' % (key, value) for key, value in self.__dict__.items()] return '%s(%s)' % (self.__class__.__name__, ', '.join(L)) def __eq__(self, other): return isinstance(other, self.__class__) and self.__dict__ == other.__dict__ def __ne__(self, other): return not (self == other) class JobSummaryResult: """ Attributes: - summaries """ thrift_spec = ( None, # 0 (1, TType.SET, 'summaries', (TType.STRUCT,(JobSummary, JobSummary.thrift_spec)), None, ), # 1 ) def __init__(self, summaries=None,): self.summaries = summaries def read(self, iprot): if iprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None and fastbinary is not None: fastbinary.decode_binary(self, iprot.trans, (self.__class__, self.thrift_spec)) return iprot.readStructBegin() while True: (fname, ftype, fid) = iprot.readFieldBegin() if ftype == TType.STOP: break if fid == 1: if ftype == TType.SET: self.summaries = set() (_etype245, _size242) = iprot.readSetBegin() for _i246 in range(_size242): _elem247 = JobSummary() _elem247.read(iprot) self.summaries.add(_elem247) iprot.readSetEnd() else: iprot.skip(ftype) else: iprot.skip(ftype) iprot.readFieldEnd() iprot.readStructEnd() def write(self, oprot): if oprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and self.thrift_spec is not None and fastbinary is not None: oprot.trans.write(fastbinary.encode_binary(self, (self.__class__, self.thrift_spec))) return oprot.writeStructBegin('JobSummaryResult') if self.summaries is not None: oprot.writeFieldBegin('summaries', TType.SET, 1) oprot.writeSetBegin(TType.STRUCT, len(self.summaries)) for iter248 in self.summaries: iter248.write(oprot) oprot.writeSetEnd() oprot.writeFieldEnd() oprot.writeFieldStop() oprot.writeStructEnd() def validate(self): return def __hash__(self): value = 17 value = (value * 31) ^ hash(self.summaries) return value def __repr__(self): L = ['%s=%r' % (key, value) for key, value in self.__dict__.items()] return '%s(%s)' % (self.__class__.__name__, ', '.join(L)) def __eq__(self, other): return isinstance(other, self.__class__) and self.__dict__ == other.__dict__ def __ne__(self, other): return not (self == other) class GetLocksResult: """ Attributes: - locks """ thrift_spec = ( None, # 0 (1, TType.SET, 'locks', (TType.STRUCT,(Lock, Lock.thrift_spec)), None, ), # 1 ) def __init__(self, locks=None,): self.locks = locks def read(self, iprot): if iprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None and fastbinary is not None: fastbinary.decode_binary(self, iprot.trans, (self.__class__, self.thrift_spec)) return iprot.readStructBegin() while True: (fname, ftype, fid) = iprot.readFieldBegin() if ftype == TType.STOP: break if fid == 1: if ftype == TType.SET: self.locks = set() (_etype252, _size249) = iprot.readSetBegin() for _i253 in range(_size249): _elem254 = Lock() _elem254.read(iprot) self.locks.add(_elem254) iprot.readSetEnd() else: iprot.skip(ftype) else: iprot.skip(ftype) iprot.readFieldEnd() iprot.readStructEnd() def write(self, oprot): if oprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and self.thrift_spec is not None and fastbinary is not None: oprot.trans.write(fastbinary.encode_binary(self, (self.__class__, self.thrift_spec))) return oprot.writeStructBegin('GetLocksResult') if self.locks is not None: oprot.writeFieldBegin('locks', TType.SET, 1) oprot.writeSetBegin(TType.STRUCT, len(self.locks)) for iter255 in self.locks: iter255.write(oprot) oprot.writeSetEnd() oprot.writeFieldEnd() oprot.writeFieldStop() oprot.writeStructEnd() def validate(self): return def __hash__(self): value = 17 value = (value * 31) ^ hash(self.locks) return value def __repr__(self): L = ['%s=%r' % (key, value) for key, value in self.__dict__.items()] return '%s(%s)' % (self.__class__.__name__, ', '.join(L)) def __eq__(self, other): return isinstance(other, self.__class__) and self.__dict__ == other.__dict__ def __ne__(self, other): return not (self == other) class ConfigSummaryResult: """ Attributes: - summary """ thrift_spec = ( None, # 0 (1, TType.STRUCT, 'summary', (ConfigSummary, ConfigSummary.thrift_spec), None, ), # 1 ) def __init__(self, summary=None,): self.summary = summary def read(self, iprot): if iprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None and fastbinary is not None: fastbinary.decode_binary(self, iprot.trans, (self.__class__, self.thrift_spec)) return iprot.readStructBegin() while True: (fname, ftype, fid) = iprot.readFieldBegin() if ftype == TType.STOP: break if fid == 1: if ftype == TType.STRUCT: self.summary = ConfigSummary() self.summary.read(iprot) else: iprot.skip(ftype) else: iprot.skip(ftype) iprot.readFieldEnd() iprot.readStructEnd() def write(self, oprot): if oprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and self.thrift_spec is not None and fastbinary is not None: oprot.trans.write(fastbinary.encode_binary(self, (self.__class__, self.thrift_spec))) return oprot.writeStructBegin('ConfigSummaryResult') if self.summary is not None: oprot.writeFieldBegin('summary', TType.STRUCT, 1) self.summary.write(oprot) oprot.writeFieldEnd() oprot.writeFieldStop() oprot.writeStructEnd() def validate(self): return def __hash__(self): value = 17 value = (value * 31) ^ hash(self.summary) return value def __repr__(self): L = ['%s=%r' % (key, value) for key, value in self.__dict__.items()] return '%s(%s)' % (self.__class__.__name__, ', '.join(L)) def __eq__(self, other): return isinstance(other, self.__class__) and self.__dict__ == other.__dict__ def __ne__(self, other): return not (self == other) class GetPendingReasonResult: """ Attributes: - reasons """ thrift_spec = ( None, # 0 (1, TType.SET, 'reasons', (TType.STRUCT,(PendingReason, PendingReason.thrift_spec)), None, ), # 1 ) def __init__(self, reasons=None,): self.reasons = reasons def read(self, iprot): if iprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None and fastbinary is not None: fastbinary.decode_binary(self, iprot.trans, (self.__class__, self.thrift_spec)) return iprot.readStructBegin() while True: (fname, ftype, fid) = iprot.readFieldBegin() if ftype == TType.STOP: break if fid == 1: if ftype == TType.SET: self.reasons = set() (_etype259, _size256) = iprot.readSetBegin() for _i260 in range(_size256): _elem261 = PendingReason() _elem261.read(iprot) self.reasons.add(_elem261) iprot.readSetEnd() else: iprot.skip(ftype) else: iprot.skip(ftype) iprot.readFieldEnd() iprot.readStructEnd() def write(self, oprot): if oprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and self.thrift_spec is not None and fastbinary is not None: oprot.trans.write(fastbinary.encode_binary(self, (self.__class__, self.thrift_spec))) return oprot.writeStructBegin('GetPendingReasonResult') if self.reasons is not None: oprot.writeFieldBegin('reasons', TType.SET, 1) oprot.writeSetBegin(TType.STRUCT, len(self.reasons)) for iter262 in self.reasons: iter262.write(oprot) oprot.writeSetEnd() oprot.writeFieldEnd() oprot.writeFieldStop() oprot.writeStructEnd() def validate(self): return def __hash__(self): value = 17 value = (value * 31) ^ hash(self.reasons) return value def __repr__(self): L = ['%s=%r' % (key, value) for key, value in self.__dict__.items()] return '%s(%s)' % (self.__class__.__name__, ', '.join(L)) def __eq__(self, other): return isinstance(other, self.__class__) and self.__dict__ == other.__dict__ def __ne__(self, other): return not (self == other) class StartJobUpdateResult: """ Result of the startUpdate call. Attributes: - key: Unique identifier for the job update. """ thrift_spec = ( None, # 0 (1, TType.STRUCT, 'key', (JobUpdateKey, JobUpdateKey.thrift_spec), None, ), # 1 ) def __init__(self, key=None,): self.key = key def read(self, iprot): if iprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None and fastbinary is not None: fastbinary.decode_binary(self, iprot.trans, (self.__class__, self.thrift_spec)) return iprot.readStructBegin() while True: (fname, ftype, fid) = iprot.readFieldBegin() if ftype == TType.STOP: break if fid == 1: if ftype == TType.STRUCT: self.key = JobUpdateKey() self.key.read(iprot) else: iprot.skip(ftype) else: iprot.skip(ftype) iprot.readFieldEnd() iprot.readStructEnd() def write(self, oprot): if oprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and self.thrift_spec is not None and fastbinary is not None: oprot.trans.write(fastbinary.encode_binary(self, (self.__class__, self.thrift_spec))) return oprot.writeStructBegin('StartJobUpdateResult') if self.key is not None: oprot.writeFieldBegin('key', TType.STRUCT, 1) self.key.write(oprot) oprot.writeFieldEnd() oprot.writeFieldStop() oprot.writeStructEnd() def validate(self): return def __hash__(self): value = 17 value = (value * 31) ^
#!/usr/bin/env python3 # # Copyright (c) <NAME> and the University of Texas MD Anderson Cancer Center # Distributed under the terms of the 3-clause BSD License. import contextlib import logging import os import subprocess import sys import time from collections import OrderedDict, defaultdict from textwrap import dedent import pandas as pd import pkg_resources from ipykernel.ipkernel import IPythonKernel from IPython.core.display import HTML from IPython.utils.tokenutil import line_at_cursor, token_at_cursor from jupyter_client import manager from sos._version import __sos_version__, __version__ from sos.eval import SoS_eval, SoS_exec, interpolate from sos.syntax import SOS_SECTION_HEADER from sos.utils import (format_duration, WorkflowDict, env, log_to_file, short_repr) from ._version import __version__ as __notebook_version__ from .completer import SoS_Completer from .inspector import SoS_Inspector from .step_executor import PendingTasks from .workflow_executor import runfile, NotebookLoggingHandler from .magics import SoS_Magics class FlushableStringIO: '''This is a string buffer for output, but it will only keep the first 200 lines and the last 10 lines. ''' def __init__(self, kernel, name, args, kwargs): self.kernel = kernel self.name = name def write(self, content): if content.startswith('HINT: '): content = content.splitlines() hint_line = content[0][6:].strip() content = '\n'.join(content[1:]) self.kernel.send_response(self.kernel.iopub_socket, 'display_data', { 'metadata': {}, 'data': {'text/html': HTML( f'<div class="sos_hint">{hint_line}</div>').data} }) if content: if self.kernel._meta['capture_result'] is not None: self.kernel._meta['capture_result'].append( ('stream', {'name': self.name, 'text': content})) self.kernel.send_response(self.kernel.iopub_socket, 'stream', {'name': self.name, 'text': content}) def flush(self): pass __all__ = ['SoS_Kernel'] class subkernel(object): # a class to information on subkernel def __init__(self, name=None, kernel=None, language='', color='', options={}): self.name = name self.kernel = kernel self.language = language self.color = color self.options = options def __repr__(self): return f'subkernel {self.name} with kernel {self.kernel} for language {self.language} with color {self.color}' # translate a message to transient_display_data message def make_transient_msg(msg_type, content, title, append=False, page='Info'): if msg_type == 'display_data': return { 'title': title, 'data': content.get('data', {}), 'metadata': {'append': append, 'page': page} } elif msg_type == 'stream': if content['name'] == 'stdout': return { 'title': title, 'data': { 'text/plain': content['text'], 'application/vnd.jupyter.stdout': content['text'] }, 'metadata': {'append': append, 'page': page} } else: return { 'title': title, 'data': { 'text/plain': content['text'], 'application/vnd.jupyter.stderr': content['text'] }, 'metadata': {'append': append, 'page': page} } else: raise ValueError( f"failed to translate message {msg_type} to transient_display_data message") class Subkernels(object): # a collection of subkernels def __init__(self, kernel): self.sos_kernel = kernel self.language_info = kernel.supported_languages from jupyter_client.kernelspec import KernelSpecManager km = KernelSpecManager() specs = km.find_kernel_specs() # get supported languages self._kernel_list = [] lan_map = {} for x in self.language_info.keys(): for lname, knames in kernel.supported_languages[x].supported_kernels.items(): for kname in knames: if x != kname: lan_map[kname] = (lname, self.get_background_color(self.language_info[x], lname), getattr(self.language_info[x], 'options', {})) # kernel_list has the following items # # 1. displayed name # 2. kernel name # 3. language name # 4. color for spec in specs.keys(): if spec == 'sos': # the SoS kernel will be default theme color. self._kernel_list.append( subkernel(name='SoS', kernel='sos', options={ 'variable_pattern': r'^\s[_A-Za-z0-9\.]+\s$', 'assignment_pattern': r'^\s([_A-Za-z0-9\.]+)\s=.$'})) elif spec in lan_map: # e.g. ir ==> R self._kernel_list.append( subkernel(name=lan_map[spec][0], kernel=spec, language=lan_map[spec][0], color=lan_map[spec][1], options=lan_map[spec][2])) else: # undefined language also use default theme color self._kernel_list.append(subkernel(name=spec, kernel=spec)) def kernel_list(self): return self._kernel_list # now, no kernel is found, name has to be a new name and we need some definition # if kernel is defined def add_or_replace(self, kdef): for idx, x in enumerate(self._kernel_list): if x.name == kdef.name: self._kernel_list[idx] = kdef return self._kernel_list[idx] else: self._kernel_list.append(kdef) return self._kernel_list[-1] def get_background_color(self, plugin, lan): # if a single color is defined, it is used for all supported # languages if isinstance(plugin.background_color, str): # return the same background color for all inquiry return plugin.background_color else: # return color for specified, or any color if unknown inquiry is made return plugin.background_color.get(lan, next(iter(plugin.background_color.values()))) def find(self, name, kernel=None, language=None, color=None, notify_frontend=True): # find from subkernel name def update_existing(idx): x = self._kernel_list[idx] if (kernel is not None and kernel != x.kernel) or (language not in (None, '', 'None') and language != x.language): raise ValueError( f'Cannot change kernel or language of predefined subkernel {name} {x}') if color is not None: if color == 'default': if self._kernel_list[idx].language: self._kernel_list[idx].color = self.get_background_color( self.language_info[self._kernel_list[idx].language], self._kernel_list[idx].language) else: self._kernel_list[idx].color = '' else: self._kernel_list[idx].color = color if notify_frontend: self.notify_frontend() # if the language module cannot be loaded for some reason if name in self.sos_kernel._failed_languages: raise self.sos_kernel._failed_languages[name] # find from language name (subkernel name, which is usually language name) for idx, x in enumerate(self._kernel_list): if x.name == name: if x.name == 'SoS' or x.language or language is None: update_existing(idx) return x else: if not kernel: kernel = name break # find from kernel name for idx, x in enumerate(self._kernel_list): if x.kernel == name: # if exist language or no new language defined. if x.language or language is None: update_existing(idx) return x else: # otherwise, try to use the new language kernel = name break if kernel is not None: # in this case kernel should have been defined in kernel list if kernel not in [x.kernel for x in self._kernel_list]: raise ValueError( f'Unrecognized Jupyter kernel name {kernel}. Please make sure it is properly installed and appear in the output of command "jupyter kenelspec list"') # now this a new instance for an existing kernel kdef = [x for x in self._kernel_list if x.kernel == kernel][0] if not language: if color == 'default': if kdef.language: color = self.get_background_color( self.language_info[kdef.language], kdef.language) else: color = kdef.color new_def = self.add_or_replace(subkernel(name, kdef.kernel, kdef.language, kdef.color if color is None else color, getattr(self.language_info[kdef.language], 'options', {}) if kdef.language else {})) if notify_frontend: self.notify_frontend() return new_def else: # if language is defined, if ':' in language: # if this is a new module, let us create an entry point and load from pkg_resources import EntryPoint mn, attr = language.split(':', 1) ep = EntryPoint(name=kernel, module_name=mn, attrs=tuple(attr.split('.'))) try: plugin = ep.resolve() self.language_info[name] = plugin # for convenience, we create two entries for, e.g. R and ir # but only if there is no existing definition for supported_lan, supported_kernels in plugin.supported_kernels.items(): for supported_kernel in supported_kernels: if name != supported_kernel and supported_kernel not in self.language_info: self.language_info[supported_kernel] = plugin if supported_lan not in self.language_info: self.language_info[supported_lan] = plugin except Exception as e: raise RuntimeError( f'Failed to load language {language}: {e}') # if color == 'default': color = self.get_background_color(plugin, kernel) new_def = self.add_or_replace(subkernel(name, kdef.kernel, kernel, kdef.color if color is None else color, getattr(plugin, 'options', {}))) else: # if should be defined ... if language not in self.language_info: raise RuntimeError( f'Unrecognized language definition {language}, which should be a known language name or a class in the format of package.module:class') # self.language_info[name] = self.language_info[language] if color == 'default': color = self.get_background_color( self.language_info[name], language) new_def = self.add_or_replace(subkernel(name, kdef.kernel, language, kdef.color if color is None else color, getattr(self.language_info[name], 'options', {}))) if notify_frontend: self.notify_frontend() return new_def elif language is not None: # kernel is not defined and we only have language if ':' in language: # if this is a new module, let us create an entry point and load from pkg_resources import EntryPoint mn, attr = language.split(':', 1) ep = EntryPoint(name='__unknown__', module_name=mn, attrs=tuple(attr.split('.'))) try: plugin = ep.resolve() self.language_info[name] = plugin except Exception as e: raise RuntimeError( f'Failed to load language {language}: {e}') if name in plugin.supported_kernels: # if name is defined in the module, only search kernels for this language avail_kernels = [x for x in plugin.supported_kernels[name] if x in [y.kernel for y in self._kernel_list]] else: # otherwise we search all supported kernels avail_kernels = [x for x in sum(plugin.supported_kernels.values(), []) if x in [y.kernel for y in self._kernel_list]] if not avail_kernels: raise ValueError( 'Failed to find any of the kernels {} supported by language {}. Please make sure it is properly installed and appear in the output of command "jupyter kenelspec list"'.format( ', '.join(sum(plugin.supported_kernels.values(), [])), language)) # use the first available kernel # find the language that has the kernel lan_name = list({x: y for x, y in plugin.supported_kernels.items( ) if avail_kernels[0] in y}.keys())[0] if color == 'default': color = self.get_background_color(plugin, lan_name) new_def = self.add_or_replace(subkernel(name, avail_kernels[0], lan_name, self.get_background_color(plugin, lan_name) if color is None else color, getattr(plugin, 'options', {}))) else: # if a language name is specified (not a path to module), if should be defined in setup.py if language not in self.language_info: raise RuntimeError( f'Unrecognized language definition {language}') # plugin = self.language_info[language] if language in plugin.supported_kernels: avail_kernels =
<filename>venv/Lib/site-packages/dash/testing/browser.py # pylint: disable=missing-docstring import os import sys import time import logging import warnings import percy from selenium import webdriver from selenium.webdriver.support import expected_conditions as EC from selenium.webdriver.common.by import By from selenium.webdriver.support.wait import WebDriverWait from selenium.webdriver.common.keys import Keys from selenium.webdriver.common.desired_capabilities import DesiredCapabilities from selenium.webdriver.common.action_chains import ActionChains from selenium.common.exceptions import ( WebDriverException, TimeoutException, MoveTargetOutOfBoundsException, ) from dash.testing.wait import ( text_to_equal, style_to_equal, contains_text, until, ) from dash.testing.dash_page import DashPageMixin from dash.testing.errors import ( DashAppLoadingError, BrowserError, TestingTimeoutError, ) from dash.testing.consts import SELENIUM_GRID_DEFAULT logger = logging.getLogger(__name__) class Browser(DashPageMixin): def __init__( self, browser, remote=False, remote_url=None, headless=False, options=None, download_path="", percy_run=True, percy_finalize=True, percy_assets_root="", wait_timeout=10, ): self._browser = browser.lower() self._remote_url = remote_url self._remote = ( True if remote_url and remote_url != SELENIUM_GRID_DEFAULT else remote ) self._headless = headless self._options = options self._download_path = download_path self._wait_timeout = wait_timeout self._percy_finalize = percy_finalize self._percy_run = percy_run self._driver = until(self.get_webdriver, timeout=1) self._driver.implicitly_wait(2) self._wd_wait = WebDriverWait(self.driver, wait_timeout) self._last_ts = 0 self._url = None self._window_idx = 0 # switch browser tabs if self._percy_run: self.percy_runner = percy.Runner( loader=percy.ResourceLoader( webdriver=self.driver, base_url="/assets", root_dir=percy_assets_root, ) ) self.percy_runner.initialize_build() logger.info("initialize browser with arguments") logger.info(" headless => %s", self._headless) logger.info(" download_path => %s", self._download_path) logger.info(" percy asset root => %s", os.path.abspath(percy_assets_root)) def __enter__(self): return self def __exit__(self, exc_type, exc_val, traceback): try: self.driver.quit() if self._percy_run and self._percy_finalize: logger.info("percy runner finalize build now") self.percy_runner.finalize_build() else: logger.info("percy finalize relies on CI job") except WebDriverException: logger.exception("webdriver quit was not successful") except percy.errors.Error: logger.exception("percy runner failed to finalize properly") def visit_and_snapshot( self, resource_path, hook_id, wait_for_callbacks=True, assert_check=True, stay_on_page=False, ): try: path = resource_path.lstrip("/") if path != resource_path: logger.warning("we stripped the left '/' in resource_path") self.driver.get("{}/{}".format(self.server_url.rstrip("/"), path)) # wait for the hook_id to present and all callbacks get fired self.wait_for_element_by_id(hook_id) self.percy_snapshot(path, wait_for_callbacks=wait_for_callbacks) if assert_check: assert not self.driver.find_elements_by_css_selector( "div.dash-debug-alert" ), "devtools should not raise an error alert" if not stay_on_page: self.driver.back() except WebDriverException as e: logger.exception("snapshot at resource %s error", path) raise e def percy_snapshot(self, name="", wait_for_callbacks=False): """percy_snapshot - visual test api shortcut to `percy_runner.snapshot`. It also combines the snapshot `name` with the python version. """ snapshot_name = "{} - py{}.{}".format( name, sys.version_info.major, sys.version_info.minor ) logger.info("taking snapshot name => %s", snapshot_name) try: if wait_for_callbacks: # the extra one second sleep adds safe margin in the context # of wait_for_callbacks time.sleep(1) until(self._wait_for_callbacks, timeout=40, poll=0.3) except TestingTimeoutError: # API will log the error but this TimeoutError should not block # the test execution to continue and it will still do a snapshot # as diff reference for the build run. logger.error( "wait_for_callbacks failed => status of invalid rqs %s", list(_ for _ in self.redux_state_rqs if not _.get("responseTime")), ) logger.debug("full content of the rqs => %s", self.redux_state_rqs) self.percy_runner.snapshot(name=snapshot_name) def take_snapshot(self, name): """Hook method to take snapshot when a selenium test fails. The snapshot is placed under. - `/tmp/dash_artifacts` in linux - `%TEMP` in windows with a filename combining test case name and the running selenium session id """ target = "/tmp/dash_artifacts" if not self._is_windows() else os.getenv("TEMP") if not os.path.exists(target): try: os.mkdir(target) except OSError: logger.exception("cannot make artifacts") self.driver.save_screenshot( "{}/{}_{}.png".format(target, name, self.session_id) ) def find_element(self, selector): """find_element returns the first found element by the css `selector` shortcut to `driver.find_element_by_css_selector`.""" return self.driver.find_element_by_css_selector(selector) def find_elements(self, selector): """find_elements returns a list of all elements matching the css `selector`. shortcut to `driver.find_elements_by_css_selector`. """ return self.driver.find_elements_by_css_selector(selector) def _get_element(self, elem_or_selector): if isinstance(elem_or_selector, str): return self.find_element(elem_or_selector) return elem_or_selector def _wait_for(self, method, args, timeout, msg): """Abstract generic pattern for explicit WebDriverWait.""" _wait = ( self._wd_wait if timeout is None else WebDriverWait(self.driver, timeout) ) logger.debug( "method, timeout, poll => %s %s %s", method, _wait._timeout, # pylint: disable=protected-access _wait._poll, # pylint: disable=protected-access ) return _wait.until(method(*args), msg) def wait_for_element(self, selector, timeout=None): """wait_for_element is shortcut to `wait_for_element_by_css_selector` timeout if not set, equals to the fixture's `wait_timeout`.""" return self.wait_for_element_by_css_selector(selector, timeout) def wait_for_element_by_css_selector(self, selector, timeout=None): """Explicit wait until the element is present, timeout if not set, equals to the fixture's `wait_timeout` shortcut to `WebDriverWait` with `EC.presence_of_element_located`.""" return self._wait_for( EC.presence_of_element_located, ((By.CSS_SELECTOR, selector),), timeout, "timeout {}s => waiting for selector {}".format( timeout if timeout else self._wait_timeout, selector ), ) def wait_for_element_by_id(self, element_id, timeout=None): """Explicit wait until the element is present, timeout if not set, equals to the fixture's `wait_timeout` shortcut to `WebDriverWait` with `EC.presence_of_element_located`.""" return self._wait_for( EC.presence_of_element_located, ((By.ID, element_id),), timeout, "timeout {}s => waiting for element id {}".format( timeout if timeout else self._wait_timeout, element_id ), ) def wait_for_style_to_equal(self, selector, style, val, timeout=None): """Explicit wait until the element's style has expected `value` timeout if not set, equals to the fixture's `wait_timeout` shortcut to `WebDriverWait` with customized `style_to_equal` condition.""" return self._wait_for( method=style_to_equal, args=(selector, style, val), timeout=timeout, msg="style val => {} {} not found within {}s".format( style, val, timeout if timeout else self._wait_timeout ), ) def wait_for_text_to_equal(self, selector, text, timeout=None): """Explicit wait until the element's text equals the expected `text`. timeout if not set, equals to the fixture's `wait_timeout` shortcut to `WebDriverWait` with customized `text_to_equal` condition. """ return self._wait_for( method=text_to_equal, args=(selector, text), timeout=timeout, msg="text -> {} not found within {}s".format( text, timeout if timeout else self._wait_timeout ), ) def wait_for_contains_text(self, selector, text, timeout=None): """Explicit wait until the element's text contains the expected `text`. timeout if not set, equals to the fixture's `wait_timeout` shortcut to `WebDriverWait` with customized `contains_text` condition. """ return self._wait_for( method=contains_text, args=(selector, text), timeout=timeout, msg="text -> {} not found inside element within {}s".format( text, timeout if timeout else self._wait_timeout ), ) def wait_for_page(self, url=None, timeout=10): """wait_for_page navigates to the url in webdriver wait until the renderer is loaded in browser. use the `server_url` if url is not provided. """ self.driver.get(self.server_url if url is None else url) try: self.wait_for_element_by_css_selector( self.dash_entry_locator, timeout=timeout ) except TimeoutException: logger.exception("dash server is not loaded within %s seconds", timeout) logger.debug(self.get_logs()) raise DashAppLoadingError( "the expected Dash react entry point cannot be loaded" " in browser\n HTML => {}\n Console Logs => {}\n".format( self.driver.find_element_by_tag_name("body").get_property( "innerHTML" ), "\n".join((str(log) for log in self.get_logs())), ) ) def select_dcc_dropdown(self, elem_or_selector, value=None, index=None): dropdown = self._get_element(elem_or_selector) dropdown.click() menu = dropdown.find_element_by_css_selector("div.Select-menu-outer") logger.debug("the available options are %s", "\|".join(menu.text.split("\n"))) options = menu.find_elements_by_css_selector("div.VirtualizedSelectOption") if options: if isinstance(index, int): options[index].click() return for option in options: if option.text == value: option.click() return logger.error( "cannot find matching option using value=%s or index=%s", value, index, ) def toggle_window(self): """Switch between the current working window and the new opened one.""" idx = (self._window_idx + 1) % 2 self.switch_window(idx=idx) self._window_idx += 1 def switch_window(self, idx=0): """Switch to window by window index shortcut to `driver.switch_to.window`.""" if len(self.driver.window_handles) <= idx: raise BrowserError("there is no second window in Browser") self.driver.switch_to.window(self.driver.window_handles[idx]) def open_new_tab(self, url=None): """Open a new tab in browser url is not set, equals to `server_url`.""" self.driver.execute_script( 'window.open("{}", "new window")'.format( self.server_url if url is None else url ) ) def get_webdriver(self): try: return getattr(self, "_get_{}".format(self._browser))() except WebDriverException: logger.exception("<<<Webdriver not initialized correctly>>>") def _get_wd_options(self): options = ( self._options[0] if self._options and isinstance(self._options, list) else getattr(webdriver, self._browser).options.Options() ) if self._headless: options.headless = True return options def _get_chrome(self): options = self._get_wd_options() capabilities = DesiredCapabilities.CHROME capabilities["loggingPrefs"] = {"browser": "SEVERE"} if "DASH_TEST_CHROMEPATH" in os.environ: options.binary_location = os.environ["DASH_TEST_CHROMEPATH"] options.add_experimental_option( "prefs", { "download.default_directory": self.download_path, "download.prompt_for_download": False, "download.directory_upgrade": True, "safebrowsing.enabled": False, "safebrowsing.disable_download_protection": True, }, ) chrome = ( webdriver.Remote( command_executor=self._remote_url, options=options, desired_capabilities=capabilities, ) if self._remote else webdriver.Chrome(options=options, desired_capabilities=capabilities) ) # https://bugs.chromium.org/p/chromium/issues/detail?id=696481 if self._headless: # pylint: disable=protected-access chrome.command_executor._commands["send_command"] = ( "POST", "/session/$sessionId/chromium/send_command", ) params = { "cmd": "Page.setDownloadBehavior", "params": {"behavior": "allow", "downloadPath": self.download_path}, } res = chrome.execute("send_command", params) logger.debug("enabled headless download returns %s", res) chrome.set_window_position(0, 0) return chrome def _get_firefox(self): options = self._get_wd_options() capabilities = DesiredCapabilities.FIREFOX capabilities["loggingPrefs"] = {"browser": "SEVERE"} capabilities["marionette"] = True # https://developer.mozilla.org/en-US/docs/Download_Manager_preferences fp = webdriver.FirefoxProfile() fp.set_preference("browser.download.dir", self.download_path) fp.set_preference("browser.download.folderList", 2) fp.set_preference( "browser.helperApps.neverAsk.saveToDisk", "application/octet-stream", # this MIME is generic for binary ) return ( webdriver.Remote( command_executor=self._remote_url, options=options, desired_capabilities=capabilities, ) if self._remote else webdriver.Firefox( firefox_profile=fp, options=options, capabilities=capabilities ) ) @staticmethod def _is_windows(): return sys.platform == "win32" def multiple_click(self, elem_or_selector, clicks): """multiple_click click the element with number of `clicks`.""" for _ in range(clicks): self._get_element(elem_or_selector).click() def clear_input(self, elem_or_selector): """Simulate key press to clear the input.""" elem = self._get_element(elem_or_selector) logger.debug("clear input with %s => %s", elem_or_selector, elem) ( ActionChains(self.driver) .move_to_element(elem) .pause(0.2) .click(elem) .send_keys(Keys.END) .key_down(Keys.SHIFT) .send_keys(Keys.HOME) .key_up(Keys.SHIFT) .send_keys(Keys.DELETE) ).perform() def zoom_in_graph_by_ratio( self, elem_or_selector, start_fraction=0.5, zoom_box_fraction=0.2,
#!/usr/bin/env python """Web server for the NDVI Time Series Tool application. The code in this file runs on App Engine. It's called when the user loads the web page, requests a map or chart and if he wants to export an image. The App Engine code does most of the communication with EE. It uses the EE Python library and the service account specified in config.py. The exception is that when the browser loads map tiles it talks directly with EE. The map handler generates a unique client ID for the Channel API connection, injects it into the index.html template, and returns the page contents. When the user changes the options in the UI and clicks the compute button, the /mapid handler will generated map IDs for each image band. When the user requests a chart the /chart handler generates and returns a small chart over the Channel API. Also a full screen version is temporary available (ids are saved with the Memcache API) where the chart can be saved as image or table. When the user exports a file, the /export handler then kicks off an export runner (running asynchronously) to create the EE task and poll for the task's completion. When the EE task completes, the file is stored for 5 hours in the service account's Drive folder and an download link is sent to the user's browser using the Channel API. To clear the service account's Drive folder a cron job runs every hour and deletes all files older than 5 hours. Another export method is the /download handler that generates a download url directly from the EE. With this method the computing is done on the fly, because of that the download is not very stable and the file size is limited by 1024 MB. """ import math import traceback import json import logging import os import random import socket import string import time import calendar import urlparse import re from datetime import datetime # ctypes PATH KeyError fix os.environ.setdefault("PATH", '') import httplib2 import firebase_admin from firebase_admin import auth as firebase_auth import ee import jinja2 from oauth2client.service_account import ServiceAccountCredentials import webapp2 import gviz_api from google.appengine.api import taskqueue from google.appengine.api import urlfetch from google.appengine.api import memcache from google.appengine.api import users import config import drive ############################################################################### # Initialization. # ############################################################################### # Debug flag controls the output of the stacktrace if errors occur DEBUG = True # The timeout for URL Fetch, Socket and Earth Engine (seconds). # Note: Normal request are terminated after 60 seconds, background requests after 10 Minutes URL_FETCH_TIMEOUT = 600 # 10 Minuten # Check https://developers.google.com/drive/scopes for all available scopes. # Compines the Drive, Earth Engine and Firebase Scopes OAUTH_SCOPES = ["https://www.googleapis.com/auth/drive"] + ["https://www.googleapis.com/auth/earthengine","https://www.googleapis.com/auth/devstorage.full_control"] + ["https://www.googleapis.com/auth/userinfo.email","https://www.googleapis.com/auth/firebase.database"] # Our App Engine service account's credentials for Earth Engine and Google Drive CREDENTIALS = ServiceAccountCredentials.from_json_keyfile_name(config.SERVICE_ACC_JSON_KEYFILE, OAUTH_SCOPES) # Initialize the EE API. ee.Initialize(CREDENTIALS) # Set some timeouts ee.data.setDeadline(URL_FETCH_TIMEOUT*1000) # in milliseconds (default no limit) socket.setdefaulttimeout(URL_FETCH_TIMEOUT) urlfetch.set_default_fetch_deadline(URL_FETCH_TIMEOUT) # The Jinja templating system we use to dynamically generate HTML. See: # http://jinja.pocoo.org/docs/dev/ JINJA2_ENVIRONMENT = jinja2.Environment( loader=jinja2.FileSystemLoader(os.path.dirname(__file__)), autoescape=True, extensions=["jinja2.ext.autoescape"]) # An authenticated Drive helper object for the app service account. DRIVE_HELPER = drive.DriveHelper(CREDENTIALS) # The resolution of the exported images (meters per pixel). EXPORT_RESOLUTION = 30 # The maximum number of pixels in an exported image. EXPORT_MAX_PIXELS = 10e10 # The frequency to poll for export EE task completion (seconds). TASK_POLL_FREQUENCY = 10 ############################################################################### # Web request handlers. # ############################################################################### class DataHandler(webapp2.RequestHandler): """A servlet base class for responding to data queries. We use this base class to wrap our web request handlers with try/except blocks and set per-thread values (e.g. URL_FETCH_TIMEOUT). """ def get(self): self.Handle(self.DoGet) def post(self): self.Handle(self.DoPost) def DoGet(self): """Processes a GET request and returns a JSON-encodable result.""" raise NotImplementedError() def DoPost(self): """Processes a POST request and returns a JSON-encodable result.""" raise NotImplementedError() def Handle(self, handle_function): """Responds with the result of the handle_function or errors, if any.""" try: response = handle_function() except Exception as e: if DEBUG: response = {"error": str(e) + " - " + traceback.format_exc()} else: response = {"error": str(e)} if response: self.response.headers["Content-Type"] = "application/json" self.response.out.write(json.dumps(response)) class MapHandler(DataHandler): """A servlet to handle requests to load the main web page.""" def DoGet(self): """Returns the main web page with Firebase details included.""" client_id = _GetUniqueString() template = JINJA2_ENVIRONMENT.get_template("templates/index.html") self.response.out.write(template.render({ # channel token expire in 24 hours "clientId": client_id, "firebaseToken": create_custom_token(client_id), "firebaseConfig": "templates/%s" % config.FIREBASE_CONFIG, "display_splash": "none" })) class MapIdHandler(DataHandler): """A servlet that generates the map IDs for each band based on the selected options""" def DoPost(self): """Returns the map IDs of the requested options. HTTP Parameters: regression: the regression type [poly1,poly2,poly3,zhuWood] source: the source satellite [all,land5,land7,land8] start: the start year to filter the satellite images (including) end: the end year to filter the satellite images (including) cloudscore: the max cloudscore for the ee.Algorithms.Landsat.simpleCloudScore [1-100] Higher means that the pixel is more likley to be a cloud point: an array of two double values representing coordinates like [<longitude>,<latitude>] region: an array of arrays representing a region [[<longitude>,<latitude>],[<longitude>,<latitude>],...] client_id: the unique id that is used for the channel api Returns: A dictionary with a key called 'bands' containing an array of dictionaries like {"name":<band name>,"mapid":<mapid>,"token":<token>}. """ # reads the request options options = _ReadOptions(self.request) # creates an image based on the options image = _GetImage(options) # _GetImage returns None if the collection is empty if image is None: return {"error": "No images in collection. Change your options."} bands = image.bandNames().getInfo() layers = [] for band in bands: # create a map overlay for each band mapid = image.select(band).visualize().getMapId() layers.append({"name":band, "mapid": mapid["mapid"], "token": mapid["token"]}) return {"bands":layers} class ChartHandler(DataHandler): """A servlet to handle chart requests""" def DoGet(self): """Returns the full screen view of a chart. HTTP Parameters: id: the unique chart id (key value for the Memcache API). Returns: A html page with the full screen chart """ chart_id = self.request.get("id") # load chart options from Memcache API chart_options = memcache.get(chart_id) if chart_options is None: return {"error":"Chart id doesn't exist!"} else: # read template file f = open("templates/full_chart.html", "r") full_chart = f.read() f.close() # style chart view corresponding to the regression type if chart_options["regression"] == "zhuWood": chart_options["chart_style"] = "height: 40%;" chart_options["chartArea"] = "{width: \"80%\"}" else: chart_options["chart_style"] = "height: 60%; max-width: 1000px;" chart_options["chartArea"] = "{width: \"70%\"}" # output html page self.response.set_status(200) self.response.headers["Content-Type"] = "text/html" self.response.out.write(full_chart % chart_options) return def DoPost(self): """Starts an ChartRunnerHandler to asynchronously generate a chart. HTTP Parameters: regression: the regression type [poly1,poly2,poly3,zhuWood] source: the source satellite [all,land5,land7,land8] start: the start year to filter the satellite images (including) end: the end year to filter the satellite images (including) cloudscore: the max cloudscore for the ee.Algorithms.Landsat.simpleCloudScore [1-100] Higher means that the pixel is more likley to be a cloud point: an array of two double values representing coordinates like [<longitude>,<latitude>] client_id: the unique id that is used for the channel api """ # read request options options = _ReadOptions(self.request) # Kick off an export runner to start and monitor the EE export task. # Note: The work "task" is used by both Earth Engine and App Engine to refer # to two different things. "TaskQueue" is an async App Engine service. # only execute once even if task fails taskqueue.add(url="/chartrunner", params={"options":json.dumps(options)}, retry_options=taskqueue.TaskRetryOptions(task_retry_limit=0,task_age_limit=1)) # notify client browser that the chart creation has started _SendMessage(options["client_id"],"chart-" + options["filename"],"info","Chart creation at [%s/%s] in progress." % (options["point"][1],options["point"][0])) class ChartRunnerHandler(webapp2.RequestHandler): """A servlet for handling async chart task requests.""" def post(self): """Generates a small chart that is displayed as alert in the clients browser and creates the full screen version that is saved with the Memcache API. HTTP Parameters: regression: the regression type [poly1,poly2,poly3,zhuWood] source: the source satellite [all,land5,land7,land8] start: the start year to filter the satellite images (including) end: the end year to filter the satellite images (including) cloudscore: the max cloudscore for the ee.Algorithms.Landsat.simpleCloudScore [1-100] Higher means that the pixel is more likley to be a cloud point: an array of two double values representing coordinates like [<longitude>,<latitude>] client_id: the unique id that is used for the channel api """ # load the options options = json.loads(self.request.get("options")) # create the chart try: chart = _GetChart(options) except Exception as e: if DEBUG: _SendMessage(options["client_id"],"chart-" + options["filename"],"danger","Chart creation failed.", str(e) + " - " + traceback.format_exc()) else: _SendMessage(options["client_id"],"chart-" + options["filename"],"danger","Chart creation failed.", str(e)) return # _GetChart returns None if the collection is
# Copyright 2018 Jetperch LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from joulescope.usb import core as usb_core from joulescope.usb.api import DeviceEvent from joulescope.usb.impl_tools import RunUntilDone from joulescope.usb.core import SetupPacket, ControlTransferResponse from joulescope.usb.scan_info import INFO from typing import List import time import threading from contextlib import contextmanager import platform import numpy as np import os import sys import struct import ctypes import ctypes.util from ctypes import Structure, c_uint8, c_uint16, c_uint32, c_uint, \ c_int, c_char, c_ssize_t, c_void_p, POINTER, pointer, byref import logging log = logging.getLogger(__name__) STRING_LENGTH_MAX = 255 CONTROL_TRANSFER_TIMEOUT_MS = 1000 # default in milliseconds find_lib = ctypes.util.find_library('usb-1.0') if find_lib is None: if platform.system() == 'Darwin' and getattr(sys, 'frozen', False): os_version = platform.uname().release.split('.')[0] find_lib = os.path.join(sys._MEIPASS, '%s_libusb-1.0.0.dylib' % os_version) log.info('Darwin lib: %s', find_lib) else: raise RuntimeError('Could not import libusb') _lib = ctypes.cdll.LoadLibrary(find_lib) class DescriptorType: DEVICE = 0x01 CONFIG = 0x02 STRING = 0x03 INTERFACE = 0x04 ENDPOINT = 0x05 BOS = 0x0f DEVICE_CAPABILITY = 0x10 HID = 0x21 REPORT = 0x22 PHYSICAL = 0x23 HUB = 0x29 SUPERSPEED_HUB = 0x2a SS_ENDPOINT_COMPANION = 0x30 class TransferType: CONTROL = 0 ISOCHRONOUS = 1 BULK = 2 INTERRUPT = 3 BULK_STREAM = 4 class TransferStatus: COMPLETED = 0 ERROR = 1 TIMED_OUT = 2 CANCELLED = 3 STALL = 4 NO_DEVICE = 5 OVERFLOW = 6 class TransferFlags: SHORT_NOT_OK = 1 << 0 FREE_BUFFER = 1 << 1 FREE_TRANSFER = 1 << 2 ADD_ZERO_PACKET = 1 << 3 class ReturnCodes: SUCCESS = 0 ERROR_IO = -1 ERROR_INVALID_PARAM = -2 ERROR_ACCESS = -3 ERROR_NO_DEVICE = -4 ERROR_NOT_FOUND = -5 ERROR_BUSY = -6 ERROR_TIMEOUT = -7 ERROR_OVERFLOW = -8 ERROR_PIPE = -9 ERROR_INTERRUPTED = -10 ERROR_NO_MEM = -11 ERROR_NOT_SUPPORTED = -12 ERROR_OTHER = -99 class _libusb_device_descriptor(Structure): _fields_ = [ ('bLength', c_uint8), ('bDescriptorType', c_uint8), ('bcdUSB', c_uint16), ('bDeviceClass', c_uint8), ('bDeviceSubClass', c_uint8), ('bDeviceProtocol', c_uint8), ('bMaxPacketSize0', c_uint8), ('idVendor', c_uint16), ('idProduct', c_uint16), ('bcdDevice', c_uint16), ('iManufacturer', c_uint8), ('iProduct', c_uint8), ('iSerialNumber', c_uint8), ('bNumConfigurations', c_uint8)] # typedef void (LIBUSB_CALL libusb_transfer_cb_fn)(struct libusb_transfer transfer); libusb_transfer_cb_fn = ctypes.CFUNCTYPE(None, c_void_p) class _libusb_transfer(Structure): _fields_ = [ ('dev_handle', c_void_p), ('flags', c_uint8), ('endpoint_id', c_uint8), ('endpoint_type', c_uint8), ('timeout_ms', c_uint), ('status', c_uint), ('length', c_int), ('actual_length', c_int), ('callback', libusb_transfer_cb_fn), ('user_data', c_void_p), ('buffer', POINTER(c_uint8)), ('num_iso_packets', c_int), # struct libusb_iso_packet_descriptor iso_packet_desc[ZERO_SIZED_ARRAY]; ] # typedef struct libusb_context libusb_context; - c_void_p # typedef struct libusb_device libusb_device; - c_void_p # typedef struct libusb_device_handle libusb_device_handle; c_void_p # int LIBUSB_CALL libusb_init(libusb_context *ctx); _lib.libusb_init.restype = c_int _lib.libusb_init.argtypes = [POINTER(c_void_p)] # void LIBUSB_CALL libusb_exit(libusb_context ctx); _lib.libusb_exit.restype = None _lib.libusb_exit.argtypes = [c_void_p] # ssize_t LIBUSB_CALL libusb_get_device_list(libusb_context ctx, # libusb_device list); _lib.libusb_get_device_list.restype = c_ssize_t _lib.libusb_get_device_list.argtypes = [c_void_p, POINTER(POINTER(c_void_p))] # void LIBUSB_CALL libusb_free_device_list(libusb_device list, # int unref_devices); _lib.libusb_free_device_list.restype = None _lib.libusb_free_device_list.argtypes = [POINTER(c_void_p), c_int] # int LIBUSB_CALL libusb_open(libusb_device dev, libusb_device_handle dev_handle); _lib.libusb_open.restype = c_int _lib.libusb_open.argtypes = [c_void_p, POINTER(c_void_p)] # void LIBUSB_CALL libusb_close(libusb_device_handle dev_handle); _lib.libusb_close.restype = None _lib.libusb_close.argtypes = [c_void_p] # int LIBUSB_CALL libusb_set_configuration(libusb_device_handle dev_handle, # int configuration); _lib.libusb_set_configuration.restype = c_int _lib.libusb_set_configuration.argtypes = [c_void_p, c_int] # int LIBUSB_CALL libusb_claim_interface(libusb_device_handle dev_handle, # int interface_number); _lib.libusb_claim_interface.restype = c_int _lib.libusb_claim_interface.argtypes = [c_void_p, c_int] # int LIBUSB_CALL libusb_release_interface(libusb_device_handle dev_handle, # int interface_number); _lib.libusb_release_interface.restype = c_int _lib.libusb_release_interface.argtypes = [c_void_p, c_int] # int LIBUSB_CALL libusb_set_interface_alt_setting(libusb_device_handle dev_handle, # int interface_number, int alternate_setting); _lib.libusb_set_interface_alt_setting.restype = c_int _lib.libusb_set_interface_alt_setting.argtypes = [c_void_p, c_int, c_int] # int LIBUSB_CALL libusb_get_device_descriptor(libusb_device dev, # struct libusb_device_descriptor desc); _lib.libusb_get_device_descriptor.restype = c_int _lib.libusb_get_device_descriptor.argtypes = [c_void_p, POINTER(_libusb_device_descriptor)] # int LIBUSB_CALL libusb_control_transfer(libusb_device_handle dev_handle, # uint8_t request_type, uint8_t bRequest, uint16_t wValue, uint16_t wIndex, # unsigned char data, uint16_t wLength, unsigned int timeout); _lib.libusb_control_transfer.restype = c_int _lib.libusb_control_transfer.argtypes = [c_void_p, c_uint8, c_uint8, c_uint16, c_uint16, POINTER(c_uint8), c_uint16, c_int] # struct libusb_transfer * LIBUSB_CALL libusb_alloc_transfer(int iso_packets); _lib.libusb_alloc_transfer.restype = POINTER(_libusb_transfer) _lib.libusb_alloc_transfer.argtypes = [c_int] # int LIBUSB_CALL libusb_submit_transfer(struct libusb_transfer transfer); _lib.libusb_submit_transfer.restype = c_int _lib.libusb_submit_transfer.argtypes = [POINTER(_libusb_transfer)] # int LIBUSB_CALL libusb_cancel_transfer(struct libusb_transfer transfer); _lib.libusb_cancel_transfer.restype = c_int _lib.libusb_cancel_transfer.argtypes = [POINTER(_libusb_transfer)] # void LIBUSB_CALL libusb_free_transfer(struct libusb_transfer transfer); _lib.libusb_free_transfer.restype = None _lib.libusb_free_transfer.argtypes = [POINTER(_libusb_transfer)] class TimeVal(Structure): _fields_ = [ ("tv_sec", ctypes.c_long), ("tv_usec", ctypes.c_long) ] # int LIBUSB_CALL libusb_handle_events_timeout(libusb_context ctx, # struct timeval tv); _lib.libusb_handle_events_timeout.restype = c_int _lib.libusb_handle_events_timeout.argtypes = [c_void_p, POINTER(TimeVal)] # int LIBUSB_CALL libusb_handle_events(libusb_context ctx) _lib.libusb_handle_events.restype = c_int _lib.libusb_handle_events.argtypes = [c_void_p] class HotplugFlag: NONE = 0 ENUMERATE = 1 << 0 class HotplugEvent: DEVICE_ARRIVED = 0x01 DEVICE_LEFT = 0x02 HOTPLUG_MATCH_ANY = -1 # typedef int (LIBUSB_CALL libusb_hotplug_callback_fn)(libusb_context ctx, # libusb_device device, # libusb_hotplug_event event, # void user_data); _libusb_hotplug_callback_fn = ctypes.CFUNCTYPE(c_int, c_void_p, c_void_p, c_int, c_void_p) # int LIBUSB_CALL libusb_hotplug_register_callback(libusb_context ctx, # libusb_hotplug_event events, # libusb_hotplug_flag flags, # int vendor_id, int product_id, # int dev_class, # libusb_hotplug_callback_fn cb_fn, # void user_data, # libusb_hotplug_callback_handle callback_handle); _lib.libusb_hotplug_register_callback.restype = c_int _lib.libusb_hotplug_register_callback.argtypes = [c_void_p, c_int, c_int, c_int, c_int, c_int, _libusb_hotplug_callback_fn, c_void_p, POINTER(c_int)] # void LIBUSB_CALL libusb_hotplug_deregister_callback(libusb_context ctx, # libusb_hotplug_callback_handle callback_handle); _lib.libusb_hotplug_deregister_callback.restype = c_int _lib.libusb_hotplug_deregister_callback.argtypes = [c_void_p, c_int] class Capability: HAS_CAPABILITY = 0x0000 HAS_HOTPLUG = 0x0001 HAS_HID_ACCESS = 0x0100 SUPPORTS_DETACH_KERNEL_DRIVER = 0x0101 # int LIBUSB_CALL libusb_has_capability(uint32_t capability); _lib.libusb_has_capability.restype = c_int _lib.libusb_has_capability.argtypes = [c_uint32] def _libusb_context_create(): ctx = c_void_p() rc = _lib.libusb_init(pointer(ctx)) if rc: raise RuntimeError('Could not open libusb') return ctx def _libusb_context_destroy(ctx): _lib.libusb_exit(ctx) @contextmanager def _libusb_context(): ctx = _libusb_context_create() try: yield ctx finally: _libusb_context_destroy(ctx) def _path_split(path): vid, pid, serial_number = path.split('/') return int(vid, 16), int(pid, 16), serial_number def _get_string_descriptor(device, index): request_type = usb_core.RequestType(direction='in', type_='standard', recipient='device').u8 byte_buffer = bytearray(STRING_LENGTH_MAX) buffer_type = c_uint8 * STRING_LENGTH_MAX buffer = buffer_type.from_buffer(byte_buffer) # determine default language rv = _lib.libusb_control_transfer(device, request_type, usb_core.Request.GET_DESCRIPTOR, (DescriptorType.STRING << 8), 0, buffer, STRING_LENGTH_MAX, 1000) if rv < 0: raise RuntimeError('control_transfer could not get language: %d' % (rv, )) langid = int(byte_buffer[2]) \| (int(byte_buffer[3]) << 8) rv = _lib.libusb_control_transfer(device, request_type, usb_core.Request.GET_DESCRIPTOR, (DescriptorType.STRING << 8) \| (index & 0xff), langid, buffer, STRING_LENGTH_MAX, 1000) if rv < 0: raise RuntimeError('control transfer could not get string descriptor: %d' % (rv, )) buffer_len = min(rv, byte_buffer[0]) # byte 0 is length, byte 1 is string identifier return byte_buffer[2:buffer_len].decode('UTF-16-LE') _transfer_callback_discard_fn = libusb_transfer_cb_fn(lambda x: None) """Default null callback that is always safe.""" class Transfer: def __init__(self, size): try: self.size = len(size) # also serves as list-like duck-typing test self.buffer = np.frombuffer(size, dtype=np.uint8) log.debug('Transfer: copy buffer %d', self.size) except TypeError: self.size = size self.buffer = np.empty(self.size, dtype=np.uint8) log.debug('Transfer: create buffer %d', self.size) self.transfer = _lib.libusb_alloc_transfer(0) # type: _libusb_transfer self.addr = ctypes.addressof(self.transfer.contents) transfer = self.transfer[0] self.buffer_ptr = self.buffer.ctypes.data_as(POINTER(c_uint8)) transfer.buffer = self.buffer_ptr transfer.flags = 0 transfer.length = self.size transfer.actual_length = 0 transfer.user_data = None transfer.num_iso_packets = 0 transfer.status = TransferStatus.COMPLETED transfer.timeout_ms = 1000 # milliseconds transfer.callback = _transfer_callback_discard_fn def __del__(self): _lib.libusb_free_transfer(self.transfer) class ControlTransferAsync: def __init__(self, handle): """Manage asynchronous control transfers. :param handle: The device handle. """ self._handle = handle self._transfer_callback_fn = libusb_transfer_cb_fn(self._transfer_callback) self._commands = [] # Pending control transfer commands as list of [cbk_fn, setup_packet, buffer] self._transfer_pending = None # type: Transfer self._time_start = None self.stop_code = None def __str__(self): return 'ControlTransferAsync()' def _transfer_callback(self, transfer_void_ptr): if self._transfer_pending is None: log.warning('Transfer callback when none pending') return if self._transfer_pending.addr != transfer_void_ptr: log.warning('Transfer mismatch') return transfer, self._transfer_pending = self._transfer_pending, None if self._commands: self._finish(self._commands.pop(0), transfer) else: log.warning('Transfer callback when no commands') self._issue() def _abort_all(self): commands, self._commands = self._commands, [] for cbk_fn, setup_packet, _ in commands: try: response = usb_core.ControlTransferResponse(setup_packet, TransferStatus.CANCELLED, None) cbk_fn(response) except Exception: log.exception('in callback while aborting') def close(self): if self._handle and self._transfer_pending: log.info('ControlTransferAsync.close cancel pending transfer, %d', len(self._commands)) transfer, self._transfer_pending = self._transfer_pending, None transfer.transfer[0].callback = _transfer_callback_discard_fn _lib.libusb_cancel_transfer(transfer.transfer) # callback function will be invoked later else: log.info('ControlTransferAsync.close %d', len(self._commands)) self._handle = None self._abort_all() def pend(self, cbk_fn, setup_packet: usb_core.SetupPacket, buffer=None): """Pend an asynchronous Control Transfer. :param cbk_fn: The function to call when the control transfer completes. A :class:`usb_core.ControlTransferResponse` is the sole argument. :param setup_packet: :param buffer: The buffer (if length > 0) for write transactions. :return: True if pending, False on error. """ if self.stop_code is not None: response = usb_core.ControlTransferResponse(setup_packet, self.stop_code, None) cbk_fn(response) return False command = [cbk_fn, setup_packet, buffer] was_empty = not bool(self._commands) self._commands.append(command) if was_empty: return self._issue() return True def _issue(self): if not self._commands: return True if not self._handle: log.info('_issue but handle not valid') self._abort_all() return False log.debug('preparing') _, setup_packet, buffer = self._commands[0] hdr = struct.pack('<BBHHH', setup_packet.request_type, setup_packet.request, setup_packet.value, setup_packet.index, setup_packet.length) if buffer is not None: transfer = Transfer(hdr + buffer) else: transfer = Transfer(len(hdr) + setup_packet.length) transfer.buffer[:len(hdr)] = np.frombuffer(hdr, dtype=np.uint8) t = transfer.transfer[0] t.dev_handle = self._handle t.endpoint_id = 0 t.endpoint_type = TransferType.CONTROL t.callback = self._transfer_callback_fn self._transfer_pending = transfer self._time_start = time.time() rv = _lib.libusb_submit_transfer(transfer.transfer) if 0 == rv: log.debug('libusb_submit_transfer [control]') else: log.warning('libusb_submit_transfer [control] => %d', rv) if t.status == 0: if rv == ReturnCodes.ERROR_NO_DEVICE: log.info('control transfer but no device') t.status = TransferStatus.NO_DEVICE else: t.status = TransferStatus.ERROR if self.stop_code is None: self.stop_code = DeviceEvent.COMMUNICATION_ERROR
<reponame>kustodian/google-cloud-sdk """Generated client library for cloudbuild version v1.""" # NOTE: This file is autogenerated and should not be edited by hand. from apitools.base.py import base_api from googlecloudsdk.third_party.apis.cloudbuild.v1 import cloudbuild_v1_messages as messages class CloudbuildV1(base_api.BaseApiClient): """Generated client library for service cloudbuild version v1.""" MESSAGES_MODULE = messages BASE_URL = u'https://cloudbuild.googleapis.com/' _PACKAGE = u'cloudbuild' _SCOPES = [u'https://www.googleapis.com/auth/cloud-platform'] _VERSION = u'v1' _CLIENT_ID = '1042881264118.apps.googleusercontent.com' _CLIENT_SECRET = 'x_Tw5K8nnjoRAqULM9PFAC2b' _USER_AGENT = 'x_Tw5K8nnjoRAqULM9PFAC2b' _CLIENT_CLASS_NAME = u'CloudbuildV1' _URL_VERSION = u'v1' _API_KEY = None def __init__(self, url='', credentials=None, get_credentials=True, http=None, model=None, log_request=False, log_response=False, credentials_args=None, default_global_params=None, additional_http_headers=None, response_encoding=None): """Create a new cloudbuild handle.""" url = url or self.BASE_URL super(CloudbuildV1, self).__init__( url, credentials=credentials, get_credentials=get_credentials, http=http, model=model, log_request=log_request, log_response=log_response, credentials_args=credentials_args, default_global_params=default_global_params, additional_http_headers=additional_http_headers, response_encoding=response_encoding) self.operations = self.OperationsService(self) self.projects_builds = self.ProjectsBuildsService(self) self.projects_triggers = self.ProjectsTriggersService(self) self.projects = self.ProjectsService(self) class OperationsService(base_api.BaseApiService): """Service class for the operations resource.""" _NAME = u'operations' def __init__(self, client): super(CloudbuildV1.OperationsService, self).__init__(client) self._upload_configs = { } def Cancel(self, request, global_params=None): r"""Starts asynchronous cancellation on a long-running operation. The server. makes a best effort to cancel the operation, but success is not guaranteed. If the server doesn't support this method, it returns `google.rpc.Code.UNIMPLEMENTED`. Clients can use Operations.GetOperation or other methods to check whether the cancellation succeeded or whether the operation completed despite cancellation. On successful cancellation, the operation is not deleted; instead, it becomes an operation with an Operation.error value with a google.rpc.Status.code of 1, corresponding to `Code.CANCELLED`. Args: request: (CloudbuildOperationsCancelRequest) input message global_params: (StandardQueryParameters, default: None) global arguments Returns: (Empty) The response message. """ config = self.GetMethodConfig('Cancel') return self._RunMethod( config, request, global_params=global_params) Cancel.method_config = lambda: base_api.ApiMethodInfo( flat_path=u'v1/operations/{operationsId}:cancel', http_method=u'POST', method_id=u'cloudbuild.operations.cancel', ordered_params=[u'name'], path_params=[u'name'], query_params=[], relative_path=u'v1/{+name}:cancel', request_field=u'cancelOperationRequest', request_type_name=u'CloudbuildOperationsCancelRequest', response_type_name=u'Empty', supports_download=False, ) def Get(self, request, global_params=None): r"""Gets the latest state of a long-running operation. Clients can use this. method to poll the operation result at intervals as recommended by the API service. Args: request: (CloudbuildOperationsGetRequest) input message global_params: (StandardQueryParameters, default: None) global arguments Returns: (Operation) The response message. """ config = self.GetMethodConfig('Get') return self._RunMethod( config, request, global_params=global_params) Get.method_config = lambda: base_api.ApiMethodInfo( flat_path=u'v1/operations/{operationsId}', http_method=u'GET', method_id=u'cloudbuild.operations.get', ordered_params=[u'name'], path_params=[u'name'], query_params=[], relative_path=u'v1/{+name}', request_field='', request_type_name=u'CloudbuildOperationsGetRequest', response_type_name=u'Operation', supports_download=False, ) def List(self, request, global_params=None): r"""Lists operations that match the specified filter in the request. If the. server doesn't support this method, it returns `UNIMPLEMENTED`. NOTE: the `name` binding allows API services to override the binding to use different resource name schemes, such as `users//operations`. To override the binding, API services can add a binding such as `"/v1/{name=users/}/operations"` to their service configuration. For backwards compatibility, the default name includes the operations collection id, however overriding users must ensure the name binding is the parent resource, without the operations collection id. Args: request: (CloudbuildOperationsListRequest) input message global_params: (StandardQueryParameters, default: None) global arguments Returns: (ListOperationsResponse) The response message. """ config = self.GetMethodConfig('List') return self._RunMethod( config, request, global_params=global_params) List.method_config = lambda: base_api.ApiMethodInfo( flat_path=u'v1/operations', http_method=u'GET', method_id=u'cloudbuild.operations.list', ordered_params=[u'name'], path_params=[u'name'], query_params=[u'filter', u'pageSize', u'pageToken'], relative_path=u'v1/{+name}', request_field='', request_type_name=u'CloudbuildOperationsListRequest', response_type_name=u'ListOperationsResponse', supports_download=False, ) class ProjectsBuildsService(base_api.BaseApiService): """Service class for the projects_builds resource.""" _NAME = u'projects_builds' def __init__(self, client): super(CloudbuildV1.ProjectsBuildsService, self).__init__(client) self._upload_configs = { } def Cancel(self, request, global_params=None): r"""Cancels a build in progress. Args: request: (CloudbuildProjectsBuildsCancelRequest) input message global_params: (StandardQueryParameters, default: None) global arguments Returns: (Build) The response message. """ config = self.GetMethodConfig('Cancel') return self._RunMethod( config, request, global_params=global_params) Cancel.method_config = lambda: base_api.ApiMethodInfo( http_method=u'POST', method_id=u'cloudbuild.projects.builds.cancel', ordered_params=[u'projectId', u'id'], path_params=[u'id', u'projectId'], query_params=[], relative_path=u'v1/projects/{projectId}/builds/{id}:cancel', request_field=u'cancelBuildRequest', request_type_name=u'CloudbuildProjectsBuildsCancelRequest', response_type_name=u'Build', supports_download=False, ) def Create(self, request, global_params=None): r"""Starts a build with the specified configuration. This method returns a long-running `Operation`, which includes the build ID. Pass the build ID to `GetBuild` to determine the build status (such as `SUCCESS` or `FAILURE`). Args: request: (CloudbuildProjectsBuildsCreateRequest) input message global_params: (StandardQueryParameters, default: None) global arguments Returns: (Operation) The response message. """ config = self.GetMethodConfig('Create') return self._RunMethod( config, request, global_params=global_params) Create.method_config = lambda: base_api.ApiMethodInfo( http_method=u'POST', method_id=u'cloudbuild.projects.builds.create', ordered_params=[u'projectId'], path_params=[u'projectId'], query_params=[], relative_path=u'v1/projects/{projectId}/builds', request_field=u'build', request_type_name=u'CloudbuildProjectsBuildsCreateRequest', response_type_name=u'Operation', supports_download=False, ) def Get(self, request, global_params=None): r"""Returns information about a previously requested build. The `Build` that is returned includes its status (such as `SUCCESS`, `FAILURE`, or `WORKING`), and timing information. Args: request: (CloudbuildProjectsBuildsGetRequest) input message global_params: (StandardQueryParameters, default: None) global arguments Returns: (Build) The response message. """ config = self.GetMethodConfig('Get') return self._RunMethod( config, request, global_params=global_params) Get.method_config = lambda: base_api.ApiMethodInfo( http_method=u'GET', method_id=u'cloudbuild.projects.builds.get', ordered_params=[u'projectId', u'id'], path_params=[u'id', u'projectId'], query_params=[], relative_path=u'v1/projects/{projectId}/builds/{id}', request_field='', request_type_name=u'CloudbuildProjectsBuildsGetRequest', response_type_name=u'Build', supports_download=False, ) def List(self, request, global_params=None): r"""Lists previously requested builds. Previously requested builds may still be in-progress, or may have finished successfully or unsuccessfully. Args: request: (CloudbuildProjectsBuildsListRequest) input message global_params: (StandardQueryParameters, default: None) global arguments Returns: (ListBuildsResponse) The response message. """ config = self.GetMethodConfig('List') return self._RunMethod( config, request, global_params=global_params) List.method_config = lambda: base_api.ApiMethodInfo( http_method=u'GET', method_id=u'cloudbuild.projects.builds.list', ordered_params=[u'projectId'], path_params=[u'projectId'], query_params=[u'filter', u'pageSize', u'pageToken'], relative_path=u'v1/projects/{projectId}/builds', request_field='', request_type_name=u'CloudbuildProjectsBuildsListRequest', response_type_name=u'ListBuildsResponse', supports_download=False, ) def Retry(self, request, global_params=None): r"""Creates a new build based on the specified build. This method creates a new build using the original build request, which may or may not result in an identical build. For triggered builds: * Triggered builds resolve to a precise revision; therefore a retry of a triggered build will result in a build that uses the same revision. For non-triggered builds that specify `RepoSource`: * If the original build built from the tip of a branch, the retried build will build from the tip of that branch, which may not be the same revision as the original build. * If the original build specified a commit sha or revision ID, the retried build will use the identical source. For builds that specify `StorageSource`: * If the original build pulled source from Google Cloud Storage without specifying the generation of the object, the new build will use the current object, which may be different from the original build source. * If the original build pulled source from Cloud Storage and specified the generation of the object, the new build will attempt to use the same object, which may or may not be available depending on the bucket's lifecycle management settings. Args: request: (CloudbuildProjectsBuildsRetryRequest) input message global_params: (StandardQueryParameters, default: None) global arguments Returns: (Operation) The response message. """ config = self.GetMethodConfig('Retry') return self._RunMethod( config, request, global_params=global_params) Retry.method_config = lambda: base_api.ApiMethodInfo( http_method=u'POST', method_id=u'cloudbuild.projects.builds.retry', ordered_params=[u'projectId', u'id'], path_params=[u'id', u'projectId'], query_params=[], relative_path=u'v1/projects/{projectId}/builds/{id}:retry', request_field=u'retryBuildRequest', request_type_name=u'CloudbuildProjectsBuildsRetryRequest', response_type_name=u'Operation', supports_download=False, ) class ProjectsTriggersService(base_api.BaseApiService): """Service class for the projects_triggers resource.""" _NAME = u'projects_triggers' def __init__(self, client): super(CloudbuildV1.ProjectsTriggersService, self).__init__(client) self._upload_configs = { } def Create(self, request, global_params=None): r"""Creates a new `BuildTrigger`. This API is experimental. Args: request: (CloudbuildProjectsTriggersCreateRequest) input message global_params: (StandardQueryParameters, default: None) global arguments Returns: (BuildTrigger) The response message. """ config = self.GetMethodConfig('Create') return self._RunMethod( config, request, global_params=global_params) Create.method_config = lambda: base_api.ApiMethodInfo( http_method=u'POST', method_id=u'cloudbuild.projects.triggers.create', ordered_params=[u'projectId'], path_params=[u'projectId'], query_params=[], relative_path=u'v1/projects/{projectId}/triggers', request_field=u'buildTrigger', request_type_name=u'CloudbuildProjectsTriggersCreateRequest', response_type_name=u'BuildTrigger', supports_download=False, ) def Delete(self, request, global_params=None): r"""Deletes a `BuildTrigger` by its project ID and trigger ID. This API is experimental. Args: request: (CloudbuildProjectsTriggersDeleteRequest) input message global_params: (StandardQueryParameters, default: None) global arguments Returns: (Empty) The response message. """ config = self.GetMethodConfig('Delete') return self._RunMethod( config, request, global_params=global_params) Delete.method_config = lambda: base_api.ApiMethodInfo( http_method=u'DELETE', method_id=u'cloudbuild.projects.triggers.delete', ordered_params=[u'projectId', u'triggerId'], path_params=[u'projectId', u'triggerId'], query_params=[], relative_path=u'v1/projects/{projectId}/triggers/{triggerId}', request_field='', request_type_name=u'CloudbuildProjectsTriggersDeleteRequest', response_type_name=u'Empty', supports_download=False, ) def Get(self, request, global_params=None): r"""Returns information about a `BuildTrigger`. This API is experimental. Args: request: (CloudbuildProjectsTriggersGetRequest) input message global_params: (StandardQueryParameters, default: None) global arguments Returns: (BuildTrigger) The response message. """ config = self.GetMethodConfig('Get') return self._RunMethod( config, request, global_params=global_params) Get.method_config = lambda: base_api.ApiMethodInfo( http_method=u'GET', method_id=u'cloudbuild.projects.triggers.get', ordered_params=[u'projectId', u'triggerId'], path_params=[u'projectId', u'triggerId'], query_params=[], relative_path=u'v1/projects/{projectId}/triggers/{triggerId}', request_field='', request_type_name=u'CloudbuildProjectsTriggersGetRequest', response_type_name=u'BuildTrigger', supports_download=False, ) def List(self, request, global_params=None): r"""Lists existing `BuildTrigger`s. This API is experimental. Args: request: (CloudbuildProjectsTriggersListRequest) input message global_params: (StandardQueryParameters, default: None) global arguments Returns: (ListBuildTriggersResponse) The response message. """ config = self.GetMethodConfig('List') return self._RunMethod( config, request, global_params=global_params) List.method_config = lambda: base_api.ApiMethodInfo( http_method=u'GET', method_id=u'cloudbuild.projects.triggers.list', ordered_params=[u'projectId'], path_params=[u'projectId'], query_params=[u'pageSize', u'pageToken'], relative_path=u'v1/projects/{projectId}/triggers', request_field='', request_type_name=u'CloudbuildProjectsTriggersListRequest', response_type_name=u'ListBuildTriggersResponse', supports_download=False, ) def Patch(self, request, global_params=None): r"""Updates a `BuildTrigger` by its project ID and trigger ID. This API is experimental. Args: request: (CloudbuildProjectsTriggersPatchRequest) input message global_params: (StandardQueryParameters, default: None) global arguments Returns: (BuildTrigger) The response message. """ config = self.GetMethodConfig('Patch') return self._RunMethod( config, request, global_params=global_params) Patch.method_config = lambda: base_api.ApiMethodInfo( http_method=u'PATCH', method_id=u'cloudbuild.projects.triggers.patch', ordered_params=[u'projectId', u'triggerId'], path_params=[u'projectId', u'triggerId'], query_params=[], relative_path=u'v1/projects/{projectId}/triggers/{triggerId}', request_field=u'buildTrigger', request_type_name=u'CloudbuildProjectsTriggersPatchRequest', response_type_name=u'BuildTrigger', supports_download=False, ) def Run(self, request, global_params=None): r"""Runs a `BuildTrigger` at a particular source revision. Args: request: (CloudbuildProjectsTriggersRunRequest) input message global_params: (StandardQueryParameters, default: None) global arguments Returns: (Operation) The response message. """ config = self.GetMethodConfig('Run') return self._RunMethod( config, request, global_params=global_params) Run.method_config = lambda: base_api.ApiMethodInfo(
method PARAMS: name: str A name/alias given to the model by the user layers: list of integers List of neuron size for each layer dropout: float Level of dropout recurrentDropout: float Level of recurrent dropout alpha: float Alpha of the leaky relu function training: boolean Whether dropout should be use at time of prediction enrolWindow: int Number of samples used to make each prediction RETURNS: model: MachineLearningModel Object with typical machine learning methods like train, predict etc. """ global _X_train, _y_train, _default_LSTM_args model = models.kerasGRU( params = { 'name': name, 'X_train': _X_train, 'y_train': _y_train, 'args': { 'activation': activation, 'loss': loss, 'optimizer': optimizer, 'metrics': metrics, 'epochs': epochs, 'batchSize': batchSize, 'verbose': verbose, 'callbacks': callbacks, 'enrolWindow': enrolWindow, 'validationSize': validationSize, 'testSize': testSize, }, }, layers=layers, dropout=dropout, recurrentDropout=recurrentDropout, alpha=alpha, training=training, ) return model def Linear( name, ): """ FUNCTION: Used to create a Linear Machine Learning model PARAMS: name: str A name/alias given to the model by the user RETURNS: model: MachineLearningModel Object with typical machine learning methods like train, predict etc. """ global _X_train, _y_train model = models.sklearnLinear( params={ 'name': name, 'X_train': _X_train, 'y_train': _y_train, }, ) return model def Linear_Regularized( name, alphas=(0.1, 1.0, 10.0), folds=10, ): """ FUNCTION: Used to create a Linear Machine Learning model with built-in regularization and cross validation PARAMS: name: str A name/alias given to the model by the user alphas: tuple of floats Set of regluarization strenght parameters to try folds: int Number of cross validation folds RETURNS: model: MachineLearningModel Object with typical machine learning methods like train, predict etc. """ global _X_train, _y_train model = models.sklearnRidgeCV( params={ 'name': name, 'X_train': _X_train, 'y_train': _y_train, }, alphas = alphas, folds = folds, ) return model def ElasticNet( name, alphas=(0.1, 1.0, 10.0), l1_ratio=0.5, ): """ FUNCTION: Used to create a iterative regularization path fitting Machine Learning model PARAMS: name: str A name/alias given to the model by the user alphas: tuple of floats Set of regluarization strenght parameters to try l1_ratio: float ratio between L1 and L2 regularization RETURNS: model: MachineLearningModel Object with typical machine learning methods like train, predict etc. """ global _X_train, _y_train model = models.sklearnElasticNetCV( params={ 'name': name, 'X_train': _X_train, 'y_train': _y_train, }, alphas = alphas, l1_ratio = l1_ratio, ) return model def DecisionTree( name, ): """ FUNCTION: Used to create a decision tree regressor PARAMS: name: str A name/alias given to the model by the user RETURNS: model: MachineLearningModel Object with typical machine learning methods like train, predict etc. """ global _X_train, _y_train model = models.sklearnDecisionTree( params={ 'name': name, 'X_train': _X_train, 'y_train': _y_train, }, ) return model def RandomForest( name, ): """ FUNCTION: Used to create a random forest (decision) tree regressor PARAMS: name: str A name/alias given to the model by the user RETURNS: model: MachineLearningModel Object with typical machine learning methods like train, predict etc. """ global _X_train, _y_train model = models.sklearnRandomForest( params={ 'name': name, 'X_train': _X_train, 'y_train': _y_train, }, ) return model def BaggingRegressor( name, ): """ FUNCTION: Used to create a bagging regressor model, which aggregates base regressors a achieve a final prediction PARAMS: name: str A name/alias given to the model by the user RETURNS: model: MachineLearningModel Object with typical machine learning methods like train, predict etc. """ global _X_train, _y_train model = models.sklearnBagging( params={ 'name': name, 'X_train': _X_train, 'y_train': _y_train, }, ) return model def AdaBoostRegressor( name, ): """ FUNCTION: Used to create an AdaBoost regressor, which fits additional regressor copies with different weights according to previous predictions PARAMS: name: str A name/alias given to the model by the user RETURNS: model: MachineLearningModel Object with typical machine learning methods like train, predict etc. """ global _X_train, _y_train model = models.sklearnAdaBoost( params={ 'name': name, 'X_train': _X_train, 'y_train': _y_train, }, ) return model def SupportVectorMachine( name, ): """ FUNCTION: Used to create a Support Vector Machine regressor PARAMS: name: str A name/alias given to the model by the user RETURNS: model: MachineLearningModel Object with typical machine learning methods like train, predict etc. """ global _X_train, _y_train model = models.sklearnSVM( params={ 'name': name, 'X_train': _X_train, 'y_train': _y_train, }, ) return model def Ensemble(name, modelList): """ FUNCTION: Used to create an Ensemble model, combining the prediction of n>1 machine learning methods using a linear regressor PARAMS: name: str A name/alias given to the model by the user modelList: list of MachineLearningModel objects A list of machine learning models used to construct the Ensemble model RETURNS: model: EnsembleModel Ensemble model object which behaves the same as any other MachineLearningModel """ global _X_train, _y_train model = models.ensembleModel( params={ 'name': name, 'X_train': _X_train, 'y_train': _y_train, }, models=modelList, ) return model def Autoencoder_Regularized( name, l1_rate=10e-4, encodingDim=3, activation=_default_MLP_args['activation'], loss=_default_MLP_args['loss'], optimizer=_default_MLP_args['optimizer'], metrics=_default_MLP_args['metrics'], epochs=_default_MLP_args['epochs'], batchSize=_default_MLP_args['batchSize'], verbose=_default_MLP_args['verbose'], validationSize=_default_MLP_args['validationSize'], testSize=_default_MLP_args['testSize'], callbacks=_default_MLP_args['callbacks'], ): """ FUNCTION: Used to create an Autoencoder model using multilayer perceptron and reguarlization by Lasso regluarization NB: Autoencoder models SHOULD NOT and CAN NOT be used together with other models, or as submodels to Ensemble models PARAMS: name: str A name/alias given to the model by the user l1_rate: float Level of L1 regularization encodingDim: int Size of autoencoder middle layer RETURNS: model: AutoencoderModel Object with typical machine learning methods like train, predict etc. """ global _X_train, _default_MLP_args model = models.autoencoder_Regularized( params = { 'name': name, 'X_train': _X_train, 'args': { 'activation': activation, 'loss': loss, 'optimizer': optimizer, 'metrics': metrics, 'epochs': epochs, 'batchSize': batchSize, 'verbose': verbose, 'callbacks': callbacks, 'enrolWindow': 0, 'validationSize': validationSize, 'testSize': testSize, }, }, l1_rate=l1_rate, encodingDim=encodingDim, ) return model def Autoencoder_Dropout( name, dropout=0.0, encodingDim=3, activation=_default_MLP_args['activation'], loss=_default_MLP_args['loss'], optimizer=_default_MLP_args['optimizer'], metrics=_default_MLP_args['metrics'], epochs=_default_MLP_args['epochs'], batchSize=_default_MLP_args['batchSize'], verbose=_default_MLP_args['verbose'], validationSize=_default_MLP_args['validationSize'], testSize=_default_MLP_args['testSize'], callbacks=_default_MLP_args['callbacks'], ): """ FUNCTION: Used to create an Autoencoder model using multilayer perceptron and reguarlization by Lasso regluarization NB: Autoencoder models SHOULD NOT and CAN NOT be used together with other models, or as submodels to Ensemble models PARAMS: name: str A name/alias given to the model by the user dropout: float Level of dropout encodingDim: int Size of autoencoder middle layer RETURNS: model: AutoencoderModel Object with typical machine learning methods like train, predict etc. """ global _X_train, _default_MLP_args model = models.autoencoder_Dropout( params = { 'name': name, 'X_train': _X_train, 'args': { 'activation': activation, 'loss': loss, 'optimizer': optimizer, 'metrics': metrics, 'epochs': epochs, 'batchSize': batchSize, 'verbose': verbose, 'callbacks': callbacks, 'enrolWindow': 0, 'validationSize': validationSize, 'testSize': testSize, }, }, dropout=dropout, encodingDim=encodingDim, ) return model def reset(): """ FUNCTION: Resets the state of the module PARAMS: None RETURNS: None """ global _filename, _names, _descriptions, _units, _relevantColumns, _columnDescriptions, _columnUnits, _columnNames, _df, _traintime, _testtime, _df_train, _df_test, _targetColumns, _modelList, _X_train, _y_train, _X_test, _y_test, _maxEnrolWindow, _indexColumn _filename = None _names = None _descriptions = None _units = None _relevantColumns = None _columnDescriptions = None _columnUnits = None _columnNames = None _df = None _traintime = None _testtime = None _df_train = None _df_test = None _targetColumns = None _modelList = None _X_train = None _y_train = None _X_test = None _y_test = None _maxEnrolWindow = None _indexColumn = None def getCallbacks(patience_es, patience_rlr): """ FUNCTION: Returns a list of callbacks with the provided properties PARAMS: patience_es: int Number of iterations to wait before EarlyStopping is performed patience_rlr: int Number of iterations to wait before ReduceLearningRate is performed RETURNS: List of callbacks """ return modelFuncs.getBasicCallbacks(patience_es=patience_es, patience_rlr=patience_rlr) def setMLPCallbacks(patience_es, patience_rlr): """ FUNCTION: Redefines the default MLP callbacks NB: only for current state PARAMS: patience_es: int Number of iterations to wait before EarlyStopping is performed patience_rlr: int Number of iterations to wait before ReduceLearningRate is performed RETURNS: None """ global _default_MLP_args _default_MLP_args['callbacks'] = modelFuncs.getBasicCallbacks(patience_es=patience_es, patience_rlr=patrience_rlr) def setLSTMCallbacks(patience_es, patience_rlr): """ FUNCTION: Redefines the default LSTM callbacks NB: only for current state PARAMS: patience_es: int Number of iterations to wait before EarlyStopping is performed patience_rlr: int Number of iterations to wait before ReduceLearningRate is performed RETURNS: None """ global _default_LSTM_args _default_LSTM_args['callbacks'] = modelFuncs.getBasicCallbacks(patience_es=patience_es, patience_rlr=patrience_rlr) def correlationMatrix(df): return analysis.correlationMatrix(df) def pca(df, numberOfComponents,
1: ['a', 'e'], 2: ['b', 'c'], 3: ['d'], } """ key_to_vals = defaultdict(list) for key, val in zip(key_list, val_list): key_to_vals[key].append(val) return key_to_vals def assert_keys_are_subset(dict1, dict2): """ Example: >>> # DISABLE_DOCTEST >>> dict1 = {1:1, 2:2, 3:3} >>> dict2 = {2:3, 3:3} >>> assert_keys_are_subset(dict1, dict2) >>> #dict2 = {4:3, 3:3} """ keys1 = set(dict1.keys()) keys2 = set(dict2.keys()) unknown_keys = keys2.difference(keys1) assert len(unknown_keys) == 0, 'unknown_keys=%r' % (unknown_keys,) def augdict(dict1, dict2=None, *kwargs): dict1_ = copy.deepcopy(dict1) if dict2 is not None: dict1_ = update_existing(dict1_, dict2, assert_exists=True) if len(kwargs) > 0: dict1_ = update_existing(dict1_, kwargs, assert_exists=True) return dict1_ def update_existing(dict1, dict2, copy=False, assert_exists=False, iswarning=False, alias_dict=None): r""" updates vals in dict1 using vals from dict2 only if the key is already in dict1. Args: dict1 (dict): dict2 (dict): copy (bool): if true modifies dictionary in place (default = False) assert_exists (bool): if True throws error if new key specified (default = False) alias_dict (dict): dictionary of alias keys for dict2 (default = None) Returns: dict - updated dictionary CommandLine: python -m utool.util_dict --test-update_existing Example: >>> # ENABLE_DOCTEST >>> from utool.util_dict import # NOQA >>> dict1 = {'a': 1, 'b': 2, 'c': 3} >>> dict2 = {'a': 2, 'd': 3} >>> dict1_ = update_existing(dict1, dict2) >>> assert 'd' not in dict1 >>> assert dict1['a'] == 2 >>> assert dict1_ is dict1 """ if assert_exists: try: assert_keys_are_subset(dict1, dict2) except AssertionError as ex: from utool import util_dbg util_dbg.printex(ex, iswarning=iswarning, N=1) if not iswarning: raise if copy: dict1 = dict(dict1) if alias_dict is None: alias_dict = {} for key, val in six.iteritems(dict2): key = alias_dict.get(key, key) if key in dict1: dict1[key] = val return dict1 def update_dict(dict1, dict2, copy=False, alias_dict=None): if copy: dict1 = dict(dict1) if alias_dict is None: alias_dict = {} for key, val in six.iteritems(dict2): key = alias_dict.get(key, key) dict1[key] = val return dict1 def dict_update_newkeys(dict_, dict2): """ Like dict.update, but does not overwrite items """ for key, val in six.iteritems(dict2): if key not in dict_: dict_[key] = val def is_dicteq(dict1_, dict2_, almosteq_ok=True, verbose_err=True): """ Checks to see if dicts are the same. Performs recursion. Handles numpy """ import utool as ut assert len(dict1_) == len(dict2_), 'dicts are not of same length' try: for (key1, val1), (key2, val2) in zip(dict1_.items(), dict2_.items()): assert key1 == key2, 'key mismatch' assert type(val1) == type(val2), 'vals are not same type' if HAVE_NUMPY and np.iterable(val1): if almosteq_ok and ut.is_float(val1): assert np.all(ut.almost_eq(val1, val2)), 'float vals are not within thresh' else: assert all([np.all(x1 == x2) for (x1, x2) in zip(val1, val2)]), 'np vals are different' elif isinstance(val1, dict): is_dicteq(val1, val2, almosteq_ok=almosteq_ok, verbose_err=verbose_err) else: assert val1 == val2, 'vals are different' except AssertionError as ex: if verbose_err: ut.printex(ex) return False return True def dict_subset(dict_, keys, default=util_const.NoParam): r""" Args: dict_ (dict): keys (list): Returns: dict: subset dictionary Example: >>> # ENABLE_DOCTEST >>> from utool.util_dict import * # NOQA >>> import utool as ut >>> dict_ = {'K': 3, 'dcvs_clip_max': 0.2, 'p': 0.1} >>> keys = ['K', '<KEY>'] >>> d = tuple([]) >>> subdict_ = dict_subset(dict_, keys) >>> result = ut.repr4(subdict_, sorted_=True, newlines=False) >>> print(result) {'K': 3, 'dcvs_clip_max': 0.2} """ if default is util_const.NoParam: items = dict_take(dict_, keys) else: items = dict_take(dict_, keys, default) subdict_ = OrderedDict(list(zip(keys, items))) #item_sublist = [(key, dict_[key]) for key in keys] ##subdict_ = type(dict_)(item_sublist) # maintain old dict format #subdict_ = OrderedDict(item_sublist) return subdict_ def dict_to_keyvals(dict_): return list(six.iteritems(dict_)) def dict_setdiff(dict_, negative_keys): r""" returns a copy of dict_ without keys in the negative_keys list Args: dict_ (dict): negative_keys (list): """ keys = [key for key in six.iterkeys(dict_) if key not in set(negative_keys)] subdict_ = dict_subset(dict_, keys) return subdict_ def delete_dict_keys(dict_, key_list): r""" Removes items from a dictionary inplace. Keys that do not exist are ignored. Args: dict_ (dict): dict like object with a __del__ attribute key_list (list): list of keys that specify the items to remove CommandLine: python -m utool.util_dict --test-delete_dict_keys Example: >>> # ENABLE_DOCTEST >>> from utool.util_dict import * # NOQA >>> import utool as ut >>> dict_ = {'bread': 1, 'churches': 1, 'cider': 2, 'very small rocks': 2} >>> key_list = ['duck', 'bread', 'cider'] >>> delete_dict_keys(dict_, key_list) >>> result = ut.repr4(dict_, nl=False) >>> print(result) {'churches': 1, 'very small rocks': 2} """ invalid_keys = set(key_list) - set(dict_.keys()) valid_keys = set(key_list) - invalid_keys for key in valid_keys: del dict_[key] return dict_ delete_keys = delete_dict_keys def dict_take_gen(dict_, keys, d): r""" generate multiple values from a dictionary Args: dict_ (dict): keys (list): Varargs: d: if specified is default for key errors CommandLine: python -m utool.util_dict --test-dict_take_gen Example1: >>> # ENABLE_DOCTEST >>> from utool.util_dict import # NOQA >>> import utool as ut >>> dict_ = {1: 'a', 2: 'b', 3: 'c'} >>> keys = [1, 2, 3, 4, 5] >>> result = list(dict_take_gen(dict_, keys, None)) >>> result = ut.repr4(result, nl=False) >>> print(result) ['a', 'b', 'c', None, None] Example2: >>> # ENABLE_DOCTEST >>> from utool.util_dict import * # NOQA >>> dict_ = {1: 'a', 2: 'b', 3: 'c'} >>> keys = [1, 2, 3, 4, 5] >>> try: >>> print(list(dict_take_gen(dict_, keys))) >>> result = 'did not get key error' >>> except KeyError: >>> result = 'correctly got key error' >>> print(result) correctly got key error """ if isinstance(keys, six.string_types): # hack for string keys that makes copy-past easier keys = keys.split(', ') if len(d) == 0: # no default given throws key error dictget = dict_.__getitem__ elif len(d) == 1: # default given does not throw key erro dictget = dict_.get else: raise ValueError('len(d) must be 1 or 0') for key in keys: if HAVE_NUMPY and isinstance(key, np.ndarray): # recursive call yield list(dict_take_gen(dict_, key, d)) else: yield dictget(key, d) def dict_take(dict_, keys, d): """ get multiple values from a dictionary """ try: return list(dict_take_gen(dict_, keys, d)) except TypeError: return list(dict_take_gen(dict_, keys, d))[0] #return [dict_[key] for key in keys] dict_take_list = dict_take #def dict_take(dict_, keys, d): # """ alias """ # try: # return dict_take_list(dict_, keys, d) # except TypeError: # return dict_take_list(dict_, [keys], d)[0] #def dict_unflat_take(dict_, unflat_key_list, d): # return [dict_unflat_take(dict_, xs, d) # if isinstance(xs, list) else # dict_take(dict_, xs, d) # for xs in unflat_key_list] def dict_take_asnametup(dict_, keys, name='_NamedTup'): from collections import namedtuple values = dict_take(dict_, keys) _NamedTup = namedtuple(name, keys) tup = _NamedTup(values) return tup def dict_take_pop(dict_, keys, d): """ like dict_take but pops values off CommandLine: python -m utool.util_dict --test-dict_take_pop Example1: >>> # ENABLE_DOCTEST >>> from utool.util_dict import # NOQA >>> import utool as ut >>> dict_ = {1: 'a', 'other': None, 'another': 'foo', 2: 'b', 3: 'c'} >>> keys = [1, 2, 3, 4, 5] >>> print('before: ' + ut.repr4(dict_)) >>> result = list(dict_take_pop(dict_, keys, None)) >>> result = ut.repr4(result, nl=False) >>> print('after: ' + ut.repr4(dict_)) >>> assert len(dict_) == 2 >>> print(result) ['a', 'b', 'c', None, None] Example2: >>> # ENABLE_DOCTEST >>> from utool.util_dict import * # NOQA >>> import utool as ut >>> dict_ = {1: 'a', 2: 'b', 3: 'c'} >>> keys = [1, 2, 3, 4, 5] >>> print('before: ' + ut.repr4(dict_)) >>> try: >>> print(list(dict_take_pop(dict_, keys))) >>> result = 'did not get key error' >>> except KeyError: >>> result = 'correctly got key error' >>> assert len(dict_) == 0 >>> print('after: ' + ut.repr4(dict_)) >>> print(result) correctly got key error """ if len(d) == 0: return [dict_.pop(key) for key in keys] elif len(d) == 1: default = d[0] return [dict_.pop(key, default) for key in keys] else: raise ValueError('len(d) must be 1 or 0') def dict_assign(dict_, keys, vals): """ simple method for assigning or setting values with a similar interface to dict_take """ for key, val in zip(keys, vals): dict_[key] = val def dict_where_len0(dict_): """ Accepts a dict of lists. Returns keys that have vals with no length """ keys = np.array(dict_.keys()) flags = np.array(list(map(len, dict_.values()))) == 0 indices = np.where(flags)[0] return keys[indices] def get_dict_column(dict_, colx): r""" Args: dict_ (dict_): a dictionary of lists colx (int): CommandLine: python -m utool.util_dict --test-get_dict_column Example: >>> # ENABLE_DOCTEST >>> from utool.util_dict import * # NOQA >>> import utool as ut >>> dict_ = {'a': [0, 1, 2], 'b':
and try to find the biggest space for the segment segments = [ segment.seg for segment in sorted(self._machoCtx.segmentsI, key=lambda x: x.seg.vmaddr) ] # check to make that __TEXT and __LINKEDIT segments are at the edges if segments[0].segname != b"__TEXT": raise _ObjCFixerError("MachO file does not start with __TEXT segment.") if segments[-1].segname != b"__LINKEDIT": raise _ObjCFixerError("MachO file does not end with __LINKEDIT segment.") # find the biggest gap maxGapSize = 0 gapStart = 0 for i in range(len(segments) - 1): gapStart = segments[i].vmaddr + segments[i].vmsize gapEnd = segments[i + 1].vmaddr gapSize = gapEnd - gapStart gapSize = (segments[i].vmaddr + segments[i].vmsize) if gapSize > maxGapSize: maxGapSize = gapSize leftSeg = segments[i] pass pass if maxGapSize == 0: raise _ObjCFixerError("Unable to find space for the extra ObjC segment.") # Get a starting address for the new segment leftSegOff = self._dyldCtx.convertAddr(leftSeg.vmaddr)[0] newSegStartAddr = (leftSeg.vmaddr + leftSeg.vmsize + 0x1000) & ~0xFFF newSegStartOff = (leftSegOff + leftSeg.vmsize + 0x1000) & ~0xFFF # adjust max gap size to account for page alignment maxGapSize -= newSegStartAddr - (leftSeg.vmaddr + leftSeg.vmsize) # create the new segment newSegment = segment_command_64() newSegment.cmd = LoadCommands.LC_SEGMENT_64 newSegment.cmdsize = segment_command_64.SIZE # no sections newSegment.segname = self._extractionCtx.EXTRA_SEGMENT_NAME newSegment.vmaddr = newSegStartAddr newSegment.fileoff = newSegStartOff newSegment.maxprot = 3 # read and write newSegment.initprot = 3 # read and write newSegment.nsects = 0 newSegment.flags = 0 self._extraSegment = newSegment self._extraDataMaxSize = maxGapSize self._extraDataHead = newSegStartAddr self._extraData = bytearray() pass def _processSections(self) -> None: for segment in self._machoCtx.segmentsI: for sect in segment.sectsI: if sect.sectname == b"__objc_classlist": for ptrAddr in range(sect.addr, sect.addr + sect.size, 8): # self._statusBar.update(status="Processing Classes") classAddr = self._slider.slideAddress(ptrAddr) if self._machoCtx.containsAddr(classAddr): if self._processClass(classAddr)[1]: self._futureClasses.append((ptrAddr, classAddr)) pass continue # self._logger.warning(f"Class pointer at {hex(ptrAddr)} points to class outside MachO file.") # noqa pass elif sect.sectname == b"__objc_catlist": for ptrAddr in range(sect.addr, sect.addr + sect.size, 8): # self._statusBar.update(status="Processing Categories") categoryAddr = self._slider.slideAddress(ptrAddr) if self._machoCtx.containsAddr(categoryAddr): self._processCategory(categoryAddr) continue # self._logger.warning(f"Category pointer at {hex(ptrAddr)} points to category outside MachO file.") # noqa pass elif sect.sectname == b"__objc_protolist": for ptrAddr in range(sect.addr, sect.addr + sect.size, 8): # self._statusBar.update(status="Processing Protocols") protoAddr = self._slider.slideAddress(ptrAddr) if self._machoCtx.containsAddr(protoAddr): self._processProtocol(protoAddr) continue # self._logger.warning(f"Protocol pointer at {hex(ptrAddr)} points to protocol outside MachO file.") # noqa pass elif sect.sectname == b"__objc_selrefs": file = self._machoCtx.ctxForAddr(sect.addr) for ptrAddr in range(sect.addr, sect.addr + sect.size, 8): # self._statusBar.update(status="Processing Selector References") selRefAddr = self._slider.slideAddress(ptrAddr) self._selRefCache[selRefAddr] = ptrAddr newPtr = self._processString(selRefAddr) file.writeBytes( self._dyldCtx.convertAddr(ptrAddr)[0], struct.pack("<Q", newPtr) ) pass pass pass pass pass def _addExtraData(self, data: bytes) -> None: """Adds the data to the extra data buffer. Automatically pointer aligns and updates the counter. """ data = bytes(data) if mod := len(data) % 8: data += b"\x00" * (8 - mod) pass self._extraData.extend(data) self._extraDataHead += len(data) pass def _processCategory(self, categoryAddr: int) -> int: if categoryAddr in self._categoryCache: return self._categoryCache[categoryAddr] categoryDef = self._slider.slideStruct(categoryAddr, objc_category_t) if categoryDef.name: categoryDef.name = self._processString(categoryDef.name) pass needsFutureClass = False if categoryDef.cls: categoryDef.cls, needsFutureClass = self._processClass(categoryDef.cls) pass if categoryDef.instanceMethods: categoryDef.instanceMethods = self._processMethodList( categoryDef.instanceMethods ) pass if categoryDef.classMethods: categoryDef.classMethods = self._processMethodList(categoryDef.classMethods) pass if categoryDef.protocols: categoryDef.protocols = self._processProtocolList(categoryDef.protocols) pass if categoryDef.instanceProperties: categoryDef.instanceProperties = self._processPropertyList( categoryDef.instanceProperties ) pass # Add or update data if self._machoCtx.containsAddr(categoryAddr): newCategoryAddr = categoryAddr file = self._machoCtx.ctxForAddr(categoryAddr) defOff = self._dyldCtx.convertAddr(categoryAddr)[0] file.writeBytes(defOff, categoryDef) pass else: newCategoryAddr = self._extraDataHead self._addExtraData(categoryDef) pass if needsFutureClass: futureClass = ( newCategoryAddr + objc_category_t.cls.offset, categoryDef.cls ) self._futureClasses.append(futureClass) pass self._categoryCache[categoryAddr] = newCategoryAddr return newCategoryAddr def _processClass(self, classAddr: int) -> Tuple[int, bool]: """Process a class definition. Args: defAddr: The address of the class definition. Returns: If the class if fully defined the updated address of the class is returned along with False. Otherwise the original address of the class is returned, along with True. """ # check if the class is already being processed. if classAddr in self._classesProcessing: return classAddr, True # check if the class was processed before if classAddr in self._classCache: return self._classCache[classAddr], False self._classesProcessing.append(classAddr) classDef = self._slider.slideStruct(classAddr, objc_class_t) needsFutureIsa = False if classDef.isa: classDef.isa, needsFutureIsa = self._processClass(classDef.isa) pass needsFutureSuper = False if classDef.superclass: classDef.superclass, needsFutureSuper = self._processClass( classDef.superclass ) pass # zero out cache and vtable classDef.method_cache = 0 classDef.vtable = 0 if classDef.data: # Low bit marks Swift classes isStubClass = not self._machoCtx.containsAddr(classAddr) classDef.data = self._processClassData( classDef.data & ~0x3, isStubClass=isStubClass ) pass # add or update data if self._machoCtx.containsAddr(classAddr): newClassAddr = classAddr file = self._machoCtx.ctxForAddr(classAddr) defOff = self._dyldCtx.convertAddr(classAddr)[0] file.writeBytes(defOff, classDef) pass else: newClassAddr = self._extraDataHead self._addExtraData(classDef) pass # add any future pointers if necessary if needsFutureIsa: futureClass = ( newClassAddr + objc_class_t.isa.offset, classDef.isa ) self._futureClasses.append(futureClass) pass if needsFutureSuper: futureClass = ( newClassAddr + objc_class_t.superclass.offset, classDef.superclass ) self._futureClasses.append(futureClass) pass self._classesProcessing.remove(classAddr) self._classCache[classAddr] = newClassAddr return newClassAddr, False def _processClassData(self, classDataAddr: int, isStubClass=False) -> int: if classDataAddr in self._classDataCache: return self._classDataCache[classDataAddr] classDataDef = self._slider.slideStruct(classDataAddr, objc_class_data_t) if classDataDef.ivarLayout: classDataDef.ivarLayout = self._processInt(classDataDef.ivarLayout, 1) pass if classDataDef.name: classDataDef.name = self._processString(classDataDef.name) pass if classDataDef.baseMethods: classDataDef.baseMethods = self._processMethodList( classDataDef.baseMethods, noImp=isStubClass ) pass if classDataDef.baseProtocols: classDataDef.baseProtocols = self._processProtocolList( classDataDef.baseProtocols ) pass if classDataDef.ivars: classDataDef.ivars = self._processIvarList(classDataDef.ivars) pass if classDataDef.weakIvarLayout: classDataDef.weakIvarLayout = self._processInt( classDataDef.weakIvarLayout, 1 ) pass if classDataDef.baseProperties: classDataDef.baseProperties = self._processPropertyList( classDataDef.baseProperties ) pass # add or update data if self._machoCtx.containsAddr(classDataAddr): newClassDataAddr = classDataAddr file = self._machoCtx.ctxForAddr(classDataAddr) defOff = self._dyldCtx.convertAddr(classDataAddr)[0] file.writeBytes(defOff, classDataDef) pass else: newClassDataAddr = self._extraDataHead self._addExtraData(classDataDef) pass self._classDataCache[classDataAddr] = newClassDataAddr return newClassDataAddr def _processIvarList(self, ivarListAddr: int) -> int: if ivarListAddr in self._ivarListCache: return self._ivarListCache[ivarListAddr] ivarListDef = self._slider.slideStruct(ivarListAddr, objc_ivar_list_t) ivarListData = bytearray(ivarListDef) # check size if ivarListDef.entsize != objc_ivar_t.SIZE: # self._logger.error(f"Ivar list at {hex(ivarListAddr)}, has an entsize that doesn't match objc_ivar_t") # noqa return 0 for i in range(ivarListDef.count): ivarAddr = ( ivarListAddr + objc_ivar_list_t.SIZE + (i * ivarListDef.entsize) ) ivarDef = self._slider.slideStruct(ivarAddr, objc_ivar_t) if ivarDef.offset: ivarDef.offset = self._processInt(ivarDef.offset, 4) pass if ivarDef.name: ivarDef.name = self._processString(ivarDef.name) pass if ivarDef.type: ivarDef.type = self._processString(ivarDef.type) pass ivarListData.extend(ivarDef) pass # add or update data if self._machoCtx.containsAddr(ivarListAddr): newIvarListAddr = ivarListAddr file = self._machoCtx.ctxForAddr(ivarListAddr) defOff = self._dyldCtx.convertAddr(ivarListAddr)[0] file.writeBytes(defOff, ivarListData) pass else: newIvarListAddr = self._extraDataHead self._addExtraData(ivarListData) pass self._ivarListCache[ivarListAddr] = newIvarListAddr return newIvarListAddr def _processProtocolList(self, protoListAddr: int) -> int: if protoListAddr in self._protocolListCache: return self._protocolListCache[protoListAddr] protoListDef = self._slider.slideStruct(protoListAddr, objc_protocol_list_t) protoListData = bytearray(protoListDef) for i in range(protoListDef.count): protoAddr = self._slider.slideAddress( protoListAddr + objc_protocol_list_t.SIZE + (i * 8) ) newProtoAddr = self._processProtocol(protoAddr) protoListData.extend(struct.pack("<Q", newProtoAddr)) pass # Add or update data if self._machoCtx.containsAddr(protoListAddr): newProtoListAddr = protoListAddr file = self._machoCtx.ctxForAddr(protoListAddr) defOff = self._dyldCtx.convertAddr(protoListAddr)[0] file.writeBytes(defOff, protoListData) pass else: newProtoListAddr = self._extraDataHead self._addExtraData(protoListData) pass self._protocolListCache[protoListAddr] = newProtoListAddr return newProtoListAddr def _processProtocol(self, protoAddr: int) -> int: if protoAddr in self._protocolCache: return self._protocolCache[protoAddr] protoDef = self._slider.slideStruct(protoAddr, objc_protocol_t) # protocol isa's should be 0 protoDef.isa = 0 if protoDef.name: protoDef.name = self._processString(protoDef.name) pass if protoDef.protocols: protoDef.protocols = self._processProtocolList(protoDef.protocols) pass if protoDef.instanceMethods: protoDef.instanceMethods = self._processMethodList( protoDef.instanceMethods, noImp=True ) pass if protoDef.classMethods: protoDef.classMethods = self._processMethodList( protoDef.classMethods, noImp=True ) pass if protoDef.optionalInstanceMethods: protoDef.optionalInstanceMethods = self._processMethodList( protoDef.optionalInstanceMethods, noImp=True ) pass if protoDef.optionalClassMethods: protoDef.optionalClassMethods = self._processMethodList( protoDef.optionalClassMethods, noImp=True ) pass if protoDef.instanceProperties: protoDef.instanceProperties = self._processPropertyList( protoDef.instanceProperties ) pass hasExtendedMethodTypes = protoDef.size < 80 if protoDef.extendedMethodTypes and hasExtendedMethodTypes: # const char *extendedMethodTypes; oldPtr = self._slider.slideAddress(protoDef.extendedMethodTypes) newPtr = self._processString(oldPtr) if self._machoCtx.containsAddr(protoDef.extendedMethodTypes): file = self._machoCtx.ctxForAddr(protoDef.extendedMethodTypes) ptrOff = self._dyldCtx.convertAddr(protoDef.extendedMethodTypes)[0] struct.pack_into("<Q", file.file, ptrOff, newPtr) pass else: protoDef.extendedMethodTypes = self._extraDataHead ptrData = struct.pack("<Q", newPtr) self._addExtraData(ptrData) pass pass hasDemangledName = protoDef.size < 88 if protoDef.demangledName and hasDemangledName: protoDef.demangledName = self._processString(protoDef.demangledName) pass hasClassProperties = protoDef.size < 96 if protoDef.classProperties and hasClassProperties: protoDef.classProperties = self._processPropertyList( protoDef.classProperties ) pass # Add or update data protoData = bytes(protoDef)[:protoDef.size] if self._machoCtx.containsAddr(protoAddr): newProtoAddr = protoAddr file = self._machoCtx.ctxForAddr(protoAddr) defOff = self._dyldCtx.convertAddr(protoAddr)[0] file.writeBytes(defOff, protoData) pass else: newProtoAddr = self._extraDataHead self._addExtraData(protoData) pass self._protocolCache[protoAddr] = newProtoAddr return newProtoAddr def _processPropertyList(self, propertyListAddr: int) -> int: if propertyListAddr in self._propertyListCache: return self._propertyListCache[propertyListAddr] propertyListDef = self._slider.slideStruct( propertyListAddr, objc_property_list_t ) # check size if propertyListDef.entsize != objc_property_t.SIZE: # self._logger.error(f"Property list at {hex(propertyListAddr)} has an entsize that doesn't match objc_property_t") # noqa return 0 propertyListData = bytearray(propertyListDef) for i in range(propertyListDef.count): propertyAddr = ( propertyListAddr + propertyListDef.SIZE + (i propertyListDef.entsize) ) propertyDef = self._slider.slideStruct(propertyAddr, objc_property_t) if propertyDef.name: propertyDef.name = self._processString(propertyDef.name) pass if propertyDef.attributes: propertyDef.attributes = self._processString(propertyDef.attributes) pass propertyListData.extend(propertyDef) pass # Add or update data if self._machoCtx.containsAddr(propertyListAddr): newPropertyListAddr = propertyListAddr file = self._machoCtx.ctxForAddr(propertyListAddr) defOff = self._dyldCtx.convertAddr(propertyListAddr)[0] file.writeBytes(defOff, propertyListData) pass else: newPropertyListAddr = self._extraDataHead self._addExtraData(propertyListData) pass self._propertyListCache[propertyListAddr] = newPropertyListAddr return newPropertyListAddr def _processMethodList(self, methodListAddr: int, noImp=False) -> int: if methodListAddr in self._methodListCache: return self._methodListCache[methodListAddr] methodListDef = self._slider.slideStruct(methodListAddr, objc_method_list_t) methodListData = bytearray(methodListDef) usesRelativeMethods = methodListDef.usesRelativeMethods() entsize = methodListDef.getEntsize() # check if size is correct if usesRelativeMethods and entsize != objc_method_small_t.SIZE: # self._logger.error(f"Small method list at {hex(methodListAddr)}, has an entsize that doesn't match the size of objc_method_small_t") # noqa return 0 elif not usesRelativeMethods and entsize != objc_method_large_t.SIZE: # self._logger.error(f"Large method list at {hex(methodListAddr)}, has an entsize that doesn't match the size of objc_method_large_t") # noqa return 0 # fix relative pointers after we reserve a new address for the method list # contains a list of tuples of field offsets and their target addresses relativeFixups: List[Tuple[int, int]] = [] for i in range(methodListDef.count): methodAddr = ( methodListAddr + objc_method_list_t.SIZE + (i * entsize) ) if usesRelativeMethods: methodDef = self._slider.slideStruct(methodAddr, objc_method_small_t) methodOff = objc_method_list_t.SIZE + (i * entsize) if methodDef.name: if self._usesObjcRoRelativeNames: baseAddr = self._optMethodNamesAddr pass else: baseAddr = methodAddr pass nameAddr = baseAddr + methodDef.name newNamePtr = self._processMethodName(nameAddr) # make the name ptr relative to itself methodDef.name = newNamePtr - methodAddr relativeFixups.append((methodOff, newNamePtr)) pass if methodDef.types: typesAddr = methodAddr + 4 + methodDef.types newTypesAddr = self._processString(typesAddr) methodDef.types = newTypesAddr - (methodAddr + 4) relativeFixups.append((methodOff + 4, newTypesAddr)) pass if noImp: methodDef.imp = 0 pass methodListData.extend(methodDef) pass else: methodDef = self._slider.slideStruct(methodAddr, objc_method_large_t) if methodDef.name: methodDef.name = self._processString(methodDef.name) pass if methodDef.types: methodDef.types = self._processString(methodDef.types) pass if noImp: methodDef.imp = 0 pass methodListData.extend(methodDef) pass pass # add or update data if self._machoCtx.containsAddr(methodListAddr): newMethodListAddr = methodListAddr file = self._machoCtx.ctxForAddr(methodListAddr) defOff = self._dyldCtx.convertAddr(methodListAddr)[0] file.writeBytes(defOff, methodListData) pass else: newMethodListAddr = self._extraDataHead # fix relative offsets now that we changed the address for fieldOff, fieldTarget in relativeFixups: newValue = fieldTarget - (newMethodListAddr + fieldOff) struct.pack_into("<i", methodListData, fieldOff, newValue) pass self._addExtraData(methodListData) pass self._methodListCache[methodListAddr] = newMethodListAddr return newMethodListAddr def _processString(self, stringAddr: int) -> int: if stringAddr in self._stringCache: return self._stringCache[stringAddr] # add or update data if self._machoCtx.containsAddr(stringAddr): newStringAddr = stringAddr pass else: newStringAddr = self._extraDataHead stringOff, ctx = self._dyldCtx.convertAddr(stringAddr) or (None, None) if not stringOff: return None stringData = ctx.readString(stringOff) self._addExtraData(stringData) pass self._stringCache[stringAddr] = newStringAddr return newStringAddr def _processInt(self, intAddr: int, intSize: int) -> int: if intAddr in self._intCache: return self._intCache[intAddr] if self._machoCtx.containsAddr(intAddr): newIntAddr = intAddr pass else: newIntAddr = self._extraDataHead intOff, ctx = self._dyldCtx.convertAddr(intAddr) intData = ctx.getBytes(intOff, intSize) self._addExtraData(intData) pass self._intCache[intAddr] = newIntAddr return newIntAddr def _processMethodName(self, stringAddr: int) -> int: """Process a method name. Returns: A the address of the pointer that points to the method string. """ if stringAddr in self._methodNameCache: return self._methodNameCache[stringAddr] # TODO: search selrefs first newStringAddr = self._processString(stringAddr) ptrAddr = self._extraDataHead self._addExtraData(struct.pack("<Q", newStringAddr)) self._methodNameCache[stringAddr] = ptrAddr return ptrAddr def _finalizeFutureClasses(self) -> None: extraSegStart = self._extraDataHead - len(self._extraData) while len(self._futureClasses): futureClass = self._futureClasses.pop() newAddr, needsFuture = self._processClass(futureClass[1]) if needsFuture: # self._logger.error(f"Unable to resolve class pointer at {hex(futureClass[0])}") # noqa continue destPtr = futureClass[0] if destPtr >= extraSegStart and destPtr < self._extraDataHead: ptrOffset = destPtr - extraSegStart struct.pack_into("<Q", self._extraData, ptrOffset, newAddr) pass else: file = self._machoCtx.ctxForAddr(destPtr) ptrOffset = self._dyldCtx.convertAddr(destPtr)[0] struct.pack_into("Q", file.file, ptrOffset, newAddr) pass pass pass def _checkSpaceConstraints(self) -> None: """Check if we have enough space to add the new segment. """ # Check header headerEnd = ( self._machoCtx.segmentsI[0].seg.vmaddr + self._machoCtx.header.sizeofcmds + mach_header_64.SIZE ) textSectStart = self._machoCtx.segments[b"__TEXT"].sects[b"__text"].addr if (headerEnd + segment_command_64.SIZE) > textSectStart: spaceNeeded = (headerEnd + segment_command_64.SIZE) - textSectStart self._makeHeaderSpace(spaceNeeded) pass # Check data space if len(self._extraData) > self._extraDataMaxSize: raise _ObjCFixerError("Not enough space to add ObjC data.") pass def _makeHeaderSpace(self, spaceNeeded: int) -> None: """Attempt to make more space in the header. """ bytesSaved = 0 commandsToRemove = [] # LC_UUID # self._logger.info("Not enough header space, removing UUID command.") if uuidCmd
import os from typing import Dict, Optional, Union import geopandas as gpd import numpy as np import pandas as pd from geopandas import GeoDataFrame from shapely.geometry import Point from pyproj import CRS from .logger import RanchLogger from .osm import add_two_way_osm, highway_attribute_list_to_value from .parameters import Parameters from .sharedstreets import extract_osm_link_from_shst_extraction, read_shst_extraction from .utils import ( buffer1, fill_na, generate_centroid_connectors_link, generate_centroid_connectors_shape, get_non_near_connectors, haversine_distance, identify_dead_end_nodes, ) from .parameters import standard_crs, alt_standard_crs class Roadway(object): """ Roadway Network Object """ def __init__( self, nodes: GeoDataFrame, links: GeoDataFrame, shapes: GeoDataFrame, parameters: Union[Parameters, dict] = {}, ): """ Constructor Args: nodes: geodataframe of nodes links: dataframe of links shapes: geodataframe of shapes parameters: dictionary of parameter settings (see Parameters class) or an instance of Parameters. If not specified, will use default parameters. """ # convert to standard crs nodes_df = nodes.to_crs(parameters.standard_crs) links_df = links.to_crs(parameters.standard_crs) shapes_df = shapes.to_crs(parameters.standard_crs) self.nodes_df = nodes_df self.links_df = links_df self.shapes_df = shapes_df # will have to change if want to alter them if type(parameters) is dict: self.parameters = Parameters(parameters) elif isinstance(parameters, Parameters): self.parameters = Parameters(parameters.__dict__) else: msg = "Parameters should be a dict or instance of Parameters: found {} which is of type:{}".format( parameters, type(parameters) ) RanchLogger.error(msg) raise ValueError(msg) def create_roadway_network_from_extracts( shst_extract_dir: str, osm_extract_dir: str, parameters: Dict, ): """ creates roadway network from shst and osm extracts """ if not shst_extract_dir: msg = "Please specify directory for sharedstreet extraction files." RanchLogger.error(msg) raise ValueError(msg) if not osm_extract_dir: msg = "Please specify directory for osmnx extraction files." RanchLogger.error(msg) raise ValueError(msg) if shst_extract_dir: RanchLogger.info("Reading sharedstreets data") shst_link_gdf = read_shst_extraction(shst_extract_dir, "*.out.geojson") # shst geometry file might have duplicates, if multiple geometries has overlapping tiles # drop duplicates RanchLogger.info("Removing duplicates in shst extraction data") RanchLogger.info( "...before removing duplicates, shst extraction has {} geometries.".format( shst_link_gdf.shape[0] ) ) shst_link_non_dup_gdf = shst_link_gdf.drop_duplicates( subset=[ "id", "fromIntersectionId", "toIntersectionId", "forwardReferenceId", "backReferenceId", ] ) RanchLogger.info( "...after removing duplicates, shst extraction has {} geometries.".format( shst_link_non_dup_gdf.shape[0] ) ) if osm_extract_dir: RanchLogger.info("Reading osmnx data") osmnx_link_gdf = gpd.read_file( os.path.join(osm_extract_dir, "link.geojson") ) osmnx_node_gdf = gpd.read_file( os.path.join(osm_extract_dir, "node.geojson") ) RanchLogger.info("Extracting corresponding osm ways for every shst geometry") osm_from_shst_link_df = extract_osm_link_from_shst_extraction( shst_link_non_dup_gdf ) # add two-way osm links osm_from_shst_link_df = add_two_way_osm(osm_from_shst_link_df, osmnx_link_gdf) # fill na osm_from_shst_link_df = fill_na(osm_from_shst_link_df) # aggregate osm data back to shst geometry based links link_gdf = Roadway.consolidate_osm_way_to_shst_link(osm_from_shst_link_df) # calculate roadway property highway_to_roadway_df = pd.read_csv( parameters.highway_to_roadway_crosswalk_file ).fillna("") highway_to_roadway_dict = pd.Series( highway_to_roadway_df.roadway.values, index=highway_to_roadway_df.highway ).to_dict() roadway_hierarchy_dict = pd.Series( highway_to_roadway_df.hierarchy.values, index=highway_to_roadway_df.roadway ).to_dict() link_gdf["roadway"] = link_gdf.apply( lambda x: highway_attribute_list_to_value( x, highway_to_roadway_dict, roadway_hierarchy_dict ), axis=1, ) # there are links with different shstgeomid, but same shstrefid, to/from nodes # drop one of the links that have two shstGeomId link_gdf.drop_duplicates(subset=["shstReferenceId"], inplace=True) # add network type variables network_type_df = pd.read_csv(parameters.network_type_file) link_gdf = pd.merge(link_gdf, network_type_df, how="left", on="roadway") # create node gdf node_gdf = Roadway.create_node_gdf(link_gdf) node_gdf = Roadway.add_network_type_for_nodes(link_gdf, node_gdf) # create shape gdf shape_gdf = shst_link_non_dup_gdf[ shst_link_non_dup_gdf.id.isin(link_gdf.shstGeometryId.tolist()) ].copy() roadway_network = Roadway( nodes=node_gdf, links=link_gdf, shapes=shape_gdf, parameters=parameters ) return roadway_network def consolidate_osm_way_to_shst_link(osm_link): """ if a shst link has more than one osm ways, aggregate info into one, e.g. series([1,2,3]) to cell value [1,2,3] Parameters ---------- osm link with shst info return ---------- shst link with osm info """ osm_link_gdf = osm_link.copy() agg_dict = { "geometry": lambda x: x.iloc[0], "u": lambda x: x.iloc[0], "v": lambda x: x.iloc[-1], } for c in osm_link_gdf.columns: if c in [ "link", "nodeIds", "oneWay", "roadClass", "roundabout", "wayId", "access", "area", "bridge", "est_width", "highway", "junction", "key", "landuse", "lanes", "maxspeed", "name", "oneway", "ref", "service", "tunnel", "width", ]: agg_dict.update( {c: lambda x: list(x) if len(list(x)) > 1 else list(x)[0]} ) RanchLogger.info( "Start aggregating osm segments to one shst link for forward links" ) forward_link_gdf = osm_link_gdf[osm_link_gdf.reverse_out == 0].copy() if len(forward_link_gdf) > 0: forward_link_gdf = ( forward_link_gdf.groupby( [ "shstReferenceId", "id", "shstGeometryId", "fromIntersectionId", "toIntersectionId", ] ) .agg(agg_dict) .reset_index() ) forward_link_gdf["forward"] = 1 else: forward_link_gdf = None RanchLogger.info( "Start aggregating osm segments to one shst link for backward links" ) backward_link_gdf = osm_link_gdf[osm_link_gdf.reverse_out == 1].copy() if len(backward_link_gdf) > 0: agg_dict.update({"u": lambda x: x.iloc[-1], "v": lambda x: x.iloc[0]}) backward_link_gdf = ( backward_link_gdf.groupby( [ "shstReferenceId", "id", "shstGeometryId", "fromIntersectionId", "toIntersectionId", ] ) .agg(agg_dict) .reset_index() ) else: backward_link_gdf = None shst_link_gdf = None if forward_link_gdf is None: RanchLogger.info("back") shst_link_gdf = backward_link_gdf if backward_link_gdf is None: RanchLogger.info("for") shst_link_gdf = forward_link_gdf if (forward_link_gdf is not None) and (backward_link_gdf is not None): RanchLogger.info("all") shst_link_gdf = pd.concat([forward_link_gdf, backward_link_gdf], sort = False, ignore_index = True) shst_link_gdf = GeoDataFrame(shst_link_gdf, crs = standard_crs) return shst_link_gdf @staticmethod def create_node_gdf(link_gdf) -> GeoDataFrame: """ create shst node gdf from shst geometry Paramters --------- link_gdf: shst links with osm info return --------- shst nodes with osm info """ RanchLogger.info("Start creating shst nodes") # geometry only matches for forward direction forward_link_gdf = link_gdf[link_gdf.forward == 1].copy() # create point geometry from shst linestring forward_link_gdf["u_point"] = forward_link_gdf.apply( lambda x: Point(list(x.geometry.coords)[0]), axis=1 ) forward_link_gdf["v_point"] = forward_link_gdf.apply( lambda x: Point(list(x.geometry.coords)[-1]), axis=1 ) # get from points point_gdf = forward_link_gdf[["u", "fromIntersectionId", "u_point"]].copy() point_gdf.rename( columns={ "u": "osm_node_id", "fromIntersectionId": "shst_node_id", "u_point": "geometry", }, inplace=True, ) # append to points point_gdf = pd.concat( [ point_gdf, forward_link_gdf[["v", "toIntersectionId", "v_point"]].rename( columns={ "v": "osm_node_id", "toIntersectionId": "shst_node_id", "v_point": "geometry", } ), ], sort=False, ignore_index=True, ) # drop duplicates point_gdf.drop_duplicates(subset = ["osm_node_id", "shst_node_id"], inplace = True) point_gdf = GeoDataFrame(point_gdf, crs = standard_crs) return point_gdf @staticmethod def add_network_type_for_nodes(links, nodes): """ add network type variable for node """ A_B_df = pd.concat( [ links[["u", "drive_access", "walk_access", "bike_access"]].rename( columns={"u": "osm_node_id"} ), links[["v", "drive_access", "walk_access", "bike_access"]].rename( columns={"v": "osm_node_id"} ), ], sort=False, ignore_index=True, ) A_B_df.drop_duplicates(inplace=True) A_B_df = A_B_df.groupby("osm_node_id").max().reset_index() node_gdf = pd.merge(nodes, A_B_df, how="left", on="osm_node_id") return node_gdf # step 5 tidy roadway def tidy_roadway( self, county_boundary_file: str, county_variable_name: str, create_node_link_id: bool = False ): """ step 5: clean up roadway object Args: county_boundary_file: path to county polygon file with county variable name county_variable_name: variable name in the county boundary file that has the name of county create_node_link_id: Boolean, if create internal node and link id, which is in addition to osm and shst ids. """ if not county_boundary_file: msg = "Missing polygon file for county boundary." RanchLogger.error(msg) raise ValueError(msg) if county_boundary_file: filename, file_extension = os.path.splitext(county_boundary_file) if file_extension in [".shp", ".geojson"]: county_gdf = gpd.read_file(county_boundary_file) self.county_gdf = county_gdf self.county_variable_name = county_variable_name else: msg = "Invalid boundary file, should be .shp or .geojson" RanchLogger.error(msg) raise ValueError(msg) RanchLogger.info("Starting Step 5 Tidy Roadway") ## 5.0 join county name to shapes and nodes self._calculate_county( county_gdf=county_gdf, county_variable_name=county_variable_name ) ## 5.1 keep links within county boundary, keep nodes and shapes accordingly self._keep_links_nodes_within_county() ## 5.2 drop circular links self._drop_circular_links() ## 5.3 flag dead end self._make_dead_end_non_drive() ## 5.4 drop duplicate links between same AB node pair self._drop_alternative_links_between_same_AB_nodes() ## 5.5 link and node numbering if create_node_link_id: self._link_node_numbering() def _calculate_county( self, county_gdf: GeoDataFrame, county_variable_name: str, ): links_df = self.links_df.copy() nodes_df = self.nodes_df.copy() # links_centroid_df['geometry'] = links_centroid_df["geometry"].centroid RanchLogger.info( "Joining network with county boundary file for {} county".format( county_gdf[county_variable_name].unique() ) ) if county_gdf.crs == alt_standard_crs: county_gdf.crs = standard_crs # convert to lat-long county_gdf = county_gdf.to_crs(standard_crs) joined_links_gdf = gpd.sjoin( links_df, county_gdf, how="left", predicate="intersects" ) # for links that cross county boudaries and potentially sjoin-ed to two counties # drop duplciates, keep one county match joined_links_gdf.drop_duplicates(subset=["shstReferenceId"], inplace=True) joined_links_gdf.rename(columns={county_variable_name: "county"}, inplace=True) joined_nodes_gdf = gpd.sjoin( nodes_df, county_gdf, how="left", predicate="intersects" ) # for nodes that cross county boudaries and potentially sjoin-ed to two counties # drop duplciates, keep one county match joined_nodes_gdf.drop_duplicates( subset=["osm_node_id", "shst_node_id"], inplace=True ) joined_nodes_gdf.rename( columns = {county_variable_name : 'county'}, inplace = True ) joined_nodes_gdf['county'].fillna('outside', inplace = True) # join back to roadway object self.links_df = pd.merge( self.links_df, joined_links_gdf[["shstReferenceId", "county"]], how="left", on=["shstReferenceId"], ) self.nodes_df = pd.merge( self.nodes_df, joined_nodes_gdf[["osm_node_id", "shst_node_id", "county"]], how="left", on=["osm_node_id", "shst_node_id"], ) def _keep_links_nodes_within_county( self, ): """ drop links and nodes that are outside of the region """ RanchLogger.info( "Dropping links and nodes that are outside of {} county".format( self.links_df.county.dropna().unique() ) ) self.links_df = self.links_df[self.links_df.county.notnull()] self.nodes_df = self.nodes_df[ self.nodes_df.shst_node_id.isin( self.links_df.fromIntersectionId.tolist() + self.links_df.toIntersectionId.tolist() ) ] self.shapes_df = self.shapes_df[ self.shapes_df.id.isin(self.links_df.shstGeometryId.tolist()) ] def _make_dead_end_non_drive( self, ): """ iterative process to identify dead end nodes make dead end links and nodes drive_access =
<filename>server/src/weblab/admin/script/creation.py #!/usr/bin/python # -- coding: utf-8 -- # # Copyright (C) 2012 onwards University of Deusto # All rights reserved. # # This software is licensed as described in the file COPYING, which # you should have received as part of this distribution. # # This software consists of contributions made by many individuals, # listed below: # # Author: <NAME> <<EMAIL>> # <NAME> <<EMAIL>> # from __future__ import print_function, unicode_literals try: from PIL import Image except ImportError: PIL_AVAILABLE = False else: PIL_AVAILABLE = True import os import getpass import sys import stat import uuid import traceback import sqlite3 import urlparse import StringIO from collections import OrderedDict from optparse import OptionParser, OptionGroup from sqlalchemy.orm import sessionmaker from sqlalchemy import create_engine import sqlalchemy import weblab.configuration_doc as configuration_doc from weblab.util import data_filename import weblab.db.model as model from weblab.db.upgrade import DbSchedulingUpgrader import weblab.admin.deploy as deploy from weblab.admin.script.httpd_config_generate import httpd_config_generate from .utils import ordered_dump import voodoo.sessions.db_lock_data as DbLockData import voodoo.sessions.sqlalchemy_data as SessionSqlalchemyData from voodoo.dbutil import generate_getconn ######################################################################################### # # # # W E B L A B D I R E C T O R Y C R E A T I O N # # # class OptionWrapper(object): """ OptionWrapper is a wrapper of an OptionParser options object, which makes it possible to refer to options['force'] instead of options.force. """ def __init__(self, options): self._options = options def __contains__(self, name): return hasattr(self._options, name) def __getitem__(self, name): return getattr(self._options, name) def __setitem__(self, name, value): return setattr(self._options, name, value) def __getattribute__(self, name): if name == '_options': return object.__getattribute__(self, '_options') return getattr(self._options, name) def __repr__(self): return repr(self._options) class Creation(object): """ This class wraps the options for creating a new WebLab-Deusto directory """ FORCE = 'force' QUIET = 'quiet' VERBOSE = 'verbose' SOCKET_WAIT = 'socket_wait' # General information ADD_TEST_DATA = 'add_test_data' ADD_FEDERATED = 'add_fed_user' CORES = 'cores' START_PORTS = 'start_ports' SYSTEM_IDENTIFIER = 'system_identifier' ENABLE_HTTPS = 'enable_https' BASE_URL = 'base_url' ENTITY_LINK = 'entity_link' SERVER_HOST = 'server_host' POLL_TIME = 'poll_time' INLINE_LAB_SERV = 'inline_lab_serv' NO_LAB = 'no_lab' HTTP_SERVER_PORT = 'http_server_port' LAB_COPIES = 'lab_copies' ADMIN_USER = 'admin_user' ADMIN_NAME = 'admin_name' ADMIN_PASSWORD = '<PASSWORD>' ADMIN_MAIL = 'admin_mail' LOGO_PATH = 'logo_path' # XMLRPC experiment XMLRPC_EXPERIMENT = 'xmlrpc_experiment' XMLRPC_EXPERIMENT_PORT = 'xmlrpc_experiment_port' # Dummy experiment DUMMY_NAME = 'dummy_name' DUMMY_CATEGORY_NAME = 'dummy_category_name' DUMMY_COPIES = 'dummy_copies' DUMMY_SILENT = 'dummy_silent' # Visir VISIR_SERVER = 'visir_server' VISIR_SLOTS = 'visir_slots' VISIR_EXPERIMENT_NAME = 'visir_experiment_name' VISIR_BASE_URL = 'visir_base_url' VISIR_MEASUREMENT_SERVER = 'visir_measurement_server' VISIR_USE_PHP = 'visir_use_php' VISIR_LOGIN = 'visir_login' VISIR_PASSWORD = '<PASSWORD>' # Logic experiment LOGIC_SERVER = 'logic_server' # Virtual Machine experiment VM_SERVER = 'vm_server' VM_EXPERIMENT_NAME = 'vm_experiment_name' VM_STORAGE_DIR = 'vm_storage_dir' VBOX_VM_NAME = 'vbox_vm_name' VBOX_BASE_SNAPSHOT = 'vbox_base_snapshot' VM_URL = 'vm_url' HTTP_QUERY_USER_MANAGER_URL = 'http_query_user_manager_url' VM_ESTIMATED_LOAD_TIME = 'vm_estimated_load_time' # Federated laboratories ADD_FEDERATED_LOGIC = 'federated_logic' ADD_FEDERATED_ROBOT = 'federated_robot' ADD_FEDERATED_VISIR = 'federated_visir' ADD_FEDERATED_SUBMARINE = 'federated_submarine' # Sessions SESSION_STORAGE = 'session_storage' SESSION_DB_ENGINE = 'session_db_engine' SESSION_DB_HOST = 'session_db_host' SESSION_DB_PORT = 'session_db_port' SESSION_DB_NAME = 'session_db_name' SESSION_DB_USER = 'session_db_user' SESSION_DB_PASSWD = '<PASSWORD>' SESSION_REDIS_DB = 'session_redis_db' SESSION_REDIS_HOST = 'session_redis_host' SESSION_REDIS_PORT = 'session_redis_port' # Database DB_ENGINE = 'db_engine' DB_NAME = 'db_name' DB_HOST = 'db_host' DB_PORT = 'db_port' DB_USER = 'db_user' DB_PASSWD = '<PASSWORD>' # Coordination COORD_ENGINE = 'coord_engine' COORD_DB_ENGINE = 'coord_db_engine' COORD_DB_NAME = 'coord_db_name' COORD_DB_USER = 'coord_db_user' COORD_DB_PASSWD = '<PASSWORD>' COORD_DB_HOST = 'coord_db_host' COORD_DB_PORT = 'coord_db_port' COORD_REDIS_DB = 'coord_redis_db' COORD_REDIS_PASSWD = 'coord_redis_passwd' COORD_REDIS_PORT = 'coord_redis_port' COORD_REDIS_HOST = 'coord_redis_host' # Other NOT_INTERACTIVE = 'not_interactive' MYSQL_ADMIN_USER = 'mysql_admin_username' MYSQL_ADMIN_PASSWORD = '<PASSWORD>' IGNORE_LOCATIONS = 'ignore_locations' class CreationFlags(object): HTTP_SERVER_PORT = '--http-server-port' COORDINATION_ENGINES = ['sql', 'redis' ] DATABASE_ENGINES = ['mysql', 'sqlite' ] SESSION_ENGINES = ['sql', 'redis', 'memory'] def load_template(name, stdout = sys.stdout, stderr = sys.stderr): """ Reads the specified template file. Only the name needs to be specified. The file should be located in the config_templates folder. """ path = "weblab" + os.sep + "admin" + os.sep + "config_templates" + os.sep + name try: f = file(path, "r") template = f.read() f.close() except: print("Error: Could not load template file %s. Probably couldn't be found." % path, file=stderr) return template def _test_redis(what, verbose, redis_port, redis_passwd, redis_db, redis_host, stdout, stderr, exit_func): if verbose: print("Checking redis connection for %s..." % what, end="", file=stdout); stdout.flush() kwargs = {} if redis_port is not None: kwargs['port'] = redis_port if redis_passwd is not None: kwargs['password'] = <PASSWORD>_passwd if redis_db is not None: kwargs['db'] = redis_db if redis_host is not None: kwargs['host'] = redis_host try: import redis except ImportError: print("redis selected for %s; but redis module is not available. Try installing it with 'pip install redis'" % what, file=stderr) exit_func(-1) else: try: client = redis.Redis(**kwargs) client.get("this.should.not.exist") except: print("redis selected for %s; but could not use the provided configuration" % what, file=stderr) traceback.print_exc(file=stderr) exit_func(-1) else: if verbose: print("[done]", file=stdout) def uncomment_json(lines): new_lines = [] for line in lines: if '//' in line: if '"' in line or "'" in line: single_quote_open = False double_quote_open = False previous_slash = False counter = 0 comment_found = False last_c = '' for c in line: if c == '/': if previous_slash and not single_quote_open and not double_quote_open: comment_found = True break # counter is the previous one previous_slash = True else: previous_slash = False if c == '"' and last_c != '\\': double_quote_open = not double_quote_open if c == "'" and last_c != '\\': single_quote_open = not single_quote_open last_c = c counter += 1 if comment_found: new_lines.append(line[:counter - 1] + '\n') else: new_lines.append(line) else: new_lines.append(line.split('//')[0]) else: new_lines.append(line) return new_lines DB_ROOT = None DB_PASSWORD = None def _check_database_connection(what, metadata, upgrader_class, directory, verbose, db_engine, db_host, db_port, db_name, db_user, db_passwd, options, stdout, stderr, exit_func): if verbose: print("Checking database connection for %s..." % what, end="", file=stdout); stdout.flush() if db_engine == 'sqlite': base_location = os.path.join(os.path.abspath(directory), 'db', '%s.db' % db_name) if sys.platform.startswith('win'): sqlite_location = base_location location = '/' + base_location else: sqlite_location = '/' + base_location location = '/' + base_location sqlite3.connect(database = sqlite_location).close() else: if db_port is not None: port_str = ':%s' % db_port else: port_str = '' if db_engine == 'mysql': try: import MySQLdb assert MySQLdb is not None # Avoid warnings except ImportError: try: import pymysql_sa except ImportError: pass else: pymysql_sa.make_default_mysql_dialect() location = "%(user)s:%(password)s@%(host)s%(port)s/%(name)s" % { 'user' : db_user, 'password' : <PASSWORD>, 'host' : db_host, 'name' : db_name, 'port' : port_str, } db_str = "%(engine)s://%(location)s" % { 'engine' : db_engine, 'location' : location, } getconn = generate_getconn(db_engine, db_user, db_passwd, db_host, db_port, db_name, dirname = directory) pool = sqlalchemy.pool.QueuePool(getconn) try: engine = create_engine(db_str, echo = False, pool = pool) engine.execute("select 1") except Exception as e: print("error: database used for %s is misconfigured" % what, file=stderr) print("error: %s" % str(e), file=stderr) if verbose: traceback.print_exc(file=stderr) else: print("error: Use -v to get more detailed information", file=stderr) try: create_database = deploy.generate_create_database(db_engine) except Exception as e: print("error: You must create the database and the db credentials", file=stderr) print("error: reason: there was an error trying to offer you the creation of users:", str(e), file=stderr) exit_func(-1) else: if create_database is None: print("error: You must create the database and the db credentials", file=stderr) print("error: reason: weblab does not support creating a database with engine %s" % db_engine, file=stderr) exit_func(-1) else: if options[Creation.NOT_INTERACTIVE]: should_create = True else: should_create = raw_input('Would you like to create it now? (y/N) ').lower().startswith('y') if not should_create: print("not creating", file=stderr) exit_func(-1) if db_engine == 'sqlite': create_database(admin_username = None, admin_password = None, database_name = db_name, new_user = None, new_password = <PASSWORD>, db_dir = os.path.join(directory, 'db')) elif db_engine == 'mysql': if Creation.MYSQL_ADMIN_USER in options and Creation.MYSQL_ADMIN_PASSWORD in options: admin_username = options[Creation.MYSQL_ADMIN_USER] admin_password = options[Creation.MYSQL_ADMIN_PASSWORD] else: if options[Creation.NOT_INTERACTIVE]: exit_func(-5) global DB_ROOT, DB_PASSWORD if DB_ROOT is None or DB_PASSWORD is None: admin_username = raw_input("Enter the MySQL administrator username [default: root]: ") or 'root' admin_password = <PASSWORD>("Enter the MySQL administrator password: ".encode('utf8')) else: admin_username = DB_ROOT admin_password = <PASSWORD> try: create_database("Did you type your password incorrectly?", admin_username, admin_password, db_name, db_user, db_passwd, db_host, db_port) except Exception as e: print("error: could not create database. reason:", str(e), file=stderr) exit_func(-1) else: DB_ROOT = admin_username DB_PASSWORD = <PASSWORD> else: print("error: You must create the database and the db credentials", file=stderr) print("error: reason: weblab does not support gathering information to create a database with engine %s" % db_engine, file=stderr) exit_func(-1) engine = create_engine(db_str, echo = False, pool = pool) if verbose: print("[done]", file=stdout) if verbose: print("Adding information to the %s database..." % what, end="", file=stdout); stdout.flush() metadata.drop_all(engine) metadata.create_all(engine) if upgrader_class is not None: if 'alembic_version'
<filename>frb/dlas.py """ Module for assessing impact of intervening galaxies (DLAs) on FRB measurements Based on calclations presented in Prochaska & Neeleman 2017 """ from __future__ import print_function, absolute_import, division, unicode_literals import numpy as np import pdb from scipy.interpolate import interp1d from astropy import units as u from frb.io import load_dla_fits from frb.turb_scattering import Turbulence def approx_avgDM(zeval, dla_model='atan', verbose=False): """ Calculate the average DM from intervening galaxies This method is approximate (and fast) and accurate to better than 1% in-so-far as the analysis is correct. From Prochaska & Neeleman 2017 Parameters ---------- zeval : float or ndarray Redshift(s) for evaluation dla_model : str, optional Returns ------- avgDM : Quantity (depending on type of input z) Units of pc/cm*3 """ # Init mlz = _model_lz(dla_model) if isinstance(zeval, float): flg_float = True zeval = np.array([zeval]) else: flg_float = False # Error on upper bound if np.max(zeval) > 5.: raise IOError("Calculation is only valid to z=5") # Calculate zcalc = np.linspace(0., 5., 10000) dz = np.median(zcalc-np.roll(zcalc,1)) # Load DLA fits model dla_fits = load_dla_fits() # Evaluate l(z) lz = mlz['eval'](zcalc) # Average NHI avgNHI = _avgN_dbl_pow(dla_fits=dla_fits) # Take ne/nH nenH_p = dla_fits['nenH']['loglog'] nenH = nenH_p['bp'] + nenH_p['m'] (avgNHI - 20.3) # Integrate lz for n(z) cumul = np.cumsum(lz * dz) # Average <z> avgz = np.cumsum(zcalc * lz * dz) / cumul ''' # <DM> for a single DLA (rest-frame) DM_DLA = 10. (avgNHI + nenH) / u.cm 2 if verbose: print("DM for an average DLA = {} (rest-frame)".format(DM_DLA.to('pc/cm3'))) ''' # Altogether now avgDM_values = 10 avgNHI * 10 ** nenH * cumul / (1 + avgz) #/ u.cm 2 # Finish up DM_values = np.zeros_like(zeval) for kk,iz in enumerate(zeval): iminz = np.argmin(np.abs(iz - zcalc)) DM_values[kk] = avgDM_values[iminz] # Return return (DM_values / u.cm2).to('pc/cm3') def monte_DM(zeval, model='atan', nrand=100, verbose=False): """ Parameters ---------- zeval : float or ndarray Array of redshifts for evaluation model nrand : int, optional Number of samples on NHI verbose : bool, optional Returns ------- rand_DM : ndarray Random DM values Reported in pc/cm3 (unitless array) """ # Convert to array if isinstance(zeval, float): zeval = np.array([zeval]) # Init dla_fits = load_dla_fits() nenH_param = dla_fits['nenH']['loglog'] mlz = _model_lz(model) lgNmax = np.linspace(20.3, 22., 10000) intfN = _int_dbl_pow(dla_fits['fN']['dpow'], lgNmax=lgNmax) # Interpolate (cubic is very slow) interp_fN = interp1d(intfN/intfN[-1], lgNmax) # l(z) model # Evaluate l(z) in small z intervals zmax = np.max(zeval) z = np.linspace(0., zmax, 50000) dz = np.median(z-np.roll(z,1)) lz = mlz['eval'](z, param=mlz['param']) # Setup for n(z) and drawing zdla nzc = np.cumsum(lzdz) # Note nzc[0] is not 0 avgz = np.cumsum(zlzdz) / nzc interp_avgz = interp1d(z, avgz) nzc[0] = 0. interp_nz = interp1d(z, nzc) interp_revnz = interp1d((nzc-nzc[0])/nzc[-1], z) # Accurate to ~1% # rand_DM = np.zeros((nrand, zeval.size)) nz = interp_nz(zeval) for kk,inz in enumerate(nz): # Random number of DLAs rn = np.random.poisson(inz, size=nrand) ndla = np.sum(rn) if ndla == 0: continue # Draw NHI rval = np.random.uniform(size=ndla) rNHI = interp_fN(rval) # nenH nenH = nenH_param['bp'] + nenH_param['m'] (rNHI-20.3) # Draw zdla rval2 = np.random.uniform(size=ndla) zdla = interp_revnz(rval2inz/nzc[-1]) # DM values DMi = 10.(rNHI + nenH) / (1+zdla) # Generate a dummy array DMarr = np.zeros((nrand, max(rn))) cnt = 0 for jj in range(nrand): # Fill if rn[jj] > 0: DMarr[jj,:rn[jj]] = DMi[cnt:cnt+rn[jj]] cnt += rn[jj] # Fill em up rand_DM[:,kk] = np.sum(DMarr,axis=1) # Return unit_conv = (1/u.cm2).to('pc/cm3').value return rand_DM unit_conv def monte_tau(zeval, nrand=100, nHI=0.1, avg_ne=-2.6, sigma_ne=0.5, cosmo=None, lobs=50u.cm, turb=None): """ Generate random draws of tau at a series of redshifts Parameters ---------- zeval : ndarray Array of redshifts for evaluation nrand : int, optional Number of samples on NHI avg_ne : float, optional Average log10 electron density / cm3 sigma_ne : float, optional Error in log10 ne nHI : float, optional Fiducial value for n_HI; used for DL value lobs : Quantity Wavelength for analysis turb : Turbulence object, optional Usually defined internally and that is the highly recommended approach cosmo : astropy.cosmology, optional Defaults to Planck15 Returns ------- rand_tau : ndarray (nrand, nz) Random tau values reported in ms (but without explicit astropy Units) """ # Init ne_param = dict(value=avg_ne, sigma=sigma_ne) # Neeleman+15 dla_fits = load_dla_fits() if cosmo is None: from astropy.cosmology import Planck15 as cosmo # Setup NHI lgNmax = np.linspace(20.3, 22., 10000) intfN = _int_dbl_pow(dla_fits['fN']['dpow'], lgNmax=lgNmax) # Spline interp_fN = interp1d(intfN/intfN[-1], lgNmax)#, kind='cubic') # Setup z zvals = np.linspace(0., 7., 10000) nz_s = _dla_nz(zvals) nz_s[0] = 0. # Turbulence if turb is None: turb = _init_dla_turb() f_ne=turb.ne zsource = 2. turb.set_rdiff(lobs) fiducial_tau = turb.temporal_smearing(lobs, zsource) # Take out the cosmology f_D_S = cosmo.angular_diameter_distance(zsource) f_D_L = cosmo.angular_diameter_distance(turb.zL) f_D_LS = cosmo.angular_diameter_distance_z1z2(turb.zL, zsource) fiducial_tau = fiducial_tau / f_D_LS / f_D_L f_D_S * (1+turb.zL)*3 # ms/Mpc kpc_cm = (1u.kpc).to('cm').value rand_tau = np.zeros((nrand, zeval.size)) # Loop on zeval for ss,izeval in enumerate(zeval): avg_nz = _dla_nz(izeval) rn = np.random.poisson(avg_nz, size=nrand) ndla = np.sum(rn) if ndla == 0: continue # Get random NHI rval = np.random.uniform(size=ndla) rNHI = interp_fN(rval) DL = 10.rNHI / nHI / kpc_cm # Get random z imin = np.argmin(np.abs(zvals-izeval)) interp_z = interp1d(nz_s[0:imin]/nz_s[imin-1], zvals[0:imin])#, kind='cubic') rval = np.random.uniform(size=ndla) rz = interp_z(rval) # Cosmology D_S = cosmo.angular_diameter_distance(izeval) D_L = cosmo.angular_diameter_distance(rz) D_LS = cosmo.angular_diameter_distance_z1z2(rz, izeval) # Get random n_e rne = 10.(ne_param['value'] + ne_param['sigma']np.random.normal(size=ndla)) # Calculate (scale) rtau = fiducial_tau (D_LS * D_L / D_S) * (rne/f_ne.to('cm-3').value)2 / (1+rz)3 # Generate, fill taus = np.zeros((nrand, np.max(rn))) kk = 0 for jj,irn in enumerate(rn): if irn > 0: taus[jj,0:irn] = rtau[kk:kk+irn] kk += irn # Finish -- add in quadrature final_tau = np.sqrt(np.sum(taus2, axis=1)) # Save rand_tau[:,ss] = final_tau # Return return rand_tau def _avgN_dbl_pow(lgNmin=20.3, dla_fits=None): """ Calculate <NHI> for the double power-law Parameters ---------- lgNmin : float, optional Returns ------- avglgN : float log10 <NHI> """ if dla_fits is None: dla_fits = load_dla_fits() # Parameters param = dla_fits['fN']['dpow'] # Calculate fterm = 1/(param['a3']+2) - 1./(param['a4']+2) sterm = (10(lgNmin-param['Nd']))(param['a3']+2) / (param['a3']+2) # Numerator num = (10param['Nd'])2 (fterm-sterm) # Denom denom = _int_dbl_pow(param, lgNmin=lgNmin) return np.log10(num/denom) def _atan_lz(zval, param=None): """ arctan l(z) model Parameters ---------- zval : float or ndarray Returns ------- atan_lz : float or ndarray """ if param is None: dfits = load_dla_fits() param = dfits['lz']['atan'] lz = param['A'] + param['B'] np.arctan(zval-param['C']) return lz def _dla_nz(zarr, mlz=None, model='atan'): """ Calculate the number of DLAs intersected on average to a given redshift Parameters ---------- zarr : ndarray mlz : model, optional model : str, optional Returns ------- nz : ndarray """ # Load model if mlz is None: mlz = _model_lz(model) z = np.linspace(0., 10., 10000) dz = np.median(z-np.roll(z,1)) lz = mlz['eval'](z, param=mlz['param']) # Sum nz = np.cumsum(lzdz) # Interpolate onto input redshifts interp_nz = interp1d(z, nz) # Return return interp_nz(zarr) def _init_dla_turb(ne=4e-3/u.cm3, zL=1.): """ Initialize a Turbulence object for a fiducial DLA Parameters ---------- ne : Quantity Electron density Default is based on Neeleman+15 zL : float Redshift of the DLA Returns ------- turb : Turbulence object """ # Sizes l0 = 1 u.AU L0 = 0.001 * u.pc DL = 1 * u.kpc # Init turb = Turbulence(ne, l0, L0, zL) turb.set_SM_obj(DL) # Return return turb def _int_dbl_pow(param, lgNmin=20.3, lgNmax=None): """ Integrate the double power-law for f(N) For normalizing with l(z) and for doing random draws Parameters ---------- lgNmin : float, optional lgNmax : ndarray, optional If None, integrate to Infinity Returns ------- val : float or ndarray Integral of f(N) dN [modulo the j(z) term] Really just the integral of h(N) dN """ # Calculate if lgNmax is None: # Integrate to Infinity fterm = 1/(param['a3']+1) - 1./(param['a4']+1) else: # Indefinite integral fterm = np.zeros_like(lgNmax) high = lgNmax > param['Nd'] fterm[high] = 1/(param['a3']+1) - 1./(param['a4']+1) fterm[high] += (10(lgNmax[high]-param['Nd']))(param['a4']+1) / (param['a4']+1) fterm[~high] = (10(lgNmax[~high]-param['Nd']))(param['a3']+1) / (param['a3']+1) # Nmin term sterm = (10(lgNmin-param['Nd']))(param['a3']+1) / (param['a3']+1) # Finish val = 10*param['Nd'] (fterm-sterm) return val def _model_lz(name): """ Return the model for l(z) Enables multiple ways to model the DLA observations Returns
<==> x<=y """ pass def __len__(args, kwargs): """ x.__len__() <==> len(x) """ pass def __lt__(args, *kwargs): """ x.__lt__(y) <==> x<y """ pass def __mul__(args, *kwargs): """ x.__mul__(y) <==> xy """ pass def __ne__(args, kwargs): """ x.__ne__(y) <==> x!=y """ pass def __radd__(args, *kwargs): """ x.__radd__(y) <==> y+x """ pass def __rdiv__(args, *kwargs): """ x.__rdiv__(y) <==> y/x """ pass def __repr__(args, *kwargs): """ x.__repr__() <==> repr(x) """ pass def __rmul__(args, *kwargs): """ x.__rmul__(y) <==> yx """ pass def __setitem__(args, kwargs): """ x.__setitem__(i, y) <==> x[i]=y """ pass def __str__(args, *kwargs): """ x.__str__() <==> str(x) """ pass def getColor(args, *kwargs): """ Returns a list containing the color's components, in the specified color model. """ pass def setColor(args, *kwargs): """ Sets the color's components and color model. """ pass a = None b = None g = None r = None __new__ = None kByte = 1 kCMY = 2 kCMYK = 3 kFloat = 0 kHSV = 1 kOpaqueBlack = None kRGB = 0 kShort = 2 class MSelectionList(object): """ A heterogenous list of MObjects, MPlugs and MDagPaths. __init__() Initializes a new, empty MSelectionList object. __init__(MSelectionList other) Initializes a new MSelectionList object containing the same items as another list. """ def __init__(args, *kwargs): """ x.__init__(...) initializes x; see help(type(x)) for signature """ pass def __str__(args, *kwargs): """ x.__str__() <==> str(x) """ pass def add(args, *kwargs): """ add(pattern, searchChildNamespaces=False) -> self add(item, mergeWithExisting=True) -> self The first version adds to the list any nodes, DAG paths, components or plugs which match the given the pattern string. The second version adds the specific item to the list, where the item can be a plug (MPlug), a node (MObject), a DAG path (MDagPath) or a component (tuple of (MDagPath, MObject) ). """ pass def clear(args, *kwargs): """ clear() -> self Empties the selection list. """ pass def copy(args, *kwargs): """ copy(src) -> self Replaces the contents of the selection list with a copy of those from src (MSelectionList). """ pass def getComponent(args, *kwargs): """ getComponent(index) -> (MDagPath, MObject) Returns the index'th item of the list as a component, represented by a tuple containing an MDagPath and an MObject. If the item is just a DAG path without a component then MObject.kNullObj will be returned in the second element of the tuple. Raises TypeError if the item is neither a DAG path nor a component. Raises IndexError if index is out of range. """ pass def getDagPath(args, *kwargs): """ getDagPath(index) -> MDagPath Returns the DAG path associated with the index'th item of the list. Raises TypeError if the item is neither a DAG path nor a component. Raises IndexError if index is out of range. """ pass def getDependNode(args, *kwargs): """ getDependNode(index) -> MObject Returns the node associated with the index'th item, whether it be a dependency node, DAG path, component or plug. Raises IndexError if index is out of range. """ pass def getPlug(args, *kwargs): """ getPlug(index) -> MPlug Returns the index'th item of the list as a plug. Raises TypeError if the item is not a plug. Raises IndexError if index is out of range. """ pass def getSelectionStrings(args, *kwargs): """ getSelectionStrings(index=None) -> (string, string, ...) Returns a tuple containing the string representation of the specified item. For nodes, DAG paths, plugs and contiguous components the tuple will only contain a single string, but for non- contiguous components there will be a separate string for each distinct block of contiguous elements. If index is not specified then the string representations of all the items in the selection list are returned. Raises IndexError if index is out of bounds. """ pass def hasItem(args, *kwargs): """ hasItem(item) -> bool Returns True if the given item is on the selection list. For a component this means that all of the elements of the component must be on the list. A component is passed as a tuple containing the MDagPath of the DAG node and an MObject containing the component. """ pass def hasItemPartly(args, *kwargs): """ hasItemPartly(dagPath, component) -> bool Returns True if at least one of the component's elements is on the selection list. Raises TypeError if dagPath is invalid or component does not contain a component. """ pass def isEmpty(args, *kwargs): """ isEmpty() -> bool Returns True if the selection list is empty. """ pass def length(args, *kwargs): """ length() -> int Returns the number of items on the selection list. """ pass def merge(args, *kwargs): """ merge(other, strategy=kMergeNormal) -> self merge(dagPath, component, strategy=kMergeNormal) -> self The first version merges the items from another selection list in with those already on the list, using the given strategy. The second version merges the specified component with those already on the list. """ pass def remove(args, *kwargs): """ remove(index) -> self Removes the index'th item from the list. Raises IndexError if the index is out of range. """ pass def replace(args, *kwargs): """ replace(index, newItem) -> self Replaces the index'th item on the list with a new item. A component is passed as a tuple containing the MDagPath of the DAG node and an MObject containing the component. Raises IndexError if the index is out of range. """ pass def toggle(args, **kwargs): """ toggle(dagPath, component) -> self Removes from the list those elements of the given component which are already on it and adds those which are not. """ pass __new__ = None kMergeNormal = 0 kRemoveFromList = 2 kXORWithList = 1 class MFn(object): """ Static class providing constants for all API types. """ kAISEnvFacade = 961 kAddDoubleLinear = 5 kAdskMaterial = 1049 kAffect = 6 kAimConstraint = 111 kAir = 257 kAlignCurve = 41 kAlignManip = 897 kAlignSurface = 42 kAmbientLight = 303 kAngle = 270 kAngleBetween = 21 kAnimBlend = 781 kAnimBlendInOut = 782 kAnimCurve = 7 kAnimCurveTimeToAngular = 8 kAnimCurveTimeToDistance = 9 kAnimCurveTimeToTime = 10 kAnimCurveTimeToUnitless = 11 kAnimCurveUnitlessToAngular = 12 kAnimCurveUnitlessToDistance = 13 kAnimCurveUnitlessToTime = 14 kAnimCurveUnitlessToUnitless = 15 kAnimLayer = 1002 kAnisotropy = 609 kAnnotation = 271 kAnyGeometryVarGroup = 115 kArcLength = 273 kAreaLight = 305 kArrayMapper = 517 kArrowManip = 123 kAssembly = 1063 kAsset = 1000 kAttachCurve = 43 kAttachSurface = 44 kAttribute = 554 kAttribute2Double = 734 kAttribute2Float = 735 kAttribute2Int = 737 kAttribute2Long = 737 kAttribute2Short = 736 kAttribute3Double = 738 kAttribute3Float = 739 kAttribute3Int = 741 kAttribute3Long = 741 kAttribute3Short = 740
<gh_stars>1-10 import os import matplotlib as mpl if os.environ.get('DISPLAY','') == '': print('no display found. Using non-interactive Agg backend') mpl.use('Agg') import sys import json import re import matplotlib.pyplot as plt sys.path.insert(0, './include') from plot_utils import * from common import * from utils import * from data_utils import * tick = 1 # mins ## plot utilization: number of busy nodes. cap = 64 mem_cap = 128 data_range=[10, 1000000] target_qos = 0.99 cpuStr = 'cpu' memStr = 'memory' show=False plotObj = True plotOverload = False plotOverbook = False plotQoS=True plotPredictionPenalty=True plotUtilization=True loads = [plotUtilization, False, plotOverload, plotOverbook, plotQoS, plotPredictionPenalty] showBarValue = True path = "./log" arg_len = len(sys.argv) - 1 if arg_len > 0: path=sys.argv[1] # path = "./" line_num = -1 # 6024 def loadLog(filepath) : cpuUsages = [] maxCpuUsages = [] cpuRequests = [] memRequests = [] totalCpuAllocations = [] totalMemAllocations = [] memUsages = [] gpuUsages = [] cpuAllocatables = [] memAllocatables = [] requests = [] busyNodes = [] overloadNodes = [] overBookNodes = [] QoS = [] NumSatifiesPods = [] NumPods = [] PredPenalty = [] cpuUsageStd = [] memUsageStd = [] with open(filepath) as fp: line = fp.readline() # content = fp.readlines() i = 0 while line: # for line in content:ot busyNode = 0 overloadNode = 0 overBookNode = 0 totalCpuUsage = 0 totalMemUsage = 0 totalCpuAllocation = 0 totalMemAllocation = 0 totalCpuCapacity = 0 totalMemCapacity = 0 maxCpuUsage = 0 totalCpuRequest = 0 totalMemRequest = 0 maxMemUsage = 0 try: data = json.loads(line) except: print("An json.loads(line) exception occurred") continue nodeDict = data['Nodes'] cUsages = [] mUsages =[] for nodeName, node in nodeDict.items(): cpuUsage = 0 memUsage = 0 cpuAllocatable = 0 memAllocatable = 0 cpuRequest = 0 memRequest = 0 runningPodsNum = int(node['RunningPodsNum']) usageDict = node['TotalResourceUsage'] for rsName in usageDict: if(rsName==cpuStr): cpuUsage = formatCpuQuatity(usageDict[rsName]) cUsages.append(cpuUsage/cap) totalCpuUsage = totalCpuUsage+ cpuUsage if cpuUsage > maxCpuUsage: maxCpuUsage = cpuUsage elif(rsName==memStr): memUsage = formatMemQuatity(usageDict[rsName]) mUsages.append(memUsage/mem_cap) totalMemUsage = totalMemUsage+ memUsage if memUsage > maxMemUsage: maxMemUsage = memUsage allocatableDict = node['Allocatable'] for rsName in allocatableDict: if(rsName==cpuStr): cpuAllocatable = formatCpuQuatity(allocatableDict[rsName]) totalCpuCapacity = totalCpuCapacity + cpuAllocatable elif(rsName==memStr): memAllocatable = formatMemQuatity(allocatableDict[rsName]) totalMemCapacity = totalMemCapacity + memAllocatable requestDict = node['TotalResourceRequest'] for rsName in requestDict: if(rsName==cpuStr): cpuRequest = formatCpuQuatity(requestDict[rsName]) totalCpuRequest = totalCpuRequest + cpuRequest elif(rsName==memStr): memRequest = formatMemQuatity(requestDict[rsName]) totalMemRequest = totalMemRequest + memRequest allocationDict = node['TotalResourceAllocation'] for rsName in allocationDict: if(rsName==cpuStr): cpuAllocation = formatCpuQuatity(allocationDict[rsName]) totalCpuAllocation = totalCpuAllocation + cpuAllocation elif(rsName==memStr): memAllocation = formatMemQuatity(allocationDict[rsName]) totalMemAllocation = totalMemAllocation + memAllocation if(cpuUsage > cpuAllocatable or memUsage > memAllocatable): overloadNode = overloadNode+1 if(cpuRequest > cpuAllocatable or memRequest > memAllocatable): overBookNode = overBookNode +1 if(runningPodsNum > 0): busyNode = busyNode + 1 if (loads[0]): cpuUsages.append(totalCpuUsage) memUsages.append(totalMemUsage) cpuAllocatables.append(totalCpuCapacity) memAllocatables.append(totalMemCapacity) cpuRequests.append(totalCpuRequest) memRequests.append(totalMemRequest) maxCpuUsages.append(maxCpuUsage) totalCpuAllocations.append(totalCpuAllocation) totalMemAllocations.append(totalMemAllocation) memUsageStd.append(numpy.std(mUsages)) cpuUsageStd.append(numpy.std(cUsages)) if (loads[1]): busyNodes.append(busyNode) if (loads[2]): overloadNodes.append(overloadNode) if (loads[3]): overBookNodes.append(overBookNode) # Queue":{"PendingPodsNum":1,"QualityOfService":1,"PredictionPenalty":2.97} queue = data['Queue'] if (loads[4]): QoS.append(float(queue['QualityOfService'])) NumSatifiesPods.append(float(queue['NumSatifisedPods'])) NumPods.append(float(queue['NumPods'])) if (loads[5]): PredPenalty.append(float(queue['PredictionPenalty'])) i=i+1 if line_num > 0 and i >= line_num: break line = fp.readline() fp.close() return busyNodes, overloadNodes, overBookNodes, cpuUsages, memUsages, cpuRequests, \ memRequests, totalCpuAllocations, totalMemAllocations, maxCpuUsages, cpuAllocatables, memAllocatables, \ QoS, NumSatifiesPods, NumPods, PredPenalty, cpuUsageStd, memUsageStd def formatCpuQuatity(str): strArray = re.split('(\d+)', str) val = float(strArray[1]) scaleStr = strArray[2] if(scaleStr == "m"): val = val/1000 elif (scaleStr == "Mi"): val = val/1024 elif (scaleStr == ""): val = val else: print("error @ formatMemQuatity "+str) return val def formatMemQuatity(str): strArray = re.split('(\d+)', str) val = float(strArray[1]) scaleStr = strArray[2] if(scaleStr == "m"): val = val/(10001000) elif (scaleStr == "Mi"): val = val/(1024) elif (scaleStr == "Ki"): val = val/(10241024) elif (scaleStr == "Gi"): va = val elif (scaleStr == ""): # byte val = val/(102410241024) else: print("error @ formatMemQuatity "+str) return val methods = ["worstfit", "oversub", "proposed_list", "proposed_largest"] methodNames = [STR_WORSTFIT, STR_OVERSUB, STR_FLEX_F, STR_FLEX_L] colors = [COLOR_WORST_FIT, COLOR_OVER_SUB, COLOR_PROPOSED_1, COLOR_PROPOSED_2] proposed_idx = 2 # methods = ["oneshot","worstfit"] methodsNum = len(methods) busyNodes = [] overloadNodes = [] overbookNodes = [] cpuUsages = [] memUsages = [] maxCpuUsages = [] cpuAllocatables = [] memAllocatables = [] cpuAllocations = [] memAllocations = [] cpuRequests = [] memRequests = [] QoSs = [] NumSatifiesPods = [] NumPods = [] PredPenalties = [] cpuUsageStd = [] memUsageStd = [] for m in methods: b, ol, ob, u_cpu, u_mem, ur_cpu, ur_mem, a_cpu, a_mem, mu, c_cpu, c_mem, q, nsp, nps, p, cStd, mStd = loadLog(path+"/kubesim_"+m+".log") busyNodes.append(b) overloadNodes.append(ol) overbookNodes.append(ob) cpuUsages.append(u_cpu) memUsages.append(u_mem) maxCpuUsages.append(mu) cpuAllocatables.append(c_cpu) memAllocatables.append(c_mem) cpuAllocations.append(a_cpu) memAllocations.append(a_mem) cpuRequests.append(ur_cpu) memRequests.append(ur_mem) QoSs.append(q) NumSatifiesPods.append(nsp) NumPods.append(nps) PredPenalties.append(p) cpuUsageStd.append(cStd) memUsageStd.append(mStd) for i in range(methodsNum): if (len(cpuRequests[i]) < data_range[1]): data_range[1] = len(cpuRequests[i]) if (len(cpuRequests[i]) < data_range[0]): data_range[0] = 0 ############# PLOTTING ############## if not os.path.exists(FIG_PATH): os.makedirs(FIG_PATH) if plotObj: # Y_MAX = cap1.5 fig = plt.figure(figsize=FIG_ONE_COL) max_len = 0 for i in range(methodsNum): plt.plot([x / 60.0 for x in range(0,len(maxCpuUsages[i])tick,tick)], maxCpuUsages[i], color=colors[i]) if max_len < len(maxCpuUsages[i]): max_len = len(maxCpuUsages[i]) plt.plot([x / 60.0 for x in range(0,max_lentick,tick)], [cap] * max_len, color=COLOR_CAP) legends = methodNames legends.append('capacity') plt.legend(legends, loc='best') plt.xlabel(STR_TIME_HOUR) plt.ylabel(STR_CPU_CORES) plt.suptitle("Max Cpu Usage") # plt.ylim(0,Y_MAX) fig.savefig(FIG_PATH+"/max_cpu_usage.pdf", bbox_inches='tight') ## plot STD of usage Y_MAX = 0.3 fig = plt.figure(figsize=FIG_ONE_COL) max_len = 0 for i in range(methodsNum): plt.plot([x / 60.0 for x in range(0,len(memUsageStd[i])tick,tick)], memUsageStd[i], color=colors[i]) plt.legend(legends, loc='best') plt.xlabel(STR_TIME_HOUR) plt.ylabel("Memory Usage std.") plt.ylim(0,Y_MAX) plt.xlim(0,24) fig.savefig(FIG_PATH+"/std_mem_usage.pdf", bbox_inches='tight') ## Y_MAX = 0 fig = plt.figure(figsize=FIG_ONE_COL) max_len = 0 for i in range(methodsNum): plt.plot([x / 60.0 for x in range(0,len(cpuUsageStd[i])tick,tick)], cpuUsageStd[i], color=colors[i]) plt.legend(legends, loc='best') plt.xlabel(STR_TIME_HOUR) plt.ylabel("CPU Usage std.") # plt.ylim(0,Y_MAX) fig.savefig(FIG_PATH+"/std_cpu_usage.pdf", bbox_inches='tight') if plotUtilization: cpuReqUtil = [] memReqUtil = [] cpuDemandUtil = [] memDemandUtil = [] cpuUsageUtil = [] memUsageUtil = [] cpuCap = np.average(cpuAllocatables[0]) memCap = np.average(memAllocatables[0]) if memCap == 0: memCap = 1.0 if cpuCap == 0: cpuCap = 1.0 Y_MAX = 300 for i in range(methodsNum): cpuR = cpuRequests[i] memR = memRequests[i] cpuD = cpuUsages[i] memD = memUsages[i] cpuU = cpuAllocations[i] memU = memAllocations[i] cpuReqUtil.append(int(round(np.average(cpuR[data_range[0]:data_range[1]])/cpuCap100, 2))) memReqUtil.append(int(round(np.average(memR[data_range[0]:data_range[1]])/memCap100, 2))) cpuDemandUtil.append(int(round(np.average(cpuD[data_range[0]:data_range[1]])/cpuCap100, 2))) memDemandUtil.append(int(round(np.average(memD[data_range[0]:data_range[1]])/memCap100, 2))) cpuUsageUtil.append(int(round(np.average(cpuU[data_range[0]:data_range[1]])/cpuCap100,2))) memUsageUtil.append(int(round(np.average(memU[data_range[0]:data_range[1]])/memCap100,2))) x = np.arange(methodsNum) width = GBAR_WIDTH/2 ## plot # request fig, ax = plt.subplots(figsize=FIG_ONE_COL) rects = ax.bar(x - width, cpuReqUtil, width, label=STR_CPU, color=COLOR_CPU) if showBarValue: autolabel(rects, ax) rects = ax.bar(x, memReqUtil, width, label=STR_MEM, color=COLOR_MEM) if showBarValue: autolabel(rects, ax) labels = methodNames ax.set_ylabel('Request (%)') ax.set_xticks(x) ax.set_xticklabels(labels) ax.legend( loc='best') plt.ylim(0,Y_MAX) fig.savefig(FIG_PATH+"/request-avg.pdf", bbox_inches='tight') # demand fig, ax = plt.subplots(figsize=FIG_ONE_COL) rects = ax.bar(x - width, cpuDemandUtil, width, label=STR_CPU, color=COLOR_CPU) if showBarValue: autolabel(rects, ax) rects = ax.bar(x, memDemandUtil, width, label=STR_MEM, color=COLOR_MEM) if showBarValue: autolabel(rects, ax) labels = methodNames ax.set_ylabel('Demand (%)') ax.set_xticks(x) ax.set_xticklabels(labels) ax.legend( loc='best') plt.ylim(0,Y_MAX) fig.savefig(FIG_PATH+"/demand-avg.pdf", bbox_inches='tight') # usage fig, ax = plt.subplots(figsize=FIG_ONE_COL) rects = ax.bar(x - width, cpuUsageUtil, width, label=STR_CPU, color=COLOR_CPU) if showBarValue: autolabel(rects, ax) rects = ax.bar(x, memUsageUtil, width, label=STR_MEM, color=COLOR_MEM) if showBarValue: autolabel(rects, ax) labels = methodNames ax.set_ylabel('Usage (%)') ax.set_xticks(x) ax.set_xticklabels(labels) ax.legend( loc='best') plt.ylim(0,Y_MAX) fig.savefig(FIG_PATH+"/usage-avg.pdf", bbox_inches='tight') if plotUtilization: # Y_MAX = np.amax(cpuRequests) fig = plt.figure(figsize=FIG_ONE_COL) for i in range(methodsNum): plt.plot([x / 60.0 for x in range(0,len(cpuRequests[i])tick,tick)], cpuRequests[i], color=colors[i]) plt.plot([x / 60.0 for x in range(0,len(cpuAllocatables[0])tick,tick)], cpuAllocatables[0], color=COLOR_CAP) legends = methodNames legends.append('capacity') plt.legend(legends, loc='best') plt.xlabel(STR_TIME_HOUR) plt.ylabel(STR_CPU_CORES) # plt.ylim(0,Y_MAX) fig.savefig(FIG_PATH+"/total-request-cpu.pdf", bbox_inches='tight') fig = plt.figure(figsize=FIG_ONE_COL) for i in range(methodsNum): plt.plot([x / 60.0 for x in range(0,len(memRequests[i])tick,tick)], memRequests[i], color=colors[i]) plt.plot([x / 60.0 for x in range(0,len(memAllocatables[0])tick,tick)], memAllocatables[0], color=COLOR_CAP) legends = methodNames legends.append('capacity') plt.legend(legends, loc='best') plt.xlabel(STR_TIME_HOUR) plt.ylabel(STR_MEM_GB) # plt.ylim(0,Y_MAX) fig.savefig(FIG_PATH+"/total-request-mem.pdf", bbox_inches='tight') # Y_MAX = np.amax(cpuRequests) fig = plt.figure(figsize=FIG_ONE_COL) for i in range(methodsNum): plt.plot([x / 60.0 for x in range(0,len(cpuUsages[i])tick,tick)], cpuUsages[i], color=colors[i]) plt.plot([x / 60.0 for x in range(0,len(cpuAllocatables[0])tick,tick)], cpuAllocatables[0], color=COLOR_CAP) legends = methodNames legends.append('capacity') plt.legend(legends, loc='best') plt.xlabel(STR_TIME_HOUR) plt.ylabel(STR_CPU_CORES) # plt.ylim(0,Y_MAX) fig.savefig(FIG_PATH+"/total-demand-cpu.pdf", bbox_inches='tight') fig = plt.figure(figsize=FIG_ONE_COL) for i in range(methodsNum): plt.plot([x / 60.0 for x in range(0,len(memUsages[i])tick,tick)], memUsages[i], color=colors[i]) plt.plot([x / 60.0 for x in range(0,len(memAllocatables[0])tick,tick)], memAllocatables[0], color=COLOR_CAP) legends = methodNames legends.append('capacity') plt.legend(legends, loc='best') plt.xlabel(STR_TIME_HOUR) plt.ylabel(STR_MEM_GB) # plt.ylim(0,Y_MAX) fig.savefig(FIG_PATH+"/total-demand-mem.pdf", bbox_inches='tight') # Y_MAX = np.amax(cpuRequests) fig = plt.figure(figsize=FIG_ONE_COL) for i in range(methodsNum): plt.plot([x / 60.0 for x in range(0,len(cpuAllocations[i])tick,tick)], cpuAllocations[i], color=colors[i]) plt.plot([x / 60.0 for x in range(0,len(cpuAllocatables[0])tick,tick)], cpuAllocatables[0], color=COLOR_CAP) legends = methodNames legends.append('capacity') plt.legend(legends, loc='best') plt.xlabel(STR_TIME_HOUR) plt.ylabel(STR_CPU_CORES) # plt.ylim(0,Y_MAX) fig.savefig(FIG_PATH+"/total-usage-cpu.pdf", bbox_inches='tight') fig = plt.figure(figsize=FIG_ONE_COL) for i in range(methodsNum): plt.plot([x / 60.0 for x in range(0,len(memAllocations[i])tick,tick)], memAllocations[i], color=colors[i]) plt.plot([x / 60.0 for x in range(0,len(memAllocatables[0])tick,tick)], memAllocatables[0], color=COLOR_CAP) legends = methodNames legends.append('capacity') plt.legend(legends, loc='best') plt.xlabel(STR_TIME_HOUR) plt.ylabel(STR_MEM_GB)
<filename>cartopy_fun.py # Having fun with Cartopy. # inspired by the work of @pythonmaps on Twitter # eg. https://twitter.com/PythonMaps/status/1391056641546768388 # <NAME>, 10th of May 2021, MIT-License import matplotlib.pyplot as plt import pandas as pd import numpy as np from numpy import genfromtxt import time import cartopy.crs as ccrs # https://www.lfd.uci.edu/~gohlke/pythonlibs/#cartopy from matplotlib.colors import ListedColormap, LinearSegmentedColormap def get_data(url,delimiter): ''' Get the data ''' df = pd.read_csv(url, delimiter=delimiter, low_memory=False) return df def save_df(df, name): """ _ _ _ """ OUTPUT_DIR = "" name_ = OUTPUT_DIR + name + ".csv" compression_opts = dict(method=None, archive_name=name_) df.to_csv(name_, index=False, compression=compression_opts) print("--- Saving " + name_ + " ---") def show_cities(): ''' Show fixed points of interest on a map ''' cities = pd.DataFrame({'City': ['Utrecht', 'Amsterdam', 'Rotterdam', 'The Hague', 'Arnhem', 'Hilversum', 'Amersfoort', 'Almere', 'Lelystad', 'Apeldoorn', 'Den Burg', 'Harlingen', 'Zwolle', 'Gorinchem'], 'Lon': [5.1214, 4.9041, 4.4777, 4.3007, 5.8987, 5.1669, 5.3878, 5.2647, 5.4714, 5.9699, 4.7997, 5.4252, 6.0830, 4.9758], 'Lat': [52.0907, 52.3676, 51.9244, 52.0705, 51.9851, 52.2292, 52.1561, 52.3508, 52.5185, 52.2112, 53.0546, 53.1746, 52.5168, 51.8372]}) plt.scatter(cities.Lon, cities.Lat, marker = 'o', color = 'red', s = 50) for i in range(cities.shape[0]): plt.text(cities.Lon[i] + 0.02, cities.Lat[i], cities.City[i], color = 'red') def show_locations(df, how, cities): ''' Show the locations given in a dataframe df = dataframe how = points or scatter. The first reads the POI's in a loop, and adds the placename (or whatever field), the second plots a scatter which is (much) faster. cities = True/False Show the fixed POI's on the map ''' df = df[df['type']=="large_airport"] fig = plt.figure() ax = plt.axes(projection=ccrs.PlateCarree()) ax.stock_img() #show a background ax.coastlines() if how == "points": # PLOTTING AS DIFFERENT POINTS - 12.8sec for i in range (0, len(df)): plt.plot( df.iloc[i]["longitude_deg"], df.iloc[i]["latitude_deg"], markersize=1, marker='o', color='red') plt.text(df.iloc[i]["longitude_deg"], df.iloc[i]["latitude_deg"] , df.iloc[i]["iata_code"], horizontalalignment='left', fontsize='smaller', transform=ccrs.PlateCarree()) else: # PLOTTING AS SCATTERPOINTS - 5.1 seconds plt.scatter( x=df["longitude_deg"], y=df["latitude_deg"], color="red", s=1, alpha=1, transform=ccrs.PlateCarree() ) if cities: show_cities() plt.title("Large airports in the world") plt.show() def show_heatmap_with_histogram2d(df, cities): ''' # turn a CSV file with points of interests into a heatmap. # np.histogram2d calculates the number of instances in a certain area # Thanks to https://medium.com/analytics-vidhya/custom-strava-heatmap-231267dcd084 ''' df = df[df['type']=="large_airport"] Z, xedges, yedges = np.histogram2d(np.array(df.longitude_deg, dtype=float), np.array(df.latitude_deg, dtype=float), bins = 100) fig = plt.figure() # I created a new figure and set up its size ax = plt.axes(projection=ccrs.PlateCarree()) #extent = [-10, 30, 35, 75] # Europe extent = [df.longitude_deg.min(),-20, 7,df.latitude_deg.max()] # North America ax.set_extent(extent, crs=ccrs.PlateCarree()) ax.coastlines(resolution='10m', color='black', linewidth=.3) heatmap = ax.pcolormesh(xedges, yedges, Z.T, vmin=0, vmax=5, cmap = 'Set1') plt.colorbar(heatmap) plt.title("Large airports in the world") if cities: show_cities() plt.show() def show_heatmap_from_values(url, delimiter, resolution, extent, field_lon, field_lat, field_value, value_type,value_process, min_colorbar_std, max_colorbar_std, colorbar_colors, colorbar_nodes, title, log_value, show_colorbar): ''' # turn a CSV file with points of interests into a heatmap. # It takes the average of values or the frequence in a certain area # and creates a pivot table which serves as base for the heatmap # inspired by https://twitter.com/PythonMaps/status/1386727574894792707 parameters : URL : URL (string) delimiter : the delimiter (string) resolution (integer/float) extent : the exent of what to slow: lon-left, lon-right, lat-up, lat-down. (list with numbers) Take care : resolution and the extent have to be congruent otherwise there might be unexpected results. Use rounded numbers. If lon-left = -180, lon-right has to be 180, otherwise there might be unexpected results too. field_lon, field_lat, field_value : the fieldnames for resp. longitude, latitude and the value. (string) value_type : "frequence" -> it adds the a field with the name field_value and fills it with the value 1.0 np.histogram2d could also be used and might be faster. or something else : doesn't matter, it only checks if the value_type is "frequence" (string) value_proces : "sum" or "mean" -> calculates the sum or the mean (string) min_colorbar_std : for the colorbar: (mean - (min_colorbar_std * std)). Put 999 to set minimum of the colorbar to 0 (integer) max : for the colorbar: (mean + (min_colorbar_std * std)) (integer) colorbar_colors : colors of the colorbar (list with strings) colorbar_nodes : the nodes of the colorbar (list with strings) title : Title of the heatmap (string) log_value : calculate the 10log of the value -> True / False (boolean) show_colorbar : show the colorbar? -> True / False (boolean) ''' t1=time.time() df = get_data(url,delimiter) df = df[df[field_lon] >= extent[0] ] df = df[df[field_lon] <= extent[1] ] df = df[df[field_lat] >= extent[2] ] df = df[df[field_lat] <= extent[3] ] if value_type == "frequence": df.loc[:,field_value] = 1 if log_value: df.loc[:,field_value]= np.log10(df.loc[:,field_value]) df.loc[:, field_lat]= round( df.loc[:,field_lat]/resolution)resolution df.loc[:, field_lon]= round( df.loc[:,field_lon]/resolution)resolution if value_process == "mean": df = df.groupby([field_lat, field_lon]).mean() else: df = df.groupby([field_lat, field_lon]).sum().reset_index() if min_colorbar_std == 999: min_value = 0 else: min_value =df[field_value].mean() - (min_colorbar_stddf[field_value].std() ) max_value =df[field_value].mean() + (max_colorbar_stddf[field_value].std() ) # Make an dataframe with a grid with NaN-values which covers the total # area and merge it with your data-dataframe row_list = [] for lon_x in np.arange (extent[0],extent[1]+resolution,resolution): for lat_x in np.arange (extent[3],(extent[2]-resolution),-1resolution): row_list.append([lat_x, lon_x, 0]) df_temp = pd.DataFrame(row_list, columns=[field_lat, field_lon,field_value]) df = pd.merge( df_temp, df, how="outer", on=[field_lat, field_lon] ) df = df.fillna(0) # I have to fill the NaN, because otherwise I cant merge the two value-fields together field_value_x = field_value + "_x" field_value_y = field_value + "_y" df[field_value]= df[field_value_y] +df[field_value_y] df = df.drop(columns=[field_value_x], axis=1) df = df.drop(columns=[field_value_y], axis=1) # I have to repeat this otherwise I get Nan Values and duplicate columns in my pivot table when resolution<1 df[field_lat]= round(df[field_lat],2) df[field_lon]= round(df[field_lon],2) df = df.groupby([field_lat, field_lon]).sum() df = df.sort_values(by=[field_lat,field_lon]).reset_index() df = df.pivot_table(index=field_lat, columns=field_lon, values=field_value, aggfunc = np.sum) df = df.sort_values(by=field_lat, ascending=False) df = df.replace(0.0, np.nan) # i don't want a color for 0 or NaN values xedges = df.columns.tolist() yedges = df.index.tolist() values__ = df.to_numpy() fig = plt.figure() ax = plt.axes(projection=ccrs.PlateCarree()) ax.coastlines(resolution='10m', color='black', linewidth=.3) colorbar_cmap = LinearSegmentedColormap.from_list("mycmap", list(zip(colorbar_nodes, colorbar_colors))) heatmap = ax.pcolormesh(xedges, yedges, values__, vmin=min_value, vmax=max_value, cmap = colorbar_cmap) if show_colorbar: plt.colorbar(heatmap, orientation = 'horizontal') plt.title(title) t2=time.time() print (f"Done in {round(t2-t1,2)} seconds with {title}") plt.show() def show_heatmap_from_array(): ''' Show a heatmap, generated from an array # thanks to https://stackoverflow.com/a/44952031/4173718 ''' extent = [-180, 180, -90, 90] # CH4 IN THE AIR # heat_data = genfromtxt('https://raw.githubusercontent.com/rcsmit/cartopy_fun/main/MOD_LSTD_M_2021-04-01_rgb_360x180.CSV', delimiter=',') # heat_data = genfromtxt('C:\\Users\\rcxsm\\Documents\\phyton_scripts\\MOD_LSTD_M_2021-04-01_rgb_1440x720.CSV', delimiter=',') # source : https://neo.sci.gsfc.nasa.gov/view.php?datasetId=MOD_LSTD_M - april 2021, csv, 1.0 degree # heat_data[heat_data == 99999.0] = np.nan # filter out the seas and ocean # POPULATION COUNT #heat_data = genfromtxt('C:\\Users\\rcxsm\\Documents\\phyton_scripts\\gpw_v4_population_count_rev11_2020_1_deg.csv', delimiter=',') #heat_data = genfromtxt('C:\\Users\\rcxsm\\Documents\\phyton_scripts\\gpw_v4_population_count_rev11_2020_15_min.asc', delimiter=' ') heat_data = genfromtxt('gpw_v4_population_count_rev11_2020_2pt5_min.asc', delimiter=' ') # https://sedac.ciesin.columbia.edu/data/set/gpw-v4-population-count-rev11/data-download # 30 Second (approx. 1km) # 2.5 Minute (approx. 5km) # 15 Minute (approx. 30km) # 30 Minute (approx. 55km) # 60 Minute/1 Degree (approx. 110km) heat_data[heat_data == -9999] = np.nan # filter out the seas and ocean heat_data = np.flip(heat_data) #no idea why I have to flip the data twice heat_data = np.flip(heat_data,1) #heat_data = np.log10(heat_data) print (heat_data) #std = np.matrix.std(heat_data) #print (f"{mean} {std}") mean = np.nanmean(heat_data) std = np.nanstd(heat_data) print (mean) fig, ax = plt.subplots() ax = plt.axes(projection=ccrs.PlateCarree()) #ax.stock_img() ax.coastlines(resolution='10m', color='black', linewidth=.3, alpha=.5) lon = np.linspace(extent[0],extent[1],heat_data.shape[1]) lat = np.linspace(extent[2],extent[3],heat_data.shape[0]) Lon, Lat = np.meshgrid(lon,lat) neo_colors = [ "white", "cyan","blue", "purple", "magenta", "red", "orange", "yellow", "lightyellow"] nodes = [0.0, 0.125, 0.25, 0.375, 0.5, 0.625, 0.75, 0.825, 1.0] neo_cmap = LinearSegmentedColormap.from_list("mycmap", list(zip(nodes, neo_colors))) #heatmap = ax.pcolormesh(Lon,Lat,(heat_data) ,vmin=-25, vmax=50, cmap = neo_cmap) heatmap = ax.pcolormesh(Lon,Lat,(heat_data), vmin=0, vmax=(mean+(1std)), cmap = 'YlOrBr') plt.colorbar(heatmap) plt.title("LAND SURFACE TEMPERATURE [DAY] \nHeatmap based on a np.array from a CSV file") plt.show() def rasterio_and_geotiff(): # https://geoscripting-wur.github.io/PythonRaster/ # under construction pass def main(): # url = 'C:\\Users\\rcxsm\\Documents\\phyton_scripts\\airports.csv' #10 mb big file, takes a lot of time to process ! # url = "https://ourairports.com/data/airports.csv" #10 mb big file, takes a lot of time to process ! # url = 'https://raw.githubusercontent.com/rcsmit/cartopy_fun/main/airports2.csv' # df = get_data(url,",") # SHOW THE LOCATIONS ON A MAP # show_locations(df, "points", False) # show the airports on a map # GENERATE A HEATMAP WITH A GENERATED HISTOGRAM #show_show_heatmap_with_histogram2d(df, False) # number of airports in a certain area # GENERATE A HEATMAP FROM AN ARRAY WITH VALUES #show_heatmap_from_array() # land temperatures # GENERATE A
# and does not need any further processing return p else: return ctypes.c_void_p(p) else: error = yami4py.yami4_get_error(result) yami4py.yami4_destroy_result(result) raise YAMIError(str(error)) def _string(result): """Extracts the string result from underlying library.""" if result == None: raise YAMIError("Not enough memory to allocate result object.") if yami4py.yami4_is_success(result): s = yami4py.yami4_get_string(result) yami4py.yami4_destroy_result(result) return str(s) else: error = yami4py.yami4_get_error(result) yami4py.yami4_destroy_result(result) raise YAMIError(str(error)) def _binary(result): """Extracts the binary result from underlying library.""" if result == None: raise YAMIError("Not enough memory to allocate result object.") if yami4py.yami4_is_success(result): p = yami4py.yami4_get_pointer(result) size = yami4py.yami4_get_int_i(result) yami4py.yami4_destroy_result(result) # TODO: faster method? a custom buffer class, perhaps? bin = array.array("B", size * "\x00") if _use_standard_extension_API: for i in range(size): # note: bytes (c_bytes) from ctypes are signed, # whereas bytearray expects unsigned ranges v = yami4py.yami4_read_from_binary_array(p, i) if v >= 0: bin[i] = v else: bin[i] = 256 + v else: p = ctypes.cast(p, ctypes.POINTER(ctypes.c_byte)) for i in range(size): # note: bytes (c_bytes) from ctypes are signed, # whereas bytearray expects unsigned ranges v = p[i] if v >= 0: bin[i] = v else: bin[i] = 256 + v return bin.tostring() else: error = yami4py.yami4_get_error(result) yami4py.yami4_destroy_result(result) raise YAMIError(str(error)) def _check(result): """Checks result for error condition.""" if result == None: raise YAMIError("Not enough memory to allocate result object.") if yami4py.yami4_is_success(result) == 0: error = yami4py.yami4_get_error(result) yami4py.yami4_destroy_result(result) raise YAMIError(str(error)) yami4py.yami4_destroy_result(result) def _utf8(s): """Converts the given string to sequence of bytes according to UTF-8.""" return s.encode("utf8") # API bound to dynamic library: class OutgoingMessage(object): """Outgoing message. The handler allowing to track the progress of outgoing message, inspect its state and to obtain the reply content. Note: The objects of this class can be safely used from multiple threads.""" POSTED = 1 TRANSMITTED = 2 ABANDONED = 3 REPLIED = 4 REJECTED = 5 def __init__(self, msg): self.__msg = msg def __enter__(self): return self def __exit__(self, exc_type, exc_val, exc_tb): self.close() def __del__(self): if self.__msg != None: self.close() def close(self): """Deallocates internal resources associated with this object. This function is called automatically if the object is used as a context manager.""" yami4py.yami4_destroy_outgoing_message(self.__msg) self.__msg = None def get_state(self): """Returns the state of this message. This function allows to inspect the progress of the message transmission and returns a 3-tuple: state, sent and total_byte_count. During transmission the sent value is always smaller than total_byte_count. When these two values become equal, it means that the transmission was either succesful or abandoned.""" result = yami4py.yami4_outgoing_message_get_state(self.__msg) state = yami4py.yami4_get_int_i(result) bytes_sent = yami4py.yami4_get_int_j(result) total_byte_count = yami4py.yami4_get_int_k(result) yami4py.yami4_destroy_result(result) return state, bytes_sent, total_byte_count def wait_for_transmission(self, timeout = 0): """Waits for the transmission to finish. Waits for the transmission to finish - that is, to either send all the message data or to abandon it. If the timeout value is greater than 0, it means relative timeout in milliseconds; the function returns True if the transmission was finished before the timeout expired and False otherwise. If the timeout value is non-positive, there is no timeout and the function can wait indefinitely. After this function returns True the state of the message is either TRANSMITTED, ABANDONED, REPLIED or REJECTED.""" _check(yami4py.yami4_outgoing_message_wait_for_transmission( self.__msg, timeout)) def wait_for_completion(self, timeout = 0): """Waits for the full message roundtrip. Waits for the full message roundtrip - that is, for some confirmation that the message has been received and reacted upon by the target agent. If the timeout value is greater than 0, it means relative timeout in milliseconds; the function returns True if the message was completed before the timeout expired and False otherwise. If the timeout value is non-positive, there is no timeout and the function can wait indefinitely. After this function returns True the state of the message is either ABANDONED, REPLIED or REJECTED. Note: This function should not be called if the intended semantics of the message is "one-way" - in this case this function would block indefinitely.""" _check(yami4py.yami4_outgoing_message_wait_for_completion( self.__msg, timeout)) def get_reply(self): """Provides access to the reply content.""" params = Parameters() params.deserialize(_binary( yami4py.yami4_outgoing_message_get_raw_reply(self.__msg))) return params def get_exception_msg(self): """Returns the human-readable reason for message rejection.""" return _string( yami4py.yami4_outgoing_message_get_exception_msg(self.__msg)) class IncomingMessage(object): """Incoming message. The handler allowing to inspect the details of the incoming message and sent back replies or rejection notifications. The user code interacts with objects of this type mainly in the functors that are provided during object registration and that are later called back when the incoming message arrives. The handler objects can be stored aside for further processing even after the callback returns, but should not be kept alive longer than the agent itself. Note: The objects of this class are not supposed to be used from multiple threads.""" def __init__(self, msg): self.__msg = msg def __del__(self): if self.__msg != None: self.close() def close(self): """Deallocates internal resources associated with this object. This function is called automatically if the object is used as a context manager.""" yami4py.yami4_destroy_incoming_message(self.__msg) self.__msg = None def get_source(self): """Returns the source of this incoming message.""" return _string( yami4py.yami4_incoming_message_get_source(self.__msg)) def get_object_name(self): """Returns the destination object name.""" return _string( yami4py.yami4_incoming_message_get_object_name(self.__msg)) def get_message_name(self): """Returns the message name.""" return _string( yami4py.yami4_incoming_message_get_message_name(self.__msg)) def get_parameters(self): """Provides access to the message content.""" params = Parameters() params.deserialize(_binary( yami4py.yami4_incoming_message_get_raw_content(self.__msg))) return params def reply(self, content = {}, priority = 0): """Sends back the reply. Sends back the reply to the message identified by this object. The reply (or rejection) can be sent only once.""" serialized_content = serialize(content) yami4py.yami4_incoming_message_reply( self.__msg, serialized_content, len(serialized_content), priority) def reject(self, reason = "", priority = 0): """Sends back the rejection (exception) notification. Sends back the rejection to the message identified by this object. The rejection (or reply) can be sent only once.""" yami4py.yami4_incoming_message_reject( self.__msg, _utf8(reason), priority) class Agent(object): """Message broker. The message broker that encapsulates physical channel management, incoming and outgoing message queues, listeners and resource management. A single agent object can manage many listeners, which are responsible for accepting remote connections, and many incoming and outgoing connections. The agent objects can be created and destroyed without constraints on the stack, on the free store or as static objects. The objects of this class can be safely used by multiple threads.""" # connection event values NEW_INCOMING_CONNECTION = 1 NEW_OUTGOING_CONNECTION = 2 CONNECTION_CLOSED = 3 class OptionNames(object): # core option names TCP_LISTEN_BACKLOG = "tcp_listen_backlog" TCP_REUSEADDR = "tcp_reuseaddr" TCP_NONBLOCKING = "tcp_nonblocking" TCP_CONNECT_TIMEOUT = "tcp_connect_timeout" TCP_NODELAY = "tcp_nodelay" TCP_KEEPALIVE = "tcp_keepalive" TCP_FRAME_SIZE = "tcp_frame_size" UDP_FRAME_SIZE = "udp_frame_size" UNIX_LISTEN_BACKLOG = "unix_listen_backlog" UNIX_NONBLOCKING = "unix_nonblocking" UNIX_FRAME_SIZE = "unix_frame_size" FILE_NONBLOCKING = "file_nonblocking" FILE_FRAME_SIZE = "file_frame_size" # C++ general-purpose option names DISPATCHER_THREADS = "dispatcher_threads" CONNECTION_RETRIES = "connection_retries" CONNECTION_RETRY_DELAY_SPREAD = "connection_retry_delay_spread" OUTGOING_HIGH_WATER_MARK = "outgoing_high_water_mark" OUTGOING_LOW_WATER_MARK = "outgoing_low_water_mark" INCOMING_HIGH_WATER_MARK = "incoming_high_water_mark" INCOMING_LOW_WATER_MARK = "incoming_low_water_mark" # note: this is not available in Python # and hardcoded for the underlying C++ component #DELIVER_AS_RAW_BINARY = "deliver_as_raw_binary" # additional Python settings INCOMING_QUEUE_MAX_LENGTH = "incoming_queue_max_length" class __DispatcherThread(threading.Thread): """Dispatcher thread that consumes incoming messages from the queue and delivers them to registered callable entities.""" def __init__(self, agent, object_map, object_map_lock, connection_event_callback): self.__agent = agent self.__objects = object_map self.__objects_lock = object_map_lock self.__connection_event_callback = connection_event_callback threading.Thread.__init__(self) def run(self): while True: # first check if there is a regular incoming message msg_ptr = yami4py.yami4_agent_get_next_incoming_message( self.__agent) if msg_ptr == None: # there is no incoming message -> check connection events conn_event = _string( yami4py.yami4_agent_get_next_connection_event( self.__agent)) if conn_event: if self.__connection_event_callback: if conn_event[0] == 'i': event = Agent.NEW_INCOMING_CONNECTION elif conn_event[0] == 'o': event = Agent.NEW_OUTGOING_CONNECTION else: event = Agent.CONNECTION_CLOSED connection_name = conn_event[2:] try: self.__connection_event_callback( connection_name, event) except: # ignore exceptions from user code pass # continue checking the queue continue else: # no incoming message and no connection event # -> agent closing return else: # process incoming message msg = IncomingMessage(_pointer(msg_ptr)) object_name = msg.get_object_name() handler = None self.__objects_lock.acquire() try: try: handler = self.__objects[object_name] except KeyError: # no such object -> try the default handler if "" in self.__objects: handler = self.__objects[""] if handler != None: # object handler found, call
#!/usr/bin/env python3 # -- coding: utf-8 -- #**************************************************************************************************************************************************** # Copyright 2017 NXP # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright notice, # this list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above copyright notice, # this list of conditions and the following disclaimer in the documentation # and/or other materials provided with the distribution. # # * Neither the name of the NXP. nor the names of # its contributors may be used to endorse or promote products derived from # this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND # ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED # WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. # IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, # INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, # BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, # DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF # LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE # OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF # ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. # #**************************************************************************************************************************************************** import argparse import hashlib import io import json import os import subprocess import sys __g_verbosityLevel = 0 __g_debugEnabled = False __g_licenseFilename = "License.json" __g_Image = "Image" __g_Model = "Model" __g_Video = "Video" __g_extensions = [ ('.bmp', __g_Image), ('.dds', __g_Image), ('.hdr', __g_Image), ('.jpg', __g_Image), ('.ktx', __g_Image), ('.png', __g_Image), ('.psd', __g_Image), ('.tga', __g_Image), ('.tiff', __g_Image), ('.3ds', __g_Model), ('.fbx', __g_Model), ('.fsf', __g_Model), ('.obj', __g_Model), ('.nff', __g_Model), # video ('.avi', __g_Video), ('.fsf', __g_Video), ('.mp4', __g_Video), ('.mpg', __g_Video), ('.mpeg', __g_Video), ('.mkv', __g_Video), ] __g_ignore = "example.jpg" __g_ignoreDir = [ ".Config/Templates.gen/Android/Copy/res/drawable-hdpi", ".Config/Templates.gen/Android/Copy/res/drawable-ldpi", ".Config/Templates.gen/Android/Copy/res/drawable-mdpi", ".Config/Templates.gen/Android/Copy/res/drawable-xhdpi", ".Config/Templates.gen/Android/Copy/res/drawable-xxhdpi", ".Config/Templates.gen/Android/Copy/res/drawable-xxxhdpi", ".Config/Templates.gen/AndroidGradleCMake/Copy/app/src/main/res/mipmap-hdpi", ".Config/Templates.gen/AndroidGradleCMake/Copy/app/src/main/res/mipmap-ldpi", ".Config/Templates.gen/AndroidGradleCMake/Copy/app/src/main/res/mipmap-mdpi", ".Config/Templates.gen/AndroidGradleCMake/Copy/app/src/main/res/mipmap-xhdpi", ".Config/Templates.gen/AndroidGradleCMake/Copy/app/src/main/res/mipmap-xxhdpi", ".Config/Templates.gen/AndroidGradleCMake/Copy/app/src/main/res/mipmap-xxxhdpi", ] class Config(object): def __init__(self, verbosityLevel): super(Config, self).__init__() self.VerbosityLevel = verbosityLevel self.IsVerbose = verbosityLevel > 0 def LogPrint(self, str): if self.IsVerbose: print(str) sys.stdout.flush() class JsonLicense(object): def __init__(self, sourceDict = {}): super(JsonLicense, self).__init__() self.Origin = "" self.License = "" self.Url = "" self.Tags = "" self.TagsIdList = [] self.SourceDict = sourceDict def SetTags(self, tags): self.Tags = tags self.TagsIdList = [entry.lower() for entry in tags.split(';') if len(entry) > 0] def Compare(self, license): return self.Origin == license.Origin and self.License == license.License and self.Url == license.Url and self.Tags == license.Tags and self.SourceDict == license.SourceDict def GetExtensionList(extensions): return [extension[0] for extension in extensions] def GetTitle(): return 'FslResourceScan V0.1.0 alpha' def ShowTitleIfNecessary(): global __g_verbosityLevel if __g_verbosityLevel > 0: print((GetTitle())) def ReadBinaryFile(filename): content = None with open(filename, "rb") as theFile: content = theFile.read() return content def WriteFile(filename, content): with open(filename, "w") as theFile: theFile.write(content) def ReadJsonFile(filename): content = ReadBinaryFile(filename) return json.loads(content) def WriteJsonFile(filename, dict): with io.open(filename, 'w', encoding='utf-8') as currentFile: currentFile.write(str(json.dumps(dict, ensure_ascii=False, indent=2))) def ToUnixStylePath(path): if path == None: return None return path.replace("\\", "/") def GetDirectoryName(path): return ToUnixStylePath(os.path.dirname(path)) def Join(path1, path2): return ToUnixStylePath(os.path.join(path1, path2)) class Resource(object): def __init__(self, sourcePath, relativeSkipChars): super(Resource, self).__init__() self.SourcePath = sourcePath self.SourceDirectory = GetDirectoryName(sourcePath) self.License = None self.RelativePath = sourcePath[relativeSkipChars:] def ScanForFiles(path, extensionList, ignoreFiles): foundFiles = [] for root, dirs, files in os.walk(path): for file in files: fileId = file.lower() for extension in extensionList: if fileId.endswith(extension) and not fileId in ignoreFiles: foundFiles.append(ToUnixStylePath(os.path.join(root, file))) break return foundFiles def HashFile(filename, blocksize=65536): hasher = hashlib.sha256() with open(filename, "rb") as theFile: buf = theFile.read(blocksize) while len(buf) > 0: hasher.update(buf) buf = theFile.read(blocksize) return hasher.hexdigest() def BuildFileContentHashDict(config, files): dictHash = {} for file in files: hash = HashFile(file) if hash in dictHash: dictHash[hash].append(file) else: dictHash[hash] = [file] return dictHash def BuildFileLengthDict(files): dict = {} for file in files: fileLength = os.stat(file).st_size if fileLength in dict: dict[fileLength].append(file) else: dict[fileLength] = [file] return dict def BuildFileContentHashDict(files): dict = {} for file in files: hash = HashFile(file) if hash in dict: dict[hash].append(file) else: dict[hash] = [file] return dict def BuildDuplicatedList(fileName, files): srcFilename = files[0] srcContentSet = set(ReadBinaryFile(srcFilename)) matchingFiles = [fileName] for file in files: content = ReadBinaryFile(file) if len(srcContentSet.intersection(content)) == len(srcContentSet): matchingFiles.append(file) return matchingFiles def BuildDuplicatedDict(config, files, uniqueFiles): dict = {} while(len(files) > 1): srcFile = files[0] remainingFiles = files[1:] matchingFiles = BuildDuplicatedList(srcFile, remainingFiles) if len(matchingFiles) > 1: dict[srcFile] = matchingFiles else: uniqueFiles.append(key) # Remove all non duplicated files files.remove(srcFile) files = [] for file in remainingFiles: if not file in matchingFiles: files.append(file) return dict def BuildUniqueFileDictByContent(config, files, uniqueFiles): # we start by sorting files by their hash # this should limit the amount of files that have to be byte compared quite a bit duplicationDict = {} dictHash = BuildFileContentHashDict(files) for fileList in list(dictHash.values()): if len(fileList) > 1: newDuplicationDict = BuildDuplicatedDict(config, fileList, uniqueFiles) duplicationDict.update(newDuplicationDict) else: uniqueFiles.append(fileList[0]) return duplicationDict def BuildUniqueFileDict(config, files, uniqueFiles): # we start by sorting files by their size # this should limit the amount of files that have to be byte compared quite a bit dictFileLength = BuildFileLengthDict(files) #config.LogPrint("Initial bins {0}".format(len(dictFileLength))) duplicationDict = {} for fileList in list(dictFileLength.values()): if len(fileList) > 1: newDuplicationDict = BuildUniqueFileDictByContent(config, fileList, uniqueFiles) duplicationDict.update(newDuplicationDict) else: uniqueFiles.append(fileList[0]) return duplicationDict def GetFileExtension(filename): filename, fileExtension = os.path.splitext(filename) return fileExtension def BuildExtensionDict(extensions): dict = {} for extension in extensions: dict[extension[0]] = extension[1] return dict def GetContentTypeByExtension(extensionDict, filename): filenameExtension = GetFileExtension(filename).lower() return extensionDict[filenameExtension] if filenameExtension in extensionDict else "" def BuildResourceDirectorySet(uniqueFiles, duplicatedFilesDict): # build unique dir list resourceDirSet = set() for entry in uniqueFiles: dirName = GetDirectoryName(entry) if not dirName in resourceDirSet: resourceDirSet.add(dirName) for fileList in list(duplicatedFilesDict.values()): for entry in fileList: dirName = GetDirectoryName(entry) if not dirName in resourceDirSet: resourceDirSet.add(dirName) return resourceDirSet class LicenseManager(object): def __init__(self): super(LicenseManager, self).__init__() self.KeyOrigin = "Origin" self.KeyLicense = "License" self.KeyComment = "Comment" self.KeyTags = "Tags" self.KeyURL = "URL" def TryReadLicense(self, config, filename): if not os.path.isfile(filename): return None content = None try: content = ReadJsonFile(filename) except (Exception) as ex: print("ERROR: Exception while parsing {0}".format(filename)) raise if not self.KeyOrigin in content: config.LogPrint("ERROR: '{0}' not present in file '{1}'".format(self.KeyOrigin, filename)); return None if not self.KeyLicense in content: config.LogPrint("ERROR: '{0}' not present in file '{1}'".format(self.KeyLicense, filename)); return None license = JsonLicense(content) license.Origin = content[self.KeyOrigin] license.License = content[self.KeyLicense] license.Comment = content[self.KeyComment] if self.KeyComment in content else "" license.URL = content[self.KeyURL] if self.KeyURL in content else "" license.SetTags(content[self.KeyTags] if self.KeyTags in content else "") return license def SaveLicense(self, filename, license): #contentDict = {} #self.__AddKeyIfNeeded(contentDict, self.KeyOrigin, license.Origin) #self.__AddKeyIfNeeded(contentDict, self.KeyLicense, license.License) #self.__AddKeyIfNeeded(contentDict, self.KeyURL, license.URL) WriteJsonFile(filename, license.SourceDict) def __AddKeyIfNeeded(self, dict, key, value): if len(value) <= 0: return dict[key] = value def BuildDirectoryLicenseDict(config, resourceDirectories, licenseFilename): licenseManager = LicenseManager() licenseDict = {} for dir in resourceDirectories: license = licenseManager.TryReadLicense(config, Join(dir, licenseFilename)) if license != None: licenseDict[dir] = license return licenseDict def TagListWithLicenses(inputDirectory, files, directoryLicenseDict): inputDirectory = ToUnixStylePath(inputDirectory) skipChars = len(inputDirectory) if inputDirectory.endswith('/') else len(inputDirectory)+1 res = [] for entry in files: resource = Resource(entry, skipChars) if resource.SourceDirectory in directoryLicenseDict: resource.License = directoryLicenseDict[resource.SourceDirectory] res.append(resource) return res; def TagDictWithLicenses(inputDirectory, fileDict, directoryLicenseDict): inputDirectory = ToUnixStylePath(inputDirectory) skipChars = len(inputDirectory) if inputDirectory.endswith('/') else len(inputDirectory)+1 res = {} for key, value in fileDict.items(): keyFilename = key[skipChars:] res[keyFilename] = TagListWithLicenses(inputDirectory, value, directoryLicenseDict) return res; def WriteCSV(dstFilename, extensions, uniqueEntries, duplicatedEntryDict): #count = len(uniqueFiles) #for list in duplicatedFilesDict.values(): # count += len(list) #config.LogPrint("Found {0} resource files".format(count)) uniqueEntries.sort(key=lambda s: s.SourcePath.lower()) sortedDuplicatedFiles = list(duplicatedEntryDict.keys()) sortedDuplicatedFiles.sort() for fileList in list(duplicatedEntryDict.values()): fileList.sort(key=lambda s: s.SourcePath.lower()); extensionDict = BuildExtensionDict(extensions) lines = [] lines.append("Unique files ({0});;Origin;License;Type;Comment;URL".format(len(uniqueEntries))) for entry in uniqueEntries: contentType = GetContentTypeByExtension(extensionDict, entry.RelativePath) if entry.License == None: lines.append("{0};;;;{1};;".format(entry.RelativePath, contentType)) else: lines.append("{0};;{1};{2};{3};{4};{5}".format(entry.RelativePath, entry.License.Origin, entry.License.License, contentType, entry.License.Comment, entry.License.URL)) lines.append("\n") lines.append("Duplicated files ({0})".format(len(duplicatedEntryDict))) for key in sortedDuplicatedFiles: lines.append("{0};;;;{1};;".format(key, GetContentTypeByExtension(extensionDict, key))) for entry in duplicatedEntryDict[key]: contentType = GetContentTypeByExtension(extensionDict, entry.RelativePath) if entry.License == None: lines.append(";{0};;;{1};;".format(entry.RelativePath, contentType)) else: lines.append(";{0};{1};{2};{3};{4};{5}".format(entry.RelativePath, entry.License.Origin, entry.License.License, contentType, entry.License.Comment, entry.License.URL)) WriteFile(dstFilename, "\n".join(lines)); def PrintIssueDirectories(fileList, dict): uniqueDirs = set() for entry in fileList: if not entry.SourceDirectory in uniqueDirs: uniqueDirs.add(entry.SourceDirectory) for value in list(dict.values()): for entry in value: if not entry.SourceDirectory in uniqueDirs: uniqueDirs.add(entry.SourceDirectory) if len(uniqueDirs) > 0: print("Investigate license for the following directories:") uniqueDirs = list(uniqueDirs) uniqueDirs.sort() for entry in uniqueDirs: print(" {0}".format(entry)) def Filter(config, ignoreDirList, inputDirectory, files): inputDirectory = ToUnixStylePath(inputDirectory)
start: self.quiet += 1 else: self.quiet -= 1 if tag == "style": if start: self.style += 1 else: self.style -= 1 if tag in ["body"]: self.quiet = 0 # sites like 9rules.com never close <head> if tag == "blockquote": if start: self.p() self.o("> ", force=True) self.start = True self.blockquote += 1 else: self.blockquote -= 1 self.p() def no_preceding_space(self: HTML2Text) -> bool: return bool( self.preceding_data and re.match(r"[^\s]", self.preceding_data[-1]) ) if tag in ["em", "i", "u"] and not self.ignore_emphasis: if start and no_preceding_space(self): emphasis = " " + self.emphasis_mark else: emphasis = self.emphasis_mark self.o(emphasis) if start: self.stressed = True if tag in ["strong", "b"] and not self.ignore_emphasis: if start and no_preceding_space(self): strong = " " + self.strong_mark else: strong = self.strong_mark self.o(strong) if start: self.stressed = True if tag in ["del", "strike", "s"]: if start and no_preceding_space(self): strike = " ~~" else: strike = "~~" self.o(strike) if start: self.stressed = True if self.google_doc: if not self.inheader: # handle some font attributes, but leave headers clean self.handle_emphasis(start, tag_style, parent_style) if tag in ["kbd", "code", "tt"] and not self.pre: self.o("`") # TODO: `` `this` `` self.code = not self.code if tag == "abbr": if start: self.abbr_title = None self.abbr_data = "" if "title" in attrs: self.abbr_title = attrs["title"] else: if self.abbr_title is not None: assert self.abbr_data is not None self.abbr_list[self.abbr_data] = self.abbr_title self.abbr_title = None self.abbr_data = None if tag == "q": if not self.quote: self.o(self.open_quote) else: self.o(self.close_quote) self.quote = not self.quote def link_url(self: HTML2Text, link: str, title: str = "") -> None: url = urlparse.urljoin(self.baseurl, link) title = ' "{}"'.format(title) if title.strip() else "" self.o("]({url}{title})".format(url=escape_md(url), title=title)) if tag == "a" and not self.ignore_links: if start: if ( "href" in attrs and attrs["href"] is not None and not (self.skip_internal_links and attrs["href"].startswith("#")) ): self.astack.append(attrs) self.maybe_automatic_link = attrs["href"] self.empty_link = True if self.protect_links: attrs["href"] = "<" + attrs["href"] + ">" else: self.astack.append(None) else: if self.astack: a = self.astack.pop() if self.maybe_automatic_link and not self.empty_link: self.maybe_automatic_link = None elif a: assert a["href"] is not None if self.empty_link: self.o("[") self.empty_link = False self.maybe_automatic_link = None if self.inline_links: title = a.get("title") or "" title = escape_md(title) link_url(self, a["href"], title) else: i = self.previousIndex(a) if i is not None: a_props = self.a[i] else: self.acount += 1 a_props = AnchorElement(a, self.acount, self.outcount) self.a.append(a_props) self.o("][" + str(a_props.count) + "]") if tag == "img" and start and not self.ignore_images: if "src" in attrs: assert attrs["src"] is not None if not self.images_to_alt: attrs["href"] = attrs["src"] alt = attrs.get("alt") or self.default_image_alt # If we have images_with_size, write raw html including width, # height, and alt attributes if self.images_as_html or ( self.images_with_size and ("width" in attrs or "height" in attrs) ): self.o("<img src='" + attrs["src"] + "' ") if "width" in attrs: assert attrs["width"] is not None self.o("width='" + attrs["width"] + "' ") if "height" in attrs: assert attrs["height"] is not None self.o("height='" + attrs["height"] + "' ") if alt: self.o("alt='" + alt + "' ") self.o("/>") return # If we have a link to create, output the start if self.maybe_automatic_link is not None: href = self.maybe_automatic_link if ( self.images_to_alt and escape_md(alt) == href and self.absolute_url_matcher.match(href) ): self.o("<" + escape_md(alt) + ">") self.empty_link = False return else: self.o("[") self.maybe_automatic_link = None self.empty_link = False # If we have images_to_alt, we discard the image itself, # considering only the alt text. if self.images_to_alt: self.o(escape_md(alt)) else: self.o("![" + escape_md(alt) + "]") if self.inline_links: href = attrs.get("href") or "" self.o( "(" + escape_md(urlparse.urljoin(self.baseurl, href)) + ")" ) else: i = self.previousIndex(attrs) if i is not None: a_props = self.a[i] else: self.acount += 1 a_props = AnchorElement(attrs, self.acount, self.outcount) self.a.append(a_props) self.o("[" + str(a_props.count) + "]") if tag == "dl" and start: self.p() if tag == "dt" and not start: self.pbr() if tag == "dd" and start: self.o(" ") if tag == "dd" and not start: self.pbr() if tag in ["ol", "ul"]: # Google Docs create sub lists as top level lists if not self.list and not self.lastWasList: self.p() if start: if self.google_doc: list_style = google_list_style(tag_style) else: list_style = tag numbering_start = list_numbering_start(attrs) self.list.append(ListElement(list_style, numbering_start)) else: if self.list: self.list.pop() if not self.google_doc and not self.list: self.o("\n") self.lastWasList = True else: self.lastWasList = False if tag == "li": self.pbr() if start: if self.list: li = self.list[-1] else: li = ListElement("ul", 0) if self.google_doc: nest_count = self.google_nest_count(tag_style) else: nest_count = len(self.list) # TODO: line up <ol><li>s > 9 correctly. self.o(" " * nest_count) if li.name == "ul": self.o(self.ul_item_mark + " ") elif li.name == "ol": li.num += 1 self.o(str(li.num) + ". ") self.start = True if tag in ["table", "tr", "td", "th"]: if self.ignore_tables: if tag == "tr": if start: pass else: self.soft_br() else: pass elif self.bypass_tables: if start: self.soft_br() if tag in ["td", "th"]: if start: self.o("<{}>\n\n".format(tag)) else: self.o("\n</{}>".format(tag)) else: if start: self.o("<{}>".format(tag)) else: self.o("</{}>".format(tag)) else: if tag == "table": if start: self.table_start = True if self.pad_tables: self.o("<" + config.TABLE_MARKER_FOR_PAD + ">") self.o(" \n") else: if self.pad_tables: self.o("</" + config.TABLE_MARKER_FOR_PAD + ">") self.o(" \n") if tag in ["td", "th"] and start: if self.split_next_td: self.o("\| ") self.split_next_td = True if tag == "tr" and start: self.td_count = 0 if tag == "tr" and not start: self.split_next_td = False self.soft_br() if tag == "tr" and not start and self.table_start: # Underline table header self.o("\|".join(["---"] * self.td_count)) self.soft_br() self.table_start = False if tag in ["td", "th"] and start: self.td_count += 1 if tag == "pre": if start: self.startpre = True self.pre = True else: self.pre = False if self.mark_code: self.out("\n[/code]") self.p() # TODO: Add docstring for these one letter functions def pbr(self) -> None: "Pretty print has a line break" if self.p_p == 0: self.p_p = 1 def p(self) -> None: "Set pretty print to 1 or 2 lines" self.p_p = 1 if self.single_line_break else 2 def soft_br(self) -> None: "Soft breaks" self.pbr() self.br_toggle = " " def o( self, data: str, puredata: bool = False, force: Union[bool, str] = False ) -> None: """ Deal with indentation and whitespace """ if self.abbr_data is not None: self.abbr_data += data if not self.quiet: if self.google_doc: # prevent white space immediately after 'begin emphasis' # marks ('*' and '_') lstripped_data = data.lstrip() if self.drop_white_space and not (self.pre or self.code): data = lstripped_data if lstripped_data != "": self.drop_white_space = 0 if puredata and not self.pre: # This is a very dangerous call ... it could mess up # all handling of   when not handled properly # (see entityref) data = re.sub(r"\s+", r" ", data) if data and data[0] == " ": self.space = True data = data[1:] if not data and not force: return if self.startpre: # self.out(" :") #TODO: not output when already one there if not data.startswith("\n") and not data.startswith("\r\n"): # <pre>stuff... data = "\n" + data if self.mark_code: self.out("\n[code]") self.p_p = 0 bq = ">" self.blockquote if not (force and data and data[0] == ">") and self.blockquote: bq += " " if self.pre: if not self.list: bq += " " # else: list content is already partially indented bq += " " * len(self.list) data = data.replace("\n", "\n" + bq) if self.startpre: self.startpre = False if self.list: # use existing initial indentation data = data.lstrip("\n") if self.start: self.space = False self.p_p = 0 self.start = False if force == "end": # It's the end. self.p_p = 0 self.out("\n") self.space = False if self.p_p: self.out((self.br_toggle + "\n" + bq) * self.p_p) self.space = False self.br_toggle = "" if self.space: if not self.lastWasNL: self.out(" ") self.space = False if self.a and ( (self.p_p == 2 and self.links_each_paragraph) or force == "end" ): if force == "end": self.out("\n") newa = [] for link in self.a: if self.outcount > link.outcount: self.out( " [" + str(link.count) + "]: " + urlparse.urljoin(self.baseurl, link.attrs["href"]) ) if "title" in link.attrs: assert link.attrs["title"] is not None self.out(" (" + link.attrs["title"] + ")") self.out("\n")
os.getcwd() self.run_dir = os.path.join(cwd, self.run_dir) print(self.run_dir) if os.path.isdir(self.run_dir): shutil.rmtree(self.run_dir, ignore_errors=True) if pgm_dir: shutil.copytree(pgm_dir, self.run_dir) if pgm_files: os.makedirs(self.run_dir) for f in pgm_files: shutil.copy(f, self.run_dir) # pre_passes_str (str): pre_passes_str is a string that contains specific passes that we want to use when we reinitialize the training (when we use reset) self.pre_passes_str= "-prune-eh -functionattrs -ipsccp -globalopt -mem2reg -deadargelim -sroa -early-cse -loweratomic -instcombine -loop-simplify" # pre_passes (list): pre_passes is a list of integer that contains the indices of the passes written in pre_passes_str. self.pre_passes = getcycle.passes2indice(self.pre_passes_str) self.passes = [] # passes (list): passes is a list that contains the passes used for the Rl training self.best_passes = [] # best_passes (list): best_passes is a list that contains the best passes recorded. (we update the list when the recoded time of cycle count is less than min_cycles) self.pgm = pgm # pgm_name (str): pgm_name is the file name of the program we are optimizing (which is written in C programming language) self.pgm_name = pgm.replace('.c','') # bc (str): bs is the file name of the program we are optimizing after being compiled to IR (hardware-independent intermediate representation) self.bc = self.pgm_name + '.prelto.2.bc' self.original_obs = [] # original_obs(list): original_obs is a list that contains the original values of the observatyions features. def __del__(self): """ This function closes the log_file (which is a file we use to record information about each episode) when delete_run_dir and log_results are True. Also deletes the entire directory tree of run_dir (the running directory) if run_dir is an existing directory. """ if self.delete_run_dir: if self.log_results: self.log_file.close() if os.path.isdir(self.run_dir): shutil.rmtree(self.run_dir) def get_Ox_rewards(self, level=3, sim=False, clang_opt=False): """ Examples : >>> print(get_0x_rewards(self, level=3, clang_opt=False, sim=False)) -45 Args: level (int): This is an integer that represents different groups of optimizations implemented in the compiler. Each optimization level is hand-picked by the compiler-designer to benefit specific benchmarks. Defaults to 3. sim (bool): sim is a Boolean that should be set to True if we want the subprocessor to run the “make clean p v -s” command, and we should set it to False if we want the subprocessor to run the “make clean accelerationCycle -s” command instead. Defaults to False. clang_opt (bool): clang_opt is a Boolean that should be set to True if we want to use the clang option when running the HLS, and should be set to False otherwise. Returns: Returns the negative number of cycle counts it took to run the synthesized circuit made by using the passes set in the 0x optimization. Which represents for the RL agent the reward. """ from gym_hls.envs.getox import getOxCycles cycle = getOxCycles(self.pgm_name, self.run_dir, level=level, clang_opt=clang_opt, sim=sim) return -cycle def print_info(self,message, end = '\n'): """ This function is used to print information the episodes of the RL agent. Args: message (str): message is a string that will contain information about the episode of the Rl agent that we want to print on our terminal end (str): end is a string that prints a new line. """ sys.stdout.write('\x1b[1;34m' + message.strip() + '\x1b[0m' + end) def get_cycles(self, passes, sim=False): """ Examples : >>>print(get_cycles(self, [“-correlated-propagation”, “-scalarrepl”, “-lowerinvoke”])) (55, True) Args: passes (list): passes is a list that contains the passes used for the Rl training sim (bool): sim (bool, optional): sim should be True if you want the arguments used to launch the process to be “make clean p v -s”, or sim should be False if you want the argument used to launch the process to be "make clean accelerationCycle -s". Defaults to False Returns: Returns a tuple where the first element is an integer that represents the number of cycle counts it took to run the synthesized circuit (the second element doesn’t matter). """ if self.shrink: actual_passes = [self.eff_pass_indices[index] for index in passes] else: actual_passes = passes cycle, _ = getcycle.getHWCycles(self.pgm_name, actual_passes, self.run_dir, sim=sim) return cycle def get_rewards(self, diff=True, sim=False): """ Examples : >>>print(get_cycles(self)) -55 Args: diff (bool): diff is a boolean that is set to True if we want the reward to be the difference of previous cycle count and the current cycle count. Otherwise, if diff is False, the reward is equal to – the current cycle count. sim (bool, optional): sim should be True if you want the arguments used to launch the process to be “make clean p v -s”, or sim should be False if you want the argument used to launch the process to be "make clean accelerationCycle -s". Defaults to False Returns: Returns an integer that represents the reward for the RL agent (it shows the improvement of the circuit), and we get it either by calculating the difference between previous cycle count and the current cycle count or the negative value of the current cycle count. """ if self.shrink: actual_passes = [self.eff_pass_indices[index] for index in self.passes] else: actual_passes = self.passes cycle, done = getcycle.getHWCycles(self.pgm_name, actual_passes, self.run_dir, sim=sim) if cycle == 10000000: cycle = 2 * self.O0_cycles # print("pass: {}".format(self.passes)) # print("prev_cycles: {}".format(self.prev_cycles)) if(self.verbose): self.print_info("passes: {}".format(actual_passes)) self.print_info("program: {} -- ".format(self.pgm_name)+" cycle: {} -- prev_cycles: {}".format(cycle, self.prev_cycles)) try: cyc_dict = pickle.load(open('cycles_chstone.pkl','rb')) except: cyc_dict = {} if self.pgm_name in cyc_dict: if cyc_dict[self.pgm_name]['cycle']>cycle: cyc_dict[self.pgm_name]['cycle'] = cycle cyc_dict[self.pgm_name]['passes'] = self.passes else: cyc_dict[self.pgm_name] = {} cyc_dict[self.pgm_name]['cycle'] = cycle cyc_dict[self.pgm_name]['passes'] = self.passes output = open('cycles_chstone.pkl', 'wb') pickle.dump(cyc_dict, output) output.close() if (cycle < self.min_cycles): self.min_cycles = cycle self.best_passes = actual_passes if (diff): rew = self.prev_cycles - cycle self.prev_cycles = cycle else: rew = -cycle # print("rew: {}".format(rew)) return rew, done def get_obs(self,get_normalizer=False): """ Examples : >>>print(get_obs()) [1, 0, 0, 0, 1] Args: get_normalizer (bool): get_normalizer is a boolean that should be set to True if we want to get a normalizer value that is used to normalize the list of observation features. Defaults to False. Returns: Returns a list or a tuple that contains the list of the observation features that we need to feed as input to the RL agent. """ feats = getfeatures.run_stats(self.bc, self.run_dir) normalizer=feats[-5] + 1 if self.shrink: actual_feats = [feats[index] for index in self.eff_feat_indices] else: actual_feats = feats if self.binary_obs: actual_feats = [1 if feat > 0 else 0 for feat in actual_feats] if not get_normalizer: return actual_feats else: return actual_feats,normalizer return actual_feats # reset() resets passes to [] # reset(init=[1,2,3]) resets passes to [1,2,3] def reset(self, init=None, get_obs=True, get_rew=False, ret=True, sim=False): """ Examples : >>>print(reset()) [0, 0, 0, 0] Args: init (list, optional): init is a list of integer that is equal to (set to) the new passes list. Defaults to None. get_obs (bool, optional): get_obs is a Boolean that is set to True when we decide to get the list of observation features after we reset. It should be set to False otherwise. Defaults to True. get_rew (bool, optional): get_rew is a Boolean that is set to True when we decide to get the reward after we reset. It should be set to False otherwise. Defaults to False. ret (bool, optional): ret is a Boolean that is set to True when we decide to get the reward or the list of observation features after we reset. It should be set to False otherwise. Defaults to True. sim (bool, optional): sim should be True if you want the arguments used to launch the process to be “make clean p v -s”, or sim should be False if you want the argument used to launch the process to be "make clean accelerationCycle -s". Defaults to False. Defaults to False. Returns: Returns an integer for the reward or a list for the observation features, or a tuple of both an integer and a list for the reward and the observation features, or zero if ret if False. """ #self.min_cycles = 10000000 self.passes = [] if self.feature_type ==
for signature in signature_solutions_aggregate: if signature['ss_id'] not in signature_list and signature['percentage'] != 0: signature_list.append(signature['ss_id']) signature_aggregate.append(signature) if others['percentage'] != 0: signature_aggregate.append(others) project_mapping = get_project_aggregate(year, active_projects, operating_unit=operating_unit, budget_source=budget_source) project_serializer = ProjectAggregateSerializer(project_mapping) signature_serializer = SignatureSolutionsAggregateSerializer(signature_aggregate, many=True) data = { 'project': project_serializer.data, 'signature_solutions': signature_serializer.data, } return self.jp_response(s_code='HTTP_200_OK', data=data) except Exception as e: print(e) return self.jp_error_response('HTTP_500_INTERNAL_SERVER_ERROR', 'EXCEPTION', [str(e), ]) class SignatureSolutionsDetailsView(APIView, ResponseViewMixin): def get(self, request, args, kwargs): try: year = request.GET.get('year', '') ss_id = request.GET.get('ss_id', '') if not year: return self.jp_error_response('HTTP_400_BAD_REQUEST', 'UNKNOWN_QUERY', ['Please provide a year']) active_projects = get_active_projects_for_year(year) donor_query = Q(project__in=active_projects) & Q(year=year) & \ Q(project__project_active__year=year) top_recipient_offices = DonorFundSplitUp.objects.filter(donor_query & Q(project__operating_unit__isnull=False) & Q(output__signature_solution__ss_id=ss_id))\ .values('project__operating_unit').annotate( total_expense=Coalesce(Sum('expense'), 0), total_budget=Coalesce(Sum('budget'), 0), name=F('project__operating_unit__name'), iso3=F('project__operating_unit__iso3')) \ .order_by('-total_budget')[0:10] recipient_offices_serializer = SignatureSolutionOperatingUnitSerializer(top_recipient_offices, many=True) budget_sources = DonorFundSplitUp.objects.filter(donor_query & Q(output__signature_solution__ss_id=ss_id)) \ .values('organisation') \ .annotate(total_expense=Coalesce(Sum('expense'), 0), total_budget=Coalesce(Sum('budget'), 0), short_name=F('organisation__short_name'), organisation_name=F('organisation__org_name'))\ .order_by('-total_budget')[0:10] budget_sources_serializer = SectorBudgetSourcesSerializer(budget_sources, many=True, context={'request': request}) signature_aggregate = [] signature_solutions_aggregate = [] signature_list = [] for signature_solutions in SignatureSolution.objects.filter(ss_id=ss_id): aggregate = get_signature_solutions_aggregate(year, active_projects, signature_solution=signature_solutions) if aggregate: signature_solutions_aggregate.append(aggregate) signature_solutions_aggregate = sorted(signature_solutions_aggregate, key=lambda signature: signature['percentage'], reverse=True) for signature in signature_solutions_aggregate: if signature['ss_id'] not in signature_list: signature_list.append(signature['ss_id']) signature_aggregate.append(signature) signature_serializer = SignatureSolutionsAggregateSerializer(signature_aggregate, many=True) data = { 'top_recipient_offices': recipient_offices_serializer.data, 'budget_sources': budget_sources_serializer.data, 'aggregate': signature_serializer.data } return self.jp_response(s_code='HTTP_200_OK', data=data) except Exception as e: return self.jp_error_response('HTTP_500_INTERNAL_SERVER_ERROR', 'EXCEPTION', [str(e), ]) class SignatureSolutionsOutcomeView(APIView, ResponseViewMixin): def get(self, request, args, *kwargs): try: year = request.GET.get('year', '') ss_id = request.GET.get('ss_id', '') if not year: return self.jp_error_response('HTTP_400_BAD_REQUEST', 'UNKNOWN_QUERY', ['Please provide a year']) active_projects = get_active_projects_for_year(year) sign = [] signature_solution = SignatureSolution.objects.values('sp_id').filter(ss_id=ss_id) num_signature_solution = len(signature_solution) percent_signature_solution = {sp_id['sp_id']: sp_id['sp_id__count'] 100/num_signature_solution for sp_id in signature_solution.annotate(Count('sp_id'))} for k, v in percent_signature_solution.items(): sector_name = Sector.objects.get(code=k) aggregate = get_sector_aggregate(year, active_projects=active_projects, sector=sector_name) sector_budget = aggregate['budget'] sector_color = sector_name.color sign.append({'sector_id': k, 'sector_name': sector_name, 'percent': v, 'sector_color': sector_color, 'budget': sector_budget}) signature_outcome_serializer = SignatureSolutionOutcomeSerializer(sign, many=True, context={'request': request}) data = { 'percentage': signature_outcome_serializer.data } return self.jp_response(s_code='HTTP_200_OK', data=data) except Exception as e: return self.jp_error_response('HTTP_500_INTERNAL_SERVER_ERROR', 'EXCEPTION', [str(e), ]) class SectorSignatureSolutionView(APIView, ResponseViewMixin): def get(self, request, args, kwargs): try: year = request.GET.get('year', '') code = request.GET.get('code', '') if not year: return self.jp_error_response('HTTP_400_BAD_REQUEST', 'UNKNOWN_QUERY', ['Please provide a year']) active_projects = get_active_projects_for_year(year) sectors = [] sector = Sector.objects.values('signaturesolution__ss_id').filter(code=code) num_sector = len(sector) percent_sector = {ss_id['signaturesolution__ss_id']: ss_id['signaturesolution__ss_id__count'] 100/num_sector for ss_id in sector.annotate(Count('signaturesolution__ss_id'))} for k, v in percent_sector.items(): signature_solution = SignatureSolution.objects.values('name').filter(ss_id=k).distinct() signature_solution_name = signature_solution[0]['name'] for signature in SignatureSolution.objects.filter(ss_id=k): aggregate = get_signature_solutions_aggregate(year, active_projects=active_projects, signature_solution=signature) ss_budget = aggregate['budget'] sectors.append({'signature_solution_id': k, 'percent': v, 'signature_solution_name': signature_solution_name, 'budget': ss_budget}) sector_sort = sorted(sectors, key=lambda k: k['signature_solution_id']) sector_signature_solution_serializer = SectorSignatureSolutionSerializer(sector_sort, many=True, context={'request': request}) data = { 'percentage': sector_signature_solution_serializer.data } return self.jp_response(s_code='HTTP_200_OK', data=data) except Exception as e: return self.jp_error_response('HTTP_500_INTERNAL_SERVER_ERROR', 'EXCEPTION', [str(e), ]) # class SdgTargetDetailView(APIView, ResponseViewMixin): # def get(self, request, args, kwargs): # try: # year = request.GET.get('year', '') # sdg_target = request.GET.get('sdg_target', '') # operating_unit = request.GET.get('operating_unit', '') # budget_source = request.GET.get('budget_source', '') # if not year: # return self.jp_error_response('HTTP_400_BAD_REQUEST', 'UNKNOWN_QUERY', # ['Please provide a year']) # if year and int(year) >= SP_START_YEAR: # active_projects = get_active_projects_for_year(year) # donor_query = Q(project__in=active_projects) & Q(year=year) & \ # Q(project__project_active__year=year) # query = Q(project_id__in=active_projects) & Q(donorfundsplitup__year=year) & \ # Q(project_active__year=year) # # if sdg_target and sdg_target != '0': # query.add(Q(outputtarget__target_id=sdg_target), Q. AND) # donor_query.add(Q(output__outputtarget__target_id=sdg_target), Q. AND) # if operating_unit: # query.add(Q(operating_unit=operating_unit) \| # Q(operating_unit__bureau__code=operating_unit), Q.AND) # donor_query.add(Q(project__operating_unit=operating_unit) \| # Q(project__operating_unit__bureau__code=operating_unit), Q.AND) # if budget_source: # budget_query = Q(donorfundsplitup__organisation__ref_id=budget_source) \| \ # Q(donorfundsplitup__organisation__type_level_3=budget_source) # donor_budget_query = Q(organisation__ref_id=budget_source) \| \ # Q(organisation__type_level_3=budget_source) # query.add(budget_query, Q.AND) # donor_query.add(donor_budget_query, Q.AND) # target_percent = [] # sdg_obj = SdgTargets.objects.values('sdg', 'description').get(target_id=sdg_target) # target_agg = get_target_aggregate(year, target=sdg_target, operating_unit=operating_unit, # budget_source=budget_source) # aggregate_results1 = Project.objects.filter(query)\ # .aggregate(projects=Count('project_id', distinct=True), # budget_sources=Count('donorfundsplitup__organisation', distinct=True)) # # target_percent.append({'total_budget': target_agg['target_budget'], # 'total_expense': target_agg['target_expense'], # 'total_projects': aggregate_results1['projects'], # 'budget_sources': aggregate_results1['budget_sources'], # 'target_desc': sdg_obj['description'], # 'target_id': sdg_target, # 'sdg': sdg_obj['sdg'] # }) # budget_sources = DonorFundSplitUp.objects.filter(donor_query).values('organisation') \ # .annotate(total_expense=Coalesce(Sum('expense'), 0), # total_budget=Coalesce(Sum('budget'), 0), # short_name=F('organisation__short_name'), # organisation_name=F('organisation__org_name')) \ # .order_by('-total_budget')[0:10] # budget_sources_serializer = SdgBudgetSourcesSerializer(budget_sources, many=True, # context={'request': request}) # top_recipient_offices = DonorFundSplitUp.objects.filter(donor_query & # Q(project__operating_unit__isnull=False)) \ # .values('project__operating_unit') \ # .annotate(total_expense=Coalesce(Sum('expense'), 0), # total_budget=Coalesce(Sum('budget'), 0), # name=F('project__operating_unit__name'), # iso3=F('project__operating_unit__iso3')).order_by( # '-total_budget')[0:10] # recipient_offices_serializer = SdgOperatingUnitSerializer(top_recipient_offices, many=True) # # data = { # 'aggregate': target_percent, # 'budget_sources': budget_sources_serializer.data, # 'top_recipient_offices': recipient_offices_serializer.data # } # return self.jp_response(s_code='HTTP_200_OK', data=data) # except Exception as e: # return self.jp_error_response('HTTP_500_INTERNAL_SERVER_ERROR', 'EXCEPTION', [str(e), ]) class SdgTargetDetailView(APIView, ResponseViewMixin): def get(self, request, args, *kwargs): try: year = request.GET.get('year', '') sdg_target = request.GET.get('sdg_target', '') operating_unit = request.GET.get('operating_unit', '') budget_source = request.GET.get('budget_source', '') if not year: return self.jp_error_response('HTTP_400_BAD_REQUEST', 'UNKNOWN_QUERY', ['Please provide a year']) data = dict() if year and int(year) >= SDG_START_YEAR: active_projects = get_active_projects_for_year(year) donor_query = Q(project__in=active_projects) & Q(year=year) & \ Q(project__project_active__year=year) query = Q(project_id__in=active_projects) & Q(donorfundsplitup__year=year) & \ Q(project_active__year=year) if sdg_target and sdg_target != '0': query.add(Q(outputtarget__target_id=sdg_target) & Q(outputtarget__year=year), Q. AND) donor_query.add(Q(output__outputtarget__target_id=sdg_target) & Q(output__outputtarget__year=year), Q. AND) if operating_unit: query.add(Q(operating_unit=operating_unit) \| Q(operating_unit__bureau__code=operating_unit), Q.AND) donor_query.add(Q(project__operating_unit=operating_unit) \| Q(project__operating_unit__bureau__code=operating_unit), Q.AND) if budget_source: budget_query = Q(donorfundsplitup__organisation__ref_id=budget_source) \| \ Q(donorfundsplitup__organisation__type_level_3=budget_source) donor_budget_query = Q(organisation__ref_id=budget_source) \| \ Q(organisation__type_level_3=budget_source) query.add(budget_query, Q.AND) donor_query.add(donor_budget_query, Q.AND) target_percent = [] sdg_obj = SdgTargets.objects.values('sdg', 'description', 'sdg__name').get(target_id=sdg_target) target_agg = get_target_aggregate_new(year, sdg_target, sdg_obj['sdg'], operating_unit=operating_unit, budget_source=budget_source, active_projects=active_projects) aggregate_results1 = Project.objects.filter(query)\ .aggregate(projects=Count('project_id', distinct=True), budget_sources=Count('donorfundsplitup__organisation', distinct=True)) target_percent.append({'total_budget': target_agg['target_budget'], 'total_expense': target_agg['target_expense'], 'total_projects': aggregate_results1['projects'], 'budget_sources': aggregate_results1['budget_sources'], 'target_desc': sdg_obj['description'], 'target_id': sdg_target, 'sdg': sdg_obj['sdg'], 'sdg_name': sdg_obj['sdg__name'], }) budget_sources = DonorFundSplitUp.objects.filter(donor_query).values('organisation') \ .annotate(total_expense=Coalesce(Sum('expense'), 0), total_budget=Coalesce(Sum('budget'), 0), short_name=F('organisation__short_name'), organisation_name=F('organisation__org_name')) \ .order_by('-total_budget')[0:10] budget_sources_data = [] for source in budget_sources: budget_data = get_target_aggregate_new(year, target=sdg_target, sdg=sdg_obj['sdg'], budget_source=source['organisation'], active_projects=active_projects) if budget_data: top_budget_source = {'total_expense': budget_data['target_expense'], 'total_budget': budget_data['target_budget'], 'short_name': source['short_name'], 'organisation_name': source['organisation_name'] } budget_sources_data.append(top_budget_source) top_budget_sources = sorted(budget_sources_data, key=lambda k: k['total_budget'], reverse=True) budget_sources_serializer = SdgBudgetSourcesSerializer(top_budget_sources, many=True, context={'request': request}) top_recipient_offices = DonorFundSplitUp.objects.filter(donor_query & Q(project__operating_unit__isnull=False)) \ .values('project__operating_unit') \ .annotate(total_expense=Coalesce(Sum('expense'), 0), total_budget=Coalesce(Sum('budget'), 0), name=F('project__operating_unit__name'), iso3=F('project__operating_unit__iso3')).order_by( '-total_budget')[0:10] top_recipient_offices_data = [] for recipient in top_recipient_offices: recipient_data = get_target_aggregate_new(year, target=sdg_target, sdg=sdg_obj['sdg'], operating_unit=recipient['project__operating_unit'], active_projects=active_projects) if recipient_data: top_recipient = {'total_expense': recipient_data['target_expense'], 'total_budget': recipient_data['target_budget'], 'name': recipient['name'], 'iso3': recipient['iso3'], } top_recipient_offices_data.append(top_recipient) recipient_offices = sorted(top_recipient_offices_data, key=lambda k: k['total_budget'], reverse=True) recipient_offices_serializer = SdgOperatingUnitSerializer(recipient_offices, many=True) data = { 'aggregate': target_percent, 'budget_sources': budget_sources_serializer.data, 'top_recipient_offices': recipient_offices_serializer.data } return self.jp_response(s_code='HTTP_200_OK', data=data) except Exception as e: return self.jp_error_response('HTTP_500_INTERNAL_SERVER_ERROR', 'EXCEPTION', [str(e), ]) class SdgTargetView(APIView, ResponseViewMixin): def get(self, request, args, *kwargs): try: year = request.GET.get('year', '') budget_source = request.GET.get('budget_source', '') operating_unit = request.GET.get('operating_unit', '') sdg = request.GET.get('sdg', '') if not year: return self.jp_error_response('HTTP_400_BAD_REQUEST', 'UNKNOWN_QUERY', ['Please provide a year']) target_percent = [] active_projects = get_active_projects_for_year(year, operating_unit=operating_unit, budget_source=budget_source) if year and int(year) >= SDG_START_YEAR: targets = SdgTargets.objects.filter(sdg=sdg).values('target_id', 'description') for target in targets: target_id = target['target_id'] target_agg = get_target_aggregate_new(year, target_id, sdg, operating_unit=operating_unit, budget_source=budget_source, active_projects=active_projects) if target_agg['budget_percentage'] > 0: target_percent.append({'target_budget': target_agg['target_budget'], 'target_expense': target_agg['target_expense'], 'target_percentage': target_agg['budget_percentage'], 'target_id': target_id, 'target_description': target['description']}) result = [] result_str = [] for target_val in target_percent: if target_val['target_id'].split('.')[1][0:2].isdigit(): result.append(int(target_val['target_id'].split('.')[1][0:2])) else: result_str.append(target_val['target_id'].split('.')[1][0:2]) outputs = sorted(result) + sorted(result_str) target_data = [] for output in outputs: for target_per in target_percent: if target_per['target_id'].split('.')[1][0:2] == str(output) : target_data.append(target_per) sdg_target_serializer = SdgTargetSerializer(target_data, many=True, context={'request': request}) data = { 'percentage': sdg_target_serializer.data } return self.jp_response(s_code='HTTP_200_OK', data=data) except Exception as e: print(e) return self.jp_error_response('HTTP_500_INTERNAL_SERVER_ERROR', 'EXCEPTION', [str(e), ]) class SdgView(APIView, ResponseViewMixin): def get(self, request, args, *kwargs): try: import json year = request.GET.get('year', '') operating_unit = request.GET.get('operating_unit', '') budget_source = request.GET.get('budget_source') sdg_code = request.GET.get('sdg', None) if not year: return self.jp_error_response('HTTP_400_BAD_REQUEST', 'UNKNOWN_QUERY', ['Please provide a year']) try: sdg = SDGSunburst.objects.get(sdg_year=year).response except Exception as e: sdg = get_sdg_sunburst(year, operating_unit, budget_source, sdg_code) return self.jp_response(s_code='HTTP_200_OK', data=json.loads(sdg)) except Exception as e: return self.jp_error_response('HTTP_500_INTERNAL_SERVER_ERROR', 'EXCEPTION', [str(e), ]) class SectorView(APIView, ResponseViewMixin): def get(self, request, args, **kwargs): try: search = self.request.GET.get('search', '') year = process_query_params(request.GET.get('year', '')) operating_unit = request.GET.get('operating_unit', '') donor = process_query_params(request.GET.get('donor', '')) new_sector = Sector.objects.filter(Q(code__in=NEW_SECTOR_CODES)).values() old_sector = Sector.objects.filter(Q(code__in=OLD_SECTOR_CODES)).values() if search: old_sector = old_sector.filter(Q(code__icontains=search) \| Q(sector__icontains=search)) old_sector = list(old_sector) new_sector = list(new_sector) if search == '' or search.lower() == 'others' or search == '0': other_valid_sectors = get_valid_sectors(year, operating_unit, donor, sector='0') if other_valid_sectors: old_sector.append({'code': "0", 'sector': "Others", 'color': NULL_SECTOR_COLOR_CODE, 'start_year': '2015', 'end_year': '2017'}) new_sector.append({'code': "0", 'sector': "Others", 'color': NULL_SECTOR_COLOR_CODE, 'start_year': '2018', 'end_year': '2021'}) sector = { 'new_focus': new_sector, '2015-2017': old_sector } data = { 'sector': sector } return self.jp_response(s_code='HTTP_200_OK', data=data) except Exception as e: return self.jp_error_response('HTTP_500_INTERNAL_SERVER_ERROR', 'EXCEPTION', [str(e), ]) def get_map_data(year, sdg='', budget_source='', recipient_country='', sector='', project_id='', budget_type='', signature_solution='', sdg_target='', marker_type='', marker_id='', provide_output=''): active_projects = get_active_projects_for_year(year, operating_unit=recipient_country, budget_source=budget_source) projects_query = get_project_query(year, operating_unit=recipient_country, budget_source=budget_source, sdg=sdg, sector=sector) projects = Project.objects.filter(projects_query & Q(project_id__in=active_projects)).distinct() fund_query = get_fund_split_query(year, budget_source=budget_source, operating_unit=recipient_country, sdg=sdg, sector=sector, project_id=project_id, budget_type=budget_type, signature_solution=signature_solution, sdg_target=sdg_target, marker_type=marker_type, marker_id=marker_id) donor_query = fund_query & Q(project__in=projects) countries = DonorFundSplitUp.objects.filter(donor_query).distinct().prefetch_related('project', 'output') countries = countries.values('output__operating_unit') \ .annotate(project_count=Count('project', distinct=True), output_count=Count('output', distinct=True), donor_count=Count('organisation', distinct=True), total_budget=Coalesce(Sum('budget'), 0), total_expense=Coalesce(Sum('expense'), 0), operating_unit_name=F('output__operating_unit__name'), operating_unit_iso3=F('output__operating_unit__iso3'), operating_unit_iso2=F('output__operating_unit__iso2'), operating_unit_unit_type=F('output__operating_unit__unit_type'), operating_unit_latitude=F('output__operating_unit__latitude'), operating_unit_longitude=F('output__operating_unit__longitude'), ) if year and int(year) >= SDG_START_YEAR and sdg or int(year) >= SDG_START_YEAR and sdg_target: serializer = MapDetailsSdgSerializer(countries, many=True, context={'year':
#!/usr/bin/env python # # Copyright (C) 2017 ShadowMan # """ A high-level overview of the framing is given in the following figure. B 0 * * * * * * * 1 * * * * * * * 2 * * * * * * * 3 * * * * * * * - \| \| \| \| \| 0 \| 1 \| 2 \| 3 \| i 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 \| +-+-+-+-+-------+-+-------------+-------------------------------+ \|F\|R\|R\|R\| opcode\|M\| Payload len \| Extended payload length \| \|I\|S\|S\|S\| (4) \|A\| (7) \| (16/64) \| \|N\|V\|V\|V\| \|S\| \| (if payload len==126/127) \| \| \|1\|2\|3\| \|K\| \| \| +-+-+-+-+-------+-+-------------+ - - - - - - - - - - - - - - - + \| Extended payload length continued, if payload len == 127 \| + - - - - - - - - - - - - - - - +-------------------------------+ \| \|Masking-key, if MASK set to 1 \| +-------------------------------+-------------------------------+ \| Masking-key (continued) \| Payload Data \| +-------------------------------- - - - - - - - - - - - - - - - + : Payload Data continued ... : + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + \| Payload Data continued ... \| +---------------------------------------------------------------+ FIN: 1 bit Indicates that this is the final fragment in a message. The first fragment MAY also be the final fragment. RSV1, RSV2, RSV3: 1 bit each MUST be 0 unless an extension is negotiated that defines meanings for non-zero values. If a nonzero value is received and none of the negotiated extensions defines the meaning of such a nonzero value, the receiving endpoint MUST _Fail the WebSocket Connection_. Opcode: 4 bits Defines the interpretation of the "Payload data". If an unknown opcode is received, the receiving endpoint MUST _Fail the WebSocket Connection_. The following values are defined. * %x0 denotes a continuation frame * %x1 denotes a text frame * %x2 denotes a binary frame * %x3-7 are reserved for further non-control frames * %x8 denotes a connection close * %x9 denotes a ping * %xA denotes a pong * %xB-F are reserved for further control frames Mask: 1 bit Defines whether the "Payload data" is masked. If set to 1, a masking key is present in masking-key, and this is used to unmask the "Payload data" as per Section 5.3. All frames sent from client to server have this bit set to 1. Payload length: 7 bits, 7+16 bits, or 7+64 bits The length of the "Payload data", in bytes: if 0-125, that is the payload length. If 126, the following 2 bytes interpreted as a 16-bit unsigned integer are the payload length. If 127, the following 8 bytes interpreted as a 64-bit unsigned integer (the most significant bit MUST be 0) are the payload length. Multi-byte length quantities are expressed in network byte order. Note that in all cases, the minimal number of bytes MUST be used to encode the length, for example, the length of a 124-byte-long string can't be encoded as the sequence 126, 0, 124. The payload length is the length of the "Extension data" + the length of the "Application data". The length of the "Extension data" may be zero, in which case the payload length is the length of the "Application data". Masking-key: 0 or 4 bytes All frames sent from the client to the server are masked by a 32-bit value that is contained within the frame. This field is present if the mask bit is set to 1 and is absent if the mask bit is set to 0. Payload data: (x+y) bytes The "Payload data" is defined as "Extension data" concatenated with "Application data". Extension data: x bytes The "Extension data" is 0 bytes unless an extension has been negotiated. Any extension MUST specify the length of the "Extension data", or how that length may be calculated, and how the extension use MUST be negotiated during the opening handshake. If present, the "Extension data" is included in the total payload length. Application data: y bytes Arbitrary "Application data", taking up the remainder of the frame after any "Extension data". The length of the "Application data" is equal to the payload length minus the length of the "Extension data". """ import os import abc import struct from websocket.utils import ( generic, ws_utils, exceptions, packet, logger ) def ws_transform_payload_data(data, mask_key): if not isinstance(mask_key, int): # from string transition to int if isinstance(mask_key, str): mask_key = int(mask_key, 16) else: raise KeyError('mask key must be hex int') if not isinstance(data, (str, bytes)): raise KeyError('data must be str or bytes type') # Octet i of the transformed data is the XOR of octet i of the original # data with octet at index i modulo 4 of the masking key mask_key_octet = { 0: (mask_key & 0xff000000) >> 24, 1: (mask_key & 0x00ff0000) >> 16, 2: (mask_key & 0x0000ff00) >> 8, 3: mask_key & 0x000000ff } transformed_string = b'' for index, value in enumerate(generic.to_bytes(data)): transformed_string += struct.pack( '!B', (value ^ mask_key_octet[index % 4]) & 0xff) return transformed_string def parse_frame_length(frame_header): if not isinstance(frame_header, (str, bytes)): raise KeyError('frame_header must be str or bytes type') header = packet.ByteArray(frame_header) if len(header) < 2: logger.warning('receive less than 2-bytes') raise RuntimeError('frame header less than 2-bytes') # first bit is MASK flag payload_length = packet.bits_to_integer(header.get_bits(1)[1:]) # if 0-125, that is the payload length if payload_length <= 125: # if frame is client-to-server, payload length does not include mask-key if header.get_bits(1)[0] is 1: return payload_length + 6 return payload_length + 2 # If 126, the following 2 bytes interpreted as a # 16-bit unsigned integer are the payload length elif payload_length == 126: # Payload length field is in [2-4)bytes if len(header) < 4: raise exceptions.FrameHeaderParseError( 'payload length flag is 126, but header length is {}'.format( len(header))) if header.get_bits(1)[0] is 1: return packet.bits_to_integer( generic.flatten_list(header.get_bits(2, 2))) + 8 return packet.bits_to_integer( generic.flatten_list(header.get_bits(2, 2))) + 4 # If 127, the following 8 bytes interpreted as a # 64-bit unsigned integer (the most significant bit # MUST be 0) are the payload length. elif payload_length == 127: # Payload length field is in [2-10)bytes if len(header) < 10: raise exceptions.FrameHeaderParseError( 'payload length flag is 127, but header length is {}'.format( len(header))) if header.get_bits(1)[0] is 1: return packet.bits_to_integer( generic.flatten_list(header.get_bits(2, 2))) + 14 return packet.bits_to_integer( generic.flatten_list(header.get_bits(2, 2))) + 10 raise exceptions.FatalError('internal error') # using for judge frame type Text_Frame = b'Text Frame' Binary_Frame = b'Binary Frame' Close_Frame = b'Close Frame' class FrameBase(object, metaclass=abc.ABCMeta): _global_frame_type = { 0x0: b'Continuation Frame', 0x1: b'Text Frame', 0x2: b'Binary Frame', 0x3: b'Non-Control Frame', 0x4: b'Non-Control Frame', 0x5: b'Non-Control Frame', 0x6: b'Non-Control Frame', 0x7: b'Non-Control Frame', 0x8: b'Close Frame', 0x9: b'Ping Frame', 0xA: b'Pong Frame', 0xB: b'Control Frame', 0xC: b'Control Frame', 0xD: b'Control Frame', 0xE: b'Control Frame', 0xF: b'Control Frame', } def __init__(self, byte_array): if not isinstance(byte_array, packet.ByteArray): raise RuntimeError('the byte array is invalid') # initializing all websocket-frame flags self._flag_fin = 1 self._flag_rsv1 = 0 self._flag_rsv2 = 0 self._flag_rsv3 = 0 self._flag_opcode = 1 # Byte index: 2 self._flag_mask = 0 self._flag_payload_length = 0 self._payload_length = 0 # Byte index: [3,7) self._mask_key = False # payload data self._payload_data = None self._byte_array = byte_array # parse frame self.parse_octet() def parse_octet(self): # first byte(8-bits) # +-+-+-+-+-------+ # \|F\|R\|R\|R\| opcode\| # \|I\|S\|S\|S\| (4) \| # \|N\|V\|V\|V\| \| # \| \|1\|2\|3\| \| # +-+-+-+-+-------+ self._flag_fin = self._byte_array.get_bit(0, 0) self._flag_rsv1 = self._byte_array.get_bit(0, 1) self._flag_rsv2 = self._byte_array.get_bit(0, 2) self._flag_rsv3 = self._byte_array.get_bit(0, 3) self._flag_opcode = packet.bits_to_integer( self._byte_array.get_bits(0)[4:]) # second byte(8-bits) # +-+-------------+ # \|M\| Payload len \| # \|A\| (7) \| # \|S\| \| # \|K\| \| # +-+-+-+-+-------+ self._flag_mask = self._byte_array.get_bit(1, 0)
brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Window, brush) brush = QtGui.QBrush(QtGui.QColor(0, 0, 0)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Shadow, brush) brush = QtGui.QBrush(QtGui.QColor(127, 255, 127)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.AlternateBase, brush) brush = QtGui.QBrush(QtGui.QColor(255, 255, 220)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.ToolTipBase, brush) brush = QtGui.QBrush(QtGui.QColor(0, 0, 0)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.ToolTipText, brush) brush = QtGui.QBrush(QtGui.QColor(0, 0, 0, 128)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.PlaceholderText, brush) brush = QtGui.QBrush(QtGui.QColor(0, 0, 0)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.WindowText, brush) brush = QtGui.QBrush(QtGui.QColor(0, 255, 0)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Button, brush) brush = QtGui.QBrush(QtGui.QColor(127, 255, 127)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Light, brush) brush = QtGui.QBrush(QtGui.QColor(63, 255, 63)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Midlight, brush) brush = QtGui.QBrush(QtGui.QColor(0, 127, 0)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Dark, brush) brush = QtGui.QBrush(QtGui.QColor(0, 170, 0)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Mid, brush) brush = QtGui.QBrush(QtGui.QColor(0, 0, 0)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Text, brush) brush = QtGui.QBrush(QtGui.QColor(255, 255, 255)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.BrightText, brush) brush = QtGui.QBrush(QtGui.QColor(0, 0, 0)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.ButtonText, brush) brush = QtGui.QBrush(QtGui.QColor(255, 255, 255)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Base, brush) brush = QtGui.QBrush(QtGui.QColor(0, 255, 0)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Window, brush) brush = QtGui.QBrush(QtGui.QColor(0, 0, 0)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Shadow, brush) brush = QtGui.QBrush(QtGui.QColor(127, 255, 127)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.AlternateBase, brush) brush = QtGui.QBrush(QtGui.QColor(255, 255, 220)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.ToolTipBase, brush) brush = QtGui.QBrush(QtGui.QColor(0, 0, 0)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.ToolTipText, brush) brush = QtGui.QBrush(QtGui.QColor(0, 0, 0, 128)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.PlaceholderText, brush) brush = QtGui.QBrush(QtGui.QColor(0, 127, 0)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.WindowText, brush) brush = QtGui.QBrush(QtGui.QColor(0, 255, 0)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Button, brush) brush = QtGui.QBrush(QtGui.QColor(127, 255, 127)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Light, brush) brush = QtGui.QBrush(QtGui.QColor(63, 255, 63)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Midlight, brush) brush = QtGui.QBrush(QtGui.QColor(0, 127, 0)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Dark, brush) brush = QtGui.QBrush(QtGui.QColor(0, 170, 0)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Mid, brush) brush = QtGui.QBrush(QtGui.QColor(0, 127, 0)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Text, brush) brush = QtGui.QBrush(QtGui.QColor(255, 255, 255)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.BrightText, brush) brush = QtGui.QBrush(QtGui.QColor(0, 127, 0)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.ButtonText, brush) brush = QtGui.QBrush(QtGui.QColor(0, 255, 0)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Base, brush) brush = QtGui.QBrush(QtGui.QColor(0, 255, 0)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Window, brush) brush = QtGui.QBrush(QtGui.QColor(0, 0, 0)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Shadow, brush) brush = QtGui.QBrush(QtGui.QColor(0, 255, 0)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.AlternateBase, brush) brush = QtGui.QBrush(QtGui.QColor(255, 255, 220)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.ToolTipBase, brush) brush = QtGui.QBrush(QtGui.QColor(0, 0, 0)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.ToolTipText, brush) brush = QtGui.QBrush(QtGui.QColor(0, 0, 0, 128)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.PlaceholderText, brush) self.main_progressBarUserName.setPalette(palette) self.main_progressBarUserName.setProperty("value", 100) self.main_progressBarUserName.setAlignment(QtCore.Qt.AlignCenter) self.main_progressBarUserName.setTextDirection(QtWidgets.QProgressBar.TopToBottom) self.main_progressBarUserName.setObjectName("main_progressBarUserName") self.horizontalLayout_11.addWidget(self.main_progressBarUserName) self.verticalLayout_9.addLayout(self.horizontalLayout_11) self.verticalLayout_13.addLayout(self.verticalLayout_9) self.groupBox_9 = QtWidgets.QGroupBox(self.splitter_8) self.groupBox_9.setMaximumSize(QtCore.QSize(16777215, 16777215)) self.groupBox_9.setObjectName("groupBox_9") self.verticalLayout_48 = QtWidgets.QVBoxLayout(self.groupBox_9) self.verticalLayout_48.setObjectName("verticalLayout_48") self.verticalLayout_47 = QtWidgets.QVBoxLayout() self.verticalLayout_47.setObjectName("verticalLayout_47") self.verticalLayout_23 = QtWidgets.QVBoxLayout() self.verticalLayout_23.setObjectName("verticalLayout_23") self.main_editNikWidth = QtWidgets.QLineEdit(self.groupBox_9) font = QtGui.QFont() font.setFamily("Courier New") font.setPointSize(10) font.setBold(True) font.setWeight(75) self.main_editNikWidth.setFont(font) self.main_editNikWidth.setText("") self.main_editNikWidth.setAlignment(QtCore.Qt.AlignCenter) self.main_editNikWidth.setClearButtonEnabled(True) self.main_editNikWidth.setObjectName("main_editNikWidth") self.verticalLayout_23.addWidget(self.main_editNikWidth) self.horizontalLayout_16 = QtWidgets.QHBoxLayout() self.horizontalLayout_16.setObjectName("horizontalLayout_16") spacerItem6 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout_16.addItem(spacerItem6) self.main_pb_ShowNikWidth = QtWidgets.QPushButton(self.groupBox_9) self.main_pb_ShowNikWidth.setMinimumSize(QtCore.QSize(100, 0)) self.main_pb_ShowNikWidth.setMaximumSize(QtCore.QSize(100, 23)) self.main_pb_ShowNikWidth.setAutoRepeat(False) self.main_pb_ShowNikWidth.setAutoDefault(False) self.main_pb_ShowNikWidth.setDefault(False) self.main_pb_ShowNikWidth.setFlat(False) self.main_pb_ShowNikWidth.setObjectName("main_pb_ShowNikWidth") self.horizontalLayout_16.addWidget(self.main_pb_ShowNikWidth) self.main_pb_clearNikWidth = QtWidgets.QPushButton(self.groupBox_9) self.main_pb_clearNikWidth.setMinimumSize(QtCore.QSize(23, 23)) self.main_pb_clearNikWidth.setMaximumSize(QtCore.QSize(23, 23)) self.main_pb_clearNikWidth.setText("") self.main_pb_clearNikWidth.setObjectName("main_pb_clearNikWidth") self.horizontalLayout_16.addWidget(self.main_pb_clearNikWidth) spacerItem7 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout_16.addItem(spacerItem7) self.verticalLayout_23.addLayout(self.horizontalLayout_16) self.verticalLayout_47.addLayout(self.verticalLayout_23) self.main_tbShowNikWidth = QtWidgets.QTextBrowser(self.groupBox_9) font = QtGui.QFont() font.setPointSize(9) self.main_tbShowNikWidth.setFont(font) self.main_tbShowNikWidth.setObjectName("main_tbShowNikWidth") self.verticalLayout_47.addWidget(self.main_tbShowNikWidth) self.verticalLayout_48.addLayout(self.verticalLayout_47) self.groupBox_7 = QtWidgets.QGroupBox(self.splitter_9) self.groupBox_7.setObjectName("groupBox_7") self.horizontalLayout_37 = QtWidgets.QHBoxLayout(self.groupBox_7) self.horizontalLayout_37.setObjectName("horizontalLayout_37") self.splitter_7 = QtWidgets.QSplitter(self.groupBox_7) self.splitter_7.setLineWidth(2) self.splitter_7.setOrientation(QtCore.Qt.Horizontal) self.splitter_7.setObjectName("splitter_7") self.splitter_6 = QtWidgets.QSplitter(self.splitter_7) self.splitter_6.setLineWidth(2) self.splitter_6.setOrientation(QtCore.Qt.Vertical) self.splitter_6.setObjectName("splitter_6") self.groupBox_4 = QtWidgets.QGroupBox(self.splitter_6) self.groupBox_4.setObjectName("groupBox_4") self.verticalLayout_50 = QtWidgets.QVBoxLayout(self.groupBox_4) self.verticalLayout_50.setObjectName("verticalLayout_50") self.verticalLayout_49 = QtWidgets.QVBoxLayout() self.verticalLayout_49.setObjectName("verticalLayout_49") self.verticalLayout_17 = QtWidgets.QVBoxLayout() self.verticalLayout_17.setObjectName("verticalLayout_17") self.main_editIP = QtWidgets.QLineEdit(self.groupBox_4) font = QtGui.QFont() font.setFamily("Courier New") font.setPointSize(10) font.setBold(True) font.setWeight(75) self.main_editIP.setFont(font) self.main_editIP.setText("") self.main_editIP.setAlignment(QtCore.Qt.AlignCenter) self.main_editIP.setClearButtonEnabled(True) self.main_editIP.setObjectName("main_editIP") self.verticalLayout_17.addWidget(self.main_editIP) self.horizontalLayout_14 = QtWidgets.QHBoxLayout() self.horizontalLayout_14.setObjectName("horizontalLayout_14") spacerItem8 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout_14.addItem(spacerItem8) self.main_pb_ShowIPInfo = QtWidgets.QPushButton(self.groupBox_4) self.main_pb_ShowIPInfo.setMinimumSize(QtCore.QSize(100, 0)) self.main_pb_ShowIPInfo.setMaximumSize(QtCore.QSize(100, 23)) self.main_pb_ShowIPInfo.setAutoRepeat(False) self.main_pb_ShowIPInfo.setAutoDefault(False) self.main_pb_ShowIPInfo.setDefault(False) self.main_pb_ShowIPInfo.setFlat(False) self.main_pb_ShowIPInfo.setObjectName("main_pb_ShowIPInfo") self.horizontalLayout_14.addWidget(self.main_pb_ShowIPInfo) self.main_pb_clearSearchIP = QtWidgets.QPushButton(self.groupBox_4) self.main_pb_clearSearchIP.setMinimumSize(QtCore.QSize(23, 23)) self.main_pb_clearSearchIP.setMaximumSize(QtCore.QSize(23, 23)) self.main_pb_clearSearchIP.setText("") self.main_pb_clearSearchIP.setObjectName("main_pb_clearSearchIP") self.horizontalLayout_14.addWidget(self.main_pb_clearSearchIP) spacerItem9 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout_14.addItem(spacerItem9) self.verticalLayout_17.addLayout(self.horizontalLayout_14) self.verticalLayout_49.addLayout(self.verticalLayout_17) self.main_tbShowIPInfo = QtWidgets.QTextBrowser(self.groupBox_4) font = QtGui.QFont() font.setPointSize(9) self.main_tbShowIPInfo.setFont(font) self.main_tbShowIPInfo.setObjectName("main_tbShowIPInfo") self.verticalLayout_49.addWidget(self.main_tbShowIPInfo) self.verticalLayout_50.addLayout(self.verticalLayout_49) self.groupBox_8 = QtWidgets.QGroupBox(self.splitter_6) self.groupBox_8.setMaximumSize(QtCore.QSize(16777215, 16777215)) self.groupBox_8.setObjectName("groupBox_8") self.verticalLayout_52 = QtWidgets.QVBoxLayout(self.groupBox_8) self.verticalLayout_52.setObjectName("verticalLayout_52") self.verticalLayout_51 = QtWidgets.QVBoxLayout() self.verticalLayout_51.setObjectName("verticalLayout_51") self.verticalLayout_22 = QtWidgets.QVBoxLayout() self.verticalLayout_22.setObjectName("verticalLayout_22") self.main_editEMAIL = QtWidgets.QLineEdit(self.groupBox_8) font = QtGui.QFont() font.setFamily("Courier New") font.setPointSize(10) font.setBold(True) font.setWeight(75) self.main_editEMAIL.setFont(font) self.main_editEMAIL.setText("") self.main_editEMAIL.setAlignment(QtCore.Qt.AlignCenter) self.main_editEMAIL.setClearButtonEnabled(True) self.main_editEMAIL.setObjectName("main_editEMAIL") self.verticalLayout_22.addWidget(self.main_editEMAIL) self.horizontalLayout_15 = QtWidgets.QHBoxLayout() self.horizontalLayout_15.setObjectName("horizontalLayout_15") spacerItem10 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout_15.addItem(spacerItem10) self.main_pb_ShowEMAIL = QtWidgets.QPushButton(self.groupBox_8) self.main_pb_ShowEMAIL.setMinimumSize(QtCore.QSize(100, 0)) self.main_pb_ShowEMAIL.setMaximumSize(QtCore.QSize(100, 23)) self.main_pb_ShowEMAIL.setAutoRepeat(False) self.main_pb_ShowEMAIL.setAutoDefault(False) self.main_pb_ShowEMAIL.setDefault(False) self.main_pb_ShowEMAIL.setFlat(False) self.main_pb_ShowEMAIL.setObjectName("main_pb_ShowEMAIL") self.horizontalLayout_15.addWidget(self.main_pb_ShowEMAIL) self.main_pb_clearEMAIL = QtWidgets.QPushButton(self.groupBox_8) self.main_pb_clearEMAIL.setMinimumSize(QtCore.QSize(23, 23)) self.main_pb_clearEMAIL.setMaximumSize(QtCore.QSize(23, 23)) self.main_pb_clearEMAIL.setText("") self.main_pb_clearEMAIL.setObjectName("main_pb_clearEMAIL") self.horizontalLayout_15.addWidget(self.main_pb_clearEMAIL) spacerItem11 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout_15.addItem(spacerItem11) self.verticalLayout_22.addLayout(self.horizontalLayout_15) self.verticalLayout_51.addLayout(self.verticalLayout_22) self.main_tbShowEMAIL = QtWidgets.QTextBrowser(self.groupBox_8) font = QtGui.QFont() font.setPointSize(9) self.main_tbShowEMAIL.setFont(font) self.main_tbShowEMAIL.setObjectName("main_tbShowEMAIL") self.verticalLayout_51.addWidget(self.main_tbShowEMAIL) self.verticalLayout_52.addLayout(self.verticalLayout_51) self.groupBox_11 = QtWidgets.QGroupBox(self.splitter_7) self.groupBox_11.setMinimumSize(QtCore.QSize(250, 0)) self.groupBox_11.setMaximumSize(QtCore.QSize(340, 16777215)) self.groupBox_11.setObjectName("groupBox_11") self.verticalLayout_54 = QtWidgets.QVBoxLayout(self.groupBox_11) self.verticalLayout_54.setObjectName("verticalLayout_54") self.verticalLayout_53 = QtWidgets.QVBoxLayout() self.verticalLayout_53.setObjectName("verticalLayout_53") self.verticalLayout_28 = QtWidgets.QVBoxLayout() self.verticalLayout_28.setObjectName("verticalLayout_28") self.label = QtWidgets.QLabel(self.groupBox_11) self.label.setAlignment(QtCore.Qt.AlignCenter) self.label.setObjectName("label") self.verticalLayout_28.addWidget(self.label) self.horizontalLayout_36 = QtWidgets.QHBoxLayout() self.horizontalLayout_36.setObjectName("horizontalLayout_36") spacerItem12 = QtWidgets.QSpacerItem(94, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout_36.addItem(spacerItem12) self.main_editGeoObject = QtWidgets.QLineEdit(self.groupBox_11) self.main_editGeoObject.setMinimumSize(QtCore.QSize(90, 0)) self.main_editGeoObject.setMaximumSize(QtCore.QSize(180, 16777215)) font = QtGui.QFont() font.setFamily("Courier New") font.setPointSize(10) font.setBold(True) font.setWeight(75) self.main_editGeoObject.setFont(font) self.main_editGeoObject.setText("") self.main_editGeoObject.setAlignment(QtCore.Qt.AlignCenter) self.main_editGeoObject.setClearButtonEnabled(True) self.main_editGeoObject.setObjectName("main_editGeoObject") self.horizontalLayout_36.addWidget(self.main_editGeoObject) spacerItem13 = QtWidgets.QSpacerItem(94, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout_36.addItem(spacerItem13) self.verticalLayout_28.addLayout(self.horizontalLayout_36) self.horizontalLayout_20 = QtWidgets.QHBoxLayout() self.horizontalLayout_20.setObjectName("horizontalLayout_20") spacerItem14 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout_20.addItem(spacerItem14) self.main_pb_ShowGeoLatLon = QtWidgets.QPushButton(self.groupBox_11) self.main_pb_ShowGeoLatLon.setMinimumSize(QtCore.QSize(100, 23)) self.main_pb_ShowGeoLatLon.setMaximumSize(QtCore.QSize(100, 23)) self.main_pb_ShowGeoLatLon.setAutoRepeat(False) self.main_pb_ShowGeoLatLon.setAutoDefault(False) self.main_pb_ShowGeoLatLon.setDefault(False) self.main_pb_ShowGeoLatLon.setFlat(False) self.main_pb_ShowGeoLatLon.setObjectName("main_pb_ShowGeoLatLon") self.horizontalLayout_20.addWidget(self.main_pb_ShowGeoLatLon) self.main_pb_clearGeoObject = QtWidgets.QPushButton(self.groupBox_11) self.main_pb_clearGeoObject.setMinimumSize(QtCore.QSize(23, 23)) self.main_pb_clearGeoObject.setMaximumSize(QtCore.QSize(23, 23)) self.main_pb_clearGeoObject.setText("") self.main_pb_clearGeoObject.setObjectName("main_pb_clearGeoObject") self.horizontalLayout_20.addWidget(self.main_pb_clearGeoObject) spacerItem15 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout_20.addItem(spacerItem15) self.verticalLayout_28.addLayout(self.horizontalLayout_20) self.verticalLayout_53.addLayout(self.verticalLayout_28) self.main_tbShowGeoObjectLatLon = QtWidgets.QTextBrowser(self.groupBox_11) font = QtGui.QFont() font.setPointSize(8) self.main_tbShowGeoObjectLatLon.setFont(font) self.main_tbShowGeoObjectLatLon.setObjectName("main_tbShowGeoObjectLatLon") self.verticalLayout_53.addWidget(self.main_tbShowGeoObjectLatLon) self.line = QtWidgets.QFrame(self.groupBox_11) self.line.setFrameShape(QtWidgets.QFrame.HLine) self.line.setFrameShadow(QtWidgets.QFrame.Sunken) self.line.setObjectName("line") self.verticalLayout_53.addWidget(self.line) self.verticalLayout_29 = QtWidgets.QVBoxLayout() self.verticalLayout_29.setContentsMargins(-1, -1, -1, 6) self.verticalLayout_29.setObjectName("verticalLayout_29") self.horizontalLayout_27 = QtWidgets.QHBoxLayout() self.horizontalLayout_27.setObjectName("horizontalLayout_27") spacerItem16 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout_27.addItem(spacerItem16) self.label_2 = QtWidgets.QLabel(self.groupBox_11) self.label_2.setAlignment(QtCore.Qt.AlignCenter) self.label_2.setObjectName("label_2") self.horizontalLayout_27.addWidget(self.label_2) spacerItem17 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout_27.addItem(spacerItem17) self.verticalLayout_29.addLayout(self.horizontalLayout_27) self.horizontalLayout_22 = QtWidgets.QHBoxLayout() self.horizontalLayout_22.setObjectName("horizontalLayout_22") spacerItem18 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout_22.addItem(spacerItem18) self.main_editGeo_Lat = QtWidgets.QLineEdit(self.groupBox_11) self.main_editGeo_Lat.setMaximumSize(QtCore.QSize(110, 16777215)) font = QtGui.QFont() font.setFamily("Courier New") font.setPointSize(9) font.setBold(False) font.setWeight(50) self.main_editGeo_Lat.setFont(font) self.main_editGeo_Lat.setText("") self.main_editGeo_Lat.setAlignment(QtCore.Qt.AlignCenter) self.main_editGeo_Lat.setClearButtonEnabled(False) self.main_editGeo_Lat.setObjectName("main_editGeo_Lat") self.horizontalLayout_22.addWidget(self.main_editGeo_Lat) spacerItem19 = QtWidgets.QSpacerItem(20, 20, QtWidgets.QSizePolicy.Fixed, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout_22.addItem(spacerItem19) self.main_editGeo_Lon = QtWidgets.QLineEdit(self.groupBox_11) self.main_editGeo_Lon.setMaximumSize(QtCore.QSize(110, 16777215)) font = QtGui.QFont() font.setFamily("Courier New") font.setPointSize(9) font.setBold(False) font.setWeight(50) self.main_editGeo_Lon.setFont(font) self.main_editGeo_Lon.setText("") self.main_editGeo_Lon.setAlignment(QtCore.Qt.AlignCenter) self.main_editGeo_Lon.setClearButtonEnabled(False) self.main_editGeo_Lon.setObjectName("main_editGeo_Lon") self.horizontalLayout_22.addWidget(self.main_editGeo_Lon) spacerItem20 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout_22.addItem(spacerItem20) self.verticalLayout_29.addLayout(self.horizontalLayout_22) self.horizontalLayout_21 = QtWidgets.QHBoxLayout() self.horizontalLayout_21.setObjectName("horizontalLayout_21") spacerItem21 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout_21.addItem(spacerItem21) self.main_pb_ShowGeoInfo = QtWidgets.QPushButton(self.groupBox_11) self.main_pb_ShowGeoInfo.setMinimumSize(QtCore.QSize(100, 23)) self.main_pb_ShowGeoInfo.setMaximumSize(QtCore.QSize(100, 23)) self.main_pb_ShowGeoInfo.setAutoRepeat(False) self.main_pb_ShowGeoInfo.setAutoDefault(False) self.main_pb_ShowGeoInfo.setDefault(False) self.main_pb_ShowGeoInfo.setFlat(False) self.main_pb_ShowGeoInfo.setObjectName("main_pb_ShowGeoInfo") self.horizontalLayout_21.addWidget(self.main_pb_ShowGeoInfo) self.main_pb_clearGeoInfo = QtWidgets.QPushButton(self.groupBox_11) self.main_pb_clearGeoInfo.setMinimumSize(QtCore.QSize(23, 23)) self.main_pb_clearGeoInfo.setMaximumSize(QtCore.QSize(23, 23)) self.main_pb_clearGeoInfo.setText("") self.main_pb_clearGeoInfo.setObjectName("main_pb_clearGeoInfo") self.horizontalLayout_21.addWidget(self.main_pb_clearGeoInfo) spacerItem22 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout_21.addItem(spacerItem22) self.verticalLayout_29.addLayout(self.horizontalLayout_21) self.verticalLayout_53.addLayout(self.verticalLayout_29) self.main_tbShowGeoObjectInfo = QtWidgets.QTextBrowser(self.groupBox_11) font = QtGui.QFont() font.setPointSize(9) self.main_tbShowGeoObjectInfo.setFont(font) self.main_tbShowGeoObjectInfo.setObjectName("main_tbShowGeoObjectInfo") self.verticalLayout_53.addWidget(self.main_tbShowGeoObjectInfo) self.verticalLayout_54.addLayout(self.verticalLayout_53) self.horizontalLayout_37.addWidget(self.splitter_7) self.horizontalLayout_38.addWidget(self.splitter_9) self.tabMain.addTab(self.tabCommon, "") self.tabGSM = QtWidgets.QWidget() self.tabGSM.setObjectName("tabGSM") self.horizontalLayout_18 = QtWidgets.QHBoxLayout(self.tabGSM) self.horizontalLayout_18.setObjectName("horizontalLayout_18") self.splitter_13 = QtWidgets.QSplitter(self.tabGSM) self.splitter_13.setOrientation(QtCore.Qt.Horizontal) self.splitter_13.setObjectName("splitter_13") self.splitter_12 = QtWidgets.QSplitter(self.splitter_13) self.splitter_12.setOrientation(QtCore.Qt.Horizontal) self.splitter_12.setObjectName("splitter_12") self.splitter_10 = QtWidgets.QSplitter(self.splitter_12) self.splitter_10.setOrientation(QtCore.Qt.Vertical) self.splitter_10.setObjectName("splitter_10") self.groupBox_10 = QtWidgets.QGroupBox(self.splitter_10) self.groupBox_10.setObjectName("groupBox_10") self.horizontalLayout_32 = QtWidgets.QHBoxLayout(self.groupBox_10) self.horizontalLayout_32.setObjectName("horizontalLayout_32") self.verticalLayout_55 = QtWidgets.QVBoxLayout() self.verticalLayout_55.setObjectName("verticalLayout_55") self.verticalLayout_24 = QtWidgets.QVBoxLayout() self.verticalLayout_24.setObjectName("verticalLayout_24") self.main_editIMSI = QtWidgets.QLineEdit(self.groupBox_10) font = QtGui.QFont() font.setFamily("Courier New") font.setPointSize(10) font.setBold(True) font.setWeight(75) self.main_editIMSI.setFont(font) self.main_editIMSI.setText("") self.main_editIMSI.setAlignment(QtCore.Qt.AlignCenter) self.main_editIMSI.setClearButtonEnabled(True) self.main_editIMSI.setObjectName("main_editIMSI") self.verticalLayout_24.addWidget(self.main_editIMSI) self.horizontalLayout_31 = QtWidgets.QHBoxLayout() self.horizontalLayout_31.setObjectName("horizontalLayout_31") spacerItem23 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout_31.addItem(spacerItem23) self.main_pb_ShowIMSI = QtWidgets.QPushButton(self.groupBox_10) self.main_pb_ShowIMSI.setMinimumSize(QtCore.QSize(100, 0)) self.main_pb_ShowIMSI.setMaximumSize(QtCore.QSize(100, 23)) self.main_pb_ShowIMSI.setAutoRepeat(False) self.main_pb_ShowIMSI.setAutoDefault(False) self.main_pb_ShowIMSI.setDefault(False) self.main_pb_ShowIMSI.setFlat(False) self.main_pb_ShowIMSI.setObjectName("main_pb_ShowIMSI") self.horizontalLayout_31.addWidget(self.main_pb_ShowIMSI) self.main_pb_clearIMSI = QtWidgets.QPushButton(self.groupBox_10) self.main_pb_clearIMSI.setMinimumSize(QtCore.QSize(23, 23)) self.main_pb_clearIMSI.setMaximumSize(QtCore.QSize(23, 23)) self.main_pb_clearIMSI.setText("") self.main_pb_clearIMSI.setObjectName("main_pb_clearIMSI") self.horizontalLayout_31.addWidget(self.main_pb_clearIMSI) spacerItem24 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout_31.addItem(spacerItem24) self.verticalLayout_24.addLayout(self.horizontalLayout_31) self.verticalLayout_55.addLayout(self.verticalLayout_24) self.main_tbShowIMSI = QtWidgets.QTextBrowser(self.groupBox_10) font = QtGui.QFont() font.setPointSize(9) self.main_tbShowIMSI.setFont(font) self.main_tbShowIMSI.setObjectName("main_tbShowIMSI") self.verticalLayout_55.addWidget(self.main_tbShowIMSI) self.horizontalLayout_32.addLayout(self.verticalLayout_55) self.groupBox_13 = QtWidgets.QGroupBox(self.splitter_10) self.groupBox_13.setObjectName("groupBox_13") self.horizontalLayout_39 = QtWidgets.QHBoxLayout(self.groupBox_13) self.horizontalLayout_39.setObjectName("horizontalLayout_39") self.verticalLayout_56 = QtWidgets.QVBoxLayout() self.verticalLayout_56.setObjectName("verticalLayout_56") self.verticalLayout_25 = QtWidgets.QVBoxLayout() self.verticalLayout_25.setObjectName("verticalLayout_25") self.main_editIMEI = QtWidgets.QLineEdit(self.groupBox_13) font = QtGui.QFont() font.setFamily("Courier New") font.setPointSize(10) font.setBold(True) font.setWeight(75) self.main_editIMEI.setFont(font) self.main_editIMEI.setText("") self.main_editIMEI.setAlignment(QtCore.Qt.AlignCenter) self.main_editIMEI.setClearButtonEnabled(True) self.main_editIMEI.setObjectName("main_editIMEI") self.verticalLayout_25.addWidget(self.main_editIMEI) self.horizontalLayout_40 = QtWidgets.QHBoxLayout() self.horizontalLayout_40.setObjectName("horizontalLayout_40") spacerItem25 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout_40.addItem(spacerItem25) self.main_pb_ShowIMEI = QtWidgets.QPushButton(self.groupBox_13) self.main_pb_ShowIMEI.setMinimumSize(QtCore.QSize(100, 0)) self.main_pb_ShowIMEI.setMaximumSize(QtCore.QSize(100, 23)) self.main_pb_ShowIMEI.setAutoRepeat(False) self.main_pb_ShowIMEI.setAutoDefault(False) self.main_pb_ShowIMEI.setDefault(False) self.main_pb_ShowIMEI.setFlat(False) self.main_pb_ShowIMEI.setObjectName("main_pb_ShowIMEI") self.horizontalLayout_40.addWidget(self.main_pb_ShowIMEI) self.main_pb_clearIMEI = QtWidgets.QPushButton(self.groupBox_13) self.main_pb_clearIMEI.setMinimumSize(QtCore.QSize(23, 23)) self.main_pb_clearIMEI.setMaximumSize(QtCore.QSize(23, 23)) self.main_pb_clearIMEI.setText("") self.main_pb_clearIMEI.setObjectName("main_pb_clearIMEI") self.horizontalLayout_40.addWidget(self.main_pb_clearIMEI) spacerItem26 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout_40.addItem(spacerItem26) self.verticalLayout_25.addLayout(self.horizontalLayout_40) self.verticalLayout_56.addLayout(self.verticalLayout_25) self.main_tbShowIMEI = QtWidgets.QTextBrowser(self.groupBox_13) font = QtGui.QFont() font.setPointSize(9) self.main_tbShowIMEI.setFont(font) self.main_tbShowIMEI.setObjectName("main_tbShowIMEI") self.verticalLayout_56.addWidget(self.main_tbShowIMEI) self.horizontalLayout_39.addLayout(self.verticalLayout_56) self.splitter_11 = QtWidgets.QSplitter(self.splitter_12) self.splitter_11.setOrientation(QtCore.Qt.Vertical) self.splitter_11.setObjectName("splitter_11") self.groupBox_15 = QtWidgets.QGroupBox(self.splitter_11) self.groupBox_15.setObjectName("groupBox_15") self.horizontalLayout_44 = QtWidgets.QHBoxLayout(self.groupBox_15) self.horizontalLayout_44.setObjectName("horizontalLayout_44") self.verticalLayout_57 = QtWidgets.QVBoxLayout() self.verticalLayout_57.setObjectName("verticalLayout_57") self.verticalLayout_26 = QtWidgets.QVBoxLayout() self.verticalLayout_26.setObjectName("verticalLayout_26") self.main_editISDN = QtWidgets.QLineEdit(self.groupBox_15) font = QtGui.QFont() font.setFamily("Courier New") font.setPointSize(10) font.setBold(True) font.setWeight(75) self.main_editISDN.setFont(font) self.main_editISDN.setText("") self.main_editISDN.setAlignment(QtCore.Qt.AlignCenter) self.main_editISDN.setClearButtonEnabled(True) self.main_editISDN.setObjectName("main_editISDN") self.verticalLayout_26.addWidget(self.main_editISDN) self.horizontalLayout_43 = QtWidgets.QHBoxLayout() self.horizontalLayout_43.setObjectName("horizontalLayout_43") spacerItem27 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout_43.addItem(spacerItem27) self.main_pb_ShowISDN = QtWidgets.QPushButton(self.groupBox_15) self.main_pb_ShowISDN.setMinimumSize(QtCore.QSize(100, 0)) self.main_pb_ShowISDN.setMaximumSize(QtCore.QSize(100, 23)) self.main_pb_ShowISDN.setAutoRepeat(False) self.main_pb_ShowISDN.setAutoDefault(False) self.main_pb_ShowISDN.setDefault(False) self.main_pb_ShowISDN.setFlat(False) self.main_pb_ShowISDN.setObjectName("main_pb_ShowISDN") self.horizontalLayout_43.addWidget(self.main_pb_ShowISDN) self.main_pb_clearISDN = QtWidgets.QPushButton(self.groupBox_15) self.main_pb_clearISDN.setMinimumSize(QtCore.QSize(23, 23)) self.main_pb_clearISDN.setMaximumSize(QtCore.QSize(23, 23)) self.main_pb_clearISDN.setText("") self.main_pb_clearISDN.setObjectName("main_pb_clearISDN") self.horizontalLayout_43.addWidget(self.main_pb_clearISDN) spacerItem28 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout_43.addItem(spacerItem28) self.verticalLayout_26.addLayout(self.horizontalLayout_43) self.verticalLayout_57.addLayout(self.verticalLayout_26) self.main_tbShowMSISDN = QtWidgets.QTextBrowser(self.groupBox_15) font = QtGui.QFont() font.setPointSize(9) self.main_tbShowMSISDN.setFont(font) self.main_tbShowMSISDN.setObjectName("main_tbShowMSISDN") self.verticalLayout_57.addWidget(self.main_tbShowMSISDN) self.horizontalLayout_44.addLayout(self.verticalLayout_57) self.groupBox_16 = QtWidgets.QGroupBox(self.splitter_11) self.groupBox_16.setObjectName("groupBox_16") self.verticalLayout_11 = QtWidgets.QVBoxLayout(self.groupBox_16) self.verticalLayout_11.setObjectName("verticalLayout_11") self.verticalLayout_58 = QtWidgets.QVBoxLayout() self.verticalLayout_58.setObjectName("verticalLayout_58") self.verticalLayout_27 = QtWidgets.QVBoxLayout() self.verticalLayout_27.setObjectName("verticalLayout_27") self.main_edit_Wifi = QtWidgets.QLineEdit(self.groupBox_16) font = QtGui.QFont() font.setFamily("Courier New") font.setPointSize(10) font.setBold(True) font.setWeight(75) self.main_edit_Wifi.setFont(font) self.main_edit_Wifi.setText("") self.main_edit_Wifi.setAlignment(QtCore.Qt.AlignCenter) self.main_edit_Wifi.setClearButtonEnabled(True) self.main_edit_Wifi.setObjectName("main_edit_Wifi") self.verticalLayout_27.addWidget(self.main_edit_Wifi) self.horizontalLayout_45 = QtWidgets.QHBoxLayout() self.horizontalLayout_45.setObjectName("horizontalLayout_45") spacerItem29 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout_45.addItem(spacerItem29) self.main_pb_ShowWifi = QtWidgets.QPushButton(self.groupBox_16) self.main_pb_ShowWifi.setMinimumSize(QtCore.QSize(100, 0)) self.main_pb_ShowWifi.setMaximumSize(QtCore.QSize(100, 23)) self.main_pb_ShowWifi.setAutoRepeat(False) self.main_pb_ShowWifi.setAutoDefault(False) self.main_pb_ShowWifi.setDefault(False) self.main_pb_ShowWifi.setFlat(False) self.main_pb_ShowWifi.setObjectName("main_pb_ShowWifi") self.horizontalLayout_45.addWidget(self.main_pb_ShowWifi) self.main_pb_clearWifi = QtWidgets.QPushButton(self.groupBox_16) self.main_pb_clearWifi.setMinimumSize(QtCore.QSize(23, 23)) self.main_pb_clearWifi.setMaximumSize(QtCore.QSize(23, 23)) self.main_pb_clearWifi.setText("") self.main_pb_clearWifi.setObjectName("main_pb_clearWifi") self.horizontalLayout_45.addWidget(self.main_pb_clearWifi) spacerItem30 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout_45.addItem(spacerItem30) self.verticalLayout_27.addLayout(self.horizontalLayout_45) self.verticalLayout_58.addLayout(self.verticalLayout_27)
that are present in both. The Jaccard distance is a simple measure of the dissimilarity between two StructureGraphs (ignoring edge weights), and is defined by 1 - (size of the intersection / size of the union) of the sets of edges. This is returned with key 'dist'. Important note: all node indices are in terms of the StructureGraph this method is called from, not the 'other' StructureGraph: there is no guarantee the node indices will be the same if the underlying Structures are ordered differently. :param other: StructureGraph :param strict: if False, will compare bonds from different Structures, with node indices replaced by Species strings, will not count number of occurrences of bonds :return: """ if self.structure != other.structure and strict: return ValueError("Meaningless to compare StructureGraphs if " "corresponding Structures are different.") if strict: # sort for consistent node indices # PeriodicSite should have a proper __hash__() value, # using its frac_coords as a convenient key mapping = {tuple(site.frac_coords): self.structure.index(site) for site in other.structure} other_sorted = other.__copy__() other_sorted.sort(key=lambda site: mapping[tuple(site.frac_coords)]) edges = {(u, v, d["to_jimage"]) for u, v, d in self.graph.edges(keys=False, data=True)} edges_other = {(u, v, d["to_jimage"]) for u, v, d in other_sorted.graph.edges(keys=False, data=True)} else: edges = { (str(self.structure[u].specie), str(self.structure[v].specie)) for u, v, d in self.graph.edges(keys=False, data=True) } edges_other = { (str(other.structure[u].specie), str(other.structure[v].specie)) for u, v, d in other.graph.edges(keys=False, data=True) } if len(edges) == 0 and len(edges_other) == 0: jaccard_dist = 0 # by definition else: jaccard_dist = 1 - len(edges.intersection(edges_other)) / len(edges.union(edges_other)) return { "self": edges - edges_other, "other": edges_other - edges, "both": edges.intersection(edges_other), "dist": jaccard_dist, } def get_subgraphs_as_molecules(self, use_weights=False): """ Retrieve subgraphs as molecules, useful for extracting molecules from periodic crystals. Will only return unique molecules, not any duplicates present in the crystal (a duplicate defined as an isomorphic subgraph). :param use_weights (bool): If True, only treat subgraphs as isomorphic if edges have the same weights. Typically, this means molecules will need to have the same bond lengths to be defined as duplicates, otherwise bond lengths can differ. This is a fairly robust approach, but will treat e.g. enantiomers as being duplicates. :return: list of unique Molecules in Structure """ # creating a supercell is an easy way to extract # molecules (and not, e.g., layers of a 2D crystal) # without adding extra logic if getattr(self, "_supercell_sg", None) is None: self._supercell_sg = supercell_sg = self * (3, 3, 3) # make undirected to find connected subgraphs supercell_sg.graph = nx.Graph(supercell_sg.graph) # find subgraphs all_subgraphs = [supercell_sg.graph.subgraph(c) for c in nx.connected_components(supercell_sg.graph)] # discount subgraphs that lie across supercell boundaries # these will subgraphs representing crystals molecule_subgraphs = [] for subgraph in all_subgraphs: intersects_boundary = any(d["to_jimage"] != (0, 0, 0) for u, v, d in subgraph.edges(data=True)) if not intersects_boundary: molecule_subgraphs.append(nx.MultiDiGraph(subgraph)) # add specie names to graph to be able to test for isomorphism for subgraph in molecule_subgraphs: for n in subgraph: subgraph.add_node(n, specie=str(supercell_sg.structure[n].specie)) # now define how we test for isomorphism def node_match(n1, n2): return n1["specie"] == n2["specie"] def edge_match(e1, e2): if use_weights: return e1["weight"] == e2["weight"] return True # prune duplicate subgraphs unique_subgraphs = [] for subgraph in molecule_subgraphs: already_present = [ nx.is_isomorphic(subgraph, g, node_match=node_match, edge_match=edge_match) for g in unique_subgraphs ] if not any(already_present): unique_subgraphs.append(subgraph) # get Molecule objects for each subgraph molecules = [] for subgraph in unique_subgraphs: coords = [supercell_sg.structure[n].coords for n in subgraph.nodes()] species = [supercell_sg.structure[n].specie for n in subgraph.nodes()] molecule = Molecule(species, coords) # shift so origin is at center of mass molecule = molecule.get_centered_molecule() molecules.append(molecule) return molecules class MolGraphSplitError(Exception): """ Raised when a molecule graph is failed to split into two disconnected subgraphs """ pass class MoleculeGraph(MSONable): """ This is a class for annotating a Molecule with bond information, stored in the form of a graph. A "bond" does not necessarily have to be a chemical bond, but can store any kind of information that connects two Sites. """ def __init__(self, molecule, graph_data=None): """ If constructing this class manually, use the `with_empty_graph` method or `with_local_env_strategy` method (using an algorithm provided by the `local_env` module, such as O'Keeffe). This class that contains connection information: relationships between sites represented by a Graph structure, and an associated structure object. This class uses the NetworkX package to store and operate on the graph itself, but contains a lot of helper methods to make associating a graph with a given molecule easier. Use cases for this include storing bonding information, NMR J-couplings, Heisenberg exchange parameters, etc. :param molecule: Molecule object :param graph_data: dict containing graph information in dict format (not intended to be constructed manually, see as_dict method for format) """ if isinstance(molecule, MoleculeGraph): # just make a copy from input graph_data = molecule.as_dict()["graphs"] self.molecule = molecule self.graph = nx.readwrite.json_graph.adjacency_graph(graph_data) # tidy up edge attr dicts, reading to/from json duplicates # information for u, v, k, d in self.graph.edges(keys=True, data=True): if "id" in d: del d["id"] if "key" in d: del d["key"] # ensure images are tuples (conversion to lists happens # when serializing back from json), it's important images # are hashable/immutable if "to_jimage" in d: d["to_jimage"] = tuple(d["to_jimage"]) if "from_jimage" in d: d["from_jimage"] = tuple(d["from_jimage"]) self.set_node_attributes() @classmethod def with_empty_graph(cls, molecule, name="bonds", edge_weight_name=None, edge_weight_units=None): """ Constructor for MoleculeGraph, returns a MoleculeGraph object with an empty graph (no edges, only nodes defined that correspond to Sites in Molecule). :param molecule (Molecule): :param name (str): name of graph, e.g. "bonds" :param edge_weight_name (str): name of edge weights, e.g. "bond_length" or "exchange_constant" :param edge_weight_units (str): name of edge weight units e.g. "Å" or "eV" :return (MoleculeGraph): """ if edge_weight_name and (edge_weight_units is None): raise ValueError( "Please specify units associated " "with your edge weights. Can be " "empty string if arbitrary or " "dimensionless." ) # construct graph with one node per site # graph attributes don't change behavior of graph, # they're just for book-keeping graph = nx.MultiDiGraph( edge_weight_name=edge_weight_name, edge_weight_units=edge_weight_units, name=name, ) graph.add_nodes_from(range(len(molecule))) graph_data = json_graph.adjacency_data(graph) return cls(molecule, graph_data=graph_data) @staticmethod def with_edges(molecule, edges): """ Constructor for MoleculeGraph, using pre-existing or pre-defined edges with optional edge parameters. :param molecule: Molecule object :param edges: dict representing the bonds of the functional group (format: {(u, v): props}, where props is a dictionary of properties, including weight. Props should be None if no additional properties are to be specified. :return: mg, a MoleculeGraph """ mg = MoleculeGraph.with_empty_graph(molecule, name="bonds", edge_weight_name="weight", edge_weight_units="") for edge, props in edges.items(): try: from_index = edge[0] to_index = edge[1] except TypeError: raise ValueError("Edges must be given as (from_index, to_index)" "tuples") if props is not None: if "weight" in props.keys(): weight = props["weight"] del props["weight"] else: weight = None if len(props.items()) == 0: props = None else: weight = None nodes = mg.graph.nodes if not (from_index in nodes and to_index in nodes): raise ValueError( "Edges cannot be added if nodes are not" " present in the graph. Please check your" " indices." ) mg.add_edge(from_index, to_index, weight=weight, edge_properties=props) mg.set_node_attributes() return mg @staticmethod def with_local_env_strategy(molecule, strategy): """ Constructor for MoleculeGraph, using a strategy from :Class: `pymatgen.analysis.local_env`. :param molecule: Molecule object :param strategy: an instance of a :Class: `pymatgen.analysis.local_env.NearNeighbors` object :return: mg, a MoleculeGraph """ if not strategy.molecules_allowed: raise ValueError( "Chosen strategy is not designed for use with molecules! " "Please choose another strategy." ) extend_structure = strategy.extend_structure_molecules mg = MoleculeGraph.with_empty_graph(molecule, name="bonds", edge_weight_name="weight", edge_weight_units="") # NearNeighbor classes only (generally) work with structures # molecules have to be boxed first coords = molecule.cart_coords if extend_structure: a = max(coords[:, 0]) - min(coords[:, 0]) + 100 b = max(coords[:, 1]) - min(coords[:, 1]) + 100 c = max(coords[:, 2]) - min(coords[:, 2]) + 100 structure = molecule.get_boxed_structure(a, b, c, no_cross=True, reorder=False) else: structure = None for n in range(len(molecule)): if structure is None: neighbors = strategy.get_nn_info(molecule, n) else: neighbors = strategy.get_nn_info(structure, n) for neighbor in neighbors: # all bonds in molecules should not cross # (artificial) periodic boundaries if not
hight, n_vertical, n_horizental) # details = [0,0,0] # details[0] = node_index # details[1] = parent_index # details[2] = hight # details[3] = n_vertical # details[4] = n_horizental # p_sym = 1 # return p_sym, details # def do_vertical_stretching(self, neuron): # """ # In one of the segments that coming out from a branching points will be stretched. # """ # (branch_index,) = np.where(neuron.branch_order==2) # (end_nodes,) = np.where(neuron.branch_order==0) # nodes = np.append(branch_index,end_nodes) # parents = neuron.parent_index_for_node_subset(nodes) # n = np.floor(nodes.shape[0]np.random.rand()).astype(int) # p = np.exp(np.random.normal() self.horizental_stretch) # node_index = nodes[n] # parent_index = parents[n] # neuron.vertical_stretch(node_index, parent_index, p) # details = [0,0,0] # details[0] = node_index # details[1] = parent_index # details[2] = p # p_sym = 1 # return p_sym, details # def do_horizental_stretching(self, neuron): # (branch_index,) = np.where(neuron.branch_order==2) # (end_nodes,) = np.where(neuron.branch_order==0) # nodes = np.append(branch_index,end_nodes) # parents = neuron.parent_index_for_node_subset(nodes) # n = np.floor(nodes.shape[0]np.random.rand()).astype(int) # p = np.exp(np.random.normal() self.horizental_stretch) # node_index = nodes[n] # parent_index = parents[n] # neuron.horizental_stretch(node_index, parent_index, p) # details = [0,0,0] # details[0] = node_index # details[1] = parent_index # details[2] = p # p_sym = 1 # return p_sym, details # def undo_MCMC(self, per, details): # """ # when per == 0, details[0] is 'ext' of 'remove'. If it is 'ext', then details[1] is node_index. # if it is 'remove', details[1] = parent, details[2] = location, details[3] = ratio # """ # if per == 'extension/reduction': # undo extension/reduction # if(len(details) !=0): # if(details[0] == 'ext'): # self.undo_ext(self.neuron, details[1]) # if(details[0] == 'remove'): # self.undo_red(self.neuron, details[1], details[2], details[3]) # if per == 'extension/reduction end points': # if(len(details) !=0): # if(details[0] == 'ext'): # self.undo_ext(self.neuron, details[1]) # if(details[0] == 'remove'): # self.undo_red(self.neuron, details[1], details[2], details[3]) # if per == 'location': # undo location # if( ~ self.neuron.is_soma()): # self.undo_location(self.neuron, details[0], details[1], details[2], details[3]) # this function makes a location perturbation on the neuron # if per == 'location for important point': # undo location # if( ~ self.neuron.is_soma()): # self.undo_location_important(self.neuron, details[0], details[1], details[2], details[3]) # this function makes a location perturbation on the neuron # if per == 'location toward end': # if( ~ self.neuron.is_soma()): # self.undo_location_toward_end_nodes(self.neuron, details[0], details[1], details[2], details[3]) # if per == 'diameter': # undo diameter # if( ~ self.neuron.is_soma()): # To make sure that there is at least one node in the no_soma list # self.undo_diameter(self.neuron, details[0], details[1]) # if per == 'rotation for any node': # self.undo_rotation(self.neuron, details[0], details[1] ) # if per == 'rotation for branching': # self.undo_rotation_from_branch(self.neuron, details[0], details[1] ) # if per == 'sliding certain in distance': # undo sliding in certain distance # if(details[0] != 0): # self.undo_sliding(self.neuron, details[0], details[1]) # if per == 'sliding for branching node': # undo sliding for branch # if(details[0] != 0): # self.undo_sliding(self.neuron, details[0], details[1]) # if per == 'sliding general': # undo sliding general # if(details[0] != 0): # self.undo_sliding_general(self.neuron, details[0], details[1]) # if per == 'sliding for branching node certain distance': # do sliding only for branch # if(details[0] != 0): # self.undo_sliding(self.neuron, details[0], details[1]) # if per == 'rescale toward end': # self.undo_rescale_toward_end(self.neuron, details[0],details[1]) # if per == 'stretching vertical': # self.undo_vertical_stretching(self.neuron, details[0], details[1], details[2]) # if per == 'stretching horizental': # self.undo_horizental_stretching(self.neuron, details[0], details[1], details[2]) # if per == 'sinusidal': # self.undo_sinusidal_wave(self.neuron, details[0], details[1], details[2], details[3], details[4]) # def undo_sinusidal_wave(self, neuron, node, parent, hight, n_vertical, n_horizental): # neuron.sinudal(node_index, parent_index, -hight, n_vertical, n_horizental) # def undo_location(self, neuron, index, x, y, z): # neuron.change_location(index, - np.array([x,y,z])) # def undo_location_toward_end_nodes(self, neuron, index, x, y, z): # neuron.change_location_toward_end_nodes(index, - np.array([x,y,z])) # def undo_location_important(self, neuron, index, x, y, z): # neuron.change_location_important(index, - np.array([x,y,z])) # def undo_diameter(self, neuron, index, ratio): # neuron.change_diameter(index, 1.0 / ratio) # def undo_ext(self, neuron, index_node): # neuron.remove_node(index_node) # def undo_red(self, neuron, parent, location, ratio): # neuron.extend_node(parent, location, ratio) # def undo_rotation(self, neuron, node, matrix): # neuron.rotate(node, inv(matrix)) # def undo_rotation_from_branch(self, neuron, node, matrix): # neuron.rotate(node, inv(matrix)) # def undo_sliding(self, # neuron, # child_of_branching_node_index, # order_one_node_index): # neuron.slide(child_of_branching_node_index, order_one_node_index) # def undo_sliding_general(self, # neuron, # child_of_branching_node_index, # order_one_node_index): # neuron.slide(child_of_branching_node_index, order_one_node_index) # def undo_rescale_toward_end(self, neuron, node, rescale): # neuron.rescale_toward_end(node, 1./rescale) # def undo_vertical_stretching(self, neuron, node, parent, scale): # neuron.vertical_stretch(node, parent, 1./scale) # def undo_horizental_stretching(self, neuron, node, parent, scale): # neuron.horizental_stretch(node, parent, 1./scale) def set_measure(self, features_distribution): """ Set a probability distribution on neuron by looking at each features. To run the algorithm, a set of features is needed to make a probability distribution on the set of all neurons. Parameters ---------- features_distribution: dict the dictionary of each distributions. In the case that each features is modeled by Gaussian, features_distribution has two keys: 'mean' and 'std'. Inside each of these keys, there is another dictionary with the name of features and the value. For example: features_distribution = {'mean': {'branch_angle': 2.4,'local_angle': 2.7} 'std': {'branch_angle': .2,'local_angle': .2}} """ self.measure = features_distribution self.list_features = features_distribution['mean'].keys() self.mean_measure = np.array([]) self.std_measure = np.array([]) self.sd_measure = np.array([]) self.n_features = len(self.list_features) for ind in self.list_features: self.mean_measure = \ np.append(self.mean_measure, float(features_distribution['mean'][ind])) self.std_measure = \ np.append(self.std_measure, float(features_distribution['std'][ind]) ** 2) self.sd_measure = \ np.append(self.sd_measure, float(features_distribution['std'][ind])) self.trend = np.zeros([len(features_distribution['mean']), self.ite]) self.trend_normal = np.zeros([len(features_distribution['mean']), self.ite]) def set_probability(self, list_values): """ set the probability for perturbation list_values : dict """ l = sum(list_values.values()) self.list_values = {} for i in list_values.keys(): self.list_values[i] = list_values[i]/l self.p_prob = np.array(self.list_values.values()) self.p_list = self.list_values.keys() self._consum_prob = np.zeros(len(list_values.keys())) for i in range(self.p_prob.shape[0]): self._consum_prob[i] = sum(self.p_prob[:i+1]) def set_real_neuron(self, neuron, hist_features, value_features, vec_value): """ Set the desire features by the features of given neuron. No dependency. """ self.mean_hist, self.mean_value, self.mean_vec_value = \ dis_util.get_feature_neuron(neuron=neuron, hist_features=hist_features, value_features=value_features, vec_value=vec_value) def set_database(self, database): self.mean_hist = deepcopy(database.mean_hist) self.mean_value = deepcopy(database.mean_value) self.mean_vec_value = deepcopy(database.mean_vec_value) self.std_hist = deepcopy(database.std_hist) self.std_value = deepcopy(database.std_value) self.std_vec_value = deepcopy(database.std_vec_value) def set_feature_normalizer(self, normlizer): for name in self.std_hist.keys(): self.std_hist[name] = (1./normlizer[name]) * self.std_hist[name] for name in self.std_value.keys(): self.std_value[name] = (1./normlizer[name]) * self.std_value[name] for name in self.std_vec_value.keys(): self.std_vec_value[name] = (1./normlizer[name]) * self.std_vec_value[name] def pdf_normal(self ,x, dim): """ Return the probability density at the point x of a normal distribution with mean = 0 and variance = s """ rv = multivariate_normal(np.zeros(dim), self.varnp.eye(dim)) return rv.pdf(x) # should notice to the dimentionality of the constant return (self.cte_gauss/s)np.power(np.e,-(xx).sum()/(ss)) def normal(self, dim): random_point = np.random.normal(0, self.var, dim) rv = multivariate_normal(np.zeros(dim), self.varnp.eye(dim)) pdf = rv.pdf(random_point) return random_point, pdf def far_nodes(self, neuron, node_index, threshold): x = neuron.location[0, :] - neuron.location[0, node_index] y = neuron.location[1, :] - neuron.location[1, node_index] z = neuron.location[2, :] - neuron.location[2, node_index] (index,) = np.where(x2 + y2 + z2 > threshold2) return index def random_vector(self, mean, var): vec = np.random.normal(size = 3) vec = vec/LA.norm(vec,2) l = -1 while(l<0): l = mean + var np.random.normal() vec = vecl return vec def get_random_element_for_add_remove(self, neuron): (ind1,) = np.where(neuron.branch_order[neuron.n_soma:] == 1) whole = len(neuron.nodes_list) - neuron.n_soma total_number = len(ind1) + whole a = np.floor(total_number np.random.rand()) if(a < whole): random_node = neuron.nodes_list[neuron.n_soma + a] state = 'add' else: random_node = neuron.nodes_list[ind1[a - whole]] state = 'remove' return total_number ,random_node, state def random_rotation(self, vector, mu, kappa, n): """ input: mu, kappa, n `float64` Return three vectors: the first one is close to the given vector; these three vectors make a complete set of orthogonal space for 3D space. The first vector is choosen accroding to a distribution for the phi (the angle between the given vector and choosen one) and unifor distribution for the theta (the angle of projection of the choosen vector over the orthogonal plane) the phi angle comes from von Mises distribution. """ vector = vector/LA.norm(vector,2) a = np.random.normal(0, 1, 3) a = a - sum(avector)vector a = a/LA.norm(a,2) phi = np.random.vonmises(mu, kappa, 1) normal_vec = np.sin(phi)a + np.cos(phi)vector length = np.random.chisquare(n,1)/n random_point = lengthnormal_vec pdf = (.5/np.pi)(chi2.pdf(nlength,n)n)(vonmises.pdf(np.cos(phi), kappa)) return random_point, pdf def pdf_random_rotation(self, x, v, mu, kappa, n): """ Gives back the probability of observing the vector x, such that its angle with v is coming from a Von Mises distribution with k = self.kappa and its length coming form chi squared distribution with the parameter n. """ v = v/LA.norm(v,2) x = x/LA.norm(x,2) ang = sum(vx) return (.5/np.pi)(chi2.pdf(nLA.norm(x,2),n)n)(vonmises.pdf(ang, kappa)) def unifrom(self,size): return size(2np.random.rand(1,3)-1) def random_unitary_basis(self, kappa): #Ax1 = self.random_2d_rotation_in_3d('x', kappa) #Ay1 = self.random_2d_rotation_in_3d('y', kappa) Az1 = self.random_2d_rotation_in_3d('z', kappa) #Ax2 = self.random_2d_rotation_in_3d('x', kappa) #Ay2 = self.random_2d_rotation_in_3d('y', kappa) #Az2 = self.random_2d_rotation_in_3d('z', kappa) #A = np.dot(np.dot(Ax1,Ay1),Az1) #A = np.dot(np.dot(Az2,Ay2),Ax2) #A = np.dot(Ax1,Ay1) #B = np.dot(Ay2,Ax2) #m = np.dot(A,B) return Az1 def random_2d_rotation_in_3d(self, axis, kappa): theta = np.random.vonmises(0, kappa, 1) A = np.eye(3) if axis is 'z': A[0,0] = np.cos(theta) A[1,0] = np.sin(theta) A[0,1] = - np.sin(theta) A[1,1] = np.cos(theta) return A if axis is 'y': A[0,0] = np.cos(theta) A[2,0] = np.sin(theta) A[0,2] = - np.sin(theta) A[2,2] = np.cos(theta) return A
#!/opt/anaconda/bin/python # -- coding: utf-8 -- # Unfortunately the `which` way of calling python can't accept command-line arguments. """ Created on Mon Nov 03 16:13:48 2014 @author: <NAME> @email: <EMAIL> OR <EMAIL> A selection of alignment routines designed for registering and summing stacks of images or diffraction patterns in the field of electron microscopy. """ from __future__ import division, print_function, absolute_import, unicode_literals import numpy as np if np.version.version.split('.')[1] == 7: print( "WARNING: NUMPY VERSION 1.7 DETECTED, ZORRO IS DESIGNED FOR >1.10" ) print( "CHECK YOUR ENVIRONMENT VARIABLES TO SEE IF EMAN2 HAS HIJACKED YOUR PYTHON DISTRIBUTION" ) import numexprz as nz # Now see which numexpr we have, by the dtype of float (whether it casts or not) try: # Now see which numexpr we have, by the dtype of float (whether it casts or not) tdata = np.complex64( 1.0 + 2.0j ) fftw_dtype = nz.evaluate( 'tdata + tdata' ).dtype float_dtype = nz.evaluate( 'real(tdata+tdata)' ).dtype except: fftw_dtype = 'complex128' float_dtype = 'float64' import scipy.optimize import scipy.ndimage import scipy.stats import time try: import ConfigParser as configparser except: import configparser # Python 3 # Here we have to play some games depending on where the file was called from # with the use of absolute_import # print( "__name__ of zorro: " + str(__name__) ) try: import zorro_util as util import zorro_plotting as plot except ImportError: from . import zorro_util as util from . import zorro_plotting as plot import mrcz import os, os.path, tempfile, sys import subprocess # Should we disable Multiprocessing on Windows due to general bugginess in the module? import multiprocessing as mp try: import pyfftw except: print( "Zorro did not find pyFFTW package: get it at https://pypi.python.org/pypi/pyFFTW" ) try: import tables except: print( "Zorro did not find pyTables installation for HDF5 file support" ) import matplotlib.pyplot as plt # Numpy.pad is bad at dealing with interpreted strings if sys.version_info >= (3,0): symmetricPad = u'symmetric' constantPad = u'constant' else: symmetricPad = b'symmetric' constantPad = b'constant' #### OBJECT-ORIENTED INTERFACE #### class ImageRegistrator(object): # Should be able to handle differences in translation, rotation, and scaling # between images def __init__( self ): # Declare class members self.verbose = 0 self.umask = 2 # Meta-information for processing, not saved in configuration files. self.METApriority = 0.0 self.METAstatus = u'new' self.METAmtime = 0.0 self.METAsize = 0 self.xcorrMode = 'zorro' # 'zorro', 'unblur v1.02', 'motioncorr v2.1' # FFTW_PATIENT is bugged for powers of 2, so use FFTW_MEASURE as default self.fftw_effort = u"FFTW_MEASURE" # TODO: change this to drop into cachePath self.n_threads = nz.nthreads # Number of cores to limit FFTW to, if None uses all cores self.cachePath = tempfile.gettempdir() # CALIBRATIONS self.pixelsize = None # Typically we use nanometers, the same units as Digital Micrograph self.voltage = 300.0 # Accelerating voltage, kV self.C3 = 2.7 # Spherical aberration of objective, mm self.gain = None self.detectorPixelSize = None # Physical dimensions of detector pixel (5 um for K2) # Timings self.bench = {} # Dict holds various benchmark times for the code self.saveC = False # Save the cross-correlation within +/- maxShift # INFORMATION REDUCTION # The SNR at high spatial frequencies tends to be lower due to how information transfer works, so # removing/filtering those frequencies can improve stability of the registration. YMMV, IMHO, etc. self.Brad = 512 # Gaussian low-pass applied to data before registration, units are radius in Fourier space, or equivalent point-spread function in real-space self.Bmode = u'opti' # can be a real-space Gaussian convolution, 'conv' or Fourier filter, 'fourier', or 'opti' for automatic Brad # For Bmode = 'fourier', a range of available filters can be used: gaussian, gauss_trunc, butterworth.order (order is an int), hann, hamming self.BfiltType = u'gaussian' self.fouCrop = [3072,3072] # Size of FFT in frequency-space to crop to (e.g. [2048,2048]) self.reloadData = True # Data self.images = None self.imageSum = None self.filtSum = None # Dose-filtered, Wiener-filtered, etc. representations go here self.gainRef = None # For application of gain reference in Zorro rather than Digital Micrograph/TIA/etc. self.gainInfo = { "Horizontal": True, "Vertical": True, "Diagonal":False, "GammaParams": [ 0.12035633, -1.04171635, -0.03363192, 1.03902726], } # One of None, 'dose', 'dose,background', 'dosenorm', 'gaussLP', 'gaussLP,background' # also 'hot' can be in the comma-seperated list for pre-filtering of hot pixels self.filterMode = None # Dose filt param = [dosePerFrame, critDoseA, critDoseB, critDoseC, cutoffOrder, missingStartFrame] self.doseFiltParam = [None, 0.24499, -1.6649, 2.8141, 32, 0] # for 'hot' in filterMode self.hotpixInfo = { u"logisticK":6.0, u"relax":0.925, u"maxSigma":8.0, u"psf": u"K2", u"guessHotpix":0, u"guessDeadpix":0, u"decorrOutliers":False, u"cutoffLower":-4.0, u"cutoffUpper":3.25, u"neighborPix":0 } self.FFTSum = None # If you want to use one mask, it should have dims [1,N_Y,N_X]. This is # to ensure Cythonized code can interact safely with Numpy self.incohFouMag = None # Incoherent Fourier magnitude, for CTF determination, resolution checks self.masks = None self.maskSum = None self.C = None # Results self.translations = None self.transEven = None # For even-odd tiled FRC, the half-stack translations self.transOdd = None # For even-odd tiled FRC, the half-stack translations self.velocities = None # pixel velocity, in pix/frame, to find frames that suffer from excessive drift self.rotations = None # rotations, for polar-transformed data self.scales = None # scaling, for polar-transformed data self.errorDictList = [] # A list of dictionaries of errors and such from different runs on the same data. self.trackCorrStats = False self.corrStats = None self.doLazyFRC = True self.doEvenOddFRC = False self.FRC = None # A Fourier ring correlation # Filtering # TODO: add more fine control over filtering options # CTF currently supports CTFFIND4.1 or GCTF self.CTFProgram = None # None, "ctffind4.1", or "gctf", 'ctffind4.1,sum' works on (aligned) sum, same for 'gctf,sum' self.CTFInfo = { u'DefocusU':None, u'DefocusV': None, u'DefocusAngle':None, u'CtfFigureOfMerit':None, u'FinalResolution': None, u'AmplitudeContrast':0.07, u'AdditionalPhaseShift':None, } self.CTFDiag = None # Diagnostic image from CTFFIND4.1 or GCTF # DEPRICATED ctf stuff #self.doCTF = False #self.CTF4Results = None # Micrograph number, DF1, DF2, Azimuth, Additional Phase shift, CC, and max spacing fit-to #self.CTF4Diag = None # Registration parameters self.shapePadded = [4096,4096] self.shapeOriginal = None self.shapeBinned = None self.subPixReg = 16 # fraction of a pixel to REGISTER image shift to # Subpixel alignment method: None (shifts still registered subpixally), lanczos, or fourier # lanczos is cheaper computationally and has fewer edge artifacts self.shiftMethod = u'lanczos' self.maxShift = 100 # Generally should be 1/2 distance to next lattice spacing # Pre-shift every image by that of the previous frame, useful for high-resolution where one can jump a lattice # i.e. should be used with small values for maxShift self.preShift = False # Solver weighting can be raw max correlation coeffs (None), normalized to [0,1] by the # min and max correlations ('norm'), or 'logistic' function weighted which # requires corrThres to be set. self.peakLocMode = u'interpolated' # interpolated (oversampled), or a RMS-best fit like fitlaplacian self.weightMode = u'autologistic' # autologistic, normalized, unweighted, logistic, or corr self.peaksigThres = 6.0 self.logisticK = 5.0 self.logisticNu = 0.15 self.originMode = u'centroid' # 'centroid' or None self.suppressOrigin = True # Delete the XC pixel at (0,0). Only necessary if gain reference is bad, but defaults to on. # Triangle-matrix indexing parameters self.triMode = u'diag' # Can be: tri, diag, auto, first self.startFrame = 0 self.endFrame = 0 self.diagStart = 0 # XC to neighbour frame on 0, next-nearest neighbour on +1, etc. self.diagWidth = 5 self.autoMax = 10 self.corrThres = None # Use with 'auto' mode to stop doing cross-correlations if the values drop below the threshold self.velocityThres = None # Pixel velocity threshold (pix/frame), above which to throw-out frames with too much motion blur. #### INPUT/OUTPUT #### self.files = { u"config":None, u"stack":None, u"mask":None, u"sum":None, u"align":None, u"figurePath":None, u"xc":None, u"moveRawPath":None, u"original":None, u"gainRef":None, u"stdout": None, u"automatch":None, u"rejected":None, u"compressor": None, u"clevel": 1 } #self.savePDF = False self.savePNG = True self.saveMovie = True self.doCompression = False self.compress_ext = ".bz2" #### PLOTTING #### self.plotDict = { u"imageSum":True, u"imageFirst":False, u"FFTSum":True, u"polarFFTSum":True, u"filtSum":True, u'stats': False, u"corrTriMat":False,
__all__ = ('Embed',) from ...backend.utils import copy_docs from ..utils import parse_time from .embed_base import ( EmbedBase, EmbedFooter, EmbedImage, EmbedThumbnail, EmbedVideo, EmbedProvider, EmbedAuthor, EmbedField, ) class Embed(EmbedBase): """ Represents Discord embedded content. There are two defined embed classes, the other one is ``EmbedCore``. Embeds are easier to build with this class than with the other, and faster to serialize, because it stores the objects as raw serializable data, but it also means it has worse operation support, because it needs to convert the raw data back. Attributes ---------- _data : `dict` of (`str`, `Any`) items The raw data of the embed. It should not be accessed directly. There are several properties and methods to do operations on them. Examples -------- Example of using local embed file: ```py # Imports from hata import Embed, ReuAsyncIO # Building the embed embed = Embed() embed.add_image('attachment://image.png') # Sending the message with (await ReuAsyncIO('some_file_path')) as file: await client.message_create(channel, embed=embed, file=('image.png', file)) ``` Note that you should use async io wrappers, but one which do not closes on `.close` either, but it resets itself instead, because if the request fails, the io would be closed and the request could not be done the second time. """ __slots__ = ('_data',) def __init__( self, title=None, description=None, color=None, url=None, timestamp=None, type_='rich', ): """ Creates an embed instance. Accepts the base parameters of the embed. Parameters ---------- title : `str`, Optional The title of the embed. Shows at the top with intense white characters. description : `str`, Optional The main content of the embed. color : ``Color`` or `int`, Optional The color code of the embed. Passing `0` means black, not like at the case of roles. url : `str`, Optional Url of the embed. If defined, the embed's `title` will show up as a hyper link pointing to the `url`. timestamp : `datetime`, optional Timestamp of the embed's content. Shows up next to the `footer` separated with a `'\|'` character. type_ : `None` or `str`, Optional The type of the embed. Defaults to `'rich'`. """ self._data = data = {} if title is not None: data['title'] = title if description is not None: data['description'] = description if color is not None: data['color'] = color if url is not None: data['url'] = url if timestamp is not None: data['timestamp'] = timestamp.isoformat() if type_ is not None: data['type'] = type_ @copy_docs(EmbedBase.__len__) def __len__(self): data = self._data result = 0 try: title = data['title'] except KeyError: pass else: result += len(title) try: description = data['description'] except KeyError: pass else: result += len(description) try: author_data = data['author'] except KeyError: pass else: try: author_name = author_data['name'] except KeyError: pass else: result += len(author_name) try: footer_data = data['footer'] except KeyError: pass else: result += len(footer_data['text']) try: field_datas = data['fields'] except KeyError: pass else: for field_data in field_datas: result += len(field_data['name']) result += len(field_data['value']) return result @copy_docs(EmbedBase.__bool__) def __bool__(self): data = self._data data_length = len(data) if data_length == 0: return False if data_length == 1: try: field_datas = data['fields'] except KeyError: pass else: if not field_datas: return False return True @property def contents(self): """ Returns the embed's contents. The embeds contents are the following: - `.title` - `.description` - `.author.name` - `.footer.text` - `.fields[n].name` - `.fields[n].value` Returns ------- contents : `list` of `str` """ data = self._data result = [] try: title = data['title'] except KeyError: pass else: result.append(title) try: description = data['description'] except KeyError: pass else: result.append(description) try: author_data = data['author'] except KeyError: pass else: try: author_name = author_data['name'] except KeyError: pass else: result.append(author_name) try: footer_data = data['footer'] except KeyError: pass else: result.append(footer_data['text']) try: field_datas = data['fields'] except KeyError: pass else: for field_data in field_datas: result.append(field_data['name']) result.append(field_data['value']) return result @classmethod def from_data(cls, data): """ Creates an embed from the data sent by Discord. Parameters ---------- data : `dict` of (`str`, `Any`) items Embed data received from Discord. Returns ------- self : ``Embed`` """ self = object.__new__(cls) self._data = data return self def to_data(self): """ Returns the embed's `._data`. This method is for compatibility with other embed-likes. When sending embed in message this method is called for getting it's data. Returns ------- data : `dict` of (`str`, `Any`) items """ return self._data @copy_docs(EmbedBase.clear) def clear(self): data = self._data fields = data.get('fields', None) data.clear() if fields is not None: fields.clear() data['fields'] = fields # Properties # `.author` @property def author(self): """ A get-set-del property for accessing the embed's author. Accepts and returns `None` or an ``EmbedAuthor`` object. """ try: author_data = self._data['author'] except KeyError: return None return EmbedAuthor.from_data(author_data) @author.setter def author(self, value): self._data['author'] = value.to_data() @author.deleter def author(self): try: del self._data['author'] except KeyError: pass # `.color` @property def color(self): """ A get-set-del property for accessing the embed's color. Accepts and returns `None` or a ``Color`` (/ `int`) object. """ return self._data.get('color', None) @color.setter def color(self, value): self._data['color'] = value @color.deleter def color(self): try: del self._data['color'] except KeyError: pass # `.description` @property def description(self): """ A get-set-del property for accessing the embed's description. Accepts and returns `None` or a `str` instance. """ return self._data.get('description', None) @description.setter def description(self, value): self._data['description'] = value @description.deleter def description(self): try: del self._data['description'] except KeyError: pass # `.fields` @property def fields(self): try: field_datas = self._data['fields'] except KeyError: self._data['fields'] = field_datas = [] return _EmbedFieldsProxy(field_datas) @fields.setter def fields(self, value): """ A get-set-del property for accessing the embed's fields. Accepts an `iterable` of ``EmbedField``objects. Meanwhile returns an ``_EmbedFieldsProxy`` instance, through what the respective embed's fields can be modified directly. """ data = self._data try: fields_data = data['fields'] except KeyError: fields_data = data['fields'] = [] if type(value) is _EmbedFieldsProxy: new_fields_data = value._data else: new_fields_data = list(field.to_data() for field in value) fields_data.clear() fields_data.extend(new_fields_data) @fields.deleter def fields(self): try: field_datas = self._data['fields'] except KeyError: pass else: field_datas.clear() # `.footer` @property def footer(self): """ A get-set-del property for accessing the embed's footer. Accepts and returns `None` or an ``EmbedFooter`` object. """ try: footer_data = self._data['footer'] except KeyError: return None return EmbedFooter.from_data(footer_data) @footer.setter def footer(self, value): self._data['footer'] = value.to_data() @footer.deleter def footer(self): try: del self._data['footer'] except KeyError: pass # `.image` @property def image(self): """ A get-set-del property for accessing the embed's image. Accepts and returns `None` or an ``EmbedImage`` object. """ try: image_data = self._data['image'] except KeyError: return None return EmbedImage.from_data(image_data) @image.setter def image(self, value): self._data['image'] = value.to_data() @image.deleter def image(self): try: del self._data['image'] except KeyError: pass # `.provider` @property def provider(self): """ A get-del property for accessing the embed's provider. Returns `None` or an ``EmbedProvider`` object. Embed providers cannot be set, they are receive only. """ try: provider_data = self._data['provider'] except KeyError: return None return EmbedProvider.from_data(provider_data) @provider.deleter def provider(self): try: del self._data['provider'] except KeyError: pass # `.thumbnail` @property def thumbnail(self): """ A get-set-del property for accessing the embed's thumbnail. Accepts and returns `None` or an ``EmbedThumbnail`` object. """ try: thumbnail_data = self._data['thumbnail'] except KeyError: return None return EmbedThumbnail.from_data(thumbnail_data) @thumbnail.setter def thumbnail(self, value): self._data['thumbnail'] = value.to_data() @thumbnail.deleter def thumbnail(self): try: self._data['thumbnail'] except KeyError: pass # `.timestamp` @property def timestamp(self): """ A get-set-del property for accessing the embed's timestamp. Accepts and returns `None` or a `datetime` object. """ try: timestamp_value = self._data['timestamp'] except KeyError: return None return parse_time(timestamp_value) @timestamp.setter def timestamp(self, value): self._data['timestamp'] = value.isoformat() @timestamp.deleter def timestamp(self): try: del self._data['timestamp'] except KeyError: pass # `.title` @property def title(self): """ A get-set-del property for accessing the embed's title. Accepts and returns `None` or a `str` instance. """ return self._data.get('title', None) @title.setter def title(self, value): self._data['title'] = value @title.deleter def title(self): try: del self._data['title'] except KeyError: pass # `.type` @property def type(self): """ A get-set-del property for accessing the embed's type. Accepts and returns `None` or a `str` instance. """ return self._data.get('type', None) @type.setter def type(self, value): self._data['type'] = value @type.deleter def type(self): try: del self._data['type'] except KeyError: pass # `.url` @property def url(self): """ A get-set-del property for accessing the embed's url. Accepts and returns `None` or a

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#!/usr/bin/env python import numpy as np import scipy.io as io import matplotlib.pyplot as plt from scipy.stats import norm import argparse import copy import tqdm from hdphmm.utils import timeseries as ts def initialize(): parser = argparse.ArgumentParser(description='Generate timeseries with different underlying models') parser.add_argument('-t', '--type', default='AR', type=str, help="Underlying dynamical model. Only AR is fully" "implemented.") parser.add_argument('-d', '--ndraws', default=2000, type=int, help='Number of time steps to take') parser.add_argument('-n', '--ntraj', default=4, type=int, help='Number of trajetories to generate') parser.add_argument('-f', '--format', nargs='+', default='npz', type=str, help='Format of output array (mat or npz)') parser.add_argument('-nd', '--ndimensions', default=1, type=int, help='Number of dimensions of trajectory.') # Define transition matrix. Either provide your own, or let this script generate one parser.add_argument('-T', '--transition_matrix', nargs='+', action='append', type=float, help='Define a transition ' 'matrix. If provided, this will be used to determine the number of states. Each row of the ' 'transition matrix should be passed with a separate -T flag') parser.add_argument('-s', '--nstates', default=3, type=int, help='Number of states to switch between') parser.add_argument('-slip', '--slip', default=0.01, type=float, help='Determines how frequently things will ' 'switch states. A slip of 0 results in a transition matrix with only ones on the diagonal. ' 'Higher values of slip will give smaller and smaller ratios of diagonals to the rest.') # Autoregressive parameters parser.add_argument('-r', '--order', default=1, type=int, help='Autoregressive order (number of time lags that ' 'yt depends on.') parser.add_argument('-phis', '--phis', nargs='+', action='append', type=float, help='Define autoregressive' 'coefficients for each state. Coefficients for each state should be passed in order with ' 'separate -phis flags. If this is not specified, the coefficients will be randomly generated' 'for you.') # noise parameters parser.add_argument('-cov', '--covariance', nargs='+', action='append', type=float, help='Covariance matrix for ' 'each state. Pass matrix for each state with a separate flag.') # phantom state linking parser.add_argument('-l', '--link', action="store_true", help='Link together the independent trajetories with a' 'phantom state in between.') parser.add_argument('-pl', '--phantom_length', default=100, type=int, help='Number of time steps for phantom state ' 'linkage') return parser class StateError(Exception): """ Raised if an undefined reaction is attempted """ def __init__(self, message): super().__init__(message) class GenARData: def __init__(self, params=None, dim=3, transition_matrix=None, phis=None, nstates=None, slip=0.25, order=1, cov=None, stdmax=1, mu=None): """ Given a transition matrix, generate timeseries using Markov Chains :param params: a dictionary containing parameters of an AR HMM process. This is how one should pass the parameters generated by hdphmm.InfiniteHMM stored in InfiniteHMM.converged_params. :param dim: number of dimensions of trajectory data :param transition_matrix: a list of N N-length lists. Each list represents a row of the transition matrix. If \ None or False is passed, a transition matrix will be generated randomly :param phis: a list of N order-length lists of autoregressive coefficients for each state. In order of phi_1, \ phi_2 etc. If None, these will be randomly generated :param nstates: number of states :param slip: determines ratio of diagonal elements to the rest. A higher 'high' will give you a smaller ratio \ of diagonals to the rest and vice versa. A high of zero will result in an identity matrix, meaning there will \ be no transitions from the initial state :param order: autoregressive order. Only specified if type is 'AR' :param cov: covariance matrix of multivariate Gaussian white noise for each state. If None, a random covariance matrix will be generated :param stdmax: maximum standard deviation of Gaussian white noise. This is only used if stds=None :type params: dict :type dim: int :type transition_matrix: list of lists :type nstates: int :type slip: float :type order: int :type stds: list :type stdmax: float :type phis: list of lists """ self.T = None self.dwells = [] self.hops = [] if params is not None: self.T = params['T'] self.nstates = self.T.shape[1] self.phis = params['A'] self.cov = params['sigma'] self.pi_init = params['pi_init'] self.dim = self.cov.shape[1] self.order = self.phis.shape[1] self.mu = params['mu'] #self.count_matrix = self._get_count_matrix(params['z']) else: self.dim = dim self.order = order if transition_matrix is not None: self.T = np.array(transition_matrix) self.nstates = self.T.shape[1] # define number of states based on provided transition matrix else: if not nstates: raise StateError("If no transition matrix is provided, the number of states must be specified") self.nstates = nstates self.generate_transition_matrix(slip) self.phis = np.zeros([self.nstates, order, dim, dim]) if phis is not None: # only works for r = 1 for s in range(self.nstates): try: self.phis[s, 0, ...] = np.array(phis[s]).reshape(dim, dim) except IndexError: raise IndexError('You have not provided enough phi matrices for the number of requested states') else: # NOTE: for multidimensional case, off-diagonal terms in each phi coefficient matrix are set to zero. # I'm not sure what the stabilty rules are for the multidimensional case self.phis = np.zeros([1, order, dim, dim, self.nstates]) for s in range(self.nstates): self.phis[0, s, ...] = generate_ar_parameters(order, dim) self.cov = np.zeros([self.nstates, dim, dim]) if cov is None: for s in range(self.nstates): A = np.random.uniform(0, stdmax, size=(dim, dim)) self.cov[0, ..., s] = A @ A.T else: for s in range(self.nstates): self.cov[s, ...] = np.array(cov[s]).reshape(dim, dim) self.mu = np.zeros([self.nstates, self.dim]) if mu is not None: for s in range(self.nstates): self.mu[s, :] = mu[s] self.state_labels = np.arange(self.nstates) self.traj = None self.state_sequence = None def _get_count_matrix(self, z): nclusters = len(np.unique(z)) # need A, sigma, transition matrix, pi_init count_matrix = np.zeros([nclusters, nclusters]) found_states = np.unique(z) ndx_dict = {found_states[i]: i for i in range(len(found_states))} count_matrix = np.zeros([nclusters, nclusters]) for frame in range(1, nT - 1): # start at frame 1. May need to truncate more as equilibration transitioned_from = [ndx_dict[i] for i in ihmm_final.z[:, frame - 1]] transitioned_to = [ndx_dict[i] for i in ihmm_final.z[:, frame]] for pair in zip(transitioned_from, transitioned_to): count_matrix[pair[0], pair[1]] += 1 # for frame in range(1, z.shape[1]): # start at frame 1. May need to truncate more as equilibration # transitioned_from = z[:, frame - 1] # transitioned_to = z[:, frame] # for pair in zip(transitioned_from, transitioned_to): # count_matrix[pair[0], pair[1]] += 1 return count_matrix def generate_transition_matrix(self, high): """ generate a semi-random transition matrix :param high: determines ratio of diagonal elements to the rest. A higher 'high' will give you a smaller ratio of diagonals to the rest and vice versa. A high of zero will result in an identity matrix, meaning there will be no transitions from the initial state :type high: float """ T = np.eye(self.nstates) # start with identify matrix T += np.random.uniform(0, high, size=(self.nstates, self.nstates)) # add random draws from uniform distribution self.T = T / T.sum(axis=1, keepdims=1) # normalize so sum of rows is 1 def gen_trajectory(self, ndraws, ntraj, bound_dimensions=None, progress=True, state_no=None, resample_T=False, alpha=1): """ Generate time series with chosen underlying dynamics :param ndraws: number of sequential points to generate :param ntraj: number of independent trajectories to generate :param unbound_dimensions: indices of dimensions whose mean should not be fixed. :param progress: show progress bar :param state_no: if not None, generate a trajectory for the given state index :param resample_T: resample the rows of the transition matrix by drawing from a dirichlet process :param alpha: multiply rows of transition matrix by this number :type ndraws: int :type ntraj: int :type unbound_dimensions: NoneType, list or np.ndarray :type progress: bool :type state_no: int or NoneType :type resample_T: bool :type alpha: int or float """ return self._gen_ar_hmm(ndraws, ntraj, bound_dimensions=bound_dimensions, state_no=state_no, progress=progress, resample_T=resample_T, alpha=alpha) #return self._gen_ar_hmm_fixed_distance(10) def _gen_ar_hmm_fixed_distance(self, length, bound_dimensions=None): """ Generate a mean-zero autoregressive timeseries based on the transition matrix and autoregressive parameters. The timeseries is defined as: yt = \sum_{n=1}^{r} phi_n * y_{t-n} + \epsilon_t where r is autoregressive order and \epsilon_t is Gaussian white noise with state-dependent variance :param ndraws: number of points to generate for timeseries :param phis: autoregressive coefficients for each state (n_phis x n_states) :type ndraws: int :type phis: np.ndarray """ state_labels = np.arange(self.nstates) current_state = np.random.choice(state_labels, p=self.pi_init) previous_state = current_state mean = np.zeros(self.dim) traj = [np.array([0, 0]) for i in range(self.order)] # r, z zeroed = [np.array([0, 0]) for i in range(self.order)] # traj = np.zeros([self.order + 1, self.dim]) # zeroed = np.zeros_like(traj) tlen = self.order n = 0 # while np.abs(traj[-1][1]) < length: self.traj = np.zeros([4807, 100, 2]) for i in
:param async_req bool :param str id: (required) :param list[ApiParameter] parameters: :param str run_name: name to identify the run on the Kubeflow Pipelines UI, defaults to component name :return: ApiRunCodeResponse If the method is called asynchronously, returns the request thread. """ all_params = ['id', 'parameters', 'run_name'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method run_dataset" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'id' is set if ('id' not in params or params['id'] is None): raise ValueError("Missing the required parameter `id` when calling `run_dataset`") # noqa: E501 collection_formats = {} path_params = {} if 'id' in params: path_params['id'] = params['id'] # noqa: E501 query_params = [] if 'run_name' in params: query_params.append(('run_name', params['run_name'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params = None if 'parameters' in params: body_params = params['parameters'] # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/datasets/{id}/run', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='ApiRunCodeResponse', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def set_featured_datasets(self, dataset_ids, kwargs): # noqa: E501 """set_featured_datasets # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.set_featured_datasets(dataset_ids, async_req=True) >>> result = thread.get() :param async_req bool :param list[str] dataset_ids: Array of dataset IDs to be featured. (required) :return: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.set_featured_datasets_with_http_info(dataset_ids, kwargs) # noqa: E501 else: (data) = self.set_featured_datasets_with_http_info(dataset_ids, kwargs) # noqa: E501 return data def set_featured_datasets_with_http_info(self, dataset_ids, kwargs): # noqa: E501 """set_featured_datasets # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.set_featured_datasets_with_http_info(dataset_ids, async_req=True) >>> result = thread.get() :param async_req bool :param list[str] dataset_ids: Array of dataset IDs to be featured. (required) :return: None If the method is called asynchronously, returns the request thread. """ all_params = ['dataset_ids'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method set_featured_datasets" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'dataset_ids' is set if ('dataset_ids' not in params or params['dataset_ids'] is None): raise ValueError("Missing the required parameter `dataset_ids` when calling `set_featured_datasets`") # noqa: E501 collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'dataset_ids' in params: body_params = params['dataset_ids'] # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/datasets/featured', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def upload_dataset(self, uploadfile, kwargs): # noqa: E501 """upload_dataset # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.upload_dataset(uploadfile, async_req=True) >>> result = thread.get() :param async_req bool :param file uploadfile: The dataset YAML file to upload. Can be a GZip-compressed TAR file (.tgz, .tar.gz) or a YAML file (.yaml, .yml). Maximum size is 32MB. (required) :param str name: :return: ApiDataset If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.upload_dataset_with_http_info(uploadfile, kwargs) # noqa: E501 else: (data) = self.upload_dataset_with_http_info(uploadfile, kwargs) # noqa: E501 return data def upload_dataset_with_http_info(self, uploadfile, kwargs): # noqa: E501 """upload_dataset # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.upload_dataset_with_http_info(uploadfile, async_req=True) >>> result = thread.get() :param async_req bool :param file uploadfile: The dataset YAML file to upload. Can be a GZip-compressed TAR file (.tgz, .tar.gz) or a YAML file (.yaml, .yml). Maximum size is 32MB. (required) :param str name: :return: ApiDataset If the method is called asynchronously, returns the request thread. """ all_params = ['uploadfile', 'name'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method upload_dataset" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'uploadfile' is set if ('uploadfile' not in params or params['uploadfile'] is None): raise ValueError("Missing the required parameter `uploadfile` when calling `upload_dataset`") # noqa: E501 collection_formats = {} path_params = {} query_params = [] if 'name' in params: query_params.append(('name', params['name'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {} if 'uploadfile' in params: local_var_files['uploadfile'] = params['uploadfile'] # noqa: E501 body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['multipart/form-data']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/datasets/upload', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='ApiDataset', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def upload_dataset_file(self, id, uploadfile, kwargs): # noqa: E501 """upload_dataset_file # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.upload_dataset_file(id, uploadfile, async_req=True) >>> result = thread.get() :param async_req bool :param str id: The id of the dataset. (required) :param file uploadfile: The file to upload, overwriting existing. Can be a GZip-compressed TAR file (.tgz), a YAML file (.yaml), Python script (.py), or Markdown file (.md) (required) :return: ApiDataset If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.upload_dataset_file_with_http_info(id, uploadfile, kwargs) # noqa: E501 else: (data) = self.upload_dataset_file_with_http_info(id, uploadfile, kwargs) # noqa: E501 return data def upload_dataset_file_with_http_info(self, id, uploadfile, kwargs): # noqa: E501 """upload_dataset_file # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.upload_dataset_file_with_http_info(id, uploadfile, async_req=True) >>> result = thread.get() :param async_req bool :param str id: The id of the dataset. (required) :param file uploadfile: The file to upload, overwriting existing. Can be a GZip-compressed TAR file (.tgz), a YAML file (.yaml), Python script (.py), or Markdown file (.md) (required) :return: ApiDataset If the method is called asynchronously, returns the request thread. """ all_params = ['id', 'uploadfile'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method upload_dataset_file" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'id' is set if ('id' not in params or params['id'] is None): raise ValueError("Missing the required parameter `id` when calling `upload_dataset_file`") # noqa: E501 # verify the required parameter 'uploadfile' is set if ('uploadfile' not in params or params['uploadfile'] is None): raise ValueError("Missing the required parameter `uploadfile` when calling `upload_dataset_file`") # noqa: E501 collection_formats = {} path_params = {} if 'id' in params: path_params['id'] = params['id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} if 'uploadfile' in params: local_var_files['uploadfile'] = params['uploadfile'] # noqa: E501 body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['multipart/form-data']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/datasets/{id}/upload', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='ApiDataset', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def upload_dataset_from_url(self, url, **kwargs): # noqa: E501 """upload_dataset_from_url # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.upload_dataset_from_url(url, async_req=True) >>> result = thread.get() :param async_req bool :param
9 * m.b1400) m.e1 = Constraint(expr= m.x1 - 0.2 * m.x141 == 0) m.e2 = Constraint(expr= m.x2 - 0.2 * m.x142 == 0) m.e3 = Constraint(expr= m.x3 - 0.2 * m.x143 == 0) m.e4 = Constraint(expr= m.x4 - 0.2 * m.x144 == 0) m.e5 = Constraint(expr= m.x5 - 0.2 * m.x145 == 0) m.e6 = Constraint(expr= m.x6 - 0.2 * m.x146 == 0) m.e7 = Constraint(expr= m.x7 - 0.2 * m.x147 == 0) m.e8 = Constraint(expr= m.x8 - 0.2 * m.x148 == 0) m.e9 = Constraint(expr= m.x9 - 0.2 * m.x149 == 0) m.e10 = Constraint(expr= m.x10 - 0.2 * m.x150 == 0) m.e11 = Constraint(expr= m.x11 - 0.2 * m.x151 == 0) m.e12 = Constraint(expr= m.x12 - 0.2 * m.x152 == 0) m.e13 = Constraint(expr= m.x13 - 0.2 * m.x153 == 0) m.e14 = Constraint(expr= m.x14 - 0.2 * m.x154 == 0) m.e15 = Constraint(expr= m.x15 - 0.2 * m.x155 == 0) m.e16 = Constraint(expr= m.x16 - 0.2 * m.x156 == 0) m.e17 = Constraint(expr= m.x17 - 0.2 * m.x157 == 0) m.e18 = Constraint(expr= m.x18 - 0.2 * m.x158 == 0) m.e19 = Constraint(expr= m.x19 - 0.2 * m.x159 == 0) m.e20 = Constraint(expr= m.x20 - 0.2 * m.x160 == 0) m.e21 = Constraint(expr= m.x21 - 0.5 * m.x161 == 0) m.e22 = Constraint(expr= m.x22 - 0.5 * m.x162 == 0) m.e23 = Constraint(expr= m.x23 - 0.5 * m.x163 == 0) m.e24 = Constraint(expr= m.x24 - 0.5 * m.x164 == 0) m.e25 = Constraint(expr= m.x25 - 0.5 * m.x165 == 0) m.e26 = Constraint(expr= m.x26 - 0.5 * m.x166 == 0) m.e27 = Constraint(expr= m.x27 - 0.5 * m.x167 == 0) m.e28 = Constraint(expr= m.x28 - 0.5 * m.x168 == 0) m.e29 = Constraint(expr= m.x29 - 0.7 * m.x169 == 0) m.e30 = Constraint(expr= m.x30 - 0.7 * m.x170 == 0) m.e31 = Constraint(expr= m.x31 - 0.7 * m.x171 == 0) m.e32 = Constraint(expr= m.x32 - 0.7 * m.x172 == 0) m.e33 = Constraint(expr= m.x33 - 0.7 * m.x173 == 0) m.e34 = Constraint(expr= m.x34 - 0.7 * m.x174 == 0) m.e35 = Constraint(expr= m.x35 - 0.7 * m.x175 == 0) m.e36 = Constraint(expr= m.x36 - 0.7 * m.x176 == 0) m.e37 = Constraint(expr= m.x37 - 1.2 * m.x177 == 0) m.e38 = Constraint(expr= m.x38 - 1.2 * m.x178 == 0) m.e39 = Constraint(expr= m.x39 - 1.2 * m.x179 == 0) m.e40 = Constraint(expr= m.x40 - 1.2 * m.x180 == 0) m.e41 = Constraint(expr= m.x41 - 1.2 * m.x181 == 0) m.e42 = Constraint(expr= m.x42 - 1.2 * m.x182 == 0) m.e43 = Constraint(expr= m.x43 - 1.2 * m.x183 == 0) m.e44 = Constraint(expr= m.x44 - 1.2 * m.x184 == 0) m.e45 = Constraint(expr= m.x45 - 0.5 * m.x185 == 0) m.e46 = Constraint(expr= m.x46 - 0.5 * m.x186 == 0) m.e47 = Constraint(expr= m.x47 - 0.5 * m.x187 == 0) m.e48 = Constraint(expr= m.x48 - 0.5 * m.x188 == 0) m.e49 = Constraint(expr= m.x49 - 0.7 * m.x189 == 0) m.e50 = Constraint(expr= m.x50 - 0.7 * m.x190 == 0) m.e51 = Constraint(expr= m.x51 - 0.7 * m.x191 == 0) m.e52 = Constraint(expr= m.x52 - 0.7 * m.x192 == 0) m.e53 = Constraint(expr= m.x53 - 1.2 * m.x193 == 0) m.e54 = Constraint(expr= m.x54 - 1.2 * m.x194 == 0) m.e55 = Constraint(expr= m.x55 - 1.2 * m.x195 == 0) m.e56 = Constraint(expr= m.x56 - 1.2 * m.x196 == 0) m.e57 = Constraint(expr= m.x57 - 1.2 * m.x197 == 0) m.e58 = Constraint(expr= m.x58 - 1.2 * m.x198 == 0) m.e59 = Constraint(expr= m.x59 - 1.2 * m.x199 == 0) m.e60 = Constraint(expr= m.x60 - 1.2 * m.x200 == 0) m.e61 = Constraint(expr= m.x61 - 1.2 * m.x201 == 0) m.e62 = Constraint(expr= m.x62 - 1.2 * m.x202 == 0) m.e63 = Constraint(expr= m.x63 - 1.2 * m.x203 == 0) m.e64 = Constraint(expr= m.x64 - 1.2 * m.x204 == 0) m.e65 = Constraint(expr= m.x65 - 1.2 * m.x205 == 0) m.e66 = Constraint(expr= m.x66 - 1.2 * m.x206 == 0) m.e67 = Constraint(expr= m.x67 - 1.2 * m.x207 == 0) m.e68 = Constraint(expr= m.x68 - 1.2 * m.x208 == 0) m.e69 = Constraint(expr= m.x69 - 0.3 * m.x209 == 0) m.e70 = Constraint(expr= m.x70 - 0.3 * m.x210 == 0) m.e71 = Constraint(expr= m.x71 - 0.3 * m.x211 == 0) m.e72 = Constraint(expr= m.x72 - 0.3 * m.x212 == 0) m.e73 = Constraint(expr= m.x73 - 0.9 * m.x213 == 0) m.e74 = Constraint(expr= m.x74 - 0.9 * m.x214 == 0) m.e75 = Constraint(expr= m.x75 - 0.9 * m.x215 == 0) m.e76 = Constraint(expr= m.x76 - 0.9 * m.x216 == 0) m.e77 = Constraint(expr= m.x77 - 0.3 * m.x217 == 0) m.e78 = Constraint(expr= m.x78 - 0.3 * m.x218 == 0) m.e79 = Constraint(expr= m.x79 - 0.3 * m.x219 == 0) m.e80 = Constraint(expr= m.x80 - 0.3 * m.x220 == 0) m.e81 = Constraint(expr= m.x81 - 0.9 * m.x221 == 0) m.e82 = Constraint(expr= m.x82 - 0.9 * m.x222 == 0) m.e83 = Constraint(expr= m.x83 - 0.9 * m.x223 == 0) m.e84 = Constraint(expr= m.x84 - 0.9 * m.x224 == 0) m.e85 = Constraint(expr= m.x85 - 0.4 * m.x225 == 0) m.e86 = Constraint(expr= m.x86 - 0.4 * m.x226 == 0) m.e87 = Constraint(expr= m.x87 - 0.4 * m.x227 == 0) m.e88 = Constraint(expr= m.x88 - 0.4 * m.x228 == 0) m.e89 = Constraint(expr= m.x89 - 0.4 * m.x229 == 0) m.e90 = Constraint(expr= m.x90 - 0.4 * m.x230 == 0) m.e91 = Constraint(expr= m.x91 - 0.4 * m.x231 == 0) m.e92 = Constraint(expr= m.x92 - 0.4 * m.x232 == 0) m.e93 = Constraint(expr= m.x93 - 0.4 * m.x233 == 0) m.e94 = Constraint(expr= m.x94 - 0.4 * m.x234 == 0) m.e95 = Constraint(expr= m.x95 - 0.4 * m.x235 == 0) m.e96 = Constraint(expr= m.x96 - 0.4 * m.x236 == 0) m.e97 = Constraint(expr= m.x97 - 1.6 * m.x237 == 0) m.e98 = Constraint(expr= m.x98 - 1.6 * m.x238 == 0) m.e99 = Constraint(expr= m.x99 - 1.6 * m.x239 == 0) m.e100 = Constraint(expr= m.x100 - 1.6 * m.x240 == 0) m.e101 = Constraint(expr= m.x101 - 1.6 * m.x241 == 0) m.e102 = Constraint(expr= m.x102 - 1.6 * m.x242 == 0) m.e103 = Constraint(expr= m.x103 - 1.6 * m.x243 == 0) m.e104 = Constraint(expr= m.x104 - 1.6 * m.x244 == 0) m.e105 = Constraint(expr= m.x105 - 1.1 * m.x245 == 0) m.e106 = Constraint(expr= m.x106 - 1.1 * m.x246 == 0) m.e107 = Constraint(expr= m.x107 - 1.1 * m.x247 == 0) m.e108 = Constraint(expr= m.x108 - 1.1 * m.x248 == 0) m.e109 = Constraint(expr= m.x109 - 1.1 * m.x249 == 0) m.e110 = Constraint(expr= m.x110 - 1.1 * m.x250 == 0) m.e111 = Constraint(expr= m.x111 - 1.1 * m.x251 == 0) m.e112 = Constraint(expr= m.x112 - 1.1 * m.x252 == 0) m.e113 = Constraint(expr= m.x113 - 0.7 * m.x253 == 0) m.e114 = Constraint(expr= m.x114 - 0.7 * m.x254 == 0) m.e115 = Constraint(expr= m.x115 - 0.7 * m.x255 == 0) m.e116 = Constraint(expr= m.x116 - 0.7 * m.x256 == 0) m.e117 = Constraint(expr= m.x117 - 0.7 * m.x257 == 0) m.e118 = Constraint(expr= m.x118 - 0.7 * m.x258 == 0) m.e119 = Constraint(expr= m.x119 - 0.7 * m.x259 == 0) m.e120 = Constraint(expr= m.x120 - 0.7 * m.x260 == 0) m.e121 = Constraint(expr= m.x121 - 0.7 * m.x261 == 0) m.e122 = Constraint(expr= m.x122 - 0.7 * m.x262 == 0) m.e123 = Constraint(expr= m.x123 - 0.7 * m.x263 == 0) m.e124 = Constraint(expr= m.x124 - 0.7 * m.x264 == 0) m.e125 = Constraint(expr= m.x125 - 0.2 * m.x265 == 0) m.e126 = Constraint(expr= m.x126 - 0.2 * m.x266 == 0) m.e127 = Constraint(expr= m.x127 - 0.2 * m.x267 == 0) m.e128 = Constraint(expr= m.x128 - 0.2 * m.x268 == 0) m.e129 = Constraint(expr= m.x129 - 0.7 * m.x269 == 0) m.e130 = Constraint(expr= m.x130 - 0.7 * m.x270 == 0) m.e131 = Constraint(expr= m.x131 - 0.7 * m.x271 == 0) m.e132 = Constraint(expr= m.x132 - 0.7 * m.x272 == 0) m.e133 = Constraint(expr= m.x133 - 0.3 * m.x273 == 0) m.e134 = Constraint(expr= m.x134 - 0.3 * m.x274 == 0) m.e135 = Constraint(expr= m.x135 - 0.3 * m.x275 == 0) m.e136 = Constraint(expr= m.x136 - 0.3 * m.x276 == 0) m.e137 = Constraint(expr= m.x137 - 0.9 * m.x277 == 0) m.e138 = Constraint(expr= m.x138 - 0.9 * m.x278 == 0) m.e139 = Constraint(expr= m.x139 - 0.9 * m.x279 == 0) m.e140 = Constraint(expr= m.x140 - 0.9 * m.x280 == 0) m.e141 = Constraint(expr= m.x101 >= 1.2) m.e142 = Constraint(expr= m.x102 >= 1.15) m.e143 = Constraint(expr= m.x103 >= 1.1) m.e144 = Constraint(expr=
#!/usr/bin/env python # -- coding: utf-8 -- ############################################################################### # Copyright Kitware Inc. # # Licensed under the Apache License, Version 2.0 ( the "License" ); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ############################################################################### # flake8: noqa: E501 class TcgaCodes(object): DISEASE_STUDIES = { # 'Study Abbreviation': 'Study Name', 'LAML': 'Acute Myeloid Leukemia', 'ACC': 'Adrenocortical carcinoma', 'BLCA': 'Bladder Urothelial Carcinoma', 'LGG': 'Brain Lower Grade Glioma', 'BRCA': 'Breast invasive carcinoma', 'CESC': 'Cervical squamous cell carcinoma and endocervical adenocarcinoma', 'CHOL': 'Cholangiocarcinoma', 'LCML': 'Chronic Myelogenous Leukemia', 'COAD': 'Colon adenocarcinoma', 'CNTL': 'Controls', 'ESCA': 'Esophageal carcinoma ', 'FPPP': 'FFPE Pilot Phase II', 'GBM': 'Glioblastoma multiforme', 'HNSC': 'Head and Neck squamous cell carcinoma', 'KICH': 'Kidney Chromophobe', 'KIRC': 'Kidney renal clear cell carcinoma', 'KIRP': 'Kidney renal papillary cell carcinoma', 'LIHC': 'Liver hepatocellular carcinoma', 'LUAD': 'Lung adenocarcinoma', 'LUSC': 'Lung squamous cell carcinoma', 'DLBC': 'Lymphoid Neoplasm Diffuse Large B-cell Lymphoma', 'MESO': 'Mesothelioma', 'MISC': 'Miscellaneous', 'OV': 'Ovarian serous cystadenocarcinoma', 'PAAD': 'Pancreatic adenocarcinoma', 'PCPG': 'Pheochromocytoma and Paraganglioma', 'PRAD': 'Prostate adenocarcinoma', 'READ': 'Rectum adenocarcinoma', 'SARC': 'Sarcoma', 'SKCM': 'Skin Cutaneous Melanoma', 'STAD': 'Stomach adenocarcinoma', 'TGCT': 'Testicular Germ Cell Tumors', 'THYM': 'Thymoma', 'THCA': 'Thyroid carcinoma', 'UCS': 'Uterine Carcinosarcoma', 'UCEC': 'Uterine Corpus Endometrial Carcinoma', 'UVM': 'Uveal Melanoma', } REPOSITORY_TYPES = { 'bcr', # 'Biospecimen Core Resource' 'cgcc', 'gsc', } DATA_PROVIDERS = { 'biotab', # Clinical metadata, skip 'intgen.org', 'nationwidechildrens.org', 'genome.wustl.edu', 'supplemental' # unknown, appears under 'tumor/ov/bcr/', skip } DATA_TYPES = { 'bio', # XML format clinical metadata, skip 'biotab', # CSV format clinical metadata, skip 'pathology_reports', # PDF format pathology reports, skip 'diagnostic_images', # SVS format images, use 'tissue_images', # SVS format images, use 'minbio' # unknown, appears under 'tumor/gbm/bcr/intgen.org/', skip } SLIDE_LOCATION = { 'TS': 'Top Slide', 'MS': 'Middle Slide', 'BS': 'Bottom Slide', 'DX': 'Top Slide', } TISSUE_SOURCE_SITE = { # 'TSS Code': ('Source Site', 'Study Name', 'BCR'), '01': ('International Genomics Consortium', 'Ovarian serous cystadenocarcinoma', 'IGC'), '02': ('MD Anderson Cancer Center', 'Glioblastoma multiforme', 'IGC'), '04': ('Gynecologic Oncology Group', 'Ovarian serous cystadenocarcinoma', 'IGC'), '05': ('Indivumed', 'Lung adenocarcinoma', 'IGC'), '06': ('Henry Ford Hospital', 'Glioblastoma multiforme', 'IGC'), '07': ('TGen', 'Cell Line Control', 'IGC'), '08': ('UCSF', 'Glioblastoma multiforme', 'IGC'), '09': ('UCSF', 'Ovarian serous cystadenocarcinoma', 'IGC'), '10': ('MD Anderson Cancer Center', 'Ovarian serous cystadenocarcinoma', 'IGC'), '11': ('MD Anderson Cancer Center', 'Lung squamous cell carcinoma', 'IGC'), '12': ('Duke', 'Glioblastoma multiforme', 'IGC'), '13': ('Memorial Sloan Kettering', 'Ovarian serous cystadenocarcinoma', 'IGC'), '14': ('Emory University', 'Glioblastoma multiforme', 'IGC'), '15': ('Mayo Clinic - Rochester', 'Glioblastoma multiforme', 'IGC'), '16': ('Toronto Western Hospital', 'Glioblastoma multiforme', 'IGC'), '17': ('Washington University', 'Lung adenocarcinoma', 'IGC'), '18': ('Princess Margaret Hospital (Canada)', 'Lung squamous cell carcinoma', 'IGC'), '19': ('Case Western', 'Glioblastoma multiforme', 'IGC'), '1Z': ('Johns Hopkins', 'Thymoma', 'NCH'), '20': ('Fox Chase Cancer Center', 'Ovarian serous cystadenocarcinoma', 'IGC'), '21': ('Fox Chase Cancer Center', 'Lung squamous cell carcinoma', 'IGC'), '22': ('Mayo Clinic - Rochester', 'Lung squamous cell carcinoma', 'IGC'), '23': ('<NAME>', 'Ovarian serous cystadenocarcinoma', 'IGC'), '24': ('Washington University', 'Ovarian serous cystadenocarcinoma', 'IGC'), '25': ('<NAME> - Rochester', 'Ovarian serous cystadenocarcinoma', 'IGC'), '26': ('University of Florida', 'Glioblastoma multiforme', 'IGC'), '27': ('Milan - Italy, Fondazione IRCCS Instituto Neuroligico C. Besta', 'Glioblastoma multiforme', 'IGC'), '28': ('<NAME>', 'Glioblastoma multiforme', 'IGC'), '29': ('Duke', 'Ovarian serous cystadenocarcinoma', 'IGC'), '2A': ('Memorial Sloan Kettering Cancer Center', 'Prostate adenocarcinoma', 'NCH'), '2E': ('University of Kansas Medical Center', 'Uterine Corpus Endometrial Carcinoma', 'NCH'), '2F': ('Erasmus MC', 'Bladder Urothelial Carcinoma', 'NCH'), '2G': ('Erasmus MC', 'Testicular Germ Cell Tumors', 'NCH'), '2H': ('Erasmus MC', 'Esophageal carcinoma ', 'NCH'), '2J': ('<NAME>', 'Pancreatic adenocarcinoma', 'NCH'), '2K': ('Greenville Health System', 'Kidney renal papillary cell carcinoma', 'NCH'), '2L': ('Technical University of Munich', 'Pancreatic adenocarcinoma', 'NCH'), '2M': ('Technical University of Munich', 'Esophageal carcinoma ', 'NCH'), '2N': ('Technical University of Munich', 'Stomach adenocarcinoma', 'NCH'), '2P': ('University of California San Diego', 'Pancreatic adenocarcinoma', 'NCH'), '2V': ('University of California San Diego', 'Liver hepatocellular carcinoma', 'NCH'), '2W': ('University of New Mexico', 'Cervical squamous cell carcinoma and endocervical adenocarcinoma', 'NCH'), '2X': ('ABS IUPUI', 'Testicular Germ Cell Tumors', 'NCH'), '2Y': ('Moffitt Cancer Center', 'Liver hepatocellular carcinoma', 'NCH'), '2Z': ('Moffitt Cancer Center', 'Kidney renal papillary cell carcinoma', 'NCH'), '30': ('Harvard', 'Ovarian serous cystadenocarcinoma', 'IGC'), '31': ('Imperial College', 'Ovarian serous cystadenocarcinoma', 'IGC'), '32': ('St. Joseph\'s Hospital (AZ)', 'Glioblastoma multiforme', 'IGC'), '33': ('Johns Hopkins', 'Lung squamous cell carcinoma', 'IGC'), '34': ('University of Pittsburgh', 'Lung squamous cell carcinoma', 'IGC'), '35': ('Cureline', 'Lung adenocarcinoma', 'IGC'), '36': ('BC Cancer Agency', 'Ovarian serous cystadenocarcinoma', 'IGC'), '37': ('Cureline', 'Lung squamous cell carcinoma', 'IGC'), '38': ('UNC', 'Lung adenocarcinoma', 'IGC'), '39': ('MSKCC', 'Lung squamous cell carcinoma', 'IGC'), '3A': ('Moffitt Cancer Center', 'Pancreatic adenocarcinoma', 'NCH'), '3B': ('Moffitt Cancer Center', 'Sarcoma', 'NCH'), '3C': ('Columbia University', 'Breast invasive carcinoma', 'NCH'), '3E': ('Columbia University', 'Pancreatic adenocarcinoma', 'NCH'), '3G': ('MD Anderson Cancer Center', 'Thymoma', 'NCH'), '3H': ('MD Anderson Cancer Center', 'Mesothelioma', 'NCH'), '3J': ('Carle Cancer Center', 'Breast invasive carcinoma', 'NCH'), '3K': ('Boston Medical Center', 'Liver hepatocellular carcinoma', 'NCH'), '3L': ('Albert Einstein Medical Center', 'Colon adenocarcinoma', 'NCH'), '3M': ('University of Kansas Medical Center', 'Stomach adenocarcinoma', 'NCH'), '3N': ('Greenville Health System', 'Skin Cutaneous Melanoma', 'NCH'), '3P': ('Greenville Health System', 'Ovarian serous cystadenocarcinoma', 'NCH'), '3Q': ('Greenville Health Systems', 'Thymoma', 'NCH'), '3R': ('University of New Mexico', 'Sarcoma', 'NCH'), '3S': ('University of New Mexico', 'Thymoma', 'NCH'), '3T': ('Emory University', 'Thymoma', 'NCH'), '3U': ('University of Chicago', 'Mesothelioma', 'NCH'), '3W': ('University of California San Diego', 'Sarcoma', 'NCH'), '3X': ('Alberta Health Services', 'Cholangiocarcinoma', 'NCH'), '3Z': ('Mary Bird Perkins Cancer Center - Our Lady of the Lake', 'Kidney renal clear cell carcinoma', 'NCH'), '41': ('Christiana Healthcare', 'Glioblastoma multiforme', 'IGC'), '42': ('Christiana Healthcare', 'Ovarian serous cystadenocarcinoma', 'IGC'), '43': ('Christiana Healthcare', 'Lung squamous cell carcinoma', 'IGC'), '44': ('Christiana Healthcare', 'Lung adenocarcinoma', 'IGC'), '46': ('St. Joseph\'s Medical Center (MD)', 'Lung squamous cell carcinoma', 'IGC'), '49': ('Johns Hopkins', 'Lung adenocarcinoma', 'IGC'), '4A': ('Mary Bird Perkins Cancer Center - Our Lady of the Lake', 'Kidney renal papillary cell carcinoma', 'NCH'), '4B': ('Mary Bird Perkins Cancer Center - Our Lady of the Lake', 'Lung adenocarcinoma', 'NCH'), '4C': ('Mary Bird Perkins Cancer Center - Our Lady of the Lake', 'Thyroid carcinoma', 'NCH'), '4D': ('Molecular Response', 'Ovarian serous cystadenocarcinoma', 'NCH'), '4E': ('Molecular Response', 'Uterine Corpus Endometrial Carcinoma', 'NCH'), '4G': ('Sapienza University of Rome', 'Cholangiocarcinoma', 'NCH'), '4H': ('Proteogenex, Inc.', 'Breast invasive carcinoma', 'NCH'), '4J': ('Proteogenex, Inc.', 'Cervical squamous cell carcinoma and endocervical adenocarcinoma', 'NCH'), '4K': ('Proteogenex, Inc.', 'Testicular Germ Cell Tumors', 'NCH'), '4L': ('Proteogenex, Inc.', 'Prostate adenocarcinoma', 'NCH'), '4N': ('<NAME>kins Cancer Center - Our Lady of the Lake', 'Colon adenocarcinoma', 'NCH'), '4P': ('Duke University', 'Head and Neck squamous cell carcinoma', 'NCH'), '4Q': ('Duke University', 'Sarcoma', 'NCH'), '4R': ('Duke University', 'Liver hepatocellular carcinoma', 'NCH'), '4S': ('Duke University', 'Prostate adenocarcinoma', 'NCH'), '4T': ('Duke University', 'Colon adenocarcinoma', 'NCH'), '4V': ('Hospital Louis Pradel', 'Thymoma', 'NCH'), '4W': ('University of Miami', 'Glioblastoma multiforme', 'NCH'), '4X': ('Yale University', 'Thymoma', 'NCH'), '4Y': ('Medical College of Wisconsin', 'Sarcoma', 'NCH'), '4Z': ('Barretos Cancer Hospital', 'Bladder Urothelial Carcinoma', 'NCH'), '50': ('University of Pittsburgh', 'Lung adenocarcinoma', 'IGC'), '51': ('UNC', 'Lung squamous cell carcinoma', 'IGC'), '52': ('University of Miami', 'Lung squamous cell carcinoma', 'IGC'), '53': ('University of Miami', 'Lung adenocarcinoma', 'IGC'), '55': ('International Genomics Consortium', 'Lung adenocarcinoma', 'IGC'), '56': ('International Genomics Consortium', 'Lung squamous cell carcinoma', 'IGC'), '57': ('International Genomics Consortium', 'Ovarian serous cystadenocarcinoma', 'IGC'), '58': ('Thoraxklinik at University Hospital Heidelberg', 'Lung squamous cell carcinoma', 'IGC'), '59': ('Roswell Park', 'Ovarian serous cystadenocarcinoma', 'IGC'), '5A': ('Wake Forest University', 'Cholangiocarcinoma', 'NCH'), '5B': ('Medical College of Wisconsin', 'Uterine Corpus Endometrial Carcinoma', 'NCH'), '5C': ('Cureline', 'Liver hepatocellular carcinoma', 'NCH'), '5D': ('University of Miami', 'Sarcoma', 'NCH'), '5F': ('Duke University', 'Thyroid carcinoma', 'NCH'), '5G': ('Cleveland Clinic Foundation', 'Thymoma', 'NCH'), '5H': ('Retina Consultants Houston', 'Uveal Melanoma', 'NCH'), '5J': ('Cureline', 'Acute Myeloid Leukemia', 'NCH'), '5K': ('St. Joseph\'s Hospital AZ', 'Thymoma', 'NCH'),
EMPTY_BOARD = [ [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], ] EMPTY_BOARD_STRING = " \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n" BOARD_WITH_ROW_2_COLUMN_4_ALIVE = [ # 0 1 2 3 4 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], # 0 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], # 1 [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], # 2 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], ] BOARD_WITH_ROW_2_COLUMN_4_ALIVE_STRING = " \n \n * \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n" BOARD_WITH_SQUARE_AT_ROW_2_COLUMN_4_ALIVE = [ # 0 1 2 3 4 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], # 0 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], # 1 [0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], # 2 [0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 0, 0, 0,
j, k, m)y(m, n, p) answer(i, j, k, n, p, z) self.failUnless(isEqual(result, answer, tol=tol), "Mismatch: %s != %s" % (result, answer)) return #--------------------------------------------------------------------------- # thirdranktensor . fourthranktensor #--------------------------------------------------------------------------- def testThirdRankTensorDotFourthRankTensor(self): for dim in dims: r3type = eval("ThirdRankTensor%id" % dim) r4type = eval("FourthRankTensor%id" % dim) r5type = eval("FifthRankTensor%id" % dim) x = fillRandom(r3type) y = fillRandom(r4type) result = innerProduct(x, y) answer = r5type() for i in xrange(dim): for j in xrange(dim): for k in xrange(dim): for m in xrange(dim): for n in xrange(dim): for p in xrange(dim): z = answer(i, j, m, n, p) + x(i, j, k)y(k, m, n, p) answer(i, j, m, n, p, z) self.failUnless(isEqual(result, answer, tol=tol), "Mismatch: %s != %s" % (result, answer)) return #--------------------------------------------------------------------------- # thirdranktensor . thirdranktensor #--------------------------------------------------------------------------- def testThirdRankTensorDotThirdRankTensor(self): for dim in dims: r3type = eval("ThirdRankTensor%id" % dim) r4type = eval("FourthRankTensor%id" % dim) x = fillRandom(r3type) y = fillRandom(r3type) result = innerProduct(x, y) answer = r4type() for i in xrange(dim): for j in xrange(dim): for k in xrange(dim): for m in xrange(dim): for n in xrange(dim): z = answer(i, j, m, n) + x(i, j, k)y(k, m, n) answer(i, j, m, n, z) self.failUnless(isEqual(result, answer, tol=tol), "Mismatch: %s != %s" % (result, answer)) return #=============================================================================== # Test class for outer product. #=============================================================================== class TestOuterProduct(unittest.TestCase): #--------------------------------------------------------------------------- # scalar x value #--------------------------------------------------------------------------- def testScalarOuterThing(self): for typestring in ("Vector%id", "Tensor%id", "SymTensor%id", "ThirdRankTensor%id"): for dim in dims: ttype = eval(typestring % dim) x = rangen.uniform(ranrange) y = fillRandom(ttype) result = outerProduct(x, y) answer = ttype() for i in xrange(ttype.numElements): answer[i] = xy[i] self.failUnless(isEqual(result, answer, tol=tol), "Mismatch for %s: %s != %s" % (ttype.__name__, result, answer)) return #--------------------------------------------------------------------------- # value x scalar #--------------------------------------------------------------------------- def testThingOuterScalar(self): for typestring in ("Vector%id", "Tensor%id", "SymTensor%id", "ThirdRankTensor%id"): for dim in dims: ttype = eval(typestring % dim) x = rangen.uniform(ranrange) y = fillRandom(ttype) result = outerProduct(y, x) answer = ttype() for i in xrange(ttype.numElements): answer[i] = xy[i] self.failUnless(isEqual(result, answer, tol=tol), "Mismatch for %s: %s != %s" % (ttype.__name__, result, answer)) return #--------------------------------------------------------------------------- # vector x vector #--------------------------------------------------------------------------- def testVectorOuterVector(self): for dim in dims: type = eval("Vector%id" % dim) ttype = eval("Tensor%id" % dim) x = fillRandom(type) y = fillRandom(type) result = outerProduct(x, y) answer = ttype() for i in xrange(dim): for j in xrange(dim): answer(i, j, x[i]y[j]) self.failUnless(isEqual(result, answer, tol=tol), "Mismatch: %s != %s" % (result, answer)) return #--------------------------------------------------------------------------- # tensor x vector #--------------------------------------------------------------------------- def testTensorOuterVector(self): for typestring in ("Tensor%id", "SymTensor%id"): for dim in dims: vtype = eval("Vector%id" % dim) ttype = eval(typestring % dim) trttype = eval("ThirdRankTensor%id" % dim) x = fillRandom(ttype) y = fillRandom(vtype) result = outerProduct(x, y) answer = trttype() for i in xrange(dim): for j in xrange(dim): for k in xrange(dim): answer(i, j, k, x(i,j)y(k)) self.failUnless(isEqual(result, answer, tol=tol), "Mismatch: %s != %s" % (result, answer)) return #--------------------------------------------------------------------------- # vector x tensor #--------------------------------------------------------------------------- def testVectorOuterTensor(self): for typestring in ("Tensor%id", "SymTensor%id"): for dim in dims: vtype = eval("Vector%id" % dim) ttype = eval(typestring % dim) trttype = eval("ThirdRankTensor%id" % dim) x = fillRandom(vtype) y = fillRandom(ttype) result = outerProduct(x, y) answer = trttype() for i in xrange(dim): for j in xrange(dim): for k in xrange(dim): answer(i, j, k, x(i)y(j,k)) self.failUnless(isEqual(result, answer, tol=tol), "Mismatch: %s != %s" % (result, answer)) return #=============================================================================== # Test class for double inner product. #=============================================================================== class TestDoubleInnerProduct(unittest.TestCase): #--------------------------------------------------------------------------- # tensor .. tensor #--------------------------------------------------------------------------- def testTensorDoubleDotTensor(self): for ttypestring1 in ("Tensor%id", "SymTensor%id"): for ttypestring2 in ("Tensor%id", "SymTensor%id"): for dim in dims: t1type = eval(ttypestring1 % dim) t2type = eval(ttypestring2 % dim) x = fillRandom(t1type) y = fillRandom(t2type) result = innerDoubleProduct(x, y) result2 = x.doubledot(y) answer = 0.0 for i in xrange(dim): for j in xrange(dim): answer += x(i,j)y(j,i) self.failUnless(abs(result - answer) < 1.0e-10, "Mismatch: %s != %s" % (result, answer)) self.failUnless(abs(result2 - answer) < 1.0e-10, "Mismatch: %s != %s" % (result2, answer)) return #--------------------------------------------------------------------------- # tensor .. thirdranktensor #--------------------------------------------------------------------------- def testTensorDoubleDotThirdRankTensor(self): for ttypestring in ("Tensor%id", "SymTensor%id"): for dim in dims: vtype = eval("Vector%id" % dim) r2type = eval(ttypestring % dim) r3type = eval("ThirdRankTensor%id" % dim) x = fillRandom(r2type) y = fillRandom(r3type) result = innerDoubleProduct(x, y) answer = vtype() for i in xrange(dim): for j in xrange(dim): for k in xrange(dim): answer[k] += x(i, j)y(j, i, k) self.failUnless(isEqual(result, answer, tol=tol), "Mismatch: %s != %s" % (result, answer)) return #--------------------------------------------------------------------------- # thirdranktensor .. tensor #--------------------------------------------------------------------------- def testThirdRankTensorDoubleDotTensor(self): for ttypestring in ("Tensor%id", "SymTensor%id"): for dim in dims: vtype = eval("Vector%id" % dim) r2type = eval(ttypestring % dim) r3type = eval("ThirdRankTensor%id" % dim) x = fillRandom(r3type) y = fillRandom(r2type) result = innerDoubleProduct(x, y) answer = vtype() for i in xrange(dim): for j in xrange(dim): for k in xrange(dim): answer[i] += x(i, j, k)y(k, j) self.failUnless(isEqual(result, answer, tol=tol), "Mismatch: %s != %s" % (result, answer)) return #--------------------------------------------------------------------------- # thirdranktensor .. thirdranktensor #--------------------------------------------------------------------------- def testThirdRankTensorDoubleDotThirdRankTensor(self): for dim in dims: r2type = eval("Tensor%id" % dim) r3type = eval("ThirdRankTensor%id" % dim) x = fillRandom(r3type) y = fillRandom(r3type) result = innerDoubleProduct(x, y) answer = r2type() for i in xrange(dim): for j in xrange(dim): for k in xrange(dim): for m in xrange(dim): z = answer(i,m) + x(i,j,k)y(k,j,m) answer(i,m,z) self.failUnless(isEqual(result, answer, tol=tol), "Mismatch: %s != %s" % (result, answer)) return #--------------------------------------------------------------------------- # tensor .. fourthranktensor #--------------------------------------------------------------------------- def testTensorDoubleDotFourthRankTensor(self): for ttypestring in ("Tensor%id", "SymTensor%id"): for dim in dims: ttype = eval("Tensor%id" % dim) r2type = eval(ttypestring % dim) r4type = eval("FourthRankTensor%id" % dim) x = fillRandom(r2type) y = fillRandom(r4type) result = innerDoubleProduct(x, y) answer = ttype() for i in xrange(dim): for j in xrange(dim): for k in xrange(dim): for m in xrange(dim): z = answer(k, m) + x(i, j)y(j, i, k, m) answer(k, m, z) self.failUnless(isEqual(result, answer, tol=tol), "Mismatch: %s != %s" % (result, answer)) return #--------------------------------------------------------------------------- # fourthranktensor .. tensor #--------------------------------------------------------------------------- def testFourthRankTensorDoubleDotTensor(self): for ttypestring in ("Tensor%id", "SymTensor%id"): for dim in dims: ttype = eval("Tensor%id" % dim) r2type = eval(ttypestring % dim) r4type = eval("FourthRankTensor%id" % dim) x = fillRandom(r4type) y = fillRandom(r2type) result = innerDoubleProduct(x, y) answer = ttype() for i in xrange(dim): for j in xrange(dim): for k in xrange(dim): for m in xrange(dim): z = answer(i, j) + x(i, j, k, m)y(m, k) answer(i, j, z) self.failUnless(isEqual(result, answer, tol=tol), "Mismatch: %s != %s" % (result, answer)) return #--------------------------------------------------------------------------- # thirdranktensor .. fourthranktensor #--------------------------------------------------------------------------- def testThirdRankTensorDoubleDotFourthRankTensor(self): for dim in dims: r3type = eval("ThirdRankTensor%id" % dim) r4type = eval("FourthRankTensor%id" % dim) x = fillRandom(r3type) y = fillRandom(r4type) result = innerDoubleProduct(x, y) answer = r3type() for i in xrange(dim): for j in xrange(dim): for k in xrange(dim): for m in xrange(dim): for n in xrange(dim): z = answer(i, m, n) + x(i, j, k)y(k, j, m, n) answer(i, m, n, z) self.failUnless(isEqual(result, answer, tol=tol), "Mismatch: %s != %s" % (result, answer)) return #--------------------------------------------------------------------------- # fourthranktensor .. thirdranktensor #--------------------------------------------------------------------------- def testFourthRankTensorDoubleDotThirdRankTensor(self): for dim in dims: r3type = eval("ThirdRankTensor%id" % dim) r4type = eval("FourthRankTensor%id" % dim) x = fillRandom(r4type) y = fillRandom(r3type) result = innerDoubleProduct(x, y) answer = r3type() for i in xrange(dim): for j in xrange(dim): for k in xrange(dim): for m in xrange(dim): for n in xrange(dim): z = answer(i, j, n) + x(i, j, k, m)y(m, k, n) answer(i, j, n, z) self.failUnless(isEqual(result, answer, tol=tol), "Mismatch: %s != %s" % (result, answer)) return #--------------------------------------------------------------------------- # fourthranktensor .. fourthranktensor #--------------------------------------------------------------------------- def testFourthRankTensorDoubleDotFourthRankTensor(self): for dim in dims: r4type = eval("FourthRankTensor%id" % dim) x = fillRandom(r4type) y = fillRandom(r4type) result = innerDoubleProduct(x, y) answer = r4type() for i in xrange(dim): for j in xrange(dim): for k in xrange(dim): for m in xrange(dim): for n in xrange(dim): for p in xrange(dim): z = answer(i, j, n, p) + x(i, j, k, m)*y(m, k, n, p) answer(i, j, n, p, z) self.failUnless(isEqual(result, answer, tol=tol), "Mismatch: %s != %s" % (result, answer)) return #--------------------------------------------------------------------------- # tensor .. fifthranktensor #--------------------------------------------------------------------------- def testTensorDoubleDotFifthRankTensor(self): for ttypestring in ("Tensor%id", "SymTensor%id"): for dim in dims: r2type = eval(ttypestring % dim) r3type = eval("ThirdRankTensor%id" % dim) r5type = eval("FifthRankTensor%id" % dim)
<reponame>ark0015/DetectorDesignSensitivities<filename>Functions/waveform_Wphase.py import numpy as np def Get_Waveform(source,pct_of_peak=0.01): """Uses Mass Ratio (q <= 18), aligned spins (abs(a/m)~0.85 or when q=1 abs(a/m)<0.98), fitting coefficients for QNM type, and sampling rate Returns the frequency, the Phenom amplitude of the inspiral-merger-ringdown Uses methods found in <https://arxiv.org/abs/1508.07253> and <https://arxiv.org/abs/1508.07250> Parameters ---------- source : object source object from StrainandNoise, contains all source parameters pct_of_peak : float, optional the percentange of the strain at merger that dictates the maximum frequency the waveform is calculated at in geometrized units (G=c=1) Returns ------- Mf : numpy array of floats the waveform frequencies in geometrized units (G=c=1) fullwaveform : numpy array of floats the waveform strain in geometrized units (G=c=1) """ f_low = source.f_low N = source.nfreqs q = source.q x1 = source.chi1 x2 = source.chi2 fitcoeffs = source._fitcoeffs #M = m1+m2 #Total Mass #q = m2/m1 #Mass Ratio: Paper tested up to 18 #eta = m1m2/M2 reduced mass: Paper tested up to 0.05 (q=18) eta = q/(q+1)2 x_PN = chi_PN(eta,x1,x2) #PN reduced spin parameter a_f = a_final(x1,x2,q,eta) #dimensionless spin ################## #Finds f_ringdown and f_damp from fit taken from <https://arxiv.org/abs/gr-qc/0512160> n = 0 #QNM indices l = 2 m = 2 numn = 3 #number of n's included in the table index = (l-2)(2l+1)numn + (l-m)numn + n f_fit = fitcoeffs[index][3:6] q_fit = fitcoeffs[index][6:9] omega_RD = f_fit[0]+f_fit[1](1-a_f)*f_fit[2] #M omega_{lmn} tau = 2(q_fit[0]+q_fit[1](1-a_f)q_fit[2])/omega_RD #tau_{lmn}/M = 2 Q_{lmn}/(M omega_{lmn}) ######################## f_RD = omega_RD/2/np.pi f_damp = 1/tau/2/np.pi Gamma1 = Lambda(eta,x_PN,4) Gamma2 = Lambda(eta,x_PN,5) Gamma3 = Lambda(eta,x_PN,6) f_peak = Calc_f_peak(f_RD,f_damp,[Gamma1,Gamma2,Gamma3]) f1 = 0.014 f3 = f_peak f2 = (f1+f3)/2 f1_phase = 0.018 f2_phase = 0.5f_RD cutoffFreq = Find_Cutoff_Freq(f_RD,f_damp,[Gamma1,Gamma2,Gamma3],pct_of_peak=pct_of_peak) #If lowest frequency is greater than cutoffFreq, then raise error. if f_low >= cutoffFreq: raise ValueError('Lower frequency bound (ie. f_low) must be lower than that of the merger ringdown.') Mf = np.logspace(np.log10(f_low),np.log10(cutoffFreq),N) #Mf_phase = np.logspace(log10(0.0035),log10(1.15f_RD),N) #Mf_phase = np.logspace(log10(0.0035),log10(0.12),N) v1 = A_insp(f1,eta,x1,x2,x_PN) v2 = Lambda(eta,x_PN,3) v3 = A_MR(f3,f_RD,f_damp,[Gamma1,Gamma2,Gamma3]) fund1 = DA_insp(f1,eta,x1,x2,x_PN) fund3 = DA_MR(f3,f_RD,f_damp,[Gamma1,Gamma2,Gamma3]) ############################# #Calculate Solutions to eqn 21 in intermediate region Del_solns = A_intermediate(f1,f2,f3,v1,v2,v3,fund1,fund3) # Solutions to eqn 21 ############################## #Calculate all sections of waveform and Paste together indxf1 = np.argmin(np.abs(Mf-f1)) indxfpeak = np.argmin(np.abs(Mf-f_peak)) tmpinspiral = A_norm(Mf[0:indxf1+1],eta)A_insp(Mf[0:indxf1+1],eta,x1,x2,x_PN) tmpintermediate = A_norm(Mf[indxf1+1:indxfpeak],eta)A_int(Mf[indxf1+1:indxfpeak],Del_solns) tmpmergerringdown = A_norm(Mf[indxfpeak:],eta)A_MR(Mf[indxfpeak:],f_RD,f_damp,[Gamma1,Gamma2,Gamma3]) fullwaveform = np.hstack((tmpinspiral,tmpintermediate,tmpmergerringdown)) ############################## #Calculate all section of waveform Phase indxf1_phase = np.argmin(np.abs(Mf-f1_phase)) indxf2_phase = np.argmin(np.abs(Mf-f2_phase)) tc=0.0 ############################## #Calculate Phase connections alpha0 and Beta0: dphi_ins = Dphi_ins(f1_phase,eta,x1,x2,x_PN,tc) phi_ins = Phi_ins(f1_phase,eta,x1,x2,x_PN,tc) beta1 = etadphi_ins - Dphi_int(f1_phase,eta,x_PN,0.0) beta0 = etaphi_ins - Phi_int(f1_phase,eta,x_PN,beta1,0.0) alpha1 = Dphi_int(f2_phase,eta,x_PN,beta1) - Dphi_MR(f2_phase,eta,x_PN,f_RD,f_damp,0.0) alpha0 = Phi_int(f2_phase,eta,x_PN,beta1,beta0) - Phi_MR(f2_phase,eta,x_PN,f_RD,f_damp,alpha1,0.0) dinspiral_phase = Dphi_ins(Mf[:indxf1_phase+1],eta,x1,x2,x_PN,tc) dintermediate_phase = (1/eta)Dphi_int(Mf[indxf1_phase+1:indxf2_phase],eta,x_PN,beta1) dmerger_ringdown_phase = (1/eta)Dphi_MR(Mf[indxf2_phase:],eta,x_PN,f_RD,f_damp,alpha1) inspiral_phase = Phi_ins(Mf[0:indxf1_phase+1],eta,x1,x2,x_PN,tc) intermediate_phase = (1/eta)Phi_int(Mf[indxf1_phase+1:indxf2_phase],eta,x_PN,beta1,beta0) merger_ringdown_phase = (1/eta)Phi_MR(Mf[indxf2_phase:],eta,x_PN,f_RD,f_damp,alpha1,alpha0) ############################ #Join subsections of phase and amplitude fullphase = np.hstack((inspiral_phase,intermediate_phase,merger_ringdown_phase)) return [Mf,fullwaveform,fullphase] def A_norm(freqs,eta): """Calculates the constant scaling factor A_0 Parameters ---------- freqs : array The frequencies in Natural units (Mf, G=c=1) of the waveform eta : float The reduced mass ratio """ const = np.sqrt(2eta/3/np.pi(1/3)) return constfreqs*-(7/6) def A_insp(freqs,eta,x1,x2,X_PN): """Calculates the Inspiral Amplitude Parameters ---------- freqs : array The frequencies in Natural units (Mf, G=c=1) of the waveform eta : float The reduced mass ratio x1 : float The dimensionless spin parameter abs(a/m) for black hole m1. x2 : float The dimensionless spin parameter abs(a/m) for black hole m2. x_PN : float The PN reduced spin parameter """ A_PN = 0.0 A_higher = 0.0 for i in range(7): A_PN = A_PN + PN_coeffs(eta,x1,x2,i)(np.pifreqs)(i/3) if i >= 1 and i <= 3: A_higher = A_higher + Lambda(eta,X_PN,i-1)freqs((6+i)/3) return (A_PN + A_higher) def DA_insp(freqs,eta,x1,x2,X_PN): """Calculates Derivative of the inspiral amplitude. Parameters ---------- freqs : array The frequencies in Natural units (Mf, G=c=1) of the waveform eta : float The reduced mass ratio x1 : float The dimensionless spin parameter abs(a/m) for black hole m1. x2 : float The dimensionless spin parameter abs(a/m) for black hole m2. x_PN : float The PN reduced spin parameter """ DA_PN = 0.0 DA_higher = 0.0 for i in range(7): PN_const = np.pi(i/3)(i/3)PN_coeffs(eta,x1,x2,i) DA_PN = DA_PN + PN_const(freqs)((i-3)/3) if i >= 1 and i <= 3: higher_const = ((6+i)/3)Lambda(eta,X_PN,i-1) DA_higher = DA_higher + higher_constfreqs((i+3)/3) return DA_PN + DA_higher def A_MR(freqs,f_RD,f_damp,Gammas): """Calculates the Normalized Merger-Ringdown Amplitude Parameters ---------- freqs : array The frequencies in Natural units (Mf, G=c=1) of the waveform f_RD : float Frequency of the Ringdown transition f_damp : float Damping frequency Gammas : array-like Normalizes lorentzian to correct shape """ varf = freqs-f_RD fg_d = Gammas[2]f_damp return (Gammas[0]fg_d)/(varf2+fg_d2)np.exp(-(Gammas[1]/fg_d)varf) def DA_MR(freqs,f_RD,f_damp,Gammas): """Calculates Derivative of the Merger-Ringdown Amplitude Parameters ---------- freqs : array The frequencies in Natural units (Mf, G=c=1) of the waveform f_RD : float Frequency of the Ringdown transition f_damp : float Damping frequency Gammas : array-like Normalizes lorentzian to correct shape """ varf = freqs-f_RD fg_d = Gammas[2]f_damp A_MR_0 = A_MR(freqs,f_RD,f_damp,Gammas) return -A_MR_0(2varf/(varf2+fg_d2)+Gammas[1]/fg_d) def A_intermediate(f1,f2,f3,v1,v2,v3,d1,d3): """Solves system of equations for intermediate amplitude matching""" Mat = np.array([[1., f1, f12, f13, f14],[1., f2, f22, f23, f24],[1., f3, f32, f33, f3*4], \ [0., 1., 2f1, 3f12, 4f1*3],[0., 1., 2f3, 3f32, 4f3*3]],dtype='float') a = np.array([v1,v2,v3,d1,d3],dtype='float') return np.linalg.solve(Mat,a) def A_int(freqs,delt): """Calculates the Intermediate Amplitude Parameters ---------- freqs : array The frequencies in Natural units (Mf, G=c=1) of the waveform delt : array Coefficient solutions to match the inspiral to the merger-ringdown portion of the waveform """ return (delt[0]+delt[1]freqs+delt[2]freqs2+delt[3]freqs*3+delt[4]freqs*4) ########################################################################### #Phase portion of waveform ########################################################################### def Phi_ins(freqs,eta,x1,x2,x_PN,t_c,phi_c,sigma0): """Calculates the Inspiral Phase Parameters ---------- freqs : array The frequencies in Natural units (Mf, G=c=1) of the waveform eta : float The reduced mass ratio x1 : float The dimensionless spin parameter abs(a/m) for black hole m1. x2 : float The dimensionless spin parameter abs(a/m) for black hole m2. x_PN : float The PN reduced spin parameter t_c : float Coalescence time?? """ #t_c = 0.0 #???? #phi_c = 0.0 #???????? #sigma0 = 0.0 #??? sigma1 = Lambda(eta,x_PN,7) sigma2 = Lambda(eta,x_PN,8) sigma3 = Lambda(eta,x_PN,9) sigma4 = Lambda(eta,x_PN,10) TF2_expansion = 0.0 TF2_const = 3/(128eta) piMf = np.pifreqs for i in range(5): #First four of summation, others need to be separate for log(piMf) factors. TF2_expansion = TF2_expansion + PN_coeffs_phase(eta,x1,x2,i)(piMf)((i-5)/3) TF2_expansion = (TF2_expansion + (1+np.log10(piMf))PN_coeffs_phase(eta,x1,x2,5) + (PN_coeffs_phase(eta,x1,x2,6) - (6848/63)np.log10(64piMf))(piMf)(1/3) + PN_coeffs_phase(eta,x1,x2,7)(piMf)*(2/3)) phi_TF2 = 2t_cpiMf - phi_c - np.pi/4 + TF2_constTF2_expansion return (phi_TF2 + (1/eta)(sigma0 + sigma1freqs + (3/4sigma2)freqs*(4/3) + (3/5sigma3)freqs(5/3) + (.5sigma4)freqs2)) def Dphi_ins(freqs,eta,x1,x2,x_PN,t_c): """Calculates the Inspiral Phase Derivative Parameters ---------- freqs : array The frequencies in Natural units (Mf, G=c=1) of the waveform eta : float The reduced mass ratio x1 : float The dimensionless spin parameter abs(a/m) for black hole m1. x2 : float The dimensionless spin parameter abs(a/m) for black hole m2. x_PN : float The PN reduced spin parameter t_c : float Coalescence time?? """ #t_c = -200.0 #???? sigma1 = Lambda(eta,x_PN,7) sigma2 = Lambda(eta,x_PN,8) sigma3 = Lambda(eta,x_PN,9) sigma4 = Lambda(eta,x_PN,10) TF2_const_deriv = 3/(128eta) TF2_expans_deriv = 0.0 for i in range(5): TF2_expans_deriv = TF2_expans_deriv + (PN_coeffs_phase(eta,x1,x2,i)np.pi((i-5)/3)((i-5)/3))freqs((i-8)/3) Dphi_TF2 = (2np.pit_c + TF2_const_deriv(TF2_expans_deriv + PN_coeffs_phase(eta,x1,x2,4)/freqs + (PN_coeffs_phase(eta,x1,x2,5)-(6848/63)(3+np.log10((64np.pi)freqs)))(np.pi*(1/3)/3)freqs*(-2/3) + ((2/3)np.pi*(2/3)PN_coeffs_phase(eta,x1,x2,6))freqs(-1/3))) return(Dphi_TF2 + (1/eta)(sigma1 + sigma2freqs(1/3) + sigma3freqs*(2/3) + sigma4freqs)) def Phi_MR(freqs,eta,x_PN,f_RD,f_damp,alpha1,alpha0): """Calculates the Normalized Merger-Ringdown Phase Parameters ---------- freqs : array The frequencies in Natural units (Mf, G=c=1) of the waveform eta : float The reduced mass ratio x_PN : float The PN reduced spin parameter f_RD : float Frequency of the Ringdown transition f_damp : float Damping frequency alpha1 : float ?? alpha0 : float ?? """ ########################### #Calculates phase for merger ringdown (eqn 14) alpha2 = Lambda(eta,x_PN,15) alpha3 = Lambda(eta,x_PN,16) alpha4 = Lambda(eta,x_PN,17) alpha5 = Lambda(eta,x_PN,18) lorentzian = (freqs - alpha5f_RD)/f_damp return (alpha0 + alpha1freqs - alpha2/freqs + (4./3.alpha3)freqs*(.75) + alpha4np.arctan(lorentzian)) def Dphi_MR(freqs,eta,x_PN,f_RD,f_damp,alpha1): """Calculates Derivative of the Merger-Ringdown Phase Derivative Parameters ---------- freqs : array The frequencies in Natural units (Mf, G=c=1) of the waveform eta : float The
# -- coding: utf-8 -- """ featherpmm.py: Extends feather file format with a paired file that contains extra metadata. If the feather file is /path/to/foo.feather, the metadata file is /path/to/foo.pmm. """ from __future__ import division import os import datetime import numpy as np import sys try: import pyarrow.feather as feather except ImportError: feather = None from pmmif import pmm PANDAS_STRING_DTYPE = np.dtype('O') PANDAS_BOOL_DTYPE = np.dtype('bool') PANDAS_FLOAT_DTYPE = np.dtype('float64') NULL_STRING = '∅' NULL_SUFFIX = '_' + NULL_STRING UTF8 = 'UTF-8' isPython2 = sys.version_info.major < 3 if isPython2: bytes_type = str # type: type unicode_type = unicode # type: type else: bytes_type = bytes unicode_type = str class Dataset(object): """ Container for a Pandas dataframe and a metadata object from PMM. Metadata fulfils two functions: 1. It allows association of 'intended' types of columns, in cases for which Pandas forces promotion. For example: - Integer columns that contain nulls are promoted to float in Pandas, but can be marked as 'integer' in the metadata. - Boolean columns that contain nulls are promoted to Object in Pandas, but can be marked as 'boolean' in the metadata. 2. It allows additional annotations to be associated with the dataframe, and with individual columns of the dataframe. - Tags (with optional values) - Descriptions A dataset object has two attributes: - df is an ordinary Pandas dataframe - md is a PMM Metadata object """ def __init__(self, df, md=None, name=None): """ Create Dataset object. - df is a Pandas dataframe. - md is a PMM metadata object, and is optional. - name is the name to be associated with the dataset, for the case where no metadata is provided. If no metadata is provided, the metadata is inferred from the dataframe. """ self.df = df self.md = md or _create_pmm_metadata(df, name) def add_field(self, name, col, pmmtype=None): """ Add a new field to the dataset. Adds the field to the Pandas dataframe, and declares its type in the PMM metadata. The type, if provided, must be one of: 'boolean', 'integer', 'real', 'string', 'datestamp'. If the type is not specified, the type is inferred from the dataframe. The advantages of using add_field, as opposed to just creating it directly in the dataframe, are: - the option of specifying an intended type, even if Pandas has promoted the type in the dataframe. - creating a corresponding entry in the metadata, to facilitate tagging, descriptions, etc. """ self.df[name] = col self.declare_field(name, pmmtype) def declare_field(self, name, pmmtype=None): """ Declare the type of a field in the dataset, which must already exist in the Pandas dataframe. This is intended for use when a dataframe has been created without any metadata (for example, from a CSV file), and then more detailed type information needs to be declared for existing fields. The type, if provided, must be one of the types described for add_field, above. """ if self.df[name].isnull().all(): # all null or no records if pmmtype == 'string': self.df[name] = self.df[name].astype(np.dtype('O')) elif pmmtype == 'datestamp': self.df[name] = (self.df[name] .astype(np.dtype('datetime64[ns]'))) fieldMetadata = _create_pmm_field(self.df[name], pmmtype=pmmtype) self.md.add_field(name, fieldMetadata) def tag_field(self, colname, tagname, value=None): """ Add a tag to a field. The tag can optionally have a value, but by default does not. The field must already exist in the metadata. If a value is provided, it must be of one of the following Python types: - None - bool - int - float - str - datetime.datetime or it can be a (potentially nested) list or dictionary over these types with string keys. """ self.md[colname].tags[tagname] = value def tag_dataset(self, tagname, value=None): """ Add a tag to the dataset. The tag can optionally have a value, but by default does not. If a value is provided, it must have one of the types described in tag_field above. """ self.md.tags[tagname] = value def update_metadata(self): """ Update the metadata to bring it into line with the dataset. After calling this method, all of the fields that exist in the dataset will now exist in the metadata too, and the metadata will not contain any fields that do not appear in the dataframe. It will infer types in the metadata for any fields that do not already have metadata, but will not alter the types of existing fields in the metadata. """ _reset_fields_from_dataframe(self) def merge_metadata(self, other, fields=None): """ After merging another dataset in (via pd.merge), we don't have any of the metadata associated with the fields that have come in from that other dataset. This function takes the 'other metadata' and brings it into the metadata for the main dataset. The new metadata wins if the fields exists in both if: 1. the fields parameter is left empty, or 2. the fields parameter is a list but field is not in the list """ newfields = [fx.name for fx in other.md.fields] for f in newfields: if (fields is None) or (f in fields): if f in [fx.name for fx in self.md.fields]: del self.md.fields[[fx.name for fx in self.md.fields].index(f)] _add_metadata_from_other_dataset(self.md, other.md) _reset_fields_from_dataframe(self) def append(self, other): """ Append another dataset to an existing one. The second dataframe is appended to the first one, and the metadata for any fields that only exist in the second one is added to the first one. """ self.df = self.df.append(other.df, ignore_index=True) _add_metadata_from_other_dataset(self.md, other.md) _reset_fields_from_dataframe(self) def read_dataframe(featherpath): """ Similar to feather.read_dataframe except that it also reads the corresponding .pmm file, if present, and returns a Dataset object rather than a dataframe. The Dataset object contains the Pandas dataframe in its df attribute, and the metadata in its md attribute. """ if feather is None: raise Exception('Feather-format is not available') df = feather.read_feather(featherpath) pmmpath, datasetname = _split_feather_path(featherpath) if os.path.exists(pmmpath): md = pmm.load(pmmpath) else: md = _create_pmm_metadata(df, datasetname) df = _recover_problematical_all_null_columns(Dataset(df, md)) return Dataset(df, md) def write_dataframe(dataset, featherpath): """ Similar to feather.write_dataframe except that it also writes a corresponding .pmm file, and expects a Dataset object rather than a dataframe. The Dataset object contains the Pandas dataframe in its df attribute, and the metadata in its md attribute. """ if feather is None: raise Exception('Feather-format is not available') pmmpath, datasetname = _split_feather_path(featherpath) if dataset.md is None: dataset.md = _create_pmm_metadata(dataset.df, datasetname) _reset_fields_from_dataframe(dataset) df = _sanitize_problematical_all_null_columns(dataset) try: # feather doesn't always write file correctly if it already exists if os.path.exists(featherpath): os.remove(featherpath) feather.write_feather(df, featherpath) dataset.md.save(pmmpath) except: # feather leaves dud files around if it fails to write if os.path.exists(featherpath): os.remove(featherpath) if os.path.exists(pmmpath): os.remove(pmmpath) raise # # The rest of the functions below are internal to this module, and should not # be called from outside. # def _sanitize_problematical_all_null_columns(ds): """ Feather doesn't like all-null string columns or all-null boolean columns, so this method transforms them before saving. They are transformed into float64 fields with NaN et every value, and the pandas column name gets a '_∅t' appended, where t is a type indicator --- b for boolean, s for string or u for unknown. """ origdf, md = ds.df, ds.md df = origdf[list(origdf)] # Copye nTransformed = 0 fieldnames = list(df.columns) nRecords = len(df.index) dfIsUnicode = fieldnames and type(fieldnames[0]) == unicode_type fn = _unicode_definite if dfIsUnicode else _utf8_definite null_suffix = fn(NULL_SUFFIX) for i, f in enumerate(fieldnames): if (df[f].dtype not in (PANDAS_STRING_DTYPE, PANDAS_BOOL_DTYPE) or df[f].notnull().sum() > 0): # includes bools with nulls continue if df[f].dtype == PANDAS_BOOL_DTYPE and nRecords > 0: continue typeChar = ('b' if md[f].type == 'boolean' else 's' if md[f].type == 'string' else 'u') altname = f + null_suffix + typeChar if altname in fieldnames: continue # already there. Whatever... # restore any all-null string fields df[altname] = np.array([np.nan] * nRecords, dtype=PANDAS_FLOAT_DTYPE) nTransformed += 1 fieldnames[i] = altname if nTransformed > 0: df = df[fieldnames] return df def _recover_problematical_all_null_columns(ds): """ Feather doesn't like all-null string columns or all-null boolean columns, so they are sanitized before saving; this untransforms them. """ df, md = ds.df, ds.md nTransformed = 0 fieldnames = list(df.columns) nRecords = len(df.index) dfIsUnicode = fieldnames and type(fieldnames[0]) == unicode_type fn = _unicode_definite if dfIsUnicode else _utf8_definite null_suffix = fn(NULL_SUFFIX) for i, f
<reponame>PrivateStorageio/SecureAccessTokenAuthorizer # Copyright 2022 PrivateStorage.io, LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import annotations """ A system for replicating local SQLite3 database state to remote storage. Theory of Operation =================== A function to wrap a ``sqlite3.Connection`` in a new type is provided. This new type provides facilities for accomplishing two goals: * It (can someday) presents an expanded connection interface which includes the ability to switch the database into "replicated" mode. This is an application-facing interface meant to be used when the application is ready to discharge its responsibilities in the replication process. * It (can someday) expose the usual cursor interface wrapped around the usual cursor behavior combined with extra logic to record statements which change the underlying database (DDL and DML statements). This recorded data then feeds into the above replication process once it is enabled. An application's responsibilities in the replication process are to arrange for remote storage of "snapshots" and "event streams". See the replication/recovery design document for details of these concepts. Once replication has been enabled, the application (can someday be) informed whenever the event stream changes (respecting database transactionality) and data can be shipped to remote storage as desired. It is essential to good replication performance that once replication is enabled all database-modifying actions are captured in the event stream. This is the reason for providing a ``sqlite3.Connection``-like object for use by application code rather than a separate side-car interface: it minimizes the opportunities for database changes which are overlooked by this replication system. """ __all__ = [ "ReplicationAlreadySetup", "fail_setup_replication", "setup_tahoe_lafs_replication", "with_replication", "statements_to_snapshot", "connection_to_statements", "snapshot", ] import os import re from enum import Enum from io import BytesIO from sqlite3 import Connection as _SQLite3Connection from sqlite3 import Cursor as _SQLite3Cursor from typing import ( IO, Any, Awaitable, Callable, ClassVar, Generator, Iterable, Iterator, Optional, Protocol, Sequence, ) import cbor2 from attrs import Factory, define, field, frozen from compose import compose from eliot import log_call from twisted.application.service import IService, Service from twisted.internet.defer import CancelledError, Deferred, DeferredQueue, succeed from twisted.logger import Logger from twisted.python.filepath import FilePath from twisted.python.lockfile import FilesystemLock from ._types import CapStr from .config import REPLICA_RWCAP_BASENAME, Config from .sql import Connection, Cursor, SQLRuntimeType, SQLType, statement_mutates from .tahoe import DataProvider, DirectoryEntry, ITahoeClient, attenuate_writecap # function which can set remote ZKAPAuthorizer state. Uploader = Callable[[str, DataProvider], Awaitable[None]] # function which can remove entries from ZKAPAuthorizer state. Pruner = Callable[[Callable[[str], bool]], Awaitable[None]] # functions which can list all entries in ZKAPAuthorizer state Lister = Callable[[], Awaitable[list[str]]] EntryLister = Callable[[], Awaitable[dict[str, DirectoryEntry]]] class SnapshotPolicy(Protocol): """ Encode policy rules about when to take and upload a new snapshot. """ def should_snapshot(self, snapshot_size: int, replica_sizes: list[int]) -> bool: """ Given the size of a new snapshot and the size of an existing replica (snapshot and event streams), is now a good time to take a new snapshot? """ SNAPSHOT_NAME = "snapshot" @frozen class Replica: """ Manage a specific replica. """ upload: Uploader prune: Pruner entry_lister: EntryLister async def list(self) -> list[str]: return list(await self.entry_lister()) class ReplicationJob(Enum): """ The kinds of jobs that the Replication queue knows about :ivar startup: The job that is run once when the replication service starts and which is responsible for inspecting local and remote state to determine if any actions are immediately necessary (even before any further local changes are made). :ivar event_stream: The job to upload a new event stream object. :ivar snapshot: The job to upload a new snapshot object and prune now-obsolete event stream objects. :ivar consider_snapshot: The job to inspect replica event stream and snapshot state and potentially schedule a new snapshot which will allow pruning of existing event streams. """ startup = 1 event_stream = 2 snapshot = 3 consider_snapshot = 4 @frozen class Change: """ Represent an item in a replication event stream :ivar sequence: The sequence number of this event. :ivar statement: The SQL statement associated with this event. :ivar important: Whether this change was "important" or not. :ivar arguments: Any arguments for the SQL statement. """ sequence: int statement: str arguments: Sequence[SQLType] = field(converter=tuple) important: bool @arguments.validator def _validate_arguments(self, attribute, value) -> None: """ Require that the value has as elements only values are legal SQL values. :note: attrs validators run after attrs converters. """ if all(isinstance(o, SQLRuntimeType) for o in value): return None raise ValueError("sequence contains values incompatible with SQL") @frozen class EventStream: """ A series of database operations represented as `Change` instances. :ivar version: An identifier for the schema of the serialized form of this event stream. This will appear inside the serialized form. A change to the schema will be accompanied with an increment to this value. """ changes: Sequence[Change] = field(converter=tuple) version: ClassVar[int] = 1 def highest_sequence(self) -> Optional[int]: """ :returns: the highest sequence number in this EventStream (or None if there are no events) """ if not self.changes: return None return max(change.sequence for change in self.changes) def to_bytes(self) -> IO[bytes]: """ :returns: a producer of bytes representing this EventStream. """ return BytesIO( cbor2.dumps( { "version": self.version, "events": tuple( ( event.sequence, event.statement, event.arguments, event.important, ) for event in self.changes ), } ) ) @classmethod def from_bytes(cls, stream: IO[bytes]) -> EventStream: """ :returns EventStream: an instance of EventStream from the given bytes (which should have been produced by a prior call to ``to_bytes``) """ data = cbor2.load(stream) serial_version = data.get("version", None) if serial_version != cls.version: raise ValueError( f"Unknown serialized event stream version {serial_version}" ) return cls( changes=[ # List comprehension has incompatible type List[Change]; expected List[_T_co] # https://github.com/python-attrs/attrs/issues/519 Change(args) # type: ignore for args in data["events"] ] ) class AlreadySettingUp(Exception): """ Another setup attempt is currently in progress. """ class ReplicationAlreadySetup(Exception): """ An attempt was made to setup of replication but it is already set up. """ async def fail_setup_replication(): """ A replication setup function that always fails. """ raise Exception("Test not set up for replication") async def setup_tahoe_lafs_replication(client: ITahoeClient) -> str: """ Configure the ZKAPAuthorizer plugin that lives in the Tahoe-LAFS node with the given configuration to replicate its state onto Tahoe-LAFS storage servers using that Tahoe-LAFS node. """ # Find the configuration path for this node's replica. config_path = client.get_private_path(REPLICA_RWCAP_BASENAME) # Take an advisory lock on the configuration path to avoid concurrency # shennanigans. config_lock = FilesystemLock(config_path.asTextMode().path + ".lock") if not config_lock.lock(): raise AlreadySettingUp() try: # Check to see if there is already configuration. if config_path.exists(): raise ReplicationAlreadySetup() # Create a directory with it rw_cap = await client.make_directory() # Store the resulting write-cap in the node's private directory config_path.setContent(rw_cap.encode("ascii")) finally: # On success and failure, release the lock since we're done with the # file for now. config_lock.unlock() # Attenuate it to a read-cap rocap = attenuate_writecap(rw_cap) # Return the read-cap return rocap def is_replication_setup(config: Config) -> bool: """ :return: ``True`` if and only if replication has previously been setup for the Tahoe-LAFS node associated with the given configuration. """ # Find the configuration path for this node's replica. return FilePath(config.get_private_path(REPLICA_RWCAP_BASENAME)).exists() def get_replica_rwcap(config: Config) -> CapStr: """ :return: a mutable directory capability for our replica. :raises: Exception if replication is not setup """ rwcap_file = FilePath(config.get_private_path(REPLICA_RWCAP_BASENAME)) return rwcap_file.getContent().decode("ascii") @define class _Important: """ A context-manager to set and unset the ._important flag on a _ReplicationCapableConnection """ _replication_cursor: _ReplicationCapableCursor def __enter__(self) -> None: self._replication_cursor._important = True def __exit__(self, args) -> None: self._replication_cursor._important = False return None def with_replication( connection: _SQLite3Connection, enable_replication: bool ) -> _ReplicationCapableConnection: """ Wrap the given connection in a layer which is capable of entering a "replication mode". In replication mode, the wrapper stores all changes made through the connection so that they are available to be replicated by another component. In normal mode, changes are not stored. :param connection: The SQLite3 connection to wrap. :param enable_replication: If ``True`` then the wrapper is placed in "replication mode" initially. Otherwise it
<filename>src/FinFET.py<gh_stars>1-10 #BSD 3-Clause License # #Copyright (c) 2019, The Regents of the University of Minnesota # #All rights reserved. # #Redistribution and use in source and binary forms, with or without #modification, are permitted provided that the following conditions are met: # #* Redistributions of source code must retain the above copyright notice, this # list of conditions and the following disclaimer. # #* Redistributions in binary form must reproduce the above copyright notice, # this list of conditions and the following disclaimer in the documentation # and/or other materials provided with the distribution. # #* Neither the name of the copyright holder nor the names of its # contributors may be used to endorse or promote products derived from # this software without specific prior written permission. # #THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" #AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE #IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE #DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE #FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL #DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR #SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER #CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, #OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE #OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. #!/usr/bin/env python3 # -- coding: utf-8 -- """ @author = <NAME> """ import math import json import argparse import numpy as np from thermalModel import thermalModel #direction convention #length is along x Dimesion 0 #width is along y Dimension 1 #height is along z Dimension 2 class FinFET: def __init__(self, TECH, MOS, n_gate, n_fin, f_model_param, f_tool_config): self.TECH = TECH self.MOS = MOS self.n_gate = n_gate self.n_fin = n_fin self.initialize(f_tool_config, f_model_param) self.create_model() def quant(self, a, reso): return math.ceil(a / reso) * reso def load_json(self, json_file): with open(json_file) as f: json_data = json.load(f) return json_data def initialize(self, file_tool_config, file_model_param): tool_config = self.load_json(file_tool_config) model_param = self.load_json(file_model_param) # properties l_chnl = model_param["dimensions"]["l_chnl"] t_gate = model_param["dimensions"]["t_gate"] #design dimensions t_substrate = model_param["dimensions"][ "t_substrate"] #thickness of substrate that has been modelled for after the point at which it is t_box = model_param["dimensions"][ "t_box"] #25 # thickness of box layer t_chnl = model_param["dimensions"][ "t_chnl"] #6 # thickness of channel, source and drain diffusions t_gox = model_param["dimensions"][ "t_gox"] #1 # thickness of gate oxide t_diff_ext = model_param["dimensions"][ "t_diff_ext"] #20 # height of the diffusion extension above the diffusion t_cont = model_param["dimensions"][ "t_cont"] #10 thickness of contact bar # across all fins e_gate = model_param["dimensions"][ "e_gate"] #10 # extension of gate out of diffusion l_gate_space = model_param["dimensions"][ "l_gate_space"] #35 # lenght of source and drain diffusion l_diff_ext = model_param["dimensions"][ "l_diff_ext"] #25 # length of the source and drain diffusion extension l_cont = model_param["dimensions"]["l_cont"] #10 #length of contact w_cont = model_param["dimensions"]["w_cont"] #10 #width of contact self.res = tool_config["resolution"] l_sp_diff_ext = tool_config[ "l_sp_diff_ext"] #5 # spacing between gate and either edge sp_edge = tool_config["sp_edge"] #5 # spacing to the edges t_sp_edge = tool_config["t_sp_edge"] #20 # spacing to the edges l_g2sd_junc = model_param["dimensions"]["l_g2sd_junc"] l_sd_junc = model_param["dimensions"]["l_sd_junc"] w_fin = model_param["dimensions"]["w_fin"] w_fin_space = model_param["dimensions"]["w_fin_space"] self.t_sub2gnd = tool_config[ "t_sub2gnd"] #475 # thickness of substrate to ground not represented in node self.t_cnt2gnd = tool_config[ "t_cnt2gnd"] #1000 # distance from contact to ground self.resx = self.res[0] self.resy = self.res[1] self.resz = self.res[2] resx = self.res[0] resy = self.res[1] resz = self.res[2] self.w_fin = self.quant(w_fin, resy) self.w_fin_space = self.quant(w_fin_space, resy) self.l_chnl = self.quant(l_chnl, resx) self.t_gate = self.quant(t_gate, resz) self.t_box = self.quant(t_box, resz) self.t_chnl = self.quant(t_chnl, resz) self.t_gox = self.quant(t_gox, resz) self.l_gox = self.quant(t_gox, resx) self.w_gox = self.quant(t_gox, resy) self.t_cont = self.quant(t_cont, resz) self.l_sp_edge = self.quant(sp_edge, resx) self.w_sp_edge = self.quant(sp_edge, resy) self.t_sp_edge = self.quant(t_sp_edge, resz) self.e_gate = self.quant(e_gate, resy) self.l_gate_space = self.quant(l_gate_space, resx) self.t_substrate = self.quant(t_substrate, resz) self.l_diff_ext = self.quant(l_diff_ext, resx) self.t_diff_ext = self.quant(t_diff_ext, resz) self.l_sp_diff_ext = self.quant(l_sp_diff_ext, resx) self.l_cont = self.quant(l_cont, resx) self.w_cont = self.quant(w_cont, resy) self.l_g2sd_junc = self.quant(l_g2sd_junc, resx) self.l_sd_junc = self.quant(l_sd_junc, resx) # t_sub2gnd and t_cnt2gnd do not need quatization as there are not used in mask assert self.TECH == 'SOI' or self.TECH == 'Bulk', "Undefined TECH type" self.length = 2self.l_sp_edge + 2(self.l_sd_junc + self.l_g2sd_junc) +\ (self.n_gate - 1)(self.l_chnl+self.l_gate_space) + self.l_chnl self.width = 2(self.w_sp_edge+ self.e_gate) +\ (self.n_fin -1)(2self.w_gox + self.w_fin + self.w_fin_space) +\ 2self.w_gox + self.w_fin self.height = self.t_substrate + self.t_box + self.t_chnl + self.t_cont +\ self.t_sp_edge + max(self.t_gox + self.t_gate, self.t_diff_ext) #print("%d %d %d %d %d"%(self.t_substrate, self.t_box, self.t_chnl,\ # self.t_sp_edge, max(self.t_gox + self.t_gate, self.t_diff_ext))) print("INFO: Model Dimensions LWH: %4.3f %4.3f %4.3f" % (self.length, self.width, self.height)) print("INFO: Resolution : %4.3e %4.3e %4.3e" % (self.resx, self.resy, self.resz)) self.device = thermalModel(length=self.length, width=self.width, height=self.height, resolution=self.res, n_fin=self.n_fin) self.device.set_device_parameters(channel_length=self.l_chnl, gate_thickness=self.t_gate, substrate2ground=self.t_sub2gnd, contact2ground=self.t_cnt2gnd, gate_oxide_thickness=self.t_gox) self.device.set_conductivity_table(file_model_param) print("INFO: Initialization complete") def create_substrate(self): self.device.create_substrate(thickness=self.t_substrate) or_z = self.t_substrate if self.TECH == 'SOI': #t_box origin = (0, 0, or_z) size = (self.length, self.width, self.t_box) self.device.create_t_box(origin, size) elif self.TECH == 'Bulk': origin = (0, 0, or_z) sz_y = self.w_sp_edge + self.e_gate + self.w_gox size = (self.length, sz_y, self.t_box) self.device.create_t_box(origin, size) for f in range(self.n_fin): #create the fin or_y = self.w_sp_edge + self.e_gate + self.w_gox +\ f(2self.w_gox + self.w_fin + self.w_fin_space) sz_y = self.w_fin origin = (0, or_y, or_z) size = (self.length, sz_y, self.t_box) self.device.create_diffusion(origin, size, self.MOS, finFET=1) #create the box or_y = or_y + sz_y origin = (0, or_y, or_z) if f == self.n_fin - 1: sz_y = self.width - or_y else: sz_y = 2 self.w_gox + self.w_fin_space size = (self.length, sz_y, self.t_box) self.device.create_t_box(origin, size) or_z = or_z + self.t_box return or_z def create_fins(self, or_x_in, or_z_in): #creates the fin with the surrounding gate and contact # or_x , or_z inputs #create source diffsion, channel and drain diffusions for the fin or_x = or_x_in or_z = or_z_in sz_z = self.t_chnl #source diffusion of fin for f in range(self.n_fin): sz_x = self.l_g2sd_junc or_y = self.w_sp_edge + self.e_gate + self.w_gox +\ f(2self.w_gox + self.w_fin + self.w_fin_space) sz_y = self.w_fin origin = (or_x, or_y, or_z) size = (sz_x, sz_y, sz_z) self.device.create_diffusion(origin, size, self.MOS, finFET=1) for n in range(self.n_gate): sz_x = self.l_chnl or_x = or_x_in + self.l_g2sd_junc + n * (self.l_chnl + self.l_gate_space) or_x_gate = or_x or_y = self.w_sp_edge #surround gate origin = (or_x, or_y, or_z) sz_y = self.e_gate size = (sz_x, sz_y, sz_z + self.t_gox) cond = self.device.cond['gate'] self.device.create_box(origin, size, cond) for f in range(self.n_fin): # surround gate oxide or_y = or_y + sz_y origin = (or_x, or_y, or_z) sz_y = self.w_gox size = (sz_x, sz_y, sz_z) cond = self.device.cond['SiO2'] self.device.create_box(origin, size, cond) #channel or_x = or_x_gate or_y = or_y + sz_y sz_y = self.w_fin origin = (or_x, or_y, or_z) self.device.create_channel(origin=origin, channel_width=sz_y, channel_depth=sz_z, d_type=self.MOS) # drain diffusion or_x = or_x_gate + self.l_chnl if n == self.n_gate - 1: sz_x = self.l_g2sd_junc else: sz_x = self.l_gate_space origin = (or_x, or_y, or_z) size = (sz_x, sz_y, sz_z) self.device.create_diffusion(origin, size, self.MOS, finFET=1) # surround gate oxide or_x = or_x_gate or_y = or_y + sz_y origin = (or_x, or_y, or_z) sz_x = self.l_chnl sz_y = self.w_gox size = (sz_x, sz_y, sz_z) cond = self.device.cond['SiO2'] self.device.create_box(origin, size, cond) #surround gate or_x = or_x_gate or_y = or_y + sz_y origin = (or_x, or_y, or_z) sz_x = self.l_chnl if f == self.n_fin - 1: sz_y = self.e_gate else: sz_y = self.w_fin_space size = (sz_x, sz_y, sz_z + self.t_gox) cond = self.device.cond['gate'] self.device.create_box(origin, size, cond) end_x = or_x_gate + self.l_chnl + self.l_g2sd_junc end_z = or_z + sz_z return end_x, end_z def create_SD_junction(self, or_x, or_z): #or_x input sz_z = self.t_chnl or_y = self.w_sp_edge + self.e_gate sz_y = (self.n_fin -1)(2self.w_gox + self.w_fin + self.w_fin_space)+\ 2self.w_gox + self.w_fin sz_x = self.l_sd_junc origin = (or_x, or_y, or_z) size = (sz_x, sz_y, sz_z) self.device.create_diffusion(origin, size, self.MOS) return or_x + sz_x, or_z + sz_z def create_gate_oxide(self, or_x, or_z): #gate oxide or_x_in = or_x or_y = self.w_sp_edge + self.e_gate sz_y = self.w_fin + 2 self.w_gox for n in range(self.n_gate): or_x = or_x_in +self. l_sd_junc + self.l_g2sd_junc +\ n*(self.l_chnl+self.l_gate_space) origin = (or_x, or_y, or_z) self.device.create_gate_oxide(origin=origin, channel_width=sz_y) end_x = or_x + self.l_chnl end_z = or_z + self.t_gox return
""" script to ease construction of CSDGM2-style metadata for an GeMS-style geodatabase. To use, Run ValidateDatabase to make sure that the database is complete and there are no missing DMU, Glossary, or DataSources entries In ArcCatalog, go to Customize>Options>Metadata and set Metadata Style to "FGDC CSDGM Metadata". OK and exit. In ArcCatalog, use the ArcGIS metadata editor to complete the record for the GeologicMap feature dataset. Save. NOTE THAT whatever errors or you create in this master metadata record will be faithfully propagated to metadata records for all parts of the geodatabase! Run script GeMS_MetadataCSDGM2_Arc10.1.py. This script will: Export the GeologicMap metadata record in CSDGM2 format Polish this metadata slightly for use as a master record For the geodatabase as a whole and for each entity (table, feature dataset, feature class) in the geodatabase: Copies the master record. Adds supplemental information (ArcGIS reports this in Resouce:Details) about the the GeMS standard and continents of the geodatabase. Adds a description of the entity taken from the GeMS documentation. Adds entity-attribute information taken from the GeMS documentation and the DMU, Glossary, and DataSources tables of the geodatabase. Writes this XML to a file in the directory that contains the geodatabase. Imports this XML into the geodatabase as metadata for the appropriate entity. Look at file <geodatabasename>-metadataLog.txt to see what parts of which metadata records need to be completed by hand. This will occur wherever you extend the database schema beyond the schema outlined in the GeMS documentation. *Note that this script provides for a file that automates description of your extensions to the GeMS schema so that you need not edit metadata by hand--see file my_GeMSDefinitions.py in the GeMS Scripts directory.* Inspect metadata records in ArcCatalog (the Description tab) to see that they are complete. Open saved XML files in browser to see that they are appropriate. Scan for duplicate entries. You want ISO metadata? Change your Metadata Style and fix records using the ArcCatalog metadata editor. Export as ISO of your flavor, insofar as ArcCatalog allows. Let us know how this works. Usage: prompt>GeMS_MetadataCSDGM2_Arc10.1.py <geodatabase> <NAME> and <NAME>, US Geological Survey <EMAIL>, <EMAIL> """ # 17 March 2017 Changed NCGMP09 to GeMS, etc. # 18 April 2017 Added utility functions, local definition-extension file # 12 August 2017 Modified to recognize GeoMaterial, GeoMaterialConfidence, and GeoMaterialDict. # Added number of rows in each table to gdb description in SupplementalInfo #Metadata conversion (ImportMetadata_conversion) is not supported in Pro as of 180926, but is on the roadmap. import arcpy, sys, os.path, copy, imp, glob from GeMS_Definition import enumeratedValueDomainFieldList, rangeDomainDict, unrepresentableDomainDict, attribDict, entityDict, GeoMatConfDict from GeMS_utilityFunctions import * from xml.dom.minidom import * versionString = 'GeMS_MetadataCSDGM2_Arc10.py, version of 10 December 2017' translator = arcpy.GetInstallInfo("desktop")["InstallDir"]+'Metadata/Translator/ARCGIS2FGDC.xml' debug = False ncgmp = 'GeMS' ncgmpFullRef = '"GeMS (Geologic Map Schema)--a standard format for digital publication of geologic maps, version 2.0", available at http://ngmdb.usgs.gov/Info/standards/GeMS/' eaoverviewCitation = 'Detailed descriptions of entities, attributes, and attribute values are given in metadata for constituent elements of this composite dataset. See also '+ncgmpFullRef+'.' gdbDesc0a = ' is a composite geodataset that conforms to '+ncgmpFullRef+'. ' gdbDesc0b = ' is part of a composite geodataset that conforms to '+ncgmpFullRef+'. ' gdbDesc2 = 'Metadata records associated with each element within the geodataset contain more detailed descriptions of their purposes, constituent entities, and attributes. ' gdbDesc3 = ('Two shapefile versions of the dataset are also available. The OPEN shapefile version consists '+ 'of shapefiles, DBF files, and delimited text files and retains all information in the native '+ 'geodatabase, but some programming will likely be necessary to assemble these components into '+ 'usable formats. The SIMPLE shapefile version consists only of shapefiles and is easily used, but '+ 'lacks some information present in the native geodatabase.') def __appendOrReplace(rootNode,newNode,nodeTag): if len(rootNode.getElementsByTagName(nodeTag)) == 0: rootNode.appendChild(newNode) else: rootNode.replaceChild(newNode,rootNode.getElementsByTagName(nodeTag)[0]) def __fieldNameList(fc): #Returns a list of field names from Field.name in arcpy.ListFields fldList = arcpy.ListFields(fc) nameList = [] for fld in fldList: if not fld.name in ('OBJECTID', 'SHAPE','Shape', 'Shape_Length', 'Shape_Area'): nameList.append(fld.name) return nameList def __findInlineRef(sourceID): # finds the Inline reference for each DataSource_ID query = '"DataSources_ID" = \'' + sourceID + '\'' rows = arcpy.SearchCursor(dataSources, query) row = next(rows) if not row is None: #return row.Inline return row.Source else: return "" def __newElement(dom,tag,text): nd = dom.createElement(tag) ndText = dom.createTextNode(text) nd.appendChild(ndText) return nd def __updateAttrDef(fld,dom): ##element tag names are ## attr = Attribute ## attrlabl = Attribute_Label ## attrdef = Attribute_Definition ## attrdefs = Attribute_Definition_Source labelNodes = dom.getElementsByTagName('attrlabl') for attrlabl in labelNodes: if attrlabl.firstChild.data == fld: attr = attrlabl.parentNode if fld.find('_ID') > -1: # substitute generic _ID field for specific attrdefText = attribDict['_ID'] else: attrdefText = attribDict[fld] attrdef = __newElement(dom,'attrdef',attrdefText) __appendOrReplace(attr,attrdef,'attrdef') attrdefs = __newElement(dom,'attrdefs',ncgmp) __appendOrReplace(attr,attrdefs,'attrdefs') return dom def __updateEdom(fld, defs, dom): ##element tag names are ## attr = Attribute ## attrdomv = Attribute_Domain_Values ## edom = Enumerated_Domain ## edomv = Enumerated_Domain_Value ## edomd = Enumerated_Domain_Definition ## edomvds = Enumerated_Domain_Value_Definition_Source labelNodes = dom.getElementsByTagName('attrlabl') for attrlabl in labelNodes: if attrlabl.firstChild.data == fld: attr = attrlabl.parentNode attrdomv = dom.createElement('attrdomv') for k in defs.items(): edom = dom.createElement('edom') edomv = __newElement(dom,'edomv',k[0]) edomvd = __newElement(dom,'edomvd',k[1][0]) edom.appendChild(edomv) edom.appendChild(edomvd) if len(k[1][1]) > 0: edomvds = __newElement(dom,'edomvds',k[1][1]) edom.appendChild(edomvds) attrdomv.appendChild(edom) __appendOrReplace(attr,attrdomv,'attrdomv') return dom def __updateEntityAttributes(fc, fldList, dom, logFile): """For each attribute (field) in fldList, adds attribute definition and definition source, classifies as range domain, unrepresentable-value domain or enumerated-value domain, and for range domains, adds rangemin, rangemax, and units; for unrepresentable value domains, adds unrepresentable value statement; for enumerated value domains: 1) Finds all controlled-vocabulary fields in the table sent to it 2) Builds a set of unique terms in each field, ie, the domain 3) Matches each domain value to an entry in the glossary 4) Builds a dictionary of term:(definition, source) items 5) Takes the dictionary items and put them into the metadata document as Attribute_Domain_Values Field MapUnit in table DescriptionOfMapUnits is treated as a special case. """ cantfindTerm = [] cantfindValue = [] for fld in fldList: addMsgAndPrint( ' Field: '+ fld) # if is _ID field or if field definition is available, update definition if fld.find('_ID') > -1 or fld in attribDict: dom = __updateAttrDef(fld,dom) else: cantfindTerm.append(fld) #if this is an _ID field if fld.find('_ID') > -1: dom = __updateUdom(fld,dom,unrepresentableDomainDict['_ID']) #if this is another unrepresentable-domain field if fld in unrepresentableDomainDict: dom = __updateUdom(fld,dom,unrepresentableDomainDict[fld]) #if this is a defined range-domain field elif fld in rangeDomainDict: dom = __updateRdom(fld,dom) #if this is MapUnit in DMU elif fld == 'MapUnit' and fc == 'DescriptionOfMapUnits': dom = __updateUdom(fld,dom,unrepresentableDomainDict['default']) #if this is a defined Enumerated Value Domain field elif fld in enumeratedValueDomainFieldList: valList = [] #create a search cursor on the field rows = arcpy.SearchCursor(fc,'','', fld) row = next(rows) #collect all values/terms in that field while row: if not row.getValue(fld) is None: valList.append(row.getValue(fld)) row = next(rows) #uniquify the list by converting it to a set object valList = set(valList) #create an empty dictionary object to hold the matches between the unique terms #and their definitions (grabbed from the glossary) defs = {} #for each unique term, try to create a search cursor of just one record where the term #matchs a Term field value from the glossary if fld == 'MapUnit' and fc != 'DescriptionOfMapUnits': for t in valList: query = '"MapUnit" = \'' + t + '\'' rows = arcpy.SearchCursor(DMU, query) row = next(rows) #if the searchcursor contains a row if row: #create an entry in the dictionary of term:[definition, source] key:value pairs #this is how we will enumerate through the enumerated_domain section defs[t] = [] if row.FullName != None: defs[t].append(row.FullName.encode('utf_8')) defs[t].append('this report, table DescriptionOfMapUnits') else: addMsgAndPrint('MapUnit = '+t+', FullName not defined') defs[t].append(row.Name.encode('utf_8')) defs[t].append('this report, table DescriptionOfMapUnits') else: if not t in ('',' '): cantfindValue.append([fld,t]) elif fld == 'GeoMaterialConfidence' and fc == 'DescriptionOfMapUnits': if debug: addMsgAndPrint('DMU / GeoMaterialsConfidence') defs = GeoMatConfDict elif fld == 'GeoMaterial' and fc == 'DescriptionOfMapUnits': if debug: addMsgAndPrint('DMU / GeoMaterials!') for t in valList: query = '"GeoMaterial" = \'' + t + '\'' if debug: addMsgAndPrint('query='+query) rows
type\: str pattern: ((\:\\|[0\-9a\-fA\-F]{0,4})\:)([0\-9a\-fA\-F]{0,4}\:){0,5}((([0\-9a\-fA\-F]{0,4}\:)?(\:\\|[0\-9a\-fA\-F]{0,4}))\\|(((25[0\-5]\\|2[0\-4][0\-9]\\|[01]?[0\-9]?[0\-9])\\.){3}(25[0\-5]\\|2[0\-4][0\-9]\\|[01]?[0\-9]?[0\-9])))(%[\\p{N}\\p{L}]+)? .. attribute:: metric_type (key) Metric type type\: int range: \-2147483648..2147483647 .. attribute:: source2 (key) Source of path 2 type\: union of the below types: type\: str pattern: (([0\-9]\\|[1\-9][0\-9]\\|1[0\-9][0\-9]\\|2[0\-4][0\-9]\\|25[0\-5])\\.){3}([0\-9]\\|[1\-9][0\-9]\\|1[0\-9][0\-9]\\|2[0\-4][0\-9]\\|25[0\-5])(%[\\p{N}\\p{L}]+)? type\: str pattern: ((\:\\|[0\-9a\-fA\-F]{0,4})\:)([0\-9a\-fA\-F]{0,4}\:){0,5}((([0\-9a\-fA\-F]{0,4}\:)?(\:\\|[0\-9a\-fA\-F]{0,4}))\\|(((25[0\-5]\\|2[0\-4][0\-9]\\|[01]?[0\-9]?[0\-9])\\.){3}(25[0\-5]\\|2[0\-4][0\-9]\\|[01]?[0\-9]?[0\-9])))(%[\\p{N}\\p{L}]+)? .. attribute:: destination2 (key) Destination of path 2 type\: union of the below types: type\: str pattern: (([0\-9]\\|[1\-9][0\-9]\\|1[0\-9][0\-9]\\|2[0\-4][0\-9]\\|25[0\-5])\\.){3}([0\-9]\\|[1\-9][0\-9]\\|1[0\-9][0\-9]\\|2[0\-4][0\-9]\\|25[0\-5])(%[\\p{N}\\p{L}]+)? type\: str pattern: ((\:\\|[0\-9a\-fA\-F]{0,4})\:)([0\-9a\-fA\-F]{0,4}\:){0,5}((([0\-9a\-fA\-F]{0,4}\:)?(\:\\|[0\-9a\-fA\-F]{0,4}))\\|(((25[0\-5]\\|2[0\-4][0\-9]\\|[01]?[0\-9]?[0\-9])\\.){3}(25[0\-5]\\|2[0\-4][0\-9]\\|[01]?[0\-9]?[0\-9])))(%[\\p{N}\\p{L}]+)? .. attribute:: disjoint_level (key) Disjointness level type\: int range: \-2147483648..2147483647 .. attribute:: disjoint_strict (key) Strict disjointness required type\: int range: \-2147483648..2147483647 .. attribute:: shortest_path (key) Whether path 1 or 2 should be shortest type\: int range: \-2147483648..2147483647 .. attribute:: headends_swapped Headends swapped type\: :py:class:`PceHeadendSwap <ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_xtc_oper.PceHeadendSwap>` .. attribute:: cspf_result CSPF Result type\: :py:class:`PceCspfRc <ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_xtc_oper.PceCspfRc>` .. attribute:: output_path Output PCE paths type\: list of :py:class:`OutputPath <ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_xtc_oper.Pce.Cspf.CspfPaths.CspfPath.OutputPath>` """ _prefix = 'infra-xtc-oper' _revision = '2017-08-24' def __init__(self): super(Pce.Cspf.CspfPaths.CspfPath, self).__init__() self.yang_name = "cspf-path" self.yang_parent_name = "cspf-paths" self.is_top_level_class = False self.has_list_ancestor = False self.ylist_key_names = ['af','source1','destination1','metric_type','source2','destination2','disjoint_level','disjoint_strict','shortest_path'] self._child_container_classes = OrderedDict([]) self._child_list_classes = OrderedDict([("output-path", ("output_path", Pce.Cspf.CspfPaths.CspfPath.OutputPath))]) self._leafs = OrderedDict([ ('af', YLeaf(YType.int32, 'af')), ('source1', YLeaf(YType.str, 'source1')), ('destination1', YLeaf(YType.str, 'destination1')), ('metric_type', YLeaf(YType.int32, 'metric-type')), ('source2', YLeaf(YType.str, 'source2')), ('destination2', YLeaf(YType.str, 'destination2')), ('disjoint_level', YLeaf(YType.int32, 'disjoint-level')), ('disjoint_strict', YLeaf(YType.int32, 'disjoint-strict')), ('shortest_path', YLeaf(YType.int32, 'shortest-path')), ('headends_swapped', YLeaf(YType.enumeration, 'headends-swapped')), ('cspf_result', YLeaf(YType.enumeration, 'cspf-result')), ]) self.af = None self.source1 = None self.destination1 = None self.metric_type = None self.source2 = None self.destination2 = None self.disjoint_level = None self.disjoint_strict = None self.shortest_path = None self.headends_swapped = None self.cspf_result = None self.output_path = YList(self) self._segment_path = lambda: "cspf-path" + "[af='" + str(self.af) + "']" + "[source1='" + str(self.source1) + "']" + "[destination1='" + str(self.destination1) + "']" + "[metric-type='" + str(self.metric_type) + "']" + "[source2='" + str(self.source2) + "']" + "[destination2='" + str(self.destination2) + "']" + "[disjoint-level='" + str(self.disjoint_level) + "']" + "[disjoint-strict='" + str(self.disjoint_strict) + "']" + "[shortest-path='" + str(self.shortest_path) + "']" self._absolute_path = lambda: "Cisco-IOS-XR-infra-xtc-oper:pce/cspf/cspf-paths/%s" % self._segment_path() def __setattr__(self, name, value): self._perform_setattr(Pce.Cspf.CspfPaths.CspfPath, ['af', 'source1', 'destination1', 'metric_type', 'source2', 'destination2', 'disjoint_level', 'disjoint_strict', 'shortest_path', 'headends_swapped', 'cspf_result'], name, value) class OutputPath(Entity): """ Output PCE paths .. attribute:: source Source of path type\: :py:class:`Source <ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_xtc_oper.Pce.Cspf.CspfPaths.CspfPath.OutputPath.Source>` .. attribute:: destination Destination of path type\: :py:class:`Destination <ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_xtc_oper.Pce.Cspf.CspfPaths.CspfPath.OutputPath.Destination>` .. attribute:: cost Cost type\: int range: 0..18446744073709551615 .. attribute:: hops Hop addresses type\: list of :py:class:`Hops <ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_xtc_oper.Pce.Cspf.CspfPaths.CspfPath.OutputPath.Hops>` """ _prefix = 'infra-xtc-oper' _revision = '2017-08-24' def __init__(self): super(Pce.Cspf.CspfPaths.CspfPath.OutputPath, self).__init__() self.yang_name = "output-path" self.yang_parent_name = "cspf-path" self.is_top_level_class = False self.has_list_ancestor = True self.ylist_key_names = [] self._child_container_classes = OrderedDict([("source", ("source", Pce.Cspf.CspfPaths.CspfPath.OutputPath.Source)), ("destination", ("destination", Pce.Cspf.CspfPaths.CspfPath.OutputPath.Destination))]) self._child_list_classes = OrderedDict([("hops", ("hops", Pce.Cspf.CspfPaths.CspfPath.OutputPath.Hops))]) self._leafs = OrderedDict([ ('cost', YLeaf(YType.uint64, 'cost')), ]) self.cost = None self.source = Pce.Cspf.CspfPaths.CspfPath.OutputPath.Source() self.source.parent = self self._children_name_map["source"] = "source" self._children_yang_names.add("source") self.destination = Pce.Cspf.CspfPaths.CspfPath.OutputPath.Destination() self.destination.parent = self self._children_name_map["destination"] = "destination" self._children_yang_names.add("destination") self.hops = YList(self) self._segment_path = lambda: "output-path" def __setattr__(self, name, value): self._perform_setattr(Pce.Cspf.CspfPaths.CspfPath.OutputPath, ['cost'], name, value) class Source(Entity): """ Source of path .. attribute:: af_name AFName type\: :py:class:`PceAfId <ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_xtc_oper.PceAfId>` .. attribute:: ipv4 IPv4 address type type\: str pattern: (([0\-9]\\|[1\-9][0\-9]\\|1[0\-9][0\-9]\\|2[0\-4][0\-9]\\|25[0\-5])\\.){3}([0\-9]\\|[1\-9][0\-9]\\|1[0\-9][0\-9]\\|2[0\-4][0\-9]\\|25[0\-5])(%[\\p{N}\\p{L}]+)? .. attribute:: ipv6 IPv6 address type type\: str pattern: ((\:\\|[0\-9a\-fA\-F]{0,4})\:)([0\-9a\-fA\-F]{0,4}\:){0,5}((([0\-9a\-fA\-F]{0,4}\:)?(\:\\|[0\-9a\-fA\-F]{0,4}))\\|(((25[0\-5]\\|2[0\-4][0\-9]\\|[01]?[0\-9]?[0\-9])\\.){3}(25[0\-5]\\|2[0\-4][0\-9]\\|[01]?[0\-9]?[0\-9])))(%[\\p{N}\\p{L}]+)? """ _prefix = 'infra-xtc-oper' _revision = '2017-08-24' def __init__(self): super(Pce.Cspf.CspfPaths.CspfPath.OutputPath.Source, self).__init__() self.yang_name = "source" self.yang_parent_name = "output-path" self.is_top_level_class = False self.has_list_ancestor = True self.ylist_key_names = [] self._child_container_classes = OrderedDict([]) self._child_list_classes = OrderedDict([]) self._leafs = OrderedDict([ ('af_name', YLeaf(YType.enumeration, 'af-name')), ('ipv4', YLeaf(YType.str, 'ipv4')), ('ipv6', YLeaf(YType.str, 'ipv6')), ]) self.af_name = None self.ipv4 = None self.ipv6 = None self._segment_path = lambda: "source" def __setattr__(self, name, value): self._perform_setattr(Pce.Cspf.CspfPaths.CspfPath.OutputPath.Source, ['af_name', 'ipv4', 'ipv6'], name, value) class Destination(Entity): """ Destination of path .. attribute:: af_name AFName type\: :py:class:`PceAfId <ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_xtc_oper.PceAfId>` .. attribute:: ipv4 IPv4 address type type\: str pattern: (([0\-9]\\|[1\-9][0\-9]\\|1[0\-9][0\-9]\\|2[0\-4][0\-9]\\|25[0\-5])\\.){3}([0\-9]\\|[1\-9][0\-9]\\|1[0\-9][0\-9]\\|2[0\-4][0\-9]\\|25[0\-5])(%[\\p{N}\\p{L}]+)? .. attribute:: ipv6 IPv6 address type type\: str pattern: ((\:\\|[0\-9a\-fA\-F]{0,4})\:)([0\-9a\-fA\-F]{0,4}\:){0,5}((([0\-9a\-fA\-F]{0,4}\:)?(\:\\|[0\-9a\-fA\-F]{0,4}))\\|(((25[0\-5]\\|2[0\-4][0\-9]\\|[01]?[0\-9]?[0\-9])\\.){3}(25[0\-5]\\|2[0\-4][0\-9]\\|[01]?[0\-9]?[0\-9])))(%[\\p{N}\\p{L}]+)? """ _prefix = 'infra-xtc-oper' _revision = '2017-08-24' def __init__(self): super(Pce.Cspf.CspfPaths.CspfPath.OutputPath.Destination, self).__init__() self.yang_name = "destination" self.yang_parent_name = "output-path" self.is_top_level_class = False self.has_list_ancestor = True self.ylist_key_names = [] self._child_container_classes = OrderedDict([]) self._child_list_classes = OrderedDict([]) self._leafs = OrderedDict([ ('af_name', YLeaf(YType.enumeration, 'af-name')), ('ipv4', YLeaf(YType.str, 'ipv4')), ('ipv6', YLeaf(YType.str, 'ipv6')), ]) self.af_name = None self.ipv4 = None self.ipv6 = None self._segment_path = lambda: "destination" def __setattr__(self, name, value): self._perform_setattr(Pce.Cspf.CspfPaths.CspfPath.OutputPath.Destination, ['af_name', 'ipv4', 'ipv6'], name, value) class Hops(Entity): """ Hop addresses .. attribute:: address_family Address Family type\: int range: 0..255 .. attribute:: ipv4_prefix IPv4 prefix type\: str pattern: (([0\-9]\\|[1\-9][0\-9]\\|1[0\-9][0\-9]\\|2[0\-4][0\-9]\\|25[0\-5])\\.){3}([0\-9]\\|[1\-9][0\-9]\\|1[0\-9][0\-9]\\|2[0\-4][0\-9]\\|25[0\-5])(%[\\p{N}\\p{L}]+)? .. attribute:: ipv6_prefix IPv6 prefix type\: str pattern: ((\:\\|[0\-9a\-fA\-F]{0,4})\:)([0\-9a\-fA\-F]{0,4}\:){0,5}((([0\-9a\-fA\-F]{0,4}\:)?(\:\\|[0\-9a\-fA\-F]{0,4}))\\|(((25[0\-5]\\|2[0\-4][0\-9]\\|[01]?[0\-9]?[0\-9])\\.){3}(25[0\-5]\\|2[0\-4][0\-9]\\|[01]?[0\-9]?[0\-9])))(%[\\p{N}\\p{L}]+)? """ _prefix = 'infra-xtc-oper' _revision = '2017-08-24' def __init__(self): super(Pce.Cspf.CspfPaths.CspfPath.OutputPath.Hops, self).__init__() self.yang_name = "hops" self.yang_parent_name = "output-path" self.is_top_level_class = False self.has_list_ancestor = True self.ylist_key_names = [] self._child_container_classes = OrderedDict([]) self._child_list_classes = OrderedDict([]) self._leafs = OrderedDict([ ('address_family', YLeaf(YType.uint8, 'address-family')), ('ipv4_prefix', YLeaf(YType.str, 'ipv4-prefix')), ('ipv6_prefix', YLeaf(YType.str, 'ipv6-prefix')), ]) self.address_family = None self.ipv4_prefix = None self.ipv6_prefix = None self._segment_path = lambda: "hops" def __setattr__(self, name, value): self._perform_setattr(Pce.Cspf.CspfPaths.CspfPath.OutputPath.Hops, ['address_family', 'ipv4_prefix', 'ipv6_prefix'], name, value) class TopologySummary(Entity): """ Node summary database in XTC .. attribute:: stats_topology_update Statistics on topology update type\: :py:class:`StatsTopologyUpdate <ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_xtc_oper.Pce.TopologySummary.StatsTopologyUpdate>` .. attribute:: nodes Number of PCE nodes type\: int range: 0..4294967295 .. attribute:: lookup_nodes Number of lookup nodes type\: int range: 0..4294967295 .. attribute:: prefixes Number of prefixes type\: int range: 0..4294967295 .. attribute:: prefix_sids Number of total prefix SIDs type\: int range: 0..4294967295 .. attribute:: regular_prefix_sids Number of reguar prefix SIDs type\: int range: 0..4294967295 .. attribute:: strict_prefix_sids Number of strict prefix SIDs type\: int range: 0..4294967295 .. attribute:: links Number of links type\: int range: 0..4294967295 .. attribute:: epe_links Number of EPE links type\: int range: 0..4294967295 .. attribute:: adjacency_sids Number of total adjacency SIDs type\: int range: 0..4294967295 .. attribute:: epesids Number of total EPE SIDs type\: int range: 0..4294967295 .. attribute:: protected_adjacency_sids Number of protected adjacency SIDs type\: int range: 0..4294967295 .. attribute:: un_protected_adjacency_sids Number of unprotected adjacency SIDs type\: int range: 0..4294967295 .. attribute:: topology_consistent True if topology is consistent type\: bool """ _prefix = 'infra-xtc-oper' _revision = '2017-08-24' def __init__(self): super(Pce.TopologySummary, self).__init__() self.yang_name = "topology-summary" self.yang_parent_name = "pce" self.is_top_level_class = False self.has_list_ancestor = False self.ylist_key_names = [] self._child_container_classes = OrderedDict([("stats-topology-update", ("stats_topology_update", Pce.TopologySummary.StatsTopologyUpdate))]) self._child_list_classes = OrderedDict([]) self._leafs = OrderedDict([ ('nodes', YLeaf(YType.uint32, 'nodes')), ('lookup_nodes', YLeaf(YType.uint32, 'lookup-nodes')), ('prefixes', YLeaf(YType.uint32, 'prefixes')), ('prefix_sids', YLeaf(YType.uint32, 'prefix-sids')), ('regular_prefix_sids', YLeaf(YType.uint32, 'regular-prefix-sids')), ('strict_prefix_sids', YLeaf(YType.uint32, 'strict-prefix-sids')), ('links', YLeaf(YType.uint32, 'links')), ('epe_links', YLeaf(YType.uint32, 'epe-links')), ('adjacency_sids', YLeaf(YType.uint32, 'adjacency-sids')), ('epesids', YLeaf(YType.uint32, 'epesids')), ('protected_adjacency_sids', YLeaf(YType.uint32, 'protected-adjacency-sids')), ('un_protected_adjacency_sids', YLeaf(YType.uint32, 'un-protected-adjacency-sids')), ('topology_consistent', YLeaf(YType.boolean, 'topology-consistent')), ]) self.nodes = None self.lookup_nodes = None self.prefixes = None self.prefix_sids = None self.regular_prefix_sids = None self.strict_prefix_sids = None self.links = None self.epe_links = None self.adjacency_sids = None self.epesids = None self.protected_adjacency_sids = None self.un_protected_adjacency_sids = None self.topology_consistent = None self.stats_topology_update = Pce.TopologySummary.StatsTopologyUpdate() self.stats_topology_update.parent = self self._children_name_map["stats_topology_update"] = "stats-topology-update" self._children_yang_names.add("stats-topology-update") self._segment_path = lambda: "topology-summary" self._absolute_path = lambda: "Cisco-IOS-XR-infra-xtc-oper:pce/%s" % self._segment_path() def __setattr__(self, name, value): self._perform_setattr(Pce.TopologySummary, ['nodes', 'lookup_nodes', 'prefixes', 'prefix_sids', 'regular_prefix_sids', 'strict_prefix_sids', 'links', 'epe_links', 'adjacency_sids', 'epesids', 'protected_adjacency_sids', 'un_protected_adjacency_sids', 'topology_consistent'], name, value) class StatsTopologyUpdate(Entity): """ Statistics on topology update .. attribute:: num_nodes_added Number of nodes added type\: int range: 0..4294967295 .. attribute:: num_nodes_deleted Number of nodes deleted type\: int range: 0..4294967295 .. attribute:: num_links_added Number of links added type\: int range: 0..4294967295 .. attribute:: num_links_deleted Number of links deleted type\: int range: 0..4294967295 .. attribute:: num_prefixes_added Number of prefixes added type\: int range: 0..4294967295 .. attribute:: num_prefixes_deleted Number of prefixes deleted type\: int range: 0..4294967295 """ _prefix = 'infra-xtc-oper' _revision = '2017-08-24' def
K.cast(K.less(inputs, 0), 'float32') * (K.exp(inputs - 1) * K.maximum(K.cast_to_floatx(0.0), K.minimum(K.cast_to_floatx(1.0), (inputs + 1.0)/2.0))) def get_config(self): base_config = super(HardElish, self).get_config() return dict(list(base_config.items()) def compute_output_shape(self, input_shape): return input_shape class BentID(Layer): ''' Bent's Identity Activation Function. .. math:: bentId(x) = x + \\frac{\\sqrt{x^{2}+1}-1}{2} Plot: .. figure:: _static/bent_id.png :align: center Shape: - Input: Arbitrary. Use the keyword argument `input_shape` (tuple of integers, does not include the samples axis) when using this layer as the first layer in a model. - Output: Same shape as the input. Examples: >>> X_input = Input(input_shape) >>> X = BentID()(X_input) ''' def __init__(self, kwargs): super(BentID, self).__init__(kwargs) self.supports_masking = True def call(self, inputs): return inputs + ((K.sqrt(K.pow(inputs,2)+1)-1)/2) def get_config(self): base_config = super(BentID, self).get_config() return dict(list(base_config.items()) def compute_output_shape(self, input_shape): return input_shape class WeightedTanh(Layer): ''' Weighted TanH Activation Function. .. math:: Weighted TanH(x, weight) = tanh(x * weight) Plot: .. figure:: _static/weighted_tanh.png :align: center Shape: - Input: Arbitrary. Use the keyword argument `input_shape` (tuple of integers, does not include the samples axis) when using this layer as the first layer in a model. - Output: Same shape as the input. Arguments: - weight: hyperparameter (default=1.0) Examples: >>> X_input = Input(input_shape) >>> X = WeightedTanh(weight=1.0)(X_input) ''' def __init__(self, weight=1.0, kwargs): super(WeightedTanh, self).__init__(kwargs) self.supports_masking = True self.weight = K.cast_to_floatx(weight) def call(self, inputs): return K.tanh(inputs * self.weight) def get_config(self): config = {'weight': float(self.weight)} base_config = super(WeightedTanh, self).get_config() return dict(list(base_config.items()) + list(config.items())) def compute_output_shape(self, input_shape): return input_shape class SineReLU(Layer): ''' Sine ReLU Activation Function. .. math:: SineReLU(x, \\epsilon) = \\left\\{\\begin{matrix} x , x > 0 \\\\ \\epsilon * (sin(x)-cos(x)), x \\leq 0 \\end{matrix}\\right. Plot: .. figure:: _static/sine_relu.png :align: center Shape: - Input: Arbitrary. Use the keyword argument `input_shape` (tuple of integers, does not include the samples axis) when using this layer as the first layer in a model. - Output: Same shape as the input. References: - See related Medium article: https://medium.com/@wilder.rodrigues/sinerelu-an-alternative-to-the-relu-activation-function-e46a6199997d Arguments: - epsilon: hyperparameter (default=0.01) Examples: >>> X_input = Input(input_shape) >>> X = SineReLU(epsilon=0.01)(X_input) ''' def __init__(self, epsilon=0.01, kwargs): super(SineReLU, self).__init__(kwargs) self.supports_masking = True self.epsilon = K.cast_to_floatx(epsilon) def call(self, inputs): return K.cast(K.greater_equal(inputs, 0), 'float32') * inputs + K.cast(K.less(inputs, 0), 'float32') * self.epsilon * (K.sin(inputs) - K.cos(inputs)) def get_config(self): config = {'epsilon': float(self.epsilon)} base_config = super(SineReLU, self).get_config() return dict(list(base_config.items()) + list(config.items())) def compute_output_shape(self, input_shape): return input_shape class ISRLU(Layer): ''' ISRLU Activation Function. .. math:: ISRLU(x)=\\left\\{\\begin{matrix} x, x\\geq 0 \\\\ x * (\\frac{1}{\\sqrt{1 + \\alphax^2}}), x <0 \\end{matrix}\\right. Plot: .. figure:: _static/isrlu.png :align: center Shape: - Input: Arbitrary. Use the keyword argument `input_shape` (tuple of integers, does not include the samples axis) when using this layer as the first layer in a model. - Output: Same shape as the input. Arguments: - alpha: hyperparameter α controls the value to which an ISRLU saturates for negative inputs (default = 1) References: - ISRLU paper: https://arxiv.org/pdf/1710.09967.pdf Examples: >>> X_input = Input(input_shape) >>> X = ISRLU(alpha=1.0)(X_input) ''' def __init__(self, alpha=1.0, kwargs): super(ISRLU, self).__init__(kwargs) self.supports_masking = True self.alpha = K.cast_to_floatx(alpha) def call(self, inputs): return K.cast(K.less(inputs, 0), 'float32') ISRU(alpha=self.alpha)(inputs) + K.cast(K.greater_equal(inputs, 0), 'float32') * inputs def get_config(self): config = {'alpha': float(self.alpha)} base_config = super(ISRLU, self).get_config() return dict(list(base_config.items()) + list(config.items())) def compute_output_shape(self, input_shape): return input_shape class SoftClipping(Layer): ''' Soft Clipping Activation Function. .. math:: SC(x) = 1 / \\alpha * log(\\frac{1 + e^{\\alpha * x}}{1 + e^{\\alpha * (x-1)}}) Plot: .. figure:: _static/sc.png :align: center Shape: - Input: Arbitrary. Use the keyword argument `input_shape` (tuple of integers, does not include the samples axis) when using this layer as the first layer in a model. - Output: Same shape as the input. Arguments: - alpha: hyper-parameter, which determines how close to linear the central region is and how sharply the linear region turns to the asymptotic values References: - See SC paper: https://arxiv.org/pdf/1810.11509.pdf Examples: >>> X_input = Input(input_shape) >>> X = SoftClipping(alpha=0.5)(X_input) ''' def __init__(self, alpha=0.5, kwargs): super(SoftClipping, self).__init__(kwargs) self.supports_masking = True self.alpha = K.cast_to_floatx(alpha) def call(self, inputs): return (1 / self.alpha) * K.log((1 + K.exp(self.alpha * inputs))/(1 + K.exp(self.alpha (inputs - 1)))) def get_config(self): config = {'alpha': float(self.alpha)} base_config = super(SoftClipping, self).get_config() return dict(list(base_config.items()) + list(config.items())) def compute_output_shape(self, input_shape): return input_shape class Aria2(Layer): ''' Aria-2 Activation Function. .. math:: Aria2(x, \\alpha, \\beta) = (1+e^{-\\betax})^{-\\alpha} Plot: .. figure:: _static/aria2.png :align: center Shape: - Input: Arbitrary. Use the keyword argument `input_shape` (tuple of integers, does not include the samples axis) when using this layer as the first layer in a model. - Output: Same shape as the input. Arguments: - alpha: hyper-parameter which has a two-fold effect; it reduces the curvature in 3rd quadrant as well as increases the curvature in first quadrant while lowering the value of activation (default = 1) - beta: the exponential growth rate (default = 0.5) References: - See Aria paper: https://arxiv.org/abs/1805.08878 Examples: >>> X_input = Input(input_shape) >>> X =Aria2(alpha=1.0, beta=0.5)(X_input) ''' def __init__(self, alpha=1.0, beta=0.5, kwargs): super(Aria2, self).__init__(kwargs) self.supports_masking = True self.alpha = K.cast_to_floatx(alpha) self.beta = K.cast_to_floatx(beta) def call(self, inputs): return K.pow((1 + K.exp(-self.beta * inputs)), -self.alpha) def get_config(self): config = {'alpha': float(self.alpha), 'beta': float(self.beta)} base_config = super(Aria2, self).get_config() return dict(list(base_config.items()) + list(config.items())) def compute_output_shape(self, input_shape): return input_shape class Celu(Layer): ''' CELU Activation Function. .. math:: CELU(x, \\alpha) = max(0,x) + min(0,\\alpha * (exp(x/ \\alpha)-1)) Shape: - Input: Arbitrary. Use the keyword argument `input_shape` (tuple of integers, does not include the samples axis) when using this layer as the first layer in a model. - Output: Same shape as the input. Arguments: - alpha: the α value for the CELU formulation (default=1.0) References: - See CELU paper: https://arxiv.org/abs/1704.07483 Examples: >>> X_input = Input(input_shape) >>> X = Celu(alpha=1.0)(X_input) ''' def __init__(self, alpha=1.0, kwargs): super(Celu, self).__init__(kwargs) self.supports_masking = True self.alpha = K.cast_to_floatx(alpha) def call(self, inputs): return K.cast(K.greater_equal(inputs, 0), 'float32') * inputs + K.cast(K.less(inputs, 0), 'float32') * self.alpha * (K.exp (inputs / self.alpha) - 1) def get_config(self): config = {'alpha': float(self.alpha)} base_config = super(Celu, self).get_config() return dict(list(base_config.items()) + list(config.items())) def compute_output_shape(self, input_shape): return input_shape class ReLU6(Layer): ''' RELU6 Activation Function. .. math:: RELU6(x) = min(max(0,x),6) Shape: - Input: Arbitrary. Use the keyword argument `input_shape` (tuple of integers, does not include the samples axis) when using this layer as the first layer in a model. - Output: Same shape as the input. References: - See RELU6 paper: http://www.cs.utoronto.ca/~kriz/conv-cifar10-aug2010.pdf Examples: >>> X_input = Input(input_shape) >>> X = ReLU6()(X_input) ''' def __init__(self, kwargs): super(ReLU6, self).__init__(kwargs) self.supports_masking = True def call(self, inputs): return K.cast(K.greater_equal(inputs, 6), 'float32') * 6 + K.cast(K.less(inputs, 6), 'float32') * K.relu(inputs) def get_config(self): base_config = super(ReLU6, self).get_config() return dict(list(base_config.items()) def compute_output_shape(self, input_shape): return input_shape class HardTanh(Layer): ''' Hard-TanH Activation Function. .. math:: Hard-TanH(x) = \\left\\{\\begin{matrix} 1, x > 1 \\\\ x , -1 \\leq x \\leq 1 \\\\ -1, x <- 1 \\end{matrix}\\right. Shape: - Input: Arbitrary. Use the keyword argument `input_shape` (tuple of integers, does not include the samples axis) when using this layer as the first layer in a model. - Output: Same shape as the input. Examples: >>> X_input = Input(input_shape) >>> X = HardTanh()(X_input) ''' def __init__(self, kwargs): super(HardTanh, self).__init__(kwargs) self.supports_masking = True def call(self, inputs): return K.cast(K.greater(inputs , 1), 'float32')\ + inputs * K.cast(K.less_equal(inputs, 1), 'float32') * K.cast(K.greater_equal(inputs, -1), 'float32') - K.cast(K.less(inputs, -1), 'float32') def get_config(self): base_config = super(HardTanh, self).get_config() return dict(list(base_config.items()) def compute_output_shape(self, input_shape): return input_shape class LogSigmoid(Layer): ''' Log-Sigmoid Activation Function. .. math:: Log-Sigmoid(x) = log (\\frac{1}{1+e^{-x}}) Shape: - Input: Arbitrary. Use the keyword argument `input_shape` (tuple of integers, does not include the samples axis) when using this layer as the first layer in a model. - Output: Same shape as the input. Examples: >>> X_input = Input(input_shape) >>> X = LogSigmoid()(X_input) ''' def __init__(self, kwargs): super(LogSigmoid, self).__init__(kwargs) self.supports_masking = True def call(self, inputs): return K.log(K.sigmoid(inputs)) def get_config(self): base_config = super(LogSigmoid, self).get_config() return dict(list(base_config.items()) def compute_output_shape(self, input_shape): return input_shape class TanhShrink(Layer): ''' TanH-Shrink Activation Function. .. math:: TanH-Shrink(x) = x - tanh(x) Shape: - Input: Arbitrary. Use the keyword argument `input_shape` (tuple of integers,
expected result (raised from the pexpect module). """ reporter.step('Getting device information...') self.__access_priv_exec_mode(child, eol, enable_password) try: # Get the name of the default drive. Depending on the device, it may be bootflash, # flash, slot (for linear memory cards), or disk (for CompactFlash disks) child.sendline('dir' + eol) child.expect_exact('dir') index = child.expect_exact(['More', self.device_prompts[1], ]) dir_list = str(child.before) if index == 0: # No need to get the whole directory listing, so break out child.sendcontrol('c') child.expect_exact(self.device_prompts[1]) default_file_system = dir_list.split( 'Directory of ')[1].split(':')[0].strip() if not default_file_system.startswith(('bootflash', 'flash', 'slot', 'disk',)): raise RuntimeError('Cannot get the device\'s working drive.') # If the drive is not formatted, a warning will appear, followed by another prompt. # Wait for it to pass, and get to the correct prompt index = child.expect_exact( ['before an image can be booted from this device', pexpect.TIMEOUT, ], timeout=5) if index == 0: child.expect_exact(self.device_prompts[1]) except (RuntimeError, IndexError) as ex: # RuntimeError = explicit, while IndexError = implicit if split index is out of range reporter.warn(ex.message) default_file_system = None try: # Get the IOS version child.sendline('show version \| include [IOSios] [Ss]oftware' + eol) # child.expect_exact('show version \| include') child.expect_exact(self.device_prompts[1]) software_ver = str(child.before).split( 'show version \| include [IOSios] [Ss]oftware\r')[1].split('\r')[0].strip() if not re.compile(r'[IOSios] [Ss]oftware').search(software_ver): raise RuntimeError('Cannot get the device\'s software version.') except (RuntimeError, IndexError) as ex: reporter.warn(ex.message) software_ver = None try: # Get the name of the device child.sendline('show inventory \| include DESCR:' + eol) # child.expect_exact('show inventory \| include DESCR:') child.expect_exact(self.device_prompts[1]) device_name = str(child.before).split( 'show inventory \| include DESCR:\r')[1].split('\r')[0].strip() if not re.compile(r'DESCR:').search(device_name): raise RuntimeError('Cannot get the device\'s name.') except (RuntimeError, IndexError) as ex: reporter.warn(ex.message) device_name = None try: # Get the serial number of the device child.sendline('show version \| include [Pp]rocessor [Bb]oard [IDid]' + eol) # child.expect_exact('show version \| include') child.expect_exact(self.device_prompts[1]) serial_num = str(child.before).split( 'show version \| include [Pp]rocessor [Bb]oard [IDid]\r')[1].split('\r')[0].strip() if not re.compile(r'[Pp]rocessor [Bb]oard [IDid]').search(serial_num): raise RuntimeError('Cannot get the device\'s serial number.') except (RuntimeError, IndexError) as ex: reporter.warn(ex.message) serial_num = None # Get rid of ANSI escape sequences ansi_seq = re.compile('(?:\\x1b\[)([\w;]+)(H)') default_file_system = ansi_seq.sub('', str(default_file_system)).strip() software_ver = ansi_seq.sub('', str(software_ver)).strip() device_name = ansi_seq.sub('', str(device_name)).strip() serial_num = ansi_seq.sub('', str(serial_num)).strip() reporter.success() return default_file_system, software_ver, device_name, serial_num def format_file_system(self, child, reporter, eol, device_file_system): """Format a file system (i.e., memory) on a network device. :param pexpect.spawn child: Connection in a child application object. :param labs.cisco.Reporter reporter: A reference to the popup GUI window that reports the status and progress of the script. :param str eol: EOL sequence (LF or CRLF) used by the connection. :param str device_file_system: File system to format. :return: None :rtype: None :raise ValueError: If an argument is invalid. :raise pexpect.ExceptionPexpect: If the result of a send command does not match the expected result (raised from the pexpect module). """ # Validate inputs if not device_file_system.startswith(('bootflash', 'flash', 'slot', 'disk',)): reporter.error() raise ValueError('Invalid Cisco file system name.') reporter.step('Formatting device memory...') self.__access_priv_exec_mode(child, eol) # Format the memory. Look for the final characters of the following strings: # 'Format operation may take a while. Continue? [confirm]' # 'Format operation will destroy all data in 'flash:'. Continue? [confirm]' # '66875392 bytes available (0 bytes used)' child.sendline('format {0}:'.format(device_file_system) + eol) index = 1 while index != 0: index = child.expect_exact( [pexpect.TIMEOUT, 'Continue? [confirm]', 'Enter volume ID', ], timeout=5) if index != 0: child.sendline(eol) child.expect_exact('Format of {0} complete'.format(device_file_system), timeout=120) child.sendline('show {0}'.format(device_file_system) + eol) child.expect_exact('(0 bytes used)') child.expect_exact(self.device_prompts[1]) reporter.success() def set_switch_priority(self, child, reporter, eol, switch_number=1, switch_priority=1, enable_password=None, commit=True): """Set the switch priority in the stack. :param pexpect.spawn child: Connection in a child application object. :param labs.cisco.Reporter reporter: A reference to the popup GUI window that reports the status and progress of the script. :param str eol: EOL sequence (LF or CRLF) used by the connection. :param switch_number: Switch reference number in the stack :param switch_priority: Switch priority in the stack; maximum is 15. The switch with the largest number (e.g., 15) becomes the master switch for the stack. :param str enable_password: Password to enable Privileged EXEC Mode from User EXEC Mode. :param bool commit: True to save changes to startup-config. :return: None :rtype: None :raise ValueError: If an argument is invalid. :raise pexpect.ExceptionPexpect: If the result of a send command does not match the expected result (raised from the pexpect module). """ reporter.step('Setting switch priority...') self.__access_priv_exec_mode(child, eol, enable_password=<PASSWORD>_password) # Validate inputs if not 1 <= switch_number <= 9: raise ValueError('Invalid switch stack member number.') validate_switch_priority(switch_priority) child.sendline('configure terminal' + eol) child.expect_exact(self.device_prompts[2]) child.sendline('switch {0} priority {1}'.format(switch_number, switch_priority)) index = 0 while index == 0: index = child.expect_exact( ['Do you want to continue', 'New Priority has been set successfully', ]) if index == 0: child.sendline(eol) child.sendline('end' + eol) child.expect_exact(self.device_prompts[1]) # Save changes if True if commit: self.save_running_configuration(child, eol, enable_password=enable_password) reporter.success() def set_switch_ip_addr(self, child, reporter, eol, vlan_name, vlan_port, new_ip_address, new_netmask, enable_password=None, commit=True): """Set a switch's IP address. :param pexpect.spawn child: Connection in a child application object. :param labs.cisco.Reporter reporter: A reference to the popup GUI window that reports the status and progress of the script. :param str eol: EOL sequence (LF or CRLF) used by the connection. :param str vlan_name: Virtual Local Area Network (VLAN) interface to configure. :param str vlan_port: Ethernet interface port name to configure and connect to VLAN. :param str new_ip_address: New IPv4 address for the device. :param str new_netmask: New netmask for the device. :param str enable_password: <PASSWORD> enable Privileged EXEC Mode from User EXEC Mode. :param bool commit: True to save changes to startup-config. :return: None :rtype: None :raise pexpect.ExceptionPexpect: If the result of a send command does not match the expected result (raised from the pexpect module). """ reporter.step('Setting the switch\'s IP address...') self.__access_priv_exec_mode(child, eol, enable_password=enable_password) # Validate inputs # FYI, vlan_port, while not validated, should start with F(ast), G(iga), etc. validate_ip_address(new_ip_address) validate_subnet_mask(new_netmask) child.sendline('configure terminal' + eol) child.expect_exact(self.device_prompts[2]) # Configure Ethernet port child.sendline('interface {0}'.format(vlan_port) + eol) child.expect_exact(self.device_prompts[3]) # Configure the VLAN membership mode child.sendline('switchport mode access' + eol) child.expect_exact(self.device_prompts[3]) # Assign the port to the VLAN child.sendline('switchport access {0}'.format(vlan_name) + eol) child.expect_exact(self.device_prompts[3]) # Set to forwarding state immediately, bypassing the listening and learning states # Used to prevent L2 switching loops when connecting to the remote host child.sendline('spanning-tree portfast' + eol) child.expect_exact(self.device_prompts[3]) child.sendline('no shutdown' + eol) child.expect_exact(self.device_prompts[3]) # Configure VLAN child.sendline('interface {0}'.format(vlan_name) + eol) child.expect_exact(self.device_prompts[3]) child.sendline('ip address {0} {1}'.format(new_ip_address, new_netmask) + eol) child.expect_exact(self.device_prompts[3]) child.sendline('no shutdown' + eol) child.expect_exact(self.device_prompts[3]) child.sendline('end' + eol) child.expect_exact(self.device_prompts[1]) # Save changes if True if commit: self.save_running_configuration(child, eol, enable_password=<PASSWORD>) reporter.success() def set_router_ip_addr(self, child, reporter, eol, ethernet_port, new_ip_address, new_netmask, enable_password=None, commit=True): """Set a router's IP address. :param pexpect.spawn child: Connection in a child application object. :param labs.cisco.Reporter reporter: A reference to the popup GUI window that reports the status and progress of the script. :param str eol: EOL sequence (LF or CRLF) used by the connection. :param str ethernet_port: Ethernet interface port name to configure. :param str new_ip_address: New IPv4 address for the device. :param str new_netmask: New netmask for the device. :param str enable_password: Password to enable Privileged EXEC Mode from User EXEC Mode. :param bool commit: True to save changes to startup-config. :return: None :rtype: None """ # Validate inputs # ethernet_port, while not validated, should start with F(ast), G(iga), etc. validate_ip_address(new_ip_address) validate_subnet_mask(new_netmask) reporter.step('Setting the router\'s IP address...') self.__access_priv_exec_mode(child, eol) child.sendline('configure terminal' + eol) child.expect_exact(self.device_prompts[2]) # Configure Ethernet port child.sendline('interface {0}'.format(ethernet_port) + eol) child.expect_exact(self.device_prompts[3]) child.sendline('ip address {0} {1}'.format(new_ip_address, new_netmask) + eol) child.expect_exact(self.device_prompts[3]) child.sendline('no shutdown' + eol) child.expect_exact(self.device_prompts[3]) child.sendline('end' + eol) child.expect_exact(self.device_prompts[1]) # Save changes if True if commit: self.save_running_configuration(child, eol, enable_password=<PASSWORD>_password) reporter.success() def ping_from_device(self, child, reporter, eol, destination_ip_addr, count=4, enable_password=None): """Check connectivity with another device. :param pexpect.spawn child: Connection in a child application object. :param labs.cisco.Reporter reporter: A reference to the popup GUI window that reports the status and progress of the script. :param str eol: EOL sequence (LF or CRLF) used by the connection. :param str destination_ip_addr: IPv4 address of the other device. :param int count: Number of
<gh_stars>1-10 #!/usr/bin/env python # coding: utf-8 # In[1]: """ Module containng custom Keras models and layers required for FlowNet architecture. """ try: import numpy as np import tensorflow as tf from tensorflow import keras from tensorflow.keras import backend as K except Exception as e: raise Exception("Error occured while importing dependency packages. More details:\n",e) __author__ = "<NAME>" __copyright__ = "Copyright 2020, FlowNet" __credits__ = ["<NAME>"] __license__ = "" __version__ = "0.1.0" __maintainer__ = "<NAME>" __email__ = "<EMAIL>" __status__ = "Development" class ForwardModel(tf.keras.Model): """ Model to construct FNN (Forward Neural Network) using custom Keras layers. Subclass of tf.keras.Model """ def __init__(self, space_dim=1, time_dep=False, output_dim=1, n_hid_lay=3, n_hid_nrn=20, act_func = "tanh", rhs_func = None): """ space_dim (int) -> Dimension of the space Omega where the PDE is defined. time_dep (bool) -> True if the problem is time dependent. output_dim (int) -> Dimension of the range of the solution to PDE. n_hid_layer (int) -> Number of hidden layers in the neural network. n_hid_nrn (int) -> Number of neurons in each hidden layer of the NN. act_func (string) -> Activation functions for each of the hidden layers. Has to be one of the members of keras.activations: could be one of {"tanh", "sigmoid", "elu", "relu", "exponential"} """ super(ForwardModel, self).__init__() #Defining class atributes self.space_dim = space_dim self.time_dep = time_dep self.output_dim = output_dim self.n_hid_lay = n_hid_lay self.n_hid_nrn = n_hid_nrn #Block of hidden layers self.hidden_block = [keras.layers.Dense( self.n_hid_nrn, activation=act_func, name="dense_"+str(i+1) ) for i in range(n_hid_lay)] #Final output layer self.final_layer = keras.layers.Dense(self.output_dim, name="final_layer") #Defining the rhs of PDE: P(u,delu) = f(x,t) if rhs_func != None: self.rhs_function = rhs_func else: self.rhs_function = lambda x: 0 def findGrad(self,func,input_space): """ Find gradient with respect to the domain Omega of the PDE. (tensor, tensor) -> Keras.Lambda layer arguments: ---------- func (tf tensor): function represented by tf tensor structure (Usually of size: data_size x dim_output_previous_layer). The func is usually the final output (solution u) coming out of a hidden layer input_space: argument with respect to which we need the partial derrivatives of func. Usually a list of input arguments representing the space dimension. Output: Keras.Lambda layer. Note that output of such a lambda layer will be a list of tensors with each element giving partial derrivative wrt to each element in argm. See tf.Keras.Lambda and tf.gradients for more details. """ try: return keras.layers.Lambda(lambda z: [tf.gradients(z[0],x_i, unconnected_gradients='zero') for x_i in z[1] ]) ([func, input_space]) except Exception as e: raise Exception("Error occured in finding the time derrivative lambda layer of type {} as follows: \n{}".format(type(e)),e) def findTimeDer(self,func,input_time): """ (tensor, tensor) -> Keras.Lambda layer arguments: ---------- func (tf tensor): function represented by tf tensor structure (Usually of size: data_size x dim_output_previous_layer). The func is usually the final output (solution u) coming out of a hidden layer input_time: TensorFlow tensor. This should be the element of the input list which corresponds to the time dimension. Used only if the problem is time_dependent. Output: Keras.Lambda layer. Note that output of such a lambda layer will be a tensor of size m x 1 representing the time derrivative of output func. See tf.Keras.Lambda and tf.gradients for more details. """ assert (self.time_dep), "Tried taking time derrivative even though the problem is not time dependent." try: return keras.layers.Lambda(lambda z: tf.gradients(z[0],z[1], unconnected_gradients='zero') [0]) ([func, input_time]) except Exception as e: raise Exception("Error occured in find gradient lambda layer of type {} as follows: \n{} ".format(type(e)),e) def findLaplace(self,first_der,input_space): """ (tensor, tensor) -> Keras.Lambda layer Returns lambda layer to find the laplacian of the solution to pde. arguments: ---------- first_der (tf tensor): function represented by tf tensor structure (Usually of size: data_size x dim_output_previous_layer). The func is input_space: argument with respect to which we need the partial derrivatives of func. Usually a list of input arguments representing the space dimension. Output: Keras.Lambda layer. This lambda layer outputs the laplacian of solution function u. See tf.Keras.Lambda and tf.gradients for more details. """ try: # list containng diagonal entries of hessian matrix. Note that tf.gradients #returns a list of tensors and hence thats why we have a [0] at the end of #the tf.gradients fucntion as tf.gradients(func,argm) [0] del_sq_layer = keras.layers.Lambda( lambda z: [ tf.gradients(z[0][i], z[1][i], unconnected_gradients='zero') [0] for i in range(len(z[1])) ] ) ([first_der,input_space]) return sum(del_sq_layer) except Exception as e: raise Exception("Error occured in find laplacian lambda layer of type {} as follows: \n{}".format(type(e)),e) #final layer representing the lhs P(x,t) of PDE P(x,t)=0 def findPdeLayer(self, laplacian, input_arg, time_der=0): """ (tensor, tensor, tensor) -> Keras.Lambda layer Returns lambda layer to find the actual pde P(u,delu,x,t) such that P(u,delu,x,t)=0. arguments: ---------- laplacian (tf tensor): laplacian with respect to space dim . input_arg: list of inputs corresponding to both space and time dimension. Last elemetn of the list corresponds to the temporal dimension. Output: Keras.Lambda layer. This lambda layer outputs the PDE P(u,delu, x,t). See tf.Keras.Lambda and tf.gradients for more details. """ try: # return keras.layers.Lambda(lambda z: z[0] - z[1] - tf.sin(z[2][0]+z[2][1]) - # 2z[2][2]tf.sin(z[2][0]+z[2][1])) ([time_der, laplacian, input_arg]) return keras.layers.Lambda(lambda z: z[0] - z[1] - self.rhs_function(input_arg)) ([time_der, laplacian, input_arg]) except Exception as e: raise Exception("Error occured in finding pde lambda layer of type {} as follows: \n{}".format(type(e)),e) def get_config(self): #getting basic config using the parent model class base_config = super().get_config() return {**base_config, "space_dim": self.space_dim, "time_dep": self.time_dep, "output_dim": self.output_dim, "n_hid_lay": self.n_hid_lay, "n_hid_nrn": self.n_hid_nrn, "act_func": self.act_func } def from_config(self, config, custom_objects): super().from_config(config) def call(self, inputs, training=False): """ Call function which wll be used while training, prediciton and evaluation of the ForwardModel. arguments: ---------- inputs (list of tensors) -> last element of the list corresponds to temporal diimension if self.time_dep = True. If possible, always feed the data from the data processing method in flowDataProcess module. training (bool) -> True if calling the function for training. False for prediction and evaluation. Value of triainng will be automatically taken care of by Keras. Note that inputs should always be given as a list with the last element of the list representing the dimension corresponding to time. """ if self.time_dep: try: assert(len(inputs) > 1) input_space = inputs[:-1] input_time = inputs[-1] except Exception as e: raise Exception("Error occured while separating spacial and temporal data from inputs, make sure that spacio-temporal data is being used to for training and x=[space_dim1,..,space_dimn,time_dim]. More details on error below:\n", type(e), e) else: input_space = inputs #concatening all the input data (space and time dimensions) making it #read to be passed to the hidden layers hidden_output = keras.layers.concatenate(inputs) #hidden layers for layer_id in range(self.n_hid_lay): hidden_output = self.hidden_block[layer_id] (hidden_output) #output layer, this is typically the solution function output_layer = self.final_layer(hidden_output) if training: #pde specific layers grad_layer = self.findGrad(output_layer, input_space) laplace_layer = self.findLaplace(grad_layer, input_space) if self.time_dep: time_der_layer = self.findTimeDer(output_layer, input_time) else: time_der_layer=0 pde_layer = self.findPdeLayer(laplace_layer, inputs, time_der_layer) return output_layer, pde_layer elif not training: #only outputting the function value if not tranining. return output_layer # In[3]: class Poission(ForwardModel): """ Doc string goes here """ def __init__(self, space_dim=1, perm_tensor=None, output_dim=1, n_hid_lay=3, n_hid_nrn=20, act_func = "tanh", rhs_func = None): """ talk about super initialization """ super().__init__(space_dim=space_dim, time_dep=False, output_dim=output_dim, n_hid_lay=n_hid_lay, n_hid_nrn=n_hid_nrn, act_func = act_func, rhs_func = rhs_func) self._perm_tensor = perm_tensor if perm_tensor else tf.eye(space_dim) #final layer representing the lhs P(x) of PDE P(x)=0 def findPdeLayer(self, laplacian, input_arg): """ (tensor, tensor, tensor) -> Keras.Lambda layer Returns lambda layer to find the actual pde P(u,delu,x,t) such that P(u,delu,x,t)=0. arguments: ---------- laplacian (tf tensor): laplacian with respect to space dim . input_arg: list of inputs corresponding to both space and time dimension. Last elemetn of the list corresponds to the temporal
next_case_line_number: int = 0 for line in code_except_decorator: # print(f'{line_number} : {line}') if "case " in line: next_case_line_number = line_number next_case = line.strip() if "done()" in line: match_obligations.add(line_number, next_case_line_number, next_case) line_number += 1 # print(match_obligations) def new_transition(self, event): if not hasattr(self, '__data_object__') or self.__data_object__ is None: self.__data_object__ = match_obligations result = transition_function(self, event) if isinstance(result, int): # print(f'++> {self.__data_object__}') self.__data_object__.remove(result) # print(f'--> {self.__data_object__}') if self.__data_object__.empty(): return ok # we are done else: return self # we are not done yet else: return result # something else (ok, error, or another state) return new_transition def initial(state_class: State) -> State: """ Decorator function which introduces the Boolean variable 'is_initial' (assigning it the value True, although that is not important) in its argument state. It allows us to annotate states as follows: @initial class Init(State): ... This has the same effect as: class Init(State): ... Init.is_initial = True :param state_class: the state to decorate. :return: the decorated state. """ state_class.is_initial = True return state_class def mk_state_vector(arg: State \| List[State]) -> List[State]: """ Turns a state or a list of states into a list of states. If the argument is a single state s, the singular state vector [s] is returned. If the argument is a list of states, it is returned unchanged. :param arg: a state or a list of states. :return: the list of states. """ if isinstance(arg, list): return arg else: return [arg] class Monitor: """ Any user defined monitor class must extend this class. It defines a monitor. """ def __init__(self): """ monitors: A monitor can have sub-monitors, stored in this variable. is_top_monitor: Is True iff. this monitor is the topmost monitor in a monitor hierarchy, that is: not a sub-monitor of another monitor. Used for printing purposes in that only the topmost monitor prints out certain debugging information. states: The state vector of the monitor: the set of all active states. states_indexed: Indexed states, used for slicing. errors: Detected errors during monitoring. event_count: Counts the events as they come in. option_show_state_event: When True, state and event will be printed on transition errors. option_print_summary: When True, a summary of the analysis is printed for the top monitor. """ self.monitors: List[Monitor] = [] self.is_top_monitor: bool = True self.states: Set[State] = set([]) self.states_indexed : Dict[object, Set[State]] = {} self.errors: List[str] = [] self.event_count: int = 0 self.option_show_state_event: bool = True self.option_print_summary: bool = True # Create always state if outermost transitions exist outer_transitions = inspect.getmembers(self, predicate=is_transition_method) if len(outer_transitions) > 0: always = type("Always", (AlwaysState,), {}) setattr(always, "is_initial", True) (name, method) = outer_transitions[0] setattr(always, name, method.__func__) setattr(self, "Always", always) # Locate all state classes (subclassing State): state_classes = inspect.getmembers(self, predicate=is_state_class) if len(state_classes) > 0: # Add initial states: initial_state_found = False for (state_name, state_class) in state_classes: if hasattr(state_class, 'is_initial'): self.add_state_to_state_vector(self.states, state_class()) initial_state_found = True if not initial_state_found: (name, the_first_class) = state_classes[0] self.add_state_to_state_vector(self.states, the_first_class()) # Debug initial states # print(f'Initial states of {self.get_monitor_name()}:') # for state in self.states: # print(state) # print() def set_event_count(self, initial_value: int): """ Sets the initial value of `event_count` to a different value than 0. This is used for example when processing CSV files, where there is a header row, which should be counted as an 'event' so that `event_count` will correspond to row number in the CSV file. :param initial_value: the initial value of `event_count`. """ self.event_count = initial_value def get_monitor_name(self) -> str: return self.__class__.__name__ def key(self, event) -> Optional[object]: """ Returns indexing key of event. Returns None by default but can be overwritten by user. :param event: event to extract index from. :return: the index of the event. """ return None def monitor_this(self, monitors: "Monitor"): """ Records one or more monitors as sub-monitors of this monitor. Each event submitted to this monitor is also submitted to the sub-monitors. Likewise when end() is called on this monitor, end() is also called on the sub-monitors. :param monitors: the monitors to record as sub-monitors. """ for monitor in monitors: monitor.is_top_monitor = False self.monitors.append(monitor) def is_relevant(self, event: Event) -> bool: """ Returns True if the event should be monitored. By default all submitted events are monitored. This method is meant to be overridden by the user. :param event: the incoming event. :return: True if the event should be monitored. """ return True def eval(self, event: Event): """ This method is used to submit events to the monitor. The monitor evaluates the event against the states in the state vector. The eval method is called recursively on sub-monitors, so it is only necessary to call it on the topmost monitor. :param event: the submitted event. """ global __monitor__ __monitor__ = self self.event_count += 1 if DEBUG_PROGRESS and self.is_top_monitor and self.event_count % DEBUG_PROGRESS == 0: debug(f'---------------------> {self.event_count}') if DEBUG and self.is_top_monitor: debug_frame("=", f'Event {self.event_count} {event}') for monitor in self.monitors: monitor.eval(event) if DEBUG: debug_frame("#", f'Monitor {self.get_monitor_name()}') if self.is_relevant(event): index = self.key(event) if index is None: new_states = self.eval_states(event, self.states) if new_states is not None: self.states = new_states for (idx, states) in self.states_indexed.items(): new_states = self.eval_states(event, states) if new_states is not None: self.states_indexed[idx] = new_states else: if index in self.states_indexed: states = self.states_indexed[index] else: states = self.states new_states = self.eval_states(event, states) if new_states is not None: self.states_indexed[index] = new_states if DEBUG: debug(f'\n{self}') def eval_states(self, event: Event, states: Set[State]) -> Optional[Set[State]]: """ Evaluates an event on each state in a set of states. :param event: the event to evaluate. :param states: the set of states to evaluate it on. :return: the resulting set of states. None is returned if no transitions fired. """ transition_triggered = False states_to_remove = set([]) states_to_add = set([]) new_states = set([]) for source_state in states: resulting_states = source_state.eval(event) # returns None or a list of states if DEBUG: debug(f'{source_state} results in {mk_string("[",", ","]", resulting_states)}') if resulting_states is not None: transition_triggered = True states_to_remove.add(source_state) for target_state in resulting_states: if target_state == ok: pass elif isinstance(target_state, ErrorState): self.report_transition_error(source_state, event, target_state.message) elif isinstance(target_state, InfoState): self.report_transition_information(source_state, event, target_state.message) else: self.add_state_to_state_vector(states_to_add, target_state) if transition_triggered: new_states = states new_states = new_states - states_to_remove new_states = new_states.union(states_to_add) return new_states else: return None def end(self): """ Terminates monitoring for the monitor. This includes looking for hot states of type HotState, which should not occur, and then printing out a summary of the verification. The end() method is called recursively on sub-monitors, so it only needs to be called on the top-most monitor. """ if self.is_top_monitor: print() print('Terminating monitoring!') print() for monitor in self.monitors: monitor.end() print_frame("+", f'Terminating monitor {self.get_monitor_name()}') for state in self.get_all_states(): if isinstance(state, HotState) or isinstance(state, HotNextState): self.report_end_error(f'terminates in hot state {state}') if self.is_top_monitor and self.option_print_summary: self.print_summary() def verify(self, trace: List[Event]): ''' Verifies a trace, which is a list of events. It calls eval on each event and calls end() at the end of the trace. :param trace: the trace. ''' for event in trace: self.eval(event) self.end() def __str__(self) -> str: monitor_name = self.__class__.__name__ suffix = " states:" bar_length = len(monitor_name) + len(suffix) result = f'{"-" bar_length}\n' result += monitor_name + suffix + "\n" for state in self.states: result += f'{state}\n' for (index, states) in self.states_indexed.items(): if states: result += f'index {index}:\n' for state in states: result += f' {state}\n' result += f'{"-" * bar_length}\n' return result def add_state_to_state_vector(self, states: Set[State], state: State): """ Adds a state to a state vector. Also sets the monitor field of the state to self (the monitor the state is part of). :param states: the state vector to add state to. :param state: the state to become initial states. """ state.set_monitor_to(self) states.add(state) def report_transition_error(self, state: State, event: Event, msg: str): """ Reports an error caused by taking a transition that results in an ErrorState. :param state: the state in which the transition is taken. :param event: the event that causes the transition to be taken. :param msg: the error message provided by user. """ message =
is visible. When the last pending initializer is removed, and no failing result is set, the initializers struct will be set to nil and the object is considered as initialized and visible to all clients. type: list contains: name: description: - name of the process that is responsible for initializing this object. type: str result: description: - If result is set with the Failure field, the object will be persisted to storage and then deleted, ensuring that other clients can observe the deletion. type: complex contains: api_version: description: - APIVersion defines the versioned schema of this representation of an object. Servers should convert recognized schemas to the latest internal value, and may reject unrecognized values. type: str code: description: - Suggested HTTP return code for this status, 0 if not set. type: int details: description: - Extended data associated with the reason. Each reason may define its own extended details. This field is optional and the data returned is not guaranteed to conform to any schema except that defined by the reason type. type: complex contains: causes: description: - The Causes array includes more details associated with the StatusReason failure. Not all StatusReasons may provide detailed causes. type: list contains: field: description: - 'The field of the resource that has caused this error, as named by its JSON serialization. May include dot and postfix notation for nested attributes. Arrays are zero-indexed. Fields may appear more than once in an array of causes due to fields having multiple errors. Optional. Examples: "name" - the field "name" on the current resource "items[0].name" - the field "name" on the first array entry in "items"' type: str message: description: - A human-readable description of the cause of the error. This field may be presented as-is to a reader. type: str reason: description: - A machine-readable description of the cause of the error. If this value is empty there is no information available. type: str group: description: - The group attribute of the resource associated with the status StatusReason. type: str kind: description: - The kind attribute of the resource associated with the status StatusReason. On some operations may differ from the requested resource Kind. type: str name: description: - The name attribute of the resource associated with the status StatusReason (when there is a single name which can be described). type: str retry_after_seconds: description: - If specified, the time in seconds before the operation should be retried. type: int uid: description: - UID of the resource. (when there is a single resource which can be described). type: str kind: description: - Kind is a string value representing the REST resource this object represents. Servers may infer this from the endpoint the client submits requests to. Cannot be updated. In CamelCase. type: str message: description: - A human-readable description of the status of this operation. type: str metadata: description: - Standard list metadata. type: complex contains: resource_version: description: - String that identifies the server's internal version of this object that can be used by clients to determine when objects have changed. Value must be treated as opaque by clients and passed unmodified back to the server. Populated by the system. Read-only. type: str self_link: description: - SelfLink is a URL representing this object. Populated by the system. Read-only. type: str reason: description: - A machine-readable description of why this operation is in the "Failure" status. If this value is empty there is no information available. A Reason clarifies an HTTP status code but does not override it. type: str status: description: - 'Status of the operation. One of: "Success" or "Failure".' type: str labels: description: - Map of string keys and values that can be used to organize and categorize (scope and select) objects. May match selectors of replication controllers and services. type: complex contains: str, str name: description: - Name must be unique within a namespace. Is required when creating resources, although some resources may allow a client to request the generation of an appropriate name automatically. Name is primarily intended for creation idempotence and configuration definition. Cannot be updated. type: str namespace: description: - Namespace defines the space within each name must be unique. An empty namespace is equivalent to the "default" namespace, but "default" is the canonical representation. Not all objects are required to be scoped to a namespace - the value of this field for those objects will be empty. Must be a DNS_LABEL. Cannot be updated. type: str owner_references: description: - List of objects depended by this object. If ALL objects in the list have been deleted, this object will be garbage collected. If this object is managed by a controller, then an entry in this list will point to this controller, with the controller field set to true. There cannot be more than one managing controller. type: list contains: api_version: description: - API version of the referent. type: str block_owner_deletion: description: - If true, AND if the owner has the "foregroundDeletion" finalizer, then the owner cannot be deleted from the key-value store until this reference is removed. Defaults to false. To set this field, a user needs "delete" permission of the owner, otherwise 422 (Unprocessable Entity) will be returned. type: bool controller: description: - If true, this reference points to the managing controller. type: bool kind: description: - Kind of the referent. type: str name: description: - Name of the referent. type: str uid: description: - UID of the referent. type: str resource_version: description: - An opaque value that represents the internal version of this object that can be used by clients to determine when objects have changed. May be used for optimistic concurrency, change detection, and the watch operation on a resource or set of resources. Clients must treat these values as opaque and passed unmodified back to the server. They may only be valid for a particular resource or set of resources. Populated by the system. Read-only. Value must be treated as opaque by clients and . type: str self_link: description: - SelfLink is a URL representing this object. Populated by the system. Read-only. type: str uid: description: - UID is the unique in time and space value for this object. It is typically generated by the server on successful creation of a resource and is not allowed to change on PUT operations. Populated by the system. Read-only. type: str spec: description: - spec holds all the input necessary to produce a new build, and the conditions when to trigger them. type: complex contains: completion_deadline_seconds: description: - completionDeadlineSeconds is an optional duration in seconds, counted from the time when a build pod gets scheduled in the system, that the build may be active on a node before the system actively tries to terminate the build; value must be positive integer type: int failed_builds_history_limit: description: - failedBuildsHistoryLimit is the number of old failed builds to retain. If not specified, all failed builds are retained. type: int node_selector: description: - nodeSelector is a selector which must be true for the build pod to fit on a node If nil, it can be overridden by default build nodeselector values for the cluster. If set to an empty map or a map with any values, default build nodeselector values are ignored. type: complex contains: str, str output: description: - output describes the Docker image the Strategy should produce. type: complex contains: image_labels: description: - imageLabels define a list of labels that are applied to the resulting image. If there are multiple labels with the same name then the last one in the list is used. type: list contains: name: description: - name defines the name of the label. It must have non-zero length. type: str value: description: - value defines the
<reponame>misakadam97/cell_mrcnn from os import listdir, mkdir, path from os.path import join, isdir, basename, split from glob import glob import numpy as np import skimage.draw from skimage.io import imread from cell_mrcnn.utils import correct_central_brightness, subtract_bg, convert_to_bit8, \ get_cell_mrcnn_path_from_config_file, get_image_description, \ preproc_pipeline, load_image import read_roi import gc import pandas as pd from datetime import datetime from shutil import copyfile # data directory # data_dir = get_cell_mrcnn_path_from_config_file() # dataset_dir = join(data_dir, 'annotated_datasets/') # Import Mask RCNN from cell_mrcnn import utils from PIL import Image def calculate_percentiles(im_paths): # takes ~2mins # on 2048x2048 images the below percentiles leave about _ pixels out: # 99: 42k # 99.9: 4.2k # 99.99: 420 # 99.999: 42 # 99.9999: 4 # 100: 0 , this is equal to max percentiles = [99, 99.9, 99.99, 99.999, 99.9999, 100] perc_df = pd.DataFrame(columns=percentiles, index=pd.RangeIndex(0, len(im_paths))) for i, im_path in enumerate(im_paths): im = imread(im_path) im = correct_central_brightness(im.astype(np.float)) im = subtract_bg(im) percentile_list = [] for perc in percentiles: percentile_list.append(np.percentile(im, perc)) perc_df.loc[i, :] = percentile_list perc_df.loc["min", :] = perc_df.min(axis=0) perc_df.loc["max", :] = perc_df.loc[perc_df.index[0:-1], :].max(axis=0) perc_df.loc["mean", :] = perc_df.loc[perc_df.index[0:-2], :].mean(axis=0) perc_df.loc["median", :] = perc_df.loc[perc_df.index[0:-3], :].median( axis=0) perc_df.loc["var", :] = perc_df.loc[perc_df.index[0:-4], :].var(axis=0) perc_df.loc["std", :] = perc_df.loc[perc_df.index[0:-5], :].std(axis=0) return perc_df def preprocess(channel_paths, output_folder, cutoffs): """ :param input_folder: folder of the raw images :param output_folder: preprocessed images will be saved here :param channel_paths: nested list of image paths. Can contain 1 or 2 lists. If it contains 2; 1st should be Venus, this is gona be the red channel; 2nd should be Cerulean, this will be the blue channel; and they should be sorted! :param cutoffs: cutoff for 8 bit conversion (order same as in channel paths) :return: """ if not isdir(output_folder): mkdir(output_folder) # If only Venus channel if len(channel_paths) == 1: venus_paths = channel_paths[0] for i, impath in enumerate(venus_paths): im = imread(impath) im_c = correct_central_brightness(im.astype(np.float16)) im_bg = subtract_bg(im_c) im8 = convert_to_bit8(im_bg, cutoffs[0]) fname = impath.split()[1] Image.fromarray(im8).save(join(output_folder, fname + '.png')) # If Venus and Cerulean elif len(channel_paths) == 2: red_paths, blue_paths = channel_paths[0], channel_paths[1] red_desc = [get_image_description(path)[1:] for path in red_paths] blue_desc = [get_image_description(path)[1:] for path in blue_paths] assert red_desc == blue_desc, 'images not sorted' for i, (red_path, blue_path) in enumerate(zip(red_paths, blue_paths)): print('\rCreating composite images: {}/{}' \ .format(i + 1, len(blue_paths)), end='...') try: red, blue = imread(red_path), imread(blue_path) comp = preproc_pipeline(red, blue) fname = split(red_path)[1].split('.')[0] Image.fromarray(comp).save(join(output_folder, fname + '.png')) except: print(f'image {i} processing failed') def transfer_w3_channel_images(data_dir): cit_dir = join(data_dir, 'w3/cit') cellmembrane_dir = join(data_dir, 'w3/cellmembrane') if not isdir(cit_dir): mkdir(cit_dit) if not isdir(cellmembrane_dir): mkdir(cellmembrane_dir) # get the path to all tif images im_paths = glob(join(data_dir, '*/.tif'), recursive=True) # select the non-thumbnail w3 images for i, im_path in enumerate(im_paths): print('\rSeparating cit. and cellmembrane w3 channel images: ', i + 1, '/', len(im_paths), end='') if 'thumb' in im_path: continue if im_path.split('_')[-1][:2] != 'w3': continue im = imread(im_path) im = convert_to_bit8(im) im = Image.fromarray(im) if 'cit' in im_path: im.save(join(cit_dir, im_path.split('/')[-1].split('.')[0] + '.png')) else: im.save(join(cellmembrane_dir, im_path.split('/')[-1].split('.')[0] + '.png')) def cell_groups_to_bg(image, roi_set): cell_group_rois = [roi_set[key] for key in roi_set.keys() if 'cell_group' in key] im = np.copy(image) for cg_roi in cell_group_rois: rr, cc = skimage.draw.polygon(cg_roi['y'], cg_roi['x']) mask = np.zeros((im.shape[0], im.shape[1]), dtype=np.uint8) mask[rr, cc] = 1 rand_bg = np.random.randint(im.mean() - im.std(), im.mean() + im.std(), size=mask.sum()) mask[rr, cc] = rand_bg im[rr, cc] = mask[rr, cc] return im def read_roi_or_roiset(impath): impath = impath.split('.')[0] # if rois are in a zip (image contains multiple rois) if path.isfile(join(impath + '.zip')): rois = read_roi.read_roi_zip( join(impath + '.zip')) return rois # if roi is in roi format(image only contains 1 roi) elif path.isfile(join(impath + '.roi')): rois = read_roi.read_roi_file(join(impath + '.roi')) return rois else: print("rois couldn't be found for:", impath) def calc_avg_pixel_value(image_paths, output_file=None): image = load_image(image_paths[0]) channel_n = image.shape[2] channel_mean_dict = {} for i in range(channel_n): channel_mean_dict[i] = [] for image_path in image_paths: image = load_image(image_path) for i in range(channel_n): channel_mean_dict[i].append(image[:, :, i].mean()) for i in range(channel_n): channel_mean_dict[i] = np.array(channel_mean_dict[i]).mean() means = [mean for mean in channel_mean_dict.values()] if output_file: with open(output_file, 'w') as f: for i, mean in enumerate(means): f.write('Channel {} mean: {}'.format(i+1, means[i])) return means def copy_annotated(input_folder, output_folder): """ creates a folder named the current datetime; and copies the roi.zip files and corresponding images from the input folder into it :param input_folder: :param output_folder: :return: """ # get a list of roi paths roi_paths = glob(join(input_folder, '.zip')) roi_paths.extend(glob(join(input_folder, '.roi'))) # get the corresponding image paths image_paths = [] for roi_path in roi_paths: image_paths.append(roi_path.split('.')[0] + '.png') date = datetime.now().strftime("%Y_%m_%d_%H_%M_%S") output_folder = join(output_folder, date) if not isdir(output_folder): mkdir(output_folder) for roi, im in zip(roi_paths, image_paths): copyfile(roi, join(output_folder, basename(roi))) copyfile(im, join(output_folder, basename(im))) return output_folder class CellTransformData(utils.Dataset): def load_cell(self, dataset_dir): """Load the cell dataset resize the images and rois to a uniform dimension. dataset_dir: Root directory of the dataset. subset: Subset to load: train or val """ # Add classes. We have only one class to add. self.add_class("hulab", 1, "cell") image_names = [file_name for file_name in listdir(dataset_dir) if 'png' in file_name] # todo: revisit this naming convention, come up w/ a unifrom system # just so that the orderly assigned id is in the utils. Dataset class # is in the same order as the numbers in the image names # image_names = [int(name.split('.png')[0]) for name in image_names] # image_names.sort() # image_names = [str(n) + '.png' for n in image_names] # Add images for image_name in image_names: id = image_name.split('.')[0] # Get the x, y coordinaets of points of the polygons that make up # the outline of each object instance. # RoI format: # roi = { # region1 : { # 'type' = 'freehand' # 'x' = [...] # 'y' = [...] # 'n' = 412 # 'width' = 0 # always 0, not informative # 'name' = the region name (same as the key) # 'position' = 0 # always 0, not informative # } # ...more regions # } rois = read_roi_or_roiset(join(dataset_dir, id)) im_path = join(dataset_dir, image_name) # load_mask() needs the image size to convert polygons to masks. # Unfortunately, RoI doesn't include it, so we must read # the image. This is only managable since the dataset is tiny. h, w = imread(im_path).shape[:2] self.add_image( "hulab", image_id=id, # use file name as a unique image id path=im_path, width=w, height=h, polygons=rois) def polygon_to_mask(self, image_id): """Generate instance masks for an image. Returns: masks: A bool array of shape [height, width, instance count] with one mask per instance. class_ids: a 1D array of class IDs of the instance masks. """ # If not a confonc dataset image, delegate to parent class. image_info = self.image_info[image_id] if image_info["source"] != "hulab": return super(self.__class__, self).load_mask(image_id) # Convert polygons to a bitmap mask of shape # [height, width, instance_count] info = self.image_info[image_id] # /proc/sys/vm/overcommit_memory has to be "1" for larger arrays mask = np.zeros([info["height"], info["width"], len(info["polygons"])], dtype=np.bool) for i, (key, vals) in enumerate(info["polygons"].items()): # Get indexes of pixels inside the polygon and set them to 1 rr, cc = skimage.draw.polygon(vals['y'], vals['x']) mask[rr, cc, i] = True del (rr, cc) gc.collect() # Return mask, and array of class IDs of each instance. Since we have # one class ID only, we return an array of 1s return mask, np.ones([mask.shape[-1]], dtype=np.int32) def split_to_trainval(self, dataset_dir): # set up the output directory's subfolders [mkdir(join(dataset_dir, folder)) for folder in ['train', 'val'] if folder not in listdir(dataset_dir)] np.random.seed(seed=54646) train_set = np.random.choice(self.image_ids, int(len(self.image_ids) * 0.7), replace=False) means = [] for image_id_ in self.image_ids: info = self.image_info[image_id_] im = self.load_image(image_id_) means.append(im.mean(axis=(0, 1))) mask = self.polygon_to_mask(image_id_)[0] id_ = info['id'] if image_id_ in train_set: output_dir = join(dataset_dir, 'train', str(id_)) else: output_dir = join(dataset_dir, 'val', str(id_)) if not isdir(output_dir): mkdir(output_dir) for m in range(mask.shape[2]): mask_ = Image.fromarray((mask[:, :, m] * 255).astype(np.uint8), mode='L') mask_ = mask_.convert(mode='1') mask_.save(join(output_dir, str(id_) + '_mask_' + str(m) \ + '.png')) copyfile(info['path'], join(output_dir, str(id_) + '.png')) means = np.array(means).mean(axis=0) print('Average pixel value(s) is(/are): {}'.format(means)) return means if __name__ == '__main__': # load the dataset dataset_path = join(dataset_dir, '2020_11_22_02_55_03') ds = CellTransformData() ds.load_cell(dataset_path) ds.prepare() ds.split_to_trainval(dataset_path) show_example = False if show_example: # test image_id = 12 image = ds.load_image(image_id) mask, class_ids = ds.polygon_to_mask(image_id) original_shape = image.shape # Resize image,
<gh_stars>1-10 from __future__ import print_function, division, absolute_import import numpy as np import scipy from scipy.misc import imsave, imread, imresize from sklearn.feature_extraction.image import reconstruct_from_patches_2d, extract_patches_2d from scipy.ndimage.filters import gaussian_filter from skimage.util.shape import view_as_windows from keras import backend as K import os import time #import cv2 ''' _image_scale_multiplier is a special variable which is used to alter image size. The default image size is 32x32. If a true upscaling model is used, then the input image size is 16x16, which not offer adequate training samples. ''' _image_scale_multiplier = 1 img_size = 256 * _image_scale_multiplier stride = 16 * _image_scale_multiplier assert (img_size 2) % (stride 2) == 0, "Number of images generated from strided subsample of the image needs to be \n" \ "a positive integer. Change stride such that : \n" \ "(img_size 2) / (stride 2) is a positive integer." input_path = r"input_images/" validation_path = r"val_images/" validation_set5_path = validation_path + "set5/" validation_set14_path = validation_path + "set14/" base_dataset_dir = os.path.expanduser("~") + "/Image Super Resolution Dataset/" output_path = base_dataset_dir + "train_images/train/" validation_output_path = base_dataset_dir + r"train_images/validation/" if not os.path.exists(output_path): os.makedirs(output_path) def transform_images(directory, output_directory, scaling_factor=2, max_nb_images=-1, true_upscale=False): index = 1 if not os.path.exists(output_directory + "X/"): os.makedirs(output_directory + "X/") if not os.path.exists(output_directory + "y/"): os.makedirs(output_directory + "y/") # For each image in input_images directory nb_images = len([name for name in os.listdir(directory)]) if max_nb_images != -1: print("Transforming %d images." % max_nb_images) else: assert max_nb_images <= nb_images, "Max number of images must be less than number of images in path" print("Transforming %d images." % (nb_images)) if nb_images == 0: print("Extract the training images or images from imageset_91.zip (found in the releases of the project) " "into a directory with the name 'input_images'") print("Extract the validation images or images from set5_validation.zip (found in the releases of the project) " "into a directory with the name 'val_images'") exit() for file in os.listdir(directory): img = imread(directory + file, mode='RGB') # Resize to 256 x 256 img = imresize(img, (img_size, img_size)) img=scipy.misc.imfilter(img,ftype='sharpen') # Create patches hr_patch_size = (16 * scaling_factor * _image_scale_multiplier) nb_hr_images = (img_size 2) // (stride 2) hr_samples = np.empty((nb_hr_images, hr_patch_size, hr_patch_size, 3)) image_subsample_iterator = subimage_generator(img, stride, hr_patch_size, nb_hr_images) stride_range = np.sqrt(nb_hr_images).astype(int) i = 0 for j in range(stride_range): for k in range(stride_range): hr_samples[i, :, :, :] = next(image_subsample_iterator) i += 1 lr_patch_size = 16 * _image_scale_multiplier t1 = time.time() # Create nb_hr_images 'X' and 'Y' sub-images of size hr_patch_size for each patch for i in range(nb_hr_images): ip = hr_samples[i] # Save ground truth image X imsave(output_directory + "/y/" + "%d_%d.png" % (index, i + 1), ip) # Apply Gaussian Blur to Y op = gaussian_filter(ip, sigma=0.5) print("CVVVVVVVVVVVVVVVV") #ip = np.array(ip, dtype=np.uint8) #op = cv2.bilateralFilter(ip,15,125,125) # Subsample by scaling factor to Y op = imresize(op, (lr_patch_size, lr_patch_size), interp='bicubic') if not true_upscale: # Upscale by scaling factor to Y op = imresize(op, (hr_patch_size, hr_patch_size), interp='bicubic') # Save Y imsave(output_directory + "/X/" + "%d_%d.png" % (index, i+1), op) print("Finished image %d in time %0.2f seconds. (%s)" % (index, time.time() - t1, file)) index += 1 if max_nb_images > 0 and index >= max_nb_images: print("Transformed maximum number of images. ") break print("Images transformed. Saved at directory : %s" % (output_directory)) def image_count(): return len([name for name in os.listdir(output_path + "X/")]) def val_image_count(): return len([name for name in os.listdir(validation_output_path + "X/")]) def subimage_generator(img, stride, patch_size, nb_hr_images): for _ in range(nb_hr_images): for x in range(0, img_size - patch_size, stride): for y in range(0, img_size - patch_size, stride): subimage = img[x : x + patch_size, y : y + patch_size, :] yield subimage def subimage_patch(img, stride, patch_size, nb_hr_images): heightini, widthini = img.shape[:2] #print(str(heightini)+'--'+str(widthini)) #j=0 for y in range(0, widthini , stride): #for y in range(0, heightini - patch_size, stride): for x in range(0, heightini , stride): if (x + patch_size)<widthini and (y + patch_size) <heightini: subimage = img[y : y + patch_size, x : x + patch_size, :] #height, width = subimage.shape[:2] #print(str(height)+'<<-->>'+str(width)) #print(str(x)+'--'+str(y)+'--'+str(x + patch_size)+'--'+str(y + patch_size)) #j += 1 yield subimage def make_patches(x, scale, patch_size, upscale=True, verbose=1): '''x shape: (num_channels, rows, cols)''' height, width = x.shape[:2] img_height =width * scale img_width = height * scale #x = imresize(x, (int(img_width/1.1), int(img_height/1.1) )) #imsave("intermediate.jpg", x) #x = imresize(x, (img_width,img_height)) #print("PATCH SIZE SIZE") #imsave("intermediateafter.jpg", x) #if upscale: x = imresize(x, (height * scale, width * scale), interp='bicubic') #if upscale: x = imresize(x, (height * scale, width * scale)) patches = extract_patches_2d(x, (patch_size, patch_size)) return patches def make_patchesOrig(x, scale, patch_size, upscale=False, verbose=1): '''x shape: (num_channels, rows, cols)''' height, width = x.shape[:2] if upscale: x = imresize(x, (height * scale, width * scale)) patches = extract_patches_2dv2(x, (patch_size, patch_size)) return patches def make_patchesStep(x, scale, patch_size, upscale=False,extraction_step=24, verbose=1): '''x shape: (num_channels, rows, cols)''' height, width = x.shape[:2] if upscale: x = imresize(x, (height * scale, width * scale)) patches = extract_patches_Step(x, (patch_size, patch_size),extraction_step) return patches def combine_patches(in_patches, out_shape, scale): '''Reconstruct an image from these `patches`''' print("wpatch") recon = reconstruct_from_patches_2d(in_patches, out_shape) return recon from itertools import product def reconstruct_from_patches_2dloc(patches, image_size): """Reconstruct the image from all of its patches. Patches are assumed to overlap and the image is constructed by filling in the patches from left to right, top to bottom, averaging the overlapping regions. Read more in the :ref:`User Guide <image_feature_extraction>`. Parameters ---------- patches : array, shape = (n_patches, patch_height, patch_width) or (n_patches, patch_height, patch_width, n_channels) The complete set of patches. If the patches contain colour information, channels are indexed along the last dimension: RGB patches would have `n_channels=3`. image_size : tuple of ints (image_height, image_width) or (image_height, image_width, n_channels) the size of the image that will be reconstructed Returns ------- image : array, shape = image_size the reconstructed image """ i_h, i_w = image_size[:2] p_h, p_w = patches.shape[1:3] img = np.zeros(image_size) # compute the dimensions of the patches array n_h = i_h - p_h + 1 n_w = i_w - p_w + 1 for p, (i, j) in zip(patches, product(range(n_h), range(n_w))): img[i:i + p_h, j:j + p_w] += p for i in range(i_h): for j in range(i_w): # divide by the amount of overlap # XXX: is this the most efficient way? memory-wise yes, cpu wise? img[i, j] /= float(min(i + 1, p_h, i_h - i) * min(j + 1, p_w, i_w - j)) return img # generate an overlap count map directly, this is fast Y, X = np.ogrid[0:i_h, 0:i_w] x_h = min(p_w, i_w - p_w) y_h = min(p_h, i_h - p_h) overlap_cnt = ((np.minimum(np.minimum(X+1,x_h), np.minimum(i_w-X,x_h))) (np.minimum(np.minimum(Y+1,y_h), np.minimum(i_h-Y,y_h))),1) return img/overlap_cnt def subimage_build_patch_global(img, stride, patch_size, nb_hr_images): heightini, widthini = img.shape[:2] print("///////////------") print(img.shape) #print(str(heightini)+'--'+str(widthini)) i=0 for y in range(0, widthini , stride): #for y in range(0, heightini - patch_size, stride): for x in range(0, heightini , stride): if (x + patch_size)<widthini and (y + patch_size) <heightini: i += 1 subimages= np.empty((i, patch_size, patch_size, 3)) j=0 for y in range(0, widthini , stride): #for y in range(0, heightini - patch_size, stride): for x in range(0, heightini , stride): if (x + patch_size)<widthini and (y + patch_size) <heightini: subimages[j, :, :, :] = img[y : y + patch_size, x : x + patch_size, :] #height, width = subimage.shape[:2] #print(str(height)+'<<-->>'+str(width)) #print(str(x)+'--'+str(y)+'--'+str(x + patch_size)+'--'+str(y + patch_size)) j += 1 #yield subimage print(i) return subimages def subimage_combine_patches_global(imgtrue , patches, stride, patch_size, scale): heighttrue, widthtrue = imgtrue.shape[:2] img = imresize(imgtrue, (heighttruescale, widthtruescale), interp='bicubic') heightini, widthini = img.shape[:2] print("///////////------") print(img.shape) print(patches.shape) j=0 for y in range(0, widthini , stride): #for y in range(0, heightini - patch_size, stride): for x in range(0, heightini , stride): if (x + patch_size)<widthini and (y + patch_size) <heightini: #subimages[j, :, :, :] = img[y : y + patch_size, x : x + patch_size, :] img[y : y + patch_size, x : x + patch_size, :]=patches[j, :, :, :] #height, width = subimage.shape[:2] #print(str(height)+'<<-->>'+str(width)) #print(str(x)+'--'+str(y)+'--'+str(x + patch_size)+'--'+str(y + patch_size)) j += 1 print(j) return img def image_generator(directory, scale_factor=2, target_shape=None, channels=3, small_train_images=False, shuffle=True, batch_size=32, seed=None): if not target_shape: if small_train_images: if K.image_dim_ordering() == "th": image_shape = (channels, 16 _image_scale_multiplier, 16 * _image_scale_multiplier) y_image_shape = (channels, 16 * scale_factor *
import logging import pickle from typing import Dict, List, Optional, Set, Union import click import hail as hl from gnomad.resources.grch38.gnomad import ( COHORTS_WITH_POP_STORED_AS_SUBPOP, HGDP_POPS, TGP_POPS, TGP_POP_NAMES, POPS, SEXES, SUBSETS, ) from gnomad.sample_qc.ancestry import POP_NAMES from gnomad.utils.filtering import remove_fields_from_constant from gnomad.utils.vcf import ( add_as_info_dict, adjust_vcf_incompatible_types, ALLELE_TYPE_FIELDS, AS_FIELDS, AS_VQSR_FIELDS, create_label_groups, ENTRIES, FAF_POPS, FORMAT_DICT, HISTS, INFO_DICT, IN_SILICO_ANNOTATIONS_INFO_DICT, make_info_dict, make_vcf_filter_dict, REGION_FLAG_FIELDS, RF_FIELDS, SITE_FIELDS, VQSR_FIELDS, ) from gnomad.variant_qc.pipeline import INBREEDING_COEFF_HARD_CUTOFF from gnomad.utils.annotations import region_flag_expr from joint_calling import utils, _version, resources from joint_calling.utils import get_validation_callback logger = logging.getLogger(__file__) logger.setLevel('INFO') # Add new site fields # NEW_SITE_FIELDS = [ # 'monoallelic', # 'transmitted_singleton', # ] # SITE_FIELDS.extend(NEW_SITE_FIELDS) # Remove original alleles for containing non-releasable alleles MISSING_ALLELE_TYPE_FIELDS = ['original_alleles', 'has_star'] ALLELE_TYPE_FIELDS = remove_fields_from_constant( ALLELE_TYPE_FIELDS, MISSING_ALLELE_TYPE_FIELDS ) # Remove SB (not included in VCF) and SOR (doesn't exist in v3.1) from site fields MISSING_SITES_FIELDS = ['SOR', 'SB'] SITE_FIELDS = remove_fields_from_constant(SITE_FIELDS, MISSING_SITES_FIELDS) # Remove AS_VarDP from AS fields MISSING_AS_FIELDS = ['AS_VarDP'] AS_FIELDS = remove_fields_from_constant(AS_FIELDS, MISSING_AS_FIELDS) # Make subset list (used in properly filling out VCF header descriptions and naming VCF info fields) SUBSET_LIST_FOR_VCF = SUBSETS.copy() SUBSET_LIST_FOR_VCF.append('') # Remove cohorts that have subpop frequencies stored as pop frequencies # Inclusion of these subsets significantly increases the size of storage in the VCFs because of the many subpops SUBSET_LIST_FOR_VCF = remove_fields_from_constant( SUBSET_LIST_FOR_VCF, COHORTS_WITH_POP_STORED_AS_SUBPOP ) # Remove decoy from region field flag MISSING_REGION_FIELDS = ['decoy'] REGION_FLAG_FIELDS = remove_fields_from_constant( REGION_FLAG_FIELDS, MISSING_REGION_FIELDS ) # All missing fields to remove from vcf info dict MISSING_INFO_FIELDS = ( MISSING_ALLELE_TYPE_FIELDS + MISSING_AS_FIELDS + MISSING_REGION_FIELDS + MISSING_SITES_FIELDS + RF_FIELDS ) # Remove unnecessary pop names from POP_NAMES dict POPS = {pop: POP_NAMES[pop] for pop in POPS} # Remove unnecessary pop names from FAF_POPS dict FAF_POPS = {pop: POP_NAMES[pop] for pop in FAF_POPS} # Get HGDP + TGP(KG) subset pop names HGDP_TGP_KEEP_POPS = TGP_POPS + HGDP_POPS HGDP_TGP_POPS = {} for pop in HGDP_TGP_KEEP_POPS: if pop in TGP_POP_NAMES: HGDP_TGP_POPS[pop] = TGP_POP_NAMES[pop] else: HGDP_TGP_POPS[pop] = pop.capitalize() # Used for HGDP + TGP subset MT VCF output only FORMAT_DICT.update( { 'RGQ': { 'Number': '1', 'Type': 'Integer', 'Description': 'Unconditional reference genotype confidence, encoded as a phred quality -10log10 p(genotype call is wrong)', } } ) @click.command() @click.version_option(_version.__version__) @click.option( '--mt', 'mt_path', required=True, callback=get_validation_callback(ext='mt', must_exist=True), help='path to the raw sparse Matrix Table generated by combine_gvcfs.py', ) @click.option( '--out-ht', 'out_ht_path', required=True, callback=get_validation_callback(ext='ht'), help='path to write Hail Table', ) @click.option( '--out-vcf-header-txt', 'out_vcf_header_txt_path', required=True, callback=get_validation_callback(ext='txt'), ) @click.option( '--public-subset', 'is_public_subset', is_flag=True, help='create a subset', ) @click.option( '--test', 'is_test', is_flag=True, required=True, help='Create release files using only 2 partitions on chr20, chrX, ' 'and chrY for testing purposes' ) @click.option( '--local-tmp-dir', 'local_tmp_dir', help='local directory for temporary files and Hail logs (must be local).', ) def main( mt_path: str, out_ht_path: str, out_vcf_header_txt_path: str, is_test: bool, is_public_subset: bool, local_tmp_dir: str, ): # pylint: disable=missing-function-docstring utils.init_hail(__file__, local_tmp_dir) mt = hl.read_matrix_table(mt_path) ht = mt.rows() if is_test: ht = filter_to_test(ht) # Setup of parameters and Table/MatrixTable parameter_dict = _build_parameter_dict(ht, is_public_subset) vcf_ht = _prepare_vcf_ht(ht, is_subset=is_public_subset) if is_public_subset: logger.info( 'Loading subset MT and annotating with the prepared VCF HT for VCF export...' ) entries_ht = mt.select_rows().select_entries(ENTRIES) vcf_ht = ht.annotate_rows(**vcf_ht[entries_ht.row_key]) logger.info('Cleaning up the VCF HT for final export...') vcf_ht = _cleanup_ht_for_vcf_export(vcf_ht) vcf_ht = vcf_ht.checkpoint(out_ht_path, overwrite=True) vcf_ht.describe() _prepare_vcf_header_dict( ht=ht, vcf_ht=vcf_ht, vcf_header_txt_path=out_vcf_header_txt_path, parameter_dict=parameter_dict, is_public_subset=is_public_subset, ) def _prepare_vcf_header_dict( ht, vcf_ht, vcf_header_txt_path, parameter_dict, is_public_subset, ): logger.info('Making histogram bin edges...') header_dict = prepare_vcf_header_dict( vcf_ht, subset_list=parameter_dict['subsets'], pops=parameter_dict['pops'], filtering_model_field=parameter_dict['filtering_model_field'], inbreeding_coeff_cutoff=ht.inbreeding_coeff_cutoff, ) if not is_public_subset: header_dict.pop('format') logger.info('Saving header dict to pickle...') with hl.hadoop_open(vcf_header_txt_path, 'wb') as p: pickle.dump(header_dict, p, protocol=pickle.HIGHEST_PROTOCOL) def populate_subset_info_dict( subset: str, description_text: str, pops: Dict[str, str] = POPS, faf_pops: Dict[str, str] = FAF_POPS, sexes: List[str] = SEXES, label_delimiter: str = '_', ) -> Dict[str, Dict[str, str]]: """ Call `make_info_dict` to populate INFO dictionary with specific sexes, population names, and filtering allele frequency (faf) pops for the requested subset. :param subset: Sample subset in dataset. :param description_text: Text describing the sample subset that should be added to the INFO description. :param pops: Dict of sample global population names for gnomAD genomes. Default is POPS. :param faf_pops: Dict with faf pop names (keys) and descriptions (values). Default is FAF_POPS. :param sexes: gnomAD sample sexes used in VCF export. Default is SEXES. :param label_delimiter: String to use as delimiter when making group label combinations. Default is '_'. :return: Dictionary containing Subset specific INFO header fields. """ vcf_info_dict = {} faf_label_groups = create_label_groups(pops=faf_pops, sexes=sexes) for label_group in faf_label_groups: vcf_info_dict.update( make_info_dict( prefix=subset, prefix_before_metric=True if 'gnomad' in subset else False, pop_names=faf_pops, label_groups=label_group, label_delimiter=label_delimiter, faf=True, description_text=description_text, ) ) label_groups = create_label_groups(pops=pops, sexes=sexes) for label_group in label_groups: vcf_info_dict.update( make_info_dict( prefix=subset, prefix_before_metric=True if 'gnomad' in subset else False, pop_names=pops, label_groups=label_group, label_delimiter=label_delimiter, description_text=description_text, ) ) # Add popmax to info dict vcf_info_dict.update( make_info_dict( prefix=subset, label_delimiter=label_delimiter, pop_names=pops, popmax=True, description_text=description_text, ) ) return vcf_info_dict def populate_info_dict( info_dict: Dict[str, Dict[str, str]] = INFO_DICT, subset_list: List[str] = SUBSETS, subset_pops: Dict[str, str] = POPS, gnomad_pops: Dict[str, str] = POPS, faf_pops: Dict[str, str] = FAF_POPS, sexes: List[str] = SEXES, in_silico_dict: Dict[str, Dict[str, str]] = IN_SILICO_ANNOTATIONS_INFO_DICT, label_delimiter: str = '_', bin_edges: Dict[str, str] = None, age_hist_data: str = None, ) -> Dict[str, Dict[str, str]]: """ Call `make_info_dict` and `make_hist_dict` to populate INFO dictionary with specific sexes, population names, and filtering allele frequency (faf) pops. Used during VCF export. Creates: - INFO fields for age histograms (bin freq, n_smaller, and n_larger for heterozygous and homozygous variant carriers) - INFO fields for popmax AC, AN, AF, nhomalt, and popmax population - INFO fields for AC, AN, AF, nhomalt for each combination of sample population, sex both for adj and raw data - INFO fields for filtering allele frequency (faf) annotations - INFO fields for variant histograms (hist_bin_freq for each histogram and hist_n_larger for DP histograms) :param bin_edges: Dictionary of variant annotation histograms and their associated bin edges. :param age_hist_data: Pipe-delimited string of age histograms, from `get_age_distributions`. :param info_dict: INFO dict to be populated. :param subset_list: List of sample subsets in dataset. Default is SUBSETS. :param subset_pops: Dict of sample global population names to use for all subsets in `subset_list` unless the subset is 'gnomad', in that case `gnomad_pops` is used. Default is POPS. :param gnomad_pops: Dict of sample global population names for gnomAD genomes. Default is POPS. :param faf_pops: Dict with faf pop names (keys) and descriptions (values). Default is FAF_POPS. :param sexes: gnomAD sample sexes used in VCF export. Default is SEXES. :param in_silico_dict: Dictionary of in silico predictor score descriptions. :param label_delimiter: String to use as delimiter when making group label combinations. :return: Updated INFO dictionary for VCF export. """ vcf_info_dict = info_dict.copy() # Remove MISSING_INFO_FIELDS from info dict for field in MISSING_INFO_FIELDS: vcf_info_dict.pop(field, None) # Add allele-specific fields to info dict, including AS_VQSR_FIELDS vcf_info_dict.update( add_as_info_dict(info_dict=info_dict, as_fields=AS_FIELDS + AS_VQSR_FIELDS) ) for subset in subset_list: if subset == 'gnomad': description_text = ' in gnomAD' pops = gnomad_pops else: description_text = '' if subset == '' else f' in {subset} subset' pops = subset_pops vcf_info_dict.update( populate_subset_info_dict( subset=subset, description_text=description_text, pops=pops, faf_pops=faf_pops, sexes=sexes, label_delimiter=label_delimiter, ) ) if age_hist_data: age_hist_data = '\|'.join(str(x) for x in age_hist_data) vcf_info_dict.update( make_info_dict( prefix='', label_delimiter=label_delimiter, bin_edges=bin_edges, popmax=True, age_hist_data=age_hist_data, ) ) # Add in silico prediction annotations to info_dict vcf_info_dict.update(in_silico_dict) return vcf_info_dict def make_info_expr( t: Union[hl.MatrixTable, hl.Table], hist_prefix: str = '', ) -> Dict[str, hl.expr.Expression]: """ Make Hail expression for variant annotations to be included in VCF INFO field. :param t: Table/MatrixTable containing variant annotations to be reformatted for VCF export. :param hist_prefix: Prefix to use for histograms. :return: Dictionary containing Hail expressions for relevant INFO annotations. :rtype: Dict[str, hl.expr.Expression] """ vcf_info_dict = {} # Add site-level annotations to vcf_info_dict for field in SITE_FIELDS: vcf_info_dict[field] = t['release_ht_info'][f'{field}'] # Add AS annotations to info dict for field in AS_FIELDS: vcf_info_dict[field] = t['release_ht_info'][f'{field}'] for field in VQSR_FIELDS: vcf_info_dict[field] = t['vqsr'][f'{field}'] # Add region_flag and allele_info fields to info dict for field in ALLELE_TYPE_FIELDS: vcf_info_dict[field] = t['allele_info'][f'{field}'] for field in REGION_FLAG_FIELDS: vcf_info_dict[field] = t['region_flag'][f'{field}'] # Add underscore to hist_prefix if it isn't empty if hist_prefix != '': hist_prefix += '_' # Histograms to export are: # gq_hist_alt, gq_hist_all, dp_hist_alt, dp_hist_all, ab_hist_alt # We previously dropped: # _n_smaller for all hists # _bin_edges for all hists # _n_larger for all hists EXCEPT DP hists for hist in HISTS: hist_type = f'{hist_prefix}qual_hists' hist_dict = { f'{hist}_bin_freq': hl.delimit(t[hist_type][hist].bin_freq, delimiter='\|'), } vcf_info_dict.update(hist_dict) if 'dp' in hist: vcf_info_dict.update({f'{hist}_n_larger': t[hist_type][hist].n_larger},) # Add in silico annotations to info dict vcf_info_dict['cadd_raw_score'] = t['cadd']['raw_score'] vcf_info_dict['cadd_phred']
<reponame>jeffvan-netsia/voltha_doc<filename>voltha/coordinator.py # # Copyright 2017 the original author or authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # """ Consul-based coordinator services """ from consul import ConsulException from consul.twisted import Consul from requests import ConnectionError from structlog import get_logger from twisted.internet import reactor from twisted.internet.defer import inlineCallbacks, returnValue, Deferred from twisted.internet.error import DNSLookupError from zope.interface import implementer from leader import Leader from common.utils.asleep import asleep from common.utils.message_queue import MessageQueue from voltha.registry import IComponent from worker import Worker from simplejson import dumps, loads from common.utils.deferred_utils import DeferredWithTimeout, TimeOutError log = get_logger() class StaleMembershipEntryException(Exception): pass @implementer(IComponent) class Coordinator(object): """ An app shall instantiate only one Coordinator (singleton). A single instance of this object shall take care of all external with consul, and via consul, all coordination activities with its clustered peers. Roles include: - registering an ephemeral membership entry (k/v record) in consul - participating in a symmetric leader election, and potentially assuming the leader's role. What leadership entails is not a concern for the coordination, it simply instantiates (and shuts down) a leader class when it gains (or looses) leadership. """ CONNECT_RETRY_INTERVAL_SEC = 1 RETRY_BACKOFF = [0.05, 0.1, 0.2, 0.5, 1, 2, 5] # Public methods: def __init__(self, internal_host_address, external_host_address, instance_id, rest_port, config, consul='localhost:8500', container_name_regex='^.\.([0-9]+)\..$'): log.info('initializing-coordinator') self.config = config['coordinator'] self.worker_config = config['worker'] self.leader_config = config['leader'] self.membership_watch_relatch_delay = config.get( 'membership_watch_relatch_delay', 0.1) self.tracking_loop_delay = self.config.get( 'tracking_loop_delay', 1) self.session_renewal_timeout = self.config.get( 'session_renewal_timeout', 5) self.session_renewal_loop_delay = self.config.get( 'session_renewal_loop_delay', 3) self.membership_maintenance_loop_delay = self.config.get( 'membership_maintenance_loop_delay', 5) self.session_time_to_live = self.config.get( 'session_time_to_live', 10) self.prefix = self.config.get('voltha_kv_prefix', 'service/voltha') self.leader_prefix = '/'.join((self.prefix, self.config.get( self.config['leader_key'], 'leader'))) self.membership_prefix = '/'.join((self.prefix, self.config.get( self.config['membership_key'], 'members'), '')) self.assignment_prefix = '/'.join((self.prefix, self.config.get( self.config['assignment_key'], 'assignments'), '')) self.workload_prefix = '/'.join((self.prefix, self.config.get( self.config['workload_key'], 'work'), '')) self.core_store_prefix = '/'.join((self.prefix, self.config.get( self.config['core_store_key'], 'data/core'))) self.core_store_assignment_key = self.core_store_prefix + \ '/assignment' self.core_storage_suffix = 'core_store' self.retries = 0 self.instance_id = instance_id self.internal_host_address = internal_host_address self.external_host_address = external_host_address self.rest_port = rest_port self.membership_record_key = self.membership_prefix + self.instance_id self.session_id = None self.i_am_leader = False self.leader_id = None # will be the instance id of the current leader self.shutting_down = False self.leader = None self.membership_callback = None self.worker = Worker(self.instance_id, self) self.host = consul.split(':')[0].strip() self.port = int(consul.split(':')[1].strip()) # TODO need to handle reconnect events properly self.consul = Consul(host=self.host, port=self.port) self.container_name_regex = container_name_regex self.wait_for_leader_deferreds = [] self.peers_mapping_queue = MessageQueue() def start(self): log.debug('starting') reactor.callLater(0, self._async_init) log.info('started') return self @inlineCallbacks def stop(self): log.debug('stopping') self.shutting_down = True yield self._delete_session() # this will delete the leader lock too yield self.worker.stop() if self.leader is not None: yield self.leader.stop() self.leader = None log.info('stopped') def wait_for_a_leader(self): """ Async wait till a leader is detected/elected. The deferred will be called with the leader's instance_id :return: Deferred. """ d = Deferred() if self.leader_id is not None: d.callback(self.leader_id) return d else: self.wait_for_leader_deferreds.append(d) return d # Wait for a core data id to be assigned to this voltha instance @inlineCallbacks def get_core_store_id_and_prefix(self): core_store_id = yield self.worker.get_core_store_id() returnValue((core_store_id, self.core_store_prefix)) def recv_peers_map(self): return self.peers_mapping_queue.get() def publish_peers_map_change(self, msg): self.peers_mapping_queue.put(msg) # Proxy methods for consul with retry support def kv_get(self, args, kw): return self._retry('GET', args, *kw) def kv_put(self, args, *kw): return self._retry('PUT', args, *kw) def kv_delete(self, args, *kw): return self._retry('DELETE', args, **kw) # Methods exposing key membership information @inlineCallbacks def get_members(self): """Return list of all members""" _, members = yield self.kv_get(self.membership_prefix, recurse=True) returnValue([member['Key'][len(self.membership_prefix):] for member in members]) # Private (internal) methods: @inlineCallbacks def _async_init(self): yield self._create_session() yield self._create_membership_record() yield self._start_leader_tracking() yield self.worker.start() def _backoff(self, msg): wait_time = self.RETRY_BACKOFF[min(self.retries, len(self.RETRY_BACKOFF) - 1)] self.retries += 1 log.info(msg, retry_in=wait_time) return asleep(wait_time) def _clear_backoff(self): if self.retries: log.info('reconnected-to-consul', after_retries=self.retries) self.retries = 0 @inlineCallbacks def _create_session(self): @inlineCallbacks def _create_session(): consul = yield self.get_consul() # create consul session self.session_id = yield consul.session.create( behavior='release', ttl=self.session_time_to_live, lock_delay=1) log.info('created-consul-session', session_id=self.session_id) self._start_session_tracking() yield self._retry(_create_session) @inlineCallbacks def _delete_session(self): try: yield self.consul.session.destroy(self.session_id) except Exception as e: log.exception('failed-to-delete-session', session_id=self.session_id) @inlineCallbacks def _create_membership_record(self): yield self._do_create_membership_record_with_retries() reactor.callLater(0, self._maintain_membership_record) @inlineCallbacks def _maintain_membership_record(self): try: while 1: valid_membership = yield self._assert_membership_record_valid() if not valid_membership: log.info('recreating-membership-before', session=self.session_id) yield self._do_create_membership_record_with_retries() log.info('recreating-membership-after', session=self.session_id) else: log.debug('valid-membership', session=self.session_id) # Async sleep before checking the membership record again yield asleep(self.membership_maintenance_loop_delay) except Exception, e: log.exception('unexpected-error-leader-trackin', e=e) finally: # except in shutdown, the loop must continue (after a short delay) if not self.shutting_down: reactor.callLater(self.membership_watch_relatch_delay, self._maintain_membership_record) def _create_membership_record_data(self): member_record = dict() member_record['status'] = 'alive' member_record['host_address'] = self.external_host_address return member_record @inlineCallbacks def _assert_membership_record_valid(self): try: log.info('membership-record-before') is_timeout, (_, record) = yield \ self.consul_get_with_timeout( key=self.membership_record_key, index=0, timeout=5) if is_timeout: returnValue(False) log.info('membership-record-after', record=record) if record is None or \ 'Session' not in record or \ record['Session'] != self.session_id: log.info('membership-record-change-detected', old_session=self.session_id, record=record) returnValue(False) else: returnValue(True) except Exception as e: log.exception('membership-validation-exception', e=e) returnValue(False) @inlineCallbacks def _do_create_membership_record_with_retries(self): while 1: log.info('recreating-membership', session=self.session_id) result = yield self._retry( 'PUT', self.membership_record_key, dumps(self._create_membership_record_data()), acquire=self.session_id) if result: log.info('new-membership-record-created', session=self.session_id) break else: log.warn('cannot-create-membership-record') yield self._backoff('stale-membership-record') def _start_session_tracking(self): reactor.callLater(0, self._session_tracking_loop) @inlineCallbacks def _session_tracking_loop(self): @inlineCallbacks def _redo_session(): log.info('_redo_session-before') yield self._delete_session() # Create a new consul connection/session with a TTL of 25 secs try: self.consul = Consul(host=self.host, port=self.port) self.session_id = yield self.consul.session.create( behavior='release', ttl=self.session_time_to_live, lock_delay=1) log.info('new-consul-session', session=self.session_id) except Exception as e: log.exception('could-not-create-a-consul-session', e=e) @inlineCallbacks def _renew_session(m_callback): try: log.debug('_renew_session-before') consul_ref = self.consul result = yield consul_ref.session.renew( session_id=self.session_id) log.info('just-renewed-session', result=result) if not m_callback.called: # Triggering callback will cancel the timeout timer log.info('trigger-callback-to-cancel-timout-timer') m_callback.callback(result) else: # Timeout event has already been called. Just ignore # this event log.info('renew-called-after-timout', new_consul_ref=self.consul, old_consul_ref=consul_ref) except Exception, e: # Let the invoking method receive a timeout log.exception('could-not-renew-session', e=e) try: while 1: log.debug('session-tracking-start') rcvd = DeferredWithTimeout( timeout=self.session_renewal_timeout) _renew_session(rcvd) try: _ = yield rcvd except TimeOutError as e: log.info('session-renew-timeout', e=e) # Redo the session yield _redo_session() except Exception as e: log.exception('session-renew-exception', e=e) else: log.debug('successfully-renewed-session') # Async sleep before the next session tracking yield asleep(self.session_renewal_loop_delay) except Exception as e: log.exception('renew-exception', e=e) finally: reactor.callLater(self.session_renewal_loop_delay, self._session_tracking_loop) def _start_leader_tracking(self): reactor.callLater(0, self._leadership_tracking_loop) @inlineCallbacks def _leadership_tracking_loop(self): try: # Attempt to acquire leadership lock. True indicates success; # False indicates there is already a leader. It's instance id # is then the value under the leader key service/voltha/leader. # attempt acquire leader lock log.info('leadership-attempt-before') result = yield self._retry('PUT', self.leader_prefix, self.instance_id, acquire=self.session_id) log.info('leadership-attempt-after') # read it back before being too happy; seeing our session id is a # proof and now we have the change id that we can use to reliably # track any changes. In an unlikely scenario where the leadership # key gets wiped out administratively since the previous line, # the returned record can be None. Handle it. (index, record) = yield self._retry('GET', self.leader_prefix) log.info('leader-prefix', i_am_leader=result, index=index, record=record) if record is not None: if result is True: if record['Session'] == self.session_id: yield self._assert_leadership() else: pass # confusion; need to retry leadership else: leader_id = record['Value'] yield self._assert_nonleadership(leader_id) # if record was none, we shall try leadership again last = record while last is not None: # this shall return only when update is made to leader key # or expires after 5 seconds wait is_timeout, (tmp_index, updated) = yield \ self.consul_get_with_timeout( key=self.leader_prefix, index=index, timeout=5) # Timeout means either there is a lost connectivity to # consul or there are no change to that key. Do nothing. if is_timeout: continue # After timeout event the index returned from # consul_get_with_timeout is None. If we are here it's not a # timeout, therefore the index is a valid one. index=tmp_index if updated is None or updated != last: log.info('leader-key-change', index=index, updated=updated, last=last) # leadership has changed or vacated (or forcefully # removed), apply now # If I was previoulsy the leader then assert a non # leadership role before going for election if self.i_am_leader: log.info('leaving-leaderdhip', leader=self.instance_id) yield self._assert_nonleadership(self.instance_id) break last = updated except Exception, e: log.exception('unexpected-error-leader-trackin', e=e) finally: # except in shutdown, the loop must continue (after a
# -- coding: utf-8 -- # # Copyright © Simphony Project Contributors # Licensed under the terms of the MIT License # (see simphony/__init__.py for details) import pytest import os from simphony.plugins.siepic.parser import load_spi #============================================================================== # Test the parser #============================================================================== EBeam_sequoiap_A_v2_result = { 'circuits': [ { 'name': 'EBeam_sequoiap_A_v2', 'ports': [ 'ebeam_gc_te1550$1_laser', 'ebeam_gc_te1550$1_detector1' ], 'subcircuits': 'EBeam_sequoiap_A_v2', 'params': [ { 'name': 'sch_x', 'value': -1.0 }, { 'name': 'sch_y', 'value': -1.0 } ] } ], 'subcircuits': [ { 'name': 'EBeam_sequoiap_A_v2', 'ports': ['ebeam_gc_te1550$1_laser', 'ebeam_gc_te1550$1_detector1'], 'components': [ { 'name': 'ebeam_y_1550_67', 'model': 'ebeam_y_1550', 'ports': ['N$80', 'N$81', 'N$82'], 'params': { 'library': 'Design kits/ebeam', 'lay_x': 0.00010077000000000001, 'lay_y': 0.00013824, 'sch_x': 8.339586207, 'sch_y': 11.440551724 } }, { 'name': 'ebeam_gc_te1550_68', 'model': 'ebeam_gc_te1550', 'ports': ['ebeam_gc_te1550$1_laser', 'N$80'], 'params': { 'library': 'Design kits/ebeam', 'lay_x': 7.687e-05, 'lay_y': 0.00013824, 'sch_x': 6.361655172, 'sch_y': 11.440551724 } }, { 'name': 'ebeam_gc_te1550_69', 'model': 'ebeam_gc_te1550', 'ports': ['ebeam_gc_te1550$1_detector1', 'N$83'], 'params': { 'library': 'Design kits/ebeam', 'lay_x': 7.687e-05, 'lay_y': 1.1240000000000002e-05, 'sch_x': 6.361655172, 'sch_y': 0.930206897 } }, { 'name': 'ebeam_y_1550_70', 'model': 'ebeam_y_1550', 'ports': ['N$83', 'N$85', 'N$84'], 'params': { 'library': 'Design kits/ebeam', 'lay_x': 0.00010077000000000001, 'lay_y': 1.1240000000000002e-05, 'sch_x': 8.339586207, 'sch_y': 0.930206897 } }, { 'name': 'ebeam_wg_integral_1550_72', 'model': 'ebeam_wg_integral_1550', 'ports': ['N$81', 'N$84'], 'params': { 'library': 'Design kits/ebeam', 'wg_length': 0.000189995, 'wg_width': 5e-07, 'points': '[[108.17,140.99],[138.469,140.99],[138.469,8.49],[108.17,8.49]]', 'radius': 5.0, 'lay_x': 0.000123694, 'lay_y': 7.474e-05, 'sch_x': 10.236744828, 'sch_y': 6.18537931 } }, { 'name': 'ebeam_wg_integral_1550_83', 'model': 'ebeam_wg_integral_1550', 'ports': ['N$82', 'N$85'], 'params': { 'library': 'Design kits/ebeam', 'wg_length': 0.000149995, 'wg_width': 5e-07, 'points': '[[104.92,389.16],[120.719,389.16],[120.719,267.66],[104.92,267.66]]', 'radius': 5.0, 'lay_x': 0.000116444, 'lay_y': 7.474e-05, 'sch_x': 9.636744828, 'sch_y': 6.18537931 } } ], 'params': { 'MC_uniformity_width': 0.0, 'MC_uniformity_thickness': 0.0, 'MC_resolution_x': 100.0, 'MC_resolution_y': 100.0, 'MC_grid': 1e-05, 'MC_non_uniform': 99.0 } } ], 'analyses': [ { 'definition': { 'input_unit': 'wavelength', 'input_parameter': 'start_and_stop' }, 'params': { 'minimum_loss': 80.0, 'analysis_type': 'scattering_data', 'multithreading': 'user_defined', 'number_of_threads': 1.0, 'orthogonal_identifier': 1.0, 'start': 1.5e-06, 'stop': 1.6e-06, 'number_of_points': 3000.0, 'input': ['EBeam_sequoiap_A_v2,ebeam_gc_te1550$1_detector1'], 'output': 'EBeam_sequoiap_A_v2,ebeam_gc_te1550$1_laser' } } ] } MZI4_result = { 'circuits': [ { 'name': 'MZI4', 'ports': ['ebeam_gc_te1550_detector2', 'ebeam_gc_te1550_laser1'], 'subcircuits': 'MZI4', 'params': [{'name': 'sch_x', 'value': -1.0}, {'name': 'sch_y', 'value': -1.0}] } ], 'subcircuits': [ { 'name': 'MZI4', 'ports': ['ebeam_gc_te1550_detector2', 'ebeam_gc_te1550_laser1'], 'components': [ { 'name': 'ebeam_y_1550_0', 'model': 'ebeam_y_1550', 'ports': ['N$0', 'N$2', 'N$1'], 'params': { 'library': 'Design kits/ebeam', 'lay_x': 7.4e-06, 'lay_y': 0.000127, 'sch_x': 0.478534829, 'sch_y': 8.212692343 } }, { 'name': 'ebeam_gc_te1550_1', 'model': 'ebeam_gc_te1550', 'ports': ['ebeam_gc_te1550_detector2', 'N$0'], 'params': { 'library': 'Design kits/ebeam', 'lay_x': -1.6500000000000005e-05, 'lay_y': 0.000127, 'sch_x': -1.067003336, 'sch_y': 8.212692343 } }, { 'name': 'ebeam_gc_te1550_2', 'model': 'ebeam_gc_te1550', 'ports': ['ebeam_gc_te1550_laser1', 'N$3'], 'params': { 'library': 'Design kits/ebeam', 'lay_x': -1.6500000000000005e-05, 'lay_y': 0.000254, 'sch_x': -1.067003336, 'sch_y': 16.425384686 } }, { 'name': 'ebeam_y_1550_3', 'model': 'ebeam_y_1550', 'ports': ['N$6', 'N$5', 'N$4'], 'params': { 'library': 'Design kits/ebeam', 'lay_x': 8.993e-05, 'lay_y': 0.000127, 'sch_x': 5.815491515, 'sch_y': 8.212692343, 'sch_f': 'true' } }, { 'name': 'ebeam_wg_integral_1550_4', 'model': 'ebeam_wg_integral_1550', 'ports': ['N$1', 'N$4'], 'params': { 'library': 'Design kits/ebeam', 'wg_length': 6.773e-05, 'wg_width': 5e-07, 'points': '[[14.8,124.25],[82.53,124.25]]', 'radius': 5.0, 'lay_x': 4.866500000000001e-05, 'lay_y': 0.00012425, 'sch_x': 3.147013172, 'sch_y': 8.034858453 } }, { 'name': 'ebeam_wg_integral_1550_5', 'model': 'ebeam_wg_integral_1550', 'ports': ['N$2', 'N$5'], 'params': { 'library': 'Design kits/ebeam', 'wg_length': 0.000297394, 'wg_width': 5e-07, 'points': '[[14.8,129.75],[28.64,129.75],[28.64,247.68],[75.36,247.68],[75.36,129.75],[82.53,129.75]]', 'radius': 5.0, 'lay_x': 4.866500000000001e-05, 'lay_y': 0.000188715, 'sch_x': 3.147013172, 'sch_y': 12.203608153 } }, { 'name': 'ebeam_wg_integral_1550_6', 'model': 'ebeam_wg_integral_1550', 'ports': ['N$6', 'N$3'], 'params': { 'library': 'Design kits/ebeam', 'wg_length': 0.000256152, 'wg_width': 5e-07, 'points': '[[97.33,127.0],[114.79,127.0],[114.79,254.0],[0.0,254.0]]', 'radius': 5.0, 'lay_x': 5.777e-05, 'lay_y': 0.0001905, 'sch_x': 3.735805013, 'sch_y': 12.319038514 } } ], 'params': { 'MC_uniformity_width': 0.0, 'MC_uniformity_thickness': 0.0, 'MC_resolution_x': 100.0, 'MC_resolution_y': 100.0, 'MC_grid': 1e-05, 'MC_non_uniform': 99.0 } } ], 'analyses': [ { 'definition': { 'input_unit': 'wavelength', 'input_parameter': 'start_and_stop' }, 'params': { 'minimum_loss': 80.0, 'analysis_type': 'scattering_data', 'multithreading': 'user_defined', 'number_of_threads': 1.0, 'orthogonal_identifier': 1.0, 'start': 1.5e-06, 'stop': 1.6e-06, 'number_of_points': 2000.0, 'input': ['MZI4,ebeam_gc_te1550_detector2'], 'output': 'MZI4,ebeam_gc_te1550_laser1' } } ] } top_result = { 'circuits': [ { 'name': 'top', 'ports': ['ebeam_gc_te1550_laser1', 'ebeam_gc_te1550_detector2', 'ebeam_gc_te1550_detector4', 'ebeam_gc_te1550_detector3'], 'subcircuits': 'top', 'params': [ {'name': 'sch_x', 'value': -1.0}, {'name': 'sch_y', 'value': -1.0} ] } ], 'subcircuits': [ { 'name': 'top', 'ports': ['ebeam_gc_te1550_laser1', 'ebeam_gc_te1550_detector2', 'ebeam_gc_te1550_detector4', 'ebeam_gc_te1550_detector3'], 'components': [ { 'name': 'ebeam_dc_te1550_0', 'model': 'ebeam_dc_te1550', 'ports': ['N$0', 'N$1', 'N$3', 'N$2'], 'params': {'library': 'Design kits/ebeam', 'wg_width': 5e-07, 'gap': 2e-07, 'radius': 5e-06, 'Lc': 1.5e-05, 'lay_x': 2.36e-06, 'lay_y': 1.2e-07, 'sch_x': 0.082235221, 'sch_y': 0.004181452} }, { 'name': 'ebeam_gc_te1550_1', 'model': 'ebeam_gc_te1550', 'ports': ['ebeam_gc_te1550_laser1', 'N$4'], 'params': {'library': 'Design kits/ebeam', 'lay_x': -0.00013533, 'lay_y': 1.475e-05, 'sch_x': -4.715632378, 'sch_y': 0.513970129} }, { 'name': 'ebeam_gc_te1550_2', 'model': 'ebeam_gc_te1550', 'ports': ['ebeam_gc_te1550_detector2', 'N$5'], 'params': {'library': 'Design kits/ebeam', 'lay_x': -0.00012984, 'lay_y': -7.662e-05, 'sch_x': -4.524330954, 'sch_y': -2.669857037} }, { 'name': 'ebeam_gc_te1550_3', 'model': 'ebeam_gc_te1550', 'ports': ['ebeam_gc_te1550_detector4', 'N$6'], 'params': {'library': 'Design kits/ebeam', 'lay_x': 9.456e-05, 'lay_y': -8.471e-05, 'sch_x': 3.294984096, 'sch_y': -2.951756586, 'sch_r': 180.0} }, { 'name': 'ebeam_gc_te1550_4', 'model': 'ebeam_gc_te1550', 'ports': ['ebeam_gc_te1550_detector3', 'N$7'], 'params': {'library': 'Design kits/ebeam', 'lay_x': 0.00013005, 'lay_y': 3.253e-05, 'sch_x': 4.531648495, 'sch_y': 1.133521919, 'sch_r': 180.0} }, { 'name': 'ebeam_wg_integral_1550_5', 'model': 'ebeam_wg_integral_1550', 'ports': ['N$0', 'N$5'], 'params': {'library': 'Design kits/ebeam', 'wg_length': 0.000173487, 'wg_width': 5e-07, 'points': '[[-11.14,-2.23],[-40.45,-2.23],[-40.45,-76.62],[-113.34,-76.62]]', 'radius': 5.0, 'lay_x': -6.224e-05, 'lay_y': -3.9425e-05, 'sch_x': -2.168779718, 'sch_y': -1.373781176} }, { 'name': 'ebeam_wg_integral_1550_6', 'model': 'ebeam_wg_integral_1550', 'ports': ['N$4', 'N$1'], 'params': {'library': 'Design kits/ebeam', 'wg_length': 0.000116867, 'wg_width': 5e-07, 'points': '[[-118.83,14.75],[-26.47,14.75],[-26.47,2.47],[-11.14,2.47]]', 'radius': 5.0, 'lay_x': -6.4985e-05, 'lay_y': 8.61e-06, 'sch_x': -2.26443043, 'sch_y': 0.300019174} }, { 'name': 'ebeam_wg_integral_1550_7', 'model': 'ebeam_wg_integral_1550', 'ports': ['N$8', 'N$2'], 'params': {'library': 'Design kits/ebeam', 'wg_length': 7.4217e-05, 'wg_width': 5e-07, 'points': '[[65.87,29.78],[36.16,29.78],[36.16,2.47],[15.86,2.47]]', 'radius': 5.0, 'lay_x': 4.0865e-05, 'lay_y': 1.6125e-05, 'sch_x': 1.423958598, 'sch_y': 0.561882599} }, { 'name': 'ebeam_wg_integral_1550_8', 'model': 'ebeam_wg_integral_1550', 'ports': ['N$3', 'N$6'], 'params': {'library': 'Design kits/ebeam', 'wg_length': 0.000141577, 'wg_width': 5e-07, 'points': '[[15.86,-2.23],[35.04,-2.23],[35.04,-84.71],[78.06,-84.71]]', 'radius': 5.0, 'lay_x': 4.696e-05, 'lay_y': -4.347000000000001e-05, 'sch_x': 1.636341509, 'sch_y': -1.51473095} }, { 'name': 'ebeam_y_1550_9', 'model': 'ebeam_y_1550', 'ports': ['N$8', 'N$10', 'N$9'], 'params': {'library': 'Design kits/ebeam', 'lay_x': 7.327e-05, 'lay_y': 2.978e-05, 'sch_x': 2.553124838, 'sch_y': 1.037696979} }, { 'name': 'ebeam_terminator_te1550_10', 'model': 'ebeam_terminator_te1550', 'ports': ['N$11'], 'params': {'library': 'Design kits/ebeam', 'lay_x': 9.14e-05, 'lay_y': 2.7e-07, 'sch_x': 3.184872529, 'sch_y': 0.009408267, 'sch_r': 270.0} }, { 'name': 'ebeam_wg_integral_1550_11', 'model': 'ebeam_wg_integral_1550', 'ports': ['N$9', 'N$11'], 'params': {'library': 'Design kits/ebeam', 'wg_length': 3.0488e-05, 'wg_width': 5e-07, 'points': '[[80.67,27.03],[91.4,27.03],[91.4,5.72]]', 'radius': 5.0, 'lay_x': 8.641e-05, 'lay_y': 1.675e-05, 'sch_x': 3.010993821, 'sch_y': 0.5836609940000002} }, { 'name': 'ebeam_wg_integral_1550_12', 'model': 'ebeam_wg_integral_1550', 'ports': ['N$10', 'N$7'], 'params': { 'library': 'Design kits/ebeam', 'wg_length': 3.288e-05, 'wg_width': 5e-07, 'points': '[[80.67,32.53],[113.55,32.53]]', 'radius': 5.0, 'lay_x': 9.711e-05, 'lay_y': 3.253e-05, 'sch_x': 3.383839949, 'sch_y': 1.133521919 } } ], 'params': { 'MC_uniformity_width': 0.0, 'MC_uniformity_thickness': 0.0, 'MC_resolution_x': 100.0, 'MC_resolution_y': 100.0, 'MC_grid': 1e-05, 'MC_non_uniform': 99.0 } } ], 'analyses': [ { 'definition': { 'input_unit': 'wavelength', 'input_parameter': 'start_and_stop' }, 'params': { 'minimum_loss': 80.0, 'analysis_type': 'scattering_data', 'multithreading': 'user_defined', 'number_of_threads': 1.0, 'orthogonal_identifier': 1.0, 'start': 1.5e-06, 'stop': 1.6e-06, 'number_of_points': 2000.0, 'input': ['top,ebeam_gc_te1550_detector2', 'top,ebeam_gc_te1550_detector3', 'top,ebeam_gc_te1550_detector4'], 'output': 'top,ebeam_gc_te1550_laser1' } } ] } def test_EBeam_sequoiap_A_v2(): filename = os.path.join(os.path.dirname(__file__), 'spice', 'EBeam_sequoiap_A_v2', 'EBeam_sequoiap_A_v2_main.spi') res = load_spi(filename) assert res == EBeam_sequoiap_A_v2_result def test_MZI4(): filename = os.path.join(os.path.dirname(__file__), 'spice', 'MZI4', 'MZI4_main.spi') res = load_spi(filename) assert res == MZI4_result def test_top(): filename =
"resource_field_ref") @property @pulumi.getter(name="secretKeyRef") def secret_key_ref(self) -> Optional['outputs.SeldonDeploymentSpecPredictorsSvcOrchSpecEnvValueFromSecretKeyRef']: """ Selects a key of a secret in the pod's namespace """ return pulumi.get(self, "secret_key_ref") def _translate_property(self, prop): return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop @pulumi.output_type class SeldonDeploymentSpecPredictorsSvcOrchSpecEnvValueFromConfigMapKeyRef(dict): """ Selects a key of a ConfigMap. """ def __init__(__self__, , key: str, name: Optional[str] = None, optional: Optional[bool] = None): """ Selects a key of a ConfigMap. :param str key: The key to select. :param str name: Name of the referent. More info: https://kubernetes.io/docs/concepts/overview/working-with-objects/names/#names TODO: Add other useful fields. apiVersion, kind, uid? :param bool optional: Specify whether the ConfigMap or its key must be defined """ pulumi.set(__self__, "key", key) if name is not None: pulumi.set(__self__, "name", name) if optional is not None: pulumi.set(__self__, "optional", optional) @property @pulumi.getter def key(self) -> str: """ The key to select. """ return pulumi.get(self, "key") @property @pulumi.getter def name(self) -> Optional[str]: """ Name of the referent. More info: https://kubernetes.io/docs/concepts/overview/working-with-objects/names/#names TODO: Add other useful fields. apiVersion, kind, uid? """ return pulumi.get(self, "name") @property @pulumi.getter def optional(self) -> Optional[bool]: """ Specify whether the ConfigMap or its key must be defined """ return pulumi.get(self, "optional") def _translate_property(self, prop): return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop @pulumi.output_type class SeldonDeploymentSpecPredictorsSvcOrchSpecEnvValueFromFieldRef(dict): """ Selects a field of the pod: supports metadata.name, metadata.namespace, metadata.labels, metadata.annotations, spec.nodeName, spec.serviceAccountName, status.hostIP, status.podIP, status.podIPs. """ def __init__(__self__, , field_path: str, api_version: Optional[str] = None): """ Selects a field of the pod: supports metadata.name, metadata.namespace, metadata.labels, metadata.annotations, spec.nodeName, spec.serviceAccountName, status.hostIP, status.podIP, status.podIPs. :param str field_path: Path of the field to select in the specified API version. :param str api_version: Version of the schema the FieldPath is written in terms of, defaults to "v1". """ pulumi.set(__self__, "field_path", field_path) if api_version is not None: pulumi.set(__self__, "api_version", api_version) @property @pulumi.getter(name="fieldPath") def field_path(self) -> str: """ Path of the field to select in the specified API version. """ return pulumi.get(self, "field_path") @property @pulumi.getter(name="apiVersion") def api_version(self) -> Optional[str]: """ Version of the schema the FieldPath is written in terms of, defaults to "v1". """ return pulumi.get(self, "api_version") def _translate_property(self, prop): return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop @pulumi.output_type class SeldonDeploymentSpecPredictorsSvcOrchSpecEnvValueFromResourceFieldRef(dict): """ Selects a resource of the container: only resources limits and requests (limits.cpu, limits.memory, limits.ephemeral-storage, requests.cpu, requests.memory and requests.ephemeral-storage) are currently supported. """ def __init__(__self__, , resource: str, container_name: Optional[str] = None, divisor: Optional['outputs.SeldonDeploymentSpecPredictorsSvcOrchSpecEnvValueFromResourceFieldRefDivisor'] = None): """ Selects a resource of the container: only resources limits and requests (limits.cpu, limits.memory, limits.ephemeral-storage, requests.cpu, requests.memory and requests.ephemeral-storage) are currently supported. :param str resource: Required: resource to select :param str container_name: Container name: required for volumes, optional for env vars :param 'SeldonDeploymentSpecPredictorsSvcOrchSpecEnvValueFromResourceFieldRefDivisorArgs' divisor: Specifies the output format of the exposed resources, defaults to "1" """ pulumi.set(__self__, "resource", resource) if container_name is not None: pulumi.set(__self__, "container_name", container_name) if divisor is not None: pulumi.set(__self__, "divisor", divisor) @property @pulumi.getter def resource(self) -> str: """ Required: resource to select """ return pulumi.get(self, "resource") @property @pulumi.getter(name="containerName") def container_name(self) -> Optional[str]: """ Container name: required for volumes, optional for env vars """ return pulumi.get(self, "container_name") @property @pulumi.getter def divisor(self) -> Optional['outputs.SeldonDeploymentSpecPredictorsSvcOrchSpecEnvValueFromResourceFieldRefDivisor']: """ Specifies the output format of the exposed resources, defaults to "1" """ return pulumi.get(self, "divisor") def _translate_property(self, prop): return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop @pulumi.output_type class SeldonDeploymentSpecPredictorsSvcOrchSpecEnvValueFromResourceFieldRefDivisor(dict): def __init__(__self__): pass def _translate_property(self, prop): return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop @pulumi.output_type class SeldonDeploymentSpecPredictorsSvcOrchSpecEnvValueFromSecretKeyRef(dict): """ Selects a key of a secret in the pod's namespace """ def __init__(__self__, , key: str, name: Optional[str] = None, optional: Optional[bool] = None): """ Selects a key of a secret in the pod's namespace :param str key: The key of the secret to select from. Must be a valid secret key. :param str name: Name of the referent. More info: https://kubernetes.io/docs/concepts/overview/working-with-objects/names/#names TODO: Add other useful fields. apiVersion, kind, uid? :param bool optional: Specify whether the Secret or its key must be defined """ pulumi.set(__self__, "key", key) if name is not None: pulumi.set(__self__, "name", name) if optional is not None: pulumi.set(__self__, "optional", optional) @property @pulumi.getter def key(self) -> str: """ The key of the secret to select from. Must be a valid secret key. """ return pulumi.get(self, "key") @property @pulumi.getter def name(self) -> Optional[str]: """ Name of the referent. More info: https://kubernetes.io/docs/concepts/overview/working-with-objects/names/#names TODO: Add other useful fields. apiVersion, kind, uid? """ return pulumi.get(self, "name") @property @pulumi.getter def optional(self) -> Optional[bool]: """ Specify whether the Secret or its key must be defined """ return pulumi.get(self, "optional") def _translate_property(self, prop): return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop @pulumi.output_type class SeldonDeploymentSpecPredictorsSvcOrchSpecResources(dict): """ ResourceRequirements describes the compute resource requirements. """ def __init__(__self__, , limits: Optional[Mapping[str, 'outputs.SeldonDeploymentSpecPredictorsSvcOrchSpecResourcesLimits']] = None, requests: Optional[Mapping[str, 'outputs.SeldonDeploymentSpecPredictorsSvcOrchSpecResourcesRequests']] = None): """ ResourceRequirements describes the compute resource requirements. :param Mapping[str, 'SeldonDeploymentSpecPredictorsSvcOrchSpecResourcesLimitsArgs'] limits: Limits describes the maximum amount of compute resources allowed. More info: https://kubernetes.io/docs/concepts/configuration/manage-compute-resources-container/ :param Mapping[str, 'SeldonDeploymentSpecPredictorsSvcOrchSpecResourcesRequestsArgs'] requests: Requests describes the minimum amount of compute resources required. If Requests is omitted for a container, it defaults to Limits if that is explicitly specified, otherwise to an implementation-defined value. More info: https://kubernetes.io/docs/concepts/configuration/manage-compute-resources-container/ """ if limits is not None: pulumi.set(__self__, "limits", limits) if requests is not None: pulumi.set(__self__, "requests", requests) @property @pulumi.getter def limits(self) -> Optional[Mapping[str, 'outputs.SeldonDeploymentSpecPredictorsSvcOrchSpecResourcesLimits']]: """ Limits describes the maximum amount of compute resources allowed. More info: https://kubernetes.io/docs/concepts/configuration/manage-compute-resources-container/ """ return pulumi.get(self, "limits") @property @pulumi.getter def requests(self) -> Optional[Mapping[str, 'outputs.SeldonDeploymentSpecPredictorsSvcOrchSpecResourcesRequests']]: """ Requests describes the minimum amount of compute resources required. If Requests is omitted for a container, it defaults to Limits if that is explicitly specified, otherwise to an implementation-defined value. More info: https://kubernetes.io/docs/concepts/configuration/manage-compute-resources-container/ """ return pulumi.get(self, "requests") def _translate_property(self, prop): return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop @pulumi.output_type class SeldonDeploymentSpecPredictorsSvcOrchSpecResourcesLimits(dict): def __init__(__self__): pass def _translate_property(self, prop): return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop @pulumi.output_type class SeldonDeploymentSpecPredictorsSvcOrchSpecResourcesRequests(dict): def __init__(__self__): pass def _translate_property(self, prop): return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop @pulumi.output_type class SeldonDeploymentStatus(dict): """ SeldonDeploymentStatus defines the observed state of SeldonDeployment """ def __init__(__self__, , address: Optional['outputs.SeldonDeploymentStatusAddress'] = None, deployment_status: Optional[Mapping[str, 'outputs.SeldonDeploymentStatusDeploymentStatus']] = None, description: Optional[str] = None, replicas: Optional[int] = None, service_status: Optional[Mapping[str, 'outputs.SeldonDeploymentStatusServiceStatus']] = None, state: Optional[str] = None): """ SeldonDeploymentStatus defines the observed state of SeldonDeployment :param 'SeldonDeploymentStatusAddressArgs' address: Addressable placeholder until duckv1 issue is fixed: https://github.com/kubernetes-sigs/controller-tools/issues/391 """ if address is not None: pulumi.set(__self__, "address", address) if deployment_status is not None: pulumi.set(__self__, "deployment_status", deployment_status) if description is not None: pulumi.set(__self__, "description", description) if replicas is not None: pulumi.set(__self__, "replicas", replicas) if service_status is not None: pulumi.set(__self__, "service_status", service_status) if state is not None: pulumi.set(__self__, "state", state) @property @pulumi.getter def address(self) -> Optional['outputs.SeldonDeploymentStatusAddress']: """ Addressable placeholder until duckv1 issue is fixed: https://github.com/kubernetes-sigs/controller-tools/issues/391 """ return pulumi.get(self, "address") @property @pulumi.getter(name="deploymentStatus") def deployment_status(self) -> Optional[Mapping[str, 'outputs.SeldonDeploymentStatusDeploymentStatus']]: return pulumi.get(self, "deployment_status") @property @pulumi.getter def description(self) -> Optional[str]: return pulumi.get(self, "description") @property @pulumi.getter def replicas(self) -> Optional[int]: return pulumi.get(self, "replicas") @property @pulumi.getter(name="serviceStatus") def service_status(self) -> Optional[Mapping[str, 'outputs.SeldonDeploymentStatusServiceStatus']]: return pulumi.get(self, "service_status") @property @pulumi.getter def state(self) -> Optional[str]: return pulumi.get(self, "state") def _translate_property(self, prop): return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop @pulumi.output_type class SeldonDeploymentStatusAddress(dict): """ Addressable placeholder until duckv1 issue is fixed: https://github.com/kubernetes-sigs/controller-tools/issues/391 """ def __init__(__self__, , url: Optional[str] = None): """ Addressable placeholder until duckv1 issue is fixed: https://github.com/kubernetes-sigs/controller-tools/issues/391 """ if url is not None: pulumi.set(__self__, "url", url) @property @pulumi.getter def url(self) -> Optional[str]: return pulumi.get(self, "url") def _translate_property(self, prop): return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop @pulumi.output_type class SeldonDeploymentStatusDeploymentStatus(dict): def __init__(__self__, , available_replicas: Optional[int] = None, description: Optional[str] = None, explainer_for: Optional[str] = None, name: Optional[str] = None, replicas: Optional[int] = None, status: Optional[str] = None): if available_replicas is not None: pulumi.set(__self__, "available_replicas", available_replicas) if description is not None: pulumi.set(__self__, "description", description) if explainer_for is not None: pulumi.set(__self__, "explainer_for", explainer_for) if name is not None: pulumi.set(__self__, "name", name) if replicas is not None: pulumi.set(__self__, "replicas", replicas) if status is not None: pulumi.set(__self__, "status", status) @property @pulumi.getter(name="availableReplicas") def available_replicas(self) -> Optional[int]: return pulumi.get(self, "available_replicas") @property @pulumi.getter def description(self) -> Optional[str]: return pulumi.get(self, "description") @property @pulumi.getter(name="explainerFor") def explainer_for(self) -> Optional[str]: return pulumi.get(self, "explainer_for") @property @pulumi.getter def name(self) -> Optional[str]: return pulumi.get(self, "name") @property @pulumi.getter def replicas(self) -> Optional[int]: return pulumi.get(self, "replicas") @property @pulumi.getter def status(self) -> Optional[str]: return pulumi.get(self, "status") def _translate_property(self, prop): return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop @pulumi.output_type class SeldonDeploymentStatusServiceStatus(dict): def __init__(__self__, *, explainer_for: Optional[str] = None, grpc_endpoint: Optional[str] = None, http_endpoint: Optional[str] = None,
# importando a biblioteca PySimpleGUI para a interface import PySimpleGUI as sg # Procurando brechas sobre como os usuários podem responder as perguntas. yes = ["S", "s", "sim"] no = ["N", "n", "nao", "não"] # Objetos espada = 0 flor = 0 # Primeira janela def janela_inicial(): sg.theme('Reddit') layout = [ [sg.Text('Seja bem vindo!')], [sg.Text('')], [sg.Text('Você está a entrar em uma aventura.')], [sg.Text('')], [sg.Text('Clique em ok para continuar')], [sg.Button('Sair'), sg.Button('Ok')] ] return sg.Window("Vamos ??", layout=layout, finalize=True, size=(300, 180)) # segunda janela def janela_comeco(): sg.theme('Reddit') layout = [ [sg.Text("Você é um plebeu e, em um dia, decide sair com seus amigos.\nVocês vão na taverna “O Bode Dourado”, na cidade de Odin.\nEsta cidade é um movimentado entreposto comercial a\nmeio caminho de todas as principais rotas mercantis\nentre os reinos do leste")], [sg.Text('...')], [sg.Text("Depois de uma noite de bebedeira com amigos,\nvocê acorda na manhã seguinte\nem uma floresta densa e úmida. " "\nCabeça girando e lutando contra a vontade de vomitar,\nvocê se levanta e se maravilha com o seu novo ambiente desconhecido. " "\nA paz desaparece rapidamente quando você ouve um som grotesco \nemitido atrás de você.\nUm Orc babando está correndo em sua direção. Você irá:")], [sg.Text("Pegue uma pedra próxima e jogue-a no Orc "), sg.Button('A')], [sg.Text("Deite-se e espere ser atacado. "), sg.Button('B')], [sg.Text("Correr! "), sg.Button('C')] ] return sg.Window("A história começa", layout=layout, finalize=True, size=(450, 400)) # escolha jogar pedra def janela_choicePedra(): sg.theme("Reddit") layout = [ [sg.Text("\nO Orc fica atordoado, mas recupera o controle.\nEle começa a correr em sua direção novamente. Você vai: ")], [sg.Text('Esconder-se atrás da rocha '), sg.Button('A')], [sg.Text('Jogar outra pedra'), sg.Button('B')], [sg.Text('Correr para uma caverna próxima'), sg.Button('C')] ] return sg.Window("Uma pedra ??", layout=layout, finalize=True) # escolha deitar e esperar ser atacado def janela_choiceDeitar(): sg.theme("Reddit") layout = [ [sg.Text('Essa foi rápida...\nVocê morreu')], [sg.Text('')], [sg.Button('Recomeçar??'), sg.Button('Sair..')] ] return sg.Window("Rápido como o vento", layout=layout, finalize=True, size=(300, 100)) # escolha correr def janela_choiceCorrer(): sg.theme("Reddit") layout = [ [sg.Text("\nVocê corre o mais rápido possível,\nmas a " "a velocidade do Orc é muito grande. Você irá: ")], [sg.Text('Esconder-se atrás da rocha '), sg.Button('A')], [sg.Text('Fazer uma armadilha, e então lutar'), sg.Button('B')], [sg.Text('Corra em direção a um cidade'), sg.Button('C')] ] return sg.Window("The Flash ?", layout=layout, finalize=True) # esxolha de jogar a pedra e acabar morrendo def janela_choicePedraF(): sg.theme("Reddit") layout = [ [sg.Text("\nVocê decide jogar outra pedra. " " A primeira pedra não deu muito dano.\nA pedra ricocheteou na cabeça e você erra. \nVocê morreu! ")], [sg.Text('')], [sg.Button('Recomeçar??'), sg.Button('Sair..')] ] return sg.Window("Não foi dessa vez", layout=layout, finalize=True) # escolha da caverna def janela_choiceCaverna(): sg.theme("Reddit") layout = [ [sg.Text("\nVocê estava hesitante, já que a caverna estava escura e sinistra. " " \nAntes de entrar totalmente, você nota você nota um báu parcialmente enterrado no chão. " " \nAo abrir, você encontra uma espada cintilante. Deseja pega-lá? Sim / Não? ")], [sg.Text('')], [sg.Input(key='resposta')], [sg.Button('Ok')] ] return sg.Window("A caverna pegar ou largar ", layout=layout, finalize=True) # escolha depois de pegar a espada def janela_choiceEspada(): sg.theme("Reddit") layout = [ [sg.Text( "\n O Orc continua a te perseguir, você está em apuros. O que fará em seguida? ")], [sg.Text('Se esconder em silêncio'), sg.Button('A')], [sg.Text('Lutar'), sg.Button('B')], [sg.Text('Correr'), sg.Button('C')] ] return sg.Window("E agora ??", layout=layout, finalize=True) # escolha de lutar após pegar a espada def janela_choiceEspada1(): sg.theme("Reddit") layout = [ [sg.Text("\nVocê ficou esperando. A espada cintilante atraiu " "o Orc,\nque pensou que você não era páreo.\nEnquanto ele caminhava " "cada vez mais perto,\nseu coração bate mais rápido. Como o Orc " "estendeu a mão para agarrar a espada,\nvocê perfurou a lâmina em " "seu peito. \n \n Você sobreviveu! ")], [sg.Button('Recomeçar??'), sg.Button('Sair..')] ] return sg.Window("Grande Final", layout=layout, finalize=True) # escolha de lutar sem ter pego a espada def janela_choiceEspada0(): sg.theme("Reddit") layout = [ [sg.Text( "\n Você deveria ter pegado aquela espada.\nVocê está indefeso. \n Você morreu! ")], [sg.Button('Recomeçar??'), sg.Button('Sair..')] ] return sg.Window("Por uma espada..", layout=layout, finalize=True) # escolha de ficar no escuro def janela_choiceEscuro(): sg.theme("Reddit") layout = [ [sg.Text("\nMesmo? Você vai se esconder no escuro?\nEu penso que " "Orcs podem ver muito bem no escuro, certo?\nNão tenho certeza, mas " "Vou com SIM, então ... \n Você morreu! ")], [sg.Button('Recomeçar??'), sg.Button('Sair..')] ] return sg.Window("Não muito inteligente ", layout=layout, finalize=True) # escolha de se esconder atrás da rocha def janela_choiceRocha(): sg.theme("Reddit") layout = [ [sg.Text('Você foi facilmente visto')], [sg.Text('Você morreu!')], [sg.Button('Recomeçar??'), sg.Button('Sair..')] ] return sg.Window("Não foi dessa vez", layout=layout, finalize=True, size=(270, 100)) # escolha de fazer armadilha def janela_choiceArmadilha(): sg.theme("Reddit") layout = [ [sg.Text('Você não é pareo para o orc')], [sg.Text('Você morreu')], [sg.Button('Recomeçar??'), sg.Button('Sair..')] ] return sg.Window("Não foi dessa vez", layout=layout, finalize=True, size=(270, 100)) # escolha de ir para cidade def janela_choiceCidade(): sg.theme("Reddit") layout = [ [sg.Text( "Você tenta acalmar sua respiração pesada enquanto se esconde " "\natrás de um edifício delapitado, esperando o Orc chegar " "\ncorrendo na esquina. Você nota uma flor roxa " "\nperto do seu pé. Você a pega? Sim / Não?")], [sg.Input(key='resposta')], [sg.Button('Ok')] ] return sg.Window("A cidade", layout=layout, finalize=True) # escolha de pegar a flor def janela_choiceFlor1(): sg.theme("Reddit") layout = [ [sg.Text("\nVocê rapidamente pega a flor roxa, de alguma forma " "\nesperando que isso pare o Orc. Pare! \nO Orc estava apenas procurando " "por amor. " "\nIsso foi bem estranho... Mas você sobreviveu! ")], [sg.Button('Recomeçar??'), sg.Button('Sair..')] ] return sg.Window("You win!!", layout=layout, finalize=True) # escolha de não pegar a flor def janela_choiceFlor0(): sg.theme("Reddit") layout = [ [sg.Text("\nTalvez você devesse ter pego a flor... " "\nVocê morre esmagado pelo Orc. ")], [sg.Button('Recomeçar??'), sg.Button('Sair..')] ] return sg.Window("You lose..", layout=layout, finalize=True) # definindo o valor das variaveis janela1, janela2 = janela_inicial(), None janela3 = None janela4 = None janela5 = None janela4 = None janela6 = None janelaX = None janelaC = None janelaAA = None # utilizando listas para definir o valor das variaveis yes = ["S", "s", "sim", "Sim"] no = ["N", "n", "nao", "não"] # estrutura de repetição while True: # definindo para as variaveis serem lidas em todas as janelas / verificando condições e escolhas window, event, values = sg.read_all_windows() # Escolha página inicial if window == janela1 and event == sg.WIN_CLOSED: break if window == janela1 and event == 'Sair': break if window == janela1 and event == 'Ok': janela2 = janela_comeco() janela1.hide() # inicio da história / segunda janela if window == janela2 and event == sg.WIN_CLOSED: break if window == janela2 and event == 'A': janelaAA = janela_choicePedra() janela2.hide() if window == janela2 and event == 'B': janela3 = janela_choiceDeitar() janela2.hide() if window == janela2 and event == 'C': janela3 = janela_choiceCorrer() janela2.hide() # janela choice A if window == janelaAA and event == sg.WIN_CLOSED: break if window == janelaAA and event == 'A': janela4 = janela_choiceRocha() janelaAA.hide() if window == janelaAA and event == 'B': janela4 = janela_choicePedraF() janelaAA.hide() if window == janelaAA and event == 'C': janela4 = janela_choiceCaverna() janelaAA.hide() # janela 4 if window == janela3 and event == sg.WIN_CLOSED: break if window == janela3 and event == 'A': janela5 = janela_choiceRocha() janela3.hide() if window == janela3 and event == 'B': janela5 = janela_choiceArmadilha() janela3.hide() if window == janela3 and event == 'C': janelaC = janela_choiceCidade() janela3.hide() #morte choice rocha c -> a if window == janela5 and event == sg.WIN_CLOSED: break if window == janela5 and event == ('Recomeçar??'): janela1 = janela_inicial() janela3.hide() if window == janela5 and event == ('Sair..'): break # morte choice rocha if window == janela4 and event == sg.WIN_CLOSED: break if window == janela4 and event == ('Recomeçar??'): janela1 = janela_inicial() janela4.hide() if window == janela4 and event == ('Sair..'): break # choice cidade if window == janelaC and event == sg.WIN_CLOSED: break if event == 'Ok': if window == janelaC and values['resposta'] == yes[0]: janela6 = janela_choiceFlor1() janelaC.hide() flor = 1 # adiciona a flor a seu inventario if window == janelaC and values['resposta'] == yes[1]: janela6 = janela_choiceFlor1() janelaC.hide() flor = 1 if window == janelaC and values['resposta'] == yes[2]: janela6 = janela_choiceFlor1() janelaC.hide() flor = 1 if window == janelaC and values['resposta'] == yes[3]: janela6 = janela_choiceFlor1() janelaC.hide() flor = 1 if window == janelaC and values['resposta'] == no[0]: flor = 0 janela6 = janela_choiceFlor0() janelaC.hide() if window == janelaC and
+ m.x94 == 0) m.c49 = Constraint(expr= - 40m.x24 + m.x96 == 0) m.c50 = Constraint(expr= - 40m.x25 + m.x98 == 0) m.c51 = Constraint(expr= - 40m.x26 + m.x100 == 0) m.c52 = Constraint(expr= - 40m.x2 + m.x53 == 0) m.c53 = Constraint(expr= - 40m.x3 + m.x55 == 0) m.c54 = Constraint(expr= - 40m.x4 + m.x57 == 0) m.c55 = Constraint(expr= - 40m.x5 + m.x59 == 0) m.c56 = Constraint(expr= - 40m.x6 + m.x61 == 0) m.c57 = Constraint(expr= - 40m.x7 + m.x63 == 0) m.c58 = Constraint(expr= - 40m.x8 + m.x65 == 0) m.c59 = Constraint(expr= - 40m.x9 + m.x67 == 0) m.c60 = Constraint(expr= - 40m.x10 + m.x69 == 0) m.c61 = Constraint(expr= - 40m.x11 + m.x71 == 0) m.c62 = Constraint(expr= - 40m.x12 + m.x73 == 0) m.c63 = Constraint(expr= - 40m.x13 + m.x75 == 0) m.c64 = Constraint(expr= - 40m.x14 + m.x77 == 0) m.c65 = Constraint(expr= - 40m.x15 + m.x79 == 0) m.c66 = Constraint(expr= - 40m.x16 + m.x81 == 0) m.c67 = Constraint(expr= - 40m.x17 + m.x83 == 0) m.c68 = Constraint(expr= - 40m.x18 + m.x85 == 0) m.c69 = Constraint(expr= - 40m.x19 + m.x87 == 0) m.c70 = Constraint(expr= - 40m.x20 + m.x89 == 0) m.c71 = Constraint(expr= - 40m.x21 + m.x91 == 0) m.c72 = Constraint(expr= - 40m.x22 + m.x93 == 0) m.c73 = Constraint(expr= - 40m.x23 + m.x95 == 0) m.c74 = Constraint(expr= - 40m.x24 + m.x97 == 0) m.c75 = Constraint(expr= - 40m.x25 + m.x99 == 0) m.c76 = Constraint(expr= - 40m.x26 + m.x101 == 0) m.c77 = Constraint(expr=m.x27(m.x130 - m.x127) - m.x102 == 0) m.c78 = Constraint(expr=m.x28(-0.574695771132691m.x127 - 0.287347885566345m.x128 + 0.766261028176921m.x129 + 0.574695771132691m.x136 + 0.287347885566345m.x137 - 0.766261028176921m.x138) - m.x103 == 0) m.c79 = Constraint(expr=m.x29(0.574695771132691m.x130 - 0.287347885566345m.x131 + 0.766261028176921m.x132 - 0.574695771132691m.x133 + 0.287347885566345m.x134 - 0.766261028176921m.x135) - m.x104 == 0) m.c80 = Constraint(expr=m.x30(0.287347885566345m.x128 - 0.574695771132691m.x127 + 0.766261028176921m.x129 + 0.574695771132691m.x139 - 0.287347885566345m.x140 - 0.766261028176921m.x141) - m.x105 == 0) m.c81 = Constraint(expr=m.x31(0.574695771132691m.x130 + 0.287347885566345m.x131 + 0.766261028176921m.x132 - 0.574695771132691m.x142 - 0.287347885566345m.x143 - 0.766261028176921m.x144) - m.x106 == 0) m.c82 = Constraint(expr=m.x32(0.936329177569045m.x132 - 0.351123441588392m.x131 + 0.351123441588392m.x137 - 0.936329177569045m.x138) - m.x107 == 0) m.c83 = Constraint(expr=m.x33(0.351123441588392m.x131 + 0.936329177569045m.x132 - 0.351123441588392m.x140 - 0.936329177569045m.x141) - m.x108 == 0) m.c84 = Constraint(expr=m.x34(0.936329177569045m.x129 - 0.351123441588392m.x128 + 0.351123441588392m.x134 - 0.936329177569045m.x135) - m.x109 == 0) m.c85 = Constraint(expr=m.x35(0.351123441588392m.x128 + 0.936329177569045m.x129 - 0.351123441588392m.x143 - 0.936329177569045m.x144) - m.x110 == 0) m.c86 = Constraint(expr=m.x36(m.x134 - m.x143) - m.x111 == 0) m.c87 = Constraint(expr=m.x37(m.x137 - m.x140) - m.x112 == 0) m.c88 = Constraint(expr=m.x38(m.x136 - m.x133) - m.x113 == 0) m.c89 = Constraint(expr=m.x39(m.x139 - m.x142) - m.x114 == 0) m.c90 = Constraint(expr=m.x40(0.345032779671177m.x133 + 0.75907211527659m.x134 + 0.552052447473883m.x135) - m.x115 == 0) m.c91 = Constraint(expr=m.x41(0.345032779671177m.x142 - 0.75907211527659m.x143 + 0.552052447473883m.x144) - m.x116 == 0) m.c92 = Constraint(expr=m.x42(0.75907211527659m.x137 - 0.345032779671177m.x136 + 0.552052447473883m.x138) - m.x117 == 0) m.c93 = Constraint(expr=m.x43(-0.345032779671177m.x139 - 0.75907211527659m.x140 + 0.552052447473883m.x141) - m.x118 == 0) m.c94 = Constraint(expr=m.x44(0.75907211527659m.x136 - 0.345032779671177m.x137 + 0.552052447473883m.x138) - m.x119 == 0) m.c95 = Constraint(expr=m.x45(-0.75907211527659m.x133 - 0.345032779671177m.x134 + 0.552052447473883m.x135) - m.x120 == 0) m.c96 = Constraint(expr=m.x46(0.75907211527659m.x139 + 0.345032779671177m.x140 + 0.552052447473883m.x141) - m.x121 == 0) m.c97 = Constraint(expr=m.x47(0.345032779671177m.x143 - 0.75907211527659m.x142 + 0.552052447473883m.x144) - m.x122 == 0) m.c98 = Constraint(expr=m.x48(0.468292905790847m.x142 + 0.468292905790847m.x143 + 0.749268649265355m.x144) - m.x123 == 0) m.c99 = Constraint(expr=m.x49(0.468292905790847m.x133 - 0.468292905790847m.x134 + 0.749268649265355m.x135) - m.x124 == 0) m.c100 = Constraint(expr=m.x50(-0.468292905790847m.x136 - 0.468292905790847m.x137 + 0.749268649265355m.x138) - m.x125 == 0) m.c101 = Constraint(expr=m.x51(0.468292905790847m.x140 - 0.468292905790847m.x139 + 0.749268649265355m.x141) - m.x126 == 0) m.c102 = Constraint(expr= - m.x102 - 0.574695771132691m.x103 - 0.574695771132691m.x105 == 1) m.c103 = Constraint(expr= - 0.287347885566345m.x103 + 0.287347885566345m.x105 - 0.351123441588392m.x109 + 0.351123441588392m.x110 == 10) m.c104 = Constraint(expr= 0.766261028176921m.x103 + 0.766261028176921m.x105 + 0.936329177569045m.x109 + 0.936329177569045m.x110 == -10) m.c105 = Constraint(expr= m.x102 + 0.574695771132691m.x104 + 0.574695771132691m.x106 == 0) m.c106 = Constraint(expr= - 0.287347885566345m.x104 + 0.287347885566345m.x106 - 0.351123441588392m.x107 + 0.351123441588392m.x108 == 10) m.c107 = Constraint(expr= 0.766261028176921m.x104 + 0.766261028176921m.x106 + 0.936329177569045m.x107 + 0.936329177569045m.x108 == -10) m.c108 = Constraint(expr= - 0.574695771132691m.x104 - m.x113 + 0.345032779671177m.x115 - 0.75907211527659m.x120 + 0.468292905790847m.x124 == 0.5) m.c109 = Constraint(expr= 0.287347885566345m.x104 + 0.351123441588392m.x109 + m.x111 + 0.75907211527659m.x115 - 0.345032779671177m.x120 - 0.468292905790847m.x124 == 0) m.c110 = Constraint(expr= - 0.766261028176921m.x104 - 0.936329177569045m.x109 + 0.552052447473883m.x115 + 0.552052447473883m.x120 + 0.749268649265355m.x124 == 0) m.c111 = Constraint(expr= 0.574695771132691m.x103 + m.x113 - 0.345032779671177m.x117 + 0.75907211527659m.x119 - 0.468292905790847m.x125 == 0) m.c112 = Constraint(expr= 0.287347885566345m.x103 + 0.351123441588392m.x107 + m.x112 + 0.75907211527659m.x117 - 0.345032779671177m.x119 - 0.468292905790847m.x125 == 0) m.c113 = Constraint(expr= - 0.766261028176921m.x103 - 0.936329177569045m.x107 + 0.552052447473883m.x117 + 0.552052447473883m.x119 + 0.749268649265355m.x125 == 0) m.c114 = Constraint(expr= 0.574695771132691m.x105 + m.x114 - 0.345032779671177m.x118 + 0.75907211527659m.x121 - 0.468292905790847m.x126 == 0) m.c115 = Constraint(expr= - 0.287347885566345m.x105 - 0.351123441588392m.x108 - m.x112 - 0.75907211527659m.x118 + 0.345032779671177m.x121 + 0.468292905790847m.x126 == 0) m.c116 = Constraint(expr= - 0.766261028176921m.x105 - 0.936329177569045m.x108 + 0.552052447473883m.x118 + 0.552052447473883m.x121 + 0.749268649265355m.x126 == 0) m.c117 = Constraint(expr= - 0.574695771132691m.x106 - m.x114 + 0.345032779671177m.x116 - 0.75907211527659m.x122 + 0.468292905790847m.x123 == 0.6) m.c118 = Constraint(expr= - 0.287347885566345m.x106 - 0.351123441588392m.x110 - m.x111 - 0.75907211527659m.x116 + 0.345032779671177m.x122 + 0.468292905790847m.x123 == 0) m.c119 = Constraint(expr= - 0.766261028176921m.x106 - 0.936329177569045m.x110 + 0.552052447473883m.x116 + 0.552052447473883m.x122 + 0.749268649265355m.x123 == 0) m.c120 = Constraint(expr= - m.x52 + m.x102 <= 0) m.c121 = Constraint(expr= - m.x53 - m.x102 <= 0) m.c122 = Constraint(expr= - m.x54 + m.x103 <= 0) m.c123 = Constraint(expr= - m.x55 - m.x103 <= 0) m.c124 = Constraint(expr= - m.x56 + m.x104 <= 0) m.c125 = Constraint(expr= - m.x57 - m.x104 <= 0) m.c126 = Constraint(expr= - m.x58 + m.x105 <= 0) m.c127 = Constraint(expr= - m.x59 - m.x105 <= 0) m.c128 = Constraint(expr= - m.x60 + m.x106 <= 0) m.c129 = Constraint(expr= - m.x61 - m.x106 <= 0) m.c130 = Constraint(expr= - m.x62 + m.x107 <= 0) m.c131 = Constraint(expr= - m.x63 - m.x107 <= 0) m.c132 = Constraint(expr= - m.x64 + m.x108 <= 0) m.c133 = Constraint(expr= - m.x65 - m.x108 <= 0) m.c134 = Constraint(expr= - m.x66 + m.x109 <= 0) m.c135 = Constraint(expr= - m.x67 - m.x109 <= 0) m.c136 = Constraint(expr= - m.x68 + m.x110 <= 0) m.c137 = Constraint(expr= - m.x69 - m.x110 <= 0) m.c138 = Constraint(expr= - m.x70 + m.x111 <= 0) m.c139 = Constraint(expr= - m.x71 - m.x111 <= 0) m.c140 = Constraint(expr= - m.x72 + m.x112 <= 0) m.c141 = Constraint(expr= - m.x73 - m.x112 <= 0) m.c142 = Constraint(expr= - m.x74 + m.x113 <= 0) m.c143 = Constraint(expr= - m.x75 - m.x113 <= 0) m.c144 = Constraint(expr= - m.x76 + m.x114 <= 0) m.c145 = Constraint(expr= - m.x77 - m.x114 <= 0) m.c146 = Constraint(expr= - m.x78 + m.x115 <= 0) m.c147 = Constraint(expr= - m.x79 - m.x115 <= 0) m.c148 = Constraint(expr= - m.x80 + m.x116 <= 0) m.c149 = Constraint(expr= - m.x81 - m.x116 <= 0) m.c150 = Constraint(expr= - m.x82 + m.x117 <= 0) m.c151 = Constraint(expr= - m.x83 - m.x117 <= 0) m.c152 = Constraint(expr= - m.x84 + m.x118 <= 0) m.c153 = Constraint(expr= - m.x85 - m.x118 <= 0) m.c154 = Constraint(expr= - m.x86 + m.x119 <= 0) m.c155 = Constraint(expr= - m.x87 - m.x119 <= 0) m.c156 = Constraint(expr= - m.x88 + m.x120 <= 0) m.c157 = Constraint(expr= - m.x89 - m.x120 <= 0) m.c158 = Constraint(expr= - m.x90 + m.x121 <= 0) m.c159 = Constraint(expr= - m.x91 - m.x121 <= 0) m.c160 = Constraint(expr= - m.x92 + m.x122 <= 0) m.c161 = Constraint(expr= - m.x93 - m.x122 <= 0) m.c162 = Constraint(expr= - m.x94 + m.x123 <= 0) m.c163 = Constraint(expr= - m.x95 - m.x123 <= 0) m.c164 = Constraint(expr= - m.x96 + m.x124 <= 0) m.c165 = Constraint(expr= - m.x97 - m.x124 <= 0) m.c166 = Constraint(expr= - m.x98 + m.x125 <= 0) m.c167 = Constraint(expr= - m.x99 - m.x125 <= 0) m.c168 = Constraint(expr= - m.x100 + m.x126 <= 0) m.c169 = Constraint(expr= - m.x101 - m.x126 <= 0) m.c170 = Constraint(expr= - m.x2 + 0.1m.b145 + 0.2m.b146 + 0.3m.b147 + 0.4m.b148 + 0.5m.b149 + 0.6m.b150 + 0.7m.b151 + 0.8m.b152 + 0.9m.b153 + m.b154 + 1.1m.b155 + 1.2m.b156 + 1.3m.b157 + 1.4m.b158 + 1.5m.b159 + 1.6m.b160 + 1.7m.b161 + 1.8m.b162 + 1.9m.b163 + 2m.b164 + 2.1m.b165 + 2.2m.b166 + 2.3m.b167 + 2.4m.b168 + 2.5m.b169 + 2.6m.b170 + 2.8m.b171 + 3m.b172 + 3.2m.b173 + 3.4m.b174 == 0) m.c171 = Constraint(expr= - m.x3 + 0.1m.b175 + 0.2m.b176 + 0.3m.b177 + 0.4m.b178 + 0.5m.b179 + 0.6m.b180 + 0.7m.b181 + 0.8m.b182 + 0.9m.b183 + m.b184 + 1.1m.b185 + 1.2m.b186 + 1.3m.b187 + 1.4m.b188 + 1.5m.b189 + 1.6m.b190 + 1.7m.b191 + 1.8m.b192 + 1.9m.b193 + 2m.b194 + 2.1m.b195 + 2.2m.b196 + 2.3m.b197 + 2.4m.b198 + 2.5m.b199 + 2.6m.b200 + 2.8m.b201 + 3m.b202 + 3.2m.b203 + 3.4*m.b204 == 0) m.c172 = Constraint(expr= -
<filename>scripts/addons/uvpackmaster2/panel_base.py<gh_stars>1-10 # ##### BEGIN GPL LICENSE BLOCK ##### # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License # as published by the Free Software Foundation; either version 2 # of the License, or (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to the Free Software Foundation, # Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. # # ##### END GPL LICENSE BLOCK ##### import multiprocessing from .prefs import get_prefs from .operator import * from .operator_islands import * from .operator_box import * from .operator_uv import * from .utils import * from .presets import * from .labels import UvpLabels from .register import UVP2_OT_SelectUvpEngine import bpy class UVP2_OT_SetRotStep(bpy.types.Operator): bl_idname = 'uvpackmaster2.set_rot_step' bl_label = 'Set Rotation Step' bl_description = "Set Rotation Step to one of the suggested values" rot_step : IntProperty( name='Rotation Step', description='', default=90) def execute(self, context): scene_props = context.scene.uvp2_props scene_props.rot_step = self.rot_step return {'FINISHED'} class UVP2_MT_SetRotStep(bpy.types.Menu): bl_idname = "UVP2_MT_SetRotStep" bl_label = "Set Rotation Step" STEPS = [1, 2, 3, 5, 6, 9, 10, 15, 18, 30, 45, 90, 180] def draw(self, context): layout = self.layout for step in self.STEPS: operator = layout.operator(UVP2_OT_SetRotStep.bl_idname, text=str(step)) operator.rot_step = step class UVP2_UL_DeviceList(bpy.types.UIList): def draw_item(self, context, layout, data, item, icon, active_data, active_propname, index): dev_name = str(item.name) row = layout.row() icon_id = 'NONE' if not item.supported: dev_name += ' ' + UvpLabels.FEATURE_NOT_SUPPORTED_MSG icon_id = UvpLabels.FEATURE_NOT_SUPPORTED_ICON row.label(text=dev_name, icon=icon_id) class UVP2_PT_Generic(bpy.types.Panel): def draw(self, context): self.prefs = get_prefs() self.scene_props = context.scene.uvp2_props self.draw_specific(context) def handle_prop(self, prop_name, supported, not_supported_msg, ui_elem): if supported: ui_elem.prop(self.scene_props, prop_name) else: ui_elem.enabled = False split = ui_elem.split(factor=0.4) col_s = split.column() col_s.prop(self.scene_props, prop_name) col_s = split.column() col_s.label(text=not_supported_msg) def handle_prop_enum(self, prop_name, prop_label, supported, not_supported_msg, ui_elem): prop_label_colon = prop_label + ':' if supported: ui_elem.label(text=prop_label_colon) else: split = ui_elem.split(factor=0.4) col_s = split.column() col_s.label(text=prop_label_colon) col_s = split.column() col_s.label(text=not_supported_msg) ui_elem.prop(self.scene_props, prop_name, text='') ui_elem.enabled = supported def messages_in_boxes(self, ui_elem, messages): for msg in messages: box = ui_elem.box() msg_split = split_by_chars(msg, 60) if len(msg_split) > 0: # box.separator() for msg_part in msg_split: box.label(text=msg_part) # box.separator() class UVP2_PT_MainBase(UVP2_PT_Generic): bl_idname = 'UVP2_PT_MainBase' bl_label = 'UVPackmaster2' bl_context = '' def draw_specific(self, context): layout = self.layout demo_suffix = " (DEMO)" if self.prefs.FEATURE_demo else '' row = layout.row() row.label(text=self.prefs.label_message) if not self.prefs.uvp_initialized: row.operator(UVP2_OT_UvpSetupHelp.bl_idname, icon='HELP', text='') row = layout.row() row2 = row.row() row2.enabled = False row2.prop(self.prefs, 'uvp_path') select_icon = 'FILEBROWSER' if is_blender28() else 'FILE_FOLDER' row.operator(UVP2_OT_SelectUvpEngine.bl_idname, icon=select_icon, text='') col = layout.column(align=True) col.separator() if in_debug_mode(): box = col.box() col2 = box.column(align=True) col2.label(text="Debug options:") row = col2.row(align=True) row.prop(self.prefs, "write_to_file") row = col2.row(align=True) row.prop(self.prefs, "wait_for_debugger") row = col2.row(align=True) row.prop(self.prefs, "seed") row = col2.row(align=True) row.prop(self.prefs, "test_param") col.separator() col.label(text="Engine operations:") row = col.row(align=True) row.enabled = self.prefs.FEATURE_overlap_check row.operator(UVP2_OT_OverlapCheckOperator.bl_idname) if not self.prefs.FEATURE_overlap_check: row.label(text=UvpLabels.FEATURE_NOT_SUPPORTED_MSG) col.operator(UVP2_OT_MeasureAreaOperator.bl_idname) # Validate operator row = col.row(align=True) row.enabled = self.prefs.FEATURE_validation row.operator(UVP2_OT_ValidateOperator.bl_idname, text=UVP2_OT_ValidateOperator.bl_label + demo_suffix) if not self.prefs.FEATURE_validation: row.label(text=UvpLabels.FEATURE_NOT_SUPPORTED_MSG) row = col.row(align=True) row.scale_y = 1.75 row.operator(UVP2_OT_PackOperator.bl_idname, text=UVP2_OT_PackOperator.bl_label + demo_suffix) col.label(text="Last operation status:") box = col.box() box.label(text=self.prefs['op_status'] if self.prefs['op_status'] != '' else '------') col.separator() if len(self.prefs['op_warnings']) > 0: row = col.row() row.label(text="WARNINGS:", icon=UvpLabels.FEATURE_NOT_SUPPORTED_ICON) self.messages_in_boxes(col, self.prefs['op_warnings']) col.separator() col.separator() col.label(text="Other operations:") row = col.row(align=True) row.operator(UVP2_OT_SplitOverlappingIslands.bl_idname) row = col.row(align=True) row.operator(UVP2_OT_UndoIslandSplit.bl_idname) row = col.row(align=True) row.operator(UVP2_OT_AdjustScaleToUnselected.bl_idname) if in_debug_mode(): row = col.row(align=True) row.operator(UVP2_OT_DebugIslands.bl_idname) col.separator() col.label(text='Option presets:') row = col.row(align=True) row.operator(UVP2_OT_SavePreset.bl_idname) row.operator(UVP2_OT_LoadPreset.bl_idname) row = col.row(align=True) row.operator(UVP2_OT_ResetToDefaults.bl_idname) class UVP2_PT_PackingDeviceBase(UVP2_PT_Generic): bl_label = 'Packing Devices' bl_context = '' bl_options = {'DEFAULT_CLOSED'} def draw_specific(self, context): layout = self.layout col = layout.column(align=True) col.template_list("UVP2_UL_DeviceList", "", self.prefs, "dev_array", self.prefs, "sel_dev_idx") box = col.box() box.label(text=UvpLabels.PACKING_DEVICE_WARNING) box.enabled = False # Multi device box = col.box() box.enabled = self.prefs.FEATURE_multi_device_pack row = box.row() self.handle_prop("multi_device_pack", self.prefs.FEATURE_multi_device_pack, UvpLabels.FEATURE_NOT_SUPPORTED_MSG, row) class UVP2_PT_BasicOptionsBase(UVP2_PT_Generic): bl_label = 'Basic Options' bl_context = '' def draw_specific(self, context): layout = self.layout col = layout.column(align=True) col.prop(self.prefs, "thread_count") col.prop(self.scene_props, "margin") col.prop(self.scene_props, "precision") # Rotation Resolution box = col.box() box.enabled = self.prefs.FEATURE_island_rotation row = box.row() # TODO: missing feature check row.prop(self.scene_props, "rot_enable") # box = col.box() row = box.row() row.enabled = self.scene_props.rot_enable row.prop(self.scene_props, "prerot_disable") row = col.row(align=True) row.enabled = self.scene_props.rot_enable split = row.split(factor=0.8, align=True) col_s = split.row(align=True) col_s.prop(self.scene_props, "rot_step") col_s = split.row(align=True) col_s.menu(UVP2_MT_SetRotStep.bl_idname, text='Set') # Pre validate pre_validate_name = UvpLabels.PRE_VALIDATE_NAME if self.prefs.FEATURE_demo: pre_validate_name += ' (DEMO)' box = col.box() box.enabled = self.prefs.FEATURE_validation row = box.row() self.handle_prop("pre_validate", self.prefs.FEATURE_validation, UvpLabels.FEATURE_NOT_SUPPORTED_MSG, row) class UVP2_PT_IslandRotStepBase(UVP2_PT_Generic): bl_label = 'Island Rotation Step' bl_context = '' bl_options = {'DEFAULT_CLOSED'} def draw_specific(self, context): layout = self.layout panel_enabled = True if not self.prefs.FEATURE_island_rotation_step: layout.label(text=UvpLabels.FEATURE_NOT_SUPPORTED_MSG) panel_enabled = False elif not self.scene_props.rot_enable: layout.label(text='Island rotations must be enabled to activate this panel', icon='ERROR') panel_enabled = False col = layout.column(align=True) col.enabled = panel_enabled box = col.box() row = box.row() row.prop(self.scene_props, "island_rot_step_enable") row.operator(UVP2_OT_IslandRotStepHelp.bl_idname, icon='HELP', text='') col2 = col.column(align=True) col2.enabled = self.scene_props.island_rot_step_enable row = col2.row(align=True) row.prop(self.scene_props, "island_rot_step") row = col2.row(align=True) row.operator(UVP2_OT_SetRotStepIslandParam.bl_idname) col2.separator() row = col2.row(align=True) row.operator(UVP2_OT_ResetRotStepIslandParam.bl_idname) row = col2.row(align=True) row.operator(UVP2_OT_ShowRotStepIslandParam.bl_idname) class UVP2_PT_ManualGroupingBase(UVP2_PT_Generic): bl_label = 'Manual Grouping' bl_context = '' bl_options = {'DEFAULT_CLOSED'} def draw_specific(self, context): layout = self.layout panel_enabled = True if not self.prefs.FEATURE_grouping: layout.label(text=UvpLabels.FEATURE_NOT_SUPPORTED_MSG) panel_enabled = False col = layout.column(align=True) col.enabled = panel_enabled container = col row = container.row(align=True) row.prop(self.scene_props, "manual_group_num") row.operator(UVP2_OT_ManualGroupingHelp.bl_idname, icon='HELP', text='') row = container.row(align=True) row.operator(UVP2_OT_SetManualGroupIslandParam.bl_idname) container.separator() container.label(text="Select islands assigned to a group:") row = container.row(align=True) op = row.operator(UVP2_OT_SelectManualGroupIslandParam.bl_idname, text="Select") op.select = True op = row.operator(UVP2_OT_SelectManualGroupIslandParam.bl_idname, text="Deselect") op.select = False # container.separator() # row = container.row(align=True) # row.operator(UVP2_OT_ResetManualGroupIslandParam.bl_idname) row = container.row(align=True) row.operator(UVP2_OT_ShowManualGroupIslandParam.bl_idname) class UVP2_PT_LockGroupsBase(UVP2_PT_Generic): bl_label = 'Lock Groups' bl_context = '' bl_options = {'DEFAULT_CLOSED'} def draw_specific(self, context): layout = self.layout panel_enabled = True if not self.prefs.FEATURE_lock_overlapping: layout.label(text=UvpLabels.FEATURE_NOT_SUPPORTED_MSG) panel_enabled = False col = layout.column(align=True) col.enabled = panel_enabled box = col.box() row = box.row() row.prop(self.scene_props, "lock_groups_enable") container = col.column(align=True) container.enabled = self.scene_props.lock_groups_enable row = container.row(align=True) row.prop(self.scene_props, "lock_group_num") row = container.row(align=True) row.operator(UVP2_OT_SetLockGroupIslandParam.bl_idname) row = container.row(align=True) row.operator(UVP2_OT_SetFreeLockGroupIslandParam.bl_idname) container.separator() container.label(text="Select islands assigned to a lock group:") row = container.row(align=True) op = row.operator(UVP2_OT_SelectLockGroupIslandParam.bl_idname, text="Select") op.select = True op = row.operator(UVP2_OT_SelectLockGroupIslandParam.bl_idname, text="Deselect") op.select = False # row = container.row(align=True) # row.operator(UVP2_OT_SelectNonDefaultLockGroupIslandParam.bl_idname) row = container.row(align=True) row.operator(UVP2_OT_ResetLockGroupIslandParam.bl_idname) row = container.row(align=True) row.operator(UVP2_OT_ShowLockGroupIslandParam.bl_idname) class UVP2_PT_HeuristicBase(UVP2_PT_Generic): bl_label = 'Heuristic' bl_context = '' bl_options = {'DEFAULT_CLOSED'} def draw_specific(self, context): layout = self.layout heurstic_supported, not_supported_msg = self.prefs.heuristic_supported(self.scene_props) col = layout.column(align=True) col.enabled = heurstic_supported # Heuristic search box = col.box() box.enabled = self.prefs.FEATURE_heuristic_search row = box.row() self.handle_prop("heuristic_enable", heurstic_supported, not_supported_msg, row) row.operator(UVP2_OT_HeuristicSearchHelp.bl_idname, icon='HELP', text='') col2 = col.column(align=True) col2.enabled = self.prefs.heuristic_enabled(self.scene_props) row = col2.row(align=True) row.prop(self.scene_props, "heuristic_search_time") row = col2.row(align=True) row.prop(self.scene_props, "heuristic_max_wait_time") # Advanced Heuristic box = col2.box() box.enabled = self.prefs.advanced_heuristic_available(self.scene_props) row = box.row() self.handle_prop("advanced_heuristic", self.prefs.FEATURE_advanced_heuristic, UvpLabels.FEATURE_NOT_SUPPORTED_MSG, row) class UVP2_PT_NonSquarePackingBase(UVP2_PT_Generic): bl_label = 'Non-Square Packing' bl_context = '' bl_options = {'DEFAULT_CLOSED'} def draw_specific(self, context): layout = self.layout col = layout.column(align=True) # Tex ratio box = col.box() row = box.row() self.handle_prop("tex_ratio", self.prefs.pack_ratio_supported(), UvpLabels.FEATURE_NOT_SUPPORTED_MSG, row) col.separator() row = col.row(align=True) row.operator(UVP2_OT_AdjustIslandsToTexture.bl_idname) row.operator(UVP2_OT_NonSquarePackingHelp.bl_idname, icon='HELP', text='') row = col.row(align=True) row.operator(UVP2_OT_UndoIslandsAdjustemntToTexture.bl_idname) class UVP2_PT_AdvancedOptionsBase(UVP2_PT_Generic): bl_label = 'Advanced Options' bl_context = '' bl_options = {'DEFAULT_CLOSED'} def draw_specific(self, context): layout = self.layout demo_suffix = " (DEMO)" if self.prefs.FEATURE_demo else '' col = layout.column(align=True) # Grouped pack box = col.box() col2 = box.column(align=True) col2.label(text=UvpLabels.PACK_MODE_NAME + ':') row = col2.row(align=True) row.prop(self.scene_props, "pack_mode", text='') if self.prefs.tiles_enabled(self.scene_props): row.operator(UVP2_OT_UdimSupportHelp.bl_idname, icon='HELP', text='') box = col2.box() box.prop(self.scene_props, "use_blender_tile_grid") tile_col = col2.column(align=True) tile_col.enabled = not self.scene_props.use_blender_tile_grid if self.prefs.pack_to_tiles(self.scene_props): tile_col.prop(self.scene_props, "tile_count") tile_col.prop(self.scene_props, "tiles_in_row") if self.prefs.pack_groups_together(self.scene_props): row = col2.row(align=True) row.prop(self.scene_props, "group_compactness") if self.prefs.grouping_enabled(self.scene_props): box = col.box() col3 = box.column() col3.label(text=UvpLabels.GROUP_METHOD_NAME + ':') row = col3.row(align=True) row.prop(self.scene_props, "group_method", text='') if self.scene_props.group_method == UvGroupingMethod.MANUAL.code: row.operator(UVP2_OT_ManualGroupingHelp.bl_idname, icon='HELP', text='') # col2.separator() # Pack to others box = col.box() pto_supported, pto_not_supported_msg = self.prefs.pack_to_others_supported(self.scene_props) row = box.row() self.handle_prop("pack_to_others", pto_supported, pto_not_supported_msg, row) # Fixed Scale box = col.box() fs_supported, fs_not_supported_msg = self.prefs.fixed_scale_supported(self.scene_props) row = box.row() self.handle_prop("fixed_scale", fs_supported, fs_not_supported_msg, row) # box = col.box() col2 = box.column() col2.enabled = self.prefs.fixed_scale_enabled(self.scene_props) col2.label(text=UvpLabels.FIXED_SCALE_STRATEGY_NAME + ':') row = col2.row(align=True) row.prop(self.scene_props, "fixed_scale_strategy", text='') # Normalize islands box = col.box() norm_supported, norm_not_supported_msg = self.prefs.normalize_islands_supported(self.scene_props) row = box.row() self.handle_prop("normalize_islands", norm_supported, norm_not_supported_msg,
from collections import namedtuple from itertools import islice import numpy as np import pandas as pd from dataclasses import dataclass @dataclass class BinningInfo(object): """Docstring for BinningInfo.""" variable_extents: tuple step: float num_bins: int bin_indicies: np.ndarray def build_spanning_grid_matrix(x_values, y_values, debug_print=False): """ builds a 2D matrix with entries spanning x_values across axis 0 and spanning y_values across axis 1. For example, used to build a grid of position points from xbins and ybins. Usage: all_positions_matrix, flat_all_positions_matrix, original_data_shape = build_all_positions_matrix(active_one_step_decoder.xbin_centers, active_one_step_decoder.ybin_centers) """ num_rows = len(y_values) num_cols = len(x_values) original_data_shape = (num_cols, num_rows) # original_position_data_shape: (64, 29) if debug_print: print(f'original_position_data_shape: {original_data_shape}') x_only_matrix = np.repeat(np.expand_dims(x_values, 1).T, num_rows, axis=0).T # np.shape(x_only_matrix) # (29, 64) flat_x_only_matrix = np.reshape(x_only_matrix, (-1, 1)) if debug_print: print(f'np.shape(x_only_matrix): {np.shape(x_only_matrix)}, np.shape(flat_x_only_matrix): {np.shape(flat_x_only_matrix)}') # np.shape(x_only_matrix): (64, 29), np.shape(flat_x_only_matrix): (1856, 1) y_only_matrix = np.repeat(np.expand_dims(y_values, 1), num_cols, axis=1).T # np.shape(y_only_matrix) # (29, 64) flat_y_only_matrix = np.reshape(y_only_matrix, (-1, 1)) # flat_all_positions_matrix = np.array([np.append(an_x, a_y) for (an_x, a_y) in zip(flat_x_only_matrix, flat_y_only_matrix)]) flat_all_entries_matrix = [tuple(np.append(an_x, a_y)) for (an_x, a_y) in zip(flat_x_only_matrix, flat_y_only_matrix)] # a list of position tuples (containing two elements) # reconsitute its shape: all_entries_matrix = np.reshape(flat_all_entries_matrix, (original_data_shape[0], original_data_shape[1], 2)) if debug_print: print(f'np.shape(all_positions_matrix): {np.shape(all_entries_matrix)}') # np.shape(all_positions_matrix): (1856, 2) # np.shape(all_positions_matrix): (64, 29, 2) print(f'flat_all_positions_matrix[0]: {flat_all_entries_matrix[0]}\nall_positions_matrix[0,0,:]: {all_entries_matrix[0,0,:]}') return all_entries_matrix, flat_all_entries_matrix, original_data_shape def safe_get(list, index, fallback_value): """Similar to dict's .get(key, fallback) function but for lists. Returns a fallback/default value if the index is not valid for the list, otherwise returns the value at that index. Args: list (_type_): a list-like object index (_type_): an index into the list fallback_value (_type_): any value to be returned when the indexing fails Returns: _type_: the value in the list, or the fallback_value is the index is not valid for the list. """ try: return list[index] except IndexError: return fallback_value def safe_pandas_get_group(dataframe_group, key): """ returns an empty dataframe if the key isn't found in the group.""" if key in dataframe_group.groups.keys(): return dataframe_group.get_group(key) else: original_df = dataframe_group.obj return original_df.drop(original_df.index) # class MatrixFlattenTransformer(object): # """ Supposed to allow easy transformation of data from a flattened representation to the original. # Usage: # trans = MatrixFlattenTransformer(original_data_shape) # test_all_positions_matrix = trans.unflatten(flat_all_positions_matrix) # print(f'np.shape(test_all_positions_matrix): {np.shape(test_all_positions_matrix)}') # """ # """ TODO: does not yet work. for MatrixFlattenTransformer.""" # def __init__(self, original_data_shape): # super(MatrixFlattenTransformer, self).__init__() # self.original_data_shape = original_data_shape # def flatten(self, data): # data_shape = np.shape(data) # original_flat_shape = np.prod(self.original_data_shape) # # assert np.shape(data) == self.original_data_shape, f"data passed in to flatten (with shape {np.shape(data)}) is not equal to the original data shape: {self.original_data_shape}" # assert data_shape == original_flat_shape, f"data passed in to flatten (with shape {data_shape}) is not equal to the original shape's number of items (shape: {self.original_data_shape}, original_flat_shape: {original_flat_shape}" # return np.reshape(data, (-1, 1)) # def unflatten(self, flat_data): # flat_data_shape = np.shape(flat_data) # original_data_shape_ndim = len(self.original_data_shape) # # assert (flat_data_shape[:original_data_shape_ndim] == self.original_data_shape), f"data passed in to unflatten (with shape {flat_data_shape}) must match the original data shape ({self.original_data_shape}), at least up to the number of dimensions in the original" # additional_dimensions = flat_data_shape[original_data_shape_ndim:] # return np.reshape(flat_data, (self.original_data_shape[0], self.original_data_shape[1], additional_dimensions)) def build_spanning_bins(variable_values, max_bin_size:float, debug_print=False): """ DEPRICATED! out_digitized_variable_bins include both endpoints (bin edges) Args: variable_values ([type]): [description] max_bin_size (float): [description] debug_print (bool, optional): [description]. Defaults to False. Returns: out_digitized_variable_bins [type]: [description] out_binning_info [BinningInfo]: contains info about how the binning was conducted """ raise DeprecationWarning # compute extents: curr_variable_extents = (np.nanmin(variable_values), np.nanmax(variable_values)) num_subdivisions = int(np.ceil((curr_variable_extents[1] - curr_variable_extents[0])/max_bin_size)) # get the next integer size above float_bin_size actual_subdivision_step_size = (curr_variable_extents[1] - curr_variable_extents[0]) / float(num_subdivisions) # the actual exact size of the bin if debug_print: print(f'for max_bin_size: {max_bin_size} -> num_subdivisions: {num_subdivisions}, actual_subdivision_step_size: {actual_subdivision_step_size}') # out_bin_indicies = np.arange(num_subdivisions) out_binning_info = BinningInfo(curr_variable_extents, actual_subdivision_step_size, num_subdivisions, np.arange(num_subdivisions)) out_digitized_variable_bins = np.linspace(curr_variable_extents[0], curr_variable_extents[1], num_subdivisions, dtype=float)#.astype(float) assert out_digitized_variable_bins[-1] == out_binning_info.variable_extents[1], "out_digitized_variable_bins[-1] should be the maximum variable extent!" assert out_digitized_variable_bins[0] == out_binning_info.variable_extents[0], "out_digitized_variable_bins[0] should be the minimum variable extent!" # All above arge the bin_edges return out_digitized_variable_bins, out_binning_info def compute_spanning_bins(variable_values, num_bins:int=None, bin_size:float=None): """[summary] Args: variable_values ([type]): [description] num_bins (int, optional): [description]. Defaults to None. bin_size (float, optional): [description]. Defaults to None. debug_print (bool, optional): [description]. Defaults to False. Raises: ValueError: [description] Returns: [type]: [description] Usage: ## Binning with Fixed Number of Bins: xbin, ybin, bin_info = compute_spanning_bins(pos_df.x.to_numpy(), bin_size=active_config.computation_config.grid_bin[0]) # bin_size mode print(bin_info) ## Binning with Fixed Bin Sizes: xbin, ybin, bin_info = compute_spanning_bins(pos_df.x.to_numpy(), num_bins=num_bins) # num_bins mode print(bin_info) """ assert (num_bins is None) or (bin_size is None), 'You cannot constrain both num_bins AND bin_size. Specify only one or the other.' assert (num_bins is not None) or (bin_size is not None), 'You must specify either the num_bins XOR the bin_size.' curr_variable_extents = (np.nanmin(variable_values), np.nanmax(variable_values)) if num_bins is not None: ## Binning with Fixed Number of Bins: mode = 'num_bins' xnum_bins = num_bins xbin, xstep = np.linspace(curr_variable_extents[0], curr_variable_extents[1], num=num_bins, retstep=True) # binning of x position elif bin_size is not None: ## Binning with Fixed Bin Sizes: mode = 'bin_size' xstep = bin_size xbin = np.arange(curr_variable_extents[0], (curr_variable_extents[1] + xstep), xstep, ) # binning of x position # the interval does not include this value, except in some cases where step is not an integer and floating point round-off affects the length of out. xnum_bins = len(xbin) else: raise ValueError return xbin, BinningInfo(curr_variable_extents, xstep, xnum_bins, np.arange(xnum_bins)) def compute_position_grid_size(any_1d_series, num_bins:tuple): """ Computes the required bin_sizes from the required num_bins (for each dimension independently) Usage: out_grid_bin_size, out_bins, out_bins_infos = compute_position_grid_size(curr_kdiba_pipeline.sess.position.x, curr_kdiba_pipeline.sess.position.y, num_bins=(64, 64)) active_grid_bin = tuple(out_grid_bin_size) print(f'active_grid_bin: {active_grid_bin}') # (3.776841861770752, 1.043326930905373) """ assert (len(any_1d_series)) == len(num_bins), f'(len(other_1d_series)) must be the same length as the num_bins tuple! But (len(other_1d_series)): {(len(any_1d_series))} and len(num_bins): {len(num_bins)}!' num_series = len(num_bins) out_bins = [] out_bins_info = [] out_bin_grid_step_size = np.zeros((num_series,)) for i in np.arange(num_series): xbins, xbin_info = compute_spanning_bins(any_1d_series[i], num_bins=num_bins[i]) out_bins.append(xbins) out_bins_info.append(xbin_info) out_bin_grid_step_size[i] = xbin_info.step return out_bin_grid_step_size, out_bins, out_bins_info def get_bin_centers(bin_edges): """ For a series of 1D bin edges given by bin_edges, returns the center of the bins. Output will have one less element than bin_edges. """ return (bin_edges[:-1] + np.diff(bin_edges) / 2.0) def get_bin_edges(bin_centers): """ TODO: CHECK For a series of 1D bin centers given by bin_centers, returns the edges of the bins. Reciprocal of get_bin_centers(bin_edges) """ half_bin_width = float((bin_centers[1] - bin_centers[0])) / 2.0 # TODO: assumes fixed bin width bin_starts = bin_centers - half_bin_width bin_ends = bin_centers + half_bin_width return interleave_elements(bin_starts, bin_ends) def build_pairwise_indicies(target_indicies, debug_print=False): """ Builds pairs of indicies from a simple list of indicies, for use in computing pairwise operations. Example: target_indicies = np.arange(5) # [0, 1, 2, 3, 4] out_pair_indicies = build_pairwise_indicies(target_indicies) > out_pair_indicies: [(0, 1), (1, 2), (2, 3), (3, 4)] Args: target_indicies ([type]): [description] debug_print (bool, optional): [description]. Defaults to False. Returns: [type]: [description] Usage: target_indicies = np.arange(5) out_pair_indicies = build_pairwise_indicies(target_indicies) # out_pair_indicies = list(out_pair_indicies) # print(f'out_pair_indicies: {list(out_pair_indicies)}') print(f'out_pair_indicies: {list(out_pair_indicies)}') for i, pair in enumerate(list(out_pair_indicies)): # first_item_lap_idx, next_item_lap_idx print(f'i: {i}, pair: {pair}') """ start_pairs = target_indicies[0:-1] # all but the last index end_pairs = target_indicies[1:] # from the second to the last index out_pair_indicies = list(zip(start_pairs, end_pairs)) # want to wrap in list so it isn't consumed if debug_print: print(f'target_indicies: {target_indicies}\nstart_pairs: {start_pairs}\nend_pairs: {end_pairs}') return out_pair_indicies def interleave_elements(start_points, end_points): """ Given two equal sized arrays, produces an output array of double that size that contains elements of start_points interleaved with elements of end_points Example: a_starts = ['A','B','C','D'] a_ends = ['a','b','c','d'] a_interleaved = interleave_elements(a_starts, a_ends) >> a_interleaved: ['A','a','B','b','C','c','D','d'] """ assert np.shape(start_points) == np.shape(end_points), f"start_points and end_points must be the same shape. np.shape(start_points): {np.shape(start_points)}, np.shape(end_points): {np.shape(end_points)}" start_points = np.atleast_2d(start_points) end_points = np.atleast_2d(end_points) all_points_shape = (np.shape(start_points)[0] * 2, np.shape(start_points)[1]) # it's double the length of the start_points all_points = np.zeros(all_points_shape) all_points[np.arange(0, all_points_shape[0], 2), :] = start_points # fill the even elements all_points[np.arange(1, all_points_shape[0], 2), :] = end_points # fill the odd elements assert np.shape(all_points)[0] == (np.shape(start_points)[0] * 2), f"newly created all_points is not of corrrect size! np.shape(all_points): {np.shape(all_points)}" return all_points def get_dict_subset(a_dict, included_keys=None, require_all_keys=False): """Gets a subset of a dictionary from a list of keys (included_keys) Args: a_dict ([type]): [description] included_keys ([type], optional): [description]. Defaults to None. require_all_keys: Bool, if True, requires all keys in included_keys to be in the dictionary (a_dict) Returns: [type]: [description]
import random import pygame import constants import utils from graphics_environment import Environment, Triggers import os, sys import time from graphics_fauna import Player, Npcs from dialogs import DialogFight, DialogText, DialogPlayerInventory, \ DialogInput, DialogPlayerInfo, DialogText, DialogGoodbye, \ DialogUseItemInInventory, DialogShowQuests from NEW_inventory import Conversation # ----------------------------------------------------------- # class Game # ----------------------------------------------------------- class Game: def __init__(self): user_info = utils.get_user_data() self.player_name = user_info["character_name"] self.zone_name = "" self.map_name = "" # ---- self.environment = None self.npcs = None self.player = None self.screen = None self.init_pygame() # ---- self.all_sprites = pygame.sprite.Group() self.keep_looping = True self.current_monster = None # ------------------------------------- # self.quest_histories = None def read_data(self): user_data = utils.get_user_data() self.zone_name = user_data["zone_name"] self.map_name = user_data["map_name"] if self._exception01() == True: self._change01() pygame.display.set_caption("Enter {} \| ({})".format(constants.TITLE, self.map_name)) # print(user_data) # ---- self.environment = Environment(self.zone_name, self.map_name) self.environment.read_data() self.npcs = Npcs(self.zone_name, self.map_name) self.npcs.read_data() self.player = Player(self.player_name, self.zone_name, self.map_name) self.player.read_data() # ---- if self.player.is_dead() == True: myresult = self.player.resurrect_player() # ---- if myresult == "y": utils.copy_original_player_files(self.player.profession, self.player.name) self.player.read_data() self.init_pygame() elif myresult == "n": self.keep_looping = False elif len(myresult) == 0: # esc was pressed; I'm going to take it that the player wishes to quit. self.keep_looping = False else: raise ValueError("Error!") def init_pygame(self): pygame.init() self.BG_COLOR = constants.BG_COLOR self.clock = pygame.time.Clock() pygame.display.set_caption("Enter {}".format(constants.TITLE)) self.screen = pygame.display.set_mode((constants.SCREEN_WIDTH, constants.SCREEN_HEIGHT)) self.font = pygame.font.Font(None, 40) # self.font = pygame.font.SysFont(constants.FONT_NAME, constants.FONT_SIZE) def player_died(self): s = "You're dead! Game over." mydialog = DialogText(s) mydialog.main() self.player.image = self.player.image_dead self.init_pygame() self.keep_looping = False def there_is_a_monster_on_this_tile(self, x, y): this_monster = self.npcs.get_npc_if_monster(x, y) if this_monster is None: return False return True # def there_is_an_angel_on_this_tile(self, x, y): # raise NotImplemented # this_angel = self.npcs.get_npc_if_angel(x, y) # if this_angel is None: return False # return True # def this_npc_is_a_questgiver(self, x, y): # current_npc = self.npcs.get_npc(self.player.x, self.player.y) # if current_npc is None: # s = "Error! There should be an NPC here, but there isn't." # raise ValueError(s) # if current_npc.is_a_questgiver(self.zone_name, self.map_name) == True: # return True # return False def there_is_an_npc_on_this_tile(self, x, y): this_npc = self.npcs.get_npc(x, y) if this_npc is None: return False return True def there_is_an_action_on_this_tile(self, x, y): print("Testing to see whether there is an action on tile x,y: ({},{})".format(x, y)) this_action = self.environment.actions.get_action(x, y) if this_action is None: return False return True def there_is_a_persistent_object_on_this_tile(self, x, y): if len(self.environment.persistents) == 0: return False this_persistent = self.environment.persistents.get_persistent_object(x, y) if this_persistent is None: return False return True def debugging_info(self): # ---- Debugging (after) (top)---- mylist = [] mylist.append("character_name (from player): {}".format(self.player.name)) mylist.append("x,y: ({},{})".format(self.player.x, self.player.y)) mylist.append("zone_name: {}".format(self.zone_name)) mylist.append("map_name: {}".format(self.map_name)) mylist.append("------------") mylist.append("From file:") mydict = utils.get_user_data() mylist.append("character_name: {}".format(mydict["character_name"])) mylist.append("zone_name: {}".format(mydict["zone_name"])) mylist.append("map_name: {}".format(mydict["map_name"])) mylist.append("------------") mylist.append("From Player:") mylist.append("x,y: ({},{})".format(self.player.x, self.player.y)) mylist.append("zone_name: {}".format(self.player.zone_name)) mylist.append("map_name: {}".format(self.player.map_name)) print("****************** Debugging (begin) ****************") print("-----------------------------------------------------------") print(mylist) print("---------------------------------------------------------") print("**************** Debugging (end) ****************") # ---- Debugging (after) (bottom) ---- def npc_encounter(self): # The user wants an encounter with the NPC they clicked on. current_npc = self.npcs.get_npc(self.player.x, self.player.y) if current_npc is None: s = "Error! There should be an NPC here, but there isn't." raise ValueError(s) print("This is the amount of gold the player has before he has the INTERACTION: {}".format(self.player.gold)) result, myinventory, player_gold = current_npc.have_interaction(self.environment.events, self.player) # ---- # The following line is executed when the user exists out of an NPC encounter. if result is None and myinventory is None: return False if result is None or myinventory is None: raise ValueError("Error") print("This is the amount of gold the player has BEFORE: {}".format(self.player.gold)) self.player.gold = player_gold print("This is the amount of gold the player has AFTER: {}".format(self.player.gold)) self.player.inventory = myinventory # ---- if result == "end game": self.player_died() elif result == "load next map": self.map_name = utils.get_next_map_name(self.map_name) utils.set_user_data(self.player.name, self.zone_name, self.map_name, self.player.profession) self.read_data() elif result in ["end conversation", "continue", "completed"]: # player goes on about their day pass else: s = "I don't understand this: {}".format(result) raise ValueError(s) def handle_events(self): # catch all events here for event in pygame.event.get(): if event.type == pygame.QUIT: self.keep_looping = False self.save_data() return True if event.type == pygame.KEYDOWN: print("self.player coords: x,y: {},{}: ".format(self.player.x, self.player.y)) if event.key == pygame.K_ESCAPE: self.keep_looping = False self.save_data() return True if self.player.is_dead() == True: return False if event.key == pygame.K_LEFT or event.key == pygame.K_a: if self.player.move(dx=-1, dy=0, obstacles=self.environment.obstacles) == True: # self.player.image = self.player.image_left self.player.direction = constants.LEFT self.player.my_update_image() elif event.key == pygame.K_RIGHT or event.key == pygame.K_d: if self.player.move(dx=1, obstacles=self.environment.obstacles) == True: self.player.direction = constants.RIGHT self.player.my_update_image() elif event.key == pygame.K_DOWN or event.key == pygame.K_s: if self.player.move(dy=1, obstacles=self.environment.obstacles) == True: self.player.direction = constants.DOWN self.player.my_update_image() elif event.key == pygame.K_UP or event.key == pygame.K_w: if self.player.move(dy=-1, obstacles=self.environment.obstacles) == True: self.player.direction = constants.UP self.player.my_update_image() # =============================================== elif event.key == pygame.K_h: if self.there_is_an_npc_on_this_tile(self.player.x, self.player.y) == True: message, self.player.inventory = self.npcs.do_something(self.player) if message == "load next map": print("current map name: {}".format(self.map_name)) self.map_name = utils.get_next_map_name(self.map_name) print("moving to map name: {}".format(self.map_name)) self.load_map(zone_name=self.zone_name, map_name=self.map_name, dialog_text="") elif self.there_is_an_action_on_this_tile(self.player.x, self.player.y) == True: # After an action is used, the tile is removed. print("There is an action on this tile.") if self.environment.actions.conditions_passed(self.player) == False: return False current_action = self.environment.actions.get_action(self.player.x, self.player.y) self.do_action(current_action) self.environment.actions.remove_tile(self.player.x, self.player.y) self.init_pygame() self.all_sprites = pygame.sprite.Group() return "" elif self.there_is_a_persistent_object_on_this_tile(self.player.x, self.player.y) == True: # After a persistent object is used, it remains. # (I could probably just make 'persistent' and 'temporary' values of a # field for an Actions object rather than make a separate class.) print("This is a persistent object on this tile.") # if self.environment.persistents.conditions_passed(self.player, self.environment.events) == False: # return False if self.environment.persistents.conditions_passed(self.player) == False: return False persistent_object = self.environment.persistents.get_persistent_object(self.player.x, self.player.y) if persistent_object is None: raise ValueError("Error") self.do_persistent(persistent_object) self.init_pygame() return "" # =============================================== elif event.key == pygame.K_i: # Inventory if len(self.player.inventory) == 0: raise ValueError("The player has lost their inventory!") mydialog = DialogPlayerInventory(self.player) mydialog.main() self.init_pygame() elif event.key == pygame.K_p: mydialog = DialogPlayerInfo(self.player) mydialog.main() self.init_pygame() # elif event.key == pygame.K_u: # mydialog = DialogUseItemInInventory(self.player) # mydialog.main() # self.init_pygame() elif event.key == pygame.K_q: mydialog = DialogShowQuests() mydialog.read_data() mydialog.main() else: print("I don't recognize this event.key in handle_events: {}".format(event.key)) # ------------------------------------------------------ self.check_for_trigger() def check_for_trigger(self): if self.environment.triggers is None: print("There is no TRIGGER on this tile.") return False current_trigger = self.environment.triggers.get_trigger(self.player.x, self.player.y) if not current_trigger is None: self.do_trigger(current_trigger) # self.npcs.debug_print() # raise NotImplemented def show_text(self, current_trigger): # print("debugging: in def load_map(self, current_trigger") filename = "{}.txt".format(current_trigger.data) filepath = os.path.join("data", "zones", self.zone_name, self.map_name, "texts", filename) mydict = utils.read_file(filepath)[0] for key, value in mydict.items(): # print(key, value) mytextdialog = DialogText(value) mytextdialog.main() self.init_pygame() def fire_attack(self, attack_strength): self.player.hit_points -= attack_strength if self.player.hit_points <= 0: self.player_died() def do_trigger(self, current_trigger): # print("There is a current trigger") # print("current_trigger.command = {}".format(current_trigger.command)) # current_trigger.debug_print() if current_trigger.command == "load_map": if current_trigger.conditions_fulfilled(self.zone_name, self.map_name, self.player.inventory) == False: return False # ---- self.save_data() # print("*********** dkdkkd") zone_name, map_name = current_trigger.parse_data() self.zone_name = zone_name self.map_name = map_name utils.set_user_data(self.player.name, self.zone_name, self.map_name, self.player.profession) # ---- self.read_data() elif current_trigger.command == "show_text": self.show_text(current_trigger) elif current_trigger.command == "fire_attack": if not utils.is_int(current_trigger.data): raise ValueError("Error") self.fire_attack(int(current_trigger.data)) elif current_trigger.command == "fire_attack_big": if not utils.is_int(current_trigger.data): raise ValueError("Error") self.fire_attack(int(current_trigger.data)) elif current_trigger.command == "change_npc_passive": # check to make sure that the name given in data is that of an npc # so check the npc_name_lookup.txt file in the zone directory. # if this goes through then turn that npc passive. if utils.npc_exists_in_zone(npc_name=current_trigger.data, zone_name=self.zone_name, map_name=self.map_name) == False: s = "Error! That npc ({}) does not exist in this zone ({})." s = s.format(current_trigger.data.replace(".txt", ""), self.zone_name) raise ValueError(s) raise NotImplemented elif current_trigger.command == "change_npc_agro": # check to make sure that the name given in data is that of an npc # so check the npc_name_lookup.txt file in the zone directory. # if this goes through then turn that npc agro. if utils.npc_exists_in_zone(npc_name=current_trigger.data, zone_name=self.zone_name, map_name=self.map_name) == False: s = "Error! That npc ({}) does not exist in this zone ({}).".format(current_trigger.data, self.zone_name) raise ValueError(s) the_npc = self.npcs.get_npc_by_name(current_trigger.data) if the_npc is None: s = "This name ({}) is not the name of a current npc.".format(current_trigger.data.replace(" ", "_")) print(s) return False # raise ValueError(s) the_npc.agro_level = "agro" else: current_trigger.debug_print() s = "I couldn't find that: {}".format(current_trigger.command) raise ValueError(s) return True def load_map(self, zone_name, map_name, dialog_text=""): """This loads both a zone and a map.""" if not zone_name in constants.ZONE_NAMES: s
<filename>leiaapi/generated/api/application_admin_api.py<gh_stars>0 # coding: utf-8 """ LEIA RESTful API for AI Leia API # noqa: E501 OpenAPI spec version: 1.0.0 Contact: <EMAIL> Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import import re # noqa: F401 # python 2 and python 3 compatibility library import six from leiaapi.generated.api_client import ApiClient class ApplicationAdminApi(object): """NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. Ref: https://github.com/swagger-api/swagger-codegen """ def __init__(self, api_client=None): if api_client is None: api_client = ApiClient() self.api_client = api_client def admin_create_application(self, token, kwargs): # noqa: E501 """Adds a new application to the system (admin only) # noqa: E501 Adds a new application to the system. This method is only accessible to admins. An API key will be generated for the new application when calling this method. Note or store it carefully, it will not be recoverable after this call. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.admin_create_application(token, async_req=True) >>> result = thread.get() :param async_req bool :param str token: The login token obtained via GET /login/{api_key} (required) :param Application body: :return: Application If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.admin_create_application_with_http_info(token, kwargs) # noqa: E501 else: (data) = self.admin_create_application_with_http_info(token, kwargs) # noqa: E501 return data def admin_create_application_with_http_info(self, token, kwargs): # noqa: E501 """Adds a new application to the system (admin only) # noqa: E501 Adds a new application to the system. This method is only accessible to admins. An API key will be generated for the new application when calling this method. Note or store it carefully, it will not be recoverable after this call. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.admin_create_application_with_http_info(token, async_req=True) >>> result = thread.get() :param async_req bool :param str token: The login token obtained via GET /login/{api_key} (required) :param Application body: :return: Application If the method is called asynchronously, returns the request thread. """ all_params = ['token', 'body'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method admin_create_application" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'token' is set if ('token' not in params or params['token'] is None): raise ValueError("Missing the required parameter `token` when calling `admin_create_application`") # noqa: E501 collection_formats = {} path_params = {} query_params = [] header_params = {} if 'token' in params: header_params['token'] = params['token'] # noqa: E501 form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/admin/application', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='Application', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def admin_delete_always_on_schedule(self, token, application_id, always_on_schedule_id, kwargs): # noqa: E501 """Removes a schedule from an application # noqa: E501 Removes a schedule from an application # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.admin_delete_always_on_schedule(token, application_id, always_on_schedule_id, async_req=True) >>> result = thread.get() :param async_req bool :param str token: The login token obtained via GET /login/{api_key} (required) :param str application_id: The id of the application (required) :param str always_on_schedule_id: The id of the schedule to delete (required) :return: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.admin_delete_always_on_schedule_with_http_info(token, application_id, always_on_schedule_id, kwargs) # noqa: E501 else: (data) = self.admin_delete_always_on_schedule_with_http_info(token, application_id, always_on_schedule_id, kwargs) # noqa: E501 return data def admin_delete_always_on_schedule_with_http_info(self, token, application_id, always_on_schedule_id, kwargs): # noqa: E501 """Removes a schedule from an application # noqa: E501 Removes a schedule from an application # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.admin_delete_always_on_schedule_with_http_info(token, application_id, always_on_schedule_id, async_req=True) >>> result = thread.get() :param async_req bool :param str token: The login token obtained via GET /login/{api_key} (required) :param str application_id: The id of the application (required) :param str always_on_schedule_id: The id of the schedule to delete (required) :return: None If the method is called asynchronously, returns the request thread. """ all_params = ['token', 'application_id', 'always_on_schedule_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method admin_delete_always_on_schedule" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'token' is set if ('token' not in params or params['token'] is None): raise ValueError("Missing the required parameter `token` when calling `admin_delete_always_on_schedule`") # noqa: E501 # verify the required parameter 'application_id' is set if ('application_id' not in params or params['application_id'] is None): raise ValueError("Missing the required parameter `application_id` when calling `admin_delete_always_on_schedule`") # noqa: E501 # verify the required parameter 'always_on_schedule_id' is set if ('always_on_schedule_id' not in params or params['always_on_schedule_id'] is None): raise ValueError("Missing the required parameter `always_on_schedule_id` when calling `admin_delete_always_on_schedule`") # noqa: E501 collection_formats = {} path_params = {} if 'application_id' in params: path_params['application_id'] = params['application_id'] # noqa: E501 if 'always_on_schedule_id' in params: path_params['always_on_schedule_id'] = params['always_on_schedule_id'] # noqa: E501 query_params = [] header_params = {} if 'token' in params: header_params['token'] = params['token'] # noqa: E501 form_params = [] local_var_files = {} body_params = None # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/admin/application/{application_id}/always_on_schedules/{always_on_schedule_id}', 'DELETE', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def admin_delete_application(self, token, application_id, kwargs): # noqa: E501 """Deletes an application (admin only) # noqa: E501 Retrieves a new application from the system. This method is only accessible to admins # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.admin_delete_application(token, application_id, async_req=True) >>> result = thread.get() :param async_req bool :param str token: The login token obtained via GET /login/{api_key} (required) :param str application_id: The id of the application to delete (required) :return: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.admin_delete_application_with_http_info(token, application_id, kwargs) # noqa: E501 else: (data) = self.admin_delete_application_with_http_info(token, application_id, kwargs) # noqa: E501 return data def admin_delete_application_with_http_info(self, token, application_id, kwargs): # noqa: E501 """Deletes an application (admin only) # noqa: E501 Retrieves a new application from the system. This method is only accessible to admins # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.admin_delete_application_with_http_info(token, application_id, async_req=True) >>> result = thread.get() :param async_req bool :param str token: The login token obtained via GET /login/{api_key} (required) :param str application_id: The id of the application to delete (required) :return: None If the method is called asynchronously, returns the request thread. """ all_params = ['token', 'application_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method admin_delete_application" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'token' is set if ('token' not in params or params['token'] is None): raise ValueError("Missing the required parameter `token` when calling `admin_delete_application`") # noqa: E501 # verify the required parameter 'application_id' is set if ('application_id' not in params or params['application_id'] is None): raise ValueError("Missing the required parameter `application_id` when calling `admin_delete_application`") # noqa: E501 collection_formats = {} path_params = {} if 'application_id' in params: path_params['application_id']
"""Library implementing convolutional neural networks. Authors * <NAME> 2020 * <NAME> 2020 * <NAME> 2021 * <NAME> 2021 """ import math import torch import logging import numpy as np import torch.nn as nn import torch.nn.functional as F from typing import Tuple logger = logging.getLogger(__name__) class SincConv(nn.Module): """This function implements SincConv (SincNet). <NAME>, <NAME>, "Speaker Recognition from raw waveform with SincNet", in Proc. of SLT 2018 (https://arxiv.org/abs/1808.00158) Arguments --------- input_shape : tuple The shape of the input. Alternatively use ``in_channels``. in_channels : int The number of input channels. Alternatively use ``input_shape``. out_channels : int It is the number of output channels. kernel_size: int Kernel size of the convolutional filters. stride : int Stride factor of the convolutional filters. When the stride factor > 1, a decimation in time is performed. dilation : int Dilation factor of the convolutional filters. padding : str (same, valid, causal). If "valid", no padding is performed. If "same" and stride is 1, output shape is the same as the input shape. "causal" results in causal (dilated) convolutions. padding_mode : str This flag specifies the type of padding. See torch.nn documentation for more information. groups : int This option specifies the convolutional groups. See torch.nn documentation for more information. bias : bool If True, the additive bias b is adopted. sample_rate : int, Sampling rate of the input signals. It is only used for sinc_conv. min_low_hz : float Lowest possible frequency (in Hz) for a filter. It is only used for sinc_conv. min_low_hz : float Lowest possible value (in Hz) for a filter bandwidth. Example ------- >>> inp_tensor = torch.rand([10, 16000]) >>> conv = SincConv(input_shape=inp_tensor.shape, out_channels=25, kernel_size=11) >>> out_tensor = conv(inp_tensor) >>> out_tensor.shape torch.Size([10, 16000, 25]) """ def __init__( self, out_channels, kernel_size, input_shape=None, in_channels=None, stride=1, dilation=1, padding="same", padding_mode="reflect", sample_rate=16000, min_low_hz=50, min_band_hz=50, ): super().__init__() self.out_channels = out_channels self.kernel_size = kernel_size self.stride = stride self.dilation = dilation self.padding = padding self.padding_mode = padding_mode self.sample_rate = sample_rate self.min_low_hz = min_low_hz self.min_band_hz = min_band_hz # input shape inference if input_shape is None and in_channels is None: raise ValueError("Must provide one of input_shape or in_channels") if in_channels is None: in_channels = self._check_input_shape(input_shape) # Initialize Sinc filters self._init_sinc_conv() def forward(self, x): """Returns the output of the convolution. Arguments --------- x : torch.Tensor (batch, time, channel) input to convolve. 2d or 4d tensors are expected. """ x = x.transpose(1, -1) self.device = x.device unsqueeze = x.ndim == 2 if unsqueeze: x = x.unsqueeze(1) if self.padding == "same": x = self._manage_padding( x, self.kernel_size, self.dilation, self.stride ) elif self.padding == "causal": num_pad = (self.kernel_size - 1) * self.dilation x = F.pad(x, (num_pad, 0)) elif self.padding == "valid": pass else: raise ValueError( "Padding must be 'same', 'valid' or 'causal'. Got %s." % (self.padding) ) sinc_filters = self._get_sinc_filters() wx = F.conv1d( x, sinc_filters, stride=self.stride, padding=0, dilation=self.dilation, ) if unsqueeze: wx = wx.squeeze(1) wx = wx.transpose(1, -1) return wx def _check_input_shape(self, shape): """Checks the input shape and returns the number of input channels. """ if len(shape) == 2: in_channels = 1 elif len(shape) == 3: in_channels = 1 else: raise ValueError( "sincconv expects 2d or 3d inputs. Got " + str(len(shape)) ) # Kernel size must be odd if self.kernel_size % 2 == 0: raise ValueError( "The field kernel size must be an odd number. Got %s." % (self.kernel_size) ) return in_channels def _get_sinc_filters(self,): """This functions creates the sinc-filters to used for sinc-conv. """ # Computing the low frequencies of the filters low = self.min_low_hz + torch.abs(self.low_hz_) # Setting minimum band and minimum freq high = torch.clamp( low + self.min_band_hz + torch.abs(self.band_hz_), self.min_low_hz, self.sample_rate / 2, ) band = (high - low)[:, 0] # Passing from n_ to the corresponding f_times_t domain self.n_ = self.n_.to(self.device) self.window_ = self.window_.to(self.device) f_times_t_low = torch.matmul(low, self.n_) f_times_t_high = torch.matmul(high, self.n_) # Left part of the filters. band_pass_left = ( (torch.sin(f_times_t_high) - torch.sin(f_times_t_low)) / (self.n_ / 2) ) * self.window_ # Central element of the filter band_pass_center = 2 * band.view(-1, 1) # Right part of the filter (sinc filters are symmetric) band_pass_right = torch.flip(band_pass_left, dims=[1]) # Combining left, central, and right part of the filter band_pass = torch.cat( [band_pass_left, band_pass_center, band_pass_right], dim=1 ) # Amplitude normalization band_pass = band_pass / (2 * band[:, None]) # Setting up the filter coefficients filters = band_pass.view(self.out_channels, 1, self.kernel_size) return filters def _init_sinc_conv(self): """Initializes the parameters of the sinc_conv layer.""" # Initialize filterbanks such that they are equally spaced in Mel scale high_hz = self.sample_rate / 2 - (self.min_low_hz + self.min_band_hz) mel = torch.linspace( self._to_mel(self.min_low_hz), self._to_mel(high_hz), self.out_channels + 1, ) hz = self._to_hz(mel) # Filter lower frequency and bands self.low_hz_ = hz[:-1].unsqueeze(1) self.band_hz_ = (hz[1:] - hz[:-1]).unsqueeze(1) # Maiking freq and bands learnable self.low_hz_ = nn.Parameter(self.low_hz_) self.band_hz_ = nn.Parameter(self.band_hz_) # Hamming window n_lin = torch.linspace( 0, (self.kernel_size / 2) - 1, steps=int((self.kernel_size / 2)) ) self.window_ = 0.54 - 0.46 * torch.cos( 2 * math.pi * n_lin / self.kernel_size ) # Time axis (only half is needed due to symmetry) n = (self.kernel_size - 1) / 2.0 self.n_ = ( 2 * math.pi * torch.arange(-n, 0).view(1, -1) / self.sample_rate ) def _to_mel(self, hz): """Converts frequency in Hz to the mel scale. """ return 2595 * np.log10(1 + hz / 700) def _to_hz(self, mel): """Converts frequency in the mel scale to Hz. """ return 700 * (10 ** (mel / 2595) - 1) def _manage_padding( self, x, kernel_size: int, dilation: int, stride: int, ): """This function performs zero-padding on the time axis such that their lengths is unchanged after the convolution. Arguments --------- x : torch.Tensor Input tensor. kernel_size : int Size of kernel. dilation : int Dilation used. stride : int Stride. """ # Detecting input shape L_in = x.shape[-1] # Time padding padding = get_padding_elem(L_in, stride, kernel_size, dilation) # Applying padding x = F.pad(x, padding, mode=self.padding_mode) return x class Conv1d(nn.Module): """This function implements 1d convolution. Arguments --------- out_channels : int It is the number of output channels. kernel_size : int Kernel size of the convolutional filters. input_shape : tuple The shape of the input. Alternatively use ``in_channels``. in_channels : int The number of input channels. Alternatively use ``input_shape``. stride : int Stride factor of the convolutional filters. When the stride factor > 1, a decimation in time is performed. dilation : int Dilation factor of the convolutional filters. padding : str (same, valid, causal). If "valid", no padding is performed. If "same" and stride is 1, output shape is the same as the input shape. "causal" results in causal (dilated) convolutions. groups: int Number of blocked connections from input channels to output channels. padding_mode : str This flag specifies the type of padding. See torch.nn documentation for more information. skip_transpose : bool If False, uses batch x time x channel convention of speechbrain. If True, uses batch x channel x time convention. Example ------- >>> inp_tensor = torch.rand([10, 40, 16]) >>> cnn_1d = Conv1d( ... input_shape=inp_tensor.shape, out_channels=8, kernel_size=5 ... ) >>> out_tensor = cnn_1d(inp_tensor) >>> out_tensor.shape torch.Size([10, 40, 8]) """ def __init__( self, out_channels, kernel_size, input_shape=None, in_channels=None, stride=1, dilation=1, padding="same", groups=1, bias=True, padding_mode="reflect", skip_transpose=False, ): super().__init__() self.kernel_size = kernel_size self.stride = stride self.dilation = dilation self.padding = padding self.padding_mode = padding_mode self.unsqueeze = False self.skip_transpose = skip_transpose if input_shape is None and in_channels is None: raise ValueError("Must provide one of input_shape or in_channels") if in_channels is None: in_channels = self._check_input_shape(input_shape) self.conv = nn.Conv1d( in_channels, out_channels, self.kernel_size, stride=self.stride, dilation=self.dilation, padding=0, groups=groups, bias=bias, ) def forward(self, x): """Returns the output of the convolution. Arguments --------- x : torch.Tensor (batch, time, channel) input to convolve. 2d or 4d tensors are expected. """ if not self.skip_transpose: x = x.transpose(1, -1) if self.unsqueeze: x = x.unsqueeze(1) if self.padding == "same": x = self._manage_padding( x, self.kernel_size, self.dilation, self.stride ) elif self.padding == "causal": num_pad = (self.kernel_size - 1) * self.dilation x = F.pad(x, (num_pad, 0)) elif self.padding == "valid": pass else: raise ValueError( "Padding
#!/usr/bin/env python # coding: utf-8 # In[1]: import torch import numpy as np import matplotlib.pyplot as plt import csv from PIL import Image import matplotlib as mpl from tqdm import tqdm from sklearn.manifold import TSNE import umap from sklearn.metrics import silhouette_score , silhouette_samples from sklearn.discriminant_analysis import LinearDiscriminantAnalysis # # Visualizing the Disregarding classes # # ### Load data # In[2]: def access_data(letter,shot): feat = torch.load('features'+letter+str(shot),map_location=torch.device('cpu')) classifier= torch.load('classifier'+letter,map_location=torch.device('cpu')) accuracy = torch.load('complete_class_accuracy'+letter+str(shot)+'shots',map_location=torch.device('cpu')) idx = torch.load('complete_class_accuracy'+letter+'idx'+str(shot)+'shots',map_location=torch.device('cpu')) return feat,classifier,accuracy,idx # In[3]: shot=5 letter='A' feat,classifier,acc,idx = access_data(letter,shot) print(acc.shape) print(feat.shape) print(classifier.shape) print(idx.shape) # In[4]: shot=5 letter='B' featB,classifierB,accB,idxB = access_data(letter,shot) print(accB.shape) print(featB.shape) print(classifierB.shape) print(idxB.shape) # In[5]: base_mean = feat[:64].mean(-2) base_meanB = featB[:64].mean(-2) print(base_mean.shape) # In[6]: def sphering(features): return features / torch.norm(features, p = 2, dim = 2, keepdim = True) def centering(train_features, features): return features - train_features.reshape(-1, train_features.shape[2]).mean(dim = 0).unsqueeze(0).unsqueeze(0) feat_processed = sphering(centering(sphering(feat)[:64],sphering(feat) )) # In[7]: def proj_class(i,test_features,letter='A'): if letter=='A': #one projection per 64 clesses on miniimagenet w=base_mean[i] #select weights of the i-th class else: w=base_meanB[i] proj = torch.matmul(test_features,w)/ torch.norm(w)*2 #get coef of projection and normalize try: projection_ortho = proj.unsqueeze(-1).repeat(1,640) except: projection_ortho = proj.unsqueeze(-1).repeat(1,1,640) projection_ortho = projection_ortho w #vector of projection along w projection_ortho = test_features - projection_ortho #projection on the orthogonal space of w return projection_ortho # In[8]: filenametrain = '/home/r21lafar/Documents/dataset/miniimagenetimages/train.csv' filenametest = '/home/r21lafar/Documents/dataset/miniimagenetimages/test.csv' directory = '/home/r21lafar/Documents/dataset/miniimagenetimages/images/' def opencsv(filename): file = open(filename) csvreader = csv.reader(file) header = [] header = next(csvreader) print(header) rowstrain = [] rows = [] for row in csvreader: rows.append(row) return rows test = opencsv(filenametest) train = opencsv(filenametrain) def openimg(cl,title): if cl<64: src=train if cl>=80: src=test cl-=80 if type(cl)==int: plt.figure(figsize=(5,5)) idx=int((cl+0.5)600)+np.random.randint(-100,100) filename=src[idx][0] im = Image.open(directory +filename) plt.title(title) plt.imshow(np.array(im)) # In[9]: def distance_from_base(proj,run,plot=False,letter='A'): if letter=='A': fs_run = feat[acc[0,0,run].long()] else: fs_run = featB[acc[0,0,run].long()] if proj==-1 and run ==-1: if letter=='A': proto_fs = feat[-20:].mean(1) else: proto_fs = featB[-20:].mean(1) else: fs_run = torch.gather(fs_run,dim=1,index=idx[0,run].unsqueeze(-1).repeat(1,1,640).long()) proto_fs = fs_run[:,:shot].mean(1) if proj!=0: proto_fs=proj_class(proj-1,proto_fs,letter=letter) if letter=='A': D = torch.cdist(proto_fs,base_mean) else: D = torch.cdist(proto_fs,base_meanB) if plot: plt.figure() plt.imshow(D.detach().numpy(),aspect='auto') plt.colorbar() plt.title('distance between FS class mean and base class '+letter+' mean \n (whole base dataset) projection ' +str(proj) + ' (0 is no projection)') plt.xlabel('64 base class mean') plt.ylabel('FS prototype of class') return D # ## Create FS scenarii or runs # ### 2 ways # In[10]: n_runs, batch_few_shot_runs = 20,10 n_ways=2 def ncm(train_features, features, run_classes, run_indices, n_shots,i_proj): with torch.no_grad(): dim = features.shape[2] targets = torch.arange(n_ways).unsqueeze(1).unsqueeze(0) #features = preprocess(train_features, features) scores = [] score=0 for batch_idx in range(n_runs // batch_few_shot_runs): runs = generate_runs(features, run_classes, run_indices, batch_idx) means = torch.mean(runs[:,:,:n_shots], dim = 2) var_intra = runs[:,:,:n_shots].var(2).mean(-1) var_inter = runs[:,:,:n_shots].mean(2).var(1).mean(-1).unsqueeze(1) var = torch.cat((var_intra,var_inter),dim=1) distances = torch.norm(runs[:,:,n_shots:].reshape(batch_few_shot_runs, n_ways, 1, -1, dim) - means.reshape(batch_few_shot_runs, 1, n_ways, 1, dim), dim = 4, p = 2) winners = torch.min(distances, dim = 2)[1] accuracy = (winners == targets) if batch_idx==0: full_accuracy=accuracy full_mean=means full_var = var else: full_accuracy=torch.cat((full_accuracy,accuracy),dim=0) full_mean=torch.cat((full_mean,means),dim=0) full_var=torch.cat((full_var,var),dim=0) return full_accuracy,full_mean,full_var def generate_runs(data, run_classes, run_indices, batch_idx): n_runs, n_ways, n_samples = run_classes.shape[0], run_classes.shape[1], run_indices.shape[2] run_classes = run_classes[batch_idx batch_few_shot_runs : (batch_idx + 1) * batch_few_shot_runs] run_indices = run_indices[batch_idx * batch_few_shot_runs : (batch_idx + 1) * batch_few_shot_runs] run_classes = run_classes.unsqueeze(2).unsqueeze(3).repeat(1,1,data.shape[1], data.shape[2]) run_indices = run_indices.unsqueeze(3).repeat(1, 1, 1, data.shape[2]) datas = data.unsqueeze(0).repeat(batch_few_shot_runs, 1, 1, 1) cclasses = torch.gather(datas, 1, run_classes.to(torch.int64)) res = torch.gather(cclasses, 2, run_indices) return res def define_runs(n_ways, n_shots, n_queries, num_classes, elements_per_class): shuffle_classes = torch.LongTensor(np.arange(num_classes)) run_classes = torch.LongTensor(n_runs, n_ways) run_indices = torch.LongTensor(n_runs, n_ways, n_shots + n_queries) for i in range(n_runs): run_classes[i] = torch.randperm(num_classes)[:n_ways] for j in range(n_ways): run_indices[i,j] = torch.randperm(elements_per_class[run_classes[i, j]])[:n_shots + n_queries] return run_classes, run_indices run_classes, run_indices = define_runs(n_ways, 5, 500,20, [600 for i in range(20)]) # In[11]: A,_,full_var = ncm(feat[:64], feat[-20:], run_classes, run_indices, 5,0) B,_,full_var = ncm(featB[:64], featB[-20:],run_classes, run_indices, 5,0) plt.plot(A.float().mean(-1).mean(-1),label='backbone A') plt.plot(B.float().mean(-1).mean(-1),label='backbone B') plt.legend() plt.xlabel('run') plt.ylabel('accuracy') plt.title('no projection') # In[12]: for i in tqdm(range(65)): if i!=0: feature=proj_class(i-1,feat,'A') featureB=proj_class(i-1,featB,'B') else: feature =feat featureB =featB A,meanA,varA = ncm(feature[:64], feature[-20:], run_classes, run_indices, 5,0) B,meanB,varB = ncm(featureB[:64], featureB[-20:],run_classes, run_indices, 5,0) if i==0: fullA = A.unsqueeze(0) fullB = B.unsqueeze(0) fullmeanA = meanA.unsqueeze(0) fullmeanB = meanB.unsqueeze(0) fullvarA = varA.unsqueeze(0) fullvarB = varB.unsqueeze(0) else: fullA = torch.cat((fullA, A.unsqueeze(0)) ,dim = 0) fullB = torch.cat((fullB, B.unsqueeze(0)) ,dim = 0) fullmeanA = torch.cat((fullmeanA, meanA.unsqueeze(0)) ,dim = 0) fullmeanB = torch.cat((fullmeanB, meanB.unsqueeze(0)) ,dim = 0) fullvarA = torch.cat((fullvarA, varA.unsqueeze(0)) ,dim = 0) fullvarB = torch.cat((fullvarB, varB.unsqueeze(0)) ,dim = 0) # In[12]: def what_proj(run): return fullA[:,run].float().mean(-1).mean(-1).argsort()-1 # In[13]: fullA[0,2,0].float().mean(-1) # In[14]: run=0 fullvarA[0,run,:2].mean(-1)-fullvarA[0,run,2] # In[15]: for prj in [0,1,2,3]: plt.plot(fullvarA[prj,:,:2].mean(-1)-fullvarA[prj,:,2],fullA[prj,:,:].float().mean(-1).mean(-1),'.',label='projection '+ str(prj)) plt.xlabel('intraclass var -(minus)- interclass var') plt.ylabel('accuracy of run') plt.legend() plt.title('20 runs') # In[16]: best_boost =fullA.float().mean(-1).mean(-1).max(0)[0] - fullA[0,:,:].float().mean(-1).mean(-1) worst_boost =fullA.float().mean(-1).mean(-1).min(0)[0] - fullA[0,:,:].float().mean(-1).mean(-1) # In[17]: best_boost_id = fullA[:,:,:].float().mean(-1).mean(-1).max(0)[1] worst_boost_id = fullA[:,:,:].float().mean(-1).mean(-1).min(0)[1] # In[18]: intrater = fullvarA[:,:,:2].mean(-1)-fullvarA[:,:,2] intrater_min = intrater.min(0)[1] intrater_max = intrater.max(0)[1] # In[19]: boost = torch.zeros(intrater_min.shape) for i in range(intrater_min.shape[0]): boost[i] = fullA[intrater_min[i],i].float().mean(-1).mean(-1)-fullA[0,i].float().mean(-1).mean(-1) # In[20]: boost_max = torch.zeros(intrater_min.shape) for i in range(intrater_min.shape[0]): boost_max[i] = fullA[intrater_max[i],i].float().mean(-1).mean(-1)-fullA[0,i].float().mean(-1).mean(-1) # In[21]: fullA.shape # In[ ]: # In[22]: plt.hlines(y=0 ,xmin=0,xmax = 20) plt.plot(boost,'.',label='proj with min intra - inter') plt.plot(boost_max,'.',label='proj with max intra - inter') plt.plot(best_boost,'.',label='best boost') plt.xlabel('run') plt.ylabel('boost') plt.legend() # In[23]: intrater_best_boost = torch.zeros(intrater_min.shape) for i in range(intrater_min.shape[0]): intrater_best_boost[i] = intrater[best_boost_id[i],i] # In[24]: plt.plot(intrater_best_boost,'.', label = 'best boost') plt.plot(intrater.mean(0),'.', label = 'mean intra -inter') plt.plot(intrater.min(0)[0],'.', label = 'minimum intra -inter') plt.plot(intrater.max(0)[0],'.', label = 'maximum intra -inter') plt.ylabel('intra-class - interclass variance') plt.xlabel('run') plt.legend() # In[25]: intrater.min(dim=0)[0] # In[ ]: # In[26]: get_ipython().run_line_magic('matplotlib', 'inline') run = 12 nb_sample=30 mk_size=4 plt.figure() plt.plot(fullA[:,run].float().mean(-1).mean(-1)) plt.figure() plt.plot(fullvarA[:,run].float().mean(-1).mean(-1)) FULLumap = torch.cat((base_mean,fullmeanA[0,run],feat[80+run_classes[run],:nb_sample].reshape(n_waysnb_sample,640) )) umapA=umap.UMAP().fit_transform(FULLumap) plt.figure() plt.plot(umapA[:64,0],umapA[:64,1],'o',label='base', c='b') plt.plot(umapA[64,0],umapA[64,1],'',label='proto 0', c='purple',markersize=20) plt.plot(umapA[65,0],umapA[65,1],'',label='proto 1', c='k',markersize=20) plt.plot(umapA[69:69+nb_sample,0],umapA[64+5:69+nb_sample,1],'.',label='samples 0',markersize=mk_size, c='purple') plt.plot(umapA[64+5+nb_sample:69+nb_sample2,0],umapA[64+5+nb_sample:69+nb_sample2,1],'.',label='samples 1',markersize=mk_size, c='k') plt.legend() boost = fullA[:,run].float().mean(-1).mean(-1)-fullA[0,run].float().mean(-1).mean(-1) example = what_proj(run) signboost = boost>=0. label = [str(i) for i in range(65)] couleur = ['red','green'] for i in range(len(label)): plt.annotate(label[i], (umapA[example[i],0], umapA[example[i],1]), color = couleur[signboost[example[i]]1]) # In[27]: get_ipython().run_line_magic('matplotlib', 'inline') run = 0 plt.plot(fullA[:,run].float().mean(-1).mean(-1),label='backbone A') plt.plot(fullB[:,run].float().mean(-1).mean(-1),label='backbone B') plt.legend() plt.xlabel('projection') plt.ylabel('accuracy') print(fullA[:,run].shape) # In[28]: feat.shape # In[29]: nb_samples = 100 feat_sil = feat[:,:nb_samples].reshape(-1,640) # In[30]: labels = torch.arange(0,100).unsqueeze(1).repeat(1,nb_samples).reshape(-1) # In[31]: sil = silhouette_samples(feat_sil,labels) # In[32]: sil_r = sil.reshape(100,nb_samples) # In[33]: plt.plot(sil_r.mean(1),'.') plt.xlabel('class') plt.ylabel('silhouette') plt.vlines(x=64,ymin=sil_r.mean(1).min(),ymax = sil_r.mean(1).max()) plt.vlines(x=64+20,ymin=sil_r.mean(1).min(),ymax = sil_r.mean(1).max()) # In[34]: feat.shape # In[35]: plt.plot(feat.var(1).mean(1),'.',label='intra class variance') plt.hlines(y=feat.mean(1).var(0).mean(),xmin=0,xmax=100,label='interclass variance') plt.legend() plt.xlabel('class') plt.ylabel('mean variance over features') plt.title('whole dataset') # ## Project on aligning vector # In[11]: def proj_vec(v_proj): proj = torch.matmul(features,v_proj)/ torch.norm(v_proj)2 #get coef of projection and normalize return proj # In[12]: run_classes, run_indices = define_runs(n_ways, 5, 500,20, [600 for i in range(20)]) A,full_meanA,full_varA = ncm(feat[:64], feat[-20:], run_classes, run_indices, 5,0) B,full_meanB,full_var = ncm(featB[:64], featB[-20:],run_classes, run_indices, 5,0) # In[13]: def ncm_proj(train_features, features, run_classes, run_indices, n_shots): with torch.no_grad(): dim = features.shape[2] targets = torch.arange(n_ways).unsqueeze(1).unsqueeze(0) #features = preprocess(train_features, features) scores = [] score=0 for batch_idx in range(n_runs // batch_few_shot_runs): runs = generate_runs(features, run_classes, run_indices, batch_idx) means = torch.mean(runs[:,:,:n_shots], dim = 2) v_diff = (means[:,0]-means[:,1]) #v_diff = torch.randn(means[:,0].shape) var_intra = runs[:,:,:n_shots].var(2).mean(-1) var_inter = runs[:,:,:n_shots].mean(2).var(1).mean(-1).unsqueeze(1) var = torch.cat((var_intra,var_inter),dim=1) proj_means =torch.zeros(means[:,:,0].shape) proj_runs =torch.zeros(runs[:,:,:,0].shape) for i in range(batch_few_shot_runs): proj_runs[i] = torch.matmul(v_diff[i], torch.swapaxes(runs[i],-1,-2)) proj_means[i] = torch.matmul(v_diff[i], torch.swapaxes(means[i],-1,-2)) distances = torch.norm(proj_runs[:,:,n_shots:].reshape(batch_few_shot_runs, n_ways, 1, -1, 1) - proj_means.reshape(batch_few_shot_runs, 1, n_ways, 1, 1), dim = 4, p = 2) winners = torch.min(distances, dim = 2)[1] accuracy = (winners == targets) if batch_idx==0: full_accuracy=accuracy full_mean=means full_var = var else: full_accuracy=torch.cat((full_accuracy,accuracy),dim=0) full_mean=torch.cat((full_mean,means),dim=0) full_var=torch.cat((full_var,var),dim=0) return full_accuracy,full_mean,full_var # In[ ]: n_runs = 1000 run_classes, run_indices = define_runs(n_ways, 5, 500,20, [600 for i in range(20)]) n_shots=5 # In[105]: a,b,c = ncm_proj(feat[:64], feat[-20:], run_classes, run_indices, n_shots) print(a.float().mean()) # In[104]: a,b,c = ncm(feat[:64], feat[-20:], run_classes, run_indices, n_shots,0) print(a.float().mean()) # Avec et sans projection sur l'axe reliant les deux protopypes ou templates. La performance reste la même # ## test suppression de classe orthogonale à v_diff # In[101]: def ncm_del_otho_vdif(train_features, features, run_classes, run_indices, n_shots): with torch.no_grad(): dim = features.shape[2] targets = torch.arange(n_ways).unsqueeze(1).unsqueeze(0) #features = preprocess(train_features, features) scores = [] score=0 for batch_idx in tqdm(range(n_runs // batch_few_shot_runs)): runs = generate_runs(features, run_classes, run_indices, batch_idx) for i in range(3): runs,means = remove_the_class(runs) var_intra = runs[:,:,:n_shots].var(2).mean(-1) var_inter = runs[:,:,:n_shots].mean(2).var(1).mean(-1).unsqueeze(1) var = torch.cat((var_intra,var_inter),dim=1) distances = torch.norm(runs[:,:,n_shots:].reshape(batch_few_shot_runs, n_ways, 1, -1, dim) - means.reshape(batch_few_shot_runs, 1, n_ways, 1, dim), dim = 4, p = 2) winners = torch.min(distances, dim = 2)[1] accuracy = (winners == targets) if batch_idx==0: full_accuracy=accuracy full_mean=means full_var = var else: full_accuracy=torch.cat((full_accuracy,accuracy),dim=0) full_mean=torch.cat((full_mean,means),dim=0) full_var=torch.cat((full_var,var),dim=0) return full_accuracy,full_mean,full_var def remove_the_class(runs): means = torch.mean(runs[:,:,:n_shots], dim = 2) v_diff = (means[:,0]-means[:,1]) #axis between proto 0 and proto 1 proj_base = torch.zeros(batch_few_shot_runs,base_mean.shape[0]) for j in range(batch_few_shot_runs): for i in range(base_mean.shape[0]): w = base_mean[i] proj_base[j,i] = torch.torch.matmul(v_diff[j], w)/torch.norm(w) id_proj = abs(proj_base).min(1)[1] for j in range(batch_few_shot_runs): runs[j] = proj_class(id_proj[j],runs[j]) means = torch.mean(runs[:,:,:n_shots], dim = 2) return runs,means # In[ ]: # In[99]: a,b,c = ncm_del_otho_vdif(feat[:64], feat[-20:], run_classes, run_indices, n_shots) print(a.float().mean().item()) # In[100]: a,b,c = ncm(feat_processed[:64], feat_processed[-20:], run_classes, run_indices, n_shots,0) print(a.float().mean()) a,b,c = ncm_del_otho_vdif(feat_processed[:64], feat_processed[-20:], run_classes, run_indices, n_shots) print(a.float().mean()) # ## Test du LDA / shrinkage # In[15]: def LDA(run,kwargs): s = run.shape s_shots = run[:,:n_shots].shape run_reshaped = run.reshape(s[0]s[1],-1) run_reshaped_shots = run[:,:n_shots].reshape(s_shots[0]s_shots[1],-1) target = torch.cat((torch.zeros(n_shots),torch.ones(n_shots))) clf = LinearDiscriminantAnalysis(kwargs) clf.fit(run_reshaped_shots, target) out = torch.tensor(clf.transform(run_reshaped)) out = out.reshape((s[0],s[1],-1)) return out # In[16]: def ncm_lda(train_features, features, run_classes, run_indices, n_shots,dictlda): with torch.no_grad(): dim = features.shape[2] targets = torch.arange(n_ways).unsqueeze(1).unsqueeze(0) #features = preprocess(train_features, features) scores = [] score=0 for batch_idx in tqdm(range(n_runs // batch_few_shot_runs)): runs = generate_runs(features, run_classes, run_indices, batch_idx) runs_reduced = torch.zeros((runs.shape[0],runs.shape[1],runs.shape[2],n_components)) for i,run in enumerate(runs): runs_reduced[i] = LDA(run,dictlda) means = torch.mean(runs_reduced[:,:,:n_shots], dim = 2) distances = torch.norm(runs_reduced[:,:,n_shots:].reshape(batch_few_shot_runs, n_ways, 1, -1, n_components)
# -- coding: utf-8 -- """ This module """ import attr import typing from ..core.model import ( Property, Resource, Tag, GetAtt, TypeHint, TypeCheck, ) from ..core.constant import AttrMeta #--- Property declaration --- @attr.s class PropDeliveryStreamAmazonopensearchserviceRetryOptions(Property): """ AWS Object Type = "AWS::KinesisFirehose::DeliveryStream.AmazonopensearchserviceRetryOptions" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-amazonopensearchserviceretryoptions.html Property Document: - ``p_DurationInSeconds``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-amazonopensearchserviceretryoptions.html#cfn-kinesisfirehose-deliverystream-amazonopensearchserviceretryoptions-durationinseconds """ AWS_OBJECT_TYPE = "AWS::KinesisFirehose::DeliveryStream.AmazonopensearchserviceRetryOptions" p_DurationInSeconds: int = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(int)), metadata={AttrMeta.PROPERTY_NAME: "DurationInSeconds"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-amazonopensearchserviceretryoptions.html#cfn-kinesisfirehose-deliverystream-amazonopensearchserviceretryoptions-durationinseconds""" @attr.s class PropDeliveryStreamHiveJsonSerDe(Property): """ AWS Object Type = "AWS::KinesisFirehose::DeliveryStream.HiveJsonSerDe" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-hivejsonserde.html Property Document: - ``p_TimestampFormats``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-hivejsonserde.html#cfn-kinesisfirehose-deliverystream-hivejsonserde-timestampformats """ AWS_OBJECT_TYPE = "AWS::KinesisFirehose::DeliveryStream.HiveJsonSerDe" p_TimestampFormats: typing.List[TypeHint.intrinsic_str] = attr.ib( default=None, validator=attr.validators.optional(attr.validators.deep_iterable(member_validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), iterable_validator=attr.validators.instance_of(list))), metadata={AttrMeta.PROPERTY_NAME: "TimestampFormats"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-hivejsonserde.html#cfn-kinesisfirehose-deliverystream-hivejsonserde-timestampformats""" @attr.s class PropDeliveryStreamSchemaConfiguration(Property): """ AWS Object Type = "AWS::KinesisFirehose::DeliveryStream.SchemaConfiguration" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-schemaconfiguration.html Property Document: - ``p_CatalogId``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-schemaconfiguration.html#cfn-kinesisfirehose-deliverystream-schemaconfiguration-catalogid - ``p_DatabaseName``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-schemaconfiguration.html#cfn-kinesisfirehose-deliverystream-schemaconfiguration-databasename - ``p_Region``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-schemaconfiguration.html#cfn-kinesisfirehose-deliverystream-schemaconfiguration-region - ``p_RoleARN``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-schemaconfiguration.html#cfn-kinesisfirehose-deliverystream-schemaconfiguration-rolearn - ``p_TableName``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-schemaconfiguration.html#cfn-kinesisfirehose-deliverystream-schemaconfiguration-tablename - ``p_VersionId``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-schemaconfiguration.html#cfn-kinesisfirehose-deliverystream-schemaconfiguration-versionid """ AWS_OBJECT_TYPE = "AWS::KinesisFirehose::DeliveryStream.SchemaConfiguration" p_CatalogId: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "CatalogId"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-schemaconfiguration.html#cfn-kinesisfirehose-deliverystream-schemaconfiguration-catalogid""" p_DatabaseName: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "DatabaseName"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-schemaconfiguration.html#cfn-kinesisfirehose-deliverystream-schemaconfiguration-databasename""" p_Region: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "Region"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-schemaconfiguration.html#cfn-kinesisfirehose-deliverystream-schemaconfiguration-region""" p_RoleARN: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "RoleARN"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-schemaconfiguration.html#cfn-kinesisfirehose-deliverystream-schemaconfiguration-rolearn""" p_TableName: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "TableName"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-schemaconfiguration.html#cfn-kinesisfirehose-deliverystream-schemaconfiguration-tablename""" p_VersionId: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "VersionId"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-schemaconfiguration.html#cfn-kinesisfirehose-deliverystream-schemaconfiguration-versionid""" @attr.s class PropDeliveryStreamSplunkRetryOptions(Property): """ AWS Object Type = "AWS::KinesisFirehose::DeliveryStream.SplunkRetryOptions" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-splunkretryoptions.html Property Document: - ``p_DurationInSeconds``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-splunkretryoptions.html#cfn-kinesisfirehose-deliverystream-splunkretryoptions-durationinseconds """ AWS_OBJECT_TYPE = "AWS::KinesisFirehose::DeliveryStream.SplunkRetryOptions" p_DurationInSeconds: int = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(int)), metadata={AttrMeta.PROPERTY_NAME: "DurationInSeconds"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-splunkretryoptions.html#cfn-kinesisfirehose-deliverystream-splunkretryoptions-durationinseconds""" @attr.s class PropDeliveryStreamHttpEndpointConfiguration(Property): """ AWS Object Type = "AWS::KinesisFirehose::DeliveryStream.HttpEndpointConfiguration" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-httpendpointconfiguration.html Property Document: - ``rp_Url``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-httpendpointconfiguration.html#cfn-kinesisfirehose-deliverystream-httpendpointconfiguration-url - ``p_AccessKey``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-httpendpointconfiguration.html#cfn-kinesisfirehose-deliverystream-httpendpointconfiguration-accesskey - ``p_Name``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-httpendpointconfiguration.html#cfn-kinesisfirehose-deliverystream-httpendpointconfiguration-name """ AWS_OBJECT_TYPE = "AWS::KinesisFirehose::DeliveryStream.HttpEndpointConfiguration" rp_Url: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), metadata={AttrMeta.PROPERTY_NAME: "Url"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-httpendpointconfiguration.html#cfn-kinesisfirehose-deliverystream-httpendpointconfiguration-url""" p_AccessKey: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "AccessKey"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-httpendpointconfiguration.html#cfn-kinesisfirehose-deliverystream-httpendpointconfiguration-accesskey""" p_Name: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "Name"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-httpendpointconfiguration.html#cfn-kinesisfirehose-deliverystream-httpendpointconfiguration-name""" @attr.s class PropDeliveryStreamCopyCommand(Property): """ AWS Object Type = "AWS::KinesisFirehose::DeliveryStream.CopyCommand" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-copycommand.html Property Document: - ``rp_DataTableName``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-copycommand.html#cfn-kinesisfirehose-deliverystream-copycommand-datatablename - ``p_CopyOptions``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-copycommand.html#cfn-kinesisfirehose-deliverystream-copycommand-copyoptions - ``p_DataTableColumns``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-copycommand.html#cfn-kinesisfirehose-deliverystream-copycommand-datatablecolumns """ AWS_OBJECT_TYPE = "AWS::KinesisFirehose::DeliveryStream.CopyCommand" rp_DataTableName: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), metadata={AttrMeta.PROPERTY_NAME: "DataTableName"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-copycommand.html#cfn-kinesisfirehose-deliverystream-copycommand-datatablename""" p_CopyOptions: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "CopyOptions"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-copycommand.html#cfn-kinesisfirehose-deliverystream-copycommand-copyoptions""" p_DataTableColumns: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "DataTableColumns"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-copycommand.html#cfn-kinesisfirehose-deliverystream-copycommand-datatablecolumns""" @attr.s class PropDeliveryStreamOpenXJsonSerDe(Property): """ AWS Object Type = "AWS::KinesisFirehose::DeliveryStream.OpenXJsonSerDe" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-openxjsonserde.html Property Document: - ``p_CaseInsensitive``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-openxjsonserde.html#cfn-kinesisfirehose-deliverystream-openxjsonserde-caseinsensitive - ``p_ColumnToJsonKeyMappings``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-openxjsonserde.html#cfn-kinesisfirehose-deliverystream-openxjsonserde-columntojsonkeymappings - ``p_ConvertDotsInJsonKeysToUnderscores``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-openxjsonserde.html#cfn-kinesisfirehose-deliverystream-openxjsonserde-convertdotsinjsonkeystounderscores """ AWS_OBJECT_TYPE = "AWS::KinesisFirehose::DeliveryStream.OpenXJsonSerDe" p_CaseInsensitive: bool = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(bool)), metadata={AttrMeta.PROPERTY_NAME: "CaseInsensitive"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-openxjsonserde.html#cfn-kinesisfirehose-deliverystream-openxjsonserde-caseinsensitive""" p_ColumnToJsonKeyMappings: typing.Dict[str, TypeHint.intrinsic_str] = attr.ib( default=None, validator=attr.validators.optional(attr.validators.deep_mapping(key_validator=attr.validators.instance_of(str), value_validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type))), metadata={AttrMeta.PROPERTY_NAME: "ColumnToJsonKeyMappings"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-openxjsonserde.html#cfn-kinesisfirehose-deliverystream-openxjsonserde-columntojsonkeymappings""" p_ConvertDotsInJsonKeysToUnderscores: bool = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(bool)), metadata={AttrMeta.PROPERTY_NAME: "ConvertDotsInJsonKeysToUnderscores"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-openxjsonserde.html#cfn-kinesisfirehose-deliverystream-openxjsonserde-convertdotsinjsonkeystounderscores""" @attr.s class PropDeliveryStreamOrcSerDe(Property): """ AWS Object Type = "AWS::KinesisFirehose::DeliveryStream.OrcSerDe" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-orcserde.html Property Document: - ``p_BlockSizeBytes``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-orcserde.html#cfn-kinesisfirehose-deliverystream-orcserde-blocksizebytes - ``p_BloomFilterColumns``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-orcserde.html#cfn-kinesisfirehose-deliverystream-orcserde-bloomfiltercolumns - ``p_BloomFilterFalsePositiveProbability``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-orcserde.html#cfn-kinesisfirehose-deliverystream-orcserde-bloomfilterfalsepositiveprobability - ``p_Compression``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-orcserde.html#cfn-kinesisfirehose-deliverystream-orcserde-compression - ``p_DictionaryKeyThreshold``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-orcserde.html#cfn-kinesisfirehose-deliverystream-orcserde-dictionarykeythreshold - ``p_EnablePadding``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-orcserde.html#cfn-kinesisfirehose-deliverystream-orcserde-enablepadding - ``p_FormatVersion``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-orcserde.html#cfn-kinesisfirehose-deliverystream-orcserde-formatversion - ``p_PaddingTolerance``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-orcserde.html#cfn-kinesisfirehose-deliverystream-orcserde-paddingtolerance - ``p_RowIndexStride``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-orcserde.html#cfn-kinesisfirehose-deliverystream-orcserde-rowindexstride - ``p_StripeSizeBytes``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-orcserde.html#cfn-kinesisfirehose-deliverystream-orcserde-stripesizebytes """ AWS_OBJECT_TYPE = "AWS::KinesisFirehose::DeliveryStream.OrcSerDe" p_BlockSizeBytes: int = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(int)), metadata={AttrMeta.PROPERTY_NAME: "BlockSizeBytes"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-orcserde.html#cfn-kinesisfirehose-deliverystream-orcserde-blocksizebytes""" p_BloomFilterColumns: typing.List[TypeHint.intrinsic_str] = attr.ib( default=None, validator=attr.validators.optional(attr.validators.deep_iterable(member_validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), iterable_validator=attr.validators.instance_of(list))), metadata={AttrMeta.PROPERTY_NAME: "BloomFilterColumns"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-orcserde.html#cfn-kinesisfirehose-deliverystream-orcserde-bloomfiltercolumns""" p_BloomFilterFalsePositiveProbability: float = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(float)), metadata={AttrMeta.PROPERTY_NAME: "BloomFilterFalsePositiveProbability"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-orcserde.html#cfn-kinesisfirehose-deliverystream-orcserde-bloomfilterfalsepositiveprobability""" p_Compression: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "Compression"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-orcserde.html#cfn-kinesisfirehose-deliverystream-orcserde-compression""" p_DictionaryKeyThreshold: float = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(float)), metadata={AttrMeta.PROPERTY_NAME: "DictionaryKeyThreshold"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-orcserde.html#cfn-kinesisfirehose-deliverystream-orcserde-dictionarykeythreshold""" p_EnablePadding: bool = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(bool)), metadata={AttrMeta.PROPERTY_NAME: "EnablePadding"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-orcserde.html#cfn-kinesisfirehose-deliverystream-orcserde-enablepadding""" p_FormatVersion: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "FormatVersion"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-orcserde.html#cfn-kinesisfirehose-deliverystream-orcserde-formatversion""" p_PaddingTolerance: float = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(float)), metadata={AttrMeta.PROPERTY_NAME: "PaddingTolerance"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-orcserde.html#cfn-kinesisfirehose-deliverystream-orcserde-paddingtolerance""" p_RowIndexStride: int = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(int)), metadata={AttrMeta.PROPERTY_NAME: "RowIndexStride"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-orcserde.html#cfn-kinesisfirehose-deliverystream-orcserde-rowindexstride""" p_StripeSizeBytes: int = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(int)), metadata={AttrMeta.PROPERTY_NAME: "StripeSizeBytes"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-orcserde.html#cfn-kinesisfirehose-deliverystream-orcserde-stripesizebytes""" @attr.s class PropDeliveryStreamElasticsearchBufferingHints(Property): """ AWS Object Type = "AWS::KinesisFirehose::DeliveryStream.ElasticsearchBufferingHints" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-elasticsearchbufferinghints.html Property Document: - ``p_IntervalInSeconds``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-elasticsearchbufferinghints.html#cfn-kinesisfirehose-deliverystream-elasticsearchbufferinghints-intervalinseconds - ``p_SizeInMBs``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-elasticsearchbufferinghints.html#cfn-kinesisfirehose-deliverystream-elasticsearchbufferinghints-sizeinmbs """ AWS_OBJECT_TYPE = "AWS::KinesisFirehose::DeliveryStream.ElasticsearchBufferingHints" p_IntervalInSeconds: int = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(int)), metadata={AttrMeta.PROPERTY_NAME: "IntervalInSeconds"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-elasticsearchbufferinghints.html#cfn-kinesisfirehose-deliverystream-elasticsearchbufferinghints-intervalinseconds""" p_SizeInMBs: int = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(int)), metadata={AttrMeta.PROPERTY_NAME: "SizeInMBs"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-elasticsearchbufferinghints.html#cfn-kinesisfirehose-deliverystream-elasticsearchbufferinghints-sizeinmbs""" @attr.s class PropDeliveryStreamCloudWatchLoggingOptions(Property): """ AWS Object Type = "AWS::KinesisFirehose::DeliveryStream.CloudWatchLoggingOptions" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-cloudwatchloggingoptions.html Property Document: - ``p_Enabled``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-cloudwatchloggingoptions.html#cfn-kinesisfirehose-deliverystream-cloudwatchloggingoptions-enabled - ``p_LogGroupName``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-cloudwatchloggingoptions.html#cfn-kinesisfirehose-deliverystream-cloudwatchloggingoptions-loggroupname - ``p_LogStreamName``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-cloudwatchloggingoptions.html#cfn-kinesisfirehose-deliverystream-cloudwatchloggingoptions-logstreamname """ AWS_OBJECT_TYPE = "AWS::KinesisFirehose::DeliveryStream.CloudWatchLoggingOptions" p_Enabled: bool = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(bool)), metadata={AttrMeta.PROPERTY_NAME: "Enabled"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-cloudwatchloggingoptions.html#cfn-kinesisfirehose-deliverystream-cloudwatchloggingoptions-enabled""" p_LogGroupName: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "LogGroupName"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-cloudwatchloggingoptions.html#cfn-kinesisfirehose-deliverystream-cloudwatchloggingoptions-loggroupname""" p_LogStreamName: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "LogStreamName"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-cloudwatchloggingoptions.html#cfn-kinesisfirehose-deliverystream-cloudwatchloggingoptions-logstreamname""" @attr.s class PropDeliveryStreamBufferingHints(Property): """ AWS Object Type = "AWS::KinesisFirehose::DeliveryStream.BufferingHints" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-bufferinghints.html Property Document: - ``p_IntervalInSeconds``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-bufferinghints.html#cfn-kinesisfirehose-deliverystream-bufferinghints-intervalinseconds - ``p_SizeInMBs``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-bufferinghints.html#cfn-kinesisfirehose-deliverystream-bufferinghints-sizeinmbs """ AWS_OBJECT_TYPE = "AWS::KinesisFirehose::DeliveryStream.BufferingHints" p_IntervalInSeconds: int = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(int)), metadata={AttrMeta.PROPERTY_NAME: "IntervalInSeconds"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-bufferinghints.html#cfn-kinesisfirehose-deliverystream-bufferinghints-intervalinseconds""" p_SizeInMBs: int = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(int)), metadata={AttrMeta.PROPERTY_NAME: "SizeInMBs"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-bufferinghints.html#cfn-kinesisfirehose-deliverystream-bufferinghints-sizeinmbs""" @attr.s class PropDeliveryStreamProcessorParameter(Property): """ AWS Object Type = "AWS::KinesisFirehose::DeliveryStream.ProcessorParameter" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-processorparameter.html Property Document: - ``rp_ParameterName``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-processorparameter.html#cfn-kinesisfirehose-deliverystream-processorparameter-parametername - ``rp_ParameterValue``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-processorparameter.html#cfn-kinesisfirehose-deliverystream-processorparameter-parametervalue """ AWS_OBJECT_TYPE = "AWS::KinesisFirehose::DeliveryStream.ProcessorParameter" rp_ParameterName: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), metadata={AttrMeta.PROPERTY_NAME: "ParameterName"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-processorparameter.html#cfn-kinesisfirehose-deliverystream-processorparameter-parametername""" rp_ParameterValue: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), metadata={AttrMeta.PROPERTY_NAME: "ParameterValue"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-processorparameter.html#cfn-kinesisfirehose-deliverystream-processorparameter-parametervalue""" @attr.s class PropDeliveryStreamAmazonopensearchserviceBufferingHints(Property): """ AWS Object Type = "AWS::KinesisFirehose::DeliveryStream.AmazonopensearchserviceBufferingHints" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-amazonopensearchservicebufferinghints.html Property Document: - ``p_IntervalInSeconds``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-amazonopensearchservicebufferinghints.html#cfn-kinesisfirehose-deliverystream-amazonopensearchservicebufferinghints-intervalinseconds - ``p_SizeInMBs``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-amazonopensearchservicebufferinghints.html#cfn-kinesisfirehose-deliverystream-amazonopensearchservicebufferinghints-sizeinmbs """ AWS_OBJECT_TYPE = "AWS::KinesisFirehose::DeliveryStream.AmazonopensearchserviceBufferingHints" p_IntervalInSeconds: int = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(int)), metadata={AttrMeta.PROPERTY_NAME: "IntervalInSeconds"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-amazonopensearchservicebufferinghints.html#cfn-kinesisfirehose-deliverystream-amazonopensearchservicebufferinghints-intervalinseconds""" p_SizeInMBs: int = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(int)), metadata={AttrMeta.PROPERTY_NAME: "SizeInMBs"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-amazonopensearchservicebufferinghints.html#cfn-kinesisfirehose-deliverystream-amazonopensearchservicebufferinghints-sizeinmbs""" @attr.s class PropDeliveryStreamDeliveryStreamEncryptionConfigurationInput(Property): """ AWS Object Type = "AWS::KinesisFirehose::DeliveryStream.DeliveryStreamEncryptionConfigurationInput" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-deliverystreamencryptionconfigurationinput.html Property Document: - ``rp_KeyType``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-deliverystreamencryptionconfigurationinput.html#cfn-kinesisfirehose-deliverystream-deliverystreamencryptionconfigurationinput-keytype - ``p_KeyARN``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-deliverystreamencryptionconfigurationinput.html#cfn-kinesisfirehose-deliverystream-deliverystreamencryptionconfigurationinput-keyarn """ AWS_OBJECT_TYPE = "AWS::KinesisFirehose::DeliveryStream.DeliveryStreamEncryptionConfigurationInput" rp_KeyType: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), metadata={AttrMeta.PROPERTY_NAME: "KeyType"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-deliverystreamencryptionconfigurationinput.html#cfn-kinesisfirehose-deliverystream-deliverystreamencryptionconfigurationinput-keytype""" p_KeyARN: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "KeyARN"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-deliverystreamencryptionconfigurationinput.html#cfn-kinesisfirehose-deliverystream-deliverystreamencryptionconfigurationinput-keyarn""" @attr.s class PropDeliveryStreamElasticsearchRetryOptions(Property): """ AWS Object Type = "AWS::KinesisFirehose::DeliveryStream.ElasticsearchRetryOptions" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-elasticsearchretryoptions.html Property Document: - ``p_DurationInSeconds``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-elasticsearchretryoptions.html#cfn-kinesisfirehose-deliverystream-elasticsearchretryoptions-durationinseconds """ AWS_OBJECT_TYPE = "AWS::KinesisFirehose::DeliveryStream.ElasticsearchRetryOptions" p_DurationInSeconds: int = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(int)), metadata={AttrMeta.PROPERTY_NAME: "DurationInSeconds"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-elasticsearchretryoptions.html#cfn-kinesisfirehose-deliverystream-elasticsearchretryoptions-durationinseconds""" @attr.s class PropDeliveryStreamKMSEncryptionConfig(Property): """ AWS Object Type = "AWS::KinesisFirehose::DeliveryStream.KMSEncryptionConfig" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-kmsencryptionconfig.html Property Document: - ``rp_AWSKMSKeyARN``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-kmsencryptionconfig.html#cfn-kinesisfirehose-deliverystream-kmsencryptionconfig-awskmskeyarn """ AWS_OBJECT_TYPE = "AWS::KinesisFirehose::DeliveryStream.KMSEncryptionConfig" rp_AWSKMSKeyARN: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), metadata={AttrMeta.PROPERTY_NAME: "AWSKMSKeyARN"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-kmsencryptionconfig.html#cfn-kinesisfirehose-deliverystream-kmsencryptionconfig-awskmskeyarn""" @attr.s class PropDeliveryStreamDeserializer(Property): """ AWS Object Type = "AWS::KinesisFirehose::DeliveryStream.Deserializer" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-deserializer.html Property Document: - ``p_HiveJsonSerDe``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-deserializer.html#cfn-kinesisfirehose-deliverystream-deserializer-hivejsonserde - ``p_OpenXJsonSerDe``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-deserializer.html#cfn-kinesisfirehose-deliverystream-deserializer-openxjsonserde """ AWS_OBJECT_TYPE = "AWS::KinesisFirehose::DeliveryStream.Deserializer" p_HiveJsonSerDe: typing.Union['PropDeliveryStreamHiveJsonSerDe', dict] = attr.ib( default=None, converter=PropDeliveryStreamHiveJsonSerDe.from_dict, validator=attr.validators.optional(attr.validators.instance_of(PropDeliveryStreamHiveJsonSerDe)), metadata={AttrMeta.PROPERTY_NAME: "HiveJsonSerDe"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-deserializer.html#cfn-kinesisfirehose-deliverystream-deserializer-hivejsonserde""" p_OpenXJsonSerDe: typing.Union['PropDeliveryStreamOpenXJsonSerDe', dict] = attr.ib( default=None, converter=PropDeliveryStreamOpenXJsonSerDe.from_dict, validator=attr.validators.optional(attr.validators.instance_of(PropDeliveryStreamOpenXJsonSerDe)), metadata={AttrMeta.PROPERTY_NAME: "OpenXJsonSerDe"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-deserializer.html#cfn-kinesisfirehose-deliverystream-deserializer-openxjsonserde""" @attr.s class PropDeliveryStreamKinesisStreamSourceConfiguration(Property): """ AWS Object Type = "AWS::KinesisFirehose::DeliveryStream.KinesisStreamSourceConfiguration" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-kinesisstreamsourceconfiguration.html Property Document: - ``rp_KinesisStreamARN``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-kinesisstreamsourceconfiguration.html#cfn-kinesisfirehose-deliverystream-kinesisstreamsourceconfiguration-kinesisstreamarn - ``rp_RoleARN``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-kinesisstreamsourceconfiguration.html#cfn-kinesisfirehose-deliverystream-kinesisstreamsourceconfiguration-rolearn """ AWS_OBJECT_TYPE = "AWS::KinesisFirehose::DeliveryStream.KinesisStreamSourceConfiguration" rp_KinesisStreamARN: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), metadata={AttrMeta.PROPERTY_NAME: "KinesisStreamARN"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-kinesisstreamsourceconfiguration.html#cfn-kinesisfirehose-deliverystream-kinesisstreamsourceconfiguration-kinesisstreamarn""" rp_RoleARN: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), metadata={AttrMeta.PROPERTY_NAME: "RoleARN"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-kinesisstreamsourceconfiguration.html#cfn-kinesisfirehose-deliverystream-kinesisstreamsourceconfiguration-rolearn""" @attr.s class PropDeliveryStreamRedshiftRetryOptions(Property): """ AWS Object Type = "AWS::KinesisFirehose::DeliveryStream.RedshiftRetryOptions" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-redshiftretryoptions.html Property Document: - ``p_DurationInSeconds``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-redshiftretryoptions.html#cfn-kinesisfirehose-deliverystream-redshiftretryoptions-durationinseconds """ AWS_OBJECT_TYPE = "AWS::KinesisFirehose::DeliveryStream.RedshiftRetryOptions" p_DurationInSeconds: int = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(int)), metadata={AttrMeta.PROPERTY_NAME: "DurationInSeconds"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-redshiftretryoptions.html#cfn-kinesisfirehose-deliverystream-redshiftretryoptions-durationinseconds""" @attr.s class PropDeliveryStreamRetryOptions(Property): """ AWS Object Type = "AWS::KinesisFirehose::DeliveryStream.RetryOptions" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-retryoptions.html Property Document: - ``p_DurationInSeconds``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-retryoptions.html#cfn-kinesisfirehose-deliverystream-retryoptions-durationinseconds """ AWS_OBJECT_TYPE = "AWS::KinesisFirehose::DeliveryStream.RetryOptions" p_DurationInSeconds: int = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(int)), metadata={AttrMeta.PROPERTY_NAME: "DurationInSeconds"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-retryoptions.html#cfn-kinesisfirehose-deliverystream-retryoptions-durationinseconds""" @attr.s class PropDeliveryStreamParquetSerDe(Property): """ AWS Object Type = "AWS::KinesisFirehose::DeliveryStream.ParquetSerDe" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-parquetserde.html Property Document: - ``p_BlockSizeBytes``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-parquetserde.html#cfn-kinesisfirehose-deliverystream-parquetserde-blocksizebytes - ``p_Compression``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-parquetserde.html#cfn-kinesisfirehose-deliverystream-parquetserde-compression - ``p_EnableDictionaryCompression``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-parquetserde.html#cfn-kinesisfirehose-deliverystream-parquetserde-enabledictionarycompression - ``p_MaxPaddingBytes``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-parquetserde.html#cfn-kinesisfirehose-deliverystream-parquetserde-maxpaddingbytes - ``p_PageSizeBytes``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-parquetserde.html#cfn-kinesisfirehose-deliverystream-parquetserde-pagesizebytes - ``p_WriterVersion``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-parquetserde.html#cfn-kinesisfirehose-deliverystream-parquetserde-writerversion """ AWS_OBJECT_TYPE = "AWS::KinesisFirehose::DeliveryStream.ParquetSerDe" p_BlockSizeBytes: int = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(int)), metadata={AttrMeta.PROPERTY_NAME: "BlockSizeBytes"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-parquetserde.html#cfn-kinesisfirehose-deliverystream-parquetserde-blocksizebytes""" p_Compression: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "Compression"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-parquetserde.html#cfn-kinesisfirehose-deliverystream-parquetserde-compression""" p_EnableDictionaryCompression: bool = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(bool)), metadata={AttrMeta.PROPERTY_NAME: "EnableDictionaryCompression"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-parquetserde.html#cfn-kinesisfirehose-deliverystream-parquetserde-enabledictionarycompression""" p_MaxPaddingBytes: int = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(int)), metadata={AttrMeta.PROPERTY_NAME: "MaxPaddingBytes"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-parquetserde.html#cfn-kinesisfirehose-deliverystream-parquetserde-maxpaddingbytes""" p_PageSizeBytes: int = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(int)), metadata={AttrMeta.PROPERTY_NAME: "PageSizeBytes"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-parquetserde.html#cfn-kinesisfirehose-deliverystream-parquetserde-pagesizebytes""" p_WriterVersion: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "WriterVersion"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-parquetserde.html#cfn-kinesisfirehose-deliverystream-parquetserde-writerversion""" @attr.s class PropDeliveryStreamVpcConfiguration(Property): """ AWS Object Type = "AWS::KinesisFirehose::DeliveryStream.VpcConfiguration" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-vpcconfiguration.html Property Document: - ``rp_RoleARN``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-vpcconfiguration.html#cfn-kinesisfirehose-deliverystream-vpcconfiguration-rolearn - ``rp_SecurityGroupIds``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-vpcconfiguration.html#cfn-kinesisfirehose-deliverystream-vpcconfiguration-securitygroupids - ``rp_SubnetIds``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-vpcconfiguration.html#cfn-kinesisfirehose-deliverystream-vpcconfiguration-subnetids """ AWS_OBJECT_TYPE = "AWS::KinesisFirehose::DeliveryStream.VpcConfiguration" rp_RoleARN: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), metadata={AttrMeta.PROPERTY_NAME: "RoleARN"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-vpcconfiguration.html#cfn-kinesisfirehose-deliverystream-vpcconfiguration-rolearn""" rp_SecurityGroupIds: typing.List[TypeHint.intrinsic_str] = attr.ib( default=None, validator=attr.validators.deep_iterable(member_validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), iterable_validator=attr.validators.instance_of(list)), metadata={AttrMeta.PROPERTY_NAME: "SecurityGroupIds"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-vpcconfiguration.html#cfn-kinesisfirehose-deliverystream-vpcconfiguration-securitygroupids""" rp_SubnetIds: typing.List[TypeHint.intrinsic_str] = attr.ib( default=None, validator=attr.validators.deep_iterable(member_validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), iterable_validator=attr.validators.instance_of(list)), metadata={AttrMeta.PROPERTY_NAME: "SubnetIds"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-vpcconfiguration.html#cfn-kinesisfirehose-deliverystream-vpcconfiguration-subnetids""" @attr.s class PropDeliveryStreamHttpEndpointCommonAttribute(Property): """ AWS Object Type = "AWS::KinesisFirehose::DeliveryStream.HttpEndpointCommonAttribute" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-httpendpointcommonattribute.html Property Document: - ``rp_AttributeName``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-httpendpointcommonattribute.html#cfn-kinesisfirehose-deliverystream-httpendpointcommonattribute-attributename - ``rp_AttributeValue``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-httpendpointcommonattribute.html#cfn-kinesisfirehose-deliverystream-httpendpointcommonattribute-attributevalue """ AWS_OBJECT_TYPE = "AWS::KinesisFirehose::DeliveryStream.HttpEndpointCommonAttribute" rp_AttributeName: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), metadata={AttrMeta.PROPERTY_NAME: "AttributeName"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-kinesisfirehose-deliverystream-httpendpointcommonattribute.html#cfn-kinesisfirehose-deliverystream-httpendpointcommonattribute-attributename""" rp_AttributeValue: TypeHint.intrinsic_str = attr.ib(
#!/usr/bin/env python3 # -- coding: utf-8 -- """ Created on Thu Jun 10 15:51:15 2021 @author: rosariouceda-sosa """ ########################################### # Extraction of Propbank, Verbnet and mappings # It requires verbnet3.4, verbnet3.3 and verbnet3.2 in nltk_data directory, # as well as the latest version of propbank # # In particular, it does require the last ########################################### import json import re from nltk.corpus import treebank from nltk.corpus.util import LazyCorpusLoader from VerbnetCorpusReaderEx import VerbnetCorpusReaderEx from nltk.corpus import PropbankCorpusReader from semlinkEx import query_pb_vn_mapping, query_pb_vn_mapping_1_2 from xml.etree import ElementTree from propbank_readerEx import PropbankCorpusReaderEx #from nltk.corpus import propbank propbank = LazyCorpusLoader( "propbank-latest", PropbankCorpusReaderEx, "prop.txt", r"frames/.\.xml", "verbs.txt", lambda filename: re.sub(r"^wsj/\d\d/", "", filename), treebank, ) # Must be defined after* treebank corpus. vn_dict = { "verbnet3.2": LazyCorpusLoader("verbnet3.2", VerbnetCorpusReaderEx, r"(?!\.).\.xml"), "verbnet3.3": LazyCorpusLoader("verbnet3.3", VerbnetCorpusReaderEx, r"(?!\.).\.xml"), "verbnet3.4": LazyCorpusLoader("verbnet3.4", VerbnetCorpusReaderEx, r"(?!\.).*\.xml") } #The default is 3.4 current_vn = vn_dict["verbnet3.4"] VN_FILES = "/Users/rosariouceda-sosa/Documents/usr/SemanticsSvces/verbnet/verbnet-master/verbnet3.4" VN_DIR = "/Users/rosariouceda-sosa/Documents/usr/SemanticsSvces/verbnet/" PB_DIR = "/Users/rosariouceda-sosa/Documents/usr/SemanticsSvces/propbank/" outputFile = "" logFile = "" processedGroupingsVN = {} processedMaps = [] #key is entity and the list of mappings they have. Keys for Verbnet, Propbank and WN are their id's memberToMap = {} #inverse of memberToMap mapToMember = {} #Each propbank has: [roleSet] : name, arguments, lemmas, provenance pb_index = {} #Each verbnet has: [CODE] : name, [arguments] variableName, variableType, lemmas, provenance, vn_index = {} #{roleset} admire-31.2': {'provenance': 'verbnet3.4', 'arguments' : {'ARG0' : {'description' : "XXX" , 'vnArg' : Agent}}] map_index = {} extended_semnlink_index = [] # #vnCodeToLemma = {} ###### LOG outLog = open("/Users/rosariouceda-sosa/Downloads/OutLog_ULKB_Clean.txt", "w") ########################################################### # AUXILIARY FUNCTIONS ########################################################### #IMPORTANT: To standardize both verbnet names and codes, def vn_standard(_verb: str) -> str: # return _verb.replace(".", "-") #do nothing return _verb def count_dict() -> int : highest = 0 for thisMap in mapToMember : if len(mapToMember[thisMap]) > highest: highest = len(mapToMember[thisMap]) return highest def compare_strs(_first : str, _second : str) -> bool : if (_first.lower() == _second.lower()): return 1 return 0 def checkKeyStr(_dict : {}, _key: str) -> str : if _key in _dict.keys(): return _dict[_key] else: return "" def toRDFStr(_in :str) -> str : #somewhat sloppy, but gets the data _in = _in.replace("/", "_") _in = _in.replace(":", "-") _in = _in.replace(" ", "") _in = _in.replace("(", "_") _in = _in.replace(")", "_") _in = _in.replace("'", "") _in = _in.replace(".", "-") _in = _in.replace(",", "_") _in = _in.replace("__", "_") _in = _in.replace(">", "") _in = _in.replace("<", "") _in = _in.replace("#", "-") _in = _in.replace("%", "_") _in = _in.replace("?", "_") #ADD THIS FOR THE INCONSISTENT VERBNET NAMING IN SEMLINK AND NLTK _in = _in.replace("-", "_") return _in def checkArray(_dict : {}, _name : str) -> [] : if (_name) in _dict.keys() : return _dict.get(_name) else: return [] # Whether the mapping points to 'nothing' def wrong_mapping(_term :str) -> bool : _term = _term.strip() if len(_term) == 0 : return True if _term == 'NP' or _term == 'np' or _term == 'NP>' or _term == 'np>': return True if _term == 'NM' or _term == 'nm' or _term == 'NM>' or _term == 'nm>': return True return False def clean_text(_text :str, _oneLine : bool) -> str : _text = _text.replace("\"", "\\\"") _text = _text.replace("'", "\\'") _text = _text.replace("\/", "-") _text = _text.replace("`", " ") if _oneLine : _text = _text.replace("\n", "") return _text def chunk(mappings: str) -> []: rList = mappings.split(',') for item in rList : item = item.strip() return rList # from admire-31.2 to admire def get_vn_lemma(_verb : str) -> str : return _verb.split('-', 1)[0] # from admire-31.2 to 31.2 -- The first hyphen is the one that counts def get_vn_code(_verb: str) -> str : stVerb = vn_standard(_verb) return stVerb.split('-',1)[1] #from admire.01 to admire def get_pb_lemma(_verb : str) -> str : return _verb.split('.', 1)[0] def get_vn_varName(_var : str) -> str : if _var.startswith('?') : _var = _var[1:] return _var def match_vn_codes(_first:str , _second: str) -> bool : if toRDFStr(_first) == toRDFStr(_second): return True return False def matchRDF(_item, _dict: {}) -> str : toMatch = toRDFStr(_item) for keyItem in _dict: if toMatch == toRDFStr(keyItem): return keyItem return "" # Also consider one start with another def vn_in_dict(_item:str, _dict: {}, _name: str) -> bool : for keyItem in _dict: if len (_name ) == 0 : compareTo = keyItem else : compareTo = _dict[keyItem][_name] if compareTo == _item : return True if compareTo.startswith(_item) or compareTo.startswith(_name) : return True return False def vn_to_swap(_item:str, _dict: {}, _name:str) -> str : _itemCode = get_vn_code(_item) if _itemCode not in vn_index : return "" _itemProvenance = vn_index[_itemCode]['provenance'] _itemVersion = _itemProvenance.split('.',1)[1] for keyItem in _dict: if len(_name) == 0 : compareTo = keyItem else : compareTo = _dict[keyItem][_name] compareToCode = get_vn_code(compareTo) if _itemCode == compareToCode or compareToCode.startswith(_itemCode) or _itemCode.startswith(compareToCode) : if compareToCode in vn_index : compareToProvenance = vn_index[compareToCode]['provenance'] compareToVersion = compareToProvenance.split('.', 1)[1] if compareToVersion < _itemVersion : return compareTo return "" def unmatched_roles(_have : [], _want: []) -> [] : result = [] for haveEntry in _have : haveEntry = haveEntry.lower() found = False for wantEntry in _want : if wantEntry.lower() == haveEntry : found = True if not found : result.append(haveEntry) return result def wrongly_matched_roles(_have : [], _want: []) -> [] : result = [] for haveEntry in _have : haveEntry = haveEntry.lower() found = False for wantEntry in _want : if wantEntry.lower() == haveEntry : found = True if not found : result.append(haveEntry) return result def getRoleStrFrom(_list : []) -> str : resultStr = "" noDupList = [] for item in _list : if item.startswith("?") : item = item[1:] if item not in noDupList : noDupList.append(item) for item in noDupList : if len(resultStr) == 0 : resultStr += item else : resultStr += ", " + item return resultStr def getRoleListFrom(_list : []) -> [[str]] : resultStr = "" noDupList = [] for item in _list : if item.startswith("?") : item = item[1:] if item not in noDupList : noDupList.append(item) return noDupList ###################################################################### # SEMLINK INGESTION ###################################################################### #maps from verbnet class + argument a URL #Check the variables that have been already mapped through Verbnet def map_to_url(_class: str, _param : str) -> []: global vnClassToVars, framesToVars resultList = [] if _class not in vnClassToVars : return resultList argList = vnClassToVars[_class] for argKey in argList : if argKey.lower() == _param.lower(): resultList.append(argList[argKey]) # elif _class in framesToVars : #try the frames # argList = framesToVars[_class] # for frameKey in argList : # for argKey in argList[frameKey] : # if argKey.lower() == _param.lower() : # resultList.append(argList[frameKey][argKey]) return resultList def process_semlink_1_2() : global provenance, pbToMap_params, pbToMap, semLinkFromPB # from {'mapping': '51.2', 'source': 'verbnet3.4', 'arguments': {'ARG0': 'Theme'}} # TO map_index #[{'vnVerb': 'admire-31.2', 'provenance': 'verbnet3.4', 'arguments' : {'ARG0' : {'description' : "XXX" , 'vnArg' : Agent}}] oldProvenance = provenance provenance = "semlink 1.2.2" for roleset in pb_index : if "abound.01" in roleset: print("DEBUG " + roleset) semLinkmappingList = query_pb_vn_mapping_1_2(roleset) #If there is no mapping, ignore. if not semLinkmappingList or len(semLinkmappingList) == 0 : # if outLog is not None : # outLog.write("PROPBANK NO_SEMLINK_1_2," + roleset + "\n") # outLog.flush() continue #If there is a mapping BUT we don't have the roleset, it's an issue. if roleset not in map_index : if outLog is not None : outLog.write("NO_PROPBANK SEMLINK_1_2," + roleset + "\n") outLog.flush() map_index[roleset] = {} #Grab the current map_index entry. We know it's there ourMappings = map_index[roleset] for mapping in semLinkmappingList : vnRawCode = mapping['mapping'] vnRawCode = vn_standard(vnRawCode) vnRawName = "" if vnRawCode in vn_index : vnRawName = vn_index[vnRawCode]['name'] else : # use a hack to substitute the first hyphen by a dot. Oh brother... if outLog is not None : outLog.write("NO VERBNET SEMLINK_1_2," + vnRawName + "," + vnRawCode + "\n") outLog.flush() continue #go to the next mapping #If the verbnet
# """handle input Text for Larch -- inclides translation to Python text """ from __future__ import print_function from utils import isValidName, isNumber, isLiteralStr, strip_comments, find_delims def get_DefVar(text): """ looks for defined variable statement, of the form >> def varname = exression returns (varname, expression) if this is a valid defvar statement or None, None if not a valid defvar statement """ if text.find('=') > 0 and text.startswith('def '): t = text[4:].replace('=',' = ').strip() words = t.split() if len(words) > 2 and words[1] == '=': iequal = t.find('=') iparen = t.find('(') icolon = t.find(':') if iparen < 0 : iparen = len(t)+1 if icolon < 0 : icolon = len(t)+1 # print iequal, iparen, icolon, words[0], isValidName(words[0]) if (iequal < iparen and iequal < icolon and isValidName(words[0])): return words[0], t[iequal+1:].strip() return None, None class InputText: """Input Larch Code: handles loading and reading code text, and providing blocks of compile-able python code to be converted to AST. InputText accepts and stores single or multiple lines of input text, including as from an interactive prompt, watching for blocks of code, and keepin track of whether a block are complete. When asked for the next block of code, it emits blocks of valid (hopefully!) python code ready to parsed by 'ast.parse' Uses a FIFO (First In, First Out) buffer to allow mixing of input/output. Can read from a specified input source for 'interactive' mode usage: >>> text = InputText() >>> s = 'a statement' >>> text.put(s) # add lines of text >>> text.get(s) # get complete code block, ready for Compiler.eval the following translations are made from valid Larch to valid Python: 1. Block Indentation: larch blocks may end with one of the tokens: ('end', 'endXXX', '#end', '#endXXX') for an 'XXX' block (one of 'if', 'for', 'while', 'try', and 'def') where the token starts a line of text, followed by whitespace or a comment starting with a '#'. 2. Defined Variables: larch uses 'def VarName = Expression' for a Defined Variable (the expression is stored and accessing the VarName causes the expression to be re-evaluated) The tokens are found with "def" "varname" "=" "text of expression" and then translated into _builtin._definevar_("varname", "text of expression") 3. Command Syntax: larch allows lines of code which execute a function without return value to be viewed as "commands" and written without parentheses, so that the function call function(x, y) can be written as function x, y 4. Print: as a special case of rule 3, and because Python is going through changes in the syntax of "print", print statements are translated from either "print(list)" or "print list" to _builtin._print(list) """ indent = ' '4 ps1 = ' >' ps2 = '....>' block_friends = {'if': ('else', 'elif'), 'for': ('else'), 'def': (), 'try': ('else', 'except', 'finally'), 'while': ('else') } parens = {'{':'}', '(':')', '[':']'} fcn_defvar = "_builtin.definevar" fcn_print = "_builtin._print_" nonkey = 'NONKEY' empty_frame = (None, None, -1) def __init__(self, prompt=None, interactive=True, input=None, filename=None, _larch=None): self.prompt = prompt or self.ps1 self.input = None self._larch = _larch self.interactive = interactive self.lineno = 0 self.filename = filename or '<stdin>' if interactive: self.input = input or self.__defaultInput self._fifo = [[], []] self.block = [] self.keys = [] self.current = None self.endkeys = () self.friends = () self.delims = [] self.eos = '' self.in_string = False self.input_buff = [] self.input_complete = True def readfile(self, fname): fh = open(fname, 'r') self.put(fh.read(), filename=fname, lineno=0) fh.close() def put(self, text, filename=None, lineno=None ): """add line of input code text""" fname = filename or self.filename or '<stdin>' if lineno is not None: self.lineno = lineno def addTextInput(thisline, fname): self.input_complete = self.__isComplete(thisline) self.input_buff.append((thisline, self.input_complete, self.eos, fname, self.lineno)) self.lineno += 1 text = text.split('\n') text.reverse() while len(text) > 0: addTextInput(text.pop(), fname) if self.interactive: self.prompt = self.ps2 while not self.input_complete: t = self.input() t0 = t.strip() if len(t0) > 0: addTextInput(t, fname) if self.input_complete: self.prompt = self.ps1 nkeys, nblock = self.convert() return self.input_complete def get(self): """get compile-able block of python code""" if len(self) > 0: if not self._fifo[0]: self._fifo.reverse() self._fifo[0].reverse() try: return self._fifo[0].pop() except IndexError: msg = 'InputText out of complete text' if self._larch is None: raise IndexError(msg) else: self._larch.raise_exception(None, exc=IndexError, msg=msg) return self.empty_frame def convert(self): """ Convert input buff (in self.input_buff) to valid python code and stores this (text, filename, lineno) into _fifo buffer """ indent_level = 0 oneliner = False startkeys = self.block_friends.keys() self.input_buff.reverse() while self.input_buff: text, complete, eos, fname, lineno = self.input_buff.pop() long_text = eos in '"\'' sindent = self.indent(indent_level+1) while not complete: tnext, complete, xeos, fname, lineno2 = self.input_buff.pop() if long_text: text = "%s\n%s" % (text, tnext) else: text = "%s\n %s%s" % (text, sindent, tnext) text = text.strip().rstrip() txt = text.replace('(', ' (').replace(')', ' )') if text.startswith('"') or text.startswith("'"): delim = text[0] if text[0:3] == text[0]3: delim = text[0:3] while not find_delims(text, delim=delim)[0]: tnext, complete, eos, fname, lineno2 = self.input_buff.pop() text = "%s\n %s%s" % (text, sindent, tnext) # note here the trick of replacing '#end' with '&end' so # that it is not removed by strip_comments. then below, # we look for '&end' as an end-of-block token. if txt.startswith('#end'): txt = '&end%s' % txt[4:] txt = strip_comments(txt) # thiskey, word2 = (txt.split() + [''])[0:2] words = txt.split(' ', 1) thiskey = words.pop(0).strip() word2 = '' if len(words) > 0: word2 = words[0].replace(',', ' ').split()[0] if thiskey.endswith(':'): thiskey = thiskey[:-1] prefix, oneliner = '', False if thiskey in startkeys: # check for defined variables if thiskey == 'def': dname, dexpr = get_DefVar(text) if dname is not None and dexpr is not None: if "'" in dexpr: dexpr.replace("'", "\'") text = "%s('%s', '%s')" % (self.fcn_defvar, dname, dexpr) thiskey = self.nonkey # note that we re-test* here, # as thiskey may have changed above for defined variables if thiskey in startkeys: if text.find(':') < 1: msg = "%s statement needs a ':' at\n %s" % (thiskey, text) if self._larch is None: raise SyntaxError(msg) else: self._larch.raise_exception(None, exc=SyntaxError, msg=msg, expr=text) elif text.endswith(':'): self.current = thiskey self.keys.append(thiskey) self.friends = self.block_friends[thiskey] self.endkeys = ('end', 'end%s'% thiskey, '&end', '&end%s'% thiskey) else: # one-liner form oneliner = True elif thiskey in self.endkeys: # end of block if not thiskey.startswith('&'): prefix = '#' if len(self.keys) != 0: self.current = None self.friends = () self.keys.pop() if len(self.keys)>0: self.current = self.keys[-1] self.friends = self.block_friends[self.current] self.endkeys = ('end', 'end%s'%self.current, '&end', '&end%s'%self.current) elif not text.endswith(')') and self.__isCommand(thiskey, word2): # handle 'command format', including 'print' text = '%s(%s)' % (thiskey, text[len(thiskey):].strip()) indent_level = len(self.keys) if (not oneliner and len(thiskey)>0 and (thiskey == self.current or thiskey in self.friends)): indent_level = indent_level - 1 if indent_level < 0: msg = 'impossible indent level!' if self._larch is None: raise SyntaxError(msg) else: self._larch.raise_exception(None, exc=SyntaxError, msg=msg) self.block.append('%s%s%s' % (self.indent*indent_level, prefix, text)) if len(self.keys) == 0: outtext = '\n'.join(self.block) if '\n' in outtext: outtext = outtext + '\n' self._fifo[1].append((outtext, fname, 1+lineno-len(self.block))) self.block = [] return len(self.keys), len(self.block) def clear(self): "clear the input" self._fifo = [[], []] def __isCommand(self, key, word2): """ decide if a keyword and next word are of the form 'command arg, ...' which will get translated to 'command(arg, ...)' to allow 'command syntax' """ # this could be in one long test, but we simplify: # first test key: if (not isValidName(key) or key in self.friends or key.startswith('#') or len(key) < 1 or len(word2) < 1): return False # next test word2 return (isValidName(word2) or isNumber(word2) or isLiteralStr(word2) ) def __isComplete(self, text): """returns whether input text is a complete: that is: does not contains unclosed parens or quotes and does not end with a backslash stores state information from previous textline in self.eos = char(s) to look for 'end of string' ("" == string complete) self.delims = current list of closing delims being waited for """ parens = self.parens opens = ''.join(parens.keys()) closes = ''.join(parens.values()) quotes, bslash = '\'"', '\\' prev_char = '' # txt = strip_comments(text) txt = text
""" This is the core file in the `gradio` package, and defines the Interface class, including methods for constructing the interface using the input and output types. """ import copy import csv import getpass import inspect import markdown2 import numpy as np import os import pkg_resources import requests import random import sys import time import warnings import webbrowser import weakref from gradio import networking, strings, utils, encryptor, queue from gradio.inputs import get_input_instance from gradio.outputs import get_output_instance from gradio.interpretation import quantify_difference_in_label, get_regression_or_classification_value from gradio.external import load_interface, load_from_pipeline class Interface: """ Interfaces are created with Gradio by constructing a `gradio.Interface()` object or by calling `gradio.Interface.load()`. """ instances = weakref.WeakSet() # stores references to all currently existing Interface instances @classmethod def get_instances(cls): """ :return: list of all current instances. """ return list(Interface.instances) @classmethod def load(cls, name, src=None, api_key=None, alias=None, kwargs): """ Class method to construct an Interface from an external source repository, such as huggingface. Parameters: name (str): the name of the model (e.g. "gpt2"), can include the `src` as prefix (e.g. "huggingface/gpt2") src (str): the source of the model: `huggingface` or `gradio` (or empty if source is provided as a prefix in `name`) api_key (str): optional api key for use with Hugging Face Model Hub alias (str): optional, used as the name of the loaded model instead of the default name Returns: (gradio.Interface): a Gradio Interface object for the given model """ interface_info = load_interface(name, src, api_key, alias) # create a dictionary of kwargs without overwriting the original interface_info dict because it is mutable # and that can cause some issues since the internal prediction function may rely on the original interface_info dict kwargs = dict(interface_info, kwargs) interface = cls(kwargs) interface.api_mode = True # set api mode to true so that the interface will not preprocess/postprocess return interface @classmethod def from_pipeline(cls, pipeline, kwargs): """ Class method to construct an Interface from a Hugging Face transformers.Pipeline. pipeline (transformers.Pipeline): Returns: (gradio.Interface): a Gradio Interface object from the given Pipeline """ interface_info = load_from_pipeline(pipeline) kwargs = dict(interface_info, kwargs) interface = cls(kwargs) return interface def __init__(self, fn, inputs=None, outputs=None, verbose=None, examples=None, examples_per_page=10, live=False, layout="unaligned", show_input=True, show_output=True, capture_session=None, interpretation=None, num_shap=2.0, theme=None, repeat_outputs_per_model=True, title=None, description=None, article=None, thumbnail=None, css=None, height=500, width=900, allow_screenshot=True, allow_flagging=None, flagging_options=None, encrypt=False, show_tips=None, flagging_dir="flagged", analytics_enabled=None, enable_queue=None, api_mode=None): """ Parameters: fn (Callable): the function to wrap an interface around. inputs (Union[str, List[Union[str, InputComponent]]]): a single Gradio input component, or list of Gradio input components. Components can either be passed as instantiated objects, or referred to by their string shortcuts. The number of input components should match the number of parameters in fn. outputs (Union[str, List[Union[str, OutputComponent]]]): a single Gradio output component, or list of Gradio output components. Components can either be passed as instantiated objects, or referred to by their string shortcuts. The number of output components should match the number of values returned by fn. verbose (bool): DEPRECATED. Whether to print detailed information during launch. examples (Union[List[List[Any]], str]): sample inputs for the function; if provided, appears below the UI components and can be used to populate the interface. Should be nested list, in which the outer list consists of samples and each inner list consists of an input corresponding to each input component. A string path to a directory of examples can also be provided. If there are multiple input components and a directory is provided, a log.csv file must be present in the directory to link corresponding inputs. examples_per_page (int): If examples are provided, how many to display per page. live (bool): whether the interface should automatically reload on change. layout (str): Layout of input and output panels. "horizontal" arranges them as two columns of equal height, "unaligned" arranges them as two columns of unequal height, and "vertical" arranges them vertically. capture_session (bool): DEPRECATED. If True, captures the default graph and session (needed for Tensorflow 1.x) interpretation (Union[Callable, str]): function that provides interpretation explaining prediction output. Pass "default" to use simple built-in interpreter, "shap" to use a built-in shapley-based interpreter, or your own custom interpretation function. num_shap (float): a multiplier that determines how many examples are computed for shap-based interpretation. Increasing this value will increase shap runtime, but improve results. Only applies if interpretation is "shap". title (str): a title for the interface; if provided, appears above the input and output components. description (str): a description for the interface; if provided, appears above the input and output components. article (str): an expanded article explaining the interface; if provided, appears below the input and output components. Accepts Markdown and HTML content. thumbnail (str): path to image or src to use as display picture for models listed in gradio.app/hub theme (str): Theme to use - one of "default", "huggingface", "grass", "peach". Add "dark" prefix, e.g. "darkpeach" or "darkdefault" for darktheme. css (str): custom css or path to custom css file to use with interface. allow_screenshot (bool): if False, users will not see a button to take a screenshot of the interface. allow_flagging (bool): if False, users will not see a button to flag an input and output. flagging_options (List[str]): if not None, provides options a user must select when flagging. encrypt (bool): If True, flagged data will be encrypted by key provided by creator at launch flagging_dir (str): what to name the dir where flagged data is stored. show_tips (bool): DEPRECATED. if True, will occasionally show tips about new Gradio features enable_queue (bool): DEPRECATED. if True, inference requests will be served through a queue instead of with parallel threads. Required for longer inference times (> 1min) to prevent timeout. api_mode (bool): DEPRECATED. If True, will skip preprocessing steps when the Interface is called() as a function (should remain False unless the Interface is loaded from an external repo) """ if not isinstance(fn, list): fn = [fn] if not isinstance(inputs, list): inputs = [inputs] if not isinstance(outputs, list): outputs = [outputs] self.input_components = [get_input_instance(i) for i in inputs] self.output_components = [get_output_instance(o) for o in outputs] if repeat_outputs_per_model: self.output_components = len(fn) if interpretation is None or isinstance(interpretation, list) or callable(interpretation): self.interpretation = interpretation elif isinstance(interpretation, str): self.interpretation = [interpretation.lower() for _ in self.input_components] else: raise ValueError("Invalid value for parameter: interpretation") self.predict = fn self.predict_durations = [[0, 0]] len(fn) self.function_names = [func.__name__ for func in fn] self.__name__ = ", ".join(self.function_names) if verbose is not None: warnings.warn("The `verbose` parameter in the `Interface` is deprecated and has no effect.") self.status = "OFF" self.live = live self.layout = layout self.show_input = show_input self.show_output = show_output self.flag_hash = random.getrandbits(32) self.capture_session = capture_session if capture_session is not None: warnings.warn("The `capture_session` parameter in the `Interface` will be deprecated in the near future.") self.session = None self.title = title self.description = description if article is not None: article = utils.readme_to_html(article) article = markdown2.markdown( article, extras=["fenced-code-blocks"]) self.article = article self.thumbnail = thumbnail theme = theme if theme is not None else os.getenv("GRADIO_THEME", "default") if theme not in ("default", "huggingface", "grass", "peach", "darkdefault", "darkhuggingface", "darkgrass", "darkpeach"): raise ValueError("Invalid theme name.") self.theme = theme self.height = height self.width = width if css is not None and os.path.exists(css): with open(css) as css_file: self.css = css_file.read() else: self.css = css if examples is None or isinstance(examples, str) or (isinstance(examples, list) and (len(examples) == 0 or isinstance(examples[0], list))): self.examples = examples elif isinstance(examples, list) and len(self.input_components) == 1: # If there is only one input component, examples can be provided as a regular list instead of a list of lists self.examples = [[e] for e in examples] else: raise ValueError( "Examples argument must either be a directory or a nested list, where each sublist represents a set of inputs.") self.num_shap = num_shap self.examples_per_page = examples_per_page self.simple_server = None self.allow_screenshot = allow_screenshot # For allow_flagging and analytics_enabled: (1) first check for parameter, (2) check for environment variable, (3) default to True self.allow_flagging = allow_flagging if allow_flagging is not None else os.getenv("GRADIO_ALLOW_FLAGGING", "True")=="True" self.analytics_enabled = analytics_enabled
<filename>src/app/services/plot_service.py # MIT License # # Copyright (c) 2020 OdinLabs IO # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. import itertools from collections import defaultdict from math import pi from typing import Dict from bokeh.layouts import column from bokeh.models import ColumnDataSource, FactorRange, Range1d, LinearAxis, PreText from bokeh.palettes import Spectral11 from bokeh.plotting import figure from bokeh.transform import cumsum from app.model import is_valid_aggregate, is_valid_dimension from app.model.model import CHART_PARAMETERS_AXIS, is_valid_filter TOOLS = "hover,crosshair,pan,wheel_zoom,zoom_in,zoom_out,box_zoom,undo,redo,reset,tap,save,box_select,poly_select,lasso_select," PALETTE = Spectral11 ALPHA = 0.8 class PlotService: def __init__(self): self._analytics_service = None self._dashboard_service = None self._axis_order = defaultdict(lambda: 0, {}) def init_app(self, analytics_service, dashboard_service): self._analytics_service = analytics_service self._dashboard_service = dashboard_service self._axis_order = defaultdict(lambda: len(CHART_PARAMETERS_AXIS), {axe: i for i, axe in enumerate(CHART_PARAMETERS_AXIS, 0)}) def _axis_index(self, axis, sort_by): axis_order = self._axis_order axis = sorted([(axe, axis_order[axe[sort_by]]) for axe in axis], key=lambda x: x[1]) return [axe[0] for axe in axis] def _generate_value_tooltips(self, fields, fields_legend): return [(legend, "@" + field) for field, legend in zip(fields, fields_legend)] def _color(self, values): return self._color_def(values, color_dict=dict()) def _color_def(self, values, color_dict): color_palette = itertools.cycle(PALETTE) colors = [] for c in values: color = color_dict.get(c) if not color: color_dict[c] = next(color_palette) colors.append(color_dict[c]) return colors def _bar_chart(self, labels: Dict[str, str], df): dimensions = df.index.names aggregates = df.columns if len(dimensions) == 1: aggr_0 = aggregates[0] # normal vbar and line for other aggregates factors = [factor for factor in df.index.values] data = dict({'x': factors, 'aggr_0': [v for v in df[aggr_0].values]}) data['color'] = self._color(factors) tooltips_var = [] tooltips_label = [] extra_y_ranges = {} if len(aggregates) > 1: for i in range(1, len(aggregates)): aggr_n = aggregates[i] aggr_n_name = 'aggr_' + str(i) tooltips_var.append(aggr_n) tooltips_label.append(aggr_n_name) data[aggr_n_name] = df[aggr_n] extra_y_ranges[aggr_n_name] = Range1d(start=df[aggr_n].min() - 10, end=df[aggr_n].max() + 10) p = figure(x_range=factors, tools=TOOLS, tooltips=[('Value', "@x: @aggr_0")] + self._generate_value_tooltips(tooltips_label, tooltips_var)) source = ColumnDataSource(data=data) p.vbar(x='x', top='aggr_0', source=source, width=0.9, alpha=ALPHA, legend_field='x', fill_color='color') if len(aggregates) > 1: color_palette = itertools.cycle(PALETTE) p.extra_y_ranges = extra_y_ranges for i in range(1, len(aggregates)): aggr_n = aggregates[i] aggr_n_name = 'aggr_' + str(i) color = next(color_palette) p.line(x='x', y=aggr_n_name, color=color, y_range_name=aggr_n_name, source=source) p.add_layout(LinearAxis(y_range_name=aggr_n_name, axis_label=aggr_n), 'left') p.yaxis[i].major_label_text_color = color p.yaxis[0].axis_label = aggr_0 p.legend.title = labels.get(dimensions[0], dimensions[0]) p.add_layout(p.legend[0], 'right') p.xaxis.major_label_orientation = 1 p.xgrid.grid_line_color = None return p elif len(dimensions) == 2: # stacked bar chart aggr_0 = aggregates[0] unstacked = df.unstack(-1) factors = [factor for factor in unstacked.index] data = dict({'x': factors}) aggr_column = unstacked[aggr_0] unstacked_values = [v for v in aggr_column.columns] color = [] colors = itertools.cycle(PALETTE) for memb in unstacked_values: data[memb] = [v for v in aggr_column[memb].fillna(0).values] color.append(next(colors)) p = figure(x_range=factors, tools=TOOLS, tooltips="$name @x: @$name") source = ColumnDataSource(data=data) p.vbar_stack(unstacked_values, x='x', source=source, color=color, width=0.9, alpha=ALPHA, legend_label=unstacked_values) p.legend.title = labels.get(dimensions[-1], dimensions[-1]) p.add_layout(p.legend[0], 'right') p.yaxis.axis_label = aggr_0 p.xaxis.major_label_orientation = 1 p.xgrid.grid_line_color = None return p elif len(dimensions) == 3 or len(dimensions) == 4: # group by n-1 first and stack by last dimension aggr_0 = aggregates[0] unstacked = df.unstack(-1) factors = [factor for factor in unstacked.index] data = dict({'x': factors}) aggr_column = unstacked[aggr_0] unstacked_values = [v for v in aggr_column.columns] color = [] colors = itertools.cycle(PALETTE) for memb in unstacked_values: data[memb] = [v for v in aggr_column[memb].fillna(0).values] color.append(next(colors)) source = ColumnDataSource(data=data) p = figure(x_range=FactorRange(factors), tools=TOOLS, tooltips="$name @x: @$name") p.vbar_stack(unstacked_values, x='x', source=source, color=color, width=0.9, alpha=ALPHA, legend_label=unstacked_values) p.legend.title = labels.get(dimensions[-1], dimensions[-1]) p.add_layout(p.legend[0], 'right') p.yaxis.axis_label = aggr_0 p.xaxis.major_label_orientation = 1 p.xgrid.grid_line_color = None return p else: p = PreText(text="""Bar Chart accepts 1 to 4 dimensions.""", width=500, height=100) return p def _pie_chart(self, labels: Dict[str, str], df): dimensions = df.index.names aggregates = df.columns if len(dimensions) == 1 and len(aggregates) != 0: aggr_0 = aggregates[0] category = dimensions[0] data = dict() df = df.reset_index() aggr_0_sum = df[aggr_0].sum() data['value'] = df[aggr_0] data['angle'] = df[aggr_0] / aggr_0_sum 2 * pi data['percentage'] = df[aggr_0] / aggr_0_sum * 100 data['color'] = self._color(df[category]) data['category'] = df[category] data['legend'] = [l + ": {:.2f} %".format(v) for l, v in zip(data['category'], data['percentage'])] source = ColumnDataSource(data=data) p = figure(tools=TOOLS, tooltips="@category:@value / @percentage") p.wedge(x=0, y=0, radius=0.8, start_angle=cumsum('angle', include_zero=True), end_angle=cumsum('angle'), line_color="white", fill_color='color', legend_field='legend', source=source) p.legend.title = labels.get(category, category) p.add_layout(p.legend[0], 'right') p.xaxis.major_label_orientation = 1 p.xgrid.grid_line_color = None p.toolbar.autohide = True return p else: p = PreText(text="""Pie chart accepts at most one aggregate and a dimension.""", width=500, height=100) return p def _scatter_chart(self, labels: Dict[str, str], df): dimensions = df.index.names aggregates = df.columns if len(aggregates) == 1: aggr_0 = aggregates[0] if len(dimensions) == 1: factors = [factor for factor in df.index] p = figure(x_range=factors, tools=TOOLS, tooltips="@x: @aggr_0") category = dimensions[0] data = dict({'x': factors, 'aggr_0': [v for v in df[aggr_0].values]}) p.line(x='x', y='aggr_0', source=ColumnDataSource(data=data)) p.xaxis.axis_label = labels.get(category, category) p.yaxis.axis_label = aggr_0 p.xaxis.major_label_orientation = 1 p.xgrid.grid_line_color = None p.toolbar.autohide = True return p elif len(dimensions) == 2 or len(dimensions) == 3 or len(dimensions) == 4: # unstack first unstacked = df.stack().unstack(-2).unstack(-1) p = figure(x_range=FactorRange(unstacked.index), tools=TOOLS, tooltips=self._generate_value_tooltips(['x', 'aggr_0', 'category'], ["", aggr_0, labels.get(dimensions[-1])])) color_dict = dict() stacked_columns = list(set([i[0] for i in unstacked.columns])) self._color_def(stacked_columns, color_dict=color_dict) aggr_0_values = [] factors = [] categories = [] for cat in stacked_columns: cat_column = unstacked[cat].dropna() aggr_0_values += cat_column[aggr_0].values.tolist() factors += [i for i in cat_column.index] categories += [cat for i in range(len(cat_column))] colors = [color_dict[cat_val] for cat_val in categories] data = dict({'x': factors, 'aggr_0': aggr_0_values, 'category': categories, 'color': colors}) p.scatter(x='x', y='aggr_0', size=10, fill_color='color', fill_alpha=ALPHA, source=ColumnDataSource(data), legend_group='category') p.legend.title = labels.get(dimensions[-1], dimensions[-1]) p.add_layout(p.legend[0], 'right') p.yaxis.axis_label = aggr_0 p.xaxis.axis_label = "/".join([labels.get(dim, dim) for dim in dimensions[:-1]]) p.xaxis.major_label_orientation = 1 p.xgrid.grid_line_color = None p.toolbar.autohide = True return p else: p = PreText(text="""Scatter Plot 1 aggregates accepts at most 4 dimensions.""", width=500, height=100) return p elif len(aggregates) == 2: aggr_0 = aggregates[0] aggr_1 = aggregates[1] if len(dimensions) == 1: df = df.reset_index() category = dimensions[0] data = dict() data['aggr_0'] = df[aggr_0] data['aggr_1'] = df[aggr_1] data['category'] = df[category] data['color'] = self._color(df[category]) p = figure(tools=TOOLS, tooltips=self._generate_value_tooltips(['aggr_0', 'aggr_1', 'category'], [aggr_0, aggr_1, labels.get(category, category)])) p.scatter(x='aggr_0', y='aggr_1', source=ColumnDataSource(data=data), fill_color='color', fill_alpha=ALPHA, legend_group='category') p.legend.title = labels.get(category, category) p.add_layout(p.legend[0], 'right') p.xaxis.axis_label = aggr_0 p.yaxis.axis_label = aggr_1 p.add_layout(p.legend[0], 'right') p.xaxis.major_label_orientation = 1 p.xgrid.grid_line_color = None p.toolbar.autohide = True return p elif len(dimensions) == 2 or len(dimensions) == 3 or len(dimensions) == 4: unstacked = df.stack().unstack(-2).unstack(-1) p = figure(x_range=FactorRange(unstacked.index), tools=TOOLS, tooltips=self._generate_value_tooltips(['x', 'aggr_0', 'aggr_1', 'category'], ["", aggr_0, aggr_1, labels.get(dimensions[-1])])) color_dict = dict() stacked_columns = list(set([i[0] for i in unstacked.columns])) self._color_def(stacked_columns, color_dict=color_dict) aggr_0_values = [] aggr_1_values = [] factors = [] categories = [] for cat in stacked_columns: cat_column = unstacked[cat].dropna() aggr_0_values += cat_column[aggr_0].values.tolist() aggr_1_values += cat_column[aggr_1].values.tolist() factors += [i for i in cat_column.index] categories += [cat for i in range(len(cat_column))] colors = [color_dict[cat_val] for cat_val in categories] data = dict({'x': factors, 'aggr_0': aggr_0_values, 'aggr_1': aggr_1_values, 'category': categories, 'color': colors}) p.scatter(x='x', y='aggr_0', size='aggr_1', fill_color='color', fill_alpha=ALPHA, source=ColumnDataSource(data), legend_group='category') p.legend.title = labels.get(dimensions[-1], dimensions[-1]) p.add_layout(p.legend[0], 'right') p.yaxis.axis_label = aggr_0 p.xaxis.axis_label = "/".join([labels.get(dim, dim) for dim in dimensions[:-1]]) p.xaxis.major_label_orientation = 1 p.xgrid.grid_line_color = None p.toolbar.autohide = True return p else: # get first dimension (more than one dimension => duplicates in plot) df = df.reset_index() category = dimensions[0] data = dict() data['aggr_0'] = df[aggr_0] data['aggr_1'] = df[aggr_1] data['category'] = df[category] data['color'] = self._color(df[category]) p = figure(tools=TOOLS, tooltips=self._generate_value_tooltips(['aggr_0', 'aggr_1', 'category'], [aggr_0, aggr_1, labels.get(category, category)])) p.scatter(x='aggr_0', y='aggr_1', source=ColumnDataSource(data=data), fill_color='color', fill_alpha=ALPHA, legend_group='category') p.legend.title = labels.get(category, category) p.xaxis.axis_label = aggr_0
<filename>catkit/hardware/boston/BostonDmController.py import os import sys import threading import numpy as np from catkit.interfaces.DeformableMirrorController import DeformableMirrorController from catkit.hardware.boston.DmCommand import DmCommand, convert_dm_image_to_command from catkit.multiprocessing import SharedMemoryManager # BMC is Boston's library and it only works on windows. try: sdk_path = os.environ.get('CATKIT_BOSTON_SDK_PATH') if sdk_path is not None: sys.path.append(sdk_path) import bmc else: bmc = None except ImportError: bmc = None """Interface for Boston Micro-machines deformable mirror controller that can control 2 DMs. It does so by interpreting the first half of the command for DM1, and the second for DM2. This controller cannot control the two DMs independently, it will always send a command to both.""" class BostonDmController(DeformableMirrorController): instrument_lib = bmc def _clear_state(self): self.dm1_command = None self.dm2_command = None self.dm1_command_object = None self.dm2_command_object = None self.channels = {} def initialize(self, serial_number, command_length, dac_bit_width): """ Initialize dm manufacturer specific object - this does not, nor should it, open a connection.""" self.log.info("Opening DM connection") # Create class attributes for storing individual DM commands. self._clear_state() self.serial_num = serial_number self.command_length = command_length self.dac_bit_width = dac_bit_width self.lock = threading.Lock() def send_data(self, data): # The DM controller expects the command to be unitless (normalized Volts): 0.0 - 1.0, where 1.0 := max_volts data_min = np.min(data) data_max = np.max(data) if data_min < 0 or data_max > 1: self.log.warning(f"DM command out of range and will be clipped by hardware. min:{data_min}, max:{data_max}") status = self.instrument.send_data(data) if status != self.instrument_lib.NO_ERR: raise Exception("{}: Failed to send data - {}".format(self.config_id, self.instrument.error_string(status))) def _open(self): self._clear_state() dm = self.instrument_lib.BmcDm() status = dm.open_dm(self.serial_num) if status != self.instrument_lib.NO_ERR: raise Exception("{}: Failed to connect - {}.".format(self.config_id, dm.error_string(status))) # If we get this far, a connection has been successfully opened. # Set self.instrument so that we can close if anything here subsequently fails. self.instrument = dm hardware_command_length = dm.num_actuators() if self.command_length != hardware_command_length: raise ValueError("config.ini error - '{}':'command_length' = {} but hardware gives {}.".format(self.config_id, self.command_length, hardware_command_length)) # Initialize the DM to zeros. zeros = np.zeros(self.command_length, dtype=float) try: self.send_data(zeros) # TODO: call self.apply_shape_to_both() except Exception: self._clear_state() raise else: # Store the current dm_command values in class attributes. self.dm1_command = zeros self.dm2_command = zeros.copy() # dm 1 & 2 should NOT be using the same memory self.dm_controller = self.instrument # For legacy API purposes return self.instrument def _close(self): """Close dm connection safely.""" try: try: self.log.info("Closing DM connection") # FIXME: I'm pretty sure the new SDK does this under the hood. # Set the DM to zeros. zeros = np.zeros(self.command_length, dtype=float) self.send_data(zeros) finally: self.instrument.close_dm() finally: self.instrument = None self._clear_state() def apply_shape_to_both(self, dm1_shape, dm2_shape, flat_map=True, bias=False, as_voltage_percentage=False, as_volts=False, sin_specification=None, output_path=None, channel=None, do_logging=True): """ Combines both commands and sends to the controller to produce a shape on each DM. The concept of channels is optional. If no channel is supplied, the DM shapes are applied directly onto the DM. However, if channels are used, each channel acts as an independent contribution to the total shape that is on the DM. Each contribution will be updated (= replaced) by calling apply_shape_to_both() with the name of that channel. In this way, the current contribution from each channel can be read out using the BOSTON_DM.channels[channel_name] attribute. While individual contributions can be added as delta contributions to a running total, this approach was not taken for code clarity. This comes at the cost of a few microseconds of runtime for each sent DM command. Note: if channels are used, the dm shapes are required to be numpy arrays. In this case, DmCommand objects are not allowed. A TypeError will be thrown is this is the case. :param dm<1\|2>_shape: catkit.hardware.boston.DmCommand.DmCommand or numpy array of the following shapes: 34x34, 1x952, 1x2048, 1x4096. Interpreted by default as the desired DM surface height in units of meters, but see parameters as_volts and as_voltage_percentage. When using channels, this should be a numpy array, and they should have the same shape for each channel. :param flat_map: If true, add flat map correction to the data before outputting commands :param bias: If true, add bias to the data before outputting commands :param as_voltage_percentage: Interpret the data as a voltage percentage instead of meters; Deprecated. :param as_volts: If true, interpret the data as volts instead of meters :param sin_specification: Add this sine to the data :param output_path: str, Path to save commands to if provided. Default `None` := don't save. :param channel: str or None, the DM channel on which to write this shape. Default `None` := set the entire shape. :param do_logging: boolean. Whether to emit a logging message. In fast (>100Hz) loops, the logs can be overwhelmed by log messages from the DM. Setting this to False doesn't emit a log message. Default: True. """ with self.lock: if do_logging: if channel is None: self.log.info("Applying shape to both DMs") else: self.log.info(f'Applying shape to both DMs in channel {channel}.') if channel is None: if self.channels: self.log.warn('A channel was not supplied while channels were used previously. ' + 'All channels will be reset. This may not be what you want.') self.channels = {} else: if isinstance(dm1_shape, DmCommand) or isinstance(dm2_shape, DmCommand): # DmCommand objects cannot be added together, yet. raise TypeError('DM shapes cannot be DmCommands when using channels.') # Check if dm{1,2}_shape is 2D, then convert to 1D. # This standardizes the shape stored in the channels attribute. if dm1_shape.ndim == 2: dm1_shape = convert_dm_image_to_command(dm1_shape) if dm2_shape.ndim == 2: dm2_shape = convert_dm_image_to_command(dm2_shape) self.channels[channel] = (dm1_shape, dm2_shape) # Add contributions for each channel, and use that as the dm command. dm1_shape = 0 dm2_shape = 0 for dm1, dm2 in self.channels.values(): dm1_shape += dm1 dm2_shape += dm2 if not isinstance(dm1_shape, DmCommand): dm1_shape = DmCommand(data=dm1_shape, dm_num=1, flat_map=flat_map, bias=bias, as_voltage_percentage=as_voltage_percentage, as_volts=as_volts, sin_specification=sin_specification) if not isinstance(dm2_shape, DmCommand): dm2_shape = DmCommand(data=dm2_shape, dm_num=2, flat_map=flat_map, bias=bias, as_voltage_percentage=as_voltage_percentage, as_volts=as_volts, sin_specification=sin_specification) # Ensure that the correct dm_num is set. dm1_shape.dm_num = 1 dm2_shape.dm_num = 2 if output_path is not None: dm1_shape.export_fits(output_path) dm2_shape.export_fits(output_path) # Use DmCommand class to format the commands correctly (with zeros for other DM). dm1_command = dm1_shape.to_dm_command() dm2_command = dm2_shape.to_dm_command() # Add both arrays together (first half and second half) and send to DM. full_command = dm1_command + dm2_command try: self.send_data(full_command) except Exception: # We shouldn't guarantee the state of the DM. self._clear_state() raise else: # Update both dm_command class attributes. self.dm1_command = dm1_command self.dm2_command = dm2_command self.dm1_command_object = dm1_shape self.dm2_command_object = dm2_shape def apply_shape(self, dm_shape, dm_num, flat_map=True, bias=False, as_voltage_percentage=False, as_volts=False, sin_specification=None, output_path=None): """ Forms a command for a single DM, and re-sends the existing shape to other DM. :param dm_shape: catkit.hardware.boston.DmCommand.DmCommand or numpy array of the following shapes: 34x34, 1x952, 1x2048, 1x4096. Interpreted by default as the desired DM surface height in units of meters, but see parameters as_volts and as_voltage_percentage. :param dm_num: Which DM to apply the shape to. Valid values are 1, 2. :param flat_map: If true, add flat map correction to the data before outputting commands :param bias: If true, add bias to the data before outputting commands :param as_voltage_percentage: Interpret the data as a voltage percentage instead of meters; Deprecated. :param as_volts: If true, interpret the data as volts instead of meters :param sin_specification: Add this sine to the data :param output_path: str, Path to save commands to if provided. Default `None` := don't save. """ self.log.info("Applying shape to DM " + str(dm_num)) if not isinstance(dm_shape, DmCommand): dm_shape = DmCommand(data=dm_shape, dm_num=dm_num, flat_map=flat_map, bias=bias, as_voltage_percentage=as_voltage_percentage, as_volts=as_volts, sin_specification=sin_specification) # Ensure the dm_num is correct. dm_shape.dm_num = dm_num if output_path is not None: dm_shape.export_fits(output_path) other_dm_command_object = self.dm2_command_object if dm_num == 1 else self.dm1_command_object other_dm_command_object.export_fits(output_path) # Use DmCommand class to format the single command correctly (with zeros for other DM). dm_command = dm_shape.to_dm_command() # Grab the other DM's currently applied shape. other_dm_command = self.dm2_command if dm_num == 1 else self.dm1_command # Add both arrays together (first half and second half) and send to DM. full_command = dm_command + other_dm_command try: self.send_data(full_command) except Exception: # We shouldn't guarantee the state of the DM. self._clear_state() raise else: # Update the dm_command
<reponame>wenting-zhao/sgnmt # -- coding: utf-8 -- # coding=utf-8 # Copyright 2019 The SGNMT Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """This module contains predictors that deal wit the length of the target sentence. The ``NBLengthPredictor`` assumes a negative binomial distribution on the target sentence lengths, where the parameters r and p are linear combinations of features extracted from the source sentence. The ``WordCountPredictor`` adds the number of words as cost, which can be used to prevent hypotheses from getting to short when using a language model. """ import logging import math from scipy.special import logsumexp from scipy.special import gammaln from cam.sgnmt import utils from cam.sgnmt.misc.trie import SimpleTrie from cam.sgnmt.predictors.core import Predictor, UnboundedVocabularyPredictor import numpy as np NUM_FEATURES = 5 EPS_R = 0.1; def load_external_lengths(path): """Loads a length distribution from a plain text file. The file must contain blank separated <length>:<score> pairs in each line. Args: path (string): Path to the length file. Returns: list of dicts mapping a length to its scores, one dict for each sentence. """ lengths = [] with open(path) as f: for line in f: scores = {} for pair in line.strip().split(): if ':' in pair: length, score = pair.split(':') scores[int(length)] = float(score) else: scores[int(pair)] = 0.0 lengths.append(scores) return lengths def load_external_ids(path): """ load file of ids to list """ with open(path) as f: return [int(line.strip()) for line in f] class NBLengthPredictor(Predictor): """This predictor assumes that target sentence lengths are distributed according a negative binomial distribution with parameters r,p. r is linear in features, p is the logistic of a linear function over the features. Weights can be trained using the Matlab script ``estimate_length_model.m`` Let w be the model_weights. All features are extracted from the src sentence:: r = w0 * #char + w1 * #words + w2 * #punctuation + w3 * #char/#words + w4 * #punct/#words + w10 p = logistic(w5 * #char + w6 * #words + w7 * #punctuation + w8 * #char/#words + w9 * #punct/#words + w11) target_length ~ NB(r,p) The biases w10 and w11 are optional. The predictor predicts EOS with NB(#consumed_words,r,p) """ def __init__(self, text_file, model_weights, use_point_probs, offset = 0): """Creates a new target sentence length model predictor. Args: text_file (string): Path to the text file with the unindexed source sentences, i.e. not using word ids model_weights (list): Weights w0 to w11 of the length model. See class docstring for more information use_point_probs (bool): Use point estimates for EOS token, 0.0 otherwise offset (int): Subtract this from hypothesis length before applying the NB model """ super(NBLengthPredictor, self).__init__() self.use_point_probs = use_point_probs self.offset = offset if len(model_weights) == 2NUM_FEATURES: # add biases model_weights.append(0.0) model_weights.append(0.0) if len(model_weights) != 2NUM_FEATURES+2: logging.fatal("Number of length model weights has to be %d or %d" % (2NUM_FEATURES, 2NUM_FEATURES+2)) self.r_weights = model_weights[0:NUM_FEATURES] + [model_weights[-2]] self.p_weights = model_weights[NUM_FEATURES:2NUM_FEATURES] + [model_weights[-1]] self.src_features = self._extract_features(text_file) self.n_consumed = 0 def _extract_features(self, file_name): """Extract all features from the source sentences. """ feats = [] with open(file_name) as f: for line in f: feats.append(self._analyse_sentence(line.strip())) return feats def _analyse_sentence(self, sentence): """Extract features for a single source sentence. Args: sentence (string): Source sentence string Returns: 5-tuple of features as described in the class docstring """ n_char = len(sentence) + 0.0 n_words = len(sentence.split()) + 0.0 n_punct = sum([sentence.count(s) for s in ",.:;-"]) + 0.0 return [n_char, n_words, n_punct, n_char/n_words, n_punct/n_words] def get_unk_probability(self, posterior): """If we use point estimates, return 0 (=1). Otherwise, return the 1-p(EOS), with p(EOS) fetched from ``posterior`` """ if self.use_point_probs: if self.n_consumed == 0: return self.max_eos_prob return 0.0 if self.n_consumed == 0: return 0.0 return np.log(1.0 - np.exp(posterior[utils.EOS_ID])) def predict_next(self): """Returns a dictionary with single entry for EOS. """ if self.n_consumed == 0: return {utils.EOS_ID : utils.NEG_INF} return {utils.EOS_ID : self._get_eos_prob()} def _get_eos_prob(self): """Get loglikelihood according cur_p, cur_r, and n_consumed """ eos_point_prob = self._get_eos_point_prob(max( 1, self.n_consumed - self.offset)) if self.use_point_probs: return eos_point_prob - self.max_eos_prob if not self.prev_eos_probs: self.prev_eos_probs.append(eos_point_prob) return eos_point_prob # bypass utils.log_sum because we always want to use logsumexp here prev_sum = logsumexp(np.asarray([p for p in self.prev_eos_probs])) self.prev_eos_probs.append(eos_point_prob) # Desired prob is eos_point_prob / (1-last_eos_probs_sum) return eos_point_prob - np.log(1.0-np.exp(prev_sum)) def _get_eos_point_prob(self, n): return gammaln(n + self.cur_r) \ - gammaln(n + 1) \ - gammaln(self.cur_r) \ + n np.log(self.cur_p) \ + self.cur_r * np.log(1.0-self.cur_p) def _get_max_eos_prob(self): """Get the maximum loglikelihood according cur_p, cur_r TODO: replace this brute force impl. with something smarter """ max_prob = utils.NEG_INF n_prob = max_prob n = 0 while n_prob == max_prob: n += 1 n_prob = self._get_eos_point_prob(n) max_prob = max(max_prob, n_prob) return max_prob def initialize(self, src_sentence): """Extract features for the source sentence. Note that this method does not use ``src_sentence`` as we need the string representation of the source sentence to extract features. Args: src_sentence (list): Not used """ feat = self.src_features[self.current_sen_id] + [1.0] self.cur_r = max(EPS_R, np.dot(feat, self.r_weights)); p = np.dot(feat, self.p_weights) p = 1.0 / (1.0 + math.exp(-p)) self.cur_p = max(utils.EPS_P, min(1.0 - utils.EPS_P, p)) self.n_consumed = 0 self.prev_eos_probs = [] if self.use_point_probs: self.max_eos_prob = self._get_max_eos_prob() def consume(self, word): """Increases the current history length Args: word (int): Not used """ self.n_consumed = self.n_consumed + 1 def get_state(self): """State consists of the number of consumed words, and the accumulator for previous EOS probability estimates if we don't use point estimates. """ return self.n_consumed,self.prev_eos_probs def set_state(self, state): """Set the predictor state """ self.n_consumed,self.prev_eos_probs = state def is_equal(self, state1, state2): """Returns true if the number of consumed words is the same """ n1,_ = state1 n2,_ = state2 return n1 == n2 class WordCountPredictor(Predictor): """This predictor adds the (negative) number of words as feature. This means that this predictor encourages shorter hypotheses when used with a positive weight. """ def __init__(self, word=-1, nonterminal_penalty=False, nonterminal_ids=None, min_terminal_id=0, max_terminal_id=30003, negative_wc=True, vocab_size=30003): """Creates a new word count predictor instance. Args: word (int): If this is non-negative we count only the number of the specified word. If its negative, count all words nonterminal_penalty (bool): If true, apply penalty only to tokens in a range (the range outside min/max terminal id) nonterminal_ids: file containing ids of nonterminal tokens min_terminal_id: lower bound of tokens not to penalize, if nonterminal_penalty selected max_terminal_id: upper bound of tokens not to penalize, if nonterminal_penalty selected negative_wc: If true, the score of this predictor is the negative word count. vocab_size: upper bound of tokens, used to find nonterminal range """ super(WordCountPredictor, self).__init__() val = 1.0 if negative_wc: val = -1.0 if nonterminal_penalty: if nonterminal_ids: nts = load_external_ids(nonterminal_ids) else: min_nt_range = range(0, min_terminal_id) max_nt_range = range(max_terminal_id + 1, vocab_size) nts = list(min_nt_range) + list(max_nt_range) self.posterior = {nt: val for nt in nts} self.posterior[utils.EOS_ID] = 0.0 self.posterior[utils.UNK_ID] = 0.0 self.unk_prob = 0.0 elif word < 0: self.posterior = {utils.EOS_ID : 0.0} self.unk_prob = val else: self.posterior = {word : val} self.unk_prob = 0.0 def get_unk_probability(self, posterior): return self.unk_prob def predict_next(self): return self.posterior def initialize(self, src_sentence): """Empty""" pass def consume(self, word): """Empty""" pass def get_state(self): """Returns true """ return True def set_state(self, state): """Empty""" pass def is_equal(self, state1, state2): """Returns true """ return True class WeightNonTerminalPredictor(Predictor): """This wrapper multiplies the weight of given tokens (those outside the min/max terminal range) by a factor.""" def __init__(self, slave_predictor, penalty_factor=1.0, nonterminal_ids=None, min_terminal_id=0, max_terminal_id=30003, vocab_size=30003): """Creates a new id-weighting wrapper for a predictor Args: slave_predictor: predictor to
""" Authors: <NAME>, <NAME> TUM, 2020 In order to guarantee transferability of models, Network models should follow the following conventions. Classes should be called Node Edge Network in order to guarantee correct import in other modules. """ # -------------------------------------------------------------------------------------------------------------------- # # standard distribution imports # ----------------------------- import os import logging # additional module imports (> requirements) # ------------------------------------------ import pandas as pd import numpy as np from pyproj import Transformer # src imports # ----------- from src.routing.NetworkBase import NetworkBase from src.routing.routing_imports.Router import Router # -------------------------------------------------------------------------------------------------------------------- # # global variables # ---------------- from src.misc.globals import * LOG = logging.getLogger(__name__) # import os # import pandas as pd # import imports.Router as Router def read_node_line(columns): return Node(int(columns["node_index"]), int(columns["is_stop_only"]), float(columns["pos_x"]), float(columns["pos_y"])) class Node(): def __init__(self, node_index, is_stop_only, pos_x, pos_y, node_order=None): self.node_index = node_index self.is_stop_only = is_stop_only self.pos_x = pos_x self.pos_y = pos_y # self.edges_to = {} #node_obj -> edge self.edges_from = {} #node_obj -> edge # self.travel_infos_from = {} #node_index -> (tt, dis) self.travel_infos_to = {} #node_index -> (tt, dis) # # attributes set during path calculations self.is_target_node = False # is set and reset in computeFromNodes #attributes for forwards dijkstra self.prev = None self.settled = 1 self.cost_index = -1 self.cost = None # attributes for backwards dijkstra (for bidirectional dijkstra) self.next = None self.settled_back = 1 self.cost_index_back = -1 self.cost_back = None def __str__(self): return str(self.node_index) def must_stop(self): return self.is_stop_only def get_position(self): return (self.pos_x, self.pos_y) def get_next_node_edge_pairs(self, ch_flag = False): """ :return: list of (node, edge) tuples [references to objects] in forward direction """ return self.edges_to.items() def get_prev_node_edge_pairs(self, ch_flag = False): """ :return: list of (node, edge) tuples [references to objects] in backward direction """ return self.edges_from.items() def add_next_edge_to(self, other_node, edge): #print("add next edge to: {} -> {}".format(self.node_index, other_node.node_index)) self.edges_to[other_node] = edge self.travel_infos_to[other_node.node_index] = edge.get_tt_distance() def add_prev_edge_from(self, other_node, edge): self.edges_from[other_node] = edge self.travel_infos_from[other_node.node_index] = edge.get_tt_distance() def get_travel_infos_to(self, other_node_index): return self.travel_infos_to[other_node_index] def get_travel_infos_from(self, other_node_index): return self.travel_infos_from[other_node_index] class Edge(): def __init__(self, edge_index, distance, travel_time): self.edge_index = edge_index self.distance = distance self.travel_time = travel_time # def __str__(self): return "-".join(self.edge_index) def set_tt(self, travel_time): self.travel_time = travel_time def get_tt(self): """ :return: (current) travel time on edge """ return self.travel_time def get_distance(self): """ :return: distance of edge """ return self.distance def get_tt_distance(self): """ :return: (travel time, distance) tuple """ return (self.travel_time, self.distance) # Position: (start_node_id, end_node_id, relative_pos) # -> (node_id, None, None) in case vehicle is on a node # -> relative_pos in [0.0, 1.0] # A Route is defined as list of node-indices (int) # while all given start-and end-position nodes are included class NetworkBasic(NetworkBase): def __init__(self, network_name_dir, network_dynamics_file_name=None, scenario_time=None): """ The network will be initialized. This network only uses basic routing algorithms (dijkstra and bidirectional dijkstra) :param network_name_dir: name of the network_directory to be loaded :param type: determining whether the base or a pre-processed network will be used :param scenario_time: applying travel times for a certain scenario at a given time in the scenario :param network_dynamics_file_name: file-name of the network dynamics file :type network_dynamics_file_name: str """ self.nodes = [] #list of all nodes in network (index == node.node_index) self.network_name_dir = network_name_dir self.travel_time_file_folders = self._load_tt_folder_path(network_dynamics_file_name=network_dynamics_file_name) self.loadNetwork(network_name_dir, network_dynamics_file_name=network_dynamics_file_name, scenario_time=scenario_time) self.current_dijkstra_number = 1 #used in dijkstra-class self.sim_time = 0 # TODO # self.zones = None # TODO # with open(os.sep.join([self.network_name_dir, "base","crs.info"]), "r") as f: self.crs = f.read() def loadNetwork(self, network_name_dir, network_dynamics_file_name=None, scenario_time=None): nodes_f = os.path.join(network_name_dir, "base", "nodes.csv") print(f"Loading nodes from {nodes_f} ...") nodes_df = pd.read_csv(nodes_f) self.nodes = nodes_df.apply(read_node_line, axis=1) # edges_f = os.path.join(network_name_dir, "base", "edges.csv") print(f"Loading edges from {edges_f} ...") with open(edges_f) as fhin: header = fhin.readline() for line in fhin: lc = line.strip().split(",") o_node = self.nodes[int(lc[0])] d_node = self.nodes[int(lc[1])] # for the table approach, int values are used (to avoid rounding mistakes!) tmp_edge = Edge((o_node, d_node), float(lc[2]), float(lc[3])) o_node.add_next_edge_to(d_node, tmp_edge) d_node.add_prev_edge_from(o_node, tmp_edge) print("... {} nodes loaded!".format(len(self.nodes))) if scenario_time is not None: latest_tt = None if len(self.travel_time_file_folders.keys()) > 0: tts = sorted(list(self.travel_time_file_folders.keys())) for tt in tts: if tt > scenario_time: break latest_tt = tt self.load_tt_file(latest_tt) def _load_tt_folder_path(self, network_dynamics_file_name=None): """ this method searches in the network-folder for travel_times folder. the name of the folder is defined by the simulation time from which these travel times are valid stores the corresponding time to trigger loading of new travel times ones the simulation time is reached. """ tt_folders = {} if network_dynamics_file_name is None: LOG.info("... no network dynamics file given -> read folder structure") for f in os.listdir(self.network_name_dir): time = None try: time = int(f) except: continue tt_folders[time] = os.path.join(self.network_name_dir, f) else: LOG.info("... load network dynamics file: {}".format(os.path.join(self.network_name_dir, network_dynamics_file_name))) nw_dynamics_df = pd.read_csv(os.path.join(self.network_name_dir, network_dynamics_file_name)) nw_dynamics_df.set_index("simulation_time", inplace=True) for sim_time, tt_folder_name in nw_dynamics_df["travel_time_folder"].items(): tt_folders[int(sim_time)] = os.path.join(self.network_name_dir, str(tt_folder_name)) return tt_folders def update_network(self, simulation_time, update_state = True): """This method can be called during simulations to update travel times (dynamic networks). :param simulation_time: time of simulation :type simulation_time: float :return: new_tt_flag True, if new travel times found; False if not :rtype: bool """ LOG.debug(f"update network {simulation_time}") self.sim_time = simulation_time if update_state: if self.travel_time_file_folders.get(simulation_time, None) is not None: self.load_tt_file(simulation_time) return True return False def load_tt_file(self, scenario_time): """ loads new travel time files for scenario_time """ self._reset_internal_attributes_after_travel_time_update() f = self.travel_time_file_folders[scenario_time] tt_file = os.path.join(f, "edges_td_att.csv") tmp_df = pd.read_csv(tt_file, index_col=[0,1]) for edge_index_tuple, new_tt in tmp_df["edge_tt"].iteritems(): self._set_edge_tt(edge_index_tuple[0], edge_index_tuple[1], new_tt) def _set_edge_tt(self, o_node_index, d_node_index, new_travel_time): o_node = self.nodes[o_node_index] d_node = self.nodes[d_node_index] edge_obj = o_node.edges_to[d_node] edge_obj.set_tt(new_travel_time) new_tt, dis = edge_obj.get_tt_distance() o_node.travel_infos_to[d_node_index] = (new_tt, dis) d_node.travel_infos_from[o_node_index] = (new_tt, dis) def get_node_list(self): """ :return: list of node objects. """ return self.nodes def get_number_network_nodes(self): return len(self.nodes) def get_must_stop_nodes(self): """ returns a list of node-indices with all nodes with a stop_only attribute """ return [n.node_index for n in self.nodes if n.must_stop()] def return_position_from_str(self, position_str): a, b, c = position_str.split(";") if b == "-1": return (int(a), None, None) else: return (int(a), int(b), float(c)) def return_node_coordinates(self, node_index): return self.nodes[node_index].get_position() def return_position_coordinates(self, position_tuple): """Returns the spatial coordinates of a position. :param position_tuple: (o_node, d_node, rel_pos) \| (o_node, None, None) :return: (x,y) for metric systems """ if position_tuple[1] is None: return self.return_node_coordinates(position_tuple[0]) else: c0 = np.array(self.return_node_coordinates(position_tuple[0])) c1 = np.array(self.return_node_coordinates(position_tuple[1])) c_rel = position_tuple[2] * c1 + (1 - position_tuple[2]) * c0 return c_rel[0], c_rel[1] def return_network_bounding_box(self): min_x = min([node.pos_x for node in self.nodes]) max_x = max([node.pos_x for node in self.nodes]) min_y = min([node.pos_y for node in self.nodes]) max_y = max([node.pos_y for node in self.nodes]) proj_transformer = Transformer.from_proj(self.crs, 'epsg:4326') lats, lons = proj_transformer.transform([min_x, max_x], [min_y, max_y]) return list(zip(lons, lats)) def return_positions_lon_lat(self, position_tuple_list: list) -> list: pos_list = [self.return_position_coordinates(pos) for pos in position_tuple_list] x, y = list(zip(*pos_list)) proj_transformer = Transformer.from_proj(self.crs, 'epsg:4326') lats, lons = proj_transformer.transform(x, y) return list(zip(lons, lats)) def get_section_infos(self, start_node_index, end_node_index): """ :param start_node_index_index: index of start_node of section :param end_node_index: index of end_node of section :return: (travel time, distance); if no section between nodes (None, None) """ return self.nodes[start_node_index].get_travel_infos_to(end_node_index) def return_route_infos(self, route, rel_start_edge_position, start_time): """ This method returns the information travel information along a route. The start position is given by a relative value on the first edge [0,1], where 0 means that the vehicle is at the first node. :param route: list of nodes :param rel_start_edge_position: float [0,1] determining the start position :param start_time: can be used as an offset in case the route is planned for a future time :return: (arrival time, distance to travel) """ arrival_time = start_time distance = 0 _, start_tt, start_dis = self.get_section_overhead( (route[0], route[1], rel_start_edge_position), from_start=False) arrival_time += start_tt distance += start_dis if len(route) > 2: for i in range(2, len(route)): tt, dis = self.get_section_infos(route[i-1], route[i]) arrival_time += tt distance += dis return (arrival_time, distance) def assign_route_to_network(self, route, start_time): """This method can be used for dynamic network models in which the travel times will be derived from the number of vehicles/routes assigned to the network. :param route: list of nodes :param start_time: can be used as an offset in case the route is planned for a future time :return: TODO """ pass def get_section_overhead(self, position, from_start=True, customized_section_cost_function=None): """This method computes the section overhead for a certain position. :param position: (current_edge_origin_node_index, current_edge_destination_node_index, relative_position) :param from_start: computes already traveled travel_time and distance, if False: computes rest travel time (relative_position -> 1.0-relative_position) :param customized_section_cost_function: customized routing objective function
<filename>embiggen/pipelines/compute_node_embedding.py """Sub-module with methods to compute node-embedding with a one-liner.""" import inspect import warnings from typing import Dict, List, Tuple, Union import pandas as pd import tensorflow as tf from cache_decorator import Cache from ensmallen import Graph from ..embedders import (Embedder, GraphCBOW, GraphGloVe, GraphSkipGram, Siamese, SimplE, TransE, TransH, TransR) from ..utils import has_gpus, has_nvidia_drivers, has_rocm_drivers SUPPORTED_NODE_EMBEDDING_METHODS = { "CBOW": GraphCBOW, "GloVe": GraphGloVe, "SkipGram": GraphSkipGram, "Siamese": Siamese, "TransE": TransE, "SimplE": SimplE, "TransH": TransH, "TransR": TransR, } REQUIRE_ZIPFIAN = [ "CBOW", "SkipGram" ] RANDOM_WALK_BASED_MODELS = [ "CBOW", "GloVe", "SkipGram" ] LINK_PREDICTION_BASED_MODELS = [ "Siamese", "TransR", "TransE", "TransH", "SimplE" ] assert set(RANDOM_WALK_BASED_MODELS + LINK_PREDICTION_BASED_MODELS) == set(SUPPORTED_NODE_EMBEDDING_METHODS) def get_available_node_embedding_methods() -> List[str]: """Return list of supported node embedding methods.""" return list(SUPPORTED_NODE_EMBEDDING_METHODS.keys()) def get_node_embedding_method(node_embedding_method_name: str) -> Embedder: """Return node embedding method curresponding to given name.""" return SUPPORTED_NODE_EMBEDDING_METHODS[node_embedding_method_name] def is_node_embedding_method_supported(node_embedding_method_name: str) -> bool: """Return boolean value representing if given node embedding method is supported. Parameters -------------------- node_embedding_method_name: str, Name of the node embedding method. Returns -------------------- Whether the given node embedding method is supported. """ return node_embedding_method_name in get_available_node_embedding_methods() def _train_model( graph: Graph, node_embedding_method_name: str, fit_kwargs: Dict, verbose: bool, support_mirrored_strategy: bool, kwargs: Dict ) -> Tuple[Union[pd.DataFrame, Tuple[pd.DataFrame]], pd.DataFrame]: """Return embedding computed with required node embedding method. Parameters -------------------------- graph: Graph, The graph to embed. node_embedding_method_name: str, The name of the node embedding method to use. fit_kwargs: Dict, Arguments to pass to the fit call. verbose: bool = True, Whether to show loading bars. use_mirrored_strategy: bool = True, Whether to use mirrored strategy. kwargs: Dict, Arguments to pass to the node embedding method constructor. Read the documentation of the selected method. Returns -------------------------- Tuple with node embedding and training history. """ # Creating the node embedding model model = get_node_embedding_method(node_embedding_method_name)( graph, support_mirrored_strategy=support_mirrored_strategy, kwargs ) # Fitting the node embedding model history = model.fit( verbose=verbose, fit_kwargs ) # Extracting computed embedding node_embedding = model.get_embedding_dataframe() return node_embedding, history @Cache( cache_path=[ "node_embeddings/{node_embedding_method_name}/{graph_name}/{_hash}_embedding.pkl.gz", "node_embeddings/{node_embedding_method_name}/{graph_name}/{_hash}_training_history.csv.xz", ], args_to_ignore=["devices", "use_mirrored_strategy", "verbose"] ) def _compute_node_embedding( graph: Graph, graph_name: str, # pylint: disable=unused-argument node_embedding_method_name: str, fit_kwargs: Dict, verbose: bool = True, use_mirrored_strategy: bool = True, devices: Union[List[str], str] = None, kwargs: Dict ) -> Tuple[Union[pd.DataFrame, Tuple[pd.DataFrame]], pd.DataFrame]: """Return embedding computed with required node embedding method. Specifically, this method also caches the embedding automatically. Parameters -------------------------- graph: Graph, The graph to embed. graph_name: str, The name of the graph. node_embedding_method_name: str, The name of the node embedding method to use. fit_kwargs: Dict, Arguments to pass to the fit call. verbose: bool = True, Whether to show loading bars. use_mirrored_strategy: bool = True, Whether to use mirrored strategy. devices: Union[List[str], str] = None, The devices to use. If None, all GPU devices available are used. kwargs: Dict, Arguments to pass to the node embedding method constructor. Read the documentation of the selected method. Returns -------------------------- Tuple with node embedding and training history. """ # Since the verbose kwarg may be provided also on the fit_kwargs # we normalize the parameter to avoid collisions. verbose = fit_kwargs.pop("verbose", verbose) kwargs = dict( graph=graph, node_embedding_method_name=node_embedding_method_name, fit_kwargs=fit_kwargs, verbose=verbose, support_mirrored_strategy=use_mirrored_strategy, kwargs ) if use_mirrored_strategy: strategy = tf.distribute.MirroredStrategy(devices=devices) with strategy.scope(): return _train_model(kwargs) return _train_model(kwargs) def compute_node_embedding( graph: Graph, node_embedding_method_name: str, use_mirrored_strategy: bool = True, devices: Union[List[str], str] = None, fit_kwargs: Dict = None, verbose: Union[bool, int] = True, automatically_drop_unsupported_parameters: bool = False, automatically_enable_time_memory_tradeoffs: bool = True, automatically_sort_by_decreasing_outbound_node_degree: bool = True, kwargs: Dict ) -> Tuple[pd.DataFrame, pd.DataFrame]: """Return embedding computed using SkipGram on given graph. Parameters -------------------------- graph: Graph, Graph to embed. node_embedding_method_name: str, The name of the node embedding method to use. use_mirrored_strategy: bool = True, Whether to use mirror strategy to distribute the computation across multiple devices. Note that this will be automatically disabled if the set of devices detected is only composed of one, since using MirroredStrategy adds a significant overhead and may endup limiting the device usage. devices: Union[List[str], str] = None, The devices to use. If None, all GPU devices available are used. fit_kwargs: Dict = None, Arguments to pass to the fit call. verbose: bool = True, Whether to show loading bars. automatically_drop_unsupported_parameters: bool = False, If required, we filter out the unsupported parameters. This may be useful when running a suite of experiments with a set of parameters and you do not want to bother in dropping the parameters that are only supported in a subset of methods. automatically_enable_time_memory_tradeoffs: bool = True, Whether to activate the time memory tradeoffs automatically. Often case, this is something you want enabled on your graph object. Since, generally, it is a good idea to enable these while computing a node embedding we enable these by default. automatically_sort_by_decreasing_outbound_node_degree: bool = True, Whether to automatically sort the nodes by the outbound node degree. This is necessary in order to run SkipGram efficiently with the NCE loss. It will ONLY be executed if the requested model is SkipGram. **kwargs: Dict, Arguments to pass to the node embedding method constructor. Read the documentation of the selected method to learn which methods are supported by the selected constructor. Returns -------------------------- Tuple with node embedding and training history. """ if not is_node_embedding_method_supported(node_embedding_method_name): raise ValueError( ( "The given node embedding method `{}` is not supported. " "The supported node embedding methods are `{}`." ).format( node_embedding_method_name, get_available_node_embedding_methods() ) ) # To avoid some nighmares we check availability of GPUs. if not has_gpus(): # If there are no GPUs, mirrored strategy makes no sense. if use_mirrored_strategy: use_mirrored_strategy = False warnings.warn( "It does not make sense to use mirrored strategy " "when GPUs are not available.\n" "The parameter has been disabled." ) # We check for drivers to try and give a more explainatory # warning about the absence of GPUs. if has_nvidia_drivers(): warnings.warn( "It was not possible to detect GPUs but the system " "has NVIDIA drivers installed.\n" "It is very likely there is some mis-configuration " "with your TensorFlow instance.\n" "The model will train a LOT faster if you figure " "out what may be the cause of this issue on your " "system: sometimes a simple reboot will do a lot of good.\n" "If you are currently on COLAB, remember to enable require " "a GPU instance from the menu!" ) elif has_rocm_drivers(): warnings.warn( "It was not possible to detect GPUs but the system " "has ROCM drivers installed.\n" "It is very likely there is some mis-configuration " "with your TensorFlow instance.\n" "The model will train a LOT faster if you figure " "out what may be the cause of this issue on your " "system: sometimes a simple reboot will do a lot of good." ) else: warnings.warn( "It was neither possible to detect GPUs nor GPU drivers " "of any kind on your system (neither CUDA or ROCM).\n" "The model will proceed with trainining, but it will be " "significantly slower than what would be possible " "with GPU acceleration." ) # If the fit kwargs are not given we normalize them to an empty dictionary. if fit_kwargs is None: fit_kwargs = {} # If the model requested is SkipGram and the given graph does not have sorted # node IDs according to decreasing outbound node degrees, we create the new graph # that has the node IDs sorted. if automatically_sort_by_decreasing_outbound_node_degree and node_embedding_method_name in REQUIRE_ZIPFIAN and not graph.has_nodes_sorted_by_decreasing_outbound_node_degree(): graph = graph.sort_by_decreasing_outbound_node_degree() # If required, we filter out the unsupported parameters. # This may be useful when running a suite of experiments with a set of # parameters and you do not want to bother in dropping the parameters # that are only supported in a subset of methods. if automatically_drop_unsupported_parameters and kwargs: # Get the list of supported parameters supported_parameter = inspect.signature( get_node_embedding_method(node_embedding_method_name).__init__ ).parameters # Filter out the unsupported parameters kwargs = { key: value for key, value in kwargs.items() if key in supported_parameter } # If required we enable the time memory tradeoffs. if automatically_enable_time_memory_tradeoffs: if node_embedding_method_name in RANDOM_WALK_BASED_MODELS: graph.enable( vector_sources=False, vector_destinations=True, vector_cumulative_node_degrees=True ) if node_embedding_method_name in LINK_PREDICTION_BASED_MODELS: graph.enable( vector_sources=True, vector_destinations=True, vector_cumulative_node_degrees=False ) # If devices are
<reponame>joaopbicalho/CodingInPython def get_cur_hedons(): global cur_hedons return cur_hedons def get_cur_health(): global cur_health return cur_health def offer_star(activity): global cur_star global star_counter global time_since_curstar global time_since_star global star_break global time_since_star1 global time_since_star2 global star_span time_since_curstar = 0 star_counter += 1 time_since_star = time_since_star1 + time_since_star2 if time_since_star >= 120: star_counter += -1 time_since_star1 = time_since_star2 time_since_star2 = 0 star_span = 1 if star_counter > 2: cur_star = "none" star_break = "activated" elif activity == "running": cur_star = "running" elif activity == "textbooks": cur_star = "textbooks" elif time_since_curstar < 120: if star_counter > 2: cur_star = "none" star_break = "activated" elif activity == "running": cur_star = "running" star_span = star_counter elif activity == "textbooks": cur_star = "textbooks" star_span = star_counter def perform_activity(activity, duration): global cur_health global cur_hedons global running_duration global running_counter global resting_duration global user_state global textbooks_duration global textbooks_counter global running_hed_counter global star_time global cur_star global star_counter global time_since_star global time_since_curstar global star_break global time_since_star1 global time_since_star2 global star_span global textbook_hed_counter if activity == "running": running_duration += duration resting_duration = 0 textbook_hed_counter = 0 textbooks_duration = 0 textbooks_counter = 0 if user_state == "tired" and cur_star != "running": cur_hedons += duration * (-2) cur_star = "none" time_since_curstar = "not zero" if running_duration <= 180: cur_health += duration3 user_state = "tired" if star_span == 1: time_since_star1 += duration elif star_span == 2: time_since_star2 += duration elif running_duration >180: running_counter += 1 if running_counter == 1: cur_health += (running_duration - 180) + 540 - (running_duration - duration) 3 user_state = "tired" if star_span == 1: time_since_star1 += duration elif star_span == 2: time_since_star2 += duration else: cur_health += (duration) user_state = "tired" if star_span == 1: time_since_star1 += duration elif star_span == 2: time_since_star2 += duration elif user_state == "not tired" and cur_star != "running": running_hed_counter += 1 user_state = "tired" cur_star = "none" time_since_curstar = "not zero" if running_duration <= 10: cur_hedons += running_duration * 2 user_state = "tired" cur_health += duration * 3 if star_span == 1: time_since_star1 += duration elif star_span == 2: time_since_star2 += duration elif running_duration > 10 and running_hed_counter == 1: cur_hedons += (running_duration - 10) * (-2) + 20 if running_duration <= 180: cur_health += duration * 3 if star_span == 1: time_since_star1 += duration elif star_span == 2: time_since_star2 += duration elif running_duration > 180 and running_counter == 1: running_counter += 1 cur_health += (running_duration - 180) + 540 - (running_duration - duration) * 3 if star_span == 1: time_since_star1 += duration elif star_span == 2: time_since_star2 += duration else: cur_health += duration if star_span == 1: time_since_star1 += duration elif star_span == 2: time_since_star2 += duration else: cur_hedons += duration * (-1) user_state = "tired" if running_duration <= 180: cur_health += duration * 3 if star_span == 1: time_since_star1 += duration elif star_span == 2: time_since_star2 += duration elif running_duration > 180 and running_counter == 1: running_counter += 1 cur_health += (running_duration - 180) + 540 if star_span == 1: time_since_star1 += duration elif star_span == 2: time_since_star2 += duration elif running_counter != 1: cur_health += (running_duration - 180) + 540 - (running_duration - duration) * 3 if star_span == 1: time_since_star1 += duration elif star_span == 2: time_since_star2 += duration elif cur_star == "running" and user_state == "not tired" and star_break != "activated" and time_since_curstar == 0: user_state = "tired" cur_star = "none" time_since_curstar = "not zero" if duration <= 10: cur_hedons += 5 * duration if running_duration <= 180: cur_health += duration * 3 if star_span == 1: time_since_star1 += duration elif star_span == 2: time_since_star2 += duration elif running_duration > 180 and running_counter == 1: running_counter += 1 cur_health += (running_duration - 180) + 540 - (running_duration - duration) * 3 if star_span == 1: time_since_star1 += duration elif star_span == 2: time_since_star2 += duration else: cur_health += duration if star_span == 1: time_since_star1 += duration elif star_span == 2: time_since_star2 += duration elif duration > 10: cur_hedons += (duration - 10) * (-2) + 50 if running_duration <= 180: cur_health += duration * 3 if star_span == 1: time_since_star1 += duration elif star_span == 2: time_since_star2 += duration elif running_duration > 180 and running_counter == 1: running_counter += 1 cur_health += (running_duration - 180) + 540 - (running_duration - duration) * 3 if star_span == 1: time_since_star1 += duration elif star_span == 2: time_since_star2 += duration else: cur_health += duration if star_span == 1: time_since_star1 += duration elif star_span == 2: time_since_star2 += duration elif cur_star == "running" and user_state == "tired" and star_break != "activated" and time_since_curstar == 0: user_state = "tired" cur_star = "none" time_since_curstar = "not zero" if duration <= 10: cur_hedons += duration if running_duration <= 180: cur_health += duration * 3 if star_span == 1: time_since_star1 += duration elif star_span == 2: time_since_star2 += duration elif running_duration > 180 and running_counter == 1: running_counter += 1 cur_health += (running_duration - 180) + 540 if star_span == 1: time_since_star1 += duration elif star_span == 2: time_since_star2 += duration else: cur_health += duration if star_span == 1: time_since_star1 += duration elif star_span == 2: time_since_star2 += duration elif duration > 10: cur_hedons += (duration - 10) * (-2) + 10 if running_duration <= 180: cur_health += duration * 3 if star_span == 1: time_since_star1 += duration elif star_span == 2: time_since_star2 += duration elif running_duration > 180 and running_counter == 1: running_counter += 1 cur_health += (running_duration - 180) + 540 if star_span == 1: time_since_star1 += duration elif star_span == 2: time_since_star2 += duration else: cur_health += duration if star_span == 1: time_since_star1 += duration elif star_span == 2: time_since_star2 += duration elif activity == "textbooks": resting_duration = 0 cur_health = cur_health + 2 * duration running_duration = 0 running_counter = 0 textbooks_counter += 1 textbooks_duration += duration if user_state == "tired" and cur_star != "textbooks": cur_star = "none" cur_hedons += duration * (-2) time_since_curstar = "not zero" user_state = "tired" if star_span == 1: time_since_star1 += duration elif star_span == 2: time_since_star2 += duration elif user_state == "not tired" and cur_star == "textbooks" and star_break != "activated" and time_since_curstar == 0: cur_star = "none" time_since_curstar = "not zero" user_state = "tired" if duration <= 10: cur_hedons += 4 * duration if star_span == 1: time_since_star1 += duration elif star_span == 2: time_since_star2 += duration elif duration <= 20: cur_hedons += (duration - 10) + 40 if star_span == 1: time_since_star1 += duration elif star_span == 2: time_since_star2 += duration elif duration > 20: cur_hedons += ((duration - 20) * (-1)) + 50 if star_span == 1: time_since_star1 += duration elif star_span == 2: time_since_star2 += duration elif user_state == "tired" and cur_star == "textbooks" and star_break != "activated" and time_since_curstar == 0: cur_star = "none" user_state = "tired" time_since_curstar = "not zero" if duration <= 10: cur_hedons += duration if star_span == 1: time_since_star1 += duration elif star_span == 2: time_since_star2 += duration elif duration > 10: cur_hedons += (duration - 10) * (-2) + 10 if star_span == 1: time_since_star1 += duration elif star_span == 2: time_since_star2 += duration elif user_state == "not tired" and cur_star != "textbooks": cur_star = "none" user_state = "tired" time_since_curstar = "not zero" if duration <= 20: cur_hedons += duration if star_span == 1: time_since_star1 += duration elif star_span == 2: time_since_star2 += duration elif duration > 20 and textbook_hed_counter == 0: textbook_hed_counter += 1 cur_hedons += (duration - 20) * (-1) + 20 if star_span == 1: time_since_star1 += duration elif star_span == 2: time_since_star2 += duration elif duration > 20 and textbook_hed_counter != 0: textbook_hed_counter += 1 cur_hedons += (duration) * (-1) if star_span == 1: time_since_star1 += duration elif star_span == 2: time_since_star2 += duration elif activity == "resting": running_duration = 0 textbook_hed_counter = 0 running_counter = 0 textbooks_duration = 0 textbooks_counter
memory if it is a valid point if np.isnan(y0).any(): self.mem_ban.add(x0, y0) else: self.mem_med.add(x0, y0) if self.verbose: print(" y = %s" % np.array_str(y0)) return y0 def feasible_moves(self, x0, dx): """Starting from x0, all moves within constraints and not tabu.""" # Generate candidate moves X = hj_move(x0, dx) # Remove duplicate moves (can arise if an element of dx is zero) X = np.unique(X, axis=0) # Filter by input constraints X = X[self.constraint(X)] # Filter against short term memory X = X[~self.mem_short.contains(X)] # Filter against permanent ban list # (we put points where CFD results indicate constraint violated here) X = X[~self.mem_ban.contains(X)] return X def evaluate_moves(self, x0, dx): """From a given start point, evaluate permissible candidate moves.""" X = self.feasible_moves(x0, dx) # Check which points we have seen before log_seen = self.mem_long.contains(X) X_seen = X[log_seen] X_unseen = X[~log_seen] # Re-use previous objectives from long-term mem if possible Y_seen = self.mem_long.lookup(X_seen) # Only go as far as evaluating unseen if there are actually points if X_unseen.shape[0] > 0: # Shuffle the unseen points to remove selection bias np.random.shuffle(X_unseen) # Limit the maximum parallel evaluations if self.max_parallel: # Evaluate in batches isplit = range( self.max_parallel, len(X_unseen), self.max_parallel ) X_batch = np.split(X_unseen, isplit) Y_batch = [self.objective(Xi) for Xi in X_batch] # Join results Y_unseen = np.concatenate(Y_batch) else: # Evaluate objective for unseen points Y_unseen = self.objective(X_unseen) # Increment function evaluation counter self.fevals += len(X_unseen) # Join the results together X = np.vstack((X_seen, X_unseen)) Y = np.vstack((Y_seen, Y_unseen)) # If there are no unseen points else: X = X_seen Y = Y_seen return X, Y def select_move(self, x0, y0, X, Y): """Choose next move given starting point and list of candidate moves.""" j = self.j_objective try: # Categorise the candidates for next move with respect to current with np.errstate(invalid="ignore"): b_dom = (Y[:, j] < y0[:, j]).all(axis=1) b_non_dom = (Y[:, j] > y0[:, j]).all(axis=1) b_equiv = ~np.logical_and(b_dom, b_non_dom) except IndexError: print("ERROR! in select_move") print("Y=%s", str(Y)) print("y0=%s", str(y0)) print("shape Y", Y.shape) print("shape y0", y0.shape) quit() # Convert to indices i_dom = np.where(b_dom)[0] i_non_dom = np.where(b_non_dom)[0] i_equiv = np.where(b_equiv)[0] # Choose the next point if len(i_dom) > 0: # If we have dominating points, randomly choose from them np.random.shuffle(i_dom) x1, y1 = X[i_dom[0]], Y[i_dom[0]] elif len(i_equiv) > 0: # Randomly choose from equivalent points np.random.shuffle(i_equiv) x1, y1 = X[i_equiv[0]], Y[i_equiv[0]] elif len(i_non_dom) > 0: # Randomly choose from non-dominating points np.random.shuffle(i_non_dom) x1, y1 = X[i_non_dom[0]], Y[i_non_dom[0]] else: raise Exception("No valid points to pick next move from") # Keep in matrix form x1 = np.atleast_2d(x1) y1 = np.atleast_2d(y1) return x1, y1 def pattern_move(self, x0, y0, x1, y1, dx): """If this move is in a good direction, increase move length.""" x1a = x0 + self.fac_pattern * (x1 - x0) # # If we are running objectives in parallel, do not waste the spare cores # if self.max_parallel: # # Pick (n_parallel - 1) feasible moves from the pattern move point # X1a = self.feasible_moves(x1a, dx) # X1a_unseen = X1a[~self.mem_long.contains(X1a)] # np.random.shuffle(X1a_unseen) # X1a_unseen = X1a_unseen[: (self.max_parallel -1)] # X = np.vstack(x1,X1a_unseen) # Y = self.objective(X) # else: y1a = self.objective(x1a) if (y1a < y1).all(): return x1a else: return x1 def update_med(self, X, Y): """Update the near-optimal points in medium term memory.""" if X.shape[0] == 0: flag = False else: if len(self.j_objective) == 1: flag = self.mem_med.update_best(X, Y) else: flag = self.mem_med.update_front(X, Y) return flag def search(self, x0, dx, callback=None): """Perform a search with given intial point and step size.""" # Evaluate the objective at given initial guess point, update memories y0 = self.initial_guess(x0) max_step = dx * self.fac_restart ** 2.0 # Main loop, until max evaluations reached or step size below tolerance self.i_search = 0 while self.fevals < self.max_fevals and np.any(dx > self.tol): # Save in case we want to resume later self.dx = dx.reshape(-1).tolist() self.x0 = x0.reshape(-1).tolist() self.y0 = y0.reshape(-1).tolist() # Record our progress in a memory file, if specified if self.mem_file: self.save_memories(self.mem_file) # Plot stuff if self.verbose: self.plot_long("long.pdf") self.plot_opt("opt.pdf") # If we are given a callback, evaluate it now if callback: callback(self) # Evaluate objective for permissible candidate moves X, Y = self.evaluate_moves(x0, dx) # If any objectives are NaN, add to permanent ban list inan = np.isnan(Y).any(-1) Xnan = X[inan] self.mem_ban.add(Xnan) # Delete NaN from results X, Y = X[~inan], Y[~inan] # Put new results into long-term memory self.mem_long.add(X, Y) # Put Pareto-equivalent results into medium-term memory # Flag true if we sucessfully added a point flag = self.update_med(X, Y) if self.verbose and flag: print( "NEW OPT\n x = %s\n y = %s" % tuple([np.array_str(xy) for xy in self.mem_med.get(0)]) ) # Reset counter if we added to medium memory, otherwise increment self.i_search = 0 if flag else self.i_search + 1 # Choose next point based on local search counter if self.i_search == self.i_restart: if self.verbose: print("RESTART") # RESTART: reduce step sizes and randomly select from # medium-term dx = self.fac_restart if len(self.j_objective) == 1: # Pick the current optimum if scalar objective x1, y1 = self.mem_med.get(0) else: # Pick from sparse region of Pareto from if multi-objective x1, y1 = self.mem_med.sample_sparse(self.x_regions) self.i_search = 0 elif self.i_search in self.i_intensify or X.shape[0] == 0: # INTENSIFY: Select a near-optimal point if the medium memory # is populated if self.mem_med.npts > 0: if self.verbose: print("INTENSIFY") x1, y1 = self.mem_med.sample_random() else: # If nothing in the medium-term memory, we have not found # any valid points yet, so increase step size and try again if np.all(dx <= max_step): if self.verbose: print("INCREASE STEP") dx /= self.fac_restart x1, y1 = x0, y0 else: print( "Could not find a point satisfying constraints near initial guess, quitting." ) elif self.i_search == self.i_diversify: if self.verbose: print("DIVERSIFY") # DIVERSIFY: Generate a new point in sparse design region x1 = self.mem_long.generate_sparse(self.x_regions) y1 = self.objective(x1) else: if self.verbose: print("i=%d, fevals=%d" % (self.i_search, self.fevals)) # Normally, choose the best candidate move x1, y1 = self.select_move(x0, y0, X, Y) # Check for a pattern move every i_pattern steps if not self.i_pattern is None: if np.mod(self.i_search, self.i_pattern): x1 = self.pattern_move(x0, y0, x1, y1, dx) if self.verbose: print( " x = %s\n y = %s" % tuple([np.array_str(xy) for xy in (x1, y1)]) ) # Add chosen point to short-term list (tabu) self.mem_short.add(x1) # Update current point before next iteration x0, y0 = x1, y1 # After the loop return current point return x0, y0 def resume(self, fname): self.load_memories(fname) self.mem_file = fname self.search(self.x0, self.dx) def save_memories(self, fname): """Dump the memories to a json file.""" # Assemble a dict for each memory d = {k: m.to_dict() for k, m in zip(self.MEM_KEYS, self.mem_all)} for a in ["i_search", "x0", "y0", "dx"]: d[a] = getattr(self, a) # Write the file with open(fname, "w") as f: json.dump(d, f) def load_memories(self, fname): """Populate memories from a json file.""" if self.verbose: print("READ memories from %s" % fname) # Load the file with open(fname, "r") as f: d = json.load(f) # Populate the memories for k, m in zip(self.MEM_KEYS, self.mem_all): if self.verbose: print(" %s: %d points" % (k, d[k]["npts"])) m.from_dict(d[k]) if "i_search" in d: self.i_search = d["i_search"] self.x0 = np.atleast_2d(d["x0"]) self.y0 = np.atleast_2d(d["y0"]) self.dx = np.atleast_2d(d["dx"]) def plot_long(self, fname): """Generate a graph of long-term memory.""" import matplotlib.pyplot as plt fig, ax = plt.subplots() Yl = np.flip(self.mem_long.Y, axis=0) 100.0 pts = np.arange(len(Yl)) Ym = self.mem_med.Y _, ind = find_rows(Ym, Yl) ax.plot(pts, Yl[:, 0], "k-") ax.plot(pts[ind], Yl[ind, 0], "r") ax.set_ylabel("Lost Efficiency, $\Delta \eta/\%$") ax.set_xlabel("Design Evaluations") plt.tight_layout() plt.savefig(fname) plt.close() def plot_opt(self, fname): """Generate a graph of optimisation progress.""" import matplotlib.pyplot as plt fig, ax = plt.subplots() Yl = np.flip(self.mem_long.Y, axis=0) pts = np.arange(len(Yl)) Ymin = np.empty_like(pts) for i, p in enumerate(pts): if np.all(np.isnan(Yl[: (p + 1), 0])): Ymin[i] = np.nan else: Ymin[i] = np.nanmin(Yl[: (p + 1), 0]) 100.0 ax.plot(pts, Ymin - Ymin[-1], "k-") ax.set_ylabel("Optimum Lost Efficiency Error, $\Delta \eta/\%$") ax.set_xlabel("Design Evaluations") plt.tight_layout() plt.savefig(fname)
__version__) models = MODELS_MAP[g_param[OptionsDefine.Version]] model = models.ModifyLivePlayAuthKeyRequest() model.from_json_string(json.dumps(args)) rsp = client.ModifyLivePlayAuthKey(model) result = rsp.to_json_string() try: json_obj = json.loads(result) except TypeError as e: json_obj = json.loads(result.decode('utf-8')) # python3.3 FormatOutput.output("action", json_obj, g_param[OptionsDefine.Output], g_param[OptionsDefine.Filter]) def doDescribeLiveTranscodeTemplate(args, parsed_globals): g_param = parse_global_arg(parsed_globals) cred = credential.Credential( g_param[OptionsDefine.SecretId], g_param[OptionsDefine.SecretKey], g_param[OptionsDefine.Token] ) http_profile = HttpProfile( reqTimeout=60 if g_param[OptionsDefine.Timeout] is None else int(g_param[OptionsDefine.Timeout]), reqMethod="POST", endpoint=g_param[OptionsDefine.Endpoint], proxy=g_param[OptionsDefine.HttpsProxy] ) profile = ClientProfile(httpProfile=http_profile, signMethod="HmacSHA256") mod = CLIENT_MAP[g_param[OptionsDefine.Version]] client = mod.LiveClient(cred, g_param[OptionsDefine.Region], profile) client._sdkVersion += ("_CLI_" + __version__) models = MODELS_MAP[g_param[OptionsDefine.Version]] model = models.DescribeLiveTranscodeTemplateRequest() model.from_json_string(json.dumps(args)) rsp = client.DescribeLiveTranscodeTemplate(model) result = rsp.to_json_string() try: json_obj = json.loads(result) except TypeError as e: json_obj = json.loads(result.decode('utf-8')) # python3.3 FormatOutput.output("action", json_obj, g_param[OptionsDefine.Output], g_param[OptionsDefine.Filter]) def doDescribeScreenShotSheetNumList(args, parsed_globals): g_param = parse_global_arg(parsed_globals) cred = credential.Credential( g_param[OptionsDefine.SecretId], g_param[OptionsDefine.SecretKey], g_param[OptionsDefine.Token] ) http_profile = HttpProfile( reqTimeout=60 if g_param[OptionsDefine.Timeout] is None else int(g_param[OptionsDefine.Timeout]), reqMethod="POST", endpoint=g_param[OptionsDefine.Endpoint], proxy=g_param[OptionsDefine.HttpsProxy] ) profile = ClientProfile(httpProfile=http_profile, signMethod="HmacSHA256") mod = CLIENT_MAP[g_param[OptionsDefine.Version]] client = mod.LiveClient(cred, g_param[OptionsDefine.Region], profile) client._sdkVersion += ("_CLI_" + __version__) models = MODELS_MAP[g_param[OptionsDefine.Version]] model = models.DescribeScreenShotSheetNumListRequest() model.from_json_string(json.dumps(args)) rsp = client.DescribeScreenShotSheetNumList(model) result = rsp.to_json_string() try: json_obj = json.loads(result) except TypeError as e: json_obj = json.loads(result.decode('utf-8')) # python3.3 FormatOutput.output("action", json_obj, g_param[OptionsDefine.Output], g_param[OptionsDefine.Filter]) def doUnBindLiveDomainCert(args, parsed_globals): g_param = parse_global_arg(parsed_globals) cred = credential.Credential( g_param[OptionsDefine.SecretId], g_param[OptionsDefine.SecretKey], g_param[OptionsDefine.Token] ) http_profile = HttpProfile( reqTimeout=60 if g_param[OptionsDefine.Timeout] is None else int(g_param[OptionsDefine.Timeout]), reqMethod="POST", endpoint=g_param[OptionsDefine.Endpoint], proxy=g_param[OptionsDefine.HttpsProxy] ) profile = ClientProfile(httpProfile=http_profile, signMethod="HmacSHA256") mod = CLIENT_MAP[g_param[OptionsDefine.Version]] client = mod.LiveClient(cred, g_param[OptionsDefine.Region], profile) client._sdkVersion += ("_CLI_" + __version__) models = MODELS_MAP[g_param[OptionsDefine.Version]] model = models.UnBindLiveDomainCertRequest() model.from_json_string(json.dumps(args)) rsp = client.UnBindLiveDomainCert(model) result = rsp.to_json_string() try: json_obj = json.loads(result) except TypeError as e: json_obj = json.loads(result.decode('utf-8')) # python3.3 FormatOutput.output("action", json_obj, g_param[OptionsDefine.Output], g_param[OptionsDefine.Filter]) def doDeleteRecordTask(args, parsed_globals): g_param = parse_global_arg(parsed_globals) cred = credential.Credential( g_param[OptionsDefine.SecretId], g_param[OptionsDefine.SecretKey], g_param[OptionsDefine.Token] ) http_profile = HttpProfile( reqTimeout=60 if g_param[OptionsDefine.Timeout] is None else int(g_param[OptionsDefine.Timeout]), reqMethod="POST", endpoint=g_param[OptionsDefine.Endpoint], proxy=g_param[OptionsDefine.HttpsProxy] ) profile = ClientProfile(httpProfile=http_profile, signMethod="HmacSHA256") mod = CLIENT_MAP[g_param[OptionsDefine.Version]] client = mod.LiveClient(cred, g_param[OptionsDefine.Region], profile) client._sdkVersion += ("_CLI_" + __version__) models = MODELS_MAP[g_param[OptionsDefine.Version]] model = models.DeleteRecordTaskRequest() model.from_json_string(json.dumps(args)) rsp = client.DeleteRecordTask(model) result = rsp.to_json_string() try: json_obj = json.loads(result) except TypeError as e: json_obj = json.loads(result.decode('utf-8')) # python3.3 FormatOutput.output("action", json_obj, g_param[OptionsDefine.Output], g_param[OptionsDefine.Filter]) def doDescribeLiveTranscodeDetailInfo(args, parsed_globals): g_param = parse_global_arg(parsed_globals) cred = credential.Credential( g_param[OptionsDefine.SecretId], g_param[OptionsDefine.SecretKey], g_param[OptionsDefine.Token] ) http_profile = HttpProfile( reqTimeout=60 if g_param[OptionsDefine.Timeout] is None else int(g_param[OptionsDefine.Timeout]), reqMethod="POST", endpoint=g_param[OptionsDefine.Endpoint], proxy=g_param[OptionsDefine.HttpsProxy] ) profile = ClientProfile(httpProfile=http_profile, signMethod="HmacSHA256") mod = CLIENT_MAP[g_param[OptionsDefine.Version]] client = mod.LiveClient(cred, g_param[OptionsDefine.Region], profile) client._sdkVersion += ("_CLI_" + __version__) models = MODELS_MAP[g_param[OptionsDefine.Version]] model = models.DescribeLiveTranscodeDetailInfoRequest() model.from_json_string(json.dumps(args)) rsp = client.DescribeLiveTranscodeDetailInfo(model) result = rsp.to_json_string() try: json_obj = json.loads(result) except TypeError as e: json_obj = json.loads(result.decode('utf-8')) # python3.3 FormatOutput.output("action", json_obj, g_param[OptionsDefine.Output], g_param[OptionsDefine.Filter]) def doDescribeLogDownloadList(args, parsed_globals): g_param = parse_global_arg(parsed_globals) cred = credential.Credential( g_param[OptionsDefine.SecretId], g_param[OptionsDefine.SecretKey], g_param[OptionsDefine.Token] ) http_profile = HttpProfile( reqTimeout=60 if g_param[OptionsDefine.Timeout] is None else int(g_param[OptionsDefine.Timeout]), reqMethod="POST", endpoint=g_param[OptionsDefine.Endpoint], proxy=g_param[OptionsDefine.HttpsProxy] ) profile = ClientProfile(httpProfile=http_profile, signMethod="HmacSHA256") mod = CLIENT_MAP[g_param[OptionsDefine.Version]] client = mod.LiveClient(cred, g_param[OptionsDefine.Region], profile) client._sdkVersion += ("_CLI_" + __version__) models = MODELS_MAP[g_param[OptionsDefine.Version]] model = models.DescribeLogDownloadListRequest() model.from_json_string(json.dumps(args)) rsp = client.DescribeLogDownloadList(model) result = rsp.to_json_string() try: json_obj = json.loads(result) except TypeError as e: json_obj = json.loads(result.decode('utf-8')) # python3.3 FormatOutput.output("action", json_obj, g_param[OptionsDefine.Output], g_param[OptionsDefine.Filter]) def doDescribeLiveRecordRules(args, parsed_globals): g_param = parse_global_arg(parsed_globals) cred = credential.Credential( g_param[OptionsDefine.SecretId], g_param[OptionsDefine.SecretKey], g_param[OptionsDefine.Token] ) http_profile = HttpProfile( reqTimeout=60 if g_param[OptionsDefine.Timeout] is None else int(g_param[OptionsDefine.Timeout]), reqMethod="POST", endpoint=g_param[OptionsDefine.Endpoint], proxy=g_param[OptionsDefine.HttpsProxy] ) profile = ClientProfile(httpProfile=http_profile, signMethod="HmacSHA256") mod = CLIENT_MAP[g_param[OptionsDefine.Version]] client = mod.LiveClient(cred, g_param[OptionsDefine.Region], profile) client._sdkVersion += ("_CLI_" + __version__) models = MODELS_MAP[g_param[OptionsDefine.Version]] model = models.DescribeLiveRecordRulesRequest() model.from_json_string(json.dumps(args)) rsp = client.DescribeLiveRecordRules(model) result = rsp.to_json_string() try: json_obj = json.loads(result) except TypeError as e: json_obj = json.loads(result.decode('utf-8')) # python3.3 FormatOutput.output("action", json_obj, g_param[OptionsDefine.Output], g_param[OptionsDefine.Filter]) def doDescribeLiveDelayInfoList(args, parsed_globals): g_param = parse_global_arg(parsed_globals) cred = credential.Credential( g_param[OptionsDefine.SecretId], g_param[OptionsDefine.SecretKey], g_param[OptionsDefine.Token] ) http_profile = HttpProfile( reqTimeout=60 if g_param[OptionsDefine.Timeout] is None else int(g_param[OptionsDefine.Timeout]), reqMethod="POST", endpoint=g_param[OptionsDefine.Endpoint], proxy=g_param[OptionsDefine.HttpsProxy] ) profile = ClientProfile(httpProfile=http_profile, signMethod="HmacSHA256") mod = CLIENT_MAP[g_param[OptionsDefine.Version]] client = mod.LiveClient(cred, g_param[OptionsDefine.Region], profile) client._sdkVersion += ("_CLI_" + __version__) models = MODELS_MAP[g_param[OptionsDefine.Version]] model = models.DescribeLiveDelayInfoListRequest() model.from_json_string(json.dumps(args)) rsp = client.DescribeLiveDelayInfoList(model) result = rsp.to_json_string() try: json_obj = json.loads(result) except TypeError as e: json_obj = json.loads(result.decode('utf-8')) # python3.3 FormatOutput.output("action", json_obj, g_param[OptionsDefine.Output], g_param[OptionsDefine.Filter]) def doDescribeLiveStreamPublishedList(args, parsed_globals): g_param = parse_global_arg(parsed_globals) cred = credential.Credential( g_param[OptionsDefine.SecretId], g_param[OptionsDefine.SecretKey], g_param[OptionsDefine.Token] ) http_profile = HttpProfile( reqTimeout=60 if g_param[OptionsDefine.Timeout] is None else int(g_param[OptionsDefine.Timeout]), reqMethod="POST", endpoint=g_param[OptionsDefine.Endpoint], proxy=g_param[OptionsDefine.HttpsProxy] ) profile = ClientProfile(httpProfile=http_profile, signMethod="HmacSHA256") mod = CLIENT_MAP[g_param[OptionsDefine.Version]] client = mod.LiveClient(cred, g_param[OptionsDefine.Region], profile) client._sdkVersion += ("_CLI_" + __version__) models = MODELS_MAP[g_param[OptionsDefine.Version]] model = models.DescribeLiveStreamPublishedListRequest() model.from_json_string(json.dumps(args)) rsp = client.DescribeLiveStreamPublishedList(model) result = rsp.to_json_string() try: json_obj = json.loads(result) except TypeError as e: json_obj = json.loads(result.decode('utf-8')) # python3.3 FormatOutput.output("action", json_obj, g_param[OptionsDefine.Output], g_param[OptionsDefine.Filter]) def doDescribeLiveDomain(args, parsed_globals): g_param = parse_global_arg(parsed_globals) cred = credential.Credential( g_param[OptionsDefine.SecretId], g_param[OptionsDefine.SecretKey], g_param[OptionsDefine.Token] ) http_profile = HttpProfile( reqTimeout=60 if g_param[OptionsDefine.Timeout] is None else int(g_param[OptionsDefine.Timeout]), reqMethod="POST", endpoint=g_param[OptionsDefine.Endpoint], proxy=g_param[OptionsDefine.HttpsProxy] ) profile = ClientProfile(httpProfile=http_profile, signMethod="HmacSHA256") mod = CLIENT_MAP[g_param[OptionsDefine.Version]] client = mod.LiveClient(cred, g_param[OptionsDefine.Region], profile) client._sdkVersion += ("_CLI_" + __version__) models = MODELS_MAP[g_param[OptionsDefine.Version]] model = models.DescribeLiveDomainRequest() model.from_json_string(json.dumps(args)) rsp = client.DescribeLiveDomain(model) result = rsp.to_json_string() try: json_obj = json.loads(result) except TypeError as e: json_obj = json.loads(result.decode('utf-8')) # python3.3 FormatOutput.output("action", json_obj, g_param[OptionsDefine.Output], g_param[OptionsDefine.Filter]) def doCreateLiveCallbackRule(args, parsed_globals): g_param = parse_global_arg(parsed_globals) cred = credential.Credential( g_param[OptionsDefine.SecretId], g_param[OptionsDefine.SecretKey], g_param[OptionsDefine.Token] ) http_profile = HttpProfile( reqTimeout=60 if g_param[OptionsDefine.Timeout] is None else int(g_param[OptionsDefine.Timeout]), reqMethod="POST", endpoint=g_param[OptionsDefine.Endpoint], proxy=g_param[OptionsDefine.HttpsProxy] ) profile = ClientProfile(httpProfile=http_profile, signMethod="HmacSHA256") mod = CLIENT_MAP[g_param[OptionsDefine.Version]] client = mod.LiveClient(cred, g_param[OptionsDefine.Region], profile) client._sdkVersion += ("_CLI_" + __version__) models = MODELS_MAP[g_param[OptionsDefine.Version]] model = models.CreateLiveCallbackRuleRequest() model.from_json_string(json.dumps(args)) rsp = client.CreateLiveCallbackRule(model) result = rsp.to_json_string() try: json_obj = json.loads(result) except TypeError as e: json_obj = json.loads(result.decode('utf-8')) # python3.3 FormatOutput.output("action", json_obj, g_param[OptionsDefine.Output], g_param[OptionsDefine.Filter]) def doBindLiveDomainCert(args, parsed_globals): g_param = parse_global_arg(parsed_globals) cred = credential.Credential( g_param[OptionsDefine.SecretId], g_param[OptionsDefine.SecretKey], g_param[OptionsDefine.Token] ) http_profile = HttpProfile( reqTimeout=60 if g_param[OptionsDefine.Timeout] is None else int(g_param[OptionsDefine.Timeout]), reqMethod="POST", endpoint=g_param[OptionsDefine.Endpoint], proxy=g_param[OptionsDefine.HttpsProxy] ) profile = ClientProfile(httpProfile=http_profile, signMethod="HmacSHA256") mod = CLIENT_MAP[g_param[OptionsDefine.Version]] client = mod.LiveClient(cred, g_param[OptionsDefine.Region], profile) client._sdkVersion += ("_CLI_" + __version__) models = MODELS_MAP[g_param[OptionsDefine.Version]] model = models.BindLiveDomainCertRequest() model.from_json_string(json.dumps(args)) rsp = client.BindLiveDomainCert(model) result = rsp.to_json_string() try: json_obj = json.loads(result) except TypeError as e: json_obj = json.loads(result.decode('utf-8')) # python3.3 FormatOutput.output("action", json_obj, g_param[OptionsDefine.Output], g_param[OptionsDefine.Filter]) def doDescribeLiveCallbackRules(args, parsed_globals): g_param = parse_global_arg(parsed_globals) cred = credential.Credential( g_param[OptionsDefine.SecretId], g_param[OptionsDefine.SecretKey], g_param[OptionsDefine.Token] ) http_profile = HttpProfile( reqTimeout=60 if g_param[OptionsDefine.Timeout] is None else int(g_param[OptionsDefine.Timeout]), reqMethod="POST", endpoint=g_param[OptionsDefine.Endpoint], proxy=g_param[OptionsDefine.HttpsProxy] ) profile = ClientProfile(httpProfile=http_profile, signMethod="HmacSHA256") mod = CLIENT_MAP[g_param[OptionsDefine.Version]] client = mod.LiveClient(cred, g_param[OptionsDefine.Region], profile) client._sdkVersion += ("_CLI_" + __version__) models = MODELS_MAP[g_param[OptionsDefine.Version]] model = models.DescribeLiveCallbackRulesRequest() model.from_json_string(json.dumps(args)) rsp = client.DescribeLiveCallbackRules(model) result = rsp.to_json_string() try: json_obj = json.loads(result) except TypeError as e: json_obj = json.loads(result.decode('utf-8')) # python3.3 FormatOutput.output("action", json_obj, g_param[OptionsDefine.Output], g_param[OptionsDefine.Filter]) def doDescribePlayErrorCodeDetailInfoList(args, parsed_globals): g_param = parse_global_arg(parsed_globals) cred = credential.Credential( g_param[OptionsDefine.SecretId], g_param[OptionsDefine.SecretKey], g_param[OptionsDefine.Token] ) http_profile = HttpProfile( reqTimeout=60 if g_param[OptionsDefine.Timeout] is None else int(g_param[OptionsDefine.Timeout]), reqMethod="POST", endpoint=g_param[OptionsDefine.Endpoint], proxy=g_param[OptionsDefine.HttpsProxy] ) profile = ClientProfile(httpProfile=http_profile, signMethod="HmacSHA256") mod = CLIENT_MAP[g_param[OptionsDefine.Version]] client = mod.LiveClient(cred, g_param[OptionsDefine.Region], profile) client._sdkVersion += ("_CLI_" + __version__) models = MODELS_MAP[g_param[OptionsDefine.Version]] model = models.DescribePlayErrorCodeDetailInfoListRequest() model.from_json_string(json.dumps(args)) rsp = client.DescribePlayErrorCodeDetailInfoList(model) result = rsp.to_json_string() try: json_obj = json.loads(result) except TypeError as e: json_obj = json.loads(result.decode('utf-8')) # python3.3 FormatOutput.output("action", json_obj, g_param[OptionsDefine.Output], g_param[OptionsDefine.Filter]) def doDeleteLiveRecordRule(args, parsed_globals): g_param = parse_global_arg(parsed_globals) cred = credential.Credential( g_param[OptionsDefine.SecretId], g_param[OptionsDefine.SecretKey], g_param[OptionsDefine.Token] ) http_profile = HttpProfile( reqTimeout=60 if g_param[OptionsDefine.Timeout] is None else int(g_param[OptionsDefine.Timeout]), reqMethod="POST", endpoint=g_param[OptionsDefine.Endpoint], proxy=g_param[OptionsDefine.HttpsProxy] ) profile = ClientProfile(httpProfile=http_profile, signMethod="HmacSHA256") mod = CLIENT_MAP[g_param[OptionsDefine.Version]] client = mod.LiveClient(cred, g_param[OptionsDefine.Region], profile) client._sdkVersion += ("_CLI_" + __version__) models = MODELS_MAP[g_param[OptionsDefine.Version]] model = models.DeleteLiveRecordRuleRequest() model.from_json_string(json.dumps(args)) rsp = client.DeleteLiveRecordRule(model) result = rsp.to_json_string() try: json_obj = json.loads(result) except TypeError as e: json_obj = json.loads(result.decode('utf-8')) # python3.3 FormatOutput.output("action", json_obj, g_param[OptionsDefine.Output], g_param[OptionsDefine.Filter]) def doDescribePushBandwidthAndFluxList(args, parsed_globals): g_param = parse_global_arg(parsed_globals) cred = credential.Credential( g_param[OptionsDefine.SecretId], g_param[OptionsDefine.SecretKey], g_param[OptionsDefine.Token] ) http_profile = HttpProfile( reqTimeout=60 if g_param[OptionsDefine.Timeout] is None else int(g_param[OptionsDefine.Timeout]), reqMethod="POST", endpoint=g_param[OptionsDefine.Endpoint], proxy=g_param[OptionsDefine.HttpsProxy] ) profile = ClientProfile(httpProfile=http_profile, signMethod="HmacSHA256") mod = CLIENT_MAP[g_param[OptionsDefine.Version]] client = mod.LiveClient(cred, g_param[OptionsDefine.Region], profile) client._sdkVersion += ("_CLI_" + __version__) models = MODELS_MAP[g_param[OptionsDefine.Version]] model = models.DescribePushBandwidthAndFluxListRequest() model.from_json_string(json.dumps(args)) rsp = client.DescribePushBandwidthAndFluxList(model) result = rsp.to_json_string() try: json_obj = json.loads(result) except TypeError as e: json_obj = json.loads(result.decode('utf-8')) # python3.3 FormatOutput.output("action", json_obj, g_param[OptionsDefine.Output], g_param[OptionsDefine.Filter]) def doForbidLiveStream(args, parsed_globals): g_param = parse_global_arg(parsed_globals) cred = credential.Credential( g_param[OptionsDefine.SecretId], g_param[OptionsDefine.SecretKey], g_param[OptionsDefine.Token] ) http_profile = HttpProfile( reqTimeout=60 if g_param[OptionsDefine.Timeout] is None else int(g_param[OptionsDefine.Timeout]), reqMethod="POST", endpoint=g_param[OptionsDefine.Endpoint], proxy=g_param[OptionsDefine.HttpsProxy] ) profile = ClientProfile(httpProfile=http_profile, signMethod="HmacSHA256") mod = CLIENT_MAP[g_param[OptionsDefine.Version]] client = mod.LiveClient(cred, g_param[OptionsDefine.Region], profile) client._sdkVersion += ("_CLI_" + __version__) models = MODELS_MAP[g_param[OptionsDefine.Version]] model = models.ForbidLiveStreamRequest() model.from_json_string(json.dumps(args)) rsp = client.ForbidLiveStream(model) result = rsp.to_json_string() try: json_obj = json.loads(result) except TypeError as e: json_obj = json.loads(result.decode('utf-8')) # python3.3 FormatOutput.output("action", json_obj, g_param[OptionsDefine.Output], g_param[OptionsDefine.Filter]) def doAddLiveDomain(args, parsed_globals): g_param = parse_global_arg(parsed_globals) cred = credential.Credential(
if jBase.createDatabase(val) == 0: bd = TS.SimboloBase(val, None, nodo.mode) tablaSimbolos.put(val, bd) consola += "Base de datos " + val + " creada. \n" else: consola += "Error al crear la base de datos \n" elif nodo.owner != False and nodo.mode == False: if jBase.createDatabase(val) == 0: bd = TS.SimboloBase(val, nodo.owner, None) tablaSimbolos.put(val, bd) consola += "Base de datos " + val + " creada. \n" else: consola += "Error al crear la base de datos \n" elif nodo.owner != False and nodo.mode != False: if jBase.createDatabase(val) == 0: bd = TS.SimboloBase(val, nodo.owner, nodo.mode) tablaSimbolos.put(val, bd) consola += "Base de datos " + val + " creada. \n" else: consola += "Error al crear la base de datos \n" elif nodo.replace == False and nodo.exists != False: if nodo.owner == False and nodo.mode == False: if jBase.createDatabase(val) == 0: bd = TS.SimboloBase(val, None, None) tablaSimbolos.put(val, bd) consola += "Base de datos " + val + " creada. \n" elif jBase.createDatabase(val) == 2: consola += "La base de datos " + val + " ya existe. \n" else: consola += "Error al crear la base de datos \n" elif nodo.owner == False and nodo.mode != False: if jBase.createDatabase(val) == 0: bd = TS.SimboloBase(val, None, nodo.mode) tablaSimbolos.put(val, bd) consola += "Base de datos " + val + " creada. \n" elif jBase.createDatabase(val) == 2: consola += "La base de datos " + val + " ya existe. \n" else: consola += "Error al crear la base de datos \n" elif nodo.owner != False and nodo.mode == False: if jBase.createDatabase(val) == 0: bd = TS.SimboloBase(val, nodo.owner, None) tablaSimbolos.put(val, bd) consola += "Base de datos " + val + " creada. \n" elif jBase.createDatabase(val) == 2: consola += "La base de datos " + val + " ya existe. \n" else: consola += "Error al crear la base de datos \n" elif nodo.owner != False and nodo.mode != False: if jBase.createDatabase(val) == 0: bd = TS.SimboloBase(val, nodo.owner, nodo.mode) tablaSimbolos.put(val, bd) consola += "Base de datos " + val + " creada. \n" elif jBase.createDatabase(val) == 2: consola += "La base de datos " + val + " ya existe. \n" else: consola += "Error al crear la base de datos \n" def crearTabla(nodo, tablaSimbolos): val = nodo.id global useActual global consola primarykeys = [] if nodo.herencia == False: contador = 0 nueva = TS.SimboloTabla(val, None) for col in nodo.columnas: pk = False default_ = None check = None null = True unique = False if isinstance(col, SColumna): if col.opcionales != None: for opc in col.opcionales: if isinstance(opc, SOpcionales): if opc.tipo == TipoOpcionales.PRIMARYKEY: pk = True elif opc.tipo == TipoOpcionales.DEFAULT: default_ = opc.valor elif opc.tipo == TipoOpcionales.CHECK: if opc.id == None: check = {"id": col.id + "_check", "condicion": opc.valor} listaConstraint.append( TS.Constraints(useActual, val, col.id + "_check", col.id, "check")) else: check = {"id": opc.id, "condicion": opc.valor} listaConstraint.append( TS.Constraints(useActual, val, opc.id, col.id, "check")) elif opc.tipo == TipoOpcionales.NULL: null = True elif opc.tipo == TipoOpcionales.NOTNULL: null = False elif opc.tipo == TipoOpcionales.UNIQUE: if opc.id == None: unique = col.id + "_unique" listaConstraint.append( TS.Constraints(useActual, val, col.id + "_unique", col.id, "unique")) else: unique = opc.id listaConstraint.append( TS.Constraints(useActual, val, opc.id, col.id, "unique")) colnueva = TS.SimboloColumna(col.id, col.tipo, pk, None, unique, default_, null, check, len(nueva.columnas)) if pk: primarykeys.append(colnueva.index) nueva.crearColumna(col.id, colnueva) if colnueva == None: listaSemanticos.append( Error.ErrorS("Error Semantico", "Ya existe una columna con el nombre " + col.id)) else: auxc = TS.SimboloColumna(col.id, col.tipo, pk, None, unique, default_, null, check, len(nueva.columnas)) nueva.crearColumna(col.id, auxc) contador += 1 elif isinstance(col, SColumnaUnique): for id in col.id: if nueva.modificarUnique(id.valor, True, id.valor + "_unique") == None: listaSemanticos.append( Error.ErrorS("Error Semantico", "No se encontró la columna con id " + id.valor)) else: listaConstraint.append(TS.Constraints( useActual, val, id.valor + "_unique", id.valor, "unique")) elif isinstance(col, SColumnaCheck): print("Entró al constraint") condicion = col.condicion opIzq = condicion.opIzq idcol = opIzq.valor result = False if col.id == None: result = nueva.modificarCheck( idcol, col.condicion, idcol + "_check") listaConstraint.append(TS.Constraints( useActual, val, idcol + "_check", idcol, "check")) else: result = nueva.modificarCheck(idcol, condicion, col.id) listaConstraint.append(TS.Constraints( useActual, val, col.id, idcol, "check")) if result != True: listaSemanticos.append(Error.ErrorS( "Error Semantico", "No se encontró la columna con id " + idcol)) elif isinstance(col, SColumnaFk): for i in range(len(col.idlocal)): idlocal = col.idlocal[i].valor idfk = col.idfk[i].valor columnafk = tablaSimbolos.getColumna( useActual, col.id, idfk) columnalocal = nueva.getColumna(idlocal) if columnafk != None and columnalocal != None: if columnafk.tipo.tipo == columnalocal.tipo.tipo: nueva.modificarFk(idlocal, col.id, idfk) if col.idconstraint != None: listaConstraint.append( TS.Constraints(useActual, val, col.idconstraint, columnalocal, "FK")) listaFK.append(TS.llaveForanea( useActual, val, col.id, idlocal, idfk)) else: listaSemanticos.append(Error.ErrorS("Error Semantico", "La columna %s y la columna %s no tienen el mismo tipo" % ( idlocal, idfk))) else: listaSemanticos.append( Error.ErrorS("Error Semantico", "No se encontró la columna")) elif isinstance(col, SColumnaPk): for id in col.id: if nueva.modificarPk(id.valor) == None: listaSemanticos.append( Error.ErrorS("Error Semantico", "No se encontró la columna " + id.valor)) else: primarykeys.append(nueva.getColumna(id.valor).index) base = tablaSimbolos.get(useActual) base.crearTabla(val, nueva) tt = jBase.createTable(useActual, nodo.id, contador) if len(primarykeys) > 0: jBase.alterAddPK(useActual, val, primarykeys) if tt == 0: consola += "La tabla " + nodo.id + " se creó con éxito. \n" elif tt == 1: consola += "Error en la operación al crear la tabla " + nodo.id + "\n" elif tt == 2: consola += "La base de datos " + useActual + " no existe. \n" else: consola += "La tabla " + nodo.id + " ya existe. \n" def AlterDatabase(nodo, tablaSimbolos): global consola if nodo.rename: b = jBase.alterDatabase(nodo.id.valor, nodo.idnuevo) if b == 0: base = tablaSimbolos.renameBase(nodo.id.valor, nodo.idnuevo) if base: for fk in listaFK: if fk.idbase == nodo.id.valor: fk.idbase = nodo.idnuevo for cons in listaConstraint: if cons.idbase == nodo.id.valor: cons.idbase = nodo.idnuevo consola += "La base se renombró con éxito " + nodo.idnuevo + " \n" else: consola += "Error no se pudo renombrar la base " + \ nodo.id.valor + " en la tabla de simbolos \n" elif b == 2: listaSemanticos.append(Error.ErrorS( "Error Semantico", "La base de datos " + nodo.id.valor + " no existe")) elif b == 3: listaSemanticos.append(Error.ErrorS( "Error Semantico", "La base de datos ya existe " + nodo.idnuevo)) elif b == 1: listaSemanticos.append(Error.ErrorS( "Error Semantico", "Error en la operacion.")) def AlterAddColumn(nodo, tablaSimbolos): global consola global useActual base = tablaSimbolos.get(useActual) tabla = base.getTabla(nodo.idtabla) for col in nodo.listaColumnas: auxcol = TS.SimboloColumna( col.idcolumna, col.tipo, False, None, None, None, True, None, len(tabla.columnas)) if tabla.crearColumna(col.idcolumna, auxcol): b = jBase.alterAddColumn(useActual, nodo.idtabla, col.idcolumna) if b == 0: consola += "La columna " + col.idcolumna + \ " se agregó a la tabla " + nodo.idtabla + " \n" elif b == 1: listaSemanticos.append(Error.ErrorS( "Error Semantico", "Error en la operacion.")) elif b == 2: listaSemanticos.append(Error.ErrorS( "Error Semantico", "Error la base " + useActual + "no existe")) elif b == 3: listaSemanticos.append(Error.ErrorS( "Error Semantico", "Error la tabla " + nodo.idtabla + "no existe")) else: consola += "Error al crear la columna " + col.idcolumna + " \n" def AlterRenameColumn(nodo, tablaSimbolos): base = tablaSimbolos.get(useActual) tabla = base.getTabla(nodo.idtabla) global consola op = tabla.renameColumna(nodo.idcolumna, nodo.idnuevo) if op == 0: for fk in listaFK: if fk.idcfk == nodo.idcolumna: fk.idcfk = nodo.idnuevo tablaRF = base.getTabla(fk.idtlocal) columnaRF = tablaRF.getColumna(fk.idclocal) columnaRF.foreign_key["columna"] = nodo.idnuevo elif fk.idclocal == nodo.idcolumna: fk.idclocal = nodo.idnuevo for cons in listaConstraint: if cons.idcol == nodo.idcolumna: cons.idcol = nodo.idnuevo consola += "Se cambio el nombre de la columna " + \ nodo.idcolumna + " a " + nodo.idnuevo + " con exito \n" elif op == 1: listaSemanticos.append(Error.ErrorS( "Error Semantico", "La columna con nombre " + nodo.idnuevo + " ya existe")) elif op == 2: listaSemanticos.append(Error.ErrorS( "Error Semantico", "La columna con nombre " + nodo.idactual + " no existe")) def AlterRenameTable(nodo, tablaSimbolos): global useActual global consola base = tablaSimbolos.get(useActual) op = base.renameTable(nodo.idactual, nodo.idnuevo) if op == 0: lib = jBase.alterTable(useActual, nodo.idactual, nodo.idnuevo) if lib == 0: for fk in listaFK: if fk.idtfk == nodo.idactual: fk.idtfk = nodo.idnuevo tablaRF = base.getTabla(fk.idtlocal) columnaRF = tablaRF.getColumna(fk.idclocal) columnaRF.foreign_key["tabla"] = nodo.idnuevo elif fk.idtlocal == nodo.idactual: fk.idtlocal = nodo.idnuevo for cons in listaConstraint: if cons.idtabla == nodo.idactual:
<reponame>FiskFan1999/ergochat_irctest # # (C) Copyright 2011 <NAME> <<EMAIL>> # # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License Version # 2.1 as published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with this program; if not, write to the Free Software # Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. # """SCRAM authentication mechanisms for PyXMPP SASL implementation. Normative reference: - :RFC:`5802` """ from __future__ import absolute_import, division, unicode_literals __docformat__ = "restructuredtext en" import sys import re import logging import hashlib import hmac from binascii import a2b_base64 from base64 import standard_b64encode from .core import default_nonce_factory from .exceptions import BadChallengeException, \ ExtraChallengeException, ServerScramError, BadSuccessException, \ NotAuthorizedException logger = logging.getLogger("pyxmpp2_scram") HASH_FACTORIES = { "SHA-1": hashlib.sha1, # pylint: disable=E1101 "SHA-224": hashlib.sha224, # pylint: disable=E1101 "SHA-256": hashlib.sha256, # pylint: disable=E1101 "SHA-384": hashlib.sha384, # pylint: disable=E1101 "SHA-512": hashlib.sha512, # pylint: disable=E1101 "MD-5": hashlib.md5, # pylint: disable=E1101 } VALUE_CHARS_RE = re.compile(br"^[\x21-\x2B\x2D-\x7E]+$") _QUOTED_VALUE_RE = br"(?:[\x21-\x2B\x2D-\x7E]\|=2C\|=3D)+" CLIENT_FIRST_MESSAGE_RE = re.compile( br"^(?P<gs2_header>(?:y\|n\|p=(?P<cb_name>[a-zA-z0-9.-]+))," br"(?:a=(?P<authzid>" + _QUOTED_VALUE_RE + br"))?,)" br"(?P<client_first_bare>(?P<mext>m=[^\000=]+,)?" br"n=(?P<username>" + _QUOTED_VALUE_RE + br")," br"r=(?P<nonce>[\x21-\x2B\x2D-\x7E]+)" br"(?:,.)?)$" ) SERVER_FIRST_MESSAGE_RE = re.compile( br"^(?P<mext>m=[^\000=]+,)?" br"r=(?P<nonce>[\x21-\x2B\x2D-\x7E]+)," br"s=(?P<salt>[a-zA-Z0-9/+=]+)," br"i=(?P<iteration_count>\d+)" br"(?:,.)?$" ) CLIENT_FINAL_MESSAGE_RE = re.compile( br"(?P<without_proof>c=(?P<cb>[a-zA-Z0-9/+=]+)," br"(?:r=(?P<nonce>[\x21-\x2B\x2D-\x7E]+))" br"(?:,.)?)" br",p=(?P<proof>[a-zA-Z0-9/+=]+)$" ) SERVER_FINAL_MESSAGE_RE = re.compile( br"^(?:e=(?P<error>[^,]+)\|v=(?P<verifier>[a-zA-Z0-9/+=]+)(?:,.)?)$") class SCRAMOperations(object): """Functions used during SCRAM authentication and defined in the RFC. """ def __init__(self, hash_function_name): self.hash_function_name = hash_function_name self.hash_factory = HASH_FACTORIES[hash_function_name] self.digest_size = self.hash_factory().digest_size @staticmethod def Normalize(str_): """The Normalize(str) function. This one also accepts Unicode string input (in the RFC only UTF-8 strings are used). """ # pylint: disable=C0103 if isinstance(str_, bytes): str_ = str_.decode("utf-8") return str_.encode("utf-8") def HMAC(self, key, str_): """The HMAC(key, str) function.""" # pylint: disable=C0103 return hmac.new(key, str_, self.hash_factory).digest() def H(self, str_): """The H(str) function.""" # pylint: disable=C0103 return self.hash_factory(str_).digest() if sys.version_info.major >= 3: @staticmethod # pylint: disable=C0103 def XOR(str1, str2): """The XOR operator for two byte strings.""" return bytes(a ^ b for a, b in zip(str1, str2)) else: @staticmethod # pylint: disable=C0103 def XOR(str1, str2): """The XOR operator for two byte strings.""" return "".join(chr(ord(a) ^ ord(b)) for a, b in zip(str1, str2)) def Hi(self, str_, salt, i): """The Hi(str, salt, i) function.""" # pylint: disable=C0103 Uj = self.HMAC(str_, salt + b"\000\000\000\001") # U1 result = Uj for _ in range(2, i + 1): Uj = self.HMAC(str_, Uj) # Uj = HMAC(str, Uj-1) result = self.XOR(result, Uj) # ... XOR Uj-1 XOR Uj return result @staticmethod def escape(data): """Escape the ',' and '=' characters for 'a=' and 'n=' attributes. Replaces '=' with '=3D' and ',' with '=2C'. :Parameters: - `data`: string to escape :Types: - `data`: `bytes` """ return data.replace(b'=', b'=3D').replace(b',', b'=2C') @staticmethod def unescape(data): """Unescape the ',' and '=' characters for 'a=' and 'n=' attributes. Reverse of `escape`. :Parameters: - `data`: string to unescape :Types: - `data`: `bytes` """ return data.replace(b'=2C', b',').replace(b'=3D', b'=') class SCRAMClientAuthenticator(SCRAMOperations): """Provides SCRAM SASL authentication for a client. :Ivariables: - `password`: current authentication password - `pformat`: current authentication password format - `realm`: current authentication realm """ # pylint: disable-msg=R0902 def __init__(self, hash_name, channel_binding): """Initialize a `SCRAMClientAuthenticator` object. :Parameters: - `hash_function_name`: hash function name, e.g. ``"SHA-1"`` - `channel_binding`: `True` to enable channel binding :Types: - `hash_function_name`: `unicode` - `channel_binding`: `bool` """ SCRAMOperations.__init__(self, hash_name) self.name = "SCRAM-{0}".format(hash_name) if channel_binding: self.name += "-PLUS" self.channel_binding = channel_binding self.username = None self.password = <PASSWORD> self.authzid = None self._c_nonce = None self._server_first_message = False self._client_first_message_bare = False self._gs2_header = None self._finished = False self._auth_message = None self._salted_password = <PASSWORD> self._cb_data = None @classmethod def are_properties_sufficient(cls, properties): return "username" in properties and "password" in properties def start(self, properties): self.username = properties["username"] self.password = properties["password"] self.authzid = properties.get("authzid", "") c_nonce = properties.get("nonce_factory", default_nonce_factory)() if not VALUE_CHARS_RE.match(c_nonce): c_nonce = standard_b64encode(c_nonce) self._c_nonce = c_nonce if self.channel_binding: cb_data = properties.get("channel-binding") if not cb_data: raise ValueError("No channel binding data provided") if "tls-unique" in cb_data: cb_type = "tls-unique" elif "tls-server-end-point" in cb_data: cb_type = "tls-server-end-point" elif cb_data: cb_type = cb_data.keys()[0] self._cb_data = cb_data[cb_type] cb_flag = b"p=" + cb_type.encode("utf-8") else: plus_name = self.name + "-PLUS" if plus_name in properties.get("enabled_mechanisms", []): # -PLUS is enabled (supported) on our side, # but was not selected - that means it was not included # in the server features cb_flag = b"y" else: cb_flag = b"n" if self.authzid: authzid = b"a=" + self.escape(self.authzid.encode("utf-8")) else: authzid = b"" gs2_header = cb_flag + b"," + authzid + b"," self._gs2_header = gs2_header nonce = b"r=" + c_nonce client_first_message_bare = (b"n=" + self.escape(self.username.encode("utf-8")) + b"," + nonce) self._client_first_message_bare = client_first_message_bare client_first_message = gs2_header + client_first_message_bare return client_first_message def challenge(self, challenge): """Process a challenge and return the response. :Parameters: - `challenge`: the challenge from server. :Types: - `challenge`: `bytes` :return: the response :returntype: bytes :raises: `BadChallengeException` """ # pylint: disable=R0911 if not challenge: raise BadChallengeException('Empty challenge') if self._server_first_message: return self._final_challenge(challenge) match = SERVER_FIRST_MESSAGE_RE.match(challenge) if not match: raise BadChallengeException("Bad challenge syntax: {0!r}".format(challenge)) self._server_first_message = challenge mext = match.group("mext") if mext: raise BadChallengeException("Unsupported extension received: {0!r}".format(mext)) nonce = match.group("nonce") if not nonce.startswith(self._c_nonce): raise BadChallengeException("Nonce does not start with our nonce") salt = match.group("salt") try: salt = a2b_base64(salt) except ValueError: raise BadChallengeException("Bad base64 encoding for salt: {0!r}".format(salt)) iteration_count = match.group("iteration_count") try: iteration_count = int(iteration_count) except ValueError: raise BadChallengeException("Bad iteration_count: {0!r}".format(iteration_count)) return self._make_response(nonce, salt, iteration_count) def _make_response(self, nonce, salt, iteration_count): """Make a response for the first challenge from the server. :return: the response :returntype: bytes """ self._salted_password = self.Hi(self.Normalize(self.password), salt, iteration_count) self.password = None # not needed any more if self.channel_binding: channel_binding = b"c=" + standard_b64encode(self._gs2_header + self._cb_data) else: channel_binding = b"c=" + standard_b64encode(self._gs2_header) # pylint: disable=C0103 client_final_message_without_proof = (channel_binding + b",r=" + nonce) client_key = self.HMAC(self._salted_password, b"Client Key") stored_key = self.H(client_key) auth_message = ( self._client_first_message_bare + b"," + self._server_first_message + b"," + client_final_message_without_proof ) self._auth_message = auth_message client_signature = self.HMAC(stored_key, auth_message) client_proof = self.XOR(client_key, client_signature) proof = b"p=" + standard_b64encode(client_proof) client_final_message = (client_final_message_without_proof + b"," + proof) return client_final_message def _final_challenge(self, challenge): """Process the second challenge from the server and return the response. :Parameters: - `challenge`: the challenge from server. :Types: - `challenge`: `bytes` :raises: `ExtraChallengeException`, `BadChallengeException`, `ServerScramError`, or `BadSuccessException` """ if self._finished: return ExtraChallengeException() match = SERVER_FINAL_MESSAGE_RE.match(challenge) if not match: raise BadChallengeException("Bad final message syntax: {0!r}".format(challenge)) error = match.group("error") if error: raise ServerScramError("{0!r}".format(error)) verifier = match.group("verifier") if not verifier: raise BadSuccessException("No verifier value in the final message") server_key = self.HMAC(self._salted_password, b"Server Key") server_signature = self.HMAC(server_key, self._auth_message) if server_signature != a2b_base64(verifier): raise BadSuccessException("Server verifier does not match") self._finished = True def finish(self, data): """Process success indicator from the server. Process any addiitional data passed with the success. Fail if the server was not authenticated. :Parameters: - `data`: an optional additional data with success. :Types: - `data`: `bytes` :return: username and authzid :returntype: `dict` :raises: `BadSuccessException`""" if not self._server_first_message: raise BadSuccessException("Got success too early") if self._finished: return {"username": self.username, "authzid": self.authzid} else: self._final_challenge(data) if self._finished: return {"username": self.username, "authzid": self.authzid} else: raise BadSuccessException("Something went wrong when processing additional" " data with success?") class SCRAMServerAuthenticator(SCRAMOperations): """Provides SCRAM SASL authentication for a server. """ def __init__(self, hash_name, channel_binding, password_database): """Initialize a `SCRAMClientAuthenticator` object. :Parameters: - `hash_function_name`: hash function name, e.g. ``"SHA-1"`` - `channel_binding`: `True` to enable channel binding :Types: - `hash_function_name`: `unicode` - `channel_binding`: `bool` """ SCRAMOperations.__init__(self, hash_name) self.name = "SCRAM-{0}".format(hash_name) if channel_binding: self.name += "-PLUS" self.channel_binding = channel_binding self.properties = None self.out_properties = None self.password_database = password_database self._client_first_message_bare = None self._stored_key = None self._server_key = None def start(self, properties, initial_response): self.properties = properties self._client_first_message_bare = None self.out_properties = {} if not initial_response: return b"" return self.response(initial_response) def response(self, response): if self._client_first_message_bare: logger.debug("Client final message: {0!r}".format(response)) return self._handle_final_response(response) else: logger.debug("Client first message: {0!r}".format(response)) return self._handle_first_response(response) def _handle_first_response(self, response): match = CLIENT_FIRST_MESSAGE_RE.match(response) if not match: raise NotAuthorizedException("Bad response syntax: {0!r}".format(response)) mext = match.group("mext") if mext: raise NotAuthorizedException("Unsupported extension received: {0!r}".format(mext)) gs2_header
<filename>gpsTime.py<gh_stars>0 import numpy as np from math import modf import datetime as dt import calendar def cal2jd(yr,mn,dy) : """ CAL2JD Converts calendar date to Julian date using algorithm from "Practical Ephemeris Calculations" by <NAME> (Springer-Verlag, 1989). Uses astronomical year for B.C. dates (2 BC = -1 yr). Input: yr : YYYY (int) mn : MM 01 to 12 (int) day : DD 01 to 31 (int) Output: jd : julian date (float) """ if mn > 2: y = yr m = mn else: y = yr - 1 m = mn + 12 date1=4.5+31.(10.+12.1582.) # Last day of Julian calendar (1582.10.04 Noon) date2=15.5+31.(10.+12.1582.) # First day of Gregorian calendar (1582.10.15 Noon) date=dy+31.(mn+12.yr) if date <= date1: b = -2 elif date >= date2 : b = np.fix(y/400.) - np.fix(y/100.) else: #warning('Dates between October 5 & 15, 1582 do not exist'); return if y > 0: jd = np.fix(365.25y) + np.fix(30.6001(m+1)) + b + 1720996.5 + dy else: jd = np.fix(365.25y-0.75) + np.fix(30.6001(m+1)) + b + 1720996.5 + dy return jd def yyyydoy2jd(year,doy,hh=0,mm=0,ss=0.0): """ yyyydoy2jd Take a year, day-of-year, etc and convert it into a julian day Usage: jd = yyyydoy2jd(year,doy,hh,mm,ss) Input: year - 4 digit integer doy - 3 digit, or less integer, (1 <= doy <= 366) hh - 2 digit, or less int, (0 <= hh < 24) (not required) mm - 2 digit, or less int,(0 <= ss < 60) (not required) ss - float (not required) Output: 'jd' (float) """ # # need to split seconds into two components # sec => 2 digit, or less int, (0 <= ss < 60) # ms => int 0 <= ms < 1,000,000 # ms,sec = modf(float(ss)) ms = ms * 10e5 dto = dt.datetime(int(year),01,01,int(hh),int(mm),int(sec),int(ms)) dto = dto + dt.timedelta(days=(int(doy) - 1)) mn = dto.month dy = dto.day jd = cal2jd(int(year),int(mn),int(dy)) jd = jd + float(hh)/24. + float(mm)/60./24. + float(sec)/3600./24. return jd - 2400000.5 def jd2gps(jd): """ JD2GPS Converts Julian date to GPS week number (since 1980.01.06) and seconds of week. Usage: [gpsweek,sow,rollover]=jd2gps(jd) Input: jd - Julian date Output: gpsweek - GPS week number sow - seconds of week since 0 hr, Sun. rollover - number of GPS week rollovers (modulus 1024) """ jdgps = cal2jd(1980,1,6); # beginning of GPS week numbering nweek = int(np.fix((jd-jdgps)/7.)) sow = (jd - (jdgps+nweek7)) 360024 rollover = np.fix(nweek/1024) # rollover every 1024 weeks gpsweek = int(nweek) # rollover is being returned as an array? # should just be an int return gpsweek,sow,rollover def jd2cal(jd): """ JD2CAL Converts Julian date to calendar date using algorithm from "Practical Ephemeris Calculations" by <NAME> (Springer-Verlag, 1989). Must use astronomical year for B.C. dates (2 BC = -1 yr). Non-vectorized version. See also CAL2JD, DOY2JD, GPS2JD, JD2DOW, JD2DOY, JD2GPS, JD2YR, YR2JD. Usage: [yr, mn, dy]=jd2cal(jd) Input: jd - Julian date Output: yr - year of calendar date mn - month of calendar date dy - day of calendar date (including decimal) """ a = np.fix(jd+0.5) if a < 2299161. : c = a + 1524. else: b = np.fix( (a-1867216.25) / 36524.25 ) c = a + b - np.fix(b/4.) + 1525. d = np.fix( (c-122.1)/365.25 ) e = np.fix(365.25d) f = np.fix( (c-e) / 30.6001 ) dy = c - e - np.fix(30.6001f) + np.remainder((jd+0.5),a) mn = f - 1. - 12. np.fix(f/14.) yr = d - 4715. - np.fix( (7.+mn)/10. ) return yr,mn,dy def jd2doy(jd): """ JD2DOY Converts Julian date to year and day of year. Usage: [doy,yr]=jd2doy(jd) Input: jd - Julian date Output: doy - day of year yr - year """ [yr,mn,dy] = jd2cal(jd) doy = jd - cal2jd(yr,1,0) # MM ensure the doy is 0 padded doy = "%03d" % doy return yr, doy def yyyy2yy(year): """ yy = yyyy2yy(YYYY) return the yy form of YYYY yy - last two digits of YYYY - returned as an int very messy hack """ yy = int( str(int(year))[-2] + str(int(year))[-1] ) return(yy) def dateTime2gpssow(dt): """ dateTime2gpssow Converts a datetime object into gps week, and gps seconds of week Usage: week,sow = dateTime2gpssow(dateTime) Input: dt - python datetime object Output: week - gps week (int) sow - seconds into gpsweek since 0 hr, Sunday (float) """ day = dt.day + dt.hour/24. + dt.minute/1440. + dt.second/86400. jd = cal2jd(dt.year,dt.month,day) week, sow, rollover = jd2gps(jd) return week, sow def ydhms2dt(year,doy,hh,mm,ss): """ ydhms2dt Take a year, day-of-year, etc and convert it into a date time object Usage: dto = ydhms2dt(year,day,hh,mm,ss) Input: year - 4 digit integer doy - 3 digit, or less integer, (1 <= doy <= 366) hh - 2 digit, or less int, (0 <= hh < 24) mm - 2 digit, or less int,(0 <= ss < 60) ss - float Output: 'dto' a date time object """ # # need to split seconds into two components # sec => 2 digit, or less int, (0 <= ss < 60) # ms => int 0 <= ms < 1,000,000 ms,sec = modf(float(ss)) ms = ms * 10e5 dto = dt.datetime(int(year),01,01,int(hh),int(mm),int(sec),int(ms)) dto = dto + dt.timedelta(days=(int(doy) - 1)) return dto def ymdhms2dt(year,month,day,hh,mm,ss): """ ymhms2dt Take a year, day-of-year, etc and convert it into a date time object Usage: dto = ymdhms2dt(year,month,day,hh,mm,ss) Input: year - 4 digit integer month - integer, (1 => January) day - integer hh - 2 digit, or less int, (0 <= hh < 24) mm - 2 digit, or less int,(0 <= ss < 60) ss - float Output: 'dto' a date time object """ # # need to split seconds into two components # sec => 2 digit, or less int, (0 <= ss < 60) # ms => int 0 <= ms < 1,000,000 ms,sec = modf(float(ss)) ms = ms * 10e5 dto = dt.datetime(int(year),int(month),int(day),int(hh),int(mm),int(sec),0) #dto = dt.datetime(int(year),int(month),int(day),int(hh),int(mm),int(sec),int(ms)) #dt.date(int(year),int(month),int(day)) #01,01,int(hh),int(mm),int(sec),int(ms)) #dto = dto + dt.timedelta(hours= int(hh),minutes=int(mm),seconds=int(sec)) return dto def jd2mdt(jd): """ jd2mdt Take a julian date and convert it into a matplotlib date time stamp All matplotlib date plotting is done by converting date instances into days since the 0001-01-01 UTC Usage: mp_ts = jd2mdt(jd) Input: jd julian date Output: 'mp_ts' (float) a matplot lib time stamp which is days from 0001-01-01 """ #ms,sec = modf(float(ss)) #ms = ms * 10e5 year,mon,d = jd2cal(jd) day = int(np.fix(d)) h = (d - float(day)) * 24. hh = int(np.fix(h)) m = (h - float(hh)) * 60. mm = int(np.fix(m)) s = (m - float(mm)) * 60. sec = int(np.fix(s)) ms = 0 dto = dt.datetime(int(year),int(mon),int(day),int(hh),int(mm),int(sec),int(ms)) mp_epoch = dt.datetime(1, 1, 1) DAY = 86400 td = dto - mp_epoch mp_ts = td.days + 1 + (1000000 * td.seconds + td.microseconds) / 1e6 / DAY return mp_ts def ydhms2mdt(year,doy,hh,mm,ss): """ ydhms2mdt Take a year, day-of-year, etc and convert it into a matplotlib date All matplotlib date plotting is done by converting date instances into days since the 0001-01-01 UTC Usage: mp_ts = ydhms2dt(year,day,hh,mm,ss) Input: year - 4 digit integer doy - 3 digit, or less integer, (1 <= doy <= 366) hh - 2 digit, or less int, (0 <= hh < 24) mm - 2 digit, or less int,(0 <= ss < 60) ss - float Output: 'mp_ts' (float) a matplot lib time stamp which is days from 0001-01-01 """ # # need to split seconds into two components # sec => 2 digit, or less int, (0 <= ss < 60) # ms => int 0 <= ms < 1,000,000 ms,sec = modf(float(ss)) ms = ms * 10e5 dto = dt.datetime(int(year),01,01,int(hh),int(mm),int(sec),int(ms)) dto = dto + dt.timedelta(days=(int(doy) - 1)) mp_epoch = dt.datetime(1, 1, 1) DAY = 86400 td = dto - mp_epoch mp_ts = td.days + 1 + (1000000 * td.seconds + td.microseconds) / 1e6 / DAY return mp_ts def ydhms2decyr(year,doy,hh=0,mm=0,ss=0.0): """ ydhms2decyr(year,doy,hh,mm,ss) Convert
import numpy as np import pandas as pd import re import warnings import scipy.optimize as opt from scipy.stats import norm, f, chi2, ncf, ncx2, binom from scipy.special import ncfdtrinc, chndtrinc import matplotlib.pyplot as plt import seaborn as sns from poibin import PoiBin warnings.filterwarnings("ignore") def adjust_spines(ax, spines): for loc, spine in ax.spines.items(): if loc in spines: spine.set_position(('outward', 10)) # outward by 10 points else: spine.set_color('none') # don't draw spine # turn off ticks where there is no spine if 'left' in spines: ax.yaxis.set_ticks_position('left') else: # no yaxis ticks ax.yaxis.set_ticks([]) if 'bottom' in spines: ax.xaxis.set_ticks_position('bottom') else: # no xaxis ticks ax.xaxis.set_ticks([]) return None # noinspection PyProtectedMember,PyProtectedMember,PyProtectedMember,PyProtectedMember,PyProtectedMember,PyProtectedMember,PyProtectedMember,PyProtectedMember class PCurve(object): __version__ = "0.1.0" __pcurve_app_version__ = "4.06" _REGEX_STAT_TEST = re.compile( """ ^ # Beginning of string (?P<testtype>chi2\|F\|t\|r\|z\|p) # Matches the type of test statistic (\((?P<df1>\d+)(,)?(?P<df2>\d+)?\))? #Matches the degrees of freedom =(-?) #Matches an equal sign with a potential minus sign (?P<stat>(\d)\.(\d+)) # Matches the test statistics """, re.IGNORECASE \| re.VERBOSE) _POWER_LEVELS = [0.051, 0.06, 0.07, 0.08, 0.09, 0.1, 0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.7, 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.8, 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.9, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99] @staticmethod def _bound_pvals(pvals): """ Bound a p-value to avoid precision error :param pvals: The p-values :return: The bounded p-values 2.2e-16 < p < 1-2.2e-16 """ return np.where(pvals < 2.2e-16, 2.2e-16, np.where(pvals > 1 - 2.2e-16, 1 - 2.2e-16, pvals)) @staticmethod def _format_pval(p): if p < .0001: return "< .0001" elif p < .9999: pstring = f"= {p:.4f}".strip("0") return pstring else: return "> .9999" @staticmethod def _compute_prop_lower_33(pcrit, family, df1, df2, p, ncp33): """ Compute the proportion of p-values that is expected to be smaller than `pcrit` under 33% power. :param pcrit: The p-values :return: The proportion """ # Transform the critical p-value `pcrit` into the corresponding critical value for the dist. critval_f = f.ppf(1 - pcrit, df1, df2) critval_chi = chi2.ppf(1 - pcrit, df1) # Compute the proportion of values that are expected to be larger than this under 33% power exp_smaller_f = ncf._sf(critval_f, df1, df2, ncp33) exp_smaller_chi = ncx2._sf(critval_chi, df1, ncp33) # Return the appropriate stats for the family return np.where(p > .05, np.nan, np.where(family == "F", exp_smaller_f, exp_smaller_chi)) @staticmethod def _compute_stouffer_z(pp): isnotnan = ~np.isnan(pp) pp_notnan = pp[isnotnan] return np.sum(norm._ppf(pp_notnan)) / np.sqrt(isnotnan.sum()) @staticmethod def _compute_ncp_f(df1, df2, power=1 / 3): """ Uses the inverse function of the non-central F distribution with regard to NCP to recover the NCP corresponding to a given level of power for a given F test. :param df1: Numerator degrees of freedom :param df2: Denominator degrees of freedom :param power: Desired level of power :return: """ critval = f._ppf(.95, df1, df2) return ncfdtrinc(df1, df2, 1 - power, critval) @staticmethod def _compute_ncp_chi(df, power=1 / 3): """ Uses the inverse function of the non-central Chi2 distribution with regard to NCP to recover the NCP corresponding to a given level of power for a given Chi2 test. :param df: Degrees of freedom :param power: Desired level of power :return: """ critval = chi2._ppf(.95, df) return chndtrinc(critval, df, 1 - power) def _compute_ncp_all(self, power=1 / 3): family = self._df_results["family"].values df1, df2 = self._df_results[["df1", "df2"]].to_numpy().T return np.where(family == "F", self._compute_ncp_f(df1, df2, power), self._compute_ncp_chi(df1, power)) def _parse_result(self, result_str): """ Parse a statistical result, entered as a string :param result_str: A statistical result (e.g., F(1, 234) = 12.32) :return: """ result_str_replaced = ( result_str .replace(" ", "") # Whitespaces .replace("\u2009", "") # Thin whitespaces .replace("\u2212", "-") # All possible symbols for 'minus' .replace("\u2013", "-") .replace("\uFE63", "-") .replace("\u002D", "-") .replace("\uFF0D", "-") ) match = self._REGEX_STAT_TEST.match(result_str_replaced) # See regex above if match is None: # Statistical test not recognized raise ValueError(f"The input {result_str} is not recognized. Please correct it") test_props = match.groupdict() # Test recognized, accessing properties test_type = test_props["testtype"] df1_raw = test_props["df1"] df2_raw = test_props["df2"] stat_raw = test_props["stat"] # Testing that degrees of freedom are correctly entered if (test_type == "F") and ((test_props["df1"] is None) or (test_props["df2"] is None)): raise ValueError( f"Error in {result_str}: The test statistics {test_type} requires you to specify the numerator \ and denominator degrees of freedom.") if (test_type not in ["z", "p"]) and (test_props["df1"] is None): raise ValueError( f"Error in {result_str}: The test statistics {test_type} requires you to specify the degrees of \ freedom.") stat_raw = float(stat_raw) if test_type == "F": family = "F" df1 = float(df1_raw) df2 = float(df2_raw) stat = stat_raw elif test_type == "t": family = "F" df1 = 1 df2 = float(df1_raw) stat = stat_raw * 2 elif test_type == "r": family = "F" df1 = 1 df2 = float(df1_raw) stat = (stat_raw / (np.sqrt((1 - stat_raw 2) / df2))) 2 elif test_type == "chi2": family = "Chi2" df1 = float(df1_raw) df2 = None stat = stat_raw elif test_type == "z": family = "Chi2" df1 = 1 df2 = None stat = stat_raw 2 else: family = "Chi2" df1 = 1 df2 = None stat = norm.ppf(1 - stat_raw / 2) 2 return result_str_replaced, family, df1, df2, stat def _compute_pvals(self): family = self._df_results["family"].values df1, df2, stat = self._df_results[["df1", "df2", "stat"]].to_numpy().T pval = np.where(family == "F", f._sf(stat, df1, df2), chi2._sf(stat, df1)) return self._bound_pvals(pval) def _compute_stouffer_z_at_power(self, power): # NCP and pp-values of F tests df1_f, df2_f, stat_f = self._sig_f_tests ncp_f_est = self._compute_ncp_f(df1_f, df2_f, power) pp_f_est = (ncf._cdf(stat_f, df1_f, df2_f, ncp_f_est) - (1 - power)) / power # NCP and pp-values of Chi2 tests df1_chi, stat_chi = self._sig_chi_tests ncp_chi_est = self._compute_ncp_chi(df1_chi, power) pp_chi_est = (ncx2._cdf(stat_chi, df1_chi, ncp_chi_est) - (1 - power)) / power # pp-values for all tests pp_est = self._bound_pvals(np.hstack([pp_f_est, pp_chi_est])) stouffer_at_power = self._compute_stouffer_z(pp_est) return stouffer_at_power def _solve_power_for_pct(self, pct): z = norm._ppf(pct) error = lambda est: self._compute_stouffer_z_at_power(est) - z return opt.brentq(error, .0501, .9999) def _compute_ppvals_null(self, pcurvetype="full"): """ Compute the pp-value of the p-values under the null. It simply stretches the p-value over the interval [0, 1] :param pcurvetype: The type of p-curve (full or half) :return: """ p = self._df_results["p"].values if pcurvetype == "full": return np.array([self._bound_pvals(x * 20) if x < .05 else np.nan for x in p]) else: return np.array([self._bound_pvals(x * 40) if x < .025 else np.nan for x in p]) def _compute_ppvals_33(self, pcurvetype="full"): family = self._df_results["family"].values df1, df2, stat, pvals, ncp33 = self._df_results[["df1", "df2", "stat", "p", "ncp33"]].to_numpy().T if pcurvetype == "full": pthresh = .05 # Only keep p-values smaller than .05 propsig = 1 / 3 # Under 33% power, 1/3 of p-values should be lower than .05 else: pthresh = .025 # Only keep p-values smaller than .025 # We don't know which prop of p-values should be smaller than .025 under 33% power, so compute it propsig = 3 * self._compute_prop_lower_33(.025, family, df1, df2, pvals, ncp33) # We then stretch the ppval on the [0, 1] interval. pp_33_f = (1 / propsig) * (ncf.cdf(stat, df1, df2, ncp33) - (1 - propsig)) pp_33_chi = (1 / propsig) * (ncx2.cdf(stat, df1, ncp33) - (1 - propsig)) pp_33 = np.where(family == "F", pp_33_f, pp_33_chi) return np.array([self._bound_pvals(pp) if p < pthresh else np.nan for (p, pp) in zip(pvals, pp_33)]) def _get_33_power_curve(self): family = self._df_results["family"].values df1, df2, p, ncp33 = self._df_results[["df1", "df2", "p", "ncp33"]].to_numpy().T cprop = lambda x: self._compute_prop_lower_33(x, family, df1, df2, p, ncp33) propsig = np.array([cprop(c) for c in [.01, .02, .03, .04, .05]]) diffs = np.diff(propsig, axis=0, prepend=0) # Difference of CDFs: Likelihood of p-values falling between each value props = np.nanmean(3 * diffs, axis=1) return props def _run_binom_test(self, alternative="null"): family = self._df_results["family"].values df1, df2, p, ncp33 = self._df_results[["df1", "df2", "p", "ncp33"]].to_numpy().T k_below_25 = self._n_tests['p025'] if alternative == "null": return binom(n=self._n_tests['p05'], p=.5).sf(k_below_25 - 1)
self.cmdForms['loadMacro'].descr.entryByName ebn['loadMacro']['widget'].configure(state='disabled') def __call__(self, macroName, macroFile, menuBar='menuRoot', menuButton='Macros', menuEntry=None, cascade=None, kw): """None<---loadMacro(macroName, macroFile, menuBar='menuRoot', menuButton='Macros', menuEntry=None, cascade=None, kw) """ self.doitWrapper(macroName, macroFile, menuBar=menuBar, menuButton=menuButton, menuEntry=menuEntry, cascade=cascade) def doit(self, macroName, macroFile, menuBar='menuRoot', menuButton='Macros', menuEntry=None, cascade=None): if not hasattr(self, 'macroFile') or macroFile != self.macroFile: names, macros, docs = self.getMacros(macroFile) else: names = self.macNames macros = self.macMacros docs = self.macDoc if len(names) == 0 or len(macros)==0 or len(docs)==0: return macIndex = names.index(macroName) macro = macros[macIndex] from VFCommand import Command, CommandGUI c = Command(func=macro) g = CommandGUI() if cascade: g.addMenuCommand(menuBar, menuButton, menuEntry, cascadeName=cascade) else: g.addMenuCommand(menuBar, menuButton, menuEntry) self.vf.addCommand(c, macro.__name__, g) ## class loadMacroCommand(Command): ## """ ## Command to load dynamically macro commands. ## Using the Gui the user can open a macro file. The macros available in ## that file are then displayed in a list chooser. When a macro is selected ## in the listchooser, its documentation string is deisplayed and a default ## name for the macro in the viewer is suggested. The user can also specify ## a menuBar, a menuButton as well as an optional cascade name. ## """ ## active = 0 ## def getMacros(self, file): ## """load all macro functions from file""" ## self.file = file ## _dir, file = os.path.split(file) ## if file[-3:]=='.py': file = file[:-3] ## import sys ## sys.path.insert(0, _dir) ## m = __import__(file, globals(), locals(), []) ## sys.path = sys.path[1:] ## m.__dict__['self'] = self.vf ## import types ## names = [] ## macros = [] ## doc = [] ## for key,value in m.__dict__.items(): ## if type(value)==types.FunctionType: ## names.append(key) ## macros.append(value) ## doc.append(value.__doc__) ## return names, macros, doc ## def loadMacLib_cb(self, filename): ## """Call back function for 'Open Macro library' button""" ## # self.ifd[0]['widget'] is the 'Open Macro Library' button ## self.ifd[0]['widget'].configure(relief='sunken') ## #file = os.path.split(filename)[1][:-3] ## names, macros, docs = self.getMacros(filename) ## self.macNames = names ## self.macMacros = macros ## self.macDoc = docs ## # Get a handle to the listchooser widget ## lc = self.ifd[1]['widget'] ## lc.clear() ## if len(names) == len(docs): ## entries = map(lambda x, y: (x, y), names, docs) ## else: ## entries = map(lambda x: (x, None), names) ## map(lc.add, entries) ## self.ifd[0]['widget'].configure(relief='raised') ## # set cascade name to libary Name - "mac" ## w = self.ifd[5]['widget'] ## w.delete(0, 'end') ## w.insert(0, os.path.split(filename)[1][:-3]) ## def setDefaultEntryName(self, event=None): ## """Call back function for the listchooser showing macros. ## gets the name of the currently selected macro and puts it in the entry ## type in""" ## # enable add button ## self.ifd.entryByName['Load Macro']['widget'].configure(state='normal') ## # put default name into name entry ## val = self.ifd[1]['widget'].get() ## w = self.ifd[4]['widget'] ## w.delete(0, 'end') ## w.insert(0, val[0]) ## self.selectedMac = val[0] ## def addMacro(self, macro, menuBar, menuButton, name, cascade=None): ## from VFCommand import Command, CommandGUI ## c = Command(func=macro) ## g = CommandGUI() ## if cascade: ## g.addMenuCommand(menuBar, menuButton, name, cascadeName=cascade) ## else: ## g.addMenuCommand(menuBar, menuButton, name) ## self.vf.addCommand(c, macro.__name__, g) ## ## g.register(self.vf) ## self.log(file=self.file, macroName=macro.__name__, menuBar=menuBar, ## menuButton=menuButton, name=name, cascade=cascade) ## def loadMacro_cb(self, event=None): ## bar = self.ifd[2]['widget'].get() ## menub = self.ifd[3]['widget'].get() ## name = self.ifd[4]['widget'].get() ## cascade = self.ifd[5]['widget'].get() ## if cascade=='': cascade=None ## macIndex = self.macNames.index(self.selectedMac) ## macFunc = self.macMacros[macIndex] ## self.addMacro(macFunc, bar, menub, name, cascade) ## self.ifd[0]['widget'].configure(relief='raised') ## def customizeGUI(self): ## """create the cascade menu for selecting modules to be loaded""" ## self.selectedMac = '' ## # create the for descriptor ## ifd = self.ifd = InputFormDescr(title='Load macro commands') ## if len(self.vf.libraries) is None: ## modu = __import__('ViewerFramework') ## else: ## modu = __import__(self.vf.libraries[0]) ## idir = os.path.split(modu.__file__)[0] + '/Macros' ## if not os.path.exists(idir): ## idir = None ## ifd.append( {'widgetType':'OpenButton', 'text':'Open Macro library ...', ## 'types':[('Macro Module Library', 'Mac.py'), ## ('Any Python Function', '.py')], ## 'idir':idir, ## 'title':'Open Macro File', ## 'callback': self.loadMacLib_cb } ) ## ifd.append({'title':'Choose a macro', ## 'widgetType':ListChooser, ## 'wcfg':{ ## 'command':self.setDefaultEntryName, ## 'title':'Choose a macro'}, ## 'gridcfg':{'sticky':Tkinter.E+Tkinter.W}} ) ## ifd.append({'widgetType':Tkinter.Entry, ## 'defaultValue':'menuRoot', ## 'wcfg':{'label':'menu bar'}, ## 'gridcfg':{'sticky':Tkinter.E} ## }) ## ifd.append({'widgetType':Tkinter.Entry, ## 'defaultValue':'Macros', ## 'wcfg':{'label':'menu button'}, ## 'gridcfg':{'sticky':Tkinter.E} ## }) ## ifd.append({'widgetType':Tkinter.Entry, ## 'defaultValue':'', ## 'wcfg':{'label':'menu entry'}, ## 'gridcfg':{'sticky':Tkinter.E} ## }) ## ifd.append({'widgetType':Tkinter.Entry, ## 'defaultValue':'', ## 'wcfg':{'label':'cascade'}, ## 'gridcfg':{'sticky':Tkinter.E} ## }) ## ifd.append({'name': 'Load Macro', ## 'widgetType':Tkinter.Button, ## 'text':'Load Macro', ## 'wcfg':{'bd':6}, ## 'gridcfg':{'sticky':Tkinter.E+Tkinter.W}, ## 'command': self.loadMacro_cb}) ## ifd.append({'widgetType':Tkinter.Button, ## 'text':'Dismiss', ## 'command': self.Dismiss_cb}) ## def Dismiss_cb(self): ## #self.cmdForms['loadMacro'].withdraw() ## self.ifd.form.destroy() ## self.active = 0 ## def guiCallback(self, event=None, file=None): ## if self.active: return ## self.active = 1 ## self.customizeGUI() ## self.form = self.vf.getUserInput(self.ifd, modal=0, blocking=0) ## self.ifd.entryByName['Load Macro']['widget'].configure(state='disabled') ## if file: self.loadMacLib_cb(file) ## def __call__(self, file=None, macroName=None, menuBar='menuRoot', ## menuButton='Macros', name=None, cascade=None): ## """file=None, macroName=None, menuBar='menuRoot', menuButton='Macros', ## name=None, cascade=None""" ## if not macroName: self.guiCallback(file=file) ## else: ## if file[-3:]=='.py': file = file[:-3] ## names, macros, docs = self.getMacros(file) ## i = names.index(macroName) ## if name==None: name=macroName ## self.addMacro(macros[i], menuBar, menuButton, name, cascade) class ShellCommand(Command): """Command to show/Hide the Python shell. \nPackage : ViewerFramework \nModule : basicCommand.py \nClass : ShellCommand \nCommand : Shell \nSynopsis:\n None<---Shell() """ def onAddCmdToViewer(self): if self.vf.hasGui: self.vf.GUI.pyshell.top.protocol('WM_DELETE_WINDOW', self.vf.Shell.onDestroy) def show(self): self.vf.GUI.pyshell.top.deiconify() def hide(self): self.vf.GUI.pyshell.top.withdraw() def __call__(self, args): """None<---Shell() """ if args[0]: self.show() self.vf.GUI.toolbarCheckbuttons['Shell']['Variable'].set(1) else: self.hide() self.vf.GUI.toolbarCheckbuttons['Shell']['Variable'].set(0) def guiCallback(self): on = self.vf.GUI.toolbarCheckbuttons['Shell']['Variable'].get() if on: self.show() else: self.hide() def onDestroy(self): self.vf.GUI.toolbarCheckbuttons['Shell']['Variable'].set(0) self.hide() ShellCommandGUI = CommandGUI() ShellCommandGUI.addToolBar('Shell', icon1='PyShell.gif', balloonhelp='Python IDLE Shell', index=1) class SaveSessionCommand(Command): """Command to allow the user to save the session as it is in a file. It logs all the transformation. \nPackage : Pmv \nModule : customizationCommands.py \nClass : SaveSessionCommand """ def logString(self, args, *kw): """return None as log string as we don't want to log this """ pass def guiCallback(self, event=None): ### FIXME all the logs should be in a stack and not in a file. if self.vf.logMode == 'no': self.vf.warningMsg("No log information because logMode was set to no.") return newfile = self.vf.askFileSave(types = [ ('Pmv sesion files', '.psf'), ('all files', '*.py')], defaultextension=".psf", title = 'Save Session in File:') if not newfile is None: self.doitWrapper(newfile, redraw=0) def doit(self, filename): #print "SaveSessionCommand.doit" ext = os.path.splitext(filename)[1].lower() if ext=='.psf': self.vf.saveFullSession(filename) else: import shutil # get the current log. if hasattr(self.vf, 'logAllFile'): logFileName = self.vf.logAllFile.name self.vf.logAllFile.close() if filename!=logFileName: shutil.copy(logFileName, filename) self.vf.logAllFile = open(logFileName,'a') # Add to it the transformation log. logFile = open(filename,'a') vi = self.vf.GUI.VIEWER code = vi.getViewerStateDefinitionCode('self.GUI.VIEWER') code.extend( vi.getObjectsStateDefinitionCode('self.GUI.VIEWER') ) if code: for line in code: logFile.write(line) if vi.GUI.contourTk.get(): controlpoints=vi.GUI.curvetool.getControlPoints() sensitivity=vi.GUI.d1scalewheel.get() logFile.write("self.GUI.VIEWER.GUI.curvetool.setControlPoints(%s)" %controlpoints) logFile.write("\n") logFile.write("self.GUI.VIEWER.GUI.curvetool.setSensitivity(%s)" %sensitivity) #sceneLog = self.vf.Exit.logScene() #for l in sceneLog: # l1 = l+'\n' # logFile.write(l1) logFile.close() if hasattr(self.vf, 'recentFiles'): self.vf.recentFiles.add(filename, 'readSourceMolecule') # SaveSessionCommand Command GUI SaveSessionCommandGUI = CommandGUI() SaveSessionCommandGUI.addMenuCommand( 'menuRoot', 'File', 'Current Session', index=2, cascadeName='Save', cascadeIndex=2, separatorAboveCascade=1) SaveSessionCommandGUI.addToolBar('Save', icon1='filesave.gif', type='ToolBarButton', balloonhelp='Save Session', index=1) class ExitCommand(Command): """Command to destroy application \nPackage : ViewerFramework \nModule : basicCommand.py \nClass : ExitCommand \nCommand : Exit \nSynopsis:\n None<---Exit(ask) \nask = Flag when set to 1 a form asking you if you really want to quit will popup, it will quit directly if set to 0 """ def onAddCmdToViewer(self): #print "ExitComand.onAddCmdToViewer" import warnings if self.vf.hasGui: self.vf.GUI.ROOT.protocol('WM_DELETE_WINDOW',self.askquit) def logObjectTransformations(self, object): warnings.warn( "logObjectTransformations is deprecated", DeprecationWarning, stacklevel=2) log = [] # FIXME won't work with instance matrices mat = object.GetMatrix(object) import numpy.oldnumeric as Numeric log.append("self.transformObject('rotation', '%s', matrix=%s,log=0)"%(object.fullName,tuple(object.rotation))) log.append("self.transformObject('translation', '%s', matrix=%s, log=0 )"%(object.fullName, tuple(object.translation))) log.append("self.transformObject('scale', '%s', matrix=%s, log=0 )"%(object.fullName,tuple(object.scale))) log.append("self.transformObject('pivot', '%s', matrix=%s, log=0 )"%(object.fullName,tuple(object.pivot))) return log def logObjectMaterial(self, object): warnings.warn("logObjectMaterial is deprecated", DeprecationWarning, stacklevel=2) log = [] from opengltk.OpenGL import GL log.append("from opengltk.OpenGL import GL") mat = object.materials[GL.GL_FRONT] log.append("self.setObject('%s', materials=%s, propName='ambi', matBind=%d)" % (object.fullName, repr(mat.prop[0])[6:-5],mat.binding[0])) log.append("self.setObject('%s', materials=%s, propName='diff', matBind=%d)" % (object.fullName, repr(mat.prop[1])[6:-5],mat.binding[1])) log.append("self.setObject('%s', materials=%s, propName='emis', matBind=%d)" % (object.fullName, repr(mat.prop[2])[6:-5],mat.binding[2])) log.append("self.setObject('%s', materials=%s, propName='spec', matBind=%d)" % (object.fullName, repr(mat.prop[3])[6:-5],mat.binding[3])) log.append("self.setObject('%s', materials=%s, propName='shini', matBind=%d)" % (object.fullName, repr(mat.prop[4])[6:-5],mat.binding[4])) mat = object.materials[GL.GL_BACK] log.append("self.setObject('%s', materials=%s, polyFace=GL.GL_BACK,propName='ambi', matBind=%d)" % (object.fullName, repr(mat.prop[0])[6:-5],mat.binding[0])) log.append("self.setObject('%s', materials=%s, polyFace=GL.GL_BACK,propName='diff', matBind=%d)" % (object.fullName, repr(mat.prop[1])[6:-5],mat.binding[1])) log.append("self.setObject('%s', materials=%s, polyFace=GL.GL_BACK,propName='spec', matBind=%d)" % (object.fullName, repr(mat.prop[2])[6:-5],mat.binding[2])) log.append("self.setObject('%s', materials=%s, polyFace=GL.GL_BACK,propName='emis', matBind=%d)" % (object.fullName, repr(mat.prop[3])[6:-5],mat.binding[3])) log.append("self.setObject('%s', materials=%s, polyFace=GL.GL_BACK,propName='shini', matBind=%d)" % (object.fullName, repr(mat.prop[4])[6:-5],mat.binding[4])) return log def logCameraTransformations(self, camera): warnings.warn("logCameraTransformations is deprecated", DeprecationWarning, stacklevel=2) logStr = "self.setCamera('%s', \n"%camera.name logStr = logStr + "rotation=(%9.3f, %9.3f, %9.3f, %9.3f, %9.3f, %9.3f, %9.3f, %9.3f, %9.3f, %9.3f, %9.3f, %9.3f, %9.3f, %9.3f, %9.3f, %9.3f),\n"%tuple(camera.rotation) logStr=logStr + "translation=(%9.3f, %9.3f, %9.3f),\n"%tuple(camera.translation) logStr = logStr + "scale=(%9.3f, %9.3f, %9.3f),\n"%tuple(camera.scale) logStr = logStr + "pivot=(%9.3f, %9.3f, %9.3f),\n"%tuple(camera.pivot) logStr = logStr + "lookAt=(%9.3f, %9.3f, %9.3f),\n"%tuple(camera.lookAt) logStr = logStr + "lookFrom=(%9.3f, %9.3f, %9.3f),\n"%tuple(camera.lookFrom) logStr = logStr + "direction=(%9.3f, %9.3f, %9.3f))"%tuple(camera.direction) return logStr+'\n' def logCameraProp(self, camera): warnings.warn("logCameraProp is deprecated", DeprecationWarning, stacklevel=2) logStr = "self.setCamera('%s', \n"%camera.name logStr = logStr + "width=%d, height=%d, rootx=%d, rooty=%d,"%\ (camera.width, camera.height, camera.rootx, camera.rooty) logStr = logStr + "fov=%f, near=%f, far=%f,"%\ (camera.fovy, camera.near, camera.far) logStr = logStr + "color=(%6.3f,%6.3f,%6.3f,%6.3f))"%\ tuple(camera.backgroundColor) return logStr+'\n' def logLightTransformations(self, light): warnings.warn("logLightTransformations is deprecated", DeprecationWarning, stacklevel=2) logStr = "self.setLight('%s', \n"%light.name logStr = logStr + "rotation=(%9.3f, %9.3f, %9.3f, %9.3f, %9.3f, %9.3f, %9.3f, %9.3f, %9.3f, %9.3f, %9.3f, %9.3f,
<reponame>rahul2393/python-spanner<filename>samples/samples/snippets_test.py # Copyright 2016 Google, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import time import uuid from google.api_core import exceptions from google.cloud import spanner import pytest from test_utils.retry import RetryErrors import snippets CREATE_TABLE_SINGERS = """\ CREATE TABLE Singers ( SingerId INT64 NOT NULL, FirstName STRING(1024), LastName STRING(1024), SingerInfo BYTES(MAX) ) PRIMARY KEY (SingerId) """ CREATE_TABLE_ALBUMS = """\ CREATE TABLE Albums ( SingerId INT64 NOT NULL, AlbumId INT64 NOT NULL, AlbumTitle STRING(MAX) ) PRIMARY KEY (SingerId, AlbumId), INTERLEAVE IN PARENT Singers ON DELETE CASCADE """ retry_429 = RetryErrors(exceptions.ResourceExhausted, delay=15) @pytest.fixture(scope="module") def sample_name(): return "snippets" @pytest.fixture(scope="module") def create_instance_id(): """Id for the low-cost instance.""" return f"create-instance-{uuid.uuid4().hex[:10]}" @pytest.fixture(scope="module") def lci_instance_id(): """Id for the low-cost instance.""" return f"lci-instance-{uuid.uuid4().hex[:10]}" @pytest.fixture(scope="module") def database_id(): return f"test-db-{uuid.uuid4().hex[:10]}" @pytest.fixture(scope="module") def create_database_id(): return f"create-db-{uuid.uuid4().hex[:10]}" @pytest.fixture(scope="module") def cmek_database_id(): return f"cmek-db-{uuid.uuid4().hex[:10]}" @pytest.fixture(scope="module") def default_leader_database_id(): return f"leader_db_{uuid.uuid4().hex[:10]}" @pytest.fixture(scope="module") def database_ddl(): """Sequence of DDL statements used to set up the database. Sample testcase modules can override as needed. """ return [CREATE_TABLE_SINGERS, CREATE_TABLE_ALBUMS] @pytest.fixture(scope="module") def default_leader(): """Default leader for multi-region instances.""" return "us-east4" def test_create_instance_explicit(spanner_client, create_instance_id): # Rather than re-use 'sample_isntance', we create a new instance, to # ensure that the 'create_instance' snippet is tested. retry_429(snippets.create_instance)(create_instance_id) instance = spanner_client.instance(create_instance_id) retry_429(instance.delete)() def test_create_database_explicit(sample_instance, create_database_id): # Rather than re-use 'sample_database', we create a new database, to # ensure that the 'create_database' snippet is tested. snippets.create_database(sample_instance.instance_id, create_database_id) database = sample_instance.database(create_database_id) database.drop() def test_create_instance_with_processing_units(capsys, lci_instance_id): processing_units = 500 retry_429(snippets.create_instance_with_processing_units)( lci_instance_id, processing_units, ) out, _ = capsys.readouterr() assert lci_instance_id in out assert "{} processing units".format(processing_units) in out spanner_client = spanner.Client() instance = spanner_client.instance(lci_instance_id) retry_429(instance.delete)() def test_create_database_with_encryption_config(capsys, instance_id, cmek_database_id, kms_key_name): snippets.create_database_with_encryption_key(instance_id, cmek_database_id, kms_key_name) out, _ = capsys.readouterr() assert cmek_database_id in out assert kms_key_name in out def test_get_instance_config(capsys): instance_config = "nam6" snippets.get_instance_config(instance_config) out, _ = capsys.readouterr() assert instance_config in out def test_list_instance_config(capsys): snippets.list_instance_config() out, _ = capsys.readouterr() assert "regional-us-central1" in out def test_list_databases(capsys, instance_id): snippets.list_databases(instance_id) out, _ = capsys.readouterr() assert "has default leader" in out def test_create_database_with_default_leader(capsys, multi_region_instance, multi_region_instance_id, default_leader_database_id, default_leader): retry_429 = RetryErrors(exceptions.ResourceExhausted, delay=15) retry_429(snippets.create_database_with_default_leader)( multi_region_instance_id, default_leader_database_id, default_leader ) out, _ = capsys.readouterr() assert default_leader_database_id in out assert default_leader in out def test_update_database_with_default_leader(capsys, multi_region_instance, multi_region_instance_id, default_leader_database_id, default_leader): retry_429 = RetryErrors(exceptions.ResourceExhausted, delay=15) retry_429(snippets.update_database_with_default_leader)( multi_region_instance_id, default_leader_database_id, default_leader ) out, _ = capsys.readouterr() assert default_leader_database_id in out assert default_leader in out def test_get_database_ddl(capsys, instance_id, sample_database): snippets.get_database_ddl(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert sample_database.database_id in out def test_query_information_schema_database_options(capsys, multi_region_instance, multi_region_instance_id, default_leader_database_id, default_leader): snippets.query_information_schema_database_options( multi_region_instance_id, default_leader_database_id ) out, _ = capsys.readouterr() assert default_leader in out @pytest.mark.dependency(name="insert_data") def test_insert_data(capsys, instance_id, sample_database): snippets.insert_data(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "Inserted data" in out @pytest.mark.dependency(depends=["insert_data"]) def test_delete_data(capsys, instance_id, sample_database): snippets.delete_data(instance_id, sample_database.database_id) # put it back for other tests snippets.insert_data(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "Deleted data" in out @pytest.mark.dependency(depends=["insert_data"]) def test_query_data(capsys, instance_id, sample_database): snippets.query_data(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "SingerId: 1, AlbumId: 1, AlbumTitle: Total Junk" in out @pytest.mark.dependency(name="add_column", depends=["insert_data"]) def test_add_column(capsys, instance_id, sample_database): snippets.add_column(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "Added the MarketingBudget column." in out @pytest.mark.dependency(depends=["insert_data"]) def test_read_data(capsys, instance_id, sample_database): snippets.read_data(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "SingerId: 1, AlbumId: 1, AlbumTitle: Total Junk" in out @pytest.mark.dependency(name="update_data", depends=["add_column"]) def test_update_data(capsys, instance_id, sample_database): # Sleep for 15 seconds to ensure previous inserts will be # 'stale' by the time test_read_stale_data is run. time.sleep(15) snippets.update_data(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "Updated data." in out @pytest.mark.dependency(depends=["update_data"]) def test_read_stale_data(capsys, instance_id, sample_database): # This snippet relies on test_update_data inserting data # at least 15 seconds after the previous insert snippets.read_stale_data(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "SingerId: 1, AlbumId: 1, MarketingBudget: None" in out @pytest.mark.dependency(depends=["add_column"]) def test_read_write_transaction(capsys, instance_id, sample_database): snippets.read_write_transaction(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "Transaction complete" in out @pytest.mark.dependency(depends=["add_column"]) def test_query_data_with_new_column(capsys, instance_id, sample_database): snippets.query_data_with_new_column(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "SingerId: 1, AlbumId: 1, MarketingBudget: 300000" in out assert "SingerId: 2, AlbumId: 2, MarketingBudget: 300000" in out @pytest.mark.dependency(name="add_index", depends=["insert_data"]) def test_add_index(capsys, instance_id, sample_database): snippets.add_index(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "Added the AlbumsByAlbumTitle index" in out @pytest.mark.dependency(depends=["add_index"]) def test_query_data_with_index(capsys, instance_id, sample_database): snippets.query_data_with_index(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "Go, Go, Go" in out assert "Forever Hold Your Peace" in out assert "Green" not in out @pytest.mark.dependency(depends=["add_index"]) def test_read_data_with_index(capsys, instance_id, sample_database): snippets.read_data_with_index(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "Go, Go, Go" in out assert "Forever Hold Your Peace" in out assert "Green" in out @pytest.mark.dependency(name="add_storing_index", depends=["insert_data"]) def test_add_storing_index(capsys, instance_id, sample_database): snippets.add_storing_index(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "Added the AlbumsByAlbumTitle2 index." in out @pytest.mark.dependency(depends=["add_storing_index"]) def test_read_data_with_storing_index(capsys, instance_id, sample_database): snippets.read_data_with_storing_index(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "300000" in out @pytest.mark.dependency(depends=["insert_data"]) def test_read_only_transaction(capsys, instance_id, sample_database): snippets.read_only_transaction(instance_id, sample_database.database_id) out, _ = capsys.readouterr() # Snippet does two reads, so entry should be listed twice assert out.count("SingerId: 1, AlbumId: 1, AlbumTitle: Total Junk") == 2 @pytest.mark.dependency(name="add_timestamp_column", depends=["insert_data"]) def test_add_timestamp_column(capsys, instance_id, sample_database): snippets.add_timestamp_column(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert 'Altered table "Albums" on database ' in out @pytest.mark.dependency(depends=["add_timestamp_column"]) def test_update_data_with_timestamp(capsys, instance_id, sample_database): snippets.update_data_with_timestamp(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "Updated data" in out @pytest.mark.dependency(depends=["add_timestamp_column"]) def test_query_data_with_timestamp(capsys, instance_id, sample_database): snippets.query_data_with_timestamp(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "SingerId: 1, AlbumId: 1, MarketingBudget: 1000000" in out assert "SingerId: 2, AlbumId: 2, MarketingBudget: 750000" in out @pytest.mark.dependency(name="create_table_with_timestamp") def test_create_table_with_timestamp(capsys, instance_id, sample_database): snippets.create_table_with_timestamp(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "Created Performances table on database" in out @pytest.mark.dependency(depends=["create_table_with_datatypes"]) def test_insert_data_with_timestamp(capsys, instance_id, sample_database): snippets.insert_data_with_timestamp(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "Inserted data." in out @pytest.mark.dependency(name="write_struct_data") def test_write_struct_data(capsys, instance_id, sample_database): snippets.write_struct_data(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "Inserted sample data for STRUCT queries" in out @pytest.mark.dependency(depends=["write_struct_data"]) def test_query_with_struct(capsys, instance_id, sample_database): snippets.query_with_struct(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "SingerId: 6" in out @pytest.mark.dependency(depends=["write_struct_data"]) def test_query_with_array_of_struct(capsys, instance_id, sample_database): snippets.query_with_array_of_struct(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "SingerId: 8" in out assert "SingerId: 7" in out assert "SingerId: 6" in out @pytest.mark.dependency(depends=["write_struct_data"]) def test_query_struct_field(capsys, instance_id, sample_database): snippets.query_struct_field(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "SingerId: 6" in out @pytest.mark.dependency(depends=["write_struct_data"]) def test_query_nested_struct_field(capsys, instance_id, sample_database): snippets.query_nested_struct_field(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "SingerId: 6 SongName: Imagination" in out assert "SingerId: 9 SongName: Imagination" in out @pytest.mark.dependency(name="insert_data_with_dml") def test_insert_data_with_dml(capsys, instance_id, sample_database): snippets.insert_data_with_dml(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "1 record(s) inserted." in out @pytest.mark.dependency(name="log_commit_stats") def test_log_commit_stats(capsys, instance_id, sample_database): snippets.log_commit_stats(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "1 record(s) inserted." in out assert "3 mutation(s) in transaction." in out @pytest.mark.dependency(depends=["insert_data"]) def test_update_data_with_dml(capsys, instance_id, sample_database): snippets.update_data_with_dml(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "1 record(s) updated." in out @pytest.mark.dependency(depends=["insert_data"]) def test_delete_data_with_dml(capsys, instance_id, sample_database): snippets.delete_data_with_dml(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "1 record(s) deleted." in out @pytest.mark.dependency(depends=["add_timestamp_column"]) def test_update_data_with_dml_timestamp(capsys, instance_id, sample_database): snippets.update_data_with_dml_timestamp(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "2 record(s) updated." in out @pytest.mark.dependency(name="dml_write_read_transaction") def test_dml_write_read_transaction(capsys, instance_id, sample_database): snippets.dml_write_read_transaction(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "1 record(s) inserted." in out assert "FirstName: Timothy, LastName: Campbell" in out @pytest.mark.dependency(depends=["dml_write_read_transaction"]) def test_update_data_with_dml_struct(capsys, instance_id, sample_database): snippets.update_data_with_dml_struct(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "1 record(s) updated" in out @pytest.mark.dependency(name="insert_with_dml") def test_insert_with_dml(capsys, instance_id, sample_database): snippets.insert_with_dml(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "4 record(s) inserted" in out @pytest.mark.dependency(depends=["insert_with_dml"]) def test_query_data_with_parameter(capsys, instance_id, sample_database): snippets.query_data_with_parameter(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "SingerId: 12, FirstName: Melissa, LastName: Garcia" in out @pytest.mark.dependency(depends=["add_column"]) def test_write_with_dml_transaction(capsys, instance_id, sample_database): snippets.write_with_dml_transaction(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "Transferred 200000 from Album2's budget to Album1's" in out @pytest.mark.dependency(depends=["add_column"]) def update_data_with_partitioned_dml(capsys, instance_id, sample_database): snippets.update_data_with_partitioned_dml(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "3 record(s) updated" in out @pytest.mark.dependency(depends=["insert_with_dml"]) def test_delete_data_with_partitioned_dml(capsys, instance_id, sample_database): snippets.delete_data_with_partitioned_dml(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "6 record(s) deleted" in out @pytest.mark.dependency(depends=["add_column"]) def test_update_with_batch_dml(capsys, instance_id, sample_database): snippets.update_with_batch_dml(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "Executed 2 SQL statements using Batch DML" in out @pytest.mark.dependency(name="create_table_with_datatypes") def test_create_table_with_datatypes(capsys, instance_id, sample_database): snippets.create_table_with_datatypes(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "Created Venues table on database" in out @pytest.mark.dependency( name="insert_datatypes_data", depends=["create_table_with_datatypes"], ) def test_insert_datatypes_data(capsys, instance_id, sample_database): snippets.insert_datatypes_data(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "Inserted data." in out @pytest.mark.dependency(depends=["insert_datatypes_data"]) def test_query_data_with_array(capsys, instance_id, sample_database): snippets.query_data_with_array(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "VenueId: 19, VenueName: Venue 19, AvailableDate: 2020-11-01" in out assert "VenueId: 42, VenueName: Venue 42, AvailableDate: 2020-10-01" in out @pytest.mark.dependency(depends=["insert_datatypes_data"]) def test_query_data_with_bool(capsys, instance_id, sample_database): snippets.query_data_with_bool(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "VenueId: 19, VenueName: Venue 19, OutdoorVenue: True" in out @pytest.mark.dependency(depends=["insert_datatypes_data"]) def test_query_data_with_bytes(capsys, instance_id, sample_database): snippets.query_data_with_bytes(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "VenueId: 4, VenueName: Venue 4" in out @pytest.mark.dependency(depends=["insert_datatypes_data"]) def test_query_data_with_date(capsys, instance_id, sample_database): snippets.query_data_with_date(instance_id, sample_database.database_id) out, _ = capsys.readouterr() assert "VenueId: 4, VenueName: Venue 4, LastContactDate: 2018-09-02" in out assert "VenueId: 42,
from dg_db.db_write import write_countries, write_skews, write_platforms def populate_accounts(): countries = [ ("Germany", "DE", "Central", "Gat"), ("Austria", "AT", "Central", "Gat"), ("Switzerland", "CH", "Central", "None"), ("France", "FR", "South", "None"), ("Italy", "IT", "South", "None"), ("Spain", "ES", "South", "Iberia"), ("Portugal", "PT", "South", "Iberia"), ("Denmark", "DK", "North", "None"), ("Norway", "NO", "North", "None"), ("Sweden", "SE", "North", "None"), ("Finland", "FI", "North", "None"), ("Netherlands", "NL", "North", "Benelux"), ("Belgium", "BE", "North", "Benelux"), ("UK", "UK", "UKI", "None"), ("Ireland", "IE", "UKI", "None"), ] write_countries(countries) def populate_platforms(): platforms = [ (1, "Google", "8005495881", "2059894093"), (2, "Google", "4825989316", "4302291843"), (3, "Google", "1345515457", "5635690780"), (4, "Google", "3136320411", "7447885588"), (5, "Google", "3456991375", "2037891425"), (6, "Google", "6033808683", "9021596480"), (7, "Google", "1795086707", "4753585796"), (8, "Google", "9503587615", "7310537721"), (9, "Google", "1739364726", "8212508088"), (10, "Google", "7245755087", "7575293197"), (11, "Google", "1332379677", "1904670595"), (12, "Google", "7966843210", "9271529992"), (13, "Google", "3704484034", "6338903272"), (14, "Google", "8457701491", "1150412027"), (15, "Google", "3377816067", "6186544301"), (1, "Microsoft", "X7356537", "X7356537"), (2, "Microsoft", "X7004130", "X7004130"), (3, "Microsoft", "F1071RW6", "F1071RW6"), (4, "Microsoft", "X8349003", "X8349003"), (5, "Microsoft", "B004ETLE", "B004ETLE"), (6, "Microsoft", "B004V7BM", "B004V7BM"), (7, "Microsoft", "NOTAVAIL", "NOTAVAIL"), (8, "Microsoft", "X000XGAU", "X000XGAU"), (9, "Microsoft", "B004SUHL", "B004SUHL"), (10, "Microsoft", "F107DTZT", "F107DTZT"), (11, "Microsoft", "F1074ELB", "F1074ELB"), (12, "Microsoft", "X0006AXW", "X0006AXW"), (13, "Microsoft", "B004295N", "B004295N"), (14, "Microsoft", "F107P4UT", "F107P4UT"), (15, "Microsoft", "F107MVC9", "F107MVC9") ] write_platforms(platforms) def populate_skews(): # Q2 22 records_to_insert = [ ("UK", 910534.84, 4660585.24, 0.1954, 0.0173, 0.0419, 0.0440, 0.0692, 0.0533, 0.0533, 0.0551, 0.0589, 0.2653, 0.0677, 0.0799, 0.0810, 0.0810, 0.0320), ("IE", 102951.89, 727779.08, 0.1415, 0.0180, 0.0437, 0.0459, 0.0722, 0.0556, 0.0556, 0.0574, 0.0614, 0.2339, 0.0706, 0.0833, 0.0844, 0.0844, 0.0334), ("DE", 1109019.05, 5427544.86, 0.2043, 0.0153, 0.0400, 0.0438, 0.0762, 0.0514, 0.0533, 0.0552, 0.0590, 0.2649, 0.0670, 0.0789, 0.0812, 0.0812, 0.0325), ("AT", 105074.22, 745541.88, 0.1409, 0.0160, 0.0417, 0.0457, 0.0794, 0.0536, 0.0556, 0.0576, 0.0616, 0.2335, 0.0700, 0.0826, 0.0850, 0.0842, 0.0335), ("CH", 240387.78, 1437894.37, 0.1672, 0.0218, 0.0496, 0.0595, 0.0811, 0.0811, 0.0811, 0.0991, 0.0631, 0.1295, 0.0673, 0.0737, 0.0737, 0.0804, 0.0389), ("FR", 529057.49, 2634799.26, 0.2008, 0.0170, 0.0348, 0.0382, 0.0531, 0.0448, 0.0481, 0.0498, 0.0514, 0.2308, 0.0638, 0.0705, 0.1072, 0.1440, 0.0466), ("ES", 153548.47, 807181.61, 0.1902, 0.0170, 0.0348, 0.0382, 0.0531, 0.0448, 0.0481, 0.0498, 0.0514, 0.2308, 0.0638, 0.0705, 0.1072, 0.1440, 0.0466), ("IT", 165266.60, 898683.98, 0.1839, 0.0270, 0.0505, 0.0586, 0.0631, 0.0622, 0.0712, 0.0667, 0.0703, 0.1558, 0.0930, 0.0842, 0.0842, 0.0797, 0.0337), ("PT", 39063.01, 148691.35, 0.2627, 0.0270, 0.0631, 0.0631, 0.0721, 0.0721, 0.0721, 0.0811, 0.0631, 0.0945, 0.0821, 0.0885, 0.0885, 0.0885, 0.0442), ("NO", 49170.23, 213901.64, 0.2299, 0.0270, 0.0505, 0.0586, 0.0631, 0.0622, 0.0712, 0.0667, 0.0703, 0.1558, 0.0930, 0.0842, 0.0842, 0.0797, 0.0337), ("SE", 68291.98, 315653.46, 0.2164, 0.0361, 0.0541, 0.0586, 0.0811, 0.0631, 0.0856, 0.0631, 0.0631, 0.1170, 0.0748, 0.0807, 0.0807, 0.0807, 0.0612), ("FI", 32780.15, 151513.66, 0.2164, 0.0361, 0.0541, 0.0586, 0.0811, 0.0631, 0.0856, 0.0631, 0.0631, 0.1170, 0.0748, 0.0807, 0.0807, 0.0807, 0.0613), ("DK", 122925.57, 635020.50, 0.1936, 0.0361, 0.0541, 0.0586, 0.0811, 0.0631, 0.0856, 0.0631, 0.0631, 0.1170, 0.0748, 0.0807, 0.0807, 0.0807, 0.0612), ("NL", 170456.79, 834216.40, 0.2043, 0.0406, 0.0628, 0.0628, 0.0679, 0.0679, 0.0787, 0.0763, 0.0652, 0.1213, 0.0732, 0.0848, 0.0821, 0.0795, 0.0369), ("BE", 113637.86, 617938.08, 0.1839, 0.0406, 0.0628, 0.0628, 0.0679, 0.0679, 0.0787, 0.0763, 0.0652, 0.1213, 0.0732, 0.0848, 0.0821, 0.0795, 0.0369) ] # Q1 Final (Reduced Budget) # records_to_insert = [ # ("UK", 850737, 3511354, 0.24, 0.02, 0.04, 0.04, 0.07, 0.05, 0.05, 0.06, 0.06, 0.27, 0.07, 0.08, 0.08, 0.08, 0.03), # ("IE", 92370, 564691, 0.16, 0.02, 0.04, 0.05, 0.07, 0.06, 0.06, 0.06, 0.06, 0.23, 0.07, 0.08, 0.08, 0.08, 0.03), # ("DE", 1085957, 3956971, 0.27, 0.02, 0.04, 0.04, 0.08, 0.05, 0.05, 0.06, 0.06, 0.26, 0.07, 0.08, 0.08, 0.08, 0.03), # ("AT", 98265, 508753, 0.19, 0.02, 0.04, 0.05, 0.08, 0.05, 0.06, 0.06, 0.06, 0.23, 0.07, 0.08, 0.09, 0.08, 0.03), # ("CH", 195306, 996198, 0.20, 0.02, 0.05, 0.06, 0.08, 0.08, 0.08, 0.10, 0.06, 0.13, 0.07, 0.07, 0.07, 0.08, 0.04), # ("FR", 531644, 1648781, 0.32, 0.02, 0.03, 0.04, 0.05, 0.04, 0.05, 0.05, 0.05, 0.23, 0.06, 0.07, 0.11, 0.14, 0.05), # ("ES", 126968, 590136, 0.22, 0.02, 0.03, 0.04, 0.05, 0.04, 0.05, 0.05, 0.05, 0.23, 0.06, 0.07, 0.11, 0.14, 0.05), # ("IT", 137325, 567718, 0.24, 0.03, 0.05, 0.06, 0.06, 0.06, 0.07, 0.07, 0.07, 0.16, 0.09, 0.08, 0.08, 0.08, 0.03), # ("PT", 32134, 101955, 0.32, 0.03, 0.06, 0.06, 0.07, 0.07, 0.07, 0.08, 0.06, 0.09, 0.08, 0.09, 0.09, 0.09, 0.04), # ("NO", 39934, 168357, 0.24, 0.03, 0.05, 0.06, 0.06, 0.06, 0.07, 0.07, 0.07, 0.16, 0.09, 0.08, 0.08, 0.08, 0.03), # ("SE", 55464, 167020, 0.33, 0.04, 0.05, 0.06, 0.08, 0.06, 0.09, 0.06, 0.06, 0.12, 0.07, 0.08, 0.08, 0.08, 0.06), # ("FI", 26623, 101548, 0.26, 0.04, 0.05, 0.06, 0.08, 0.06, 0.09, 0.06, 0.06, 0.12, 0.07, 0.08, 0.08, 0.08, 0.06), # ("DK", 99835, 440934, 0.23, 0.04, 0.05, 0.06, 0.08, 0.06, 0.09, 0.06, 0.06, 0.12, 0.07, 0.08, 0.08, 0.08, 0.06), # ("NL", 136775, 542948, 0.25, 0.04, 0.06, 0.06, 0.07, 0.07, 0.08, 0.08, 0.07, 0.12, 0.07, 0.08, 0.08, 0.08, 0.04), # ("BE", 91184, 398162, 0.23, 0.04, 0.06, 0.06, 0.07, 0.07, 0.08, 0.08, 0.07, 0.12, 0.07, 0.08, 0.08, 0.08, 0.04) # ] # Q1 22 # records_to_insert = [ # ("UK", 659636, 3970904, 0.1661, 0.0227, 0.0550, 0.0577, 0.0909, 0.0700, 0.0700, 0.0723, 0.0773, 0.1104, 0.0784, 0.0861, 0.0873, 0.0873, 0.0345), # ("IE", 73293, 638596, 0.1148, 0.0227, 0.0550, 0.0577, 0.0909, 0.0700, 0.0700, 0.0723, 0.0773, 0.1104, 0.0784, 0.0861, 0.0873, 0.0873, 0.0345), # ("DE", 826140, 4474841, 0.1846, 0.0201, 0.0525, 0.0575, 0.1000, 0.0675, 0.0700, 0.0725, 0.0775, 0.1100, 0.0775, 0.0850, 0.0875, 0.0875, 0.0350), # ("AT", 81706, 575337, 0.1420, 0.0201, 0.0525, 0.0575, 0.1000, 0.0675, 0.0700, 0.0725, 0.0775, 0.1100, 0.0775, 0.0850, 0.0875, 0.0875, 0.0350), # ("CH", 175908, 1126576, 0.1561, 0.0201, 0.0525, 0.0575, 0.1000, 0.0675, 0.0700, 0.0725, 0.0775, 0.1100, 0.0775, 0.0850, 0.0875, 0.0875, 0.0350), # ("FR", 359010, 1864565, 0.1925, 0.0255, 0.0525, 0.0575, 0.0800, 0.0675, 0.0725, 0.0750, 0.0775, 0.1099, 0.0850, 0.0875, 0.0875, 0.0875, 0.0350), # ("ES", 116539, 667371, 0.1746, 0.0255, 0.0525, 0.0575, 0.0800, 0.0675, 0.0725, 0.0750, 0.0775, 0.1099, 0.0850, 0.0875, 0.0875, 0.0875, 0.0350), # ("IT", 123686, 642018, 0.1927, 0.0255, 0.0525, 0.0575, 0.0800, 0.0675, 0.0725, 0.0750, 0.0775, 0.1099, 0.0850, 0.0875, 0.0875, 0.0875, 0.0350), # ("PT", 28942, 115298, 0.2510, 0.0255, 0.0525, 0.0575, 0.0800, 0.0675, 0.0725, 0.0750, 0.0775, 0.1099, 0.0850, 0.0875, 0.0875, 0.0875, 0.0350), # ("NO", 35968, 190390, 0.1889, 0.0400, 0.0600, 0.0650, 0.0900, 0.0700, 0.0950, 0.0700, 0.0700, 0.0950, 0.0725, 0.0725, 0.0725, 0.0725, 0.0550), # ("SE", 49955, 188879, 0.2645, 0.0400, 0.0600, 0.0650, 0.0900, 0.0700, 0.0950, 0.0700, 0.0700, 0.0950, 0.0725, 0.0725, 0.0725, 0.0725, 0.0550), # ("FI", 23979, 114839, 0.2088, 0.0400, 0.0600, 0.0650, 0.0900, 0.0700, 0.0950, 0.0700, 0.0700, 0.0950, 0.0725, 0.0725, 0.0725, 0.0725, 0.0550), # ("DK", 89919, 498641, 0.1803, 0.0400, 0.0600, 0.0650, 0.0900, 0.0700, 0.0950, 0.0700, 0.0700, 0.0950, 0.0725, 0.0725, 0.0725, 0.0725, 0.0550), # ("NL", 123191, 614006, 0.2006, 0.0450, 0.0697, 0.0697, 0.0753, 0.0753, 0.0873, 0.0847, 0.0723, 0.0997, 0.0703, 0.0750, 0.0727, 0.0703, 0.0327), # ("BE", 82127, 450271, 0.1824, 0.0450, 0.0697, 0.0697, 0.0753, 0.0753, 0.0873, 0.0847, 0.0723, 0.0997, 0.0703, 0.0750, 0.0727, 0.0703, 0.0327) # ] # // Q4 # records_to_insert = [ # ("UK", 492693, 2458665, 0.20, 0.02, 0.1, 0.08, 0.08, 0.08, 0.07, 0.07, 0.07, 0.07, 0.07, 0.1, 0.08, 0.08, 0.03), # ("IE", 51965, 409777, 0.13, 0.02, 0.1, 0.08, 0.08, 0.08, 0.07, 0.07, 0.07, 0.07, 0.07, 0.1, 0.08, 0.08, 0.03), # ("DE", 729221, 3698601, 0.20, 0.03, 0.07, 0.07, 0.07, 0.08, 0.08, 0.07, 0.08, 0.08, 0.12, 0.08, 0.08, 0.08, 0.04), # ("AT", 111815, 821338, 0.14, 0.03, 0.07, 0.07, 0.07, 0.08, 0.08, 0.07, 0.08, 0.08, 0.12, 0.08, 0.08, 0.08, 0.04), # ("CH", 157896, 1044068, 0.15, 0.03, 0.07, 0.07, 0.07, 0.08, 0.08, 0.07, 0.08, 0.08, 0.12, 0.08, 0.08, 0.08, 0.04), # ("FR", 230684, 1120651, 0.20, 0.02, 0.06, 0.07, 0.09, 0.09, 0.09, 0.11, 0.07, 0.11, 0.06, 0.06, 0.06, 0.07, 0.03), # ("ES", 95035, 545993, 0.17, 0.06, 0.06, 0.06, 0.07, 0.08, 0.08, 0.08, 0.08, 0.08, 0.09, 0.08, 0.08, 0.08, 0.04), # ("IT", 127896, 816425, 0.16, 0.03, 0.07, 0.07, 0.08, 0.08, 0.08, 0.09, 0.07, 0.07, 0.08, 0.08, 0.08, 0.08, 0.04), # ("PT", 16885, 60524, 0.27, 0.05, 0.06, 0.06, 0.07, 0.08, 0.08, 0.09, 0.07, 0.09, 0.08, 0.08, 0.08, 0.08, 0.03), # ("NO", 26584, 150431, 0.18, 0.02, 0.05, 0.07, 0.08, 0.07, 0.08, 0.13, 0.08, 0.1, 0.08, 0.08, 0.08, 0.08, 0.04), # ("SE", 44339, 243477, 0.18, 0.02, 0.05, 0.07, 0.08, 0.07, 0.08, 0.13, 0.08, 0.1, 0.08, 0.08, 0.08,
<reponame>mydevice/python-openstackclient # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. # from unittest import mock from unittest.mock import call from cinderclient import api_versions from osc_lib import exceptions from osc_lib import utils from openstackclient.tests.unit.volume.v2 import fakes as volume_fakes from openstackclient.volume.v2 import volume_backup class TestBackup(volume_fakes.TestVolume): def setUp(self): super(TestBackup, self).setUp() self.backups_mock = self.app.client_manager.volume.backups self.backups_mock.reset_mock() self.volumes_mock = self.app.client_manager.volume.volumes self.volumes_mock.reset_mock() self.snapshots_mock = self.app.client_manager.volume.volume_snapshots self.snapshots_mock.reset_mock() self.restores_mock = self.app.client_manager.volume.restores self.restores_mock.reset_mock() class TestBackupCreate(TestBackup): volume = volume_fakes.FakeVolume.create_one_volume() snapshot = volume_fakes.FakeSnapshot.create_one_snapshot() new_backup = volume_fakes.FakeBackup.create_one_backup( attrs={'volume_id': volume.id, 'snapshot_id': snapshot.id}) columns = ( 'availability_zone', 'container', 'description', 'id', 'name', 'object_count', 'size', 'snapshot_id', 'status', 'volume_id', ) data = ( new_backup.availability_zone, new_backup.container, new_backup.description, new_backup.id, new_backup.name, new_backup.object_count, new_backup.size, new_backup.snapshot_id, new_backup.status, new_backup.volume_id, ) def setUp(self): super(TestBackupCreate, self).setUp() self.volumes_mock.get.return_value = self.volume self.snapshots_mock.get.return_value = self.snapshot self.backups_mock.create.return_value = self.new_backup # Get the command object to test self.cmd = volume_backup.CreateVolumeBackup(self.app, None) def test_backup_create(self): arglist = [ "--name", self.new_backup.name, "--description", self.new_backup.description, "--container", self.new_backup.container, "--force", "--incremental", "--snapshot", self.new_backup.snapshot_id, self.new_backup.volume_id, ] verifylist = [ ("name", self.new_backup.name), ("description", self.new_backup.description), ("container", self.new_backup.container), ("force", True), ("incremental", True), ("snapshot", self.new_backup.snapshot_id), ("volume", self.new_backup.volume_id), ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) columns, data = self.cmd.take_action(parsed_args) self.backups_mock.create.assert_called_with( self.new_backup.volume_id, container=self.new_backup.container, name=self.new_backup.name, description=self.new_backup.description, force=True, incremental=True, snapshot_id=self.new_backup.snapshot_id, ) self.assertEqual(self.columns, columns) self.assertEqual(self.data, data) def test_backup_create_with_properties(self): self.app.client_manager.volume.api_version = \ api_versions.APIVersion('3.43') arglist = [ "--property", "foo=bar", "--property", "wow=much-cool", self.new_backup.volume_id, ] verifylist = [ ("properties", {"foo": "bar", "wow": "much-cool"}), ("volume", self.new_backup.volume_id), ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) columns, data = self.cmd.take_action(parsed_args) self.backups_mock.create.assert_called_with( self.new_backup.volume_id, container=None, name=None, description=None, force=False, incremental=False, metadata={"foo": "bar", "wow": "much-cool"}, ) self.assertEqual(self.columns, columns) self.assertEqual(self.data, data) def test_backup_create_with_properties_pre_v343(self): self.app.client_manager.volume.api_version = \ api_versions.APIVersion('3.42') arglist = [ "--property", "foo=bar", "--property", "wow=much-cool", self.new_backup.volume_id, ] verifylist = [ ("properties", {"foo": "bar", "wow": "much-cool"}), ("volume", self.new_backup.volume_id), ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) exc = self.assertRaises( exceptions.CommandError, self.cmd.take_action, parsed_args) self.assertIn("--os-volume-api-version 3.43 or greater", str(exc)) def test_backup_create_with_availability_zone(self): self.app.client_manager.volume.api_version = \ api_versions.APIVersion('3.51') arglist = [ "--availability-zone", "my-az", self.new_backup.volume_id, ] verifylist = [ ("availability_zone", "my-az"), ("volume", self.new_backup.volume_id), ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) columns, data = self.cmd.take_action(parsed_args) self.backups_mock.create.assert_called_with( self.new_backup.volume_id, container=None, name=None, description=None, force=False, incremental=False, availability_zone="my-az", ) self.assertEqual(self.columns, columns) self.assertEqual(self.data, data) def test_backup_create_with_availability_zone_pre_v351(self): self.app.client_manager.volume.api_version = \ api_versions.APIVersion('3.50') arglist = [ "--availability-zone", "my-az", self.new_backup.volume_id, ] verifylist = [ ("availability_zone", "my-az"), ("volume", self.new_backup.volume_id), ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) exc = self.assertRaises( exceptions.CommandError, self.cmd.take_action, parsed_args) self.assertIn("--os-volume-api-version 3.51 or greater", str(exc)) def test_backup_create_without_name(self): arglist = [ "--description", self.new_backup.description, "--container", self.new_backup.container, self.new_backup.volume_id, ] verifylist = [ ("description", self.new_backup.description), ("container", self.new_backup.container), ("volume", self.new_backup.volume_id), ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) columns, data = self.cmd.take_action(parsed_args) self.backups_mock.create.assert_called_with( self.new_backup.volume_id, container=self.new_backup.container, name=None, description=self.new_backup.description, force=False, incremental=False, ) self.assertEqual(self.columns, columns) self.assertEqual(self.data, data) class TestBackupDelete(TestBackup): backups = volume_fakes.FakeBackup.create_backups(count=2) def setUp(self): super(TestBackupDelete, self).setUp() self.backups_mock.get = ( volume_fakes.FakeBackup.get_backups(self.backups)) self.backups_mock.delete.return_value = None # Get the command object to mock self.cmd = volume_backup.DeleteVolumeBackup(self.app, None) def test_backup_delete(self): arglist = [ self.backups[0].id ] verifylist = [ ("backups", [self.backups[0].id]) ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) result = self.cmd.take_action(parsed_args) self.backups_mock.delete.assert_called_with( self.backups[0].id, False) self.assertIsNone(result) def test_backup_delete_with_force(self): arglist = [ '--force', self.backups[0].id, ] verifylist = [ ('force', True), ("backups", [self.backups[0].id]) ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) result = self.cmd.take_action(parsed_args) self.backups_mock.delete.assert_called_with(self.backups[0].id, True) self.assertIsNone(result) def test_delete_multiple_backups(self): arglist = [] for b in self.backups: arglist.append(b.id) verifylist = [ ('backups', arglist), ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) result = self.cmd.take_action(parsed_args) calls = [] for b in self.backups: calls.append(call(b.id, False)) self.backups_mock.delete.assert_has_calls(calls) self.assertIsNone(result) def test_delete_multiple_backups_with_exception(self): arglist = [ self.backups[0].id, 'unexist_backup', ] verifylist = [ ('backups', arglist), ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) find_mock_result = [self.backups[0], exceptions.CommandError] with mock.patch.object(utils, 'find_resource', side_effect=find_mock_result) as find_mock: try: self.cmd.take_action(parsed_args) self.fail('CommandError should be raised.') except exceptions.CommandError as e: self.assertEqual('1 of 2 backups failed to delete.', str(e)) find_mock.assert_any_call(self.backups_mock, self.backups[0].id) find_mock.assert_any_call(self.backups_mock, 'unexist_backup') self.assertEqual(2, find_mock.call_count) self.backups_mock.delete.assert_called_once_with( self.backups[0].id, False ) class TestBackupList(TestBackup): volume = volume_fakes.FakeVolume.create_one_volume() backups = volume_fakes.FakeBackup.create_backups( attrs={'volume_id': volume.name}, count=3) columns = ( 'ID', 'Name', 'Description', 'Status', 'Size', ) columns_long = columns + ( 'Availability Zone', 'Volume', 'Container', ) data = [] for b in backups: data.append(( b.id, b.name, b.description, b.status, b.size, )) data_long = [] for b in backups: data_long.append(( b.id, b.name, b.description, b.status, b.size, b.availability_zone, volume_backup.VolumeIdColumn(b.volume_id), b.container, )) def setUp(self): super(TestBackupList, self).setUp() self.volumes_mock.list.return_value = [self.volume] self.backups_mock.list.return_value = self.backups self.volumes_mock.get.return_value = self.volume self.backups_mock.get.return_value = self.backups[0] # Get the command to test self.cmd = volume_backup.ListVolumeBackup(self.app, None) def test_backup_list_without_options(self): arglist = [] verifylist = [ ("long", False), ("name", None), ("status", None), ("volume", None), ("marker", None), ("limit", None), ('all_projects', False), ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) columns, data = self.cmd.take_action(parsed_args) search_opts = { "name": None, "status": None, "volume_id": None, 'all_tenants': False, } self.volumes_mock.get.assert_not_called() self.backups_mock.get.assert_not_called() self.backups_mock.list.assert_called_with( search_opts=search_opts, marker=None, limit=None, ) self.assertEqual(self.columns, columns) self.assertItemsEqual(self.data, list(data)) def test_backup_list_with_options(self): arglist = [ "--long", "--name", self.backups[0].name, "--status", "error", "--volume", self.volume.id, "--marker", self.backups[0].id, "--all-projects", "--limit", "3", ] verifylist = [ ("long", True), ("name", self.backups[0].name), ("status", "error"), ("volume", self.volume.id), ("marker", self.backups[0].id), ('all_projects', True), ("limit", 3), ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) columns, data = self.cmd.take_action(parsed_args) search_opts = { "name": self.backups[0].name, "status": "error", "volume_id": self.volume.id, 'all_tenants': True, } self.volumes_mock.get.assert_called_once_with(self.volume.id) self.backups_mock.get.assert_called_once_with(self.backups[0].id) self.backups_mock.list.assert_called_with( search_opts=search_opts, marker=self.backups[0].id, limit=3, ) self.assertEqual(self.columns_long, columns) self.assertItemsEqual(self.data_long, list(data)) class TestBackupRestore(TestBackup): volume = volume_fakes.FakeVolume.create_one_volume() backup = volume_fakes.FakeBackup.create_one_backup( attrs={'volume_id': volume.id}) def setUp(self): super(TestBackupRestore, self).setUp() self.backups_mock.get.return_value = self.backup self.volumes_mock.get.return_value = self.volume self.restores_mock.restore.return_value = ( volume_fakes.FakeVolume.create_one_volume( {'id': self.volume['id']})) # Get the command object to mock self.cmd = volume_backup.RestoreVolumeBackup(self.app, None) def test_backup_restore(self): arglist = [ self.backup.id, self.backup.volume_id ] verifylist = [ ("backup", self.backup.id), ("volume", self.backup.volume_id) ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) result = self.cmd.take_action(parsed_args) self.restores_mock.restore.assert_called_with(self.backup.id, self.backup.volume_id) self.assertIsNotNone(result) class TestBackupSet(TestBackup): backup = volume_fakes.FakeBackup.create_one_backup( attrs={'metadata': {'wow': 'cool'}}, ) def setUp(self): super(TestBackupSet, self).setUp() self.backups_mock.get.return_value = self.backup # Get the command object to test self.cmd = volume_backup.SetVolumeBackup(self.app, None) def test_backup_set_name(self): self.app.client_manager.volume.api_version = \ api_versions.APIVersion('3.9') arglist = [ '--name', 'new_name', self.backup.id, ] verifylist = [ ('name', 'new_name'), ('backup', self.backup.id), ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) # In base command class ShowOne in cliff, abstract method take_action() # returns nothing result = self.cmd.take_action(parsed_args) self.backups_mock.update.assert_called_once_with( self.backup.id, {'name': 'new_name'}) self.assertIsNone(result) def test_backup_set_name_pre_v39(self): self.app.client_manager.volume.api_version = \ api_versions.APIVersion('3.8') arglist = [ '--name', 'new_name', self.backup.id, ] verifylist = [ ('name', 'new_name'), ('backup', self.backup.id), ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) exc = self.assertRaises( exceptions.CommandError, self.cmd.take_action, parsed_args) self.assertIn("--os-volume-api-version 3.9 or greater", str(exc)) def test_backup_set_description(self): self.app.client_manager.volume.api_version = \ api_versions.APIVersion('3.9') arglist = [ '--description', 'new_description', self.backup.id, ] verifylist = [ ('name', None), ('description', 'new_description'), ('backup', self.backup.id), ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) result = self.cmd.take_action(parsed_args) # Set expected values kwargs = { 'description': 'new_description' } self.backups_mock.update.assert_called_once_with( self.backup.id, kwargs ) self.assertIsNone(result) def test_backup_set_description_pre_v39(self): self.app.client_manager.volume.api_version = \ api_versions.APIVersion('3.8') arglist = [ '--description', 'new_description', self.backup.id, ] verifylist = [ ('name', None), ('description', 'new_description'), ('backup', self.backup.id), ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) exc = self.assertRaises( exceptions.CommandError, self.cmd.take_action, parsed_args) self.assertIn("--os-volume-api-version 3.9 or greater", str(exc)) def test_backup_set_state(self): arglist = [ '--state', 'error', self.backup.id ] verifylist = [ ('state', 'error'), ('backup', self.backup.id) ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) result = self.cmd.take_action(parsed_args) self.backups_mock.reset_state.assert_called_once_with( self.backup.id, 'error') self.assertIsNone(result) def test_backup_set_state_failed(self): self.backups_mock.reset_state.side_effect = exceptions.CommandError() arglist = [ '--state', 'error', self.backup.id ] verifylist = [ ('state', 'error'), ('backup', self.backup.id) ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) try: self.cmd.take_action(parsed_args) self.fail('CommandError should be raised.') except exceptions.CommandError as e: self.assertEqual('One or more of the set operations failed', str(e)) self.backups_mock.reset_state.assert_called_with( self.backup.id, 'error') def test_backup_set_no_property(self): self.app.client_manager.volume.api_version = \ api_versions.APIVersion('3.43') arglist = [ '--no-property', self.backup.id, ] verifylist = [ ('no_property', True), ('backup', self.backup.id), ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) result = self.cmd.take_action(parsed_args) # Set expected values kwargs = { 'metadata': {}, } self.backups_mock.update.assert_called_once_with( self.backup.id, **kwargs ) self.assertIsNone(result) def test_backup_set_no_property_pre_v343(self): self.app.client_manager.volume.api_version = \ api_versions.APIVersion('3.42') arglist = [ '--no-property', self.backup.id, ] verifylist = [ ('no_property', True), ('backup', self.backup.id), ] parsed_args = self.check_parser(self.cmd, arglist, verifylist) exc = self.assertRaises( exceptions.CommandError, self.cmd.take_action, parsed_args) self.assertIn("--os-volume-api-version 3.43 or greater", str(exc)) def test_backup_set_property(self): self.app.client_manager.volume.api_version = \ api_versions.APIVersion('3.43') arglist = [ '--property', 'foo=bar', self.backup.id, ] verifylist = [ ('properties', {'foo': 'bar'}), ('backup', self.backup.id), ] parsed_args = self.check_parser(self.cmd, arglist,
in range(maxmajorticks): if axis == xaxis: self.majorticks[axis][axissign].append(label(display=self.display, yoffset=-tmajor, font=graphfont, height=fontheight, border=0, linecolor=grey, visible=False, box=False, opacity=0)) else: self.majorticks[axis][axissign].append(label(display=self.display, xoffset=-tmajor, font=graphfont, height=fontheight, border=2, linecolor=grey, visible=False, box=False, opacity=0)) currentdisplay.select() def __del__(self): self.visible = False try: gdisplays.remove(self) except: pass def mouse(self, evt): m = evt if m.press == 'left': self.mousepos = self.display.mouse.pos+vector(10,20,30) # so mousepos != newpos self.horline.visible = True self.vertline.visible = True self.showxy.visible = True self.display.cursor.visible = False elif m.release == 'left': self.mousepos = None self.showxy.visible = False self.horline.visible = False self.vertline.visible = False self.display.cursor.visible = True newpos = self.display.mouse.pos if newpos != self.mousepos: self.mousepos = newpos xmax = self.display.range.x ymax = self.display.range.y xcenter = self.display.center.x ycenter = self.display.center.y self.horline.pos = [(xcenter-xmax,self.mousepos.y,.01), (xcenter+xmax,self.mousepos.y,.01)] self.vertline.pos = [(self.mousepos.x,ycenter-ymax,.01), (self.mousepos.x,ycenter+ymax,.01)] v = self.showxy.pos = self.mousepos if self.logx: x = 10v.x if self.logy: y = 10v.y if v.x > xcenter: self.showxy.xoffset = -10 else: self.showxy.xoffset = 10 self.showxy.text = '({0:0.4g}, {1:0.4g})'.format(v.x,v.y) def setcenter(self): x0, y0 = self.getorigin() xright = self.minmax[xaxis][posaxis] xleft = self.minmax[xaxis][negaxis] ytop = self.minmax[yaxis][posaxis] ybottom = self.minmax[yaxis][negaxis] rightpixels = self.xtitlewidth leftpixels = 0 if xleft == x0: leftpixels = 3tmajor toppixels = bottompixels = 0 if self.ytitlewidth: toppixels = 2fontheight if ybottom == y0: bottompixels = tmajor+fontheight xrange = 0.55(xright-xleft)self.width/(self.width-(rightpixels+leftpixels)) yrange = 0.55(ytop-ybottom)self.height/(self.height-(toppixels+bottompixels)) xscale = xrange/(.5self.width) yscale = yrange/(.5self.height) x1 = xright+rightpixelsxscale x2 = x0+self.ytitlewidthxscale if x2 > x1: # ytitle extends farther to the right than xaxis + xtitle xrange = 0.55(x2-xleft)self.width/(self.width-leftpixels) xscale = xrange/(.5self.width) xright = x2 rightpixels = 0 if xrange == 0: xrange = 1e-300 if yrange == 0: yrange = 1e-300 self.display.range = (xrange,yrange,0.1) self.display.center = ((xright+xleft+(rightpixels-leftpixels)xscale)/2.0, (ytop+ybottom+(toppixels-bottompixels)yscale)/2.0,0) def getorigin(self): return (self.zero[0].pos[0], self.zero[1].pos[1]) def setminorticks(self, axis, axissign, loglabel, dmajor, dminor): ## For log axis labels, show the standard uneven log tick marks if dmajor == 1, ## but for dmajor > 1, show minor tick marks at the decade locations. ## Since we have only minorticks-1 = 4 minor tick marks between major tick marks, ## if dmajor > minorticks (=5), don't show any minor tick marks. if loglabel and (dmajor > minorticks): return 0 x0,y0 = self.getorigin() limit = self.minmax[axis][axissign] if axis == xaxis: limit -= x0 else: limit -= y0 if axissign == negaxis: dminor = -dminor ntick = nmajor = nminor = 0 exclude = minorticks if loglabel and (dmajor > 1): exclude = dmajor if dminor > 0: dminor = 1 else: dminor = -1 while True: ntick += 1 tickpos = ntickdminor if (ntick % exclude) == 0: nmajor += 1 continue # no minor tick where there is a major one if loglabel: # have already excluded dmajor > minorticks (=5) if dmajor == 1: if dminor > 0: tickpos = dmajor(nmajor+logticks[(ntick-1)%exclude]) else: tickpos = dmajor(-(nmajor+1)+logticks[3-((ntick-1)%exclude)]) if dminor > 0: if tickpos > limit: break else: if tickpos < limit: break obj = self.minorticks[axis][axissign][nminor] if axis == xaxis: obj.pos = (x0+tickpos,y0,0) else: obj.pos = (x0,y0+tickpos,0) obj.visible = True nminor += 1 return nminor def axisdisplay(self, axis, axissign): # axis = 0 for x axis, 1 for y axis # axissign = 0 for negative half-axis, 1 for positive half-axis if not self.makeaxis[axis][axissign]: return sign = 1 if axissign == negaxis: sign = -1 x0,y0 = self.getorigin() if axis == xaxis: loglabel = self.logx else: loglabel = self.logy if axis == xaxis: origin = x0 else: origin = y0 if self.axis[axis][axissign] is None: # new; no axis displayed up till now if self.minmax[axis][axissign] == origin: return # zero-length axis # Display axis and axis title if axis == xaxis: axispos = ([(x0,y0,0), (self.minmax[axis][axissign],y0,0)]) titlepos = ([self.minmax[axis][posaxis],y0,0]) else: axispos = ([(x0,y0,0), (x0,self.minmax[axis][axissign],0)]) titlepos = ([x0,self.minmax[axis][posaxis]+2fontheightself.display.range.y/(.5self.width),0]) self.axis[axis][axissign] = curve(pos=axispos, color=grey, display=self.display) if axis == xaxis and self.Lxtitle.text != "": self.Lxtitle.pos = titlepos self.Lxtitle.visible = True if axis == yaxis and self.Lytitle.text != "": self.Lytitle.pos = titlepos self.Lytitle.visible = True # Determine major tick marks and labels if origin != 0: newmajormarks, form = labelnum(self.minmax[axis][posaxis] - self.minmax[axis][negaxis], loglabel) dmajor = newmajormarks[0] for n, mark in enumerate(newmajormarks): if origin > 0: newmajormarks[n] += self.minmax[axis][negaxis] else: newmajormarks[n] -= self.minmax[axis][posaxis] newmajormarks[n] = newmajormarks[n] else: if self.minmax[axis][posaxis] >= -self.minmax[axis][negaxis]: newmajormarks, form = labelnum(self.minmax[axis][posaxis], loglabel) else: if loglabel: newmajormarks, form = labelnum(self.minmax[axis][negaxis], loglabel) else: newmajormarks, form = labelnum(-self.minmax[axis][negaxis], loglabel) dmajor = newmajormarks[0] self.format[axis] = form # Display major tick marks and labels nmajor = 0 marks = [] for x1 in newmajormarks: if x1 > abs(self.minmax[axis][axissign]): break # newmajormarks can refer to opposite half-axis if axissign == posaxis and x1 < origin: continue elif axissign == negaxis and x1 < abs(origin): continue marks.append(x1) obj = self.majorticks[axis][axissign][nmajor] if loglabel: obj.text = self.format[axis].format(int(signx1)) else: obj.text = cleaneformat(self.format[axis].format(signx1)) obj.color = self.foreground obj.visible = True if axis == xaxis: obj.pos = [signx1,y0,0] obj.yoffset = -tmajor else: obj.pos = [x0,signx1,0] obj.xoffset = -tmajor nmajor = nmajor+1 # Display minor tick marks self.setminorticks(axis, axissign, loglabel, dmajor, dmajor/minorticks) if marks != []: self.majormarks[axis][axissign] = marks self.lastlabel[axis][axissign] = self.majormarks[axis][axissign][-1] else: self.lastlabel[axis][axissign] = 0 else: # Extend axis, which has grown if axis == xaxis: self.axis[axis][axissign].pos = [[x0,y0,0],[self.minmax[axis][axissign], y0, 0]] else: self.axis[axis][axissign].pos = [[x0,y0,0],[x0,self.minmax[axis][axissign],0]] # Reposition xtitle (at right) or ytitle (at top) if axis == xaxis and axissign == posaxis: self.Lxtitle.pos = (self.minmax[axis][posaxis],y0,0) if axis == yaxis and axissign == posaxis: self.Lytitle.pos = ([x0,self.minmax[axis][posaxis]+2fontheightself.display.range.y/(.5self.width),0]) # See how many majormarks are now needed, and in what format if self.minmax[axis][posaxis] >= -self.minmax[axis][negaxis]: newmajormarks, form = labelnum(self.minmax[axis][posaxis], loglabel) else: newmajormarks, form = labelnum(-self.minmax[axis][negaxis], loglabel) if (self.majormarks[axis][axissign] is not None) and (len(self.majormarks[axis][axissign]) > 0): # this axis already has major tick marks/labels olddmajor = self.majormarks[axis][axissign][0] else: olddmajor = 0. olddminor = olddmajor/minorticks dmajor = newmajormarks[0] dminor = dmajor/minorticks newformat = (form != self.format[axis]) self.format[axis] = form check = (self.minmax[axis][axissign] >= self.lastlabel[axis][axissign]+dminor) if axissign == negaxis: check = (self.minmax[axis][axissign] <= self.lastlabel[axis][axissign]-dminor) needminor = check or (dminor != olddminor) needmajor = ((self.majormarks[axis][axissign] is None) or (newmajormarks[-1] != self.majormarks[axis][axissign][-1]) or newformat) if needmajor: # need new labels start = 0 if (self.majormarks[axis][axissign] is None) or newformat or (dmajor != olddmajor): marks = [] else: for num in newmajormarks: if num > self.majormarks[axis][axissign][-1]: start = num break marks = self.majormarks[axis][axissign] for nmajor in range(maxmajorticks): obj = self.majorticks[axis][axissign][nmajor] if nmajor < len(newmajormarks): x1 = newmajormarks[nmajor] if abs(self.minmax[axis][axissign]) >= x1: if x1 < start: continue else: obj.visible = False continue else: obj.visible = False continue marks.append(x1) if loglabel: obj.text = self.format[axis].format(int(signx1)) else: obj.text = cleaneformat(self.format[axis].format(signx1)) obj.color = self.foreground obj.visible = True if axis == xaxis: obj.pos = [signx1,y0,0] else: obj.pos = [x0,signx1,0] if marks != []: self.majormarks[axis][axissign] = marks if needminor: # adjust minor tick marks nminor = self.setminorticks(axis, axissign, loglabel, dmajor, dminor) while nminor < maxminorticks: self.minorticks[axis][axissign][nminor].visible = False nminor = nminor+1 self.lastlabel[axis][axissign] = dminorint(self.minmax[axis][axissign]/dminor) def resize(self, x, y): redox = redoy = False if self.autoscale[xaxis]: if x > self.lastminmax[xaxis][posaxis]: self.minmax[xaxis][posaxis] = x+fracself.display.range[0] if (self.lastminmax[xaxis][posaxis] == 0 or (self.minmax[xaxis][posaxis] >= self.lastminmax[xaxis][posaxis])): redox = True elif x < self.lastminmax[xaxis][negaxis]: self.minmax[xaxis][negaxis] = x-fracself.display.range[0] if (self.lastminmax[xaxis][negaxis] == 0 or (self.minmax[xaxis][negaxis] <= self.lastminmax[xaxis][negaxis])): redox = True elif not self.active: redox = redoy = True if self.autoscale[yaxis]: if y > self.lastminmax[yaxis][posaxis]: self.minmax[yaxis][posaxis] = y+fracself.display.range[1] if (self.lastminmax[yaxis][posaxis] == 0 or (self.minmax[yaxis][posaxis] >= self.lastminmax[yaxis][posaxis])): redoy = True elif y < self.lastminmax[yaxis][negaxis]: self.minmax[yaxis][negaxis] = y-frac*self.display.range[1] if (self.lastminmax[yaxis][negaxis] == 0 or (self.minmax[yaxis][negaxis] <= self.lastminmax[yaxis][negaxis])): redoy = True elif not self.active: redox = redoy = True if (redox or redoy ): self.setcenter() # approximate if redox: self.axisdisplay(xaxis,posaxis) self.lastminmax[xaxis][posaxis] = self.minmax[xaxis][posaxis] self.axisdisplay(xaxis,negaxis) self.lastminmax[xaxis][negaxis] = self.minmax[xaxis][negaxis] if redoy: self.axisdisplay(yaxis,posaxis) self.lastminmax[yaxis][posaxis] = self.minmax[yaxis][posaxis] self.axisdisplay(yaxis,negaxis) self.lastminmax[yaxis][negaxis] = self.minmax[yaxis][negaxis] self.setcenter() # revised if not self.active: self.active = True gdisplays.append(self) self.display.bind("mousedown mousemove mouseup", checkGraphMouse, self) self.zero[xaxis].visible = True self.zero[yaxis].visible = True def getgdisplay(): return gdisplay() def constructorargs(obj,arguments): if 'gdisplay' in arguments: obj.gdisplay = arguments['gdisplay'] else: if lastgdisplay is None: obj.gdisplay = getgdisplay() else: obj.gdisplay = lastgdisplay if 'color' in
from pypipe import formats class Bcftools: @staticmethod def view(): return { 'cmd': 'bcftools view', 'type': None, 'log': 'log', 'out': { 'redirect': True, 'return': [ {'arg': 'out', 'type': {'b': formats.Bcf, '': formats.Vcf}, 'suffix': ''}, ] }, 'args': { 'named': { '-A': bool, '-b': bool, '-D': formats.TextFile, '-F': bool, '-G': bool, '-l': formats.TextFile, '-N': bool, '-Q': bool, '-s': formats.TextFile, '-S': bool, '-u': bool, '-c': bool, '-d': float, '-e': bool, '-g': bool, '-i': float, '-p': float, '-P': str, '-t': float, '-T': str, '-v': bool, '-1': int, '-U': int, '-X': float, }, 'unnamed': [ ('in_', {'-S': formats.Vcf, '': formats.Bcf}), ('out', str), ], }, } @staticmethod def cat(): return { 'cmd': 'bcftools cat', 'type': None, 'log': 'log', 'out': { 'redirect': True, 'return': [ {'arg': 'out', 'type': formats.Bcf, 'suffix': ''}, ], }, 'args': { 'named': { }, 'unnamed': [ ('in_', formats.Bcf), ('out', str), ], } } class Bowtie2: @staticmethod def bowtie2(): return { 'cmd': 'bowtie2', 'type': None, 'log': 'log', 'out': { 'redirect': False, 'return': [ {'arg': '-S', 'type': formats.Sam, 'suffix': ''}, ] }, 'args': { 'named': { '-x': formats.Bowtie2Index, '-S': str, '-U': { '--qseq': [formats.Qseq, 1, ','], '-f': [formats.Fasta, 1, ','], '-r': [formats.TextFile, 1, ','], '-c': str, '': [formats.Fastq, 1, ','] }, '-1': { '--qseq': [formats.Qseq, 1, ','], '-f': [formats.Fasta, 1, ','], '-r': [formats.TextFile, 1, ','], '-c': str, '': [formats.Fastq, 1, ','] }, '-2': { '--qseq': [formats.Qseq, 1, ','], '-f': [formats.Fasta, 1, ','], '-r': [formats.TextFile, 1, ','], '-c': str, '': [formats.Fastq, 1, ','] }, '-q': bool, '--qseq': bool, '-f': bool, '-r': bool, '-c': bool, '-s': int, '-u': int, '-5': int, '-3': int, '--phred33': bool, '--phred64': bool, '--solexa-quals': bool, '--int-quals': bool, '--very-fast': bool, '--fast': bool, '--sensitive': bool, '--very-sensitive': bool, '--very-fast-local': bool, '--fast-local': bool, '--sensitive-local': bool, '--very-sensitive-local': bool, '-N': int, '-L': int, '-i': str, '--n-ceil': str, '--dpad': int, '--gbar': int, '--ignore-quals': bool, '--nofw': bool, '--norc': bool, '--no-1mm-upfront': bool, '--end-to-end': bool, '--local': bool, '-k': int, '-a': bool, '-D': int, '-R': int, '--ma': int, '--mp': [int, 2, ','], '--np': int, '--rdg': [int, 2, ','], '--rfg': [int, 2, ','], '--score-min': str, '-I': int, '-X': int, '--fr': bool, '--rf': bool, '--ff': bool, '--no-mixed': bool, '--no-discordant': bool, '--dovetail': bool, '--no-contain': bool, '--no-overlap': bool, '--no-unal': bool, '--no-hd': bool, '--no-sq': bool, '--rg-id': str, '--rg': str, '--omit-sec-seq': bool, '-o': int, '-p': int, '--reorder': bool, '--mm': bool, '--qc-filter': bool, '--seed': int, '--non-deterministic': bool, '-t': bool, '--un': str, '--un-gz': str, '--un-bz2': str, '--al': str, '--al-gz': str, '--al-bz2': str, '--un-conc': str, '--un-conc-gz': str, '--un-conc-bz2': str, '--al-conc': str, '--al-conc-gz': str, '--al-conc-bz2': str, '--quiet': bool, '--met-file': str, '--met-stderr': str, '--met': int, }, 'unnamed': [ ], }, } class Bwa: @staticmethod def mem(): return { 'cmd': 'bwa mem', 'type': None, 'log': 'log', 'out': { 'redirect': True, 'return': [ {'arg': 'out', 'type': formats.Sam, 'suffix': ''}, ], }, 'args': { 'named': { '-t': int, '-k': int, '-w': int, '-d': int, '-r': float, '-c': int, '-P': bool, '-a': int, '-B': int, '-O': int, '-E': int, '-L': int, '-U': int, '-p': bool, '-R': str, '-T': int, '-C': bool, '-H': bool, '-M': bool, '-v': int, }, 'unnamed': [ ('ref', formats.BwaIndex), ('in1', formats.Fastq), ('in2', formats.Fastq), ('out', str), ], }, } @staticmethod def aln(): return { 'cmd': 'bwa aln', 'type': None, 'log': 'log', 'out': { 'redirect': True, 'return': [ {'arg': 'out', 'type': formats.Sai, 'suffix': ''}, ], }, 'args': { 'named': { '-n': int, '-o': int, '-e': int, '-d': int, '-i': int, '-l': int, '-k': int, '-t': int, '-M': int, '-O': int, '-E': int, '-R': int, '-c': bool, '-N': bool, '-q': int, '-I': bool, '-B': int, '-b': bool, '-0': bool, '-1': bool, '-2': bool, }, 'unnamed': [ ('ref', formats.BwaIndex), ('in_', formats.Fastq), ('out', str), ], }, } @staticmethod def samse(): return { 'cmd': 'bwa samse', 'type': None, 'log': 'log', 'out': { 'redirect': True, 'return': [ {'arg': 'out', 'type': formats.Sam, 'suffix': ''}, ], }, 'args': { 'named': { '-n': int, '-r': str, }, 'unnamed': [ ('ref', formats.BwaIndex), ('sai', formats.Sai), ('in_', formats.Fastq), ('out', str), ], }, } @staticmethod def sampe(): return { 'cmd': 'bwa sampe', 'type': None, 'log': 'log', 'out': { 'redirect': True, 'return': [ {'arg': 'out', 'type': formats.Sam, 'suffix': ''}, ], }, 'args': { 'named': { '-a': int, '-o': int, '-P': bool, '-n': int, '-N': int, '-r': str, }, 'unnamed': [ ('ref', formats.BwaIndex), ('sai1', formats.Sai), ('sai2', formats.Sai), ('in1', formats.Fastq), ('in2', formats.Fastq), ('out', str), ], }, } @staticmethod def bwasw(): return { 'cmd': 'bwa bwasw', 'type': None, 'log': 'log', 'out': { 'redirect': True, 'return': [ {'arg': 'out', 'type': formats.Sam, 'suffix': ''}, ] }, 'args': { 'named': { '-a': int, '-b': int, '-q': int, '-r': int, '-t': int, '-w': int, '-T': int, '-c': float, '-z': int, '-s': int, '-N': int, }, 'unnamed': [ ('ref', formats.BwaIndex), ('in1', formats.Fastq), ('in2', formats.Fastq), ('out', str), ], }, } class Freebayes: @staticmethod def freebayes(): return { 'cmd': 'freebayes', 'type': None, 'log': 'log', 'out': { 'redirect': False, 'return': [ {'arg': 'v', 'type': formats.Vcf, 'suffix': ''}, ] }, 'args': { 'named': { '-v': str, '-f': formats.Fasta, '-b': formats.Bam, '-t': formats.Bed, '-r': str, '-s': formats.TextFile, '--populations': formats.TextFile, '-A': formats.Bed, '--trace': str, '--failed-alleles': formats.Bed, '--variant-input': formats.Vcf, '-l': bool, '--haplotype-basis-alleles': bool, '--report-all-haplotype-alleles': bool, '--report-monorphic': bool, '-P': float, '-T': float, '-p': int, '-J': bool, '-Z': bool, '--reference_quality': [int, 2, ','], '-I': bool, '-i': bool, '-X': bool, '-u': bool, '-n': int, '-E': int, '--max-complex-gap': int, '--haplotype-length': int, '--min-repeat-length': int, '--min-repeat-entropy': int, '--no-partial-observation': bool, '-O': bool, '-4': bool, '-m': int, '-q': int, '-R': int, '-Y': int, '-Q': int, '-U': int, '-z': int, '--read-snp-limit': int, '-e': int, '-0': int, '-F': int, '-C': int, '-3': int, '-G': int, '--min-coverage': int, '-w': bool, '-V': bool, '-a': bool, '--observation-bias': formats.TextFile, '--base-quality-cap': int, '--experimental-gls': bool, '--prob-contamination': float, '--contamination-estimates': formats.TextFile, '--report-genotype-likelihood-max': bool, '-B': int, '--genotype-max-iterations': int, '-W': [int, 2, ','], '-S': int, '-j': bool, '-H': bool, '-D': int, '--genotype-qualities': bool, }, 'unnamed': [ ('in_', [formats.Bam, 1, ' ']), ] } } class Samtools: @staticmethod def view(): return { 'cmd': 'samtools view', 'type': None, 'log': 'log', 'out': { 'redirect': False, 'return': [ {'arg': '-o', 'type': {'-b': formats.Bam, '': formats.Sam}, 'suffix': ''}, ] }, 'args': { 'named': { '-o': str, '-b': bool, '-f': int, '-F': int, '-h': bool, '-H': bool, '-l': str, '-q': int, '-r': str, '-R': formats.Bed, '-S': bool, '-c': bool, '-t': formats.TextFile, '-u': bool, }, 'unnamed': [ ('in_', {'-S': formats.Sam, '': formats.Bam}), ], }, } @staticmethod def mpileup(): return { 'cmd': 'samtools mpileup', 'type': None, 'log': 'log', 'out': { 'redirect': True, 'return': [ {'arg': 'out', 'type': {'-u': formats.Bcf, '-g': formats.Bcf, '': formats.Pileup}, 'suffix': ''}, ], }, 'args': { 'named': { '-6': bool, '-A': bool, '-B': bool, '-b': formats.TextFile, '-C': int, '-E': bool, '-f': formats.Fasta, '-l': formats.Bed, '-q': int, '-Q': int, '-r': str, '-D': bool, '-g': bool, '-S': bool, '-u': bool, '-e': int, '-h': int, '-I': bool, '-L': bool, '-o': int, '-P': str, }, 'unnamed': [ ('in_', [formats.Bam, 1, ' ']), ('out', str), ] } } @staticmethod def cat(): return { 'cmd': 'samtools cat', 'type': None, 'log': 'log', 'out': { 'redirect': False, 'return': [ {'arg': 'o', 'type': formats.Bam, 'suffix': ''}, ], }, 'args': { 'named': { '-h': formats.Sam, }, 'unnamed': [ ('in_', formats.Bam), ('o', str) ], }, } @staticmethod def sort(): return { 'cmd': 'samtools sort', 'type': None, 'log': 'log', 'out': { 'redirect': False, 'return': [ {'arg': 'out', 'type': formats.Bam, 'suffix': '.bam'}, ], }, 'args': { 'named': { '-n': bool, '-m': int, }, 'unnamed': [ ('in_', formats.Bam), ('out', str) ], }, } @staticmethod def merge(): return { 'cmd': 'samtools merge', 'type': None, 'log': 'log', 'out': { 'redirect': False, 'return': [ {'arg': 'out', 'type': formats.Bam, 'suffix': ''}, ], }, 'args': { 'named': { '-1': bool, '-f': bool, '-h': formats.Sam, '-n': bool, '-R': str, '-r': bool, '-u': bool, }, 'unnamed': [ ('out', str), ('in_', [formats.Bam, 2, ' ']), ], }, } @staticmethod def rmdup(): return { 'cmd': 'samtools rmdup', 'type': None, 'log': 'log', 'out': { 'redirect': False, 'return': [ {'arg': 'out', 'type': formats.Bam, 'suffix': ''}, ], }, 'args': { 'named': { '-s': bool, '-S': bool, }, 'unnamed': [ ('in_', formats.Bam), ('out',
hmn_dhcp_bootstrap = self.sls_networks["HMN"].subnets()["bootstrap_dhcp"] for name, reservation in hmn_dhcp_bootstrap.reservations().items(): if str(bmc_ip) == str(reservation.ipv4_address()): reservation_found = True action_log(action, f'Removing existing IP Reservation for {self.bmc_alias} in the bootstrap_dhcp subnet of the HMN network: {reservation.name()} {reservation.ipv4_address()} {reservation.aliases()} {reservation.comment()}') del hmn_dhcp_bootstrap.reservations()[name] break if not reservation_found: action_log(action, f"Error BMC IP Reservation for {self.bmc_alias} missing from the HMN bootstrap_dhcp subnet") failed_to_find_ip = True if failed_to_find_ip: print_action(action) sys.exit(1) # Update State self.ncn_ips = ncn_ips self.bmc_ip = bmc_ip def allocate_ncn_ips(self, action: dict,): # # Allocate new NCN BMC # action_log(action, "Allocating NCN BMC IP address") bmc_ip = allocate_ip_address_in_subnet(action, self.sls_networks, "HMN", "bootstrap_dhcp") # Add BMC IP reservation to the HMN network. # Example: {"Aliases":["ncn-s001-mgmt"],"Comment":"x3000c0s13b0","IPAddress":"10.254.1.31","Name":"x3000c0s13b0"} bmc_ip_reservation = IPReservation(self.bmc_xname, bmc_ip, comment=self.bmc_xname, aliases=[self.bmc_alias]) action_log(action, f"Temporally adding NCN BMC IP reservation to bootstrap_dhcp subnet in the HMN network: {bmc_ip_reservation.to_sls()}") self.sls_networks["HMN"].subnets()["bootstrap_dhcp"].reservations().update( { bmc_ip_reservation.name(): bmc_ip_reservation } ) # # Allocate new NCN IPs in SLS # action_log(action, "") action_log(action, "Allocating NCN IP addresses") ncn_ips = {} for network_name in ["CAN", "CHN", "CMN", "HMN", "MTL", "NMN"]: if network_name not in self.sls_networks: continue ncn_ips[network_name] = allocate_ip_address_in_subnet(action, self.sls_networks, network_name, "bootstrap_dhcp", self.networks_allowed_in_dhcp_range) action_log(action, "Removing temporary NCN BMC IP reservation in the bootstrap_dhcp subnet for the HMN network") del self.sls_networks["HMN"].subnets()["bootstrap_dhcp"].reservations()[bmc_ip_reservation.name()] # Update State self.ncn_ips = ncn_ips self.bmc_ip = bmc_ip self.action_log_ncn_ips(action) # Only for new IP addresses that have been allocated: # Validate the NCN and its BMC to be added does not have an IP reservation already defined for it # Also validate that none of the IP addresses we have allocated are currently in use in SLS. fail_sls_network_check = False for network_name, sls_network in self.sls_networks.items(): for subnet in sls_network.subnets().values(): for ip_reservation in subnet.reservations().values(): # Verify no IP Reservations exist for the NCN if ip_reservation.name() == self.ncn_alias: fail_sls_network_check = True action_log(action, f'Error found existing NCN IP Reservation in subnet {subnet.name()} network {network_name} in SLS: {ip_reservation.to_sls()}') # Verify no IP Reservations exist for the NCN BMC if ip_reservation.name() == self.bmc_xname: fail_sls_network_check = True action_log(action, f'Error found existing NCN BMC IP Reservation in subnet {subnet.name()} network {network_name} in SLS: {ip_reservation.to_sls()}') # Verify no IP Reservations exist with any NCN IP if sls_network.name() in ncn_ips: allocated_ip = ncn_ips[network_name] if ip_reservation.ipv4_address() == allocated_ip: fail_sls_network_check = True action_log(action, f'Error found allocated NCN IP {allocated_ip} in subnet {subnet.name()} network {network_name} in SLS: {ip_reservation.to_sls()}') # Verify no IP Reservations exist with the NCN BMC IP if sls_network.name() == "HMN" and ip_reservation.ipv4_address() == bmc_ip: fail_sls_network_check = True action_log(action, f'Error found allocated NCN BMC IP {allocated_ip} in subnet {subnet.name()} network {network_name} in SLS: {ip_reservation.to_sls()}') if fail_sls_network_check: print_action(action) sys.exit(1) action_log(action, f'Pass {self.ncn_xname} ({self.ncn_alias}) does not currently exist in SLS Networks') action_log(action, f'Pass {self.bmc_xname} ({self.bmc_alias}) does not currently exist in SLS Networks') action_log(action, f'Pass allocated IPs for NCN {self.ncn_xname} ({self.ncn_alias}) are not currently in use in SLS Networks') action_log(action, f'Pass allocated IP for NCN BMC {self.bmc_xname} ({self.bmc_alias}) are not currently in use in SLS Networks') def action_log_ncn_ips(self, action: dict): action_log(action, "") action_log(action, "=================================") action_log(action, "Management NCN IP Allocation") action_log(action, "=================================") action_log(action, "Network \| IP Address") action_log(action, "--------\|-----------") for network in sorted(self.ncn_ips): ip = self.ncn_ips[network] action_log(action, f'{network:<8}\| {ip}') action_log(action, "") action_log(action, "=================================") action_log(action, "Management NCN BMC IP Allocation") action_log(action, "=================================") action_log(action, "Network \| IP Address") action_log(action, "--------\|-----------") action_log(action, f'HMN \| {self.bmc_ip}') action_log(action, "") def print_ncn_ips(self): print("") print(" =================================") print(" Management NCN IP Allocation") print(" =================================") print(" Network \| IP Address") print(" --------\|-----------") for network in sorted(self.ncn_ips): ip = self.ncn_ips[network] print(f' {network:<8}\| {ip}') print("") print(" =================================") print(" Management NCN BMC IP Allocation") print(" =================================") print(" Network \| IP Address") print(" --------\|-----------") print(f' HMN \| {self.bmc_ip}') print("") def validate_global_bss_bootparameters(self, action: dict): if not self.use_existing_ip_addresses: # Validate the NCN is not referenced in the Global boot parameters fail_host_records = False for host_record in self.global_bootparameters["cloud-init"]["meta-data"]["host_records"]: # Check for NCN and NCN BMC for alias in host_record["aliases"]: if alias.startswith(self.ncn_alias): action_log(action, f'Error found NCN alias in Global host_records in BSS: {host_record}') fail_host_records = True # Check for if this IP is one of our allocated IPs for network, ip in self.ncn_ips.items(): if host_record["ip"] == ip: action_log(action, f'Error found {network} IP Address {ip} in Global host_records in BSS: {host_record}') fail_host_records = True if host_record["ip"] == self.bmc_ip: action_log(action, f'Error found NCN BMC IP Address {self.bmc_ip} in Global host_records in BSS: {host_record}') fail_host_records = True if fail_host_records: print_action(action) sys.exit(1) action_log(action, f"Pass {self.ncn_xname} does not currently exist in BSS Global host_records") print_action(action) else: # Validate the NCN has the expected data in the BSS Global boot parameters fail_host_records = False for host_record in self.global_bootparameters["cloud-init"]["meta-data"]["host_records"]: for network_name, ip in self.ncn_ips.items(): # Verify each NCN IP is associated with correct NCN expected_alias = f'{self.ncn_alias}.{network_name.lower()}' if str(ip) == host_record["ip"]: expected_aliases = [expected_alias] alternate_aliases = [] # ncn-m001 on the NMN can have an alternate host record for the the PIT if network_name == "NMN": expected_aliases.append(self.ncn_alias) alternate_aliases = ["pit", "pit.nmn"] if expected_aliases == host_record["aliases"] or alternate_aliases == host_record["aliases"]: action_log(action, f"Pass found existing host_record with the IP address {ip} which contains the expected aliases of {expected_aliases}") else: fail_host_records = True action_log(action, f'Error existing host_record with IP address {ip} with aliases {host_record["aliases"]}, instead of {expected_aliases}') # Verify each NCN alias is associated with the correct IP if expected_alias in host_record["aliases"]: if str(ip) == host_record["ip"]: action_log(action, f"Pass found existing host_record for alias {expected_alias} which has the expected IP address of {ip}") else: fail_host_records = True action_log(action, f'Error existing host_record for alias {expected_alias} has the IP address of {host_record["ip"]}, instead of the expected {ip}') # Verify the NCN BMC IP is associated with correct BMC if str(self.bmc_ip) == host_record["ip"]: expected_aliases = [self.bmc_alias] if expected_aliases == host_record["aliases"]: action_log(action, f"Pass found existing BMC host_record with the IP address {self.bmc_ip} which contains the expected aliases of {expected_aliases}") else: fail_host_records = True action_log(action, f'Error existing BMC host_record with IP address {self.bmc_ip} with aliases {host_record["aliases"]}, instead of {expected_aliases}') if self.bmc_alias in host_record["aliases"]: if str(self.bmc_ip) == host_record["ip"]: action_log(action, f"Pass found existing BMC host_record for alias {self.bmc_alias} which has the expected IP address of {self.bmc_ip}") else: fail_host_records = True action_log(action, f'Error existing BMC host_record for alias {expected_alias} has the IP address of {host_record["ip"]}, instead of the expected {self.bmc_ip}') if fail_host_records: print_action(action) sys.exit(1) # Validate the NCN being added is not configured as the 'first-master-hostname' first_master_hostname = self.global_bootparameters["cloud-init"]["meta-data"]["first-master-hostname"] if first_master_hostname == self.ncn_alias: action_log(action, f'Error the NCN being added {self.ncn_alias} is currently configured as the "first-master-hostname" in the Global BSS Bootparameters') print_action(action) sys.exit(1) else: action_log(action, f'Pass the NCN being added {self.ncn_alias} is not configured as the "first-master-hostname", currently {first_master_hostname} is in the Global BSS Bootparameters.') print_action(action) def update_sls_networking(self, session: requests.Session): # Add IP Reservations for all of the networks that make sense for network_name, ip in self.ncn_ips.items(): sls_network = self.sls_networks[network_name] # CAN # Master: {"Aliases":["ncn-m002-can","time-can","time-can.local"],"Comment":"x3000c0s3b0n0","IPAddress":"10.101.5.134","Name":"ncn-m002"} # Worker: {"Aliases":["ncn-w001-can","time-can","time-can.local"],"Comment":"x3000c0s7b0n0","IPAddress":"10.101.5.136","Name":"ncn-w001"} # Storage: {"Aliases":["ncn-s001-can","time-can","time-can.local"],"Comment":"x3000c0s13b0n0","IPAddress":"10.101.5.147","Name":"ncn-s001"} # CHN # Master: {"Aliases":["ncn-m002-chn","time-chn","time-chn.local"],"Comment":"x3000c0s3b0n0","IPAddress":"10.101.5.198","Name":"ncn-m002"} # Worker: {"Aliases":["ncn-w001-chn","time-chn","time-chn.local"],"Comment":"x3000c0s7b0n0","IPAddress":"10.101.5.200","Name":"ncn-w001"} # Storage: {"Aliases":["ncn-s001-chn","time-chn","time-chn.local"],"Comment":"x3000c0s13b0n0","IPAddress":"10.101.5.211","Name":"ncn-s001"} # CMN # Master: {"Aliases":["ncn-m002-cmn","time-cmn","time-cmn.local"],"Comment":"x3000c0s3b0n0","IPAddress":"10.101.5.20","Name":"ncn-m002"} # Worker: {"Aliases":["ncn-w001-cmn","time-cmn","time-cmn.local"],"Comment":"x3000c0s7b0n0","IPAddress":"10.101.5.22","Name":"ncn-w001"} # Storage: {"Aliases":["ncn-s001-cmn","time-cmn","time-cmn.local"],"Comment":"x3000c0s13b0n0","IPAddress":"10.101.5.33","Name":"ncn-s001"} # HMN # Master: {"Aliases":["ncn-m002-hmn","time-hmn","time-hmn.local"],"Comment":"x3000c0s3b0n0","IPAddress":"10.254.1.6","Name":"ncn-m002"} # Worker: {"Aliases":["ncn-w001-hmn","time-hmn","time-hmn.local"],"Comment":"x3000c0s7b0n0","IPAddress":"10.254.1.10","Name":"ncn-w001"} # Storage: {"Aliases":["ncn-s001-hmn","time-hmn","time-hmn.local","rgw-vip.hmn"],"Comment":"x3000c0s13b0n0","IPAddress":"10.254.1.32","Name":"ncn-s001"} # MTL # Master: {"Aliases":["ncn-m002-mtl","time-mtl","time-mtl.local"],"Comment":"x3000c0s3b0n0","IPAddress":"10.1.1.3","Name":"ncn-m002"} # Worker: {"Aliases":["ncn-w001-mtl","time-mtl","time-mtl.local"],"Comment":"x3000c0s7b0n0","IPAddress":"10.1.1.5","Name":"ncn-w001"} # Storage: {"Aliases":["ncn-s001-mtl","time-mtl","time-mtl.local"],"Comment":"x3000c0s13b0n0","IPAddress":"10.1.1.16","Name":"ncn-s001"} # NMN # Master: {"Aliases":["ncn-m002-nmn","time-nmn","time-nmn.local","x3000c0s3b0n0","ncn-m002.local"],"Comment":"x3000c0s3b0n0","IPAddress":"10.252.1.5","Name":"ncn-m002"} # Worker: {"Aliases":["ncn-w001-nmn","time-nmn","time-nmn.local","x3000c0s7b0n0","ncn-w001.local"],"Comment":"x3000c0s7b0n0","IPAddress":"10.252.1.7","Name":"ncn-w001"} # Storage: {"Aliases":["ncn-s001-nmn","time-nmn","time-nmn.local","x3000c0s13b0n0","ncn-s001.local"],"Comment":"x3000c0s13b0n0","IPAddress":"10.252.1.18","Name":"ncn-s001"} # Generalizations # - All IP reservations have the NCN xname for the comment # - Following rules apply to all but CHN # - NCN Alias is the IP reservation name # - Each master/worker/storage have the following aliases # - ncn-{}-{network} # - time-{network} # - time-{network}.local # - Storage nodes on the HMN have additional alias rgw-vip.hmn # - All NCNs on the NMN have the additional aliases: # - xname # - ncn-{}.local # - The CHN # - No reservations # - have IP reservations with the node xname for the reservation name # All networks except for the CHN have the NCNs alias as the name for the reservation. The CHN has the node xname. name = self.ncn_alias # All NCN types have thier xname as the comment for their IP reservation comment = self.ncn_xname # For all
2; # break; # BIT instructions if instruction == 0x24: # $24/36 BIT zp self.BIT(OperandRef(LOC_VAL, self.zeropage())) self.pc += 1 return 1 if instruction == 0x2c: # $2C/44 BIT abs self.BIT(OperandRef(LOC_VAL, self.absolute())) self.pc += 2 return 1 # case 0x30: # if (flags & FN) BRANCH() # else pc++; # break; # BMI instruction if instruction == 0x30: # $30/48 BMI rel if (self.flags & FN): self.branch() else: self.pc += 1 return 1 # case 0xd0: # if (!(flags & FZ)) BRANCH() # else pc++; # break; # BNE instruction if instruction == 0xd0: # $D0/208 BNE rel if not (self.flags & FZ): self.branch() else: self.pc += 1 return 1 # case 0x10: # if (!(flags & FN)) BRANCH() # else pc++; # break; # BPL instruction if instruction == 0x10: # $10/16 BPL rel if not (self.flags & FN): self.branch() else: self.pc += 1 return 1 # case 0x50: # if (!(flags & FV)) BRANCH() # else pc++; # break; # BVC instruction if instruction == 0x50: # $50/80 BVC rel if not (self.flags & FV): self.branch() else: self.pc += 1 return 1 # case 0x70: # if (flags & FV) BRANCH() # else pc++; # break; # BVS instruction if instruction == 0x70: # $70/112 BVS rel if (self.flags & FV): self.branch() else: self.pc += 1 return 1 # case 0x18: # flags &= ~FC; # break; # CLC instruction if instruction == 0x18: # $18/24 CLC self.flags &= (~FC & 0xff) return 1 # case 0xd8: # flags &= ~FD; # break; # CLD instruction if instruction == 0xd8: # $D8/216 CLD self.flags &= (~FD & 0xff) return 1 # case 0x58: # flags &= ~FI; # break; # CLI instruction if instruction == 0x58: # $58/88 CLI self.flags &= (~FI & 0xff) return 1 # case 0xb8: # flags &= ~FV; # break; # CLV instruction if instruction == 0xb8: # $B8/184 CLV self.flags &= (~FV & 0xff) return 1 # case 0xc9: # CMP(a, IMMEDIATE()); # pc++; # break; # # case 0xc5: # CMP(a, MEM(ZEROPAGE())); # pc++; # break; # # case 0xd5: # CMP(a, MEM(ZEROPAGEX())); # pc++; # break; # # case 0xcd: # CMP(a, MEM(ABSOLUTE())); # pc += 2; # break; # # case 0xdd: # cpucycles += EVALPAGECROSSING_ABSOLUTEX(); # CMP(a, MEM(ABSOLUTEX())); # pc += 2; # break; # # case 0xd9: # cpucycles += EVALPAGECROSSING_ABSOLUTEY(); # CMP(a, MEM(ABSOLUTEY())); # pc += 2; # break; # # case 0xc1: # CMP(a, MEM(INDIRECTX())); # pc++; # break; # # case 0xd1: # cpucycles += EVALPAGECROSSING_INDIRECTY(); # CMP(a, MEM(INDIRECTY())); # pc++; # break; # CMP instructions if instruction == 0xc9: # $C9/201 CMP #n self.CMP(A_OPREF, OperandRef(BYTE_VAL, self.immediate())) self.pc += 1 return 1 if instruction == 0xc5: # $C5/197 CMP zp self.CMP(A_OPREF, OperandRef(LOC_VAL, self.zeropage())) self.pc += 1 return 1 if instruction == 0xd5: # $D5/213 CMP zp,X self.CMP(A_OPREF, OperandRef(LOC_VAL, self.zeropage_x())) self.pc += 1 return 1 if instruction == 0xcd: # $CD/205 CMP abs self.CMP(A_OPREF, OperandRef(LOC_VAL, self.absolute())) self.pc += 2 return 1 if instruction == 0xdd: # $DD/221 CMP abs,X self.cpucycles += self.eval_page_crossing_absolute_x() self.CMP(A_OPREF, OperandRef(LOC_VAL, self.absolute_x())) self.pc += 2 return 1 if instruction == 0xd9: # $D9/217 CMP abs,Y self.cpucycles += self.eval_page_crossing_absolute_y() self.CMP(A_OPREF, OperandRef(LOC_VAL, self.absolute_y())) self.pc += 2 return 1 if instruction == 0xc1: # $C1/193 CMP (zp,X) self.CMP(A_OPREF, OperandRef(LOC_VAL, self.indirect_x())) self.pc += 1 return 1 if instruction == 0xd1: # $D1/209 CMP (zp),Y self.cpucycles += self.eval_page_crossing_indirect_y() self.CMP(A_OPREF, OperandRef(LOC_VAL, self.indirect_y())) self.pc += 1 return 1 # case 0xe0: # CMP(x, IMMEDIATE()); # pc++; # break; # # case 0xe4: # CMP(x, MEM(ZEROPAGE())); # pc++; # break; # # case 0xec: # CMP(x, MEM(ABSOLUTE())); # pc += 2; # break; # CPX instructions if instruction == 0xe0: # $E0/224 CPX #n self.CMP(X_OPREF, OperandRef(BYTE_VAL, self.immediate())) self.pc += 1 return 1 if instruction == 0xe4: # $E4/228 CPX zp self.CMP(X_OPREF, OperandRef(LOC_VAL, self.zeropage())) self.pc += 1 return 1 if instruction == 0xec: # $EC/236 CPX abs self.CMP(X_OPREF, OperandRef(LOC_VAL, self.absolute())) self.pc += 2 return 1 # case 0xc0: # CMP(y, IMMEDIATE()); # pc++; # break; # # case 0xc4: # CMP(y, MEM(ZEROPAGE())); # pc++; # break; # # case 0xcc: # CMP(y, MEM(ABSOLUTE())); # pc += 2; # break; # CPY instructions if instruction == 0xc0: # $C0/192 CPY #n self.CMP(Y_OPREF, OperandRef(BYTE_VAL, self.immediate())) self.pc += 1 return 1 if instruction == 0xc4: # $C4/196 CPY zp self.CMP(Y_OPREF, OperandRef(LOC_VAL, self.zeropage())) self.pc += 1 return 1 if instruction == 0xcc: # $CC/204 CPY abs self.CMP(Y_OPREF, OperandRef(LOC_VAL, self.absolute())) self.pc += 2 return 1 # case 0xc6: # DEC(MEM(ZEROPAGE())); # WRITE(ZEROPAGE()); # pc++; # break; # # case 0xd6: # DEC(MEM(ZEROPAGEX())); # WRITE(ZEROPAGEX()); # pc++; # break; # # case 0xce: # DEC(MEM(ABSOLUTE())); # WRITE(ABSOLUTE()); # pc += 2; # break; # # case 0xde: # DEC(MEM(ABSOLUTEX())); # WRITE(ABSOLUTEX()); # pc += 2; # break; # DEC instructions if instruction == 0xc6: # $C6/198 DEC zp self.DEC(OperandRef(LOC_VAL, self.zeropage())) self.pc += 1 return 1 if instruction == 0xd6: # $D6/214 DEC zp,X self.DEC(OperandRef(LOC_VAL, self.zeropage_x())) self.pc += 1 return 1 if instruction == 0xce: # $CE/206 DEC abs self.DEC(OperandRef(LOC_VAL, self.absolute())) self.pc += 2 return 1 if instruction == 0xde: # $DE/222 DEC abs,X self.DEC(OperandRef(LOC_VAL, self.absolute_x())) self.pc += 2 return 1 # case 0xca: # x--; # SETFLAGS(x); # break; # DEX instruction if instruction == 0xca: # $CA/202 DEX self.x -= 1 self.x &= 0xff self.set_flags(self.x) return 1 # case 0x88: # y--; # SETFLAGS(y); # break; # DEY instruction if instruction == 0x88: # $88/136 DEY self.y -= 1 self.y &= 0xff self.set_flags(self.y) return 1 # case 0x49: # EOR(IMMEDIATE()); # pc++; # break; # # case 0x45: # EOR(MEM(ZEROPAGE())); # pc++; # break; # # case 0x55: # EOR(MEM(ZEROPAGEX())); # pc++; # break; # # case 0x4d: # EOR(MEM(ABSOLUTE())); # pc += 2; # break; # # case 0x5d: # cpucycles += EVALPAGECROSSING_ABSOLUTEX(); # EOR(MEM(ABSOLUTEX())); # pc += 2; # break; # # case 0x59: # cpucycles += EVALPAGECROSSING_ABSOLUTEY(); # EOR(MEM(ABSOLUTEY())); # pc += 2; # break; # # case 0x41: # EOR(MEM(INDIRECTX())); # pc++; # break; # # case 0x51: # cpucycles += EVALPAGECROSSING_INDIRECTY(); # EOR(MEM(INDIRECTY())); # pc++; # break; # EOR instructions if instruction == 0x49: # $49/73 EOR #n self.EOR(OperandRef(BYTE_VAL, self.immediate())) self.pc += 1 return 1 if instruction == 0x45: # $45/69 EOR zp self.EOR(OperandRef(LOC_VAL, self.zeropage())) self.pc += 1 return 1 if instruction == 0x55: # $55/85 EOR zp,X self.EOR(OperandRef(LOC_VAL, self.zeropage_x())) self.pc += 1 return 1 if instruction == 0x4d: # $4D/77 EOR abs self.EOR(OperandRef(LOC_VAL, self.absolute())) self.pc += 2 return 1 if instruction == 0x5d: # $5D/93 EOR abs,X self.cpucycles += self.eval_page_crossing_absolute_x() self.EOR(OperandRef(LOC_VAL, self.absolute_x())) self.pc += 2 return 1 if instruction == 0x59: # $59/89 EOR abs,Y self.cpucycles += self.eval_page_crossing_absolute_y() self.EOR(OperandRef(LOC_VAL, self.absolute_y())) self.pc += 2 return 1 if instruction == 0x41: # $41/65 EOR (zp,X) self.EOR(OperandRef(LOC_VAL, self.indirect_x())) self.pc += 1 return 1 if instruction == 0x51: # $51/81 EOR (zp),Y self.cpucycles += self.eval_page_crossing_indirect_y() self.EOR(OperandRef(LOC_VAL, self.indirect_y())) self.pc += 1 return 1 # case 0xe6: # INC(MEM(ZEROPAGE())); # WRITE(ZEROPAGE()); # pc++; # break; # # case 0xf6: # INC(MEM(ZEROPAGEX())); # WRITE(ZEROPAGEX()); # pc++; # break; # # case 0xee: # INC(MEM(ABSOLUTE())); # WRITE(ABSOLUTE()); # pc += 2; # break; # # case 0xfe: # INC(MEM(ABSOLUTEX())); # WRITE(ABSOLUTEX()); # pc += 2; # break; # INC instructions if instruction == 0xe6: # $E6/230 INC zp self.INC(OperandRef(LOC_VAL, self.zeropage())) self.pc += 1 return 1 if instruction == 0xf6: # $F6/246 INC zp,X self.INC(OperandRef(LOC_VAL, self.zeropage_x())) self.pc += 1 return 1 if instruction == 0xee: # $EE/238 INC abs self.INC(OperandRef(LOC_VAL, self.absolute())) self.pc += 2 return 1 if instruction == 0xfe: # $FE/254 INC abs,X self.INC(OperandRef(LOC_VAL, self.absolute_x())) self.pc += 2 return 1 # case 0xe8: # x++; # SETFLAGS(x); # break; # INX instruction if instruction == 0xe8: # $E8/232 INX self.x += 1 self.x &= 0xff self.set_flags(self.x) return 1 # case 0xc8: # y++; # SETFLAGS(y); # break; # INY instruction if instruction == 0xc8: # $C8/200 INY self.y += 1 self.y &= 0xff self.set_flags(self.y) return 1 # case 0x20: # PUSH((pc+1) >> 8); # PUSH((pc+1) &
#If gate.qubits is None, gate is assumed to be single-qubit gate #acting in parallel on all qubits. If the gate is a global idle, then #Pragma blocks are inserted (for tests like idle tomography) even #if block_between_layers==False. Set block_idles=False to disable this as well. if gate.qubits is None: if quil_for_gate == 'I': if block_idles: quil += 'PRAGMA PRESERVE_BLOCK\n' for q in gate_qubits: quil += quil_for_gate + ' ' + str(qubit_conversion[q]) + '\n' if block_idles: quil += 'PRAGMA END_PRESERVE_BLOCK\n' else: for q in gate_qubits: quil += quil_for_gate + ' ' + str(qubit_conversion[q]) + '\n' #If gate.qubits is not None, then apply the one- or multi-qubit gate to #the explicitly specified qubits. else: for q in gate_qubits: quil_for_gate += ' ' + str(qubit_conversion[q]) quil_for_gate += '\n' # Add the quil for the gate to the quil string. quil += quil_for_gate # Keeps track of the qubits that have been accounted for, and checks that hadn't been used # although that should already be checked in the .get_layer_label(), which checks for its a valid # circuit layer. assert(not set(gate_qubits).issubset(set(qubits_used))) qubits_used.extend(gate_qubits) # All gates that don't have a non-idle gate acting on them get an idle in the layer. for q in self.line_labels: if q not in qubits_used: quil += 'I' + ' ' + str(qubit_conversion[q]) + '\n' # Add in a barrier after every circuit layer if block_between_layers==True. # Including pragma blocks are critical for QCVV testing, as circuits should usually # experience minimal "behind-the-scenes" compilation (beyond necessary # conversion to native instructions) # To do: Add "barrier" as native pygsti circuit instruction, and use for indicating # where pragma blocks should be. if block_between_layers: quil += 'PRAGMA PRESERVE_BLOCK\nPRAGMA END_PRESERVE_BLOCK\n' # Add in a measurement at the end. if readout_conversion is None: for q in self.line_labels: # quil += "MEASURE {0} [{1}]\n".format(str(qubit_conversion[q]),str(qubit_conversion[q])) quil += "MEASURE {0} ro[{1}]\n".format(str(qubit_conversion[q]), str(qubit_conversion[q])) else: for q in self.line_labels: quil += "MEASURE {0} ro[{1}]\n".format(str(qubit_conversion[q]), str(readout_conversion[q])) return quil def convert_to_openqasm(self, num_qubits=None, gatename_conversion=None, qubit_conversion=None, block_between_layers=True): # TODO """ Converts this circuit to an openqasm string. Parameters ---------- gatename_conversion : dict, optional If not None, a dictionary that converts the gatenames in the circuit to the gatenames that will appear in the openqasm output. If only standard pyGSTi names are used (e.g., 'Gh', 'Gp', 'Gcnot', 'Gcphase', etc) this dictionary need not be specified, and an automatic conversion to the standard openqasm names will be implemented. qubit_conversion : dict, optional If not None, a dictionary converting the qubit labels in the circuit to the desired qubit labels in the openqasm output. Can be left as None if the qubit labels are either (1) integers, or (2) of the form 'Qi' for integer i. In this case they are converted to integers (i.e., for (1) the mapping is trivial, for (2) the mapping strips the 'Q'). Returns ------- str An openqasm string. """ # create standard conversations. if gatename_conversion is None: gatename_conversion = _itgs.get_standard_gatenames_openqasm_conversions() if qubit_conversion is None: # To tell us whether we have found a standard qubit labelling type. standardtype = False # Must first check they are strings, because cannot query q[0] for int q. if all([isinstance(q, str) for q in self.line_labels]): if all([q[0] == 'Q' for q in self.line_labels]): standardtype = True qubit_conversion = {llabel: int(llabel[1:]) for llabel in self.line_labels} if all([isinstance(q, int) for q in self.line_labels]): qubit_conversion = {q: q for q in self.line_labels} standardtype = True if not standardtype: raise ValueError( "No standard qubit labelling conversion is available! Please provide `qubit_conversion`.") if num_qubits is None: num_qubits = len(self.line_labels) #Currently only using 'Iz' as valid intermediate measurement ('IM') label. #Todo: Expand to all intermediate measurements. if 'Iz' in self.str: # using_IMs = True num_IMs = self.str.count('Iz') else: # using_IMs = False num_IMs = 0 num_IMs_used = 0 # Init the openqasm string. openqasm = 'OPENQASM 2.0;\ninclude "qelib1.inc";\n\n' openqasm += 'qreg q[{0}];\n'.format(str(num_qubits)) # openqasm += 'creg cr[{0}];\n'.format(str(num_qubits)) openqasm += 'creg cr[{0}];\n'.format(str(num_qubits + num_IMs)) openqasm += '\n' depth = self.num_layers() # Go through the layers, and add the openqasm for each layer in turn. for l in range(depth): # Get the layer, without identity gates and containing each gate only once. layer = self.get_layer_label(l) # For keeping track of which qubits have a gate on them in the layer. qubits_used = [] # Go through the (non-self.identity) gates in the layer and convert them to openqasm for gate in layer.components: gate_qubits = gate.qubits if (gate.qubits is not None) else self.line_labels assert(len(gate_qubits) <= 2), 'Gates on more than 2 qubits given; this is currently not supported!' # Find the openqasm for the gate. if gate.name.__str__() != 'Iz': openqasm_for_gate = gatename_conversion[gate.name] #If gate.qubits is None, gate is assumed to be single-qubit gate #acting in parallel on all qubits. if gate.qubits is None: for q in gate_qubits: openqasm += openqasm_for_gate + ' q[' + str(qubit_conversion[q]) + '];\n' else: for q in gate_qubits: openqasm_for_gate += ' q[' + str(qubit_conversion[q]) + ']' if q != gate_qubits[-1]: openqasm_for_gate += ', ' openqasm_for_gate += ';\n' else: assert len(gate.qubits) == 1 q = gate.qubits[0] # classical_bit = num_IMs_used openqasm_for_gate = "measure q[{0}] -> cr[{1}];\n".format(str(qubit_conversion[q]), num_IMs_used) num_IMs_used += 1 # Add the openqasm for the gate to the openqasm string. openqasm += openqasm_for_gate # Keeps track of the qubits that have been accounted for, and checks that hadn't been used # although that should already be checked in the .get_layer_label(), which checks for its a valid # circuit layer. assert(not set(gate_qubits).issubset(set(qubits_used))) qubits_used.extend(gate_qubits) # All gates that don't have a non-idle gate acting on them get an idle in the layer. for q in self.line_labels: if q not in qubits_used: openqasm += 'id' + ' q[' + str(qubit_conversion[q]) + '];\n' # Add in a barrier after every circuit layer if block_between_layers==True. # Including barriers is critical for QCVV testing, circuits should usually # experience minimal "behind-the-scenes" compilation (beyond necessary # conversion to native instructions). # To do: Add "barrier" as native pygsti circuit instruction, and use for indicating # where pragma blocks should be. if block_between_layers: openqasm += 'barrier ' for q in self.line_labels[:-1]: openqasm += 'q[{0}], '.format(str(qubit_conversion[q])) openqasm += 'q[{0}];\n'.format(str(qubit_conversion[self.line_labels[-1]])) # openqasm += ';' # Add in a measurement at the end. for q in self.line_labels: # openqasm += "measure q[{0}] -> cr[{1}];\n".format(str(qubit_conversion[q]), str(qubit_conversion[q])) openqasm += "measure q[{0}] -> cr[{1}];\n".format(str(qubit_conversion[q]), str(num_IMs_used + qubit_conversion[q])) return openqasm def simulate(self, model, return_all_outcomes=False): """ Compute the outcome probabilities of this Circuit using `model` as a model for the gates. The order of the outcome strings (e.g., '0100') is w.r.t. to the ordering of the qubits in the circuit. That is, the ith element of the outcome string corresponds to the qubit with label `self.qubit_labels[i]`. Parameters ---------- model : Model A description of the gate and SPAM operations corresponding to the labels stored in this Circuit. If this model is over more qubits than the circuit, the output will be the probabilities for the qubits in the circuit marginalized over the other qubits. But, the simulation is over the full set of qubits in the model, and so the time taken for the simulation scales with the number of qubits in the model. For models whereby "spectator" qubits do not affect the qubits in this circuit (such as with perfect gates), more efficient simulations will be obtained by first creating a model only over the qubits in this circuit. return_all_outcomes: bool, optional Whether to include outcomes in the returned dictionary that have zero probability. When False, the threshold for discarding an outcome as z ero probability is 10^-12. Returns ------- probs : dictionary A dictionary with keys equal to all (`return_all_outcomes` is True) or possibly only some (`return_all_outcomes` is False) of the possible outcomes, and values that are float probabilities. """ # These results is a dict with strings of outcomes (normally bits) ordered according
:type DnsQueryType: str :param UserName: 登录服务器的账号 :type UserName: str :param PassWord: 登录服务器的密码 :type PassWord: str :param UseSecConn: 是否使用安全链接SSL， 0 不使用，1 使用 :type UseSecConn: int :param NeedAuth: FTP登录验证方式 0 不验证 1 匿名登录 2 需要身份验证 :type NeedAuth: int :param ReqDataType: 请求数据类型。0 表示请求为字符串类型。1表示为二进制类型 :type ReqDataType: int :param ReqData: 发起TCP, UDP请求的协议请求数据 :type ReqData: str :param RespDataType: 响应数据类型。0 表示响应为字符串类型。1表示为二进制类型 :type RespDataType: int :param RespData: 预期的UDP请求的回应数据 :type RespData: str :param RedirectFollowNum: 跟随跳转次数 :type RedirectFollowNum: int """ self.TaskId = None self.TaskName = None self.Period = None self.CatTypeName = None self.CgiUrl = None self.AgentGroupId = None self.PolicyGroupId = None self.Status = None self.AddTime = None self.Type = None self.TopicId = None self.AlarmStatus = None self.Host = None self.Port = None self.CheckStr = None self.CheckType = None self.UserAgent = None self.Cookie = None self.PostData = None self.SslVer = None self.IsHeader = None self.DnsSvr = None self.DnsCheckIp = None self.DnsQueryType = None self.UserName = None self.PassWord = None self.UseSecConn = None self.NeedAuth = None self.ReqDataType = None self.ReqData = None self.RespDataType = None self.RespData = None self.RedirectFollowNum = None def _deserialize(self, params): self.TaskId = params.get("TaskId") self.TaskName = params.get("TaskName") self.Period = params.get("Period") self.CatTypeName = params.get("CatTypeName") self.CgiUrl = params.get("CgiUrl") self.AgentGroupId = params.get("AgentGroupId") self.PolicyGroupId = params.get("PolicyGroupId") self.Status = params.get("Status") self.AddTime = params.get("AddTime") self.Type = params.get("Type") self.TopicId = params.get("TopicId") self.AlarmStatus = params.get("AlarmStatus") self.Host = params.get("Host") self.Port = params.get("Port") self.CheckStr = params.get("CheckStr") self.CheckType = params.get("CheckType") self.UserAgent = params.get("UserAgent") self.Cookie = params.get("Cookie") self.PostData = params.get("PostData") self.SslVer = params.get("SslVer") self.IsHeader = params.get("IsHeader") self.DnsSvr = params.get("DnsSvr") self.DnsCheckIp = params.get("DnsCheckIp") self.DnsQueryType = params.get("DnsQueryType") self.UserName = params.get("UserName") self.PassWord = params.get("PassWord") self.UseSecConn = params.get("UseSecConn") self.NeedAuth = params.get("NeedAuth") self.ReqDataType = params.get("ReqDataType") self.ReqData = params.get("ReqData") self.RespDataType = params.get("RespDataType") self.RespData = params.get("RespData") self.RedirectFollowNum = params.get("RedirectFollowNum") memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class CreateAgentGroupRequest(AbstractModel): """CreateAgentGroup请求参数结构体 """ def __init__(self): r""" :param GroupName: 拨测分组名称，不超过32个字符 :type GroupName: str :param IsDefault: 是否为默认分组，取值可为 0 或 1。取 1 时表示设置为默认分组 :type IsDefault: int :param Agents: Province, Isp 需要成对地进行选择。参数对的取值范围。参见：DescribeAgents 的返回结果。 :type Agents: list of CatAgent """ self.GroupName = None self.IsDefault = None self.Agents = None def _deserialize(self, params): self.GroupName = params.get("GroupName") self.IsDefault = params.get("IsDefault") if params.get("Agents") is not None: self.Agents = [] for item in params.get("Agents"): obj = CatAgent() obj._deserialize(item) self.Agents.append(obj) memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class CreateAgentGroupResponse(AbstractModel): """CreateAgentGroup返回参数结构体 """ def __init__(self): r""" :param GroupId: 拨测分组Id :type GroupId: int :param RequestId: 唯一请求 ID，每次请求都会返回。定位问题时需要提供该次请求的 RequestId。 :type RequestId: str """ self.GroupId = None self.RequestId = None def _deserialize(self, params): self.GroupId = params.get("GroupId") self.RequestId = params.get("RequestId") class CreateProbeTasksRequest(AbstractModel): """CreateProbeTasks请求参数结构体 """ def __init__(self): r""" :param BatchTasks: 批量任务名-地址 :type BatchTasks: list of ProbeTaskBasicConfiguration :param TaskType: 任务类型 :type TaskType: int :param Nodes: 拨测节点 :type Nodes: list of str :param Interval: 拨测间隔 :type Interval: int :param Parameters: 拨测参数 :type Parameters: str :param TaskCategory: 任务分类 <li>1 = PC</li> <li> 2 = Mobile </li> :type TaskCategory: int :param Cron: 定时任务cron表达式 :type Cron: str :param Tag: 资源标签值 :type Tag: list of Tag """ self.BatchTasks = None self.TaskType = None self.Nodes = None self.Interval = None self.Parameters = None self.TaskCategory = None self.Cron = None self.Tag = None def _deserialize(self, params): if params.get("BatchTasks") is not None: self.BatchTasks = [] for item in params.get("BatchTasks"): obj = ProbeTaskBasicConfiguration() obj._deserialize(item) self.BatchTasks.append(obj) self.TaskType = params.get("TaskType") self.Nodes = params.get("Nodes") self.Interval = params.get("Interval") self.Parameters = params.get("Parameters") self.TaskCategory = params.get("TaskCategory") self.Cron = params.get("Cron") if params.get("Tag") is not None: self.Tag = [] for item in params.get("Tag"): obj = Tag() obj._deserialize(item) self.Tag.append(obj) memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class CreateProbeTasksResponse(AbstractModel): """CreateProbeTasks返回参数结构体 """ def __init__(self): r""" :param RequestId: 唯一请求 ID，每次请求都会返回。定位问题时需要提供该次请求的 RequestId。 :type RequestId: str """ self.RequestId = None def _deserialize(self, params): self.RequestId = params.get("RequestId") class CreateTaskExRequest(AbstractModel): """CreateTaskEx请求参数结构体 """ def __init__(self): r""" :param CatTypeName: http, https, ping, tcp, ftp, smtp, udp, dns 之一 :type CatTypeName: str :param Url: 拨测的URL，例如：www.qq.com (URL域名解析需要能解析出具体的IP) :type Url: str :param Period: 拨测周期。取值可为1,5,15,30之一, 单位：分钟。精度不能低于用户等级规定的最小精度 :type Period: int :param TaskName: 拨测任务名称不能超过32个字符。同一个用户创建的任务名不可重复 :type TaskName: str :param AgentGroupId: 拨测分组ID，体现本拨测任务要采用哪些运营商作为拨测源。一般可直接填写本用户的默认拨测分组。参见：DescribeAgentGroups 接口，本参数使用返回结果里的GroupId的值。注意： Type为0时，AgentGroupId为必填 :type AgentGroupId: int :param Host: 指定域名(如需要) :type Host: str :param IsHeader: 是否为Header请求（非0 发起Header 请求。为0，且PostData 非空，发起POST请求。为0，PostData 为空，发起GET请求） :type IsHeader: int :param SslVer: URL中含有"https"时有用。缺省为SSLv23。需要为 TLSv1_2, TLSv1_1, TLSv1, SSLv2, SSLv23, SSLv3 之一 :type SslVer: str :param PostData: POST请求数据。空字符串表示非POST请求 :type PostData: str :param UserAgent: 用户Agent信息 :type UserAgent: str :param CheckStr: 要在结果中进行匹配的字符串 :type CheckStr: str :param CheckType: 1 表示通过检查结果是否包含CheckStr 进行校验 :type CheckType: int :param Cookie: 需要设置的Cookie信息 :type Cookie: str :param TaskId: 任务ID，用于验证且修改任务时传入原任务ID :type TaskId: int :param UserName: 登录服务器的账号。如果为空字符串，表示不用校验用户密码。只做简单连接服务器的拨测 :type UserName: str :param PassWord: 登录服务器的密码 :type PassWord: str :param ReqDataType: 缺省为0。0 表示请求为字符串类型。1表示为二进制类型 :type ReqDataType: int :param ReqData: 发起TCP, UDP请求的协议请求数据 :type ReqData: str :param RespDataType: 缺省为0。0 表示响应为字符串类型。1表示为二进制类型 :type RespDataType: int :param RespData: 预期的UDP请求的回应数据。字符串型，只需要返回的结果里包含本字符串算校验通过。二进制型，则需要严格等于才算通过 :type RespData: str :param DnsSvr: 目的DNS服务器可以为空字符串 :type DnsSvr: str :param DnsCheckIp: 需要检验是否在DNS IP列表的IP。可以为空字符串，表示不校验 :type DnsCheckIp: str :param DnsQueryType: 需要为下列值之一。缺省为A。A, MX, NS, CNAME, TXT, ANY :type DnsQueryType: str :param UseSecConn: 是否使用安全链接SSL， 0 不使用，1 使用 :type UseSecConn: int :param NeedAuth: FTP登录验证方式， 0 不验证， 1 匿名登录， 2 需要身份验证 :type NeedAuth: int :param Port: 拨测目标的端口号 :type Port: int :param Type: Type=0 默认（站点监控）Type=2 可用率监控 :type Type: int :param IsVerify: IsVerify=0 非验证任务 IsVerify=1 验证任务，不传则默认为0 :type IsVerify: int :param RedirectFollowNum: 跟随跳转次数，取值范围0-5，不传则表示不跟随 :type RedirectFollowNum: int """ self.CatTypeName = None self.Url = None self.Period = None self.TaskName = None self.AgentGroupId = None self.Host = None self.IsHeader = None self.SslVer = None self.PostData = None self.UserAgent = None self.CheckStr = None self.CheckType = None self.Cookie = None self.TaskId = None self.UserName = None self.PassWord = <PASSWORD> self.ReqDataType = None self.ReqData = None self.RespDataType = None self.RespData = None self.DnsSvr = None self.DnsCheckIp = None self.DnsQueryType = None self.UseSecConn = None self.NeedAuth = None self.Port = None self.Type = None self.IsVerify = None self.RedirectFollowNum = None def _deserialize(self, params): self.CatTypeName = params.get("CatTypeName") self.Url = params.get("Url") self.Period = params.get("Period") self.TaskName = params.get("TaskName") self.AgentGroupId = params.get("AgentGroupId") self.Host = params.get("Host") self.IsHeader = params.get("IsHeader") self.SslVer = params.get("SslVer") self.PostData = params.get("PostData") self.UserAgent = params.get("UserAgent") self.CheckStr = params.get("CheckStr") self.CheckType = params.get("CheckType") self.Cookie = params.get("Cookie") self.TaskId = params.get("TaskId") self.UserName = params.get("UserName") self.PassWord = params.get("PassWord") self.ReqDataType = params.get("ReqDataType") self.ReqData = params.get("ReqData") self.RespDataType = params.get("RespDataType") self.RespData = params.get("RespData") self.DnsSvr = params.get("DnsSvr") self.DnsCheckIp = params.get("DnsCheckIp") self.DnsQueryType = params.get("DnsQueryType") self.UseSecConn = params.get("UseSecConn") self.NeedAuth = params.get("NeedAuth") self.Port = params.get("Port") self.Type = params.get("Type") self.IsVerify = params.get("IsVerify") self.RedirectFollowNum = params.get("RedirectFollowNum") memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class CreateTaskExResponse(AbstractModel): """CreateTaskEx返回参数结构体 """ def __init__(self): r""" :param ResultId: 拨测结果查询ID。接下来可以使用查询拨测是否能够成功，验证能否通过。 :type ResultId: int :param TaskId: 拨测任务ID。验证通过后，创建任务时使用，传递给CreateTask 接口。 :type TaskId: int :param RequestId: 唯一请求 ID，每次请求都会返回。定位问题时需要提供该次请求的 RequestId。 :type RequestId: str """ self.ResultId = None self.TaskId = None self.RequestId = None def _deserialize(self, params): self.ResultId = params.get("ResultId") self.TaskId = params.get("TaskId") self.RequestId = params.get("RequestId") class DataPoint(AbstractModel): """时延等数据，数据点 """ def __init__(self): r""" :param LogTime: 数据点的时间 :type LogTime: str :param MetricValue: 数据值 :type MetricValue: float """ self.LogTime = None self.MetricValue = None def _deserialize(self, params): self.LogTime = params.get("LogTime") self.MetricValue = params.get("MetricValue") memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class DataPointMetric(AbstractModel): """包含MetricName的DataPoint数据 """ def __init__(self): r""" :param MetricName: 数据项 :type MetricName: str :param Points: 数据点的时间和值 :type Points: list of DataPoint """ self.MetricName = None self.Points = None def _deserialize(self, params): self.MetricName = params.get("MetricName") if params.get("Points") is not None: self.Points = [] for item in params.get("Points"): obj = DataPoint() obj._deserialize(item) self.Points.append(obj) memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class DeleteAgentGroupRequest(AbstractModel): """DeleteAgentGroup请求参数结构体 """ def __init__(self): r""" :param GroupId: 拨测分组id :type GroupId: int """ self.GroupId = None def _deserialize(self, params): self.GroupId = params.get("GroupId") memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class
<filename>likeyoubot_kaiser.py import likeyoubot_game as lybgame import likeyoubot_kaiser_scene as lybscene from likeyoubot_configure import LYBConstant as lybconstant import time import sys import tkinter from tkinter import ttk from tkinter import font import copy class LYBKaiser(lybgame.LYBGame): work_list = [ '게임 시작', '로그인', '자동 사냥', '메인 퀘스트', '지역 퀘스트', '퀵슬롯 등록', '퀘스트', '우편', '일괄 분해', '알림', '[반복 시작]', '[반복 종료]', '[작업 대기]', '[작업 예약]', '' ] nox_kaiser_icon_list = [ 'nox_kaiser_icon' ] momo_kaiser_icon_list = [ 'momo_kaiser_icon' ] character_move_list = [ "↑", "↗", "→", "↘", "↓", "↙", "←", "↖" ] slot_item_list = [ '없음', '소형 체력 물약', '중형 체력 물약', '속도의 물약', '전투의 물약', '증폭 마법석', '펫 소환 주문서', ] def __init__(self, game_name, game_data_name, window): lybgame.LYBGame.__init__(self, lybconstant.LYB_GAME_KAISER, lybconstant.LYB_GAME_DATA_KAISER, window) def process(self, window_image): rc = super(LYBKaiser, self).process(window_image) if rc < 0: return rc return rc def custom_check(self, window_image, window_pixel): pb_name = 'skip' (loc_x, loc_y), match_rate = self.locationOnWindowPart( self.window_image, self.resource_manager.pixel_box_dic[pb_name], custom_below_level=(130, 130, 130), custom_top_level=(255, 255, 255), custom_threshold=0.9, custom_flag=1, custom_rect=(560, 240, 600, 280) ) if loc_x != -1: self.logger.warn('건너뛰기: ' + str(match_rate)) self.mouse_click(pb_name) # 패배! # (loc_x, loc_y), match_rate = self.locationResourceOnWindowPart( # self.window_image, # 'defeat_press_key_loc', # custom_below_level=(250, 250, 250), # custom_top_level=(255, 255, 255), # custom_threshold=0.7, # custom_flag=1, # custom_rect=(280, 190, 360, 230) # ) # if loc_x != -1: # self.logger.warn('전투 패배: ' + str(match_rate)) # self.mouse_click('defeat_press_key_0') return '' def get_screen_by_location(self, window_image): scene_name = self.scene_init_screen(window_image) if len(scene_name) > 0: return scene_name scene_name = self.popup_scene(window_image) if len(scene_name) > 0: return scene_name # scene_name = self.jeontoo_scene(window_image) # if len(scene_name) > 0: # return scene_name # scene_name = self.scene_google_play_account_select(window_image) # if len(scene_name) > 0: # return scene_name return '' def popup_scene(self, window_image): loc_name = 'popup_scene_loc' match_rate = self.rateMatchedResource(self.window_pixels, loc_name, custom_below_level=100, custom_top_level=255) self.logger.debug(loc_name + ' ' + str(match_rate)) if match_rate > 0.7: return 'popup_scene' return '' # def jeontoo_scene(self, window_image): # (loc_x, loc_y), match_rate = self.locationResourceOnWindowPart( # self.window_image, # 'jeontoo_scene_loc', # custom_below_level=(100, 100, 100), # custom_top_level=(255, 255, 255), # custom_threshold=0.7, # custom_flag=1, # custom_rect=(5, 90, 80, 130) # ) # if match_rate > 0.7: # return 'jeontoo_scene' # return '' def scene_init_screen(self, window_image): loc_x = -1 loc_y = -1 if self.player_type == 'nox': for each_icon in LYBKaiser.nox_kaiser_icon_list: (loc_x, loc_y), match_rate = self.locationOnWindowPart( window_image, self.resource_manager.pixel_box_dic[each_icon], custom_threshold=0.8, custom_flag=1, custom_rect=(80, 110, 570, 300) ) # print('[DEBUG] nox yh icon:', (loc_x, loc_y), match_rate) if loc_x != -1: break elif self.player_type == 'momo': for each_icon in LYBKaiser.momo_kaiser_icon_list: (loc_x, loc_y), match_rate = self.locationOnWindowPart( window_image, self.resource_manager.pixel_box_dic[each_icon], custom_threshold=0.8, custom_flag=1, custom_rect=(30, 10, 610, 300) ) # print('[DEBUG] momo yh icon:', (loc_x, loc_y), match_rate) if loc_x != -1: break if loc_x == -1: return '' return 'init_screen_scene' def scene_google_play_account_select(self, window_image): loc_x_list = [] loc_y_list = [] (loc_x, loc_y) = lybgame.LYBGame.locationOnWindow( window_image, self.resource_manager.pixel_box_dic['google_play_letter'] ) loc_x_list.append(loc_x) loc_y_list.append(loc_y) for i in range(6): (loc_x, loc_y) = lybgame.LYBGame.locationOnWindow( window_image, self.resource_manager.pixel_box_dic['google_play_letter_' + str(i)] ) loc_x_list.append(loc_x) loc_y_list.append(loc_y) for each_loc in loc_x_list: if each_loc == -1: return '' else: continue return 'google_play_account_select_scene' def clear_scene(self): last_scene = self.scene_dic self.scene_dic = {} for scene_name, scene in last_scene.items(): if ( 'google_play_account_select_scene' in scene_name or 'logo_screen_scene' in scene_name or 'connect_account_scene' in scene_name ): self.scene_dic[scene_name] = last_scene[scene_name] def add_scene(self, scene_name): self.scene_dic[scene_name] = lybscene.LYBKaiserScene(scene_name) self.scene_dic[scene_name].setLoggingQueue(self.logging_queue) self.scene_dic[scene_name].setGameObject(self) class LYBKaiserTab(lybgame.LYBGameTab): def __init__(self, root_frame, configure, game_options, inner_frame_dics, width, height, game_name=lybconstant.LYB_GAME_KAISER): lybgame.LYBGameTab.__init__(self, root_frame, configure, game_options, inner_frame_dics, width, height, game_name) def set_work_list(self): lybgame.LYBGameTab.set_work_list(self) for each_work in LYBKaiser.work_list: self.option_dic['work_list_listbox'].insert('end', each_work) self.configure.common_config[self.game_name]['work_list'].append(each_work) def set_option(self): ############################################### # 메인 퀘스트 진행 # ############################################### # frame = ttk.Frame(self.inner_frame_dic['frame_top'], relief=self.frame_relief) # label = tkinter.Label( # master = frame, # text = "메인 퀘스트를 ", # anchor = tkinter.W, # justify = tkinter.LEFT, # font = lybconstant.LYB_FONT # # fg='White' if brightness < 120 else 'Black', # # bg=bg_colour # ) # # countif.place( # # x=lybconstant.LYB_PADDING, # # y=lybconstant.LYB_PADDING, # # width=lybconstant.LYB_LABEL_WIDTH, height=lybconstant.LYB_LABEL_HEIGHT # # ) # label.pack(side=tkinter.LEFT) # option_name_mq = lybconstant.LYB_DO_STRING_DURATION_MAIN_QUEST # self.option_dic[option_name_mq] = tkinter.StringVar(frame) # self.option_dic[option_name_mq].trace('w', lambda args: self.callback_main_quest_stringvar(args, option_name=option_name_mq)) # if not option_name_mq in self.configure.common_config[self.game_name]: # self.configure.common_config[self.game_name][option_name_mq] = 20 # entry = tkinter.Entry( # master = frame, # relief = 'sunken', # textvariable = self.option_dic[option_name_mq], # justify = tkinter.RIGHT, # width = 5, # font = lybconstant.LYB_FONT # ) # entry.pack(side=tkinter.LEFT) # label = tkinter.Label( # master = frame, # text = "분 동안 진행합니다.", # justify = tkinter.LEFT, # font = lybconstant.LYB_FONT # # fg='White' if brightness < 120 else 'Black', # # bg=bg_colour # ) # label.pack(side=tkinter.LEFT) # frame.pack(anchor=tkinter.W) # PADDING frame = ttk.Frame( master = self.master, relief = self.frame_relief ) frame.pack(pady=5) self.inner_frame_dic['options'] = ttk.Frame( master = self.master, relief = self.frame_relief ) self.option_dic['option_note'] = ttk.Notebook( master = self.inner_frame_dic['options'] ) self.inner_frame_dic['common_tab_frame'] = ttk.Frame( master = self.option_dic['option_note'], relief = self.frame_relief ) self.inner_frame_dic['common_tab_frame'].pack(anchor=tkinter.NW, fill=tkinter.BOTH, expand=True) self.option_dic['option_note'].add(self.inner_frame_dic['common_tab_frame'], text='일반') self.inner_frame_dic['work_tab_frame'] = ttk.Frame( master = self.option_dic['option_note'], relief = self.frame_relief ) self.inner_frame_dic['work_tab_frame'].pack(anchor=tkinter.NW, fill=tkinter.BOTH, expand=True) self.option_dic['option_note'].add(self.inner_frame_dic['work_tab_frame'], text='작업') self.inner_frame_dic['notify_tab_frame'] = ttk.Frame( master = self.option_dic['option_note'], relief = self.frame_relief ) self.inner_frame_dic['notify_tab_frame'].pack(anchor=tkinter.NW, fill=tkinter.BOTH, expand=True) self.option_dic['option_note'].add(self.inner_frame_dic['notify_tab_frame'], text='알림') # ------ # 일반 탭 좌측 frame_l = ttk.Frame(self.inner_frame_dic['common_tab_frame']) frame_label = ttk.LabelFrame(frame_l, text='설정') frame_label_inner = ttk.LabelFrame(frame_label, text='소형 체력 물약') frame = ttk.Frame(frame_label_inner) self.option_dic[lybconstant.LYB_DO_STRING_KAISER_CONFIG + 'auto_potion_set'] = tkinter.BooleanVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_KAISER_CONFIG + 'auto_potion_set'].trace( 'w', lambda args: self.callback_auto_potion_set(args, lybconstant.LYB_DO_STRING_KAISER_CONFIG + 'auto_potion_set') ) if not lybconstant.LYB_DO_STRING_KAISER_CONFIG + 'auto_potion_set' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_KAISER_CONFIG + 'auto_potion_set'] = False check_box = ttk.Checkbutton( master = frame, text = '물약 소진시 현재 작업 종료', variable = self.option_dic[lybconstant.LYB_DO_STRING_KAISER_CONFIG + 'auto_potion_set'], onvalue = True, offvalue = False ) check_box.pack(anchor=tkinter.W, side=tkinter.LEFT) frame.pack(anchor=tkinter.NW) frame = ttk.Frame(frame_label_inner) label = ttk.Label( master = frame, text = self.get_option_text("물약 슬롯 번호") ) label.pack(side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_KAISER_CONFIG + 'auto_potion_number'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_KAISER_CONFIG + 'auto_potion_number'].trace( 'w', lambda args: self.callback_auto_potion_number(args, lybconstant.LYB_DO_STRING_KAISER_CONFIG + 'auto_potion_number') ) combobox_list = [] for i in range(1, 5): combobox_list.append(str(i)) if not lybconstant.LYB_DO_STRING_KAISER_CONFIG + 'auto_potion_number' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_KAISER_CONFIG + 'auto_potion_number'] = 1 combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_KAISER_CONFIG + 'auto_potion_number'], state = "readonly", height = 10, width = 5, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_KAISER_CONFIG + 'auto_potion_number']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) frame.pack(anchor=tkinter.NW) frame_label_inner.pack(anchor=tkinter.NW, padx=5, pady=5) frame_label_inner = ttk.LabelFrame(frame_label, text='수동 체력 물약') frame = ttk.Frame(frame_label_inner) self.option_dic[lybconstant.LYB_DO_STRING_KAISER_CONFIG + 'potion_set'] = tkinter.BooleanVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_KAISER_CONFIG + 'potion_set'].trace( 'w', lambda args: self.callback_potion_set(args, lybconstant.LYB_DO_STRING_KAISER_CONFIG + 'potion_set') ) if not lybconstant.LYB_DO_STRING_KAISER_CONFIG + 'potion_set' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_KAISER_CONFIG + 'potion_set'] = False check_box = ttk.Checkbutton( master = frame, text = '물약 소진시 현재 작업 종료', variable = self.option_dic[lybconstant.LYB_DO_STRING_KAISER_CONFIG + 'potion_set'], onvalue = True, offvalue = False ) check_box.pack(anchor=tkinter.W, side=tkinter.LEFT) frame.pack(anchor=tkinter.NW) frame = ttk.Frame(frame_label_inner) label = ttk.Label( master = frame, text = self.get_option_text("수동 회복 물약 사용(HP %)") ) label.pack(side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_KAISER_CONFIG + 'potion_hp'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_KAISER_CONFIG + 'potion_hp'].trace( 'w', lambda args: self.callback_potion_hp(args, lybconstant.LYB_DO_STRING_KAISER_CONFIG + 'potion_hp') ) combobox_list = [] for i in range(50, 91): combobox_list.append(str(i)) if not lybconstant.LYB_DO_STRING_KAISER_CONFIG + 'potion_hp' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_KAISER_CONFIG + 'potion_hp'] = 70 combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_KAISER_CONFIG + 'potion_hp'], state = "readonly", height = 10, width = 5, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_KAISER_CONFIG + 'potion_hp']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) frame.pack(anchor=tkinter.NW) frame = ttk.Frame(frame_label_inner) label = ttk.Label( master = frame, text = self.get_option_text("수동 회복 물약 슬롯 번호") ) label.pack(side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_KAISER_CONFIG + 'potion_number'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_KAISER_CONFIG + 'potion_number'].trace( 'w', lambda args: self.callback_potion_number(args, lybconstant.LYB_DO_STRING_KAISER_CONFIG + 'potion_number') ) combobox_list = [] for i in range(1, 5): combobox_list.append(str(i)) if not lybconstant.LYB_DO_STRING_KAISER_CONFIG + 'potion_number' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_KAISER_CONFIG + 'potion_number'] = 2 combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_KAISER_CONFIG + 'potion_number'], state = "readonly", height = 10, width = 5, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_KAISER_CONFIG + 'potion_number']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) frame.pack(anchor=tkinter.NW) frame_label_inner.pack(anchor=tkinter.NW, padx=5, pady=5) frame_label.pack(anchor=tkinter.NW, padx=5, pady=5) frame_l.pack(side=tkinter.LEFT, anchor=tkinter.NW) # 일반 탭 중간 frame_m = ttk.Frame(self.inner_frame_dic['common_tab_frame']) frame_m.pack(side=tkinter.LEFT, anchor=tkinter.NW) # 일반 탭 우측 frame_r = ttk.Frame(self.inner_frame_dic['common_tab_frame']) frame_r.pack(side=tkinter.LEFT, anchor=tkinter.NW) # 작업 탭 좌측 frame_l = ttk.Frame(self.inner_frame_dic['work_tab_frame']) frame_label = ttk.LabelFrame(frame_l, text='자동 사냥') frame = ttk.Frame(frame_label) label = ttk.Label( master = frame, text = self.get_option_text("진행 시간(초)") ) label.pack(side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_KAISER_WORK + 'auto_play_duration'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_KAISER_WORK + 'auto_play_duration'].trace( 'w', lambda args: self.callback_auto_play_duration(args, lybconstant.LYB_DO_STRING_KAISER_WORK + 'auto_play_duration') ) combobox_list = [] for i in range(0, 86401, 60): combobox_list.append(str(i)) if not lybconstant.LYB_DO_STRING_KAISER_WORK + 'auto_play_duration' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_KAISER_WORK + 'auto_play_duration'] = 1800 combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_KAISER_WORK + 'auto_play_duration'], state = "readonly", height = 10, width = 5, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_KAISER_WORK + 'auto_play_duration']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) frame.pack(anchor=tkinter.NW) frame = ttk.Frame(frame_label) label = ttk.Label( master = frame, text = self.get_option_text("자동 전환 감지 횟수") ) label.pack(side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_KAISER_WORK + 'auto_limit_count'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_KAISER_WORK + 'auto_limit_count'].trace( 'w', lambda args: self.callback_auto_limit_count(args, lybconstant.LYB_DO_STRING_KAISER_WORK + 'auto_limit_count') ) combobox_list = [] for i in range(2, 101): combobox_list.append(str(i)) if not lybconstant.LYB_DO_STRING_KAISER_WORK + 'auto_limit_count' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_KAISER_WORK + 'auto_limit_count'] = 5 combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_KAISER_WORK + 'auto_limit_count'], state = "readonly", height = 10, width = 5, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_KAISER_WORK + 'auto_limit_count']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) frame.pack(anchor=tkinter.NW) frame_label.pack(anchor=tkinter.NW, padx=5, pady=5) frame_label = ttk.LabelFrame(frame_l, text='메인 퀘스트') frame = ttk.Frame(frame_label) label = ttk.Label( master = frame, text = self.get_option_text("진행 시간(초)") ) label.pack(side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_KAISER_WORK + 'main_quest_duration'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_KAISER_WORK + 'main_quest_duration'].trace( 'w', lambda args: self.callback_main_quest_duration(args, lybconstant.LYB_DO_STRING_KAISER_WORK + 'main_quest_duration') ) combobox_list = [] for i in range(0, 86401, 60): combobox_list.append(str(i)) if not lybconstant.LYB_DO_STRING_KAISER_WORK + 'main_quest_duration' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_KAISER_WORK + 'main_quest_duration'] = 1800 combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_KAISER_WORK + 'main_quest_duration'], state = "readonly", height = 10, width = 5, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_KAISER_WORK + 'main_quest_duration']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) frame.pack(anchor=tkinter.NW) frame = ttk.Frame(frame_label) label = ttk.Label( master = frame, text = self.get_option_text("퀘스트 지역 이탈 판정 횟수") ) label.pack(side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_KAISER_WORK + 'main_quest_distance'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_KAISER_WORK + 'main_quest_distance'].trace( 'w', lambda args: self.callback_main_quest_distance(args, lybconstant.LYB_DO_STRING_KAISER_WORK + 'main_quest_distance') ) combobox_list = [] for i in range(1, 101): combobox_list.append(str(i)) if not lybconstant.LYB_DO_STRING_KAISER_WORK + 'main_quest_distance' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_KAISER_WORK + 'main_quest_distance'] = 3 combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_KAISER_WORK + 'main_quest_distance'], state = "readonly", height = 10, width = 5, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_KAISER_WORK + 'main_quest_distance']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) frame.pack(anchor=tkinter.NW) frame = ttk.Frame(frame_label) label = ttk.Label( master = frame, text = self.get_option_text("자동 전환 감지 횟수") ) label.pack(side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_KAISER_WORK + 'main_quest_auto'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_KAISER_WORK + 'main_quest_auto'].trace( 'w', lambda args: self.callback_main_quest_auto(args, lybconstant.LYB_DO_STRING_KAISER_WORK + 'main_quest_auto') ) combobox_list = [] for i in range(2, 101): combobox_list.append(str(i)) if not lybconstant.LYB_DO_STRING_KAISER_WORK + 'main_quest_auto' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_KAISER_WORK + 'main_quest_auto'] = 5 combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_KAISER_WORK + 'main_quest_auto'], state = "readonly", height = 10, width = 5, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_KAISER_WORK + 'main_quest_auto']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) frame.pack(anchor=tkinter.NW) frame_label.pack(anchor=tkinter.NW, padx=5, pady=5) frame_label = ttk.LabelFrame(frame_l, text='지역 퀘스트') frame = ttk.Frame(frame_label) label = ttk.Label( master = frame, text = self.get_option_text("진행 시간(초)") ) label.pack(side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_KAISER_WORK + 'local_quest_duration'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_KAISER_WORK + 'local_quest_duration'].trace( 'w', lambda args: self.callback_local_quest_duration(args, lybconstant.LYB_DO_STRING_KAISER_WORK + 'local_quest_duration') ) combobox_list = [] for i in range(0, 86401, 60): combobox_list.append(str(i)) if not lybconstant.LYB_DO_STRING_KAISER_WORK + 'local_quest_duration' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_KAISER_WORK + 'local_quest_duration'] = 1800 combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_KAISER_WORK + 'local_quest_duration'], state = "readonly", height = 10, width = 5, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_KAISER_WORK + 'local_quest_duration']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) frame.pack(anchor=tkinter.NW) frame = ttk.Frame(frame_label) label = ttk.Label( master = frame, text = self.get_option_text("퀘스트 지역 이탈 판정 거리(m)") ) label.pack(side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_KAISER_WORK + 'local_quest_distance_limit'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_KAISER_WORK + 'local_quest_distance_limit'].trace( 'w', lambda args: self.callback_local_quest_distance_limit(args, lybconstant.LYB_DO_STRING_KAISER_WORK + 'local_quest_distance_limit') ) combobox_list = [] for i in range(1, 11): combobox_list.append(str(i 10)) if not lybconstant.LYB_DO_STRING_KAISER_WORK + 'local_quest_distance_limit' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_KAISER_WORK + 'local_quest_distance_limit'] = 40 combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_KAISER_WORK + 'local_quest_distance_limit'], state = "readonly", height = 10, width = 5, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_KAISER_WORK + 'local_quest_distance_limit']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) frame.pack(anchor=tkinter.NW) frame = ttk.Frame(frame_label) label = ttk.Label( master
<filename>Bucket 1.0/Bucket Interpreter.py<gh_stars>0 #_Bucket Compiler by Pixet Bits {Version : 1.0.0.0}_---------------------------# import random as rand #Libraries LibCtrls = {"[B]" : False, "[F]" : False, "[C]" : False} #Objects SfSystVr = {"FLCnt" : 0} ActivInt = {} ActivStr = {} ActivBol = {} ActivFlt = {} IfElseSy = {"Result" : "", "inCent" : "", "isIf" : "", "isEl" : False, "Enable" : False} ActivMth = {"inMain" : False, "inTask" : False, "CallIt" : False} ActivTsk = {} #_Principal_-------------------------------------------------------------------# def Compiler(FileName) : # Name = FileName.replace(".bk", "") try : File = open(FileName) Lines = File.readlines() File.close() except FileNotFoundError : Error("File ["+Name+"] not found", "") #Tab and Enter Lines = [l.replace(" ", "") for l in Lines] Lines = [l.replace("\n", "") for l in Lines] Lines = [l.replace("\t", "") for l in Lines] #Blank lines and Coments Lines = [l for l in Lines if l != ""] Lines = [l for l in Lines if not l.startswith("//")] if Lines[0] != "[to Basic]" : Error("All Bucket classes need [to Baisc] library", Lines[0]) for y in range(0, len(Lines)) : #Tab error if Lines[y].startswith(" ") : Error("Tab error", Lines[y]) #Class Error if Lines[y].endswith("in Bucket :") : if Lines[y].startswith(Name) : continue else : Error("Namespace of class is wrong", Lines[y]) #Vars if Lines[y].startswith("int ") : Int(Lines[y]) if Lines[y].startswith("str ") : Str(Lines[y]) if Lines[y].startswith("bol ") : Bol(Lines[y]) if Lines[y].startswith("flt ") : Flt(Lines[y]) #Methods if "task :" in Lines[y] : NewTask(Lines, y) # Runner(Lines) # def Runner(lines) : # for x in range(0, len(lines)) : # #bucket open if lines[x] == "bucket open :" : ActivMth["inMain"] = True #any task if "task :" in lines[x] : ActivMth["inTask"] = True #Output if lines[x].startswith("show ") : if ActivMth["inMain"] == True : Show(lines[x]) elif ActivMth["inTask"] == True : continue else : Error("Funtion out of an method", lines[x]) #Call an Task if lines[x].startswith("call ") : if ActivMth["inMain"] == True : TaskCtrl(lines, lines[x]) else : Error("Only 'bucket open' can call tasks", lines[x]) #Set if lines[x].startswith("set ") : if ActivMth["inMain"] == True : Set(lines[x]) elif ActivMth["inTask"] == True : continue else : Error("Funtion out of an method", lines[x]) #Make if lines[x].startswith("make ") : if ActivMth["inMain"] == True : Make(lines[x]) elif ActivMth["inTask"] == True : continue else : Error("Funtion out of an method", lines[x]) #Up if lines[x].startswith("up ") : if ActivMth["inMain"] == True : Up(lines[x]) elif ActivMth["inTask"] == True : continue else : Error("Funtion out of an method", lines[x]) #Convert if lines[x].startswith("convert ") : if ActivMth["inMain"] == True : Convert(lines[x]) elif ActivMth["inTask"] == True : continue else : Error("Funtion out of an method", lines[x]) #If-Else system #If if lines[x].startswith("if ") : if ActivMth["inMain"] == True : IfElseSy["inCent"] = True IfElseSy["isIf"] = True IfElseSy["Result"] = If(lines[x]) elif ActivMth["inTask"] == True : continue else : Error("Funtion out of an method", lines[x]) #Else elif lines[x].startswith("else ") and IfElseSy["Result"] == False : if ActivMth["inMain"] == True : IfElseSy["inCent"] = True IfElseSy["isEl"] = True elif ActivMth["inTask"] == True : continue else : Error("Funtion out of an method", lines[x]) #End of statlement #Do System if lines[x].startswith("do : ") and IfElseSy["inCent"] == True : # line = lines[x].replace("do : ", "") #Is if? if IfElseSy["isIf"] == True and IfElseSy["Result"] == True : IfElseSy["Enable"] = True #Is Else? if IfElseSy["isEl"] == True and IfElseSy["Result"] == False : IfElseSy["Enable"] = True #Then if IfElseSy["Enable"] == True : #Functions if line.startswith("show ") : Show(line) if line.startswith("call ") : ActivMth["CallIt"] = True if line.startswith("set ") : Set(line) if line.startswith("up ") : Up(line) #Errors if line.startswith("int ") : Error("If-else systems cannot declare vars", line) if line.startswith("str ") : Error("If-else systems cannot declare vars", line) if line.startswith("bol ") : Error("If-else systems cannot declare vars", line) if line.startswith("flt ") : Error("If-else systems cannot declare vars", line) # #Compiler errors elif lines[x].startswith("do : ") and not IfElseSy["inCent"] == False : Error("Do out o if statlement", lines[x]) #For loop if lines[x].startswith("for ") : lLine = lines[x].replace("for ", "") limit = lLine.find("times ") count = lLine[:limit] #N° Times try : count = int(count) except ValueError : if count in ActivInt : count = ActivInt[count] else : Error("For loop needs an int", lines[x]) #Loop ForLoop(lines, x, count, lLine.replace(lLine[:limit + 6], "")) #Enders if lines[x] == "finish" : IfElseSy["inCent"] = False IfElseSy["Enable"] = False IfElseSy["isIf"] = False IfElseSy["isEl"] = False if lines[x] == "end l." : ActivMth["inMain"] = False if lines[x] == "end t." : ActivMth["inTask"] = False # return 0 # def Error(ErrorType, Line) : # print("\nCompiler error {"+ErrorType+"}\nin line : \""+str(Line)+"\"") return 0 # #_If-Else System_--------------------------------------------------------------# def If(string) : # String = (string.replace("if ", "")).replace("then :", "") String = String.replace(" ", "") limit = "" isG = False isL = False isE = False isGoE = False isLoE = False #Common if ">" in String : isG = True limit = String.find(">") if "<" in string : isL = True limit = String.find("<") #Equal if "==" in String : isE = True limit = String.find("==") #Or Equal's if "<=" in String : isGoE = True limit = String.find("<=") if ">=" in String : isLoE = True limit = String.find(">=") #------------------------------------------------------------------------------# #This is greather than? if isG == True : FstArg = String[:limit] SstArg = String[limit + 1:] #1st Arg #Declareted Vars if FstArg in ActivInt : FstArg = ActivInt[FstArg] elif FstArg in ActivFlt : FstArg = ActivFlt[FstArg] #Int elif not FstArg.endswith(".f") : try : FstArg = int(FstArg) except ValueError : Error("Unknow value", string) #Float else : FstArg = FstArg.replace(".f", "") try : FstArg = float(FstArg) except ValueError : Error("Unknow value", string) #------------------------------------------------------------------------------# #2st Arg #Declareted Vars if SstArg in ActivInt : SstArg = ActivInt[SstArg] elif SstArg in ActivFlt : SstArg = ActivFlt[SstArg] #Int elif not SstArg.endswith(".f") : try : SstArg = int(SstArg) except ValueError : Error("Unknow value", string) #Float else : SstArg = SstArg.replace(".f", "") try : SstArg = float(SstArg) except ValueError : Error("Unknow value", string) #Finally if FstArg > SstArg : return True else :return False #------------------------------------------------------------------------------# #This is less than? elif isL == True : FstArg = String[:limit] SstArg = String[limit + 1:] #1st Arg #Declareted Vars if FstArg in ActivInt : FstArg = ActivInt[FstArg] elif FstArg in ActivFlt : FstArg = ActivFlt[FstArg] #Int elif not FstArg.endswith(".f") : try : FstArg = int(FstArg) except ValueError : Error("Unknow value", string) #Float else : FstArg = FstArg.replace(".f", "") try : FstArg = float(FstArg) except ValueError : Error("Unknow value", string) #------------------------------------------------------------------------------# #2st Arg #Declareted Vars if SstArg in ActivInt : SstArg = ActivInt[SstArg] elif SstArg in ActivFlt : SstArg = ActivFlt[SstArg] #Int elif not SstArg.endswith(".f") : try : SstArg = int(SstArg) except ValueError : Error("Unknow value", string) #Float else : SstArg = SstArg.replace(".f", "") try : SstArg = float(SstArg) except ValueError : Error("Unknow value", string) #Finally if FstArg < SstArg : return True else : return False #------------------------------------------------------------------------------# elif isE == True : FstArg = String[:limit] SstArg = String[limit + 2:] #1st Arg #Declareted Vars if FstArg in ActivInt : FstArg = ActivInt[FstArg] elif FstArg in ActivStr : FstArg = ActivStr[FstArg] elif FstArg in ActivFlt : FstArg = ActivFlt[FstArg] #String elif FstArg.startswith("'") : FstArg = FstArg.replace("'", "") elif FstArg.startswith("\"") : Error("Strings must be in ''", string) #Int elif not FstArg.endswith(".f") : try : FstArg = int(FstArg) except ValueError : Error("Unknow value", string) #Float else : FstArg = FstArg.replace(".f", "") try : FstArg = float(FstArg) except ValueError : Error("Unknow value", string) #------------------------------------------------------------------------------# #2st Arg #Declareted Vars if SstArg in ActivInt : SstArg = ActivInt[SstArg] elif SstArg in ActivStr : SstArg = ActivStr[SstArg] elif SstArg in ActivFlt : SstArg = ActivFlt[SstArg] #String elif SstArg.startswith("'") : SstArg = SstArg.replace("'", "") elif SstArg.startswith("\"") : Error("Strings must be in ''", string) #Int elif not SstArg.endswith(".f") : try : SstArg = int(SstArg) except ValueError : Error("Unknow value", string) #Float else : SstArg = SstArg.replace(".f", "") try : SstArg = float(SstArg) except ValueError : Error("Unknow
in self.interface_stp_cfg: self.cur_cfg["bpdu_filter"] = "enable" self.existing["bpdu_filter"] = "enable" else: self.cur_cfg["bpdu_filter"] = "disable" self.existing["bpdu_filter"] = "disable" if self.bpdu_protection: if "stp bpdu-protection" in self.stp_cfg: self.cur_cfg["bpdu_protection"] = "enable" self.existing["bpdu_protection"] = "enable" else: self.cur_cfg["bpdu_protection"] = "disable" self.existing["bpdu_protection"] = "disable" if self.tc_protection: if "stp tc-protection" in self.stp_cfg: self.cur_cfg["tc_protection"] = "enable" self.existing["tc_protection"] = "enable" else: self.cur_cfg["tc_protection"] = "disable" self.existing["tc_protection"] = "disable" if self.tc_protection_interval: if "stp tc-protection interval" in self.stp_cfg: tmp_value = re.findall(r'stp tc-protection interval (.)', self.stp_cfg) if not tmp_value: self.module.fail_json( msg='Error: Can not find tc-protection interval on the device.') self.cur_cfg["tc_protection_interval"] = tmp_value[0] self.existing["tc_protection_interval"] = tmp_value[0] else: self.cur_cfg["tc_protection_interval"] = "null" self.existing["tc_protection_interval"] = "null" if self.tc_protection_threshold: if "stp tc-protection threshold" in self.stp_cfg: tmp_value = re.findall(r'stp tc-protection threshold (.)', self.stp_cfg) if not tmp_value: self.module.fail_json( msg='Error: Can not find tc-protection threshold on the device.') self.cur_cfg["tc_protection_threshold"] = tmp_value[0] self.existing["tc_protection_threshold"] = tmp_value[0] else: self.cur_cfg["tc_protection_threshold"] = "1" self.existing["tc_protection_threshold"] = "1" if self.cost: tmp_value = re.findall(r'stp instance (.) cost (.)', self.interface_stp_cfg) if not tmp_value: self.cur_cfg["cost"] = "null" self.existing["cost"] = "null" else: self.cur_cfg["cost"] = tmp_value[0][1] self.existing["cost"] = tmp_value[0][1] # root_protection and loop_protection should get configuration at the same time if self.root_protection or self.loop_protection: if "stp root-protection" in self.interface_stp_cfg: self.cur_cfg["root_protection"] = "enable" self.existing["root_protection"] = "enable" else: self.cur_cfg["root_protection"] = "disable" self.existing["root_protection"] = "disable" if "stp loop-protection" in self.interface_stp_cfg: self.cur_cfg["loop_protection"] = "enable" self.existing["loop_protection"] = "enable" else: self.cur_cfg["loop_protection"] = "disable" self.existing["loop_protection"] = "disable" def get_end_state(self): """ Get end state """ self.cli_get_stp_config() if self.interface and self.interface != "all": self.cli_get_interface_stp_config() if self.stp_mode: if "stp mode stp" in self.stp_cfg: self.end_state["stp_mode"] = "stp" elif "stp mode rstp" in self.stp_cfg: self.end_state["stp_mode"] = "rstp" else: self.end_state["stp_mode"] = "mstp" if self.stp_enable: if "stp disable" in self.stp_cfg: self.end_state["stp_enable"] = "disable" else: self.end_state["stp_enable"] = "enable" if self.stp_converge: if "stp converge fast" in self.stp_cfg: self.end_state["stp_converge"] = "fast" else: self.end_state["stp_converge"] = "normal" if self.edged_port: if self.interface == "all": if "stp edged-port default" in self.stp_cfg: self.end_state["edged_port"] = "enable" else: self.end_state["edged_port"] = "disable" else: if "stp edged-port enable" in self.interface_stp_cfg: self.end_state["edged_port"] = "enable" else: self.end_state["edged_port"] = "disable" if self.bpdu_filter: if self.interface == "all": if "stp bpdu-filter default" in self.stp_cfg: self.end_state["bpdu_filter"] = "enable" else: self.end_state["bpdu_filter"] = "disable" else: if "stp bpdu-filter enable" in self.interface_stp_cfg: self.end_state["bpdu_filter"] = "enable" else: self.end_state["bpdu_filter"] = "disable" if self.bpdu_protection: if "stp bpdu-protection" in self.stp_cfg: self.end_state["bpdu_protection"] = "enable" else: self.end_state["bpdu_protection"] = "disable" if self.tc_protection: if "stp tc-protection" in self.stp_cfg: self.end_state["tc_protection"] = "enable" else: self.end_state["tc_protection"] = "disable" if self.tc_protection_interval: if "stp tc-protection interval" in self.stp_cfg: tmp_value = re.findall(r'stp tc-protection interval (.)', self.stp_cfg) if not tmp_value: self.module.fail_json( msg='Error: Can not find tc-protection interval on the device.') self.end_state["tc_protection_interval"] = tmp_value[0] else: self.end_state["tc_protection_interval"] = "null" if self.tc_protection_threshold: if "stp tc-protection threshold" in self.stp_cfg: tmp_value = re.findall(r'stp tc-protection threshold (.)', self.stp_cfg) if not tmp_value: self.module.fail_json( msg='Error: Can not find tc-protection threshold on the device.') self.end_state["tc_protection_threshold"] = tmp_value[0] else: self.end_state["tc_protection_threshold"] = "1" if self.cost: tmp_value = re.findall(r'stp instance (.) cost (.)', self.interface_stp_cfg) if not tmp_value: self.end_state["cost"] = "null" else: self.end_state["cost"] = tmp_value[0][1] if self.root_protection: if "stp root-protection" in self.interface_stp_cfg: self.end_state["root_protection"] = "enable" else: self.end_state["root_protection"] = "disable" if self.loop_protection: if "stp loop-protection" in self.interface_stp_cfg: self.end_state["loop_protection"] = "enable" else: self.end_state["loop_protection"] = "disable" def present_stp(self): """ Present stp configuration """ cmds = list() # cofig stp global if self.stp_mode: if self.stp_mode != self.cur_cfg["stp_mode"]: cmd = "stp mode %s" % self.stp_mode cmds.append(cmd) self.updates_cmd.append(cmd) if self.stp_enable: if self.stp_enable != self.cur_cfg["stp_enable"]: cmd = "stp %s" % self.stp_enable cmds.append(cmd) self.updates_cmd.append(cmd) if self.stp_converge: if self.stp_converge != self.cur_cfg["stp_converge"]: cmd = "stp converge %s" % self.stp_converge cmds.append(cmd) self.updates_cmd.append(cmd) if self.edged_port: if self.interface == "all": if self.edged_port != self.cur_cfg["edged_port"]: if self.edged_port == "enable": cmd = "stp edged-port default" cmds.append(cmd) self.updates_cmd.append(cmd) else: cmd = "undo stp edged-port default" cmds.append(cmd) self.updates_cmd.append(cmd) if self.bpdu_filter: if self.interface == "all": if self.bpdu_filter != self.cur_cfg["bpdu_filter"]: if self.bpdu_filter == "enable": cmd = "stp bpdu-filter default" cmds.append(cmd) self.updates_cmd.append(cmd) else: cmd = "undo stp bpdu-filter default" cmds.append(cmd) self.updates_cmd.append(cmd) if self.bpdu_protection: if self.bpdu_protection != self.cur_cfg["bpdu_protection"]: if self.bpdu_protection == "enable": cmd = "stp bpdu-protection" cmds.append(cmd) self.updates_cmd.append(cmd) else: cmd = "undo stp bpdu-protection" cmds.append(cmd) self.updates_cmd.append(cmd) if self.tc_protection: if self.tc_protection != self.cur_cfg["tc_protection"]: if self.tc_protection == "enable": cmd = "stp tc-protection" cmds.append(cmd) self.updates_cmd.append(cmd) else: cmd = "undo stp tc-protection" cmds.append(cmd) self.updates_cmd.append(cmd) if self.tc_protection_interval: if self.tc_protection_interval != self.cur_cfg["tc_protection_interval"]: cmd = "stp tc-protection interval %s" % self.tc_protection_interval cmds.append(cmd) self.updates_cmd.append(cmd) if self.tc_protection_threshold: if self.tc_protection_threshold != self.cur_cfg["tc_protection_threshold"]: cmd = "stp tc-protection threshold %s" % self.tc_protection_threshold cmds.append(cmd) self.updates_cmd.append(cmd) # config interface stp if self.interface and self.interface != "all": tmp_changed = False cmd = "interface %s" % self.interface cmds.append(cmd) self.updates_cmd.append(cmd) if self.edged_port: if self.edged_port != self.cur_cfg["edged_port"]: if self.edged_port == "enable": cmd = "stp edged-port enable" cmds.append(cmd) self.updates_cmd.append(cmd) tmp_changed = True else: cmd = "undo stp edged-port" cmds.append(cmd) self.updates_cmd.append(cmd) tmp_changed = True if self.bpdu_filter: if self.bpdu_filter != self.cur_cfg["bpdu_filter"]: if self.bpdu_filter == "enable": cmd = "stp bpdu-filter enable" cmds.append(cmd) self.updates_cmd.append(cmd) tmp_changed = True else: cmd = "undo stp bpdu-filter" cmds.append(cmd) self.updates_cmd.append(cmd) tmp_changed = True if self.root_protection: if self.root_protection == "enable" and self.cur_cfg["loop_protection"] == "enable": self.module.fail_json( msg='Error: The interface has enable loop_protection, can not enable root_protection.') if self.root_protection != self.cur_cfg["root_protection"]: if self.root_protection == "enable": cmd = "stp root-protection" cmds.append(cmd) self.updates_cmd.append(cmd) tmp_changed = True else: cmd = "undo stp root-protection" cmds.append(cmd) self.updates_cmd.append(cmd) tmp_changed = True if self.loop_protection: if self.loop_protection == "enable" and self.cur_cfg["root_protection"] == "enable": self.module.fail_json( msg='Error: The interface has enable root_protection, can not enable loop_protection.') if self.loop_protection != self.cur_cfg["loop_protection"]: if self.loop_protection == "enable": cmd = "stp loop-protection" cmds.append(cmd) self.updates_cmd.append(cmd) tmp_changed = True else: cmd = "undo stp loop-protection" cmds.append(cmd) self.updates_cmd.append(cmd) tmp_changed = True if self.cost: if self.cost != self.cur_cfg["cost"]: cmd = "stp cost %s" % self.cost cmds.append(cmd) self.updates_cmd.append(cmd) tmp_changed = True if not tmp_changed: cmd = "interface %s" % self.interface self.updates_cmd.remove(cmd) cmds.remove(cmd) if cmds: self.cli_load_config(cmds) self.changed = True def absent_stp(self): """ Absent stp configuration """ cmds = list() if self.stp_mode: if self.stp_mode == self.cur_cfg["stp_mode"]: if self.stp_mode != "mstp": cmd = "undo stp mode" cmds.append(cmd) self.updates_cmd.append(cmd) self.changed = True if self.stp_enable: if self.stp_enable != self.cur_cfg["stp_enable"]: cmd = "stp %s" % self.stp_enable cmds.append(cmd) self.updates_cmd.append(cmd) if self.stp_converge: if self.stp_converge == self.cur_cfg["stp_converge"]: cmd = "undo stp converge" cmds.append(cmd) self.updates_cmd.append(cmd) self.changed = True if self.edged_port: if self.interface == "all": if self.edged_port != self.cur_cfg["edged_port"]: if self.edged_port == "enable": cmd = "stp edged-port default" cmds.append(cmd) self.updates_cmd.append(cmd) else: cmd = "undo stp edged-port default" cmds.append(cmd) self.updates_cmd.append(cmd) if self.bpdu_filter: if self.interface == "all": if self.bpdu_filter != self.cur_cfg["bpdu_filter"]: if self.bpdu_filter == "enable": cmd = "stp bpdu-filter default" cmds.append(cmd) self.updates_cmd.append(cmd) else: cmd = "undo stp bpdu-filter default" cmds.append(cmd) self.updates_cmd.append(cmd) if self.bpdu_protection: if self.bpdu_protection != self.cur_cfg["bpdu_protection"]: if self.bpdu_protection == "enable": cmd = "stp bpdu-protection" cmds.append(cmd) self.updates_cmd.append(cmd) else: cmd = "undo stp bpdu-protection" cmds.append(cmd) self.updates_cmd.append(cmd) if self.tc_protection: if self.tc_protection != self.cur_cfg["tc_protection"]: if self.tc_protection == "enable": cmd = "stp tc-protection" cmds.append(cmd) self.updates_cmd.append(cmd) else: cmd = "undo stp tc-protection" cmds.append(cmd) self.updates_cmd.append(cmd) if self.tc_protection_interval: if self.tc_protection_interval == self.cur_cfg["tc_protection_interval"]: cmd = "undo stp tc-protection interval" cmds.append(cmd) self.updates_cmd.append(cmd) self.changed = True if self.tc_protection_threshold: if self.tc_protection_threshold == self.cur_cfg["tc_protection_threshold"]: if self.tc_protection_threshold != "1": cmd = "undo stp tc-protection threshold" cmds.append(cmd) self.updates_cmd.append(cmd) self.changed = True # undo interface stp if self.interface and self.interface != "all": tmp_changed = False cmd = "interface %s" % self.interface cmds.append(cmd) self.updates_cmd.append(cmd) if self.edged_port: if self.edged_port != self.cur_cfg["edged_port"]: if self.edged_port == "enable": cmd = "stp edged-port enable" cmds.append(cmd) self.updates_cmd.append(cmd) tmp_changed = True else: cmd = "undo stp edged-port" cmds.append(cmd) self.updates_cmd.append(cmd) tmp_changed = True if self.bpdu_filter: if self.bpdu_filter != self.cur_cfg["bpdu_filter"]: if self.bpdu_filter == "enable": cmd = "stp bpdu-filter enable" cmds.append(cmd) self.updates_cmd.append(cmd) tmp_changed = True else: cmd = "undo stp bpdu-filter" cmds.append(cmd) self.updates_cmd.append(cmd) tmp_changed = True if self.root_protection: if self.root_protection == "enable" and self.cur_cfg["loop_protection"] == "enable": self.module.fail_json( msg='Error: The interface has enable loop_protection, can not enable root_protection.') if self.root_protection != self.cur_cfg["root_protection"]: if self.root_protection == "enable": cmd = "stp root-protection" cmds.append(cmd) self.updates_cmd.append(cmd) tmp_changed = True else: cmd = "undo stp root-protection" cmds.append(cmd) self.updates_cmd.append(cmd) tmp_changed = True if self.loop_protection: if self.loop_protection == "enable" and self.cur_cfg["root_protection"] == "enable": self.module.fail_json( msg='Error: The interface has enable root_protection, can not enable loop_protection.') if self.loop_protection != self.cur_cfg["loop_protection"]: if self.loop_protection == "enable": cmd = "stp loop-protection" cmds.append(cmd)
inverse=False, init=init, hparams=self._fparams, disable_dropout=disable_dropout, kwargs) if self.is_evaluating and check_invertibility: z_inv_inv, _, _, _ = glow.glow( "glow", z_inv, targets_mask, decoder_self_attention_bias, inverse=True, split_zs=zs, init=False, hparams=self._fparams, disable_dropout=True, kwargs) z_diff = z_q - z_inv_inv tf.summary.scalar("flow_recon_forward", tf.reduce_max(tf.abs(z_diff))) return log_p_z_base, log_abs_det def sample_p( self, targets_length, temp, check_invertibility=False, targets_mask=None, *kwargs): hparams = self._hparams if targets_mask is None: targets_mask = ops.sequence_mask(targets_length, hparams) decoder_self_attention_bias = ( common_attention.attention_bias_ignore_padding(1.0 - targets_mask)) batch_size, targets_max_length = ( common_layers.shape_list(targets_mask)[:2]) prior_shape = [batch_size, targets_max_length, hparams.latent_size] noise = tf.random.normal(prior_shape, stddev=temp) p_dist = None if hparams.prior_type == "standard_normal": z_p = noise elif hparams.prior_type == "diagonal_normal": diag_prior_params = ops.cond_prior( "diag_prior", hparams, tf.zeros(prior_shape), targets_mask, hparams.latent_size2, decoder_self_attention_bias, *kwargs) p_dist = gops.diagonal_normal(diag_prior_params, "diag_prior") z_p = p_dist.loc + p_dist.scale noise elif hparams.prior_type in ["affine", "additive", "rq"]: n_levels = len(hparams.depths.split("/")) divi = max(1, hparams.factor(n_levels-1)) flow_prior_shape = [ batch_size, targets_max_length//divi, hparams.latent_size] noise = tf.random_normal(flow_prior_shape, stddev=temp) z_p, _, _, _ = glow.glow( "glow", noise, targets_mask, decoder_self_attention_bias, inverse=True, init=False, hparams=self._fparams, disable_dropout=True, temp=temp, kwargs) if self.is_evaluating and check_invertibility: noise_inv, _, _, _ = glow.glow( "glow", z_p, targets_mask, decoder_self_attention_bias, inverse=False, init=False, hparams=self._fparams, disable_dropout=True, *kwargs) z_diff = noise - noise_inv tf.summary.scalar("flow_recon_inverse", tf.reduce_max(tf.abs(z_diff))) return z_p, p_dist def optimize(self, loss, num_async_replicas=1, use_tpu=False, variables=None): """Return a training op minimizing loss.""" lr = ops.learning_rate_schedule(self.hparams) if num_async_replicas > 1: t2t_model.log_info("Dividing learning rate by num_async_replicas: %d", num_async_replicas) lr /= math.sqrt(float(num_async_replicas)) train_op = optimize.optimize( loss, lr, self.hparams, use_tpu=use_tpu, variables=variables) return train_op def body(self, features, real_features): return self.internal(features, real_features) def infer(self, features, args, *kwargs): """Produce predictions from the model.""" del args, kwargs inputs_old = None if "inputs" in features and len(features["inputs"].shape) < 4: inputs_old = features["inputs"] features["inputs"] = tf.expand_dims(features["inputs"], 2) features["targets"] = tf.identity(features["inputs"]) # logits, _ = self(features) t2t_model.set_custom_getter_compose(self._custom_getter) tf.get_variable_scope().set_initializer( optimize.get_variable_initializer(self.hparams)) with self._eager_var_store.as_default(): self._fill_problem_hparams_features(features) # intentionally disable sharding during inference (in multi GPU) with tf.variable_scope(self.name): logits, _, _, targets_mask = self.model_fn(features) samples = tf.argmax(logits, axis=-1) samples = tf.where( tf.cast(targets_mask[..., tf.newaxis, tf.newaxis], tf.bool), samples, tf.ones_like(samples)) if inputs_old is not None: # Restore to not confuse Estimator. features["inputs"] = inputs_old return samples def model_fn(self, features): with tf.variable_scope( tf.get_variable_scope(), use_resource=True, reuse=tf.AUTO_REUSE): transformed_features = self.bottom(features) if self.hparams.activation_dtype == "bfloat16": for k, v in sorted(six.iteritems(transformed_features)): if v.dtype == tf.float32: transformed_features[k] = tf.cast(v, tf.bfloat16) t2t_model.log_info("Building model body") output, losses, monitor, targets_mask = self.body( transformed_features, features) output, losses = self._normalize_body_output((output, losses)) if "training" in losses: t2t_model.log_info( "Skipping T2TModel top and loss because training loss " "returned from body") logits = output else: logits = self.top(output, features) losses["training"] = 0.0 if (self._hparams.mode != tf.estimator.ModeKeys.PREDICT and self._hparams.mode != "attack"): losses["training"] = self.loss(logits, features) return logits, losses, monitor, targets_mask def model_fn_sharded(self, sharded_features): """Estimator model_fn sharded along batch dimension. Args: sharded_features: {str: [Tensor]}. Features sharded along batch dimension. Each list is the same length (== number of shards). Returns: sharded_logits: [Tensor]. Logits for each shard of examples. losses: {str: 0-D Tensor}. Loss averaged across shards. """ dp = self._data_parallelism # [{str: Tensor}]. Transpose of 'sharded_features'. datashard_to_features = self._to_features_per_datashard(sharded_features) sharded_logits, sharded_losses, sharded_monitors, _ = ( dp(self.model_fn, datashard_to_features)) sharded_logits, sharded_losses = dp( self.maybe_scheduled_sampling, datashard_to_features, sharded_logits, sharded_losses) if isinstance(sharded_logits[0], dict): temp_dict = {k: [] for k, _ in six.iteritems(sharded_logits[0])} for k, _ in six.iteritems(sharded_logits[0]): for l in sharded_logits: temp_dict[k].append(l[k]) sharded_logits = temp_dict losses = t2t_model.average_sharded_losses(sharded_losses) monitor = {} for key in list(sharded_monitors[0].keys()): monitor[key] = ( tf.add_n([m[key] for m in sharded_monitors]) / len(sharded_monitors)) ops.save_summary(monitor, "monitor") return sharded_logits, losses @registry.register_hparams def wmt_enro_tpu(): """HParams for Transformer model on TPU.""" hparams = transformer.transformer_base() hparams = transformer.update_hparams_for_tpu(hparams) hparams.batch_size = 512 return hparams @registry.register_hparams def iwslt_baseline_gpu(): """HParams for Transformer model on TPU.""" hparams = transformer.transformer_base() hparams.hidden_size = 256 hparams.filter_size = 1024 hparams.num_hidden_layers = 5 hparams.num_heads = 2 hparams.layer_prepostprocess_dropout = 0.1 hparams.attention_dropout = 0.1 hparams.relu_dropout = 0.1 hparams.dropout = 0.1 return hparams @registry.register_hparams def iwslt_baseline_single_gpu(): """HParams for Transformer model on TPU.""" hparams = iwslt_baseline_gpu() hparams.batch_size = 1024 hparams.learning_rate_schedule = "constantlinear_warmuprsqrt_decay" hparams.learning_rate_constant = 0.1 hparams.learning_rate_warmup_steps = 16000 return hparams @registry.register_hparams def iwslt_baseline_tpu(): """HParams for Transformer model on TPU.""" hparams = transformer.transformer_base() transformer.update_hparams_for_tpu(hparams) hparams.hidden_size = 256 hparams.filter_size = 1024 hparams.num_hidden_layers = 5 hparams.num_heads = 2 hparams.layer_prepostprocess_dropout = 0.1 hparams.attention_dropout = 0.1 hparams.relu_dropout = 0.1 hparams.dropout = 0.1 hparams.add_hparam("pos_attn", False) return hparams @registry.register_hparams def iwslt_base(): """Set of hyperparameters.""" # Model architecture flags. hparams = transformer.transformer_base() hparams.num_hidden_layers = 5 hparams.hidden_size = 256 hparams.filter_size = 1024 hparams.num_heads = 4 # Other flags. hparams.summarize_grads = False hparams.summarize_vars = False # Optimization-related flags. hparams.clip_grad_norm = 1.0 hparams.learning_rate_decay_scheme = "noam" hparams.learning_rate_warmup_steps = 8000 hparams.learning_rate = 0.2 hparams.learning_rate_schedule = ( "constantlinear_warmuprsqrt_decayrsqrt_hidden_size") hparams.learning_rate_constant = 2.0 hparams.add_hparam("predict_target_length", True) hparams.add_hparam("lendiff_bound", 30) hparams = update_hparams_for_tpu(hparams) hparams.add_hparam("pos_attn", False) return hparams @registry.register_hparams def iwslt_diag(): """Set of hyperparameters.""" hparams = iwslt_base() hparams.batch_size = 4096 # Other flags. hparams.force_full_predict = True hparams.causal_decoder_self_attention = False # VAE-related flags. hparams.add_hparam("latent_size", 256) hparams.add_hparam("anneal_min_value", 0.0) hparams.add_hparam("kl_startup_steps", 5000) hparams.add_hparam("kl_anneal_steps", 20000) hparams.add_hparam("n_posterior_layers", 3) hparams.add_hparam("n_decoder_layers", 3) hparams.add_hparam("posterior_2d_dropout", 0.20) # diagonal_normal / affine / additive / rq hparams.add_hparam("posterior_type", "diagonal_normal") # standard_normal / diagonal_normal hparams.add_hparam("prior_type", "diagonal_normal") hparams.add_hparam("decoder_2d_dropout", 0.00) # Optimization-related flags. hparams.learning_rate_warmup_steps = 8000 hparams.learning_rate_constant = 2.0 hparams.layer_prepostprocess_dropout = 0.2 hparams.attention_dropout = 0.2 hparams.relu_dropout = 0.2 hparams.dropout = 0.2 # Optimization-related flags. hparams.add_hparam("kl_reg", 0.0) hparams.add_hparam("n_gibbs_steps", 0) hparams.add_hparam("compute_kl_refinement", False) hparams.add_hparam("compute_iw_marginal", False) hparams.add_hparam("n_samples", 1) return hparams @registry.register_hparams def wmt_diag_base(): """Set of hyperparameters.""" hparams = iwslt_diag() hparams.batch_size = 4096 hparams.num_hidden_layers = 6 hparams.hidden_size = 512 hparams.filter_size = 2048 hparams.num_heads = 8 # VAE-related flags. hparams.latent_size = 512 hparams.n_posterior_layers = 4 hparams.n_decoder_layers = 6 hparams.dropout = 0.1 hparams.layer_prepostprocess_dropout = 0.1 hparams.attention_dropout = 0.1 hparams.relu_dropout = 0.1 return hparams @registry.register_hparams def wmt_diag_small(): """Set of hyperparameters.""" hparams = wmt_diag_base() hparams.n_posterior_layers = 3 hparams.n_decoder_layers = 3 hparams.kl_reg = 1e-4 return hparams @registry.register_hparams def wmt_diag_small_trueadam(): """Set of hyperparameters.""" hparams = wmt_diag_small() hparams.optimizer = "true_adam" return hparams @registry.register_hparams def wmt_diag_small_trueadam_longer(): """Set of hyperparameters.""" hparams = wmt_diag_small_trueadam() hparams.learning_rate_constant = 4.0 hparams.learning_rate_warmup_steps = 20000 return hparams @registry.register_hparams def wmt_diag_small_trueadam_shorter(): """Set of hyperparameters.""" hparams = wmt_diag_small_trueadam() hparams.learning_rate_constant = 2.0 hparams.learning_rate_warmup_steps = 4000 return hparams @registry.register_hparams def wmt_diag_base_trueadam_1e4(): """Set of hyperparameters.""" hparams = wmt_diag_base() hparams.kl_reg = 1e-4 hparams.optimizer = "true_adam" hparams.learning_rate_constant = 2.0 hparams.learning_rate_warmup_steps = 8000 return hparams @registry.register_hparams def wmt_diag_base_trueadam_longer_1e4(): """Set of hyperparameters.""" hparams = wmt_diag_base_trueadam_1e4() hparams.learning_rate_constant = 4.0 hparams.learning_rate_warmup_steps = 20000 return hparams @registry.register_hparams def wmt_diag_base_trueadam_shorter_1e4(): """Set of hyperparameters.""" hparams = wmt_diag_base_trueadam_1e4() hparams.learning_rate_constant = 2.0 hparams.learning_rate_warmup_steps = 4000 return hparams @registry.register_hparams def wmt_diag_base_1e4_trueadam(): """Set of hyperparameters.""" hparams = wmt_diag_base() hparams.kl_reg = 1e-4 hparams.optimizer = "true_adam" return hparams @registry.register_hparams def wmt_diag_base_1e4_trueadam_longer(): """Set of hyperparameters.""" hparams = wmt_diag_base_1e4_trueadam() hparams.learning_rate_constant = 4.0 hparams.learning_rate_warmup_steps = 20000 return hparams @registry.register_hparams def wmt_diag_base_1e4_trueadam_shorter(): """Set of hyperparameters.""" hparams = wmt_diag_base_1e4_trueadam() hparams.learning_rate_constant = 2.0 hparams.learning_rate_warmup_steps = 4000 return hparams @registry.register_hparams def wmt_diag_base_1e4(): """Set of hyperparameters.""" hparams = wmt_diag_base() hparams.kl_reg = 1e-4 return hparams @registry.register_hparams def wmt_diag_base_longer_1e4(): """Set of hyperparameters.""" hparams = wmt_diag_base_1e4() hparams.learning_rate_constant = 4.0 hparams.learning_rate_warmup_steps = 20000 return hparams @registry.register_hparams def wmt_diag_base_shorter_1e4(): """Set of hyperparameters.""" hparams = wmt_diag_base_1e4() hparams.learning_rate_constant = 2.0 hparams.learning_rate_warmup_steps = 4000 return hparams @registry.register_hparams def iwslt_diag_1e5(): """Set of hyperparameters.""" hparams = iwslt_diag() hparams.kl_reg = 1e-5 return hparams @registry.register_hparams def iwslt_diag_1e4(): """Set of hyperparameters.""" hparams = iwslt_diag() hparams.kl_reg = 1e-4 return hparams @registry.register_hparams def iwslt_affine(): """Set of hyperparameters.""" hparams = iwslt_diag() hparams.prior_type = "affine" hparams.batch_size = 2048 hparams.latent_size = 256 # Glow-related flags. hparams.add_hparam("depths", "4/8/8") # infer n_levels from depths hparams.add_hparam("step_fn", "glow") # glow / chunting hparams.add_hparam("affine_scale", "glow") # glow / jason hparams.add_hparam("conv_fn", "np") # np / tf hparams.add_hparam("split_plans", "cat/cat/ca") hparams.add_hparam("factor", 2) # squeezing factor hparams.add_hparam("n_layers_transform_params", 1) hparams.add_hparam("n_1x1_heads", 4) hparams.add_hparam("flow_num_heads", 4) hparams.add_hparam("flow_hidden_size", 256) hparams.add_hparam("flow_filter_size", 512) # Control max scale change. hparams.add_hparam("scale_width", 0.999) # Optimization-related flags. # hparams.learning_rate_warmup_steps = 20000 hparams.add_hparam("flow_layer_prepostprocess_dropout", 0.0) hparams.add_hparam("flow_attention_dropout", 0.0) hparams.add_hparam("flow_relu_dropout", 0.0) # hparams.optimizer_adam_beta1 = 0.9 # hparams.optimizer_adam_beta2 = 0.999 # hparams.optimizer_adam_epsilon = 1e-8 # Precision-related flags. hparams.activation_dtype = "float32" hparams.weight_dtype = "float32" return hparams @registry.register_hparams def wmt_affine(): """Set of hyperparameters.""" hparams = iwslt_affine() hparams.batch_size = 2048 # TODO(jason) : address this later. hparams.num_hidden_layers = 6 hparams.hidden_size = 256 hparams.filter_size = 1024 hparams.num_heads = 8 # VAE-related flags. hparams.latent_size = 256 hparams.n_posterior_layers = 4 hparams.n_decoder_layers = 4 hparams.layer_prepostprocess_dropout = 0.1 hparams.attention_dropout = 0.1 hparams.relu_dropout = 0.1 # Glow-related flags. hparams.flow_num_heads = 8 hparams.flow_filter_size = 512 return hparams @registry.register_hparams def wmt_affine_base(): """Set of hyperparameters.""" hparams = wmt_affine() hparams.batch_size = 2048 hparams.hidden_size = 320 hparams.latent_size = 320 hparams.flow_filter_size = 640 return hparams @registry.register_hparams def wmt_affine_base_small(): """Set of hyperparameters.""" hparams = wmt_affine_base() hparams.depths = "4/4/4" hparams.kl_reg = 1e-4 hparams.learning_rate_constant = 2.0 hparams.learning_rate_warmup_steps = 8000 return hparams @registry.register_hparams def wmt_affine_base_trueadam_small(): """Set of hyperparameters.""" hparams
<gh_stars>0 import os import re import ast import sys import json import uuid import MySQLdb import functools import threading import subprocess import unicodedata import flask, flask.views app = flask.Flask(__name__) # Don't do this! app.secret_key = "bacon" #get app directory loc = os.getcwd()+"/" #variables for registry registry = "" regmail = "" reguser = "" regpas = "" #variables for databse access dbhost = "" dbuser = "" dbpasswd = "" #read config.txt file with open(loc+"static/configs/config.txt") as f: details = f.read() f.close() for line in details.splitlines(): line = line.split() if line == []: pass elif line[0] == "registry": registry = line[2] elif line[0] == "regmail": regmail = line[2] elif line[0] == "reguser": reguser = line[2] elif line[0] == "regmail": regmail = line[2] elif line[0] == "regpas": regpas = line[2] elif line[0] == "dbhost": dbhost = line[2] elif line[0] == "dbuser": dbuser = line[2] elif line[0] == "dbpasswd": dbpasswd = line[2] grps = [] radio1 = '' radio2 = '' search1 = '' search2 = '' dld = [] dld_lsn = [] output = [] def executer(cmd): p = subprocess.Popen(cmd, stdin=subprocess.PIPE,stdout=subprocess.PIPE,stderr=subprocess.PIPE) out, err = p.communicate() return out, err def thread_executer(cmd): global dld print "in thread",cmd p = subprocess.Popen(cmd, stdout=subprocess.PIPE,stderr=subprocess.PIPE) out, err = p.communicate() temp=out.splitlines()[-2].split()[0] if temp=='Digest:': dld.remove(cmd[-1]) def downloader(cmd,image,info): global dld,loc dld.append(image) print "Downloading ",image out,err = executer(cmd) print "finished dld ",image if 'Digest' in out: try: cmd = ['docker','tag',cmd[2],image] out,err = executer(cmd) try: print loc+"static/lessons/"+image.replace('/','-') with open(loc+"static/lessons/"+image.replace('/','-'),'w') as f: f.write(info[0]+"\n"+info[1]) f.close() except: print "error writing file -",image except: print "error renaming ",image else: print "failed downloading",image while image in dld : dld.remove(image) print "exiting ",image def add_lesson(old_lesn,lesn,index,line,info): global dld,loc global dld_lsn flag = 1 print "enter loop - add_lesson" while flag: flag = 0 for item in index: if item in dld: flag = 1 print "exit loop - add_lesson" dld_lsn.remove(old_lesn) target = loc+'static/configs/lesson.txt' try: cmd=['grep','^'+lesn+' ',target] val,err = executer(cmd) #add or replace line in the configs/lesson.txt file if val: print "Replacing line" cmd=['sed','-i','/^'+lesn+' /c '+line,target] val = executer(cmd) else: print "Adding line" with open(target, 'a') as f: f.write(line) f.close() #add description about lesson in the static/lessons/ folder with open(loc+'static/lessons/'+lesn,'w') as f: f.write(info[0]+'\n'+info[1]) f.close() except: print "error writing file",lesn def thread_executer_2(cmd,args): global dld print "in thread",cmd if args[0] == 'play': try: f = open(cmd[2],'w') f.write(args[1]) f.close() f = open(cmd[3],'w') f.write(args[2]) f.close() except: print "Error creating playbook ",cmd p = subprocess.Popen(cmd,shell=False,stdin=None,stdout=None,stderr=None,close_fds=True) print "out of process",cmd def reader(fname): index=[] try: with open(fname) as f: index = f.read().splitlines() f.close() except: pass return index def db_ops(cmds,arg): global dbuser, dbpasswd, dbhost db = MySQLdb.connect(host=dbhost, user=dbuser, passwd=<PASSWORD>, db="lense") cur = db.cursor() for cmd in cmds: cur.execute(cmd) result = cur.fetchall() #commit if arg = 1 if arg == 1: db.commit() #return the results return result db.close() def filechecker(): #check and create lessons.txt if doesnot exist already path="static/configs/lesson.txt" if not os.path.exists(path): print "asdad" fh = open(path, "w") fh.write(' ') fh.close() class Main(flask.views.MethodView): def get(self): return flask.render_template('index.html') def post(self): flag = [] if 'logout' in flask.request.form: flask.session.pop('username', None) return flask.redirect(flask.url_for('index')) required = ['username', 'passwd'] for r in required: if r not in flask.request.form: flask.flash("Error: {0} is required.".format(r)) return flask.redirect(flask.url_for('index')) username = flask.request.form['username'] passwd = flask.request.form['passwd'] cmd = "SELECT * FROM users WHERE passwd='"+passwd+"' AND uname='"+username+"'" flag=db_ops([cmd],0) #flag = 1 #check if all files are available filechecker() #if username in users and users[username] == passwd: if flag: flask.session['username'] = username with open('/tmp/.esnel','w') as f: f.write(username) f.close() else: flask.flash("Username doesn't exist or incorrect password") return flask.redirect(flask.url_for('home')) def login_required(method): @functools.wraps(method) def wrapper(args, kwargs): if 'username' in flask.session: return method(args, **kwargs) else: flask.flash("A login is required to proceed!") return flask.redirect(flask.url_for('index')) return wrapper class Repo(flask.views.MethodView): @login_required def get(self): global dld_lsn global dld global registry,regmail,reguser,regpas #dld=['lesson3'] cmd=['curl','https://'+reguser+':'+regpas+'@'+registry+'/v2/_catalog'] out1, out2 = executer(cmd) temp = {'index':{},'lesns':{},'comps':{},'dld':dld,'dld_lsn':dld_lsn} try: images= ast.literal_eval(out1.splitlines()[0])['repositories'] for image in images: #check if description for component exist and add it to temp #cmd=['curl','http://test:user@registry.cs.uno.edu/'+image.replace('/','-')] cmd=['curl','http://'+reguser+':'+regpas+'@'+registry+'/'+image.replace('/','-')] out1, out2 = executer(cmd) desc=out1.splitlines() if desc[0]!='<html>' and desc[0]!='': temp['comps'][image]=[desc[0],'\n'.join(desc[1:])] #check if description for lesson exist and add it to temp, if absent image=image.split('/')[0] try: if temp['lesns'][image]: pass except: #cmd=['curl','http://test:<EMAIL>@<EMAIL>/'+image] cmd=['curl','http://'+reguser+':'+regpas+'@'+registry+'/'+image] out1, out2 = executer(cmd) desc=out1.splitlines() if desc[0]!='<html>' and desc[0]!='': temp['lesns'][image]=[desc[0],'\n'.join(desc[1:])] #check if index for lesson exist and add to temp, if absent try: if temp['index'][image]: pass except: #cmd=['curl','http://test:user@registry.cs.uno.edu/'+image+'_index'] cmd=['curl','http://'+reguser+':'+regpas+'@'+registry+'/'+image+'_index'] out1, out2 = executer(cmd) desc=out1.splitlines()[0] if desc!='<html>' and desc!='': temp['index'][image]=desc else: temp['lesns'][image]=['n/a','n/a'] else: temp['comps'][image]=['n/a','n/a'] except: print "some error in getting repo data" result = temp print result flask.flash(result) return flask.render_template('repo.html') @login_required def post(self): global dld_lsn global loc global registry,regmail,reguser,regpas flag = 0 #login to the registry server #cmd = ['docker','login','-u','test','-p','user','--email="<EMAIL>"','https://registry.cs.uno.edu'] cmd = ['docker','login','-u',reguser,'-p',regpas,'--email="'+regmail+'"','https://'+registry] out1,out2=executer(cmd) try: request = flask.request.form['lesn'] request = ast.literal_eval(request) lesn = request[0] cont = request[1] #info = cont['comps'][image] flag = 1 except: request = flask.request.form['comp'] request = ast.literal_eval(request) image = request[0] cont = request[1] info = cont['comps'][image] #download just the component image from the registry server in a thread cmd = ['docker','pull',registry+'/'+image] t = threading.Thread(name='child procs', target=downloader, args=[cmd,image,info]) t.daemon = True t.start() #return to back to web page return flask.redirect(flask.url_for('repo')) #add code if lesson is to be saved under a new name new_lsn = lesn #add lesson to the download list for lessons dld_lsn.append(lesn) #print lesn,'\n', cont new_cont = [] for comp in cont['index'][lesn].split()[1:]: print "loop main",comp image1 = comp.replace(lesn,new_lsn) image = image1.replace('-','/') new_cont.append(image1) #download image from the registry server in a thread cmd = ['docker','pull',registry+'/'+image] info = cont['comps'][image] t = threading.Thread(name='child procs', target=downloader, args=[cmd,image,info]) t.daemon = True t.start() #get description from POST and other attributes required for the lesson desc = cont['lesns'][lesn] line = new_lsn+' '+' '.join(new_cont) index = new_cont t = threading.Thread(name='child procs', target=add_lesson, args=[lesn,new_lsn,index,line,desc]) t.daemon = True t.start() return flask.redirect(flask.url_for('repo')) class Home(flask.views.MethodView): @login_required def get(self): global loc #index2 {'lesson1': {'status': 'Y', 'comps': {'lesson1/comp1': {'status': ['Y'], 'index': ['lesson1/comp1', 'latest', '252f198a8beb', 'ago 380MB', 'Y', []], 'desc': ['Web Server', 'LAMP server hosting a PHP webpage.']}}, 'desc': ['SQL Injection to Shell II', 'This exercise explains how you can, from a blind SQL injection, gain access to the administration console. Then once in the administration console, how you can run commands on the system. ']}} #check if all files are available filechecker() #--------------------------------- #check for status of containers cmd = ['docker', 'ps', '-a'] out1, out2 = executer(cmd) index3={} index4=[] tag = "" if out1: temp2=[] temp3=[] flag=0 for line in out1.splitlines(): if 'lesson' in line: var1=line.split() if var1[var1.index('ago')+1] == 'Up': index3[var1[-1]]=[var1[1],'Y'] else: index3[var1[-1]]=[var1[1],'S'] index4.append(var1[-1]) print "Home",index3,index4 index1={} temp2=[] #check downloaded images cmd = ['docker', 'images'] out1, out2 = executer(cmd) for line in out1.splitlines(): temp3 = [] flags = [] temp = line.split() if line.startswith('lesson'): status='' #555 history command no longer gives you image id of intermediate containers cmd = ["docker","history","--no-trunc",temp[0]] temp2=executer(cmd) image = [] flags = 0 for step in temp2[0].splitlines(): if '"@STEP@' in step: step = step.split() image = step[0][0:3] temp1=[] try: temp1=index3[temp[0].replace('/','-')] if image == temp1[0] : #print temp1 flags=temp1[1] else: temp1=['',''] except: temp1=['',''] temp3.append([image,temp1[1],' '.join(step[step.index('"@STEP@')+1:-2])[:-1]]) if image: temp[2]=image if not flags: try: flags=index3[temp[0].replace('/','-')][1] except: flags='N' index1[temp[0]]=[temp[0],temp[1],temp[2],' '.join(temp[-2:]),flags,temp3[::-1]] print "index",index1 temp=[] index2={} fname=loc+'static/configs/lesson.txt' with open(fname) as f: temp=f.read().splitlines() for item in temp: count1 = count2 = 0 item = item.split() index2[item[0]]={} if True: #check files and add the lesson title and description try: fbuf=[] fname=loc+'static/lessons/'+item[0] with open(fname) as f: fbuf=f.read().splitlines() index2[item[0]]['desc']=[fbuf[0],''.join(fbuf[1:])] except: index2[item[0]]['desc']=['',''] index2[item[0]]['comps']={} index2[item[0]]['status']='' #print item,index2 for key in item[1:]: #check files and add the component title and description print "--",key try: fbuf=[] fname='static/lessons/'+key with open(fname) as f: fbuf=f.read().splitlines() comp_desc = [fbuf[0],''.join(fbuf[1:])] except: comp_desc = ['',''] ip = 'n/a' try: temp3=index1[key.replace('-','/')] if temp3[4]=='Y': cmd = ['docker','inspect','--format','{{ .NetworkSettings.IPAddress}}',key] ip,err = executer(cmd) ip = ip.rstrip() count1+=1 elif temp3[4]=='N': count2+=1 except: temp3=[] index2[item[0]]['comps'][key.replace('-','/')]={'index':temp3,'desc':comp_desc,'status':[temp3[4]],'network':[ip]} #print key,comp_desc,temp3 #print index2 print item[1:],count1,count2 if count1 == len(item[1:]): index2[item[0]]['status']='Y' elif count2 == len(item[1:]) : index2[item[0]]['status']='N' else: index2[item[0]]['status']='S' #print "new" #print index3 #print index1 print "index2",index2 flask.flash(index2,'lesson') return flask.render_template('home.html') @login_required def post(self): request = flask.request.form result = {} temp1 = [] temp2 = [] print request try: if request['start-all']: print request['start-all'] try: temp=ast.literal_eval(request['start-all']) targets=temp.keys() except: pass print targets for cont in targets: image = temp[cont]['index'][2] print "starting container ",cont,image cmd = ['docker', 'run', '-Pitd', '--name='+cont.replace('/','-'), image] out1, out2 = executer(cmd) print "out-",cont,out2 except: try: if request['stop-all']: try: temp=ast.literal_eval(request['stop-all']) request=temp.keys() except: request=[request['stop-all']] print "stop all containers ",request for cont in request: cont = cont.replace('/','-') print "stopping container "+cont cmd = ['docker', 'stop', cont] out1, out2 = executer(cmd) except: try: if request['reset-all']: try: conts = ast.literal_eval(request['reset-all']) targets = conts.keys() except: targets = [request['reset-all']] for cont in targets: print
<gh_stars>0 import sys import time import stat from typing import Any import random import subprocess import glob import os import pandas as pd # type: ignore from pathlib import Path from typing import List from sys import platform import pathlib import shutil import traceback from pylpg.lpgdata import * from pylpg.lpgpythonbindings import * def execute_lpg_tsib( year: int, number_of_households: int, number_of_people_per_household: int, startdate: str = None, enddate: str = None, transportation: bool = False, energy_intensity: str = "Random", ) -> pd.DataFrame: lpe: LPGExecutor = LPGExecutor(1, False) if number_of_households < 1: print("too few households") raise Exception("Need at least one household") # basic default spec request = lpe.make_default_lpg_settings(year) # create random households for idx in range(number_of_households): hhd: HouseholdData = HouseholdData() hhd.HouseholdDataSpecification = HouseholdDataSpecificationType.ByPersons hhd.HouseholdDataPersonSpec = HouseholdDataPersonSpecification() hhd.HouseholdDataPersonSpec.Persons = [] hhd.ChargingStationSet = ( ChargingStationSets.Charging_At_Home_with_03_7_kW_output_results_to_Car_Electricity ) hhd.TravelRouteSet = ( TravelRouteSets.Travel_Route_Set_for_30km_Commuting_Distance ) hhd.TransportationDeviceSet = TransportationDeviceSets.Bus_and_two_30_km_h_Cars hhd.HouseholdDataPersonSpec.Persons = make_reasonable_family( number_of_people_per_household ) request.House.Households.append(hhd) # set more parameters if request.CalcSpec is None: raise Exception("Failed to initialize the calculation spec") if startdate is not None: request.CalcSpec.set_StartDate(startdate) if enddate is not None: request.CalcSpec.set_EndDate(enddate) request.CalcSpec.EnergyIntensityType = energy_intensity calcspecfilename = Path(lpe.calculation_directory, "calcspec.json") if transportation: request.CalcSpec.EnableTransportation = True request.CalcSpec.CalcOptions.append(CalcOption.TansportationDeviceJsons) # write to json with open(calcspecfilename, "w") as calcspecfile: jsonrequest = request.to_json(indent=4) # type: ignore calcspecfile.write(jsonrequest) # execute lpe.execute_lpg_binaries() # read the results and return as dataframe return lpe.read_all_json_results_in_directory() def make_reasonable_family(person_count: int): previousage = 0 persons = [] g = 0 for person_idx in range(person_count): if person_idx == 0: # first is an adult age = random.randint(18, 100) previousage = age g = random.randint(0, 1) elif person_idx == 1: # 2nd adult should be roughly similar age diffage = random.randint(0, 10) age = previousage - 5 + diffage if g == 0: g = 1 else: g = 0 else: # other people are children age = random.randint(0, 20) if g == 0: g = 1 else: g = 0 if g == 0: gender = Gender.Male else: gender = Gender.Female pd = PersonData(age, gender) persons.append(pd) return persons def execute_lpg_single_household( year: int, householdref: JsonReference, housetype: str, startdate: str = None, enddate: str = None, geographic_location: JsonReference = None, simulate_transportation: bool = False, chargingset: JsonReference = None, transportation_device_set: JsonReference = None, travel_route_set: JsonReference = None, random_seed: int = None, energy_intensity: str = "Random", ) -> pd.DataFrame: lpe: LPGExecutor = LPGExecutor(1, False) # basic request request = lpe.make_default_lpg_settings(year) if random_seed is not None: request.CalcSpec.RandomSeed = random_seed request.House.HouseTypeCode = housetype hhnamespec = HouseholdNameSpecification(householdref) hhn = HouseholdData( None, None, hhnamespec, "hhid", "hhname", chargingset, transportation_device_set, travel_route_set, None, HouseholdDataSpecificationType.ByHouseholdName, ) request.House.Households.append(hhn) if request.CalcSpec is None: raise Exception("Failed to initialize the calculation spec") if startdate is not None: request.CalcSpec.set_StartDate(startdate) if enddate is not None: request.CalcSpec.set_EndDate(enddate) request.CalcSpec.EnergyIntensityType = energy_intensity calcspecfilename = Path(lpe.calculation_directory, "calcspec.json") request.CalcSpec.GeographicLocation = geographic_location if simulate_transportation: request.CalcSpec.EnableTransportation = True request.CalcSpec.CalcOptions.append(CalcOption.TansportationDeviceJsons) with open(calcspecfilename, "w") as calcspecfile: jsonrequest = request.to_json(indent=4) # type: ignore calcspecfile.write(jsonrequest) lpe.execute_lpg_binaries() return lpe.read_all_json_results_in_directory() def execute_lpg_with_householdata( year: int, householddata: HouseholdData, housetype: str, startdate: str = None, enddate: str = None, simulate_transportation: bool = False, target_heating_demand: Optional[float] = None, target_cooling_demand: Optional[float] = None, calculation_index: int = 1, clear_previous_calc: bool = False, random_seed: int = None, energy_intensity: str = "Random", ): try: print( "Starting calc with " + str(calculation_index) + " for " + householddata.Name ) lpe: LPGExecutor = LPGExecutor(calculation_index, clear_previous_calc) # basic request request = lpe.make_default_lpg_settings(year) request.House.HouseTypeCode = housetype if random_seed is not None: request.CalcSpec.RandomSeed = random_seed if target_heating_demand is not None: request.House.TargetHeatDemand = target_heating_demand if target_cooling_demand is not None: request.House.TargetCoolingDemand = target_cooling_demand request.House.Households.append(householddata) if request.CalcSpec is None: raise Exception("Failed to initialize the calculation spec") if startdate is not None: request.CalcSpec.set_StartDate(startdate) if enddate is not None: request.CalcSpec.set_EndDate(enddate) request.CalcSpec.EnergyIntensityType = energy_intensity calcspecfilename = Path(lpe.calculation_directory, "calcspec.json") if simulate_transportation: request.CalcSpec.EnableTransportation = True request.CalcSpec.CalcOptions.append(CalcOption.TansportationDeviceJsons) with open(calcspecfilename, "w") as calcspecfile: jsonrequest = request.to_json(indent=4) # type: ignore calcspecfile.write(jsonrequest) lpe.execute_lpg_binaries() df = lpe.read_all_json_results_in_directory() return df except OSError as why: print("Exception: " + str(why)) traceback.print_stack() raise except: # catch all exceptions e = sys.exc_info()[0] print("Exception: " + str(e)) traceback.print_stack() raise def execute_lpg_with_many_householdata( year: int, householddata: List[HouseholdData], housetype: str, startdate: str = None, enddate: str = None, simulate_transportation: bool = False, target_heating_demand: Optional[float] = None, target_cooling_demand: Optional[float] = None, calculation_index: int = 1, clear_previous_calc: bool = False, random_seed: int = None, energy_intensity: str = "Random", ): try: print( "Starting calc with " + str(calculation_index) + " for " + str(len(householddata)) + " households" ) lpe: LPGExecutor = LPGExecutor(calculation_index, clear_previous_calc) # basic request request = lpe.make_default_lpg_settings(year) request.House.HouseTypeCode = housetype if random_seed is not None: request.CalcSpec.RandomSeed = random_seed if target_heating_demand is not None: request.House.TargetHeatDemand = target_heating_demand if target_cooling_demand is not None: request.House.TargetCoolingDemand = target_cooling_demand request.House.Households = request.House.Households + householddata if request.CalcSpec is None: raise Exception("Failed to initialize the calculation spec") if startdate is not None: request.CalcSpec.set_StartDate(startdate) if enddate is not None: request.CalcSpec.set_EndDate(enddate) request.CalcSpec.EnergyIntensityType = energy_intensity calcspecfilename = Path(lpe.calculation_directory, "calcspec.json") if simulate_transportation: request.CalcSpec.EnableTransportation = True request.CalcSpec.CalcOptions.append(CalcOption.TansportationDeviceJsons) with open(calcspecfilename, "w") as calcspecfile: jsonrequest = request.to_json(indent=4) # type: ignore calcspecfile.write(jsonrequest) lpe.execute_lpg_binaries() df = lpe.read_all_json_results_in_directory() return df except OSError as why: print("Exception: " + str(why)) traceback.print_stack() raise except: # catch all exceptions e = sys.exc_info()[0] print("Exception: " + str(e)) traceback.print_stack() raise def execute_lpg_with_householdata_with_csv_save( year: int, householddata: HouseholdData, housetype: str, startdate: str = None, enddate: str = None, simulate_transportation: bool = False, target_heating_demand: Optional[float] = None, target_cooling_demand: Optional[float] = None, calculation_index: int = 1, ): try: df = execute_lpg_with_householdata( year, householddata, housetype, startdate, enddate, simulate_transportation, target_heating_demand, target_cooling_demand, calculation_index, True, ) df_electricity = df["Electricity_HH1"] df_electricity.to_csv("R" + str(calculation_index) + ".csv") calcdir = "C" + str(calculation_index) if os.path.exists(calcdir): print("cleaning up " + calcdir) shutil.rmtree(calcdir) time.sleep(1) except OSError as why: print("Exception: " + str(why)) traceback.print_stack() raise except: # catch all exceptions e = sys.exc_info()[0] print("Exception: " + str(e)) traceback.print_stack() raise def execute_grid_calc( year: int, household_size_list: List[int], housetype: str, startdate: str = None, enddate: str = None, simulate_transportation: bool = False, chargingset: JsonReference = None, transportation_device_set: JsonReference = None, travel_route_set: JsonReference = None, ) -> pd.DataFrame: lpe: LPGExecutor = LPGExecutor(1, True) # basic request request = lpe.make_default_lpg_settings(year) request.CalcSpec.CalcOptions.clear() request.CalcSpec.CalcOptions.append(CalcOption.JsonHouseSumFiles) if len(household_size_list) < 1: raise Exception("need at least one household.") request.House.HouseTypeCode = housetype count = 1 for hs in household_size_list: hhdps: HouseholdDataPersonSpecification = HouseholdDataPersonSpecification( make_reasonable_family(hs) ) hhn = HouseholdData( hhdps, None, None, "hhid", "hhname" + str(count), chargingset, transportation_device_set, travel_route_set, None, HouseholdDataSpecificationType.ByPersons, ) request.House.Households.append(hhn) count = count + 1 if request.CalcSpec is None: raise Exception("Failed to initialize the calculation spec") if startdate is not None: request.CalcSpec.set_StartDate(startdate) if enddate is not None: request.CalcSpec.set_EndDate(enddate) calcspecfilename = Path(lpe.calculation_directory, "calcspec.json") if simulate_transportation: request.CalcSpec.EnableTransportation = True request.CalcSpec.CalcOptions.append(CalcOption.TansportationDeviceJsons) with open(calcspecfilename, "w") as calcspecfile: jsonrequest = request.to_json(indent=4) # type: ignore calcspecfile.write(jsonrequest) lpe.execute_lpg_binaries() return lpe.read_all_json_results_in_directory() class LPGExecutor: def __init__(self, calcidx: int, clear_previous_calc: bool): version = "_" self.working_directory = pathlib.Path(__file__).parent.absolute() if platform == "linux" or platform == "linux2": self.calculation_src_directory = Path( self.working_directory, "LPG" + version + "linux" ) self.simengine_src_filename = "simengine2" fullname = Path(self.calculation_src_directory, self.simengine_src_filename) print("starting to execute " + str(fullname)) os.chmod(str(fullname), stat.S_IRWXU \| stat.S_IRWXG \| stat.S_IRWXO) print("Permissions:" + str(oct(os.stat(str(fullname))[stat.ST_MODE])[-3:])) elif platform == "win32": self.calculation_src_directory = Path( self.working_directory, "LPG" + version + "win" ) self.simengine_src_filename = "simulationengine.exe" else: raise Exception("unknown operating system detected: " + platform) self.calculation_directory = Path(self.working_directory, "C" + str(calcidx)) print("Working in directory: " + str(self.calculation_directory)) if clear_previous_calc and os.path.exists(self.calculation_directory): self.error_tolerating_directory_clean(self.calculation_directory) print("Removing " + str(self.calculation_directory)) shutil.rmtree(self.calculation_directory) time.sleep(1) if not os.path.exists(self.calculation_directory): print( "copying from " + str(self.calculation_src_directory) + " to " + str(self.calculation_directory) ) shutil.copytree(self.calculation_src_directory, self.calculation_directory) print("copied to: " + str(self.calculation_directory)) def error_tolerating_directory_clean(self, path: str): mypath = str(path) if len(str(mypath)) < 10: raise Exception( "Path too short. This is suspicious. Trying to delete more than you meant to?" ) print("cleaning " + mypath) files = glob.glob(mypath + "/*", recursive=True) for file in files: if os.path.isfile(file): print("Removing " + file) os.remove(file) def execute_lpg_binaries(self) -> Any: # execute LPG pathname = Path(self.calculation_directory, self.simengine_src_filename) print("executing in " + str(self.calculation_directory)) subprocess.run( [str(pathname), "processhousejob", "-j", "calcspec.json"], cwd=str(self.calculation_directory), ) def make_default_lpg_settings(self, year: int) -> HouseCreationAndCalculationJob: print("Creating") hj = HouseCreationAndCalculationJob() hj.set_Scenario("S1").set_Year(str(year)).set_DistrictName("district") hd = HouseData() hj.House = hd hd.Name = "House" hd.HouseGuid = StrGuid("houseguid") hd.HouseTypeCode = ( HouseTypes.HT01_House_with_a_10kWh_Battery_and_a_fuel_cell_battery_charger_5_MWh_yearly_space_heating_gas_heating ) hd.TargetCoolingDemand = 10000 hd.TargetHeatDemand = 0 hd.Households = [] cs: JsonCalcSpecification = JsonCalcSpecification() hj.CalcSpec = cs cs.IgnorePreviousActivitiesWhenNeeded = True cs.LoadTypePriority = LoadTypePriority.All cs.DefaultForOutputFiles
# 1972 article n = int(n) d = len(A) if len(set(a%d for a in A)) == d: return [id for i in range(n//d)] # next, we consider an exhaustive search from sage.combinat.dlx import DLXMatrix rows = [] for i in range(n): rows.append([i+1, [(i+a)%n+1 for a in A]]) M = DLXMatrix(rows) for c in M: return [i-1 for i in c] def one_radical_difference_family(K, k): r""" Search for a radical difference family on ``K`` using dancing links algorithm. For the definition of radical difference family, see :func:`radical_difference_family`. Here, we consider only radical difference family with `\lambda = 1`. INPUT: - ``K`` -- a finite field of cardinality `q`. - ``k`` -- a positive integer so that `k(k-1)` divides `q-1`. OUTPUT: Either a difference family or ``None`` if it does not exist. ALGORITHM: The existence of a radical difference family is equivalent to a one dimensional tiling (or packing) problem in a cyclic group. This subsequent problem is solved by a call to the function :func:`one_cyclic_tiling`. Let `K^` be the multiplicative group of the finite field `K`. A radical family has the form `\mathcal B = \{x_1 B, \ldots, x_k B\}`, where `B=\{x:x^{k}=1\}` (for `k` odd) or `B=\{x:x^{k-1}=1\}\cup \{0\}` (for `k` even). Equivalently, `K^` decomposes as: .. MATH:: K^ = \Delta (x_1 B) \cup ... \cup \Delta (x_k B) = x_1 \Delta B \cup ... \cup x_k \Delta B We observe that `C=B\backslash 0` is a subgroup of the (cyclic) group `K^`, that can thus be generated by some element `r`. Furthermore, we observe that `\Delta B` is always a union of cosets of `\pm C` (which is twice larger than `C`). .. MATH:: \begin{array}{llll} (k\text{ odd} ) & \Delta B &= \{r^i-r^j:r^i\neq r^j\} &= \pm C\cdot \{r^i-1: 0 < i \leq m\}\\ (k\text{ even}) & \Delta B &= \{r^i-r^j:r^i\neq r^j\}\cup C &= \pm C\cdot \{r^i-1: 0 < i < m\}\cup \pm C \end{array} where .. MATH:: (k\text{ odd})\ m = (k-1)/2 \quad \text{and} \quad (k\text{ even})\ m = k/2. Consequently, `\mathcal B = \{x_1 B, \ldots, x_k B\}` is a radical difference family if and only if `\{x_1 (\Delta B/(\pm C)), \ldots, x_k (\Delta B/(\pm C))\}` is a partition of the cyclic group `K^/(\pm C)`. EXAMPLES:: sage: from sage.combinat.designs.difference_family import ( ....: one_radical_difference_family, ....: is_difference_family) sage: one_radical_difference_family(GF(13),4) [[0, 1, 3, 9]] The parameters that appear in [Bu95]_:: sage: df = one_radical_difference_family(GF(449), 8); df [[0, 1, 18, 25, 176, 324, 359, 444], [0, 9, 88, 162, 222, 225, 237, 404], [0, 11, 140, 198, 275, 357, 394, 421], [0, 40, 102, 249, 271, 305, 388, 441], [0, 49, 80, 93, 161, 204, 327, 433], [0, 70, 99, 197, 230, 362, 403, 435], [0, 121, 141, 193, 293, 331, 335, 382], [0, 191, 285, 295, 321, 371, 390, 392]] sage: is_difference_family(GF(449), df, 449, 8, 1) True """ q = K.cardinality() x = K.multiplicative_generator() e = k(k-1) if q%e != 1: raise ValueError("q%e is not 1") # We define A by (see the function's documentation): # ΔB = C.A if k%2 == 1: m = (k-1) // 2 r = x * ((q-1) // k) # k-th root of unity A = [ri - 1 for i in range(1,m+1)] else: m = k // 2 r = x ((q-1) // (k-1)) # (k-1)-th root of unity A = [r*i - 1 for i in range(1,m)] A.append(K.one()) # instead of the complicated multiplicative group K^/(±C) we use the # discrete logarithm to convert everything into the additive group Z/cZ c = m * (q-1) // e # cardinal of ±C from sage.groups.generic import discrete_log logA = [discrete_log(a,x)%c for a in A] # if two elements of A are equal modulo c then no tiling is possible if len(set(logA)) != m: return None # brute force tiling = one_cyclic_tiling(logA, c) if tiling is None: return None D = K.cyclotomic_cosets(r, [x*i for i in tiling]) if k%2 == 0: for d in D: d.insert(K.zero(),0) return D def radical_difference_family(K, k, l=1, existence=False, check=True): r""" Return a ``(v,k,l)``-radical difference family. Let fix an integer `k` and a prime power `q = t k(k-1) + 1`. Let `K` be a field of cardinality `q`. A `(q,k,1)`-difference family is radical* if its base blocks are either: a coset of the `k`-th root of unity for `k` odd or a coset of `k-1`-th root of unity and `0` if `k` is even (the number `t` is the number of blocks of that difference family). The terminology comes from M. Buratti article [Bu95]_ but the first constructions go back to <NAME> [Wi72]_. INPUT: - ``K`` - a finite field - ``k`` -- positive integer, the size of the blocks - ``l`` -- the `\lambda` parameter (default to `1`) - ``existence`` -- if ``True``, then return either ``True`` if Sage knows how to build such design, ``Unknown`` if it does not and ``False`` if it knows that the design does not exist. - ``check`` -- boolean (default: ``True``). If ``True`` then the result of the computation is checked before being returned. This should not be needed but ensures that the output is correct. EXAMPLES:: sage: from sage.combinat.designs.difference_family import radical_difference_family sage: radical_difference_family(GF(73),9) [[1, 2, 4, 8, 16, 32, 37, 55, 64]] sage: radical_difference_family(GF(281),5) [[1, 86, 90, 153, 232], [4, 50, 63, 79, 85], [5, 36, 149, 169, 203], [7, 40, 68, 219, 228], [9, 121, 212, 248, 253], [29, 81, 222, 246, 265], [31, 137, 167, 247, 261], [32, 70, 118, 119, 223], [39, 56, 66, 138, 263], [43, 45, 116, 141, 217], [98, 101, 109, 256, 279], [106, 124, 145, 201, 267], [111, 123, 155, 181, 273], [156, 209, 224, 264, 271]] sage: for k in range(5,10): ....: print("k = {}".format(k)) ....: list_q = [] ....: for q in range(k(k-1)+1, 2000, k(k-1)): ....: if is_prime_power(q): ....: K = GF(q,'a') ....: if radical_difference_family(K, k, existence=True): ....: list_q.append(q) ....: _ = radical_difference_family(K,k) ....: print(" ".join([str(p) for p in list_q])) k = 5 41 61 81 241 281 401 421 601 641 661 701 761 821 881 1181 1201 1301 1321 1361 1381 1481 1601 1681 1801 1901 k = 6 181 211 241 631 691 1531 1831 1861 k = 7 337 421 463 883 1723 k = 8 449 1009 k = 9 73 1153 1873 """ v = K.cardinality() x = K.multiplicative_generator() one = K.one() e = k(k-1) if (l(v-1)) % e: raise ValueError("k (k-1) = {} should be a multiple of l (v-1) ={}".format( k(k-1), l(v-1))) t = l(v-1) // e # number of blocks if t == 1: return radical_difference_set(K, k, l, existence=existence, check=check) elif l == (k-1): if existence: return True else: return K.cyclotomic_cosets(x*((v-1)//k))[1:] # all the other cases below concern the case l == 1 elif l != 1: if existence: return Unknown raise NotImplementedError("No radical families implemented for l > 2") else: D = one_radical_difference_family(K,k) if D is None: if existence: return False raise EmptySetError("No such difference family") elif existence: return True if check and not is_difference_family(K, D, v, k, l): raise RuntimeError("radical_difference_family produced a wrong " "difference family with parameters v={}, " "k={}, l={}. Please contact " "<EMAIL>".format(v,k,l)) return D def twin_prime_powers_difference_set(p, check=True): r""" Return a difference set on `GF(p) \times GF(p+2)`. The difference set is built from the following element of the Cartesian product of finite fields `GF(p) \times GF(p+2)`: - `(x,0)` with any `x` - `(x,y)` with `x` and `y` squares - `(x,y)` with `x` and `y` non-squares For more information see :wikipedia:`Difference_set`. INPUT: - ``check`` -- boolean (default: ``True``). If ``True`` then the result of the computation is checked before being returned. This should not be needed but ensures that the output is correct. EXAMPLES:: sage: from sage.combinat.designs.difference_family import twin_prime_powers_difference_set sage: G,D = twin_prime_powers_difference_set(3) sage: G The Cartesian product of (Finite Field of size 3, Finite Field of
isinstance(o.rvalue, CallExpr): call_expr = o.rvalue if self._IsInstantiation(call_expr): temp_name = 'gobj%d' % self.unique_id self.unique_id += 1 self.log('INSTANCE lval %s rval %s', lval, call_expr) self.write('%s %s', call_expr.callee.name, temp_name) # C c;, not C c(); which is most vexing parse if call_expr.args: self._WriteArgList(call_expr) self.write(';\n') self.write('%s %s = &%s;', get_c_type(lval_type), lval.name, temp_name) return # src = cast(source__SourcedFile, src) # -> source__SourcedFile* src = static_cast<source__SourcedFile>(src) if isinstance(o.rvalue, CallExpr) and o.rvalue.callee.name == 'cast': assert isinstance(lval, NameExpr) call = o.rvalue type_expr = call.args[0] subtype_name = _GetCTypeForCast(type_expr) cast_kind = _GetCastKind(self.module_path, subtype_name) # HACK: Distinguish between UP cast and DOWN cast. # osh/cmd_parse.py _MakeAssignPair does an UP cast within branches. # _t is the base type, so that means it's an upcast. if isinstance(type_expr, NameExpr) and type_expr.name.endswith('_t'): if self.decl: self.local_var_list.append((lval.name, subtype_name)) self.write_ind( '%s = %s<%s>(', lval.name, cast_kind, subtype_name) else: self.write_ind( '%s %s = %s<%s>(', subtype_name, lval.name, cast_kind, subtype_name) self.accept(call.args[1]) # variable being casted self.write(');\n') return if isinstance(lval, NameExpr): if lval.name == '_': # Skip _ = log return lval_type = self.types[lval] #c_type = get_c_type(lval_type, local=self.indent != 0) c_type = get_c_type(lval_type) # for "hoisting" to the top of the function if self.in_func_body: self.write_ind('%s = ', lval.name) if self.decl: self.local_var_list.append((lval.name, c_type)) else: # globals always get a type -- they're not mutated self.write_ind('%s %s = ', c_type, lval.name) # Special case for list comprehensions. Note that a variable has to # be on the LHS, so we can append to it. # # y = [i+1 for i in x[1:] if i] # => # y = [] # for i in x[1:]: # if i: # y.append(i+1) # (but in C++) if isinstance(o.rvalue, ListComprehension): gen = o.rvalue.generator # GeneratorExpr left_expr = gen.left_expr index_expr = gen.indices[0] seq = gen.sequences[0] cond = gen.condlists[0] # TODO: not used! # Write empty container as initialization. assert c_type.endswith(''), c_type # Hack self.write('Alloc<%s>();\n' % c_type[:-1]) over_type = self.types[seq] #self.log(' iterating over type %s', over_type) if over_type.type.fullname() == 'builtins.list': c_type = get_c_type(over_type) assert c_type.endswith(''), c_type c_iter_type = c_type.replace('List', 'ListIter', 1)[:-1] # remove * else: # Example: assoc == Optional[Dict[str, str]] c_iter_type = 'TODO_ASSOC' self.write_ind('for (%s it(', c_iter_type) self.accept(seq) self.write('); !it.Done(); it.Next()) {\n') seq_type = self.types[seq] item_type = seq_type.args[0] # get 'int' from 'List<int>' if isinstance(item_type, Instance): self.write_ind(' %s ', get_c_type(item_type)) self.accept(index_expr) self.write(' = it.Value();\n') elif isinstance(item_type, TupleType): # for x, y in pairs c_item_type = get_c_type(item_type) if isinstance(index_expr, TupleExpr): temp_name = 'tup%d' % self.unique_id self.unique_id += 1 self.write_ind(' %s %s = it.Value();\n', c_item_type, temp_name) self.indent += 1 self._write_tuple_unpacking( temp_name, index_expr.items, item_type.items) self.indent -= 1 else: raise AssertionError() else: raise AssertionError('Unexpected type %s' % item_type) if cond: self.indent += 1 self.write_ind('if (') self.accept(cond[0]) # Just the first one self.write(') {\n') self.write_ind(' %s->append(', lval.name) self.accept(left_expr) self.write(');\n') if cond: self.write_ind('}\n') self.indent -= 1 self.write_ind('}\n') return self.accept(o.rvalue) self.write(';\n') elif isinstance(lval, MemberExpr): self.write_ind('') self.accept(lval) self.write(' = ') self.accept(o.rvalue) self.write(';\n') # Collect statements that look like self.foo = 1 if isinstance(lval.expr, NameExpr) and lval.expr.name == 'self': #log(' lval.name %s', lval.name) lval_type = self.types[lval] self.member_vars[lval.name] = lval_type elif isinstance(lval, IndexExpr): # a[x] = 1 # d->set(x, 1) for both List and Dict self.write_ind('') self.accept(lval.base) self.write('->set(') self.accept(lval.index) self.write(', ') self.accept(o.rvalue) self.write(');\n') elif isinstance(lval, TupleExpr): # An assignment to an n-tuple turns into n+1 statements. Example: # # x, y = mytuple # # Tuple2<int, Str> tup1 = mytuple # int x = tup1->at0() # Str y = tup1->at1() rvalue_type = self.types[o.rvalue] c_type = get_c_type(rvalue_type) is_return = isinstance(o.rvalue, CallExpr) if is_return: assert c_type.endswith('') c_type = c_type[:-1] temp_name = 'tup%d' % self.unique_id self.unique_id += 1 self.write_ind('%s %s = ', c_type, temp_name) self.accept(o.rvalue) self.write(';\n') self._write_tuple_unpacking(temp_name, lval.items, rvalue_type.items, is_return=is_return) else: raise AssertionError(lval) def _write_body(self, body): """Write a block without the { }.""" for stmt in body: # Ignore things that look like docstrings if isinstance(stmt, ExpressionStmt) and isinstance(stmt.expr, StrExpr): continue #log('-- %d', self.indent) self.accept(stmt) def visit_for_stmt(self, o: 'mypy.nodes.ForStmt') -> T: if 0: self.log('ForStmt') self.log(' index_type %s', o.index_type) self.log(' inferred_item_type %s', o.inferred_item_type) self.log(' inferred_iterator_type %s', o.inferred_iterator_type) func_name = None # does the loop look like 'for x in func():' ? if isinstance(o.expr, CallExpr) and isinstance(o.expr.callee, NameExpr): func_name = o.expr.callee.name # special case: 'for i in xrange(3)' if func_name == 'xrange': index_name = o.index.name args = o.expr.args num_args = len(args) if num_args == 1: # xrange(end) self.write_ind('for (int %s = 0; %s < ', index_name, index_name) self.accept(args[0]) self.write('; ++%s) ', index_name) elif num_args == 2: # xrange(being, end) self.write_ind('for (int %s = ', index_name) self.accept(args[0]) self.write('; %s < ', index_name) self.accept(args[1]) self.write('; ++%s) ', index_name) elif num_args == 3: # xrange(being, end, step) # Special case to detect a constant -1. This is a static # heuristic, because it could be negative dynamically. TODO: # mylib.reverse_xrange() or something? step = args[2] if isinstance(step, UnaryExpr) and step.op == '-': comparison_op = '>' else: comparison_op = '<' self.write_ind('for (int %s = ', index_name) self.accept(args[0]) self.write('; %s %s ', index_name, comparison_op) self.accept(args[1]) self.write('; %s += ', index_name) self.accept(step) self.write(') ') else: raise AssertionError() self.accept(o.body) return reverse = False # for i, x in enumerate(...): index0_name = None if func_name == 'enumerate': assert isinstance(o.index, TupleExpr), o.index index0 = o.index.items[0] assert isinstance(index0, NameExpr), index0 index0_name = index0.name # generate int i = 0; ; ++i # type of 'x' in 'for i, x in enumerate(...)' item_type = o.inferred_item_type.items[1] index_expr = o.index.items[1] # enumerate(mylist) turns into iteration over mylist with variable i assert len(o.expr.args) == 1, o.expr.args iterated_over = o.expr.args[0] elif func_name == 'reversed': # NOTE: enumerate() and reversed() can't be mixed yet. But you CAN # reverse iter over tuples. item_type = o.inferred_item_type index_expr = o.index args = o.expr.args assert len(args) == 1, args iterated_over = args[0] reverse = True # use different iterate elif func_name == 'iteritems': item_type = o.inferred_item_type index_expr = o.index args = o.expr.args assert len(args) == 1, args # This should be a dict iterated_over = args[0] log('------------ ITERITEMS OVER %s', iterated_over) else: item_type = o.inferred_item_type index_expr = o.index iterated_over = o.expr over_type = self.types[iterated_over] #self.log(' iterating over type %s', over_type) #self.log(' iterating over type %s', over_type.type.fullname()) over_dict = False if over_type.type.fullname() == 'builtins.list': c_type = get_c_type(over_type) assert c_type.endswith(''), c_type c_iter_type = c_type.replace('List', 'ListIter', 1)[:-1] # remove * # ReverseListIter! if reverse: c_iter_type = 'Reverse' + c_iter_type elif over_type.type.fullname() == 'builtins.dict': # Iterator c_type = get_c_type(over_type) assert c_type.endswith(''), c_type c_iter_type = c_type.replace('Dict', 'DictIter', 1)[:-1] # remove over_dict = True assert not reverse elif over_type.type.fullname() == 'builtins.str': c_iter_type = 'StrIter' assert not reverse # can't reverse iterate over string yet else: # assume it's like d.iteritems()? Iterator type assert False, over_type if index0_name: # can't initialize two things in a for loop, so do it on a separate line if self.decl: self.local_var_list.append((index0_name, 'int')) self.write_ind('%s = 0;\n', index0_name) index_update = ', ++%s' % index0_name else: index_update = '' self.write_ind('for (%s it(', c_iter_type) self.accept(iterated_over) # the thing being iterated over self.write('); !it.Done(); it.Next()%s) {\n', index_update) # for x in it: ... # for i, x in enumerate(pairs): ... if isinstance(item_type, Instance) or index0_name: c_item_type = get_c_type(item_type) self.write_ind(' %s ', c_item_type) self.accept(index_expr) if over_dict: self.write(' = it.Key();\n') else: self.write(' = it.Value();\n') elif isinstance(item_type, TupleType): # for x, y in pairs if over_dict: assert isinstance(o.index, TupleExpr), o.index index_items = o.index.items assert len(index_items) == 2, index_items assert len(item_type.items) == 2, item_type.items key_type = get_c_type(item_type.items[0]) val_type = get_c_type(item_type.items[1]) self.write_ind(' %s %s = it.Key();\n', key_type, index_items[0].name) self.write_ind(' %s %s = it.Value();\n', val_type, index_items[1].name) else: # Example: # for (ListIter it(mylist); !it.Done(); it.Next()) { # Tuple2<int, Str> tup1 = it.Value(); # int i = tup1->at0(); # Str s = tup1->at1(); # log("%d %s", i, s); # } c_item_type = get_c_type(item_type) if isinstance(o.index, TupleExpr): temp_name = 'tup%d' % self.unique_id self.unique_id += 1 self.write_ind(' %s %s = it.Value();\n', c_item_type, temp_name) self.indent += 1 self._write_tuple_unpacking( temp_name, o.index.items, item_type.items) self.indent -= 1 else: self.write_ind(' %s %s = it.Value();\n', c_item_type, o.index.name) else: raise AssertionError('Unexpected type %s' % item_type) # Copy
= Var(within=Reals,bounds=(0,1),initialize=0) m.x598 = Var(within=Reals,bounds=(0,1),initialize=0) m.x599 = Var(within=Reals,bounds=(0,1),initialize=0) m.x600 = Var(within=Reals,bounds=(0,1),initialize=0) m.x601 = Var(within=Reals,bounds=(0,1),initialize=0) m.x602 = Var(within=Reals,bounds=(0,1),initialize=0) m.x603 = Var(within=Reals,bounds=(0,1),initialize=0) m.x604 = Var(within=Reals,bounds=(0,1),initialize=0) m.x605 = Var(within=Reals,bounds=(0,1),initialize=0) m.x606 = Var(within=Reals,bounds=(0,1),initialize=0) m.x607 = Var(within=Reals,bounds=(0,1),initialize=0) m.x608 = Var(within=Reals,bounds=(0,1),initialize=0) m.x609 = Var(within=Reals,bounds=(0,1),initialize=0) m.x610 = Var(within=Reals,bounds=(0,1),initialize=0) m.x611 = Var(within=Reals,bounds=(0,1),initialize=0) m.x612 = Var(within=Reals,bounds=(0,1),initialize=0) m.x613 = Var(within=Reals,bounds=(0,1),initialize=0) m.x614 = Var(within=Reals,bounds=(0,1),initialize=0) m.x615 = Var(within=Reals,bounds=(0,1),initialize=0) m.x616 = Var(within=Reals,bounds=(0,1),initialize=0) m.x617 = Var(within=Reals,bounds=(0,1),initialize=0) m.x618 = Var(within=Reals,bounds=(0,1),initialize=0) m.x619 = Var(within=Reals,bounds=(0,1),initialize=0) m.x620 = Var(within=Reals,bounds=(0,1),initialize=0) m.x621 = Var(within=Reals,bounds=(0,1),initialize=0) m.x622 = Var(within=Reals,bounds=(0,1),initialize=0) m.x623 = Var(within=Reals,bounds=(0,1),initialize=0) m.x624 = Var(within=Reals,bounds=(0,1),initialize=0) m.x625 = Var(within=Reals,bounds=(0,1),initialize=0) m.x626 = Var(within=Reals,bounds=(0,1),initialize=0) m.x627 = Var(within=Reals,bounds=(0,1),initialize=0) m.x628 = Var(within=Reals,bounds=(0,1),initialize=0) m.x629 = Var(within=Reals,bounds=(0,1),initialize=0) m.x630 = Var(within=Reals,bounds=(0,1),initialize=0) m.x631 = Var(within=Reals,bounds=(0,1),initialize=0) m.x632 = Var(within=Reals,bounds=(0,1),initialize=0) m.x633 = Var(within=Reals,bounds=(0,1),initialize=0) m.x634 = Var(within=Reals,bounds=(0,1),initialize=0) m.x635 = Var(within=Reals,bounds=(0,1),initialize=0) m.x636 = Var(within=Reals,bounds=(0,1),initialize=0) m.x637 = Var(within=Reals,bounds=(0,1),initialize=0) m.x638 = Var(within=Reals,bounds=(0,1),initialize=0) m.x639 = Var(within=Reals,bounds=(0,1),initialize=0) m.x640 = Var(within=Reals,bounds=(0,1),initialize=0) m.x641 = Var(within=Reals,bounds=(0,1),initialize=0) m.x642 = Var(within=Reals,bounds=(0,1),initialize=0) m.x643 = Var(within=Reals,bounds=(0,1),initialize=0) m.x644 = Var(within=Reals,bounds=(0,1),initialize=0) m.x645 = Var(within=Reals,bounds=(0,1),initialize=0) m.x646 = Var(within=Reals,bounds=(0,1),initialize=0) m.x647 = Var(within=Reals,bounds=(0,1),initialize=0) m.x648 = Var(within=Reals,bounds=(0,1),initialize=0) m.x649 = Var(within=Reals,bounds=(0,1),initialize=0) m.x650 = Var(within=Reals,bounds=(0,1),initialize=0) m.x651 = Var(within=Reals,bounds=(0,1),initialize=0) m.x652 = Var(within=Reals,bounds=(0,1),initialize=0) m.x653 = Var(within=Reals,bounds=(0,1),initialize=0) m.x654 = Var(within=Reals,bounds=(0,1),initialize=0) m.x655 = Var(within=Reals,bounds=(0,1),initialize=0) m.x656 = Var(within=Reals,bounds=(0,1),initialize=0) m.x657 = Var(within=Reals,bounds=(0,1),initialize=0) m.x658 = Var(within=Reals,bounds=(0,1),initialize=0) m.x659 = Var(within=Reals,bounds=(0,1),initialize=0) m.x660 = Var(within=Reals,bounds=(0,1),initialize=0) m.x661 = Var(within=Reals,bounds=(0,1),initialize=0) m.x662 = Var(within=Reals,bounds=(0,1),initialize=0) m.x663 = Var(within=Reals,bounds=(0,1),initialize=0) m.x664 = Var(within=Reals,bounds=(0,1),initialize=0) m.x665 = Var(within=Reals,bounds=(0,1),initialize=0) m.x666 = Var(within=Reals,bounds=(0,1),initialize=0) m.x667 = Var(within=Reals,bounds=(0,1),initialize=0) m.x668 = Var(within=Reals,bounds=(0,1),initialize=0) m.x669 = Var(within=Reals,bounds=(0,1),initialize=0) m.x670 = Var(within=Reals,bounds=(0,1),initialize=0) m.x671 = Var(within=Reals,bounds=(0,1),initialize=0) m.x672 = Var(within=Reals,bounds=(0,1),initialize=0) m.x673 = Var(within=Reals,bounds=(0,1),initialize=0) m.x674 = Var(within=Reals,bounds=(0,1),initialize=0) m.x675 = Var(within=Reals,bounds=(0,1),initialize=0) m.x676 = Var(within=Reals,bounds=(0,1),initialize=0) m.x677 = Var(within=Reals,bounds=(0,1),initialize=0) m.x678 = Var(within=Reals,bounds=(0,1),initialize=0) m.x679 = Var(within=Reals,bounds=(0,1),initialize=0) m.x680 = Var(within=Reals,bounds=(0,1),initialize=0) m.x681 = Var(within=Reals,bounds=(0,1),initialize=0) m.x682 = Var(within=Reals,bounds=(0,1),initialize=0) m.x683 = Var(within=Reals,bounds=(0,1),initialize=0) m.x684 = Var(within=Reals,bounds=(0,1),initialize=0) m.x685 = Var(within=Reals,bounds=(0,1),initialize=0) m.x686 = Var(within=Reals,bounds=(0,1),initialize=0) m.x687 = Var(within=Reals,bounds=(0,1),initialize=0) m.x688 = Var(within=Reals,bounds=(0,1),initialize=0) m.x689 = Var(within=Reals,bounds=(0,1),initialize=0) m.x690 = Var(within=Reals,bounds=(0,1),initialize=0) m.x691 = Var(within=Reals,bounds=(0,1),initialize=0) m.x692 = Var(within=Reals,bounds=(0,1),initialize=0) m.x693 = Var(within=Reals,bounds=(0,1),initialize=0) m.x694 = Var(within=Reals,bounds=(0,1),initialize=0) m.x695 = Var(within=Reals,bounds=(0,1),initialize=0) m.x696 = Var(within=Reals,bounds=(0,1),initialize=0) m.x697 = Var(within=Reals,bounds=(0,1),initialize=0) m.x698 = Var(within=Reals,bounds=(0,1),initialize=0) m.x699 = Var(within=Reals,bounds=(0,1),initialize=0) m.x700 = Var(within=Reals,bounds=(0,1),initialize=0) m.x701 = Var(within=Reals,bounds=(0,1),initialize=0) m.x702 = Var(within=Reals,bounds=(0,1),initialize=0) m.x703 = Var(within=Reals,bounds=(0,1),initialize=0) m.x704 = Var(within=Reals,bounds=(0,1),initialize=0) m.x705 = Var(within=Reals,bounds=(0,1),initialize=0) m.x706 = Var(within=Reals,bounds=(0,1),initialize=0) m.x707 = Var(within=Reals,bounds=(0,1),initialize=0) m.x708 = Var(within=Reals,bounds=(0,1),initialize=0) m.x709 = Var(within=Reals,bounds=(0,1),initialize=0) m.x710 = Var(within=Reals,bounds=(0,1),initialize=0) m.x711 = Var(within=Reals,bounds=(0,1),initialize=0) m.x712 = Var(within=Reals,bounds=(0,1),initialize=0) m.x713 = Var(within=Reals,bounds=(0,1),initialize=0) m.x714 = Var(within=Reals,bounds=(0,1),initialize=0) m.x715 = Var(within=Reals,bounds=(0,1),initialize=0) m.x716 = Var(within=Reals,bounds=(0,1),initialize=0) m.x717 = Var(within=Reals,bounds=(0,1),initialize=0) m.x718 = Var(within=Reals,bounds=(0,1),initialize=0) m.x719 = Var(within=Reals,bounds=(0,1),initialize=0) m.x720 = Var(within=Reals,bounds=(0,1),initialize=0) m.x721 = Var(within=Reals,bounds=(0,1),initialize=0) m.x722 = Var(within=Reals,bounds=(0,1),initialize=0) m.x723 = Var(within=Reals,bounds=(0,1),initialize=0) m.x724 = Var(within=Reals,bounds=(0,1),initialize=0) m.x725 = Var(within=Reals,bounds=(0,1),initialize=0) m.x726 = Var(within=Reals,bounds=(0,1),initialize=0) m.x727 = Var(within=Reals,bounds=(0,1),initialize=0) m.x728 = Var(within=Reals,bounds=(0,1),initialize=0) m.x729 = Var(within=Reals,bounds=(0,1),initialize=0) m.x730 = Var(within=Reals,bounds=(0,1),initialize=0) m.x731 = Var(within=Reals,bounds=(0,1),initialize=0) m.x732 = Var(within=Reals,bounds=(0,1),initialize=0) m.x733 = Var(within=Reals,bounds=(0,1),initialize=0) m.x734 = Var(within=Reals,bounds=(0,1),initialize=0) m.x735 = Var(within=Reals,bounds=(0,1),initialize=0) m.x736 = Var(within=Reals,bounds=(0,1),initialize=0) m.x737 = Var(within=Reals,bounds=(0,1),initialize=0) m.x738 = Var(within=Reals,bounds=(0,1),initialize=0) m.x739 = Var(within=Reals,bounds=(0,1),initialize=0) m.x740 = Var(within=Reals,bounds=(0,1),initialize=0) m.x741 = Var(within=Reals,bounds=(0,1),initialize=0) m.x742 = Var(within=Reals,bounds=(0,1),initialize=0) m.x743 = Var(within=Reals,bounds=(0,1),initialize=0) m.x744 = Var(within=Reals,bounds=(0,1),initialize=0) m.x745 = Var(within=Reals,bounds=(0,1),initialize=0) m.x746 = Var(within=Reals,bounds=(0,1),initialize=0) m.x747 = Var(within=Reals,bounds=(0,1),initialize=0) m.x748 = Var(within=Reals,bounds=(0,1),initialize=0) m.x749 = Var(within=Reals,bounds=(0,1),initialize=0) m.x750 = Var(within=Reals,bounds=(0,1),initialize=0) m.x751 = Var(within=Reals,bounds=(0,1),initialize=0) m.x752 = Var(within=Reals,bounds=(0,1),initialize=0) m.x753 = Var(within=Reals,bounds=(0,1),initialize=0) m.x754 = Var(within=Reals,bounds=(0,1),initialize=0) m.x755 = Var(within=Reals,bounds=(0,1),initialize=0) m.x756 = Var(within=Reals,bounds=(0,1),initialize=0) m.x757 = Var(within=Reals,bounds=(0,1),initialize=0) m.x758 = Var(within=Reals,bounds=(0,1),initialize=0) m.x759 = Var(within=Reals,bounds=(0,1),initialize=0) m.x760 = Var(within=Reals,bounds=(0,1),initialize=0) m.x761 = Var(within=Reals,bounds=(0,1),initialize=0) m.x762 = Var(within=Reals,bounds=(0,1),initialize=0) m.x763 = Var(within=Reals,bounds=(0,1),initialize=0) m.x764 = Var(within=Reals,bounds=(0,1),initialize=0) m.x765 = Var(within=Reals,bounds=(0,1),initialize=0) m.x766 = Var(within=Reals,bounds=(0,1),initialize=0) m.x767 = Var(within=Reals,bounds=(0,1),initialize=0) m.x768 = Var(within=Reals,bounds=(0,1),initialize=0) m.x769 = Var(within=Reals,bounds=(0,1),initialize=0) m.x770 = Var(within=Reals,bounds=(0,1),initialize=0) m.x771 = Var(within=Reals,bounds=(0,1),initialize=0) m.x772 = Var(within=Reals,bounds=(0,1),initialize=0) m.x773 = Var(within=Reals,bounds=(0,1),initialize=0) m.x774 = Var(within=Reals,bounds=(0,1),initialize=0) m.x775 = Var(within=Reals,bounds=(0,1),initialize=0) m.x776 = Var(within=Reals,bounds=(0,1),initialize=0) m.x777 = Var(within=Reals,bounds=(0,1),initialize=0) m.x778 = Var(within=Reals,bounds=(0,1),initialize=0) m.x779 = Var(within=Reals,bounds=(0,1),initialize=0) m.x780 = Var(within=Reals,bounds=(0,1),initialize=0) m.x781 = Var(within=Reals,bounds=(0,1),initialize=0) m.x782 = Var(within=Reals,bounds=(0,1),initialize=0) m.x783 = Var(within=Reals,bounds=(0,1),initialize=0) m.x784 = Var(within=Reals,bounds=(0,1),initialize=0) m.x785 = Var(within=Reals,bounds=(0,1),initialize=0) m.x786 = Var(within=Reals,bounds=(0,1),initialize=0) m.x787 = Var(within=Reals,bounds=(0,1),initialize=0) m.x788 = Var(within=Reals,bounds=(0,1),initialize=0) m.x789 = Var(within=Reals,bounds=(0,1),initialize=0) m.x790 = Var(within=Reals,bounds=(0,1),initialize=0) m.x791 = Var(within=Reals,bounds=(0,1),initialize=0) m.x792 = Var(within=Reals,bounds=(0,1),initialize=0) m.x793 = Var(within=Reals,bounds=(0,1),initialize=0) m.x794 = Var(within=Reals,bounds=(0,1),initialize=0) m.x795 = Var(within=Reals,bounds=(0,1),initialize=0) m.x796 = Var(within=Reals,bounds=(0,1),initialize=0) m.x797 = Var(within=Reals,bounds=(0,1),initialize=0) m.x798 = Var(within=Reals,bounds=(0,1),initialize=0) m.x799 = Var(within=Reals,bounds=(0,1),initialize=0) m.x800 = Var(within=Reals,bounds=(0,1),initialize=0) m.x801 = Var(within=Reals,bounds=(0,1),initialize=0) m.x802 = Var(within=Reals,bounds=(0,1),initialize=0) m.x803 = Var(within=Reals,bounds=(0,1),initialize=0) m.x804 = Var(within=Reals,bounds=(0,1),initialize=0) m.x805 = Var(within=Reals,bounds=(0,1),initialize=0) m.x806 = Var(within=Reals,bounds=(0,1),initialize=0) m.x807 = Var(within=Reals,bounds=(0,1),initialize=0) m.x808 = Var(within=Reals,bounds=(0,1),initialize=0) m.x809 = Var(within=Reals,bounds=(0,1),initialize=0) m.x810 = Var(within=Reals,bounds=(0,1),initialize=0) m.x811 = Var(within=Reals,bounds=(0,1),initialize=0) m.x812 = Var(within=Reals,bounds=(0,1),initialize=0) m.x813 = Var(within=Reals,bounds=(0,1),initialize=0) m.x814 = Var(within=Reals,bounds=(0,1),initialize=0) m.x815 = Var(within=Reals,bounds=(0,1),initialize=0) m.x816 = Var(within=Reals,bounds=(0,1),initialize=0) m.x817 = Var(within=Reals,bounds=(0,1),initialize=0) m.x818 = Var(within=Reals,bounds=(0,1),initialize=0) m.x819 = Var(within=Reals,bounds=(0,1),initialize=0) m.x820 = Var(within=Reals,bounds=(0,1),initialize=0) m.x821 = Var(within=Reals,bounds=(0,1),initialize=0) m.x822 = Var(within=Reals,bounds=(0,1),initialize=0) m.x823 = Var(within=Reals,bounds=(0,1),initialize=0) m.x824 = Var(within=Reals,bounds=(0,1),initialize=0) m.x825 = Var(within=Reals,bounds=(0,1),initialize=0) m.x826 = Var(within=Reals,bounds=(0,1),initialize=0) m.x827 = Var(within=Reals,bounds=(0,1),initialize=0) m.x828 = Var(within=Reals,bounds=(0,1),initialize=0) m.x829 = Var(within=Reals,bounds=(0,1),initialize=0) m.x830 = Var(within=Reals,bounds=(0,1),initialize=0) m.x831 = Var(within=Reals,bounds=(0,1),initialize=0) m.x832 = Var(within=Reals,bounds=(0,1),initialize=0) m.x833 = Var(within=Reals,bounds=(0,1),initialize=0) m.x834 = Var(within=Reals,bounds=(0,1),initialize=0) m.x835 = Var(within=Reals,bounds=(0,1),initialize=0) m.x836 = Var(within=Reals,bounds=(0,1),initialize=0) m.x837 = Var(within=Reals,bounds=(0,1),initialize=0) m.x838 = Var(within=Reals,bounds=(0,1),initialize=0) m.x839 = Var(within=Reals,bounds=(0,1),initialize=0) m.x840 = Var(within=Reals,bounds=(0,1),initialize=0) m.x841 = Var(within=Reals,bounds=(0,1),initialize=0) m.x842 = Var(within=Reals,bounds=(0,1),initialize=0) m.x843 = Var(within=Reals,bounds=(0,1),initialize=0) m.x844 = Var(within=Reals,bounds=(0,1),initialize=0) m.x845 = Var(within=Reals,bounds=(0,1),initialize=0) m.x846 = Var(within=Reals,bounds=(0,1),initialize=0) m.x847 = Var(within=Reals,bounds=(0,1),initialize=0) m.x848 = Var(within=Reals,bounds=(0,1),initialize=0) m.x849 = Var(within=Reals,bounds=(0,1),initialize=0) m.x850 = Var(within=Reals,bounds=(0,1),initialize=0) m.x851 = Var(within=Reals,bounds=(0,1),initialize=0) m.x852 = Var(within=Reals,bounds=(0,1),initialize=0) m.x853 = Var(within=Reals,bounds=(0,1),initialize=0) m.x854 = Var(within=Reals,bounds=(0,1),initialize=0) m.x855 = Var(within=Reals,bounds=(0,1),initialize=0) m.x856 = Var(within=Reals,bounds=(0,1),initialize=0) m.x857 = Var(within=Reals,bounds=(0,1),initialize=0) m.x858 = Var(within=Reals,bounds=(0,1),initialize=0) m.x859 = Var(within=Reals,bounds=(0,1),initialize=0) m.x860 = Var(within=Reals,bounds=(0,1),initialize=0) m.x861 = Var(within=Reals,bounds=(0,1),initialize=0) m.x862 = Var(within=Reals,bounds=(0,1),initialize=0) m.x863 = Var(within=Reals,bounds=(0,1),initialize=0) m.x864 = Var(within=Reals,bounds=(0,1),initialize=0) m.x865 = Var(within=Reals,bounds=(0,1),initialize=0) m.x866 = Var(within=Reals,bounds=(0,1),initialize=0) m.x867 = Var(within=Reals,bounds=(0,1),initialize=0) m.x868 = Var(within=Reals,bounds=(0,1),initialize=0) m.x869 = Var(within=Reals,bounds=(0,1),initialize=0) m.x870 = Var(within=Reals,bounds=(0,1),initialize=0) m.x871 = Var(within=Reals,bounds=(0,1),initialize=0) m.x872 = Var(within=Reals,bounds=(0,1),initialize=0) m.x873 = Var(within=Reals,bounds=(0,1),initialize=0) m.x874 = Var(within=Reals,bounds=(0,1),initialize=0) m.x875 = Var(within=Reals,bounds=(0,1),initialize=0) m.x876 = Var(within=Reals,bounds=(0,1),initialize=0) m.x877 = Var(within=Reals,bounds=(0,1),initialize=0) m.x878 = Var(within=Reals,bounds=(0,1),initialize=0) m.x879 = Var(within=Reals,bounds=(0,1),initialize=0) m.x880 = Var(within=Reals,bounds=(0,1),initialize=0) m.x881 = Var(within=Reals,bounds=(0,1),initialize=0) m.x882 = Var(within=Reals,bounds=(0,1),initialize=0) m.x883 = Var(within=Reals,bounds=(0,1),initialize=0) m.x884 = Var(within=Reals,bounds=(0,1),initialize=0) m.x885 = Var(within=Reals,bounds=(0,1),initialize=0) m.x886 = Var(within=Reals,bounds=(0,1),initialize=0) m.x887 = Var(within=Reals,bounds=(0,1),initialize=0) m.x888 = Var(within=Reals,bounds=(0,1),initialize=0) m.x889 = Var(within=Reals,bounds=(0,1),initialize=0) m.x890 = Var(within=Reals,bounds=(0,1),initialize=0) m.x891 = Var(within=Reals,bounds=(0,1),initialize=0) m.x892 = Var(within=Reals,bounds=(0,1),initialize=0) m.x893 = Var(within=Reals,bounds=(0,1),initialize=0) m.x894 = Var(within=Reals,bounds=(0,1),initialize=0) m.x895 = Var(within=Reals,bounds=(0,1),initialize=0) m.x896 = Var(within=Reals,bounds=(0,1),initialize=0) m.x897 = Var(within=Reals,bounds=(0,1),initialize=0) m.x898 = Var(within=Reals,bounds=(0,1),initialize=0) m.x899 = Var(within=Reals,bounds=(0,1),initialize=0) m.x900 = Var(within=Reals,bounds=(0,1),initialize=0) m.x901 = Var(within=Reals,bounds=(0,1),initialize=0) m.x902 = Var(within=Reals,bounds=(0,1),initialize=0) m.x903 = Var(within=Reals,bounds=(0,1),initialize=0) m.x904 = Var(within=Reals,bounds=(0,1),initialize=0) m.x905 = Var(within=Reals,bounds=(0,1),initialize=0) m.x906 = Var(within=Reals,bounds=(0,1),initialize=0) m.x907 = Var(within=Reals,bounds=(0,1),initialize=0) m.x908 = Var(within=Reals,bounds=(0,1),initialize=0) m.x909 = Var(within=Reals,bounds=(0,1),initialize=0) m.x910 = Var(within=Reals,bounds=(0,1),initialize=0) m.x911 = Var(within=Reals,bounds=(0,1),initialize=0) m.x912 = Var(within=Reals,bounds=(0,1),initialize=0) m.x913 = Var(within=Reals,bounds=(0,1),initialize=0) m.x914 = Var(within=Reals,bounds=(0,1),initialize=0) m.x915 = Var(within=Reals,bounds=(0,1),initialize=0) m.x916 = Var(within=Reals,bounds=(0,1),initialize=0) m.x917 = Var(within=Reals,bounds=(0,1),initialize=0) m.x918 = Var(within=Reals,bounds=(0,1),initialize=0) m.x919 = Var(within=Reals,bounds=(0,1),initialize=0) m.x920 = Var(within=Reals,bounds=(0,1),initialize=0) m.x921 = Var(within=Reals,bounds=(0,1),initialize=0) m.x922 = Var(within=Reals,bounds=(0,1),initialize=0) m.x923 = Var(within=Reals,bounds=(0,1),initialize=0) m.x924 = Var(within=Reals,bounds=(0,1),initialize=0) m.x925 = Var(within=Reals,bounds=(0,1),initialize=0) m.x926 = Var(within=Reals,bounds=(0,1),initialize=0) m.x927 = Var(within=Reals,bounds=(0,1),initialize=0) m.x928 = Var(within=Reals,bounds=(0,1),initialize=0) m.x929 = Var(within=Reals,bounds=(0,1),initialize=0) m.x930 = Var(within=Reals,bounds=(0,1),initialize=0) m.x931 = Var(within=Reals,bounds=(0,1),initialize=0) m.x932 = Var(within=Reals,bounds=(0,1),initialize=0) m.x933 = Var(within=Reals,bounds=(0,1),initialize=0) m.x934 = Var(within=Reals,bounds=(0,1),initialize=0) m.x935 = Var(within=Reals,bounds=(0,1),initialize=0) m.x936 = Var(within=Reals,bounds=(0,1),initialize=0) m.x937 = Var(within=Reals,bounds=(0,1),initialize=0) m.x938 = Var(within=Reals,bounds=(0,1),initialize=0) m.x939 = Var(within=Reals,bounds=(0,1),initialize=0) m.x940 = Var(within=Reals,bounds=(0,1),initialize=0) m.x941 = Var(within=Reals,bounds=(0,1),initialize=0) m.x942 = Var(within=Reals,bounds=(0,1),initialize=0) m.x943 = Var(within=Reals,bounds=(0,1),initialize=0) m.x944 = Var(within=Reals,bounds=(0,1),initialize=0) m.x945 = Var(within=Reals,bounds=(0,1),initialize=0) m.x946 = Var(within=Reals,bounds=(0,1),initialize=0) m.x947 = Var(within=Reals,bounds=(0,1),initialize=0) m.x948 = Var(within=Reals,bounds=(0,1),initialize=0) m.x949 = Var(within=Reals,bounds=(0,1),initialize=0) m.x950 = Var(within=Reals,bounds=(0,1),initialize=0) m.x951 = Var(within=Reals,bounds=(0,1),initialize=0) m.x952 = Var(within=Reals,bounds=(0,1),initialize=0) m.x953 = Var(within=Reals,bounds=(0,1),initialize=0) m.x954 = Var(within=Reals,bounds=(0,1),initialize=0) m.x955 = Var(within=Reals,bounds=(0,1),initialize=0) m.x956 = Var(within=Reals,bounds=(0,1),initialize=0) m.x957 = Var(within=Reals,bounds=(0,1),initialize=0) m.x958 = Var(within=Reals,bounds=(0,1),initialize=0) m.x959 = Var(within=Reals,bounds=(0,1),initialize=0) m.x960 = Var(within=Reals,bounds=(0,1),initialize=0) m.x961 = Var(within=Reals,bounds=(0,1),initialize=0) m.x962 = Var(within=Reals,bounds=(0,1),initialize=0) m.x963 = Var(within=Reals,bounds=(0,1),initialize=0) m.x964 = Var(within=Reals,bounds=(0,1),initialize=0) m.x965 = Var(within=Reals,bounds=(0,1),initialize=0) m.x966 = Var(within=Reals,bounds=(0,1),initialize=0) m.x967 = Var(within=Reals,bounds=(0,1),initialize=0) m.x968 = Var(within=Reals,bounds=(0,1),initialize=0) m.x969 = Var(within=Reals,bounds=(0,1),initialize=0) m.x970 = Var(within=Reals,bounds=(0,1),initialize=0) m.x971 = Var(within=Reals,bounds=(0,1),initialize=0) m.x972 = Var(within=Reals,bounds=(0,1),initialize=0) m.x973 = Var(within=Reals,bounds=(0,1),initialize=0) m.x974 = Var(within=Reals,bounds=(0,1),initialize=0) m.x975 = Var(within=Reals,bounds=(0,1),initialize=0) m.x976 = Var(within=Reals,bounds=(0,1),initialize=0) m.x977 = Var(within=Reals,bounds=(0,1),initialize=0) m.x978 = Var(within=Reals,bounds=(0,1),initialize=0) m.x979 = Var(within=Reals,bounds=(0,1),initialize=0) m.x980 = Var(within=Reals,bounds=(0,1),initialize=0) m.x981 = Var(within=Reals,bounds=(0,1),initialize=0) m.x982 = Var(within=Reals,bounds=(0,1),initialize=0) m.x983 = Var(within=Reals,bounds=(0,1),initialize=0) m.x984 = Var(within=Reals,bounds=(0,1),initialize=0) m.x985 = Var(within=Reals,bounds=(0,1),initialize=0) m.x986 = Var(within=Reals,bounds=(0,1),initialize=0) m.x987 = Var(within=Reals,bounds=(0,1),initialize=0) m.x988 = Var(within=Reals,bounds=(0,1),initialize=0) m.x989 = Var(within=Reals,bounds=(0,1),initialize=0) m.x990 = Var(within=Reals,bounds=(0,1),initialize=0) m.x991 = Var(within=Reals,bounds=(0,1),initialize=0) m.x992 = Var(within=Reals,bounds=(0,1),initialize=0) m.x993 = Var(within=Reals,bounds=(0,1),initialize=0) m.x994 = Var(within=Reals,bounds=(0,1),initialize=0) m.x995 = Var(within=Reals,bounds=(0,1),initialize=0) m.x996 = Var(within=Reals,bounds=(0,1),initialize=0) m.x997 = Var(within=Reals,bounds=(0,1),initialize=0) m.x998 = Var(within=Reals,bounds=(0,1),initialize=0) m.x999 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1000 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1001 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1002 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1003 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1004 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1005 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1006 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1007 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1008 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1009 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1010 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1011 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1012 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1013 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1014 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1015 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1016 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1017 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1018 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1019 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1020 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1021 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1022 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1023 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1024 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1025 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1026 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1027 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1028 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1029 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1030 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1031 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1032 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1033 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1034 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1035 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1036 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1037 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1038 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1039 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1040 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1041 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1042 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1043 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1044 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1045 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1046 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1047 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1048 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1049 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1050 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1051 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1052 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1053 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1054 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1055 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1056 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1057 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1058 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1059 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1060 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1061 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1062 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1063 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1064 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1065 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1066 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1067 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1068 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1069 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1070 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1071 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1072 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1073 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1074 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1075 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1076 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1077 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1078 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1079 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1080 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1081 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1082 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1083 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1084 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1085 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1086 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1087 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1088 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1089 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1090 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1091 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1092 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1093 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1094 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1095 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1096 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1097 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1098 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1099 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1100 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1101 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1102 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1103 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1104 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1105 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1106 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1107 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1108 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1109 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1110 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1111 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1112 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1113 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1114 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1115 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1116 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1117 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1118 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1119 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1120 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1121 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1122 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1123 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1124 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1125 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1126 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1127 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1128 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1129 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1130 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1131 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1132 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1133 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1134 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1135 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1136 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1137 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1138 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1139 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1140 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1141 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1142 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1143 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1144 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1145 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1146 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1147 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1148 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1149 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1150 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1151 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1152 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1153 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1154 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1155 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1156 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1157 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1158 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1159 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1160 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1161 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1162 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1163 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1164 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1165 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1166 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1167 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1168 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1169 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1170 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1171 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1172 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1173 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1174 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1175 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1176 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1177 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1178 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1179 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1180 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1181 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1182 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1183 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1184 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1185 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1186 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1187 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1188 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1189 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1190 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1191 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1192 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1193 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1194 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1195 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1196 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1197 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1198 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1199 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1200 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1201 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1202 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1203 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1204 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1205 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1206 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1207 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1208 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1209 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1210 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1211 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1212 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1213 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1214 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1215 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1216 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1217 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1218 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1219 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1220 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1221 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1222 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1223 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1224 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1225 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1226 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1227 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1228 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1229 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1230 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1231 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1232 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1233 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1234 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1235 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1236 = Var(within=Reals,bounds=(0,1),initialize=0) m.x1237
from django.contrib import admin from parler.admin import TranslatableAdmin from django.utils.html import format_html from django.forms import BaseInlineFormSet from django.shortcuts import redirect from django import forms import data_wizard # Solution to data import madness that had refused to go from django.conf import settings # allow import of projects settings at the root from django.forms import TextInput,Textarea #customize textarea row and column size from import_export.formats import base_formats from .models import (StgFacilityType,StgFacilityServiceMeasureUnits, StgFacilityOwnership,StgHealthFacility,StgServiceDomain,StgLocationCodes, FacilityServiceAvailability,FacilityServiceAvailabilityProxy, FacilityServiceProvision,StgFacilityServiceIntervention, FacilityServiceReadiness,StgFacilityServiceAreas, FacilityServiceProvisionProxy,FacilityServiceReadinesProxy) from commoninfo.admin import OverideImportExport,OverideExport,OverideImport # from publications.serializers import StgKnowledgeProductSerializer from .resources import (StgFacilityResourceExport,FacilityTypeResourceExport, FacilityServiceDomainResourceExport,StgFacilityServiceAvailabilityExport, StgFacilityServiceCapacityExport,StgFacilityServiceReadinessExport,) from regions.models import StgLocation,StgLocationCodes from django_admin_listfilter_dropdown.filters import ( DropdownFilter, RelatedDropdownFilter, ChoiceDropdownFilter, RelatedOnlyDropdownFilter) #custom from import_export.admin import (ImportExportModelAdmin, ExportMixin, ImportExportActionModelAdmin,ExportActionModelAdmin,) from authentication.models import CustomUser, CustomGroup from bootstrap_datepicker_plus import DatePickerInput # Nice date picker 06/03 from .filters import TranslatedFieldFilter #Danile solution to duplicate filters #Methods used to register global actions performed on data. See actions listbox def transition_to_pending (modeladmin, request, queryset): queryset.update(comment = 'pending') transition_to_pending.short_description = "Mark selected as Pending" def transition_to_approved (modeladmin, request, queryset): queryset.update (comment = 'approved') transition_to_approved.short_description = "Mark selected as Approved" def transition_to_rejected (modeladmin, request, queryset): queryset.update (comment = 'rejected') transition_to_rejected.short_description = "Mark selected as Rejected" @admin.register(StgFacilityType) class FacilityTypeAdmin(TranslatableAdmin,OverideExport): from django.db import models formfield_overrides = { models.CharField: {'widget': TextInput(attrs={'size':'100'})}, models.TextField: {'widget': Textarea(attrs={'rows':3, 'cols':100})}, } """ Serge requested that a user does not see other users or groups data. This method filters logged in users depending on group roles and permissions. Only the superuser can see all users and locations data while a users can only see data from registered location within his/her group/system role. If a user is not assigned to a group, he/she can only own data - 01/02/2021 """ def get_queryset(self, request): language = request.LANGUAGE_CODE qs = super().get_queryset(request).filter( translations__language_code=language).order_by( 'translations__name').distinct() # Get a query of groups the user belongs and flatten it to list object groups = list(request.user.groups.values_list('user', flat=True)) user = request.user.id user_location = request.user.location.location_id db_locations = StgLocation.objects.all().order_by('location_id') # Returns data for all the locations to the lowest location level if request.user.is_superuser: qs # returns data for AFRO and member countries elif user in groups and user_location==1: qs_admin=db_locations.filter( locationlevel__locationlevel_id__gte=1, locationlevel__locationlevel_id__lte=2) return qs def get_export_resource_class(self): return FacilityTypeResourceExport fieldsets = ( ('Health Facility Type', { 'fields':('name','shortname','description',) #afrocode may be null }), ) list_display=['name','code','shortname','description'] list_display_links =('code', 'name',) search_fields = ('code','translations__name',) #display search field list_per_page = 30 #limit records displayed on admin site to 15 exclude = ('date_created','date_lastupdated','code',) @admin.register(StgFacilityOwnership) class FacilityOwnership (TranslatableAdmin): from django.db import models formfield_overrides = { models.CharField: {'widget': TextInput(attrs={'size':'100'})}, models.TextField: {'widget': Textarea(attrs={'rows':3, 'cols':100})}, } """ Serge requested that a user does not see other users or groups data. This method filters logged in users depending on group roles and permissions. Only the superuser can see all users and locations data while a users can only see data from registered location within his/her group/system role. If a user is not assigned to a group, he/she can only own data - 01/02/2021 """ def get_queryset(self, request): language = request.LANGUAGE_CODE qs = super().get_queryset(request).filter( translations__language_code=language).order_by( 'translations__name').distinct() groups = list(request.user.groups.values_list('user', flat=True)) user = request.user.id user_location = request.user.location.location_id db_locations = StgLocation.objects.all().order_by('location_id') # Returns data for all the locations to the lowest location level if request.user.is_superuser: qs # returns data for AFRO and member countries elif user in groups and user_location==1: qs_admin=db_locations.filter( locationlevel__locationlevel_id__gte=1, locationlevel__locationlevel_id__lte=2) return qs def formfield_for_foreignkey(self, db_field, request =None, kwargs): qs = super().get_queryset(request) groups = list(request.user.groups.values_list('user', flat=True)) user = request.user.username countrycodes=StgLocationCodes.objects.values_list( 'country_code',flat=True) # This queryset is used to load specific phone code for logged in user if db_field.name == "location": if request.user.is_superuser: kwargs["queryset"] = StgLocationCodes.objects.all().order_by( 'location_id') # Looks up for the location level upto the country level else: kwargs["queryset"] = StgLocationCodes.objects.filter( location_id=request.user.location_id).order_by( 'location_id') if db_field.name == "user": kwargs["queryset"] = CustomUser.objects.filter( username=user) return super().formfield_for_foreignkey(db_field, request,kwargs) fieldsets = ( ('Facility Ownership Details', { 'fields':('name','shortname','description','location',) #afrocode may be null }), ('Logged Admin/Staff', { 'fields': ('user',) }), ) list_display=['name','code','shortname','description','location',] list_select_related = ('location','user',) list_display_links =('code', 'name',) search_fields = ('code','translations__name','translations__shortname',) #display search field list_per_page = 30 #limit records displayed on admin site to 15 exclude = ('date_created','date_lastupdated','code',) class FacilityServiceAvailabilityProxyForm(forms.ModelForm): class Meta: model = FacilityServiceAvailability fields = ('facility','domain','intervention','service','provided', 'specialunit','staff','infrastructure','supplies','date_assessed',) widgets = { 'date_assessed': DatePickerInput(), # # default date-format %m/%d/%Y will be used } class FacilityServiceAvailabilityInline(admin.TabularInline): """ Serge requested that a user does not see other users or groups data. This method filters logged in users depending on group roles and permissions. Only the superuser can see all users and locations data while a users can only see data from registered location within his/her group/system role. If a user is not assigned to a group, he/she can only own data - 01/02/2021 """ def get_queryset(self, request): qs = super().get_queryset(request) # Get a query of groups the user belongs and flatten it to list object groups = list(request.user.groups.values_list('user', flat=True)) user = request.user.username user_location = request.user.location.location_id db_locations = StgLocation.objects.all().order_by('location_id') # Returns data for all the locations to the lowest location level if request.user.is_superuser: qs # returns data for AFRO and member countries elif user in groups and user_location==1: qs_admin=db_locations.filter( locationlevel__locationlevel_id__gte=1, locationlevel__locationlevel_id__lte=2) # return data based on the location of the user logged/request location elif user in groups and user_location>1: qs=qs.filter(username=user) return qs """ Serge requested that the form for data input be restricted to user's country. Thus, this function is for filtering location to display country level. The location is used to filter the dropdownlist based on the request object's USER, If the user has superuser privileges or is a member of AFRO-DataAdmins, he/she can enter data for all the AFRO member countries otherwise, can only enter data for his/her country.=== modified 02/02/2021 """ def formfield_for_foreignkey(self, db_field, request =None, kwargs): qs = super().get_queryset(request) db_sevicedomains = StgServiceDomain.objects.all() db_sevicesubdomains=db_sevicedomains.exclude( parent_id__isnull=True).filter(category=1) if db_field.name == "domain": kwargs["queryset"]=db_sevicesubdomains return super().formfield_for_foreignkey(db_field, request,kwargs) # form = FacilityServiceAvailabilityProxyForm #overrides the default model form model = FacilityServiceAvailability # formset = LimitModelFormset extra = 1 # Used to control number of empty rows displayed. list_select_related = ('facility','domain','intervention','service',) fields = ('facility','domain','intervention','service','provided', 'specialunit','staff','infrastructure','supplies','date_assessed',) class FacilityServiceCapacityInline(admin.TabularInline): """ Serge requested that a user does not see other users or groups data. This method filters logged in users depending on group roles and permissions. Only the superuser can see all users and locations data while a users can only see data from registered location within his/her group/system role. If a user is not assigned to a group, he/she can only own data - 01/02/2021 """ def get_queryset(self, request): qs = super().get_queryset(request) # Get a query of groups the user belongs and flatten it to list object groups = list(request.user.groups.values_list('user', flat=True)) user = request.user.username user_location = request.user.location.location_id db_locations = StgLocation.objects.all().order_by('location_id') # Returns data for all the locations to the lowest location level if request.user.is_superuser: qs # returns data for AFRO and member countries elif user in groups and user_location==1: qs_admin=db_locations.filter( locationlevel__locationlevel_id__gte=1, locationlevel__locationlevel_id__lte=2) # return data based on the location of the user logged/request location elif user in groups and user_location>1: qs=qs.filter(username=user) return qs def formfield_for_foreignkey(self, db_field, request =None, kwargs): db_sevicedomains = StgServiceDomain.objects.all() db_sevicesubdomains=db_sevicedomains.exclude( parent_id__isnull=True).filter(category=2).filter( level='Level 2') db_provisionunits=StgFacilityServiceMeasureUnits.objects.select_related( 'domain') #good if db_field.name == "domain": kwargs["queryset"]=db_sevicesubdomains return super().formfield_for_foreignkey(db_field, request,kwargs) model = FacilityServiceProvision # formset = LimitModelFormset extra = 1 # Used to control number of empty rows displayed. list_select_related = ('facility','domain','units') fields = ('facility','domain','units','available','functional', 'date_assessed',) class FacilityServiceReadinessInline(admin.TabularInline): def get_queryset(self, request): qs = super().get_queryset(request) groups = list(request.user.groups.values_list('user', flat=True)) user = request.user.username user_location = request.user.location.location_id db_locations = StgLocation.objects.all().order_by('location_id') # Returns data for all the locations to the lowest location level if request.user.is_superuser: qs # returns data for AFRO and member countries elif user in groups and user_location<=2: qs_admin=db_locations.filter( locationlevel__locationlevel_id__gt=2, locationlevel__locationlevel_id__lte=3) # return data based on the location of the user logged/request location elif user in groups and user_location>1: qs=qs.filter(username=user) return qs """ Serge requested that the form for data input be restricted to user's country. Thus, this function is for filtering location to display country level. The location is used to filter the dropdownlist based on the request object's USER, If the user has superuser privileges or is a member of AFRO-DataAdmins, he/she can enter data for all the AFRO member countries otherwise, can only enter data for his/her country.=== modified 02/02/2021 """ def formfield_for_foreignkey(self, db_field, request =None,
<gh_stars>1-10 """Core async functions.""" import asyncio from pathlib import Path from ssl import SSLContext from typing import Any, Awaitable, Dict, List, Optional, Sequence, Tuple, Union import cytoolz as tlz import ujson as json from aiohttp import TCPConnector from aiohttp.typedefs import StrOrURL from aiohttp_client_cache import CachedSession, SQLiteBackend from . import utils from .exceptions import InvalidInputValue from .utils import EXPIRE, BaseRetriever __all__ = ["retrieve", "delete_url_cache", "retrieve_text", "retrieve_json", "retrieve_binary"] async def async_session( url_kwds: Tuple[Tuple[int, StrOrURL, Dict[StrOrURL, Any]], ...], read: str, r_kwds: Dict[str, Any], request_method: str, cache_name: Path, family: int, timeout: float = 5.0, expire_after: float = EXPIRE, ssl: Union[SSLContext, bool, None] = None, disable: bool = False, ) -> Awaitable[Union[str, bytes, Dict[str, Any]]]: """Create an async session for sending requests. Parameters ---------- url_kwds : list of tuples of urls and payloads A list of URLs or URLs with their payloads to be retrieved. read : str The method for returning the request; ``binary`` (bytes), ``json``, and ``text``. r_kwds : dict Keywords to pass to the response read function. ``{"content_type": None}`` if read is ``json`` else it's empty. request_method : str The request type; GET or POST. cache_name : str Path to a file for caching the session, defaults to ``./cache/aiohttp_cache.sqlite``. family : int TCP socket family timeout : float, optional Timeout for the request, defaults to 5.0. expire_after : int, optional Expiration time for the cache in seconds, defaults to -1 (never expire). ssl : bool or SSLContext, optional SSLContext to use for the connection, defaults to None. Set to False to disable SSL cetification verification. disable : bool, optional If ``True`` temporarily disable caching requests and get new responses from the server, defaults to False. Returns ------- asyncio.gather An async gather function """ cache = SQLiteBackend( cache_name=cache_name, expire_after=expire_after, allowed_methods=("GET", "POST"), timeout=timeout, ) connector = TCPConnector(family=family, ssl=ssl) async with CachedSession( json_serialize=json.dumps, cache=cache, connector=connector, trust_env=True, ) as session: _session = session.disabled() if disable else session async with _session: request_func = getattr(session, request_method.lower()) tasks = ( utils.retriever(uid, url, kwds, request_func, read, r_kwds) for uid, url, kwds in url_kwds ) return await asyncio.gather(tasks) # type: ignore def delete_url_cache( url: StrOrURL, request_method: str = "GET", cache_name: Optional[Union[Path, str]] = None, kwargs: Dict[str, Any], ) -> None: """Delete cached response associated with ``url``, along with its history (if applicable). Parameters ---------- url : str URL to be deleted from the cache request_method : str, optional HTTP request method to be deleted from the cache, defaults to ``GET``. cache_name : str, optional Path to a file for caching the session, defaults to ``./cache/aiohttp_cache.sqlite``. kwargs : dict, optional Keywords to pass to the ``cache.delete_url()``. """ loop, new_loop = utils.get_event_loop() asyncio.set_event_loop(loop) request_method = request_method.upper() valid_methods = ["GET", "POST"] if request_method not in valid_methods: raise InvalidInputValue("method", valid_methods) loop.run_until_complete( utils.delete_url(url, request_method, utils.create_cachefile(cache_name), kwargs) ) if new_loop: loop.close() def retrieve( urls: Sequence[StrOrURL], read: str, request_kwds: Optional[Sequence[Dict[str, Any]]] = None, request_method: str = "GET", max_workers: int = 8, cache_name: Optional[Union[Path, str]] = None, family: str = "both", timeout: float = 5.0, expire_after: float = EXPIRE, ssl: Union[SSLContext, bool, None] = None, disable: bool = False, ) -> List[Union[str, Dict[str, Any], bytes]]: r"""Send async requests. Parameters ---------- urls : list of str List of URLs. read : str Method for returning the request; ``binary``, ``json``, and ``text``. request_kwds : list of dict, optional List of requests keywords corresponding to input URLs (1 on 1 mapping), defaults to ``None``. For example, ``[{"params": {...}, "headers": {...}}, ...]``. request_method : str, optional Request type; ``GET`` (``get``) or ``POST`` (``post``). Defaults to ``GET``. max_workers : int, optional Maximum number of async processes, defaults to 8. cache_name : str, optional Path to a file for caching the session, defaults to ``./cache/aiohttp_cache.sqlite``. family : str, optional TCP socket family, defaults to both, i.e., IPv4 and IPv6. For IPv4 or IPv6 only pass ``ipv4`` or ``ipv6``, respectively. timeout : float, optional Timeout for the request, defaults to 5.0. expire_after : int, optional Expiration time for response caching in seconds, defaults to -1 (never expire). ssl : bool or SSLContext, optional SSLContext to use for the connection, defaults to None. Set to False to disable SSL cetification verification. disable : bool, optional If ``True`` temporarily disable caching requests and get new responses from the server, defaults to False. Returns ------- list List of responses in the order of input URLs. Examples -------- >>> import async_retriever as ar >>> stations = ["01646500", "08072300", "11073495"] >>> url = "https://waterservices.usgs.gov/nwis/site" >>> urls, kwds = zip( ... [ ... (url, {"params": {"format": "rdb", "sites": s, "siteStatus": "all"}}) ... for s in stations ... ] ... ) >>> resp = ar.retrieve(urls, "text", request_kwds=kwds) >>> resp[0].split('\n')[-2].split('\t')[1] '01646500' """ inp = BaseRetriever(urls, read, request_kwds, request_method, cache_name, family) loop, new_loop = utils.get_event_loop() asyncio.set_event_loop(loop) session = tlz.partial( async_session, read=inp.read, r_kwds=inp.r_kwds, request_method=inp.request_method, cache_name=inp.cache_name, family=inp.family, timeout=timeout, expire_after=expire_after, ssl=ssl, disable=disable, ) chunked_reqs = tlz.partition_all(max_workers, inp.url_kwds) results = (loop.run_until_complete(session(url_kwds=c)) for c in chunked_reqs) resp = [r for _, r in sorted(tlz.concat(results))] if new_loop: loop.close() return resp def retrieve_text( urls: Sequence[StrOrURL], request_kwds: Optional[Sequence[Dict[str, Any]]] = None, request_method: str = "GET", max_workers: int = 8, cache_name: Optional[Union[Path, str]] = None, family: str = "both", timeout: float = 5.0, expire_after: float = EXPIRE, ssl: Union[SSLContext, bool, None] = None, disable: bool = False, ) -> List[str]: r"""Send async requests and get the response as ``text``. Parameters ---------- urls : list of str List of URLs. request_kwds : list of dict, optional List of requests keywords corresponding to input URLs (1 on 1 mapping), defaults to ``None``. For example, ``[{"params": {...}, "headers": {...}}, ...]``. request_method : str, optional Request type; ``GET`` (``get``) or ``POST`` (``post``). Defaults to ``GET``. max_workers : int, optional Maximum number of async processes, defaults to 8. cache_name : str, optional Path to a file for caching the session, defaults to ``./cache/aiohttp_cache.sqlite``. family : str, optional TCP socket family, defaults to both, i.e., IPv4 and IPv6. For IPv4 or IPv6 only pass ``ipv4`` or ``ipv6``, respectively. timeout : float, optional Timeout for the request in seconds, defaults to 5.0. expire_after : int, optional Expiration time for response caching in seconds, defaults to -1 (never expire). ssl : bool or SSLContext, optional SSLContext to use for the connection, defaults to None. Set to False to disable SSL cetification verification. disable : bool, optional If ``True`` temporarily disable caching requests and get new responses from the server, defaults to False. Returns ------- list List of responses in the order of input URLs. Examples -------- >>> import async_retriever as ar >>> stations = ["01646500", "08072300", "11073495"] >>> url = "https://waterservices.usgs.gov/nwis/site" >>> urls, kwds = zip( ... *[ ... (url, {"params": {"format": "rdb", "sites": s, "siteStatus": "all"}}) ... for s in stations ... ] ... ) >>> resp = ar.retrieve_text(urls, kwds) >>> resp[0].split('\n')[-2].split('\t')[1] '01646500' """ resp: List[str] = retrieve( # type: ignore urls, "text", request_kwds, request_method, max_workers, cache_name, family, timeout, expire_after, ssl, disable, ) return resp def retrieve_json( urls: Sequence[StrOrURL], request_kwds: Optional[Sequence[Dict[str, Any]]] = None, request_method: str = "GET", max_workers: int = 8, cache_name: Optional[Union[Path, str]] = None, family: str = "both", timeout: float = 5.0, expire_after: float = EXPIRE, ssl: Union[SSLContext, bool, None] = None, disable: bool = False, ) -> List[Dict[str, Any]]: r"""Send async requests and get the response as ``json``. Parameters ---------- urls : list of str List of URLs. request_kwds : list of dict, optional List of requests keywords corresponding to input URLs (1 on 1 mapping), defaults to ``None``. For example, ``[{"params": {...}, "headers": {...}}, ...]``. request_method : str, optional Request type; ``GET`` (``get``) or ``POST`` (``post``). Defaults to ``GET``. max_workers : int, optional Maximum number of async processes, defaults to 8. cache_name : str, optional Path to a file for caching the session, defaults to ``./cache/aiohttp_cache.sqlite``. family : str, optional TCP socket family, defaults to both, i.e., IPv4 and IPv6. For IPv4 or IPv6 only pass ``ipv4`` or ``ipv6``, respectively. timeout : float, optional Timeout for the request, defaults to 5.0. expire_after : int, optional Expiration time for response caching
<reponame>ssalonen/pandas<filename>pandas/io/html.py<gh_stars>0 """:mod:`pandas.io.html` is a module containing functionality for dealing with HTML IO. """ import os import re import numbers import collections from distutils.version import LooseVersion import numpy as np from pandas import DataFrame, MultiIndex, isnull from pandas.io.common import _is_url, urlopen, parse_url from pandas.compat import range, lrange, lmap, u, map from pandas import compat try: import bs4 except ImportError: _HAS_BS4 = False else: _HAS_BS4 = True try: import lxml except ImportError: _HAS_LXML = False else: _HAS_LXML = True try: import html5lib except ImportError: _HAS_HTML5LIB = False else: _HAS_HTML5LIB = True ############# # READ HTML # ############# _RE_WHITESPACE = re.compile(r'([\r\n]+\|\s{2,})') def _remove_whitespace(s, regex=_RE_WHITESPACE): """Replace extra whitespace inside of a string with a single space. Parameters ---------- s : str or unicode The string from which to remove extra whitespace. regex : regex The regular expression to use to remove extra whitespace. Returns ------- subd : str or unicode `s` with all extra whitespace replaced with a single space. """ return regex.sub(' ', s.strip()) def _get_skiprows_iter(skiprows): """Get an iterator given an integer, slice or container. Parameters ---------- skiprows : int, slice, container The iterator to use to skip rows; can also be a slice. Raises ------ TypeError * If `skiprows` is not a slice, integer, or Container Raises ------ TypeError * If `skiprows` is not a slice, integer, or Container Returns ------- it : iterable A proper iterator to use to skip rows of a DataFrame. """ if isinstance(skiprows, slice): return lrange(skiprows.start or 0, skiprows.stop, skiprows.step or 1) elif isinstance(skiprows, numbers.Integral): return lrange(skiprows) elif isinstance(skiprows, collections.Container): return skiprows else: raise TypeError('{0} is not a valid type for skipping' ' rows'.format(type(skiprows))) def _read(io): """Try to read from a url, file or string. Parameters ---------- io : str, unicode, or file-like Returns ------- raw_text : str """ if _is_url(io): with urlopen(io) as url: raw_text = url.read() elif hasattr(io, 'read'): raw_text = io.read() elif os.path.isfile(io): with open(io) as f: raw_text = f.read() elif isinstance(io, compat.string_types): raw_text = io else: raise TypeError("Cannot read object of type " "'{0.__class__.__name__!r}'".format(io)) return raw_text class _HtmlFrameParser(object): """Base class for parsers that parse HTML into DataFrames. Parameters ---------- io : str or file-like This can be either a string of raw HTML, a valid URL using the HTTP, FTP, or FILE protocols or a file-like object. match : str or regex The text to match in the document. attrs : dict List of HTML <table> element attributes to match. Attributes ---------- io : str or file-like raw HTML, URL, or file-like object match : regex The text to match in the raw HTML attrs : dict-like A dictionary of valid table attributes to use to search for table elements. Notes ----- To subclass this class effectively you must override the following methods: * :func:`_build_doc` * :func:`_text_getter` * :func:`_parse_td` * :func:`_parse_tables` * :func:`_parse_tr` * :func:`_parse_thead` * :func:`_parse_tbody` * :func:`_parse_tfoot` See each method's respective documentation for details on their functionality. """ def __init__(self, io, match, attrs): self.io = io self.match = match self.attrs = attrs def parse_tables(self): tables = self._parse_tables(self._build_doc(), self.match, self.attrs) return (self._build_table(table) for table in tables) def _parse_raw_data(self, rows): """Parse the raw data into a list of lists. Parameters ---------- rows : iterable of node-like A list of row elements. text_getter : callable A callable that gets the text from an individual node. This must be defined by subclasses. column_finder : callable A callable that takes a row node as input and returns a list of the column node in that row. This must be defined by subclasses. Raises ------ AssertionError * If `text_getter` is not callable * If `column_finder` is not callable Returns ------- data : list of list of strings """ data = [[_remove_whitespace(self._text_getter(col)) for col in self._parse_td(row)] for row in rows] return data def _text_getter(self, obj): """Return the text of an individual DOM node. Parameters ---------- obj : node-like A DOM node. Returns ------- text : str or unicode The text from an individual DOM node. """ raise NotImplementedError def _parse_td(self, obj): """Return the td elements from a row element. Parameters ---------- obj : node-like Returns ------- columns : list of node-like These are the elements of each row, i.e., the columns. """ raise NotImplementedError def _parse_tables(self, doc, match, attrs): """Return all tables from the parsed DOM. Parameters ---------- doc : tree-like The DOM from which to parse the table element. match : str or regular expression The text to search for in the DOM tree. attrs : dict A dictionary of table attributes that can be used to disambiguate mutliple tables on a page. Raises ------ AssertionError * If `match` does not match any text in the document. Returns ------- tables : list of node-like A list of <table> elements to be parsed into raw data. """ raise NotImplementedError def _parse_tr(self, table): """Return the list of row elements from the parsed table element. Parameters ---------- table : node-like A table element that contains row elements. Returns ------- rows : list of node-like A list row elements of a table, usually <tr> or <th> elements. """ raise NotImplementedError def _parse_thead(self, table): """Return the header of a table. Parameters ---------- table : node-like A table element that contains row elements. Returns ------- thead : node-like A <thead>...</thead> element. """ raise NotImplementedError def _parse_tbody(self, table): """Return the body of the table. Parameters ---------- table : node-like A table element that contains row elements. Returns ------- tbody : node-like A <tbody>...</tbody> element. """ raise NotImplementedError def _parse_tfoot(self, table): """Return the footer of the table if any. Parameters ---------- table : node-like A table element that contains row elements. Returns ------- tfoot : node-like A <tfoot>...</tfoot> element. """ raise NotImplementedError def _build_doc(self): """Return a tree-like object that can be used to iterate over the DOM. Returns ------- obj : tree-like """ raise NotImplementedError def _build_table(self, table): header = self._parse_raw_thead(table) body = self._parse_raw_tbody(table) footer = self._parse_raw_tfoot(table) return header, body, footer def _parse_raw_thead(self, table): thead = self._parse_thead(table) res = [] if thead: res = lmap(self._text_getter, self._parse_th(thead[0])) return np.array(res).squeeze() if res and len(res) == 1 else res def _parse_raw_tfoot(self, table): tfoot = self._parse_tfoot(table) res = [] if tfoot: res = lmap(self._text_getter, self._parse_td(tfoot[0])) return np.array(res).squeeze() if res and len(res) == 1 else res def _parse_raw_tbody(self, table): tbody = self._parse_tbody(table) try: res = self._parse_tr(tbody[0]) except IndexError: res = self._parse_tr(table) return self._parse_raw_data(res) class _BeautifulSoupHtml5LibFrameParser(_HtmlFrameParser): """HTML to DataFrame parser that uses BeautifulSoup under the hood. See Also -------- pandas.io.html._HtmlFrameParser pandas.io.html._LxmlFrameParser Notes ----- Documentation strings for this class are in the base class :class:`pandas.io.html._HtmlFrameParser`. """ def __init__(self, args, kwargs): super(_BeautifulSoupHtml5LibFrameParser, self).__init__(args, **kwargs) from bs4 import SoupStrainer self._strainer = SoupStrainer('table') def _text_getter(self, obj): return obj.text def _parse_td(self, row): return row.find_all(('td', 'th')) def _parse_tr(self, element): return element.find_all('tr') def _parse_th(self, element): return element.find_all('th') def _parse_thead(self, table): return table.find_all('thead') def _parse_tbody(self, table): return table.find_all('tbody') def _parse_tfoot(self, table): return table.find_all('tfoot') def _parse_tables(self, doc, match, attrs): element_name = self._strainer.name tables = doc.find_all(element_name, attrs=attrs) if not tables: # known sporadically working release raise AssertionError('No tables found') mts = [table.find(text=match) for table in tables] matched_tables = [mt for mt in mts if mt is not None] tables = list(set(mt.find_parent(element_name) for mt in matched_tables)) if not tables: raise AssertionError("No tables found matching " "'{0}'".format(match.pattern)) return tables def _setup_build_doc(self): raw_text = _read(self.io) if not raw_text: raise AssertionError('No text parsed from document: ' '{0}'.format(self.io)) return raw_text def _build_doc(self): from bs4 import BeautifulSoup return BeautifulSoup(self._setup_build_doc(), features='html5lib') def _build_node_xpath_expr(attrs): """Build an xpath expression to simulate bs4's ability to pass in kwargs to search for attributes when using the lxml parser. Parameters ---------- attrs : dict A dict of HTML attributes. These are NOT checked for validity. Returns ------- expr : unicode An XPath expression that checks for the given HTML attributes. """ # give class attribute as class_ because class is a python keyword if 'class_' in attrs: attrs['class'] = attrs.pop('class_') s = (u("@{k}='{v}'").format(k=k, v=v) for k, v in compat.iteritems(attrs)) return u('[{0}]').format(' and '.join(s)) _re_namespace = {'re': 'http://exslt.org/regular-expressions'} _valid_schemes = 'http', 'file', 'ftp' class _LxmlFrameParser(_HtmlFrameParser): """HTML to DataFrame parser that uses lxml under the hood. Warning ------- This parser
#----------------------------------------------------------------------------- # Copyright (c) 2012 - 2017, Anaconda, Inc. All rights reserved. # # Powered by the Bokeh Development Team. # # The full license is in the file LICENSE.txt, distributed with this software. #----------------------------------------------------------------------------- #----------------------------------------------------------------------------- # Boilerplate #----------------------------------------------------------------------------- import pytest ; pytest #----------------------------------------------------------------------------- # Imports #----------------------------------------------------------------------------- # External imports from flaky import flaky # Bokeh imports from bokeh._testing.util.selenium import ( RECORD, alt_click, enter_text_in_cell, get_table_cell, get_table_column_cells, get_table_row, get_table_selected_rows, shift_click, sort_table_column, ) from bokeh.layouts import column from bokeh.models import ( Button, ColumnDataSource, CustomAction, CustomJS, DataTable, NumberEditor, Plot, Range1d, Rect, TableColumn, TapTool, ) #----------------------------------------------------------------------------- # Tests #----------------------------------------------------------------------------- pytest_plugins = ( "bokeh._testing.plugins.project", ) def _is_cds_data_patch(evt): return evt['kind'] == 'ModelChanged' and evt['attr'] == 'data' def has_cds_data_patches(msgs): for msg in msgs: if msg.msgtype == "PATCH-DOC": if any(_is_cds_data_patch(evt) for evt in msg.content.get('events', [])): return True return False @pytest.mark.selenium class Test_DataTableSource(object): def test_server_source_patch_does_not_duplicate_data_update_event(self, bokeh_server_page) -> None: def modify_doc(doc): data = {'x': [1,2,3,4], 'y': [10,20,30,40]} source = ColumnDataSource(data) plot = Plot(plot_height=400, plot_width=400, x_range=Range1d(0, 1), y_range=Range1d(0, 1), min_border=0) plot.add_tools(CustomAction(callback=CustomJS(args=dict(s=source), code=RECORD("data", "s.data")))) table = DataTable(columns=[ TableColumn(field="x"), TableColumn(field="y") ], source=source, editable=False) btn = Button(label="Click Me!", css_classes=["foo"]) @btn.on_click def btn_click(): source.patch({"x": [(0, 42)]}) doc.add_root(column(plot, table, btn)) page = bokeh_server_page(modify_doc) page.click_custom_action() results = page.results assert results == {'data': {'x': [1,2,3,4], 'y': [10,20,30,40]}} button = page.driver.find_element_by_class_name('foo') button.click() page.click_custom_action() results = page.results assert results == {'data': {'x': [42,2,3,4], 'y': [10,20,30,40]}} # if the server receives something back like: # # Message 'PATCH-DOC' (revision 1) content: { # 'events': [{ # 'kind': 'ModelChanged', # 'model': {'id': '1001'}, # 'attr': 'data', 'new': {'x': [42, 2, 3, 4], 'y': [10, 20, 30, 40]} # }], # 'references': [] # } # # Then that means the client got our patch message and erroneously ping # ponged a full data update back to us assert not has_cds_data_patches(page.message_test_port.received) # XXX (bev) disabled until https://github.com/bokeh/bokeh/issues/7970 is resolved #assert page.has_no_console_errors() def test_server_source_stream_does_not_duplicate_data_update_event(self, bokeh_server_page) -> None: def modify_doc(doc): data = {'x': [1,2,3,4], 'y': [10,20,30,40]} source = ColumnDataSource(data) plot = Plot(plot_height=400, plot_width=400, x_range=Range1d(0, 1), y_range=Range1d(0, 1), min_border=0) plot.add_tools(CustomAction(callback=CustomJS(args=dict(s=source), code=RECORD("data", "s.data")))) table = DataTable(columns=[ TableColumn(field="x"), TableColumn(field="y") ], source=source, editable=False) btn = Button(label="Click Me!", css_classes=["foo"]) @btn.on_click def btn_click(): source.stream({"x": [5], "y": [50]}) doc.add_root(column(plot, table, btn)) page = bokeh_server_page(modify_doc) page.click_custom_action() results = page.results assert results == {'data': {'x': [1,2,3,4], 'y': [10,20,30,40]}} button = page.driver.find_element_by_class_name('foo') button.click() page.click_custom_action() results = page.results assert results == {'data': {'x': [1,2,3,4,5], 'y': [10,20,30,40,50]}} # if the server receives something back like: # # Message 'PATCH-DOC' (revision 1) content: { # 'events': [{ # 'kind': 'ModelChanged', # 'model': {'id': '1001'}, # 'attr': 'data', 'new': {'x': [1, 2, 3, 4, 5], 'y': [10, 20, 30, 40, 50]} # }], # 'references': [] # } # # Then that means the client got our stream message and erroneously ping # ponged a full data update back to us assert not has_cds_data_patches(page.message_test_port.received) # XXX (bev) disabled until https://github.com/bokeh/bokeh/issues/7970 is resolved #assert page.has_no_console_errors() def test_server_source_update_does_not_duplicate_data_update_event(self, bokeh_server_page) -> None: def modify_doc(doc): data = {'x': [1,2,3,4], 'y': [10,20,30,40]} source = ColumnDataSource(data) plot = Plot(plot_height=400, plot_width=400, x_range=Range1d(0, 1), y_range=Range1d(0, 1), min_border=0) plot.add_tools(CustomAction(callback=CustomJS(args=dict(s=source), code=RECORD("data", "s.data")))) table = DataTable(columns=[ TableColumn(field="x"), TableColumn(field="y") ], source=source, editable=False) btn = Button(label="Click Me!", css_classes=["foo"]) @btn.on_click def btn_click(): source.data = {'x': [5,6,7,8], 'y': [50,60,70,80]} doc.add_root(column(plot, table, btn)) page = bokeh_server_page(modify_doc) page.click_custom_action() results = page.results assert results == {'data': {'x': [1,2,3,4], 'y': [10,20,30,40]}} button = page.driver.find_element_by_class_name('foo') button.click() page.click_custom_action() results = page.results assert results == {'data': {'x': [5,6,7,8], 'y': [50,60,70,80]}} # if the server receives something back like: # # Message 'PATCH-DOC' (revision 1) content: { # 'events': [{ # 'kind': 'ModelChanged', # 'model': {'id': '1001'}, # 'attr': 'data', 'new': {'x': [1, 2, 3, 4, 5], 'y': [10, 20, 30, 40, 50]} # }], # 'references': [] # } # # Then that means the client got our stream message and erroneously ping # ponged a full data update back to us assert not has_cds_data_patches(page.message_test_port.received) # XXX (bev) disabled until https://github.com/bokeh/bokeh/issues/7970 is resolved #assert page.has_no_console_errors() def test_server_edit_does_not_duplicate_data_update_event(self, bokeh_server_page) -> None: def modify_doc(doc): data = {'x': [1,2,3,4], 'y': [10,20,30,40]} source = ColumnDataSource(data) plot = Plot(plot_height=400, plot_width=400, x_range=Range1d(0, 1), y_range=Range1d(0, 1), min_border=0) plot.add_tools(CustomAction(callback=CustomJS(args=dict(s=source), code=RECORD("data", "s.data")))) table = DataTable(columns=[ TableColumn(field="x"), TableColumn(field="y", editor=NumberEditor()) ], source=source, editable=True) doc.add_root(column(plot, table)) page = bokeh_server_page(modify_doc) page.click_custom_action() results = page.results assert results == {'data': {'x': [1,2,3,4], 'y': [10,20,30,40]}} cell = get_table_cell(page.driver, 3, 2) assert cell.text == '30' enter_text_in_cell(page.driver, cell, '100') page.click_custom_action() results = page.results assert results == {'data': {'x': [1,2,3,4], 'y': [10, 20, 100, 40]}} # if the server receives something back like: # # Message 'PATCH-DOC' (revision 1) content: { # 'events': [{ # 'kind': 'ModelChanged', # 'model': {'id': '1001'}, # 'attr': 'data', 'new': {'x': [1,2,3,4], 'y': [10, 20, 100, 40]} # }], # 'references': [] # } # # Then that means the client got our stream message and erroneously ping # ponged a full data update back to us assert not has_cds_data_patches(page.message_test_port.received) # XXX (bev) disabled until https://github.com/bokeh/bokeh/issues/7970 is resolved #assert page.has_no_console_errors() def test_server_basic_selection(self, bokeh_server_page) -> None: data = {'x': [1,2,3,4,5,6], 'y': [60,50,40,30,20,10]} source = ColumnDataSource(data) def modify_doc(doc): plot = Plot(plot_height=400, plot_width=400, x_range=Range1d(0, 1), y_range=Range1d(0, 1), min_border=0) plot.add_tools(CustomAction(callback=CustomJS(args=dict(s=source), code=RECORD("indices", "s.selected.indices")))) table = DataTable(columns=[ TableColumn(field="x"), TableColumn(field="y") ], source=source, editable=False) doc.add_root(column(plot, table)) page = bokeh_server_page(modify_doc) page.click_custom_action() results = page.results assert results == {'indices': []} assert set(source.selected.indices) == set([]) assert get_table_selected_rows(page.driver) == set([]) # select the third row row = get_table_row(page.driver, 3) row.click() page.click_custom_action() results = page.results assert results == {'indices': [2]} assert source.selected.indices == [2] assert get_table_selected_rows(page.driver) == set([2]) # select the first row row = get_table_row(page.driver, 1) row.click() page.click_custom_action() results = page.results assert results == {'indices': [0]} assert source.selected.indices == [0] assert get_table_selected_rows(page.driver) == set([0]) # XXX (bev) disabled until https://github.com/bokeh/bokeh/issues/7970 is resolved #assert page.has_no_console_errors() def test_server_basic_mulitselection(self, bokeh_server_page) -> None: data = {'x': [1,2,3,4,5,6], 'y': [60,50,40,30,20,10]} source = ColumnDataSource(data) def modify_doc(doc): plot = Plot(plot_height=400, plot_width=400, x_range=Range1d(0, 1), y_range=Range1d(0, 1), min_border=0) plot.add_tools(CustomAction(callback=CustomJS(args=dict(s=source), code=RECORD("indices", "s.selected.indices")))) table = DataTable(columns=[ TableColumn(field="x"), TableColumn(field="y") ], source=source, editable=False) doc.add_root(column(plot, table)) page = bokeh_server_page(modify_doc) page.click_custom_action() results = page.results assert results == {'indices': []} assert set(source.selected.indices) == set([]) assert get_table_selected_rows(page.driver) == set([]) # select the third row row = get_table_row(page.driver, 2) row.click() row = get_table_row(page.driver, 4) shift_click(page.driver, row) page.click_custom_action() results = page.results assert set(results['indices']) == set([1, 2, 3]) assert set(source.selected.indices) == set([1, 2, 3]) assert get_table_selected_rows(page.driver) == set([1, 2, 3]) row = get_table_row(page.driver, 6) alt_click(page.driver, row) page.click_custom_action() results = page.results assert set(results['indices']) == set([1, 2, 3, 5]) assert set(source.selected.indices) == set([1, 2, 3, 5]) assert get_table_selected_rows(page.driver) == set([1, 2, 3, 5]) # XXX (bev) disabled until https://github.com/bokeh/bokeh/issues/7970 is resolved #assert page.has_no_console_errors() @flaky(max_runs=5) def test_server_sorted_after_data_update(self, bokeh_server_page) -> None: data = {'x': [1,2,5,6], 'y': [60,50,20,10]} source = ColumnDataSource(data) def modify_doc(doc): plot = Plot(plot_height=400, plot_width=400, x_range=Range1d(0, 1), y_range=Range1d(0, 1), min_border=0) plot.add_tools(CustomAction(callback=CustomJS(args=dict(s=source), code=RECORD("data", "s.data")))) table = DataTable(columns=[ TableColumn(field="x", title="x", sortable=True), TableColumn(field="y", title="y", sortable=True) ], source=source, editable=False) button = Button(css_classes=["foo"]) def cb(): source.data = {'x': [0,1,2,3,4,5,6,7], 'y': [70,60,50,40,30,20,10,0]} button.on_click(cb) doc.add_root(column(plot, table, button)) page = bokeh_server_page(modify_doc) page.click_custom_action() results = page.results assert results == {'data': {'x': [1,2,5,6], 'y': [60,50,20,10]}} assert get_table_column_cells(page.driver, 1) == ['1', '2', '5', '6'] assert get_table_column_cells(page.driver, 2) == ['60', '50', '20', '10'] sort_table_column(page.driver, 1) assert get_table_column_cells(page.driver, 1) == ['1', '2', '5', '6'] assert get_table_column_cells(page.driver, 2) == ['60', '50', '20', '10'] sort_table_column(page.driver, 2, True) assert get_table_column_cells(page.driver, 1) == ['6', '5', '2', '1'] assert get_table_column_cells(page.driver, 2) == ['10', '20', '50', '60'] button = page.driver.find_element_by_class_name('foo') button.click() page.click_custom_action() results = page.results assert results == {'data': {'x': [0,1,2,3,4,5,6,7], 'y': [70,60,50,40,30,20,10,0]}} assert source.data == {'x': [0,1,2,3,4,5,6,7], 'y': [70,60,50,40,30,20,10,0]} assert get_table_column_cells(page.driver, 1) == ['7', '6', '5', '4', '3', '2', '1', '0'] assert get_table_column_cells(page.driver, 2) == ['0', '10', '20', '30', '40', '50', '60', '70'] # XXX (bev) disabled until https://github.com/bokeh/bokeh/issues/7970 is resolved #assert page.has_no_console_errors() @pytest.mark.skip def test_server_sorted_after_patch(self, bokeh_server_page) -> None: data = {'x': [1,2,5,6], 'y': [60,50,20,10]} source = ColumnDataSource(data) def modify_doc(doc): plot = Plot(plot_height=400, plot_width=400, x_range=Range1d(0, 1), y_range=Range1d(0, 1), min_border=0) plot.add_tools(CustomAction(callback=CustomJS(args=dict(s=source), code=RECORD("data", "s.data")))) table = DataTable(columns=[ TableColumn(field="x", title="x", sortable=True), TableColumn(field="y", title="y", sortable=True) ], source=source, editable=False) button = Button(css_classes=["foo"]) def cb(): source.patch({'y': [[2, 100]]}) button.on_click(cb) doc.add_root(column(plot, table, button)) page = bokeh_server_page(modify_doc) page.click_custom_action() results = page.results assert results == {'data': {'x': [1,2,5,6], 'y': [60,50,20,10]}} assert get_table_column_cells(page.driver, 1) == ['1', '2', '5',
import pandas as pd import sys import numpy as np import scipy as sp import json import os from decimal import Decimal import scipy.optimize as opt from scipy.optimize import minimize, curve_fit from scipy.special import erfc from scipy.stats import crystalball from scipy.signal import medfilt, find_peaks import pygama.analysis.histograms as pgh import pygama.utils as pgu import pygama.analysis.peak_fitting as pga import matplotlib.pyplot as plt from matplotlib.lines import Line2D plt.style.use('style.mplstyle') def main(): ## this code takes the peaks from thorium's first-pass calibration and fits them. the values from these fits are used to then do a non-linear, second-pass calibration. peak_2615() #peak_1765() #peak_1460() #peak_609() #peak_352() def peak_2615(): if(len(sys.argv) != 2): print('Usage: fit_bkg_peaks.py [run number]') sys.exit() with open("runDB.json") as f: runDB = json.load(f) meta_dir = os.path.expandvars(runDB["meta_dir"]) #df = pd.read_hdf("{}/Spectrum_280-329.hdf5".format(meta_dir), key="df") df = pd.read_hdf("{}/Spectrum_{}.hdf5".format(meta_dir,sys.argv[1]), key="df") def gauss(x, mu, sigma, A=1): """ define a gaussian distribution, w/ args: mu, sigma, area (optional). """ return A * (1. / sigma / np.sqrt(2 * np.pi)) * np.exp(-(x - mu)*2 / (2. sigma*2)) def radford_peak(x, mu, sigma, hstep, htail, tau, bg0, a=1): """ David Radford's HPGe peak shape function """ # make sure the fractional amplitude parameters stay reasonable... if htail < 0 or htail > 1: return np.zeros_like(x) if hstep < 0 or hstep > 1: return np.zeros_like(x) bg_term = bg0 #+ xbg1 if np.any(bg_term < 0): return np.zeros_like(x) # compute the step and the low energy tail step = a * hstep * erfc((x - mu) / (sigma * np.sqrt(2))) le_tail = a * htail le_tail = erfc((x - mu) / (sigma np.sqrt(2)) + sigma / (tau * np.sqrt(2))) le_tail = np.exp((x - mu) / tau) le_tail /= (2 tau * np.exp(-(sigma / (np.sqrt(2) * tau))*2)) # add up all the peak shape components return (1 - htail) gauss(x, mu, sigma, a) + bg_term + step + le_tail hist, bins, var = pgh.get_hist(df['e_cal'], range=(2540,2680), dx=0.5) pgh.plot_hist(hist, bins, var=hist, label="data") pars, cov = pga.fit_hist(radford_peak, hist, bins, var=hist, guess=[2608.5, 1.05, 0.001, 0.02, 5, 1, 4000]) pgu.print_fit_results(pars, cov, radford_peak) pgu.plot_func(radford_peak, pars, label="chi2 fit", color='red') #x_vals = np.arange(2540,2680,0.5) #plt.plot(x_vals, radford_peak(x_vals, 2608.5, 1.05, .001, 0.02, 5, 1, 4000)) FWHM = '%.2f' % Decimal(pars[1]2) FWHM_uncertainty = '%.2f' % Decimal(np.sqrt(cov[1][1])2) peak = '%.2f' % Decimal(pars[0]) peak_uncertainty = '%.2f' % Decimal(np.sqrt(cov[0][0])) residual = '%.2f' % (2614.51 - float(peak)) chi_2_element_list = [] for i in range(len(hist)): chi_2_element = abs((radford_peak(bins[i], pars) - hist[i])2/radford_peak(bins[i], pars)) chi_2_element_list.append(chi_2_element) chi_2 = sum(chi_2_element_list) reduced_chi_2 = '%.2f' % Decimal(chi_2/len(hist)) print(reduced_chi_2) label_01 = '2614.51 keV peak fit' label_02 = 'FWHM = '+str(FWHM)+r' $\pm$ '+str(FWHM_uncertainty) label_03 = 'Peak = '+str(peak)+r' $\pm$ '+str(peak_uncertainty) label_04 = 'Residual = '+str(residual)+r' $\pm$ '+str(peak_uncertainty) colors = ['red', 'red','red', 'red'] lines = [Line2D([0], [0], color=c, lw=2) for c in colors] labels = [label_01, label_02, label_03, label_04] plt.xlim(2540,2680) plt.ylim(0,plt.ylim()[1]) plt.xlabel('Energy (keV)', ha='right', x=1.0) plt.ylabel('Counts', ha='right', y=1.0) plt.title('Fit of First-Pass Kr83m Calibration Peak') plt.tight_layout() #plt.semilogy() plt.legend(lines, labels, frameon=False, loc='upper right', fontsize='small') plt.show() def peak_1765(): if(len(sys.argv) != 2): print('Usage: fit_bkg_peaks.py [run number]') sys.exit() with open("runDB.json") as f: runDB = json.load(f) meta_dir = os.path.expandvars(runDB["meta_dir"]) #df = pd.read_hdf("{}/Spectrum_280-329.hdf5".format(meta_dir), key="df") df = pd.read_hdf("{}/Spectrum_{}.hdf5".format(meta_dir,sys.argv[1]), key="df") def gauss(x, mu, sigma, A=1): """ define a gaussian distribution, w/ args: mu, sigma, area (optional). """ return A * (1. / sigma / np.sqrt(2 * np.pi)) * np.exp(-(x - mu)*2 / (2. sigma*2)) def radford_peak(x, mu, sigma, hstep, htail, tau, bg0, a=1): """ <NAME>'s HPGe peak shape function """ # make sure the fractional amplitude parameters stay reasonable... if htail < 0 or htail > 1: return np.zeros_like(x) if hstep < 0 or hstep > 1: return np.zeros_like(x) bg_term = bg0 #+ xbg1 if np.any(bg_term < 0): return np.zeros_like(x) # compute the step and the low energy tail step = a * hstep * erfc((x - mu) / (sigma * np.sqrt(2))) le_tail = a * htail le_tail = erfc((x - mu) / (sigma np.sqrt(2)) + sigma / (tau * np.sqrt(2))) le_tail = np.exp((x - mu) / tau) le_tail /= (2 tau * np.exp(-(sigma / (np.sqrt(2) * tau))*2)) # add up all the peak shape components return (1 - htail) gauss(x, mu, sigma, a) + bg_term + step + le_tail hist, bins, var = pgh.get_hist(df['e_cal'], range=(1740,1780), dx=0.5) pgh.plot_hist(hist, bins, var=hist, label="data") pars, cov = pga.fit_hist(radford_peak, hist, bins, var=hist, guess=[1761, 1.85, 0.001, 0.02, 5, 1, 4000]) pgu.print_fit_results(pars, cov, radford_peak) pgu.plot_func(radford_peak, pars, label="chi2 fit", color='red') #x_vals = np.arange(1740,1780,0.5) #plt.plot(x_vals, radford_peak(x_vals, 1761, 1.85, .001, 0.02, 5, 1, 4000)) FWHM = '%.2f' % Decimal(pars[1]2) FWHM_uncertainty = '%.2f' % Decimal(np.sqrt(cov[1][1])2) peak = '%.2f' % Decimal(pars[0]) peak_uncertainty = '%.2f' % Decimal(np.sqrt(cov[0][0])) residual = '%.2f' % (1764.49 - float(peak)) #chi_2_element_list = [] #for i in range(len(hist)): #chi_2_element = abs((radford_peak(bins[i], pars) - hist[i])2/radford_peak(bins[i], pars)) #chi_2_element_list.append(chi_2_element) #chi_2 = sum(chi_2_element_list) #reduced_chi_2 = '%.2f' % Decimal(chi_2/len(hist)) label_01 = '1764.49 keV peak fit' label_02 = 'FWHM = '+str(FWHM)+r' $\pm$ '+str(FWHM_uncertainty) label_03 = 'Peak = '+str(peak)+r' $\pm$ '+str(peak_uncertainty) label_04 = 'Residual = '+str(residual)+r' $\pm$ '+str(peak_uncertainty) colors = ['red', 'red','red', 'red'] lines = [Line2D([0], [0], color=c, lw=2) for c in colors] labels = [label_01, label_02, label_03, label_04] plt.xlim(1740,1780) plt.ylim(0,plt.ylim()[1]) plt.xlabel('Energy (keV)', ha='right', x=1.0) plt.ylabel('Counts', ha='right', y=1.0) plt.tight_layout() #plt.semilogy() plt.legend(lines, labels, frameon=False, loc='upper right', fontsize='small') plt.show() def peak_1460(): if(len(sys.argv) != 2): print('Usage: fit_bkg_peaks.py [run number]') sys.exit() with open("runDB.json") as f: runDB = json.load(f) meta_dir = os.path.expandvars(runDB["meta_dir"]) tier_dir = os.path.expandvars(runDB["tier_dir"]) #df = pd.read_hdf("{}/Spectrum_280-329.hdf5".format(meta_dir), key="df") df = pd.read_hdf("{}/Spectrum_{}.hdf5".format(meta_dir,sys.argv[1]), key="df") #df = pd.read_hdf("{}/t2_run{}.h5".format(tier_dir,sys.argv[1])) #df['e_cal'] = 0.4054761904761905 * df['e_ftp'] + 3.113095238095184 def gauss(x, mu, sigma, A=1): """ define a gaussian distribution, w/ args: mu, sigma, area (optional). """ return A * (1. / sigma / np.sqrt(2 * np.pi)) * np.exp(-(x - mu)*2 / (2. sigma*2)) def radford_peak(x, mu, sigma, hstep, htail, tau, bg0, a=1): """ <NAME>'s HPGe peak shape function """ # make sure the fractional amplitude parameters stay reasonable... if htail < 0 or htail > 1: return np.zeros_like(x) if hstep < 0 or hstep > 1: return np.zeros_like(x) bg_term = bg0 #+ xbg1 if np.any(bg_term < 0): return np.zeros_like(x) # compute the step and the low energy tail step = a * hstep * erfc((x - mu) / (sigma * np.sqrt(2))) le_tail = a * htail le_tail = erfc((x - mu) / (sigma np.sqrt(2)) + sigma / (tau * np.sqrt(2))) le_tail = np.exp((x - mu) / tau) le_tail /= (2 tau * np.exp(-(sigma / (np.sqrt(2) * tau))*2)) # add up all the peak shape components return (1 - htail) gauss(x, mu, sigma, a) + bg_term + step + le_tail hist, bins, var = pgh.get_hist(df['e_cal'], range=(1420,1500), dx=0.5) pgh.plot_hist(hist, bins, var=hist, label="data") pars, cov = pga.fit_hist(radford_peak, hist, bins, var=hist, guess=[1460.8, 1.95, 0.001, 0.03, 4, 1, 100000]) pgu.print_fit_results(pars, cov, radford_peak) pgu.plot_func(radford_peak, pars, label="chi2 fit", color='red') #x_vals = np.arange(1420,1500,0.5) #plt.plot(x_vals, radford_peak(x_vals, 1460.8, 2.95, .001, 0.03, 5, 1, 100000)) FWHM = '%.2f' % Decimal(pars[1]2) FWHM_uncertainty = '%.2f' % Decimal(np.sqrt(cov[1][1])2) peak = '%.2f' % Decimal(pars[0]) peak_uncertainty = '%.2f' % Decimal(np.sqrt(cov[0][0])) residual = '%.2f' % (1460.82 - float(peak)) #chi_2_element_list = [] #for i in range(len(hist)): #chi_2_element = abs((radford_peak(bins[i], pars) - hist[i])2/radford_peak(bins[i], pars)) #chi_2_element_list.append(chi_2_element) #chi_2 = sum(chi_2_element_list) #reduced_chi_2 = '%.2f' % Decimal(chi_2/len(hist)) label_01 = '1460.82 keV peak fit' label_02 = 'FWHM = '+str(FWHM)+r' $\pm$ '+str(FWHM_uncertainty) label_03 = 'Peak = '+str(peak)+r' $\pm$ '+str(peak_uncertainty) label_04 = 'Residual = '+str(residual)+r' $\pm$ '+str(peak_uncertainty) colors = ['red', 'red','red', 'red'] lines = [Line2D([0], [0], color=c, lw=2) for c in colors] labels = [label_01, label_02, label_03, label_04] plt.xlim(1420,1500) plt.ylim(0,plt.ylim()[1]) plt.xlabel('Energy (keV)', ha='right', x=1.0) plt.ylabel('Counts', ha='right', y=1.0) plt.tight_layout() plt.legend(lines, labels, frameon=False, loc='upper right', fontsize='small') #plt.semilogy() plt.show() def peak_609(): if(len(sys.argv) != 2): print('Usage: fit_bkg_peaks.py [run number]') sys.exit() with open("runDB.json") as f: runDB = json.load(f) meta_dir = os.path.expandvars(runDB["meta_dir"]) #df = pd.read_hdf("{}/Spectrum_280-329.hdf5".format(meta_dir), key="df") df = pd.read_hdf("{}/Spectrum_{}.hdf5".format(meta_dir,sys.argv[1]), key="df") def gauss(x, mu, sigma, A=1): """ define a gaussian distribution, w/ args: mu, sigma, area (optional). """ return A * (1. / sigma / np.sqrt(2 * np.pi)) * np.exp(-(x - mu)*2 / (2. sigma**2)) def radford_peak(x, mu, sigma, hstep, htail, tau, bg0, a=1): """ <NAME>'s HPGe peak shape function """ # make sure the fractional amplitude
condition(self): """ Return a non-numerical health status """ status = "unknown" if self.getHitPoints() <= 0: status = "dead" elif self.getHitPoints() < self.getMaxHP() * 0.10: # Less than 10% of health remains status = "desperate" elif self.getHitPoints() < self.getMaxHP() * 0.25: # 11-25% of health remains status = "injured" elif self.getHitPoints() < self.getMaxHP() * 0.50: # 26-50% of health remains status = "drained" elif self.getHitPoints() < self.getMaxHP() * 0.75: # 51-75% of health remains status = "fatigued" elif self.getHitPoints() < self.getMaxHP() * 0.99: # 76-99% of health remains status = "healthy" elif self.getHitPoints() == self.getMaxHP(): # totally healthy status = "fresh" return status def dodge(self, basePercent=100, dodgeTxt=""): """ Return true if dodged * If basePercent is increased, chance of dodging goes down. * chances improved by dex, class, dodge skill, and dodgeBonus """ result = "dodge failed" randX = random.randint(1, 100) classMult = 2 if self.getClassName().lower() == "rogue" else 1 skillMult = self._dodge + self._dodgeBonus dodgeAdv = self.getDexterity() * (classMult + skillMult) / 10 dodgeCalc = (randX + dodgeAdv) * 2 if dodgeCalc > basePercent: result = "dodged" if dodgeTxt != "": dodgeTxt += " " dLog( "{0}{1} - character dodge calc ({2}) >? {0}odds ({3})".format( dodgeTxt, result, dodgeCalc, basePercent ), self._instanceDebug, ) if result == "dodged": return True return False def acDamageReduction(self, damage): """ reduce damage based on AC """ ac = self.getAc() # reduce AC if protection is broken for obj in self.getEquippedProtection(): if obj.isBroken(): ac -= obj.getAc() # reduce damage based on percentage: acReduction = int(damage * (0.05 * ac)) damage -= acReduction return max(0, damage) def getCircleSecs(self): """ Returns the number seconds a creature will wait given a successful circle - based on character level/stats""" secsToWait = random.randint(self.getLevel(), 20 + self.getDexterity()) return secsToWait def damageIsLethal(self, num=0): if num >= self.getHitPoints(): return True return False def takeDamage(self, damage=0, nokill=False): """ Take damage and check for death """ self.subtractHP(damage) if nokill and self.getHitPoints() <= 0: self.setNearDeathExperience() condition = self.condition() dLog(self.getName() + " takes " + str(damage) + " damage", self._instanceDebug) self.save() if self.getHitPoints() <= 0: if self.isDm(): self._spoolOut( "You would be dead if you weren't a dm." + " Resetting hp to maxhp.\n" ) self.setHitPoints(self._maxhp) else: self.processDeath() return condition def obituary(self): """ Notify/record death """ deathMsg = self.describe() + " has died" self.client.getGameObj().gameMsg(self.client.txtBanner(deathMsg) + "\n") logger.info("obituary: " + deathMsg) def processDeath(self, calculateLevelsToLose=True, silent=False): """ Do all the things related to dying """ levelsToLose = 1 if calculateLevelsToLose: levelsToLose = self.levelsToLose() for numlvl in range(1, levelsToLose + 1): self.levelDownStats() if self.getLevel() > 1: self.subtractlevel() self.setHitPoints(self.getMaxHP()) self.setPoisoned(False) self.setPlagued(False) self.save() if not silent: # primarily used for testing hundreds of deaths self._spoolOut("You are dead!\n") self.obituary() # return to starting room or guild self.client.gameObj.joinRoom(58, self) self._spoolOut(self.getRoom().display(self)) return True def searchSucceeds(self, obj, basePercent=30): """ Returns True if search succeeds * chance of success based on dex, level, and luck """ logPrefix = __class__.__name__ + " searchSucceeds: " if self.canSeeHidden(): dLog(logPrefix + "Pass - Character can see hidden", self._instanceDebug) return True percentChance = ( basePercent + self.getDexterity() + self.getLevel() + self.getLuck() ) if obj.getType() == "Creature" or obj.getType() == "Character": # +/- 10% per level difference percentChance += (self.getLevel() - obj.getLevel()) * 10 if random.randint(1, 20) == 1: # Always a 5 percent chance of success dLog(logPrefix + "Pass - Always 5% Chance", self._instanceDebug) return True randX = random.randint(1, 100) if randX <= percentChance: dLog( logPrefix + "Pass - Roll - " + str(randX) + " < " + str(percentChance), self._instanceDebug, ) return True dLog(logPrefix + "Failed", self._instanceDebug) return False def equipFist(self): """ equip fist, the default weapon - fist is a special weapon that is not in any inventory """ obj = Weapon() obj.setName("fist") obj._article = "a" obj._singledesc = "fist" obj.setMaximumDamage(self.getFistDamage()) self.equip(obj) def getFistDamage(self): """ calculate damage for the fist, the default weapon """ damage = int((self.getStrength() / 5) + (self.getLevel() / 2)) damage += self.classDict[self.getClassKey()]["baseDamage"] damage -= random.randint(0, 3) return max(0, damage) def equip(self, obj): # Deal with currently equipped item equippedObj = getattr(self, obj.getEquippedSlotName()) if equippedObj is None: # Nothing is currently equipped pass elif equippedObj == obj: # desired object is already in use return True elif obj is not None: # wearing some other item self.unEquip(obj) # Pass object so we know which slot to vacate slotName = obj.getEquippedSlotName() if slotName: setattr(self, slotName, obj) self.setAc() return True return False def unEquip(self, obj=None, slotName=""): if obj and slotName == "": # Use the current object to determine slot name if obj.isEquippable(): slotName = obj.getEquippedSlotName() if slotName == "": return False setattr(self, slotName, None) self.setAc() if self.getEquippedWeapon() is None: self.equipFist() return True def attemptToHide(self): randX = random.randint(0, 99) hidechance = self.getLevel() * 20 + self.dexterity if self.getClassName().lower() == "rogue": hidechance = 2 # double the chance of success for rogues # consider additional bonus for guild status # half the chance of success if there are already creatures in the room if len(self._roomObj.getCreatureList()) > 0: hidechance /= 2 hidechance = max(66, hidechance) # Chance to hide tops out at 66% if hidechance > randX: self.setHidden() return True return False def hearsWhispers(self): """ calculate whether a character can hear whispers in a room todo: make this more random and skill/sluck based """ if self.getClassName().lower() == "ranger": return True return False def adjustPrice(self, price): """ Adjust the price of goods depending on character attributes non-character price changes occur elsewhere """ # consider adjustments for charisma, alignment, luck return price def setMaxWeightForCharacter(self): """ Maxweight varies depending on attributes """ weight = 10 * max(7, int(self.strength)) self.setInventoryMaxWeight(weight) def fumbles(self, basePercent=20): """ Return true if player fumbles. * Fumble is a trip while attacking which causes player to unequip weapon and shield and wait 30 seconds before attacking again * random chance, partially based on dex. * if fumble, player's weapon is unequipped """ logPrefix = "char.fumbles: " fumbles = False if self.isAttackingWithFist(): return False fumbleRoll = random.randint(1, 100) percentage = basePercent - self.getDexterity() if fumbleRoll == 1: # always a 1% change of fumbling dLog(logPrefix + "Bad luck - 1% fumble triggered", self._instanceDebug) fumbles = True elif fumbleRoll < percentage: dLog( logPrefix + "Standard Roll: " + str(fumbleRoll) + " < " + str(percentage), self._instanceDebug, ) fumbles = True if fumbles: self.unEquip(slotName="_equippedWeapon") self.unEquip(slotName="_equippedShield") self.setSecondsUntilNextAttack(30) return fumbles def discardsEquippedWeapon(self): """ drop currently equipped weapon """ if self.isAttackingWithFist(): return True weaponObj = self.getEquippedWeapon() self.unEquip(slotName="_equippedWeapon") self.removeFromInventory(weaponObj) roomObj = self.getRoom() if roomObj: roomObj.addToInventory(weaponObj) return True def possibilyLoseHiddenWhenMoving(self): """ set hidden to false if you fail the roll. * when moving, there is a chance that you will not remain hidden * base chance of remaining hidden is 50% + dex * rangers and theives get improved chance = dex a ranger/thief with 20 dex has 99% chance of staying hidden """ if not self.isHidden: return False oddsOfStayingHidden = 60 + self.getDexterity() if self.getClassName() in ["rogue", "ranger"]: oddsOfStayingHidden += self.getDexterity() if random.randint(1, 100) >= oddsOfStayingHidden: self.setHidden(False) return True def processPoisonAndRegen(self, regenInterval=90, poisonInterval=60): """ At certain intervals, poison and hp regeneration kick in * poison should be faster and/or stronger than regen """ conAdj = self.getConstitution() - 12 intAdj = self.getIntelligence() - 12 regenHp = max(1, int(self.getMaxHP() / 10) + conAdj) regenMana = max(1, int(self.getMaxMana() / 8) + intAdj) poisonHp = max(1, int(self.getLevel() - conAdj)) if not self.isPlagued(): # no regen if plagued # Check the time if self.getLastRegenDate() == getNeverDate(): regenSecsRemaining = 0 else: regenSecsRemaining = regenInterval - secsSinceDate( self.getLastRegenDate() ) dLog( "regen counter: " + str(regenSecsRemaining) + " secs - " + str(self.getLastRegenDate()) + " - " + str(secsSinceDate(self.getLastRegenDate())), False, ) if regenSecsRemaining <= 0: self.addHP(regenHp) self.addMana(regenMana) self.setLastRegen() if self.isPoisoned(): # take damage if poisoned # Check the time if self.getLastPoisonDate() == getNeverDate(): poisonSecsRemaining = 0 else: poisonSecsRemaining = poisonInterval - secsSinceDate( self.getLastPoisonDate() ) dLog("poison cntr: " + str(regenSecsRemaining) + " secs", False) if poisonSecsRemaining <= 0: self._spoolOut( "As
:class:`int` :param invite_link: If user has joined the chat using an invite link, the invite link; may be null, defaults to None :type invite_link: :class:`ChatInviteLink`, optional :param old_chat_member: Previous chat member :type old_chat_member: :class:`ChatMember` :param new_chat_member: New chat member :type new_chat_member: :class:`ChatMember` """ ID: str = Field("updateChatMember", alias="@type") chat_id: int actor_user_id: int date: int invite_link: typing.Optional[ChatInviteLink] = None old_chat_member: ChatMember new_chat_member: ChatMember @staticmethod def read(q: dict) -> UpdateChatMember: return UpdateChatMember.construct(q) class UpdateChatMessageSender(Update): """ The message sender that is selected to send messages in a chat has changed :param chat_id: Chat identifier :type chat_id: :class:`int` :param message_sender_id: New value of message_sender_id; may be null if the user can't change message sender, defaults to None :type message_sender_id: :class:`MessageSender`, optional """ ID: str = Field("updateChatMessageSender", alias="@type") chat_id: int message_sender_id: typing.Optional[MessageSender] = None @staticmethod def read(q: dict) -> UpdateChatMessageSender: return UpdateChatMessageSender.construct(q) class UpdateChatMessageTtl(Update): """ The message Time To Live setting for a chat was changed :param chat_id: Chat identifier :type chat_id: :class:`int` :param message_ttl: New value of message_ttl :type message_ttl: :class:`int` """ ID: str = Field("updateChatMessageTtl", alias="@type") chat_id: int message_ttl: int @staticmethod def read(q: dict) -> UpdateChatMessageTtl: return UpdateChatMessageTtl.construct(q) class UpdateChatNotificationSettings(Update): """ Notification settings for a chat were changed :param chat_id: Chat identifier :type chat_id: :class:`int` :param notification_settings: The new notification settings :type notification_settings: :class:`ChatNotificationSettings` """ ID: str = Field("updateChatNotificationSettings", alias="@type") chat_id: int notification_settings: ChatNotificationSettings @staticmethod def read(q: dict) -> UpdateChatNotificationSettings: return UpdateChatNotificationSettings.construct(q) class UpdateChatOnlineMemberCount(Update): """ The number of online group members has changed. This update with non-zero count is sent only for currently opened chats. There is no guarantee that it will be sent just after the count has changed :param chat_id: Identifier of the chat :type chat_id: :class:`int` :param online_member_count: New number of online members in the chat, or 0 if unknown :type online_member_count: :class:`int` """ ID: str = Field("updateChatOnlineMemberCount", alias="@type") chat_id: int online_member_count: int @staticmethod def read(q: dict) -> UpdateChatOnlineMemberCount: return UpdateChatOnlineMemberCount.construct(q) class UpdateChatPendingJoinRequests(Update): """ The chat pending join requests were changed :param chat_id: Chat identifier :type chat_id: :class:`int` :param pending_join_requests: The new data about pending join requests; may be null, defaults to None :type pending_join_requests: :class:`ChatJoinRequestsInfo`, optional """ ID: str = Field("updateChatPendingJoinRequests", alias="@type") chat_id: int pending_join_requests: typing.Optional[ChatJoinRequestsInfo] = None @staticmethod def read(q: dict) -> UpdateChatPendingJoinRequests: return UpdateChatPendingJoinRequests.construct(q) class UpdateChatPermissions(Update): """ Chat permissions was changed :param chat_id: Chat identifier :type chat_id: :class:`int` :param permissions: The new chat permissions :type permissions: :class:`ChatPermissions` """ ID: str = Field("updateChatPermissions", alias="@type") chat_id: int permissions: ChatPermissions @staticmethod def read(q: dict) -> UpdateChatPermissions: return UpdateChatPermissions.construct(q) class UpdateChatPhoto(Update): """ A chat photo was changed :param chat_id: Chat identifier :type chat_id: :class:`int` :param photo: The new chat photo; may be null, defaults to None :type photo: :class:`ChatPhotoInfo`, optional """ ID: str = Field("updateChatPhoto", alias="@type") chat_id: int photo: typing.Optional[ChatPhotoInfo] = None @staticmethod def read(q: dict) -> UpdateChatPhoto: return UpdateChatPhoto.construct(q) class UpdateChatPosition(Update): """ The position of a chat in a chat list has changed. Instead of this update updateChatLastMessage or updateChatDraftMessage might be sent :param chat_id: Chat identifier :type chat_id: :class:`int` :param position: New chat position. If new order is 0, then the chat needs to be removed from the list :type position: :class:`ChatPosition` """ ID: str = Field("updateChatPosition", alias="@type") chat_id: int position: ChatPosition @staticmethod def read(q: dict) -> UpdateChatPosition: return UpdateChatPosition.construct(q) class UpdateChatReadInbox(Update): """ Incoming messages were read or the number of unread messages has been changed :param chat_id: Chat identifier :type chat_id: :class:`int` :param last_read_inbox_message_id: Identifier of the last read incoming message :type last_read_inbox_message_id: :class:`int` :param unread_count: The number of unread messages left in the chat :type unread_count: :class:`int` """ ID: str = Field("updateChatReadInbox", alias="@type") chat_id: int last_read_inbox_message_id: int unread_count: int @staticmethod def read(q: dict) -> UpdateChatReadInbox: return UpdateChatReadInbox.construct(q) class UpdateChatReadOutbox(Update): """ Outgoing messages were read :param chat_id: Chat identifier :type chat_id: :class:`int` :param last_read_outbox_message_id: Identifier of last read outgoing message :type last_read_outbox_message_id: :class:`int` """ ID: str = Field("updateChatReadOutbox", alias="@type") chat_id: int last_read_outbox_message_id: int @staticmethod def read(q: dict) -> UpdateChatReadOutbox: return UpdateChatReadOutbox.construct(q) class UpdateChatReplyMarkup(Update): """ The default chat reply markup was changed. Can occur because new messages with reply markup were received or because an old reply markup was hidden by the user :param chat_id: Chat identifier :type chat_id: :class:`int` :param reply_markup_message_id: Identifier of the message from which reply markup needs to be used; 0 if there is no default custom reply markup in the chat :type reply_markup_message_id: :class:`int` """ ID: str = Field("updateChatReplyMarkup", alias="@type") chat_id: int reply_markup_message_id: int @staticmethod def read(q: dict) -> UpdateChatReplyMarkup: return UpdateChatReplyMarkup.construct(q) class UpdateChatTheme(Update): """ The chat theme was changed :param chat_id: Chat identifier :type chat_id: :class:`int` :param theme_name: The new name of the chat theme; may be empty if theme was reset to default :type theme_name: :class:`str` """ ID: str = Field("updateChatTheme", alias="@type") chat_id: int theme_name: str @staticmethod def read(q: dict) -> UpdateChatTheme: return UpdateChatTheme.construct(q) class UpdateChatThemes(Update): """ The list of available chat themes has changed :param chat_themes: The new list of chat themes :type chat_themes: :class:`list[ChatTheme]` """ ID: str = Field("updateChatThemes", alias="@type") chat_themes: list[ChatTheme] @staticmethod def read(q: dict) -> UpdateChatThemes: return UpdateChatThemes.construct(q) class UpdateChatTitle(Update): """ The title of a chat was changed :param chat_id: Chat identifier :type chat_id: :class:`int` :param title: The new chat title :type title: :class:`str` """ ID: str = Field("updateChatTitle", alias="@type") chat_id: int title: str @staticmethod def read(q: dict) -> UpdateChatTitle: return UpdateChatTitle.construct(q) class UpdateChatUnreadMentionCount(Update): """ The chat unread_mention_count has changed :param chat_id: Chat identifier :type chat_id: :class:`int` :param unread_mention_count: The number of unread mention messages left in the chat :type unread_mention_count: :class:`int` """ ID: str = Field("updateChatUnreadMentionCount", alias="@type") chat_id: int unread_mention_count: int @staticmethod def read(q: dict) -> UpdateChatUnreadMentionCount: return UpdateChatUnreadMentionCount.construct(q) class UpdateChatVideoChat(Update): """ A chat video chat state has changed :param chat_id: Chat identifier :type chat_id: :class:`int` :param video_chat: New value of video_chat :type video_chat: :class:`VideoChat` """ ID: str = Field("updateChatVideoChat", alias="@type") chat_id: int video_chat: VideoChat @staticmethod def read(q: dict) -> UpdateChatVideoChat: return UpdateChatVideoChat.construct(q) class UpdateConnectionState(Update): """ The connection state has changed. This update must be used only to show a human-readable description of the connection state :param state: The new connection state :type state: :class:`ConnectionState` """ ID: str = Field("updateConnectionState", alias="@type") state: ConnectionState @staticmethod def read(q: dict) -> UpdateConnectionState: return UpdateConnectionState.construct(q) class UpdateDeleteMessages(Update): """ Some messages were deleted :param chat_id: Chat identifier :type chat_id: :class:`int` :param message_ids: Identifiers of the deleted messages :type message_ids: :class:`list[int]` :param is_permanent: True, if the messages are permanently deleted by a user (as opposed to just becoming inaccessible) :type is_permanent: :class:`bool` :param from_cache: True, if the messages are deleted only from the cache and can possibly be retrieved again in the future :type from_cache: :class:`bool` """ ID: str = Field("updateDeleteMessages", alias="@type") chat_id: int message_ids: list[int] is_permanent: bool from_cache: bool @staticmethod def read(q: dict) -> UpdateDeleteMessages: return UpdateDeleteMessages.construct(q) class UpdateDiceEmojis(Update): """ The list of supported dice emojis has changed :param emojis: The new list of supported dice emojis :type emojis: :class:`list[str]` """ ID: str = Field("updateDiceEmojis", alias="@type") emojis: list[str] @staticmethod def read(q: dict) -> UpdateDiceEmojis: return UpdateDiceEmojis.construct(q) class UpdateFavoriteStickers(Update): """ The list of favorite stickers was updated :param sticker_ids: The new list of file identifiers of favorite stickers :type sticker_ids: :class:`list[int]` """ ID: str = Field("updateFavoriteStickers", alias="@type") sticker_ids: list[int] @staticmethod def read(q: dict) -> UpdateFavoriteStickers: return UpdateFavoriteStickers.construct(q) class UpdateFile(Update): """ Information about a file was updated :param file: New data about the file :type file: :class:`File` """ ID: str = Field("updateFile", alias="@type") file: File @staticmethod def read(q: dict) -> UpdateFile: return UpdateFile.construct(q) class UpdateFileGenerationStart(Update): """ The file generation process needs to be started by the application :param generation_id: Unique identifier for the generation process :type generation_id: :class:`int` :param
nav_only["NAV_fx"][0]) * 100 nav_only["NAV_ret"] = nav_only["NAV_norm"].pct_change() table = {} table["meta"] = {} table["meta"]["start_date"] = (nav_only.index[0]).strftime("%m-%d-%Y") table["meta"]["end_date"] = nav_only.index[-1].strftime("%m-%d-%Y") table["meta"]["number_of_days"] = ( (nav_only.index[-1] - nav_only.index[0])).days table["meta"]["count_of_points"] = nav_only["NAV_fx"].count().astype(float) table["NAV"] = {} table["NAV"]["start"] = nav_only["NAV_fx"][0] table["NAV"]["end"] = nav_only["NAV_fx"][-1] table["NAV"]["return"] = (nav_only["NAV_fx"][-1] / nav_only["NAV_fx"][0]) - 1 table["NAV"]["avg_return"] = nav_only["NAV_ret"].mean() table["NAV"]["ann_std_dev"] = nav_only["NAV_ret"].std() * math.sqrt(365) # Include avg return + and - for ticker in tickers: if messages[ticker] == "ok": # Include new columns for return and normalized data nav_only[ticker + "_norm"] = ( nav_only[ticker + "_price"] / nav_only[ticker + "_price"][0] ) * 100 nav_only[ticker + "_ret"] = nav_only[ticker + "_norm"].pct_change() # Create Metadata table[ticker] = {} table[ticker]["start"] = nav_only[ticker + "_price"][0] table[ticker]["end"] = nav_only[ticker + "_price"][-1] table[ticker]["return"] = ( nav_only[ticker + "_price"][-1] / nav_only[ticker + "_price"][0] ) - 1 table[ticker]["comp2nav"] = table[ticker]["return"] - \ table["NAV"]["return"] table[ticker]["avg_return"] = nav_only[ticker + "_ret"].mean() table[ticker]["ann_std_dev"] = nav_only[ticker + "_ret"].std() * math.sqrt( 365 ) logging.info("[scatter_json] Success") # Create Correlation Matrix filter_col = [col for col in nav_only if col.endswith("_ret")] nav_matrix = nav_only[filter_col] corr_matrix = nav_matrix.corr(method="pearson").round(2) corr_html = corr_matrix.to_html( classes="table small text-center", border=0, justify="center" ) # Create series data for HighCharts in scatter plot format # series : [{ # name: 'NAV / BTC', # color: '[blue]', # data: [[-0.01,-0.02], [0.02, 0.04]] # },{ # name: .....}] series_hc = [] # Append NAV ticker to list of tickers, remove duplicates tickers.append("NAV") tickers = list(set(tickers)) for ticker in tickers: tmp_dict = {} if ticker == market: continue tmp_dict["name"] = "x: " + market + ", y: " + ticker tmp_dict["regression"] = 1 tmp_df = nav_matrix[[market + "_ret", ticker + "_ret"]] tmp_df.fillna(0, inplace=True) tmp_dict["data"] = list( zip(tmp_df[market + "_ret"], tmp_df[ticker + "_ret"])) series_hc.append(tmp_dict) # Now, let's return the data in the correct format as requested return jsonify( { "chart_data": series_hc, "messages": messages, "meta_data": meta_data, "table": table, "corr_html": corr_html, } ) @api.route("/transactionsandcost_json", methods=["GET"]) @login_required # Return daily data on transactions and cost for a single ticker # Takes arguments: # ticker - single ticker for filter # start - start date in the format YYMMDD (defaults to 1st transaction on ticker) # end - end date in the format YYMMDD (defaults to today) def transactionsandcost_json(): # Get arguments and assign values if needed if request.method == "GET": start_date = request.args.get("start") ticker = request.args.get("ticker") # Check if start and end dates exist, if not assign values try: start_date = datetime.strptime(start_date, "%Y-%m-%d") except (ValueError, TypeError) as e: logging.info( f"[transactionsandcost_json] Warning: {e}, " + "setting start_date to zero" ) start_date = datetime(2000, 1, 1) end_date = request.args.get("end") try: end_date = datetime.strptime(end_date, "%Y-%m-%d") except (ValueError, TypeError) as e: logging.info( f"[transactionsandcost_json] Warning: {e}, " + "setting end_date to now" ) end_date = datetime.now() # Get Transaction List df = transactions_fx() # Filter only to requested ticker # if no ticker, use BTC as default, if not BTC then the 1st in list tickers = df.trade_asset_ticker.unique().tolist() try: tickers.remove(current_user.fx()) except ValueError: pass if not ticker: if "BTC" in tickers: ticker = "BTC" else: ticker = tickers[0] # Filter only the trades for current user df = df[(df.trade_asset_ticker == ticker)] # Filter only buy and sells, ignore deposit / withdraw # For now, including Deposits and Withdrawns as well but # may consider only B and S as line below. df = df[(df.trade_operation == "B") \| (df.trade_operation == "S")] df.drop("user_id", axis=1, inplace=True) # Create a cash_flow column - so we can calculate # average price for days with multiple buys and/or sells df["cash_flow"] = df["trade_quantity"] * \ df["trade_price_fx"] + df["trade_fees_fx"] # Consolidate all transactions from a single day by grouping df = df.groupby(["date"])[["cash_value", "trade_fees", "trade_quantity", "cash_value_fx"]].agg([ "sum", "count"]) # Remove the double index for column and consolidate under one row df.columns = ["_".join(col).strip() for col in df.columns.values] # Filter to Start and End Dates passed as arguments mask = (df.index >= start_date) & (df.index <= end_date) df = df.loc[mask] # --------------------------------------------------------- # Get price of ticker passed as argument and merge into df message = {} data = price_data_fx(ticker) # If notification is an error, skip this ticker if data is None: messages = data.errors return jsonify(messages) data = data.rename(columns={'close_converted': ticker}) data = data.astype(float) # Create a DF, fill with dates and fill with operation and prices start_date = df.index.min() daily_df = pd.DataFrame(columns=["date"]) daily_df["date"] = pd.date_range(start=start_date, end=end_date) daily_df = daily_df.set_index("date") # Fill dailyNAV with prices for each ticker daily_df = pd.merge(daily_df, df, on="date", how="left") daily_df.fillna(0, inplace=True) if type(daily_df) != type(data): data = data.to_frame() daily_df = pd.merge(daily_df, data, on="date", how="left") daily_df[ticker].fillna(method="ffill", inplace=True) message = "ok" logging.info(f"[transactionandcost_json] {ticker}: Success - Merged OK") # --------------------------------------------------------- # Create additional columns on df # --------------------------------------------------------- daily_df.loc[daily_df.trade_quantity_sum > 0, "traded"] = 1 daily_df.loc[daily_df.trade_quantity_sum <= 0, "traded"] = 0 daily_df["q_cum_sum"] = daily_df["trade_quantity_sum"].cumsum() daily_df["cv_cum_sum"] = daily_df["cash_value_sum"].cumsum() daily_df["cv_fx_cum_sum"] = daily_df["cash_value_fx_sum"].cumsum() daily_df["avg_cost"] = daily_df["cv_fx_cum_sum"] / daily_df["q_cum_sum"] daily_df["price_over_cost_usd"] = daily_df[ticker] - daily_df["avg_cost"] daily_df["price_over_cost_perc"] = ( daily_df[ticker] / daily_df["avg_cost"]) - 1 daily_df["impact_on_cost_usd"] = daily_df["avg_cost"].diff() daily_df["impact_on_cost_per"] = daily_df["impact_on_cost_usd"] / \ daily_df[ticker] # Remove cost if position is too small - this avoids large numbers # Also, remove cost calculation if positions are open (from zero) daily_df.loc[daily_df.q_cum_sum <= 0.009, "price_over_cost_usd"] = np.NaN daily_df.loc[daily_df.q_cum_sum <= 0.009, "avg_cost"] = np.NaN daily_df.loc[daily_df.q_cum_sum.shift( 1) <= 0.009, "impact_on_cost_usd"] = np.NaN daily_df.loc[daily_df.q_cum_sum <= 0.009, "impact_on_cost_usd"] = np.NaN daily_df.loc[daily_df.q_cum_sum <= 0.009, "impact_on_cost_per"] = np.NaN return_dict = {} return_dict["data"] = daily_df.to_json() return_dict["message"] = message return_dict["fx"] = fxsymbol(current_user.fx()) logging.info(f"[transactionandcost_json] Success generating data") return jsonify(return_dict) @api.route("/heatmapbenchmark_json", methods=["GET"]) @login_required # Return Monthly returns for Benchmark and Benchmark difference from NAV # Takes arguments: # ticker - single ticker for filter def heatmapbenchmark_json(): # Get portfolio data first heatmap_gen, heatmap_stats, years, cols = heatmap_generator() # Now get the ticker information and run comparison if request.method == "GET": ticker = request.args.get("ticker") # Defaults to king BTC if not ticker: ticker = "BTC" # Gather the first trade date in portfolio and store # used to match the matrixes later # Panda dataframe with transactions df = pd.read_sql_table("trades", db.engine) df = df[(df.user_id == current_user.username)] # Filter the df acccoring to filter passed as arguments df["trade_date"] = pd.to_datetime(df["trade_date"]) start_date = df["trade_date"].min() start_date -= timedelta(days=1) # start on t-1 of first trade # Generate price Table now for the ticker and trim to match portfolio data = price_data_fx(ticker) mask = data.index >= start_date data = data.loc[mask] # If notification is an error, skip this ticker if data is None: messages = data.errors return jsonify(messages) data = data.rename(columns={'close_converted': ticker+'_price'}) data = data[[ticker+'_price']] data.sort_index(ascending=True, inplace=True) data["pchange"] = (data / data.shift(1)) - 1 # Run the mrh function to generate heapmap table heatmap = mrh.get(data["pchange"], eoy=True) heatmap_stats = heatmap cols = [ "Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec", "eoy", ] cols_months = [ "Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec", ] years = heatmap.index.tolist() # Create summary stats for the Ticker heatmap_stats["MAX"] = heatmap_stats[heatmap_stats[cols_months] != 0].max( axis=1) heatmap_stats["MIN"] = heatmap_stats[heatmap_stats[cols_months] != 0].min( axis=1) heatmap_stats["POSITIVES"] = heatmap_stats[heatmap_stats[cols_months] > 0].count( axis=1 ) heatmap_stats["NEGATIVES"] = heatmap_stats[heatmap_stats[cols_months] < 0].count( axis=1 ) heatmap_stats["POS_MEAN"] = heatmap_stats[heatmap_stats[cols_months] > 0].mean( axis=1 ) heatmap_stats["NEG_MEAN"] = heatmap_stats[heatmap_stats[cols_months] < 0].mean( axis=1 ) heatmap_stats["MEAN"] = heatmap_stats[heatmap_stats[cols_months] != 0].mean( axis=1) # Create the difference between the 2 df - Pandas is cool! heatmap_difference = heatmap_gen - heatmap # return (heatmap, heatmap_stats, years, cols, ticker, heatmap_diff) return simplejson.dumps( { "heatmap": heatmap.to_dict(), "heatmap_stats": heatmap_stats.to_dict(), "cols": cols, "years": years, "ticker": ticker, "heatmap_diff": heatmap_difference.to_dict(), }, ignore_nan=True, default=datetime.isoformat, ) @api.route("/drawdown_json", methods=["GET"]) @login_required # Return the largest drawdowns in a time period # Takes arguments: # ticker: Single ticker for filter (default = NAV) # start_date: If none, defaults to all available # end_date: If none, defaults to today # n_dd: Top n drawdowns to be calculated # chart: Boolean - return data for chart def drawdown_json(): # Get the arguments and store if request.method == "GET": start_date = request.args.get("start") ticker = request.args.get("ticker") n_dd = request.args.get("n_dd") chart = request.args.get("chart") if not ticker: ticker = "NAV" ticker = ticker.upper() if n_dd: try: n_dd = int(n_dd) except TypeError: n_dd = 2 if not n_dd: n_dd = 2
return not (self == other) class EndMaintenanceResult: """ Attributes: - statuses """ thrift_spec = ( None, # 0 (1, TType.SET, 'statuses', (TType.STRUCT,(HostStatus, HostStatus.thrift_spec)), None, ), # 1 ) def __init__(self, statuses=None,): self.statuses = statuses def read(self, iprot): if iprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None and fastbinary is not None: fastbinary.decode_binary(self, iprot.trans, (self.__class__, self.thrift_spec)) return iprot.readStructBegin() while True: (fname, ftype, fid) = iprot.readFieldBegin() if ftype == TType.STOP: break if fid == 1: if ftype == TType.SET: self.statuses = set() (_etype231, _size228) = iprot.readSetBegin() for _i232 in range(_size228): _elem233 = HostStatus() _elem233.read(iprot) self.statuses.add(_elem233) iprot.readSetEnd() else: iprot.skip(ftype) else: iprot.skip(ftype) iprot.readFieldEnd() iprot.readStructEnd() def write(self, oprot): if oprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and self.thrift_spec is not None and fastbinary is not None: oprot.trans.write(fastbinary.encode_binary(self, (self.__class__, self.thrift_spec))) return oprot.writeStructBegin('EndMaintenanceResult') if self.statuses is not None: oprot.writeFieldBegin('statuses', TType.SET, 1) oprot.writeSetBegin(TType.STRUCT, len(self.statuses)) for iter234 in self.statuses: iter234.write(oprot) oprot.writeSetEnd() oprot.writeFieldEnd() oprot.writeFieldStop() oprot.writeStructEnd() def validate(self): return def __hash__(self): value = 17 value = (value * 31) ^ hash(self.statuses) return value def __repr__(self): L = ['%s=%r' % (key, value) for key, value in self.__dict__.items()] return '%s(%s)' % (self.__class__.__name__, ', '.join(L)) def __eq__(self, other): return isinstance(other, self.__class__) and self.__dict__ == other.__dict__ def __ne__(self, other): return not (self == other) class RoleSummaryResult: """ Attributes: - summaries """ thrift_spec = ( None, # 0 (1, TType.SET, 'summaries', (TType.STRUCT,(RoleSummary, RoleSummary.thrift_spec)), None, ), # 1 ) def __init__(self, summaries=None,): self.summaries = summaries def read(self, iprot): if iprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None and fastbinary is not None: fastbinary.decode_binary(self, iprot.trans, (self.__class__, self.thrift_spec)) return iprot.readStructBegin() while True: (fname, ftype, fid) = iprot.readFieldBegin() if ftype == TType.STOP: break if fid == 1: if ftype == TType.SET: self.summaries = set() (_etype238, _size235) = iprot.readSetBegin() for _i239 in range(_size235): _elem240 = RoleSummary() _elem240.read(iprot) self.summaries.add(_elem240) iprot.readSetEnd() else: iprot.skip(ftype) else: iprot.skip(ftype) iprot.readFieldEnd() iprot.readStructEnd() def write(self, oprot): if oprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and self.thrift_spec is not None and fastbinary is not None: oprot.trans.write(fastbinary.encode_binary(self, (self.__class__, self.thrift_spec))) return oprot.writeStructBegin('RoleSummaryResult') if self.summaries is not None: oprot.writeFieldBegin('summaries', TType.SET, 1) oprot.writeSetBegin(TType.STRUCT, len(self.summaries)) for iter241 in self.summaries: iter241.write(oprot) oprot.writeSetEnd() oprot.writeFieldEnd() oprot.writeFieldStop() oprot.writeStructEnd() def validate(self): return def __hash__(self): value = 17 value = (value * 31) ^ hash(self.summaries) return value def __repr__(self): L = ['%s=%r' % (key, value) for key, value in self.__dict__.items()] return '%s(%s)' % (self.__class__.__name__, ', '.join(L)) def __eq__(self, other): return isinstance(other, self.__class__) and self.__dict__ == other.__dict__ def __ne__(self, other): return not (self == other) class JobSummaryResult: """ Attributes: - summaries """ thrift_spec = ( None, # 0 (1, TType.SET, 'summaries', (TType.STRUCT,(JobSummary, JobSummary.thrift_spec)), None, ), # 1 ) def __init__(self, summaries=None,): self.summaries = summaries def read(self, iprot): if iprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None and fastbinary is not None: fastbinary.decode_binary(self, iprot.trans, (self.__class__, self.thrift_spec)) return iprot.readStructBegin() while True: (fname, ftype, fid) = iprot.readFieldBegin() if ftype == TType.STOP: break if fid == 1: if ftype == TType.SET: self.summaries = set() (_etype245, _size242) = iprot.readSetBegin() for _i246 in range(_size242): _elem247 = JobSummary() _elem247.read(iprot) self.summaries.add(_elem247) iprot.readSetEnd() else: iprot.skip(ftype) else: iprot.skip(ftype) iprot.readFieldEnd() iprot.readStructEnd() def write(self, oprot): if oprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and self.thrift_spec is not None and fastbinary is not None: oprot.trans.write(fastbinary.encode_binary(self, (self.__class__, self.thrift_spec))) return oprot.writeStructBegin('JobSummaryResult') if self.summaries is not None: oprot.writeFieldBegin('summaries', TType.SET, 1) oprot.writeSetBegin(TType.STRUCT, len(self.summaries)) for iter248 in self.summaries: iter248.write(oprot) oprot.writeSetEnd() oprot.writeFieldEnd() oprot.writeFieldStop() oprot.writeStructEnd() def validate(self): return def __hash__(self): value = 17 value = (value * 31) ^ hash(self.summaries) return value def __repr__(self): L = ['%s=%r' % (key, value) for key, value in self.__dict__.items()] return '%s(%s)' % (self.__class__.__name__, ', '.join(L)) def __eq__(self, other): return isinstance(other, self.__class__) and self.__dict__ == other.__dict__ def __ne__(self, other): return not (self == other) class GetLocksResult: """ Attributes: - locks """ thrift_spec = ( None, # 0 (1, TType.SET, 'locks', (TType.STRUCT,(Lock, Lock.thrift_spec)), None, ), # 1 ) def __init__(self, locks=None,): self.locks = locks def read(self, iprot): if iprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None and fastbinary is not None: fastbinary.decode_binary(self, iprot.trans, (self.__class__, self.thrift_spec)) return iprot.readStructBegin() while True: (fname, ftype, fid) = iprot.readFieldBegin() if ftype == TType.STOP: break if fid == 1: if ftype == TType.SET: self.locks = set() (_etype252, _size249) = iprot.readSetBegin() for _i253 in range(_size249): _elem254 = Lock() _elem254.read(iprot) self.locks.add(_elem254) iprot.readSetEnd() else: iprot.skip(ftype) else: iprot.skip(ftype) iprot.readFieldEnd() iprot.readStructEnd() def write(self, oprot): if oprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and self.thrift_spec is not None and fastbinary is not None: oprot.trans.write(fastbinary.encode_binary(self, (self.__class__, self.thrift_spec))) return oprot.writeStructBegin('GetLocksResult') if self.locks is not None: oprot.writeFieldBegin('locks', TType.SET, 1) oprot.writeSetBegin(TType.STRUCT, len(self.locks)) for iter255 in self.locks: iter255.write(oprot) oprot.writeSetEnd() oprot.writeFieldEnd() oprot.writeFieldStop() oprot.writeStructEnd() def validate(self): return def __hash__(self): value = 17 value = (value * 31) ^ hash(self.locks) return value def __repr__(self): L = ['%s=%r' % (key, value) for key, value in self.__dict__.items()] return '%s(%s)' % (self.__class__.__name__, ', '.join(L)) def __eq__(self, other): return isinstance(other, self.__class__) and self.__dict__ == other.__dict__ def __ne__(self, other): return not (self == other) class ConfigSummaryResult: """ Attributes: - summary """ thrift_spec = ( None, # 0 (1, TType.STRUCT, 'summary', (ConfigSummary, ConfigSummary.thrift_spec), None, ), # 1 ) def __init__(self, summary=None,): self.summary = summary def read(self, iprot): if iprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None and fastbinary is not None: fastbinary.decode_binary(self, iprot.trans, (self.__class__, self.thrift_spec)) return iprot.readStructBegin() while True: (fname, ftype, fid) = iprot.readFieldBegin() if ftype == TType.STOP: break if fid == 1: if ftype == TType.STRUCT: self.summary = ConfigSummary() self.summary.read(iprot) else: iprot.skip(ftype) else: iprot.skip(ftype) iprot.readFieldEnd() iprot.readStructEnd() def write(self, oprot): if oprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and self.thrift_spec is not None and fastbinary is not None: oprot.trans.write(fastbinary.encode_binary(self, (self.__class__, self.thrift_spec))) return oprot.writeStructBegin('ConfigSummaryResult') if self.summary is not None: oprot.writeFieldBegin('summary', TType.STRUCT, 1) self.summary.write(oprot) oprot.writeFieldEnd() oprot.writeFieldStop() oprot.writeStructEnd() def validate(self): return def __hash__(self): value = 17 value = (value * 31) ^ hash(self.summary) return value def __repr__(self): L = ['%s=%r' % (key, value) for key, value in self.__dict__.items()] return '%s(%s)' % (self.__class__.__name__, ', '.join(L)) def __eq__(self, other): return isinstance(other, self.__class__) and self.__dict__ == other.__dict__ def __ne__(self, other): return not (self == other) class GetPendingReasonResult: """ Attributes: - reasons """ thrift_spec = ( None, # 0 (1, TType.SET, 'reasons', (TType.STRUCT,(PendingReason, PendingReason.thrift_spec)), None, ), # 1 ) def __init__(self, reasons=None,): self.reasons = reasons def read(self, iprot): if iprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None and fastbinary is not None: fastbinary.decode_binary(self, iprot.trans, (self.__class__, self.thrift_spec)) return iprot.readStructBegin() while True: (fname, ftype, fid) = iprot.readFieldBegin() if ftype == TType.STOP: break if fid == 1: if ftype == TType.SET: self.reasons = set() (_etype259, _size256) = iprot.readSetBegin() for _i260 in range(_size256): _elem261 = PendingReason() _elem261.read(iprot) self.reasons.add(_elem261) iprot.readSetEnd() else: iprot.skip(ftype) else: iprot.skip(ftype) iprot.readFieldEnd() iprot.readStructEnd() def write(self, oprot): if oprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and self.thrift_spec is not None and fastbinary is not None: oprot.trans.write(fastbinary.encode_binary(self, (self.__class__, self.thrift_spec))) return oprot.writeStructBegin('GetPendingReasonResult') if self.reasons is not None: oprot.writeFieldBegin('reasons', TType.SET, 1) oprot.writeSetBegin(TType.STRUCT, len(self.reasons)) for iter262 in self.reasons: iter262.write(oprot) oprot.writeSetEnd() oprot.writeFieldEnd() oprot.writeFieldStop() oprot.writeStructEnd() def validate(self): return def __hash__(self): value = 17 value = (value * 31) ^ hash(self.reasons) return value def __repr__(self): L = ['%s=%r' % (key, value) for key, value in self.__dict__.items()] return '%s(%s)' % (self.__class__.__name__, ', '.join(L)) def __eq__(self, other): return isinstance(other, self.__class__) and self.__dict__ == other.__dict__ def __ne__(self, other): return not (self == other) class StartJobUpdateResult: """ Result of the startUpdate call. Attributes: - key: Unique identifier for the job update. """ thrift_spec = ( None, # 0 (1, TType.STRUCT, 'key', (JobUpdateKey, JobUpdateKey.thrift_spec), None, ), # 1 ) def __init__(self, key=None,): self.key = key def read(self, iprot): if iprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None and fastbinary is not None: fastbinary.decode_binary(self, iprot.trans, (self.__class__, self.thrift_spec)) return iprot.readStructBegin() while True: (fname, ftype, fid) = iprot.readFieldBegin() if ftype == TType.STOP: break if fid == 1: if ftype == TType.STRUCT: self.key = JobUpdateKey() self.key.read(iprot) else: iprot.skip(ftype) else: iprot.skip(ftype) iprot.readFieldEnd() iprot.readStructEnd() def write(self, oprot): if oprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and self.thrift_spec is not None and fastbinary is not None: oprot.trans.write(fastbinary.encode_binary(self, (self.__class__, self.thrift_spec))) return oprot.writeStructBegin('StartJobUpdateResult') if self.key is not None: oprot.writeFieldBegin('key', TType.STRUCT, 1) self.key.write(oprot) oprot.writeFieldEnd() oprot.writeFieldStop() oprot.writeStructEnd() def validate(self): return def __hash__(self): value = 17 value = (value * 31) ^
#!/usr/bin/env python3 # # Copyright (c) <NAME> and the University of Texas MD Anderson Cancer Center # Distributed under the terms of the 3-clause BSD License. import contextlib import logging import os import subprocess import sys import time from collections import OrderedDict, defaultdict from textwrap import dedent import pandas as pd import pkg_resources from ipykernel.ipkernel import IPythonKernel from IPython.core.display import HTML from IPython.utils.tokenutil import line_at_cursor, token_at_cursor from jupyter_client import manager from sos._version import __sos_version__, __version__ from sos.eval import SoS_eval, SoS_exec, interpolate from sos.syntax import SOS_SECTION_HEADER from sos.utils import (format_duration, WorkflowDict, env, log_to_file, short_repr) from ._version import __version__ as __notebook_version__ from .completer import SoS_Completer from .inspector import SoS_Inspector from .step_executor import PendingTasks from .workflow_executor import runfile, NotebookLoggingHandler from .magics import SoS_Magics class FlushableStringIO: '''This is a string buffer for output, but it will only keep the first 200 lines and the last 10 lines. ''' def __init__(self, kernel, name, args, kwargs): self.kernel = kernel self.name = name def write(self, content): if content.startswith('HINT: '): content = content.splitlines() hint_line = content[0][6:].strip() content = '\n'.join(content[1:]) self.kernel.send_response(self.kernel.iopub_socket, 'display_data', { 'metadata': {}, 'data': {'text/html': HTML( f'<div class="sos_hint">{hint_line}</div>').data} }) if content: if self.kernel._meta['capture_result'] is not None: self.kernel._meta['capture_result'].append( ('stream', {'name': self.name, 'text': content})) self.kernel.send_response(self.kernel.iopub_socket, 'stream', {'name': self.name, 'text': content}) def flush(self): pass __all__ = ['SoS_Kernel'] class subkernel(object): # a class to information on subkernel def __init__(self, name=None, kernel=None, language='', color='', options={}): self.name = name self.kernel = kernel self.language = language self.color = color self.options = options def __repr__(self): return f'subkernel {self.name} with kernel {self.kernel} for language {self.language} with color {self.color}' # translate a message to transient_display_data message def make_transient_msg(msg_type, content, title, append=False, page='Info'): if msg_type == 'display_data': return { 'title': title, 'data': content.get('data', {}), 'metadata': {'append': append, 'page': page} } elif msg_type == 'stream': if content['name'] == 'stdout': return { 'title': title, 'data': { 'text/plain': content['text'], 'application/vnd.jupyter.stdout': content['text'] }, 'metadata': {'append': append, 'page': page} } else: return { 'title': title, 'data': { 'text/plain': content['text'], 'application/vnd.jupyter.stderr': content['text'] }, 'metadata': {'append': append, 'page': page} } else: raise ValueError( f"failed to translate message {msg_type} to transient_display_data message") class Subkernels(object): # a collection of subkernels def __init__(self, kernel): self.sos_kernel = kernel self.language_info = kernel.supported_languages from jupyter_client.kernelspec import KernelSpecManager km = KernelSpecManager() specs = km.find_kernel_specs() # get supported languages self._kernel_list = [] lan_map = {} for x in self.language_info.keys(): for lname, knames in kernel.supported_languages[x].supported_kernels.items(): for kname in knames: if x != kname: lan_map[kname] = (lname, self.get_background_color(self.language_info[x], lname), getattr(self.language_info[x], 'options', {})) # kernel_list has the following items # # 1. displayed name # 2. kernel name # 3. language name # 4. color for spec in specs.keys(): if spec == 'sos': # the SoS kernel will be default theme color. self._kernel_list.append( subkernel(name='SoS', kernel='sos', options={ 'variable_pattern': r'^\s[_A-Za-z0-9\.]+\s$', 'assignment_pattern': r'^\s([_A-Za-z0-9\.]+)\s=.$'})) elif spec in lan_map: # e.g. ir ==> R self._kernel_list.append( subkernel(name=lan_map[spec][0], kernel=spec, language=lan_map[spec][0], color=lan_map[spec][1], options=lan_map[spec][2])) else: # undefined language also use default theme color self._kernel_list.append(subkernel(name=spec, kernel=spec)) def kernel_list(self): return self._kernel_list # now, no kernel is found, name has to be a new name and we need some definition # if kernel is defined def add_or_replace(self, kdef): for idx, x in enumerate(self._kernel_list): if x.name == kdef.name: self._kernel_list[idx] = kdef return self._kernel_list[idx] else: self._kernel_list.append(kdef) return self._kernel_list[-1] def get_background_color(self, plugin, lan): # if a single color is defined, it is used for all supported # languages if isinstance(plugin.background_color, str): # return the same background color for all inquiry return plugin.background_color else: # return color for specified, or any color if unknown inquiry is made return plugin.background_color.get(lan, next(iter(plugin.background_color.values()))) def find(self, name, kernel=None, language=None, color=None, notify_frontend=True): # find from subkernel name def update_existing(idx): x = self._kernel_list[idx] if (kernel is not None and kernel != x.kernel) or (language not in (None, '', 'None') and language != x.language): raise ValueError( f'Cannot change kernel or language of predefined subkernel {name} {x}') if color is not None: if color == 'default': if self._kernel_list[idx].language: self._kernel_list[idx].color = self.get_background_color( self.language_info[self._kernel_list[idx].language], self._kernel_list[idx].language) else: self._kernel_list[idx].color = '' else: self._kernel_list[idx].color = color if notify_frontend: self.notify_frontend() # if the language module cannot be loaded for some reason if name in self.sos_kernel._failed_languages: raise self.sos_kernel._failed_languages[name] # find from language name (subkernel name, which is usually language name) for idx, x in enumerate(self._kernel_list): if x.name == name: if x.name == 'SoS' or x.language or language is None: update_existing(idx) return x else: if not kernel: kernel = name break # find from kernel name for idx, x in enumerate(self._kernel_list): if x.kernel == name: # if exist language or no new language defined. if x.language or language is None: update_existing(idx) return x else: # otherwise, try to use the new language kernel = name break if kernel is not None: # in this case kernel should have been defined in kernel list if kernel not in [x.kernel for x in self._kernel_list]: raise ValueError( f'Unrecognized Jupyter kernel name {kernel}. Please make sure it is properly installed and appear in the output of command "jupyter kenelspec list"') # now this a new instance for an existing kernel kdef = [x for x in self._kernel_list if x.kernel == kernel][0] if not language: if color == 'default': if kdef.language: color = self.get_background_color( self.language_info[kdef.language], kdef.language) else: color = kdef.color new_def = self.add_or_replace(subkernel(name, kdef.kernel, kdef.language, kdef.color if color is None else color, getattr(self.language_info[kdef.language], 'options', {}) if kdef.language else {})) if notify_frontend: self.notify_frontend() return new_def else: # if language is defined, if ':' in language: # if this is a new module, let us create an entry point and load from pkg_resources import EntryPoint mn, attr = language.split(':', 1) ep = EntryPoint(name=kernel, module_name=mn, attrs=tuple(attr.split('.'))) try: plugin = ep.resolve() self.language_info[name] = plugin # for convenience, we create two entries for, e.g. R and ir # but only if there is no existing definition for supported_lan, supported_kernels in plugin.supported_kernels.items(): for supported_kernel in supported_kernels: if name != supported_kernel and supported_kernel not in self.language_info: self.language_info[supported_kernel] = plugin if supported_lan not in self.language_info: self.language_info[supported_lan] = plugin except Exception as e: raise RuntimeError( f'Failed to load language {language}: {e}') # if color == 'default': color = self.get_background_color(plugin, kernel) new_def = self.add_or_replace(subkernel(name, kdef.kernel, kernel, kdef.color if color is None else color, getattr(plugin, 'options', {}))) else: # if should be defined ... if language not in self.language_info: raise RuntimeError( f'Unrecognized language definition {language}, which should be a known language name or a class in the format of package.module:class') # self.language_info[name] = self.language_info[language] if color == 'default': color = self.get_background_color( self.language_info[name], language) new_def = self.add_or_replace(subkernel(name, kdef.kernel, language, kdef.color if color is None else color, getattr(self.language_info[name], 'options', {}))) if notify_frontend: self.notify_frontend() return new_def elif language is not None: # kernel is not defined and we only have language if ':' in language: # if this is a new module, let us create an entry point and load from pkg_resources import EntryPoint mn, attr = language.split(':', 1) ep = EntryPoint(name='__unknown__', module_name=mn, attrs=tuple(attr.split('.'))) try: plugin = ep.resolve() self.language_info[name] = plugin except Exception as e: raise RuntimeError( f'Failed to load language {language}: {e}') if name in plugin.supported_kernels: # if name is defined in the module, only search kernels for this language avail_kernels = [x for x in plugin.supported_kernels[name] if x in [y.kernel for y in self._kernel_list]] else: # otherwise we search all supported kernels avail_kernels = [x for x in sum(plugin.supported_kernels.values(), []) if x in [y.kernel for y in self._kernel_list]] if not avail_kernels: raise ValueError( 'Failed to find any of the kernels {} supported by language {}. Please make sure it is properly installed and appear in the output of command "jupyter kenelspec list"'.format( ', '.join(sum(plugin.supported_kernels.values(), [])), language)) # use the first available kernel # find the language that has the kernel lan_name = list({x: y for x, y in plugin.supported_kernels.items( ) if avail_kernels[0] in y}.keys())[0] if color == 'default': color = self.get_background_color(plugin, lan_name) new_def = self.add_or_replace(subkernel(name, avail_kernels[0], lan_name, self.get_background_color(plugin, lan_name) if color is None else color, getattr(plugin, 'options', {}))) else: # if a language name is specified (not a path to module), if should be defined in setup.py if language not in self.language_info: raise RuntimeError( f'Unrecognized language definition {language}') # plugin = self.language_info[language] if language in plugin.supported_kernels: avail_kernels =
<filename>venv/Lib/site-packages/dash/testing/browser.py # pylint: disable=missing-docstring import os import sys import time import logging import warnings import percy from selenium import webdriver from selenium.webdriver.support import expected_conditions as EC from selenium.webdriver.common.by import By from selenium.webdriver.support.wait import WebDriverWait from selenium.webdriver.common.keys import Keys from selenium.webdriver.common.desired_capabilities import DesiredCapabilities from selenium.webdriver.common.action_chains import ActionChains from selenium.common.exceptions import ( WebDriverException, TimeoutException, MoveTargetOutOfBoundsException, ) from dash.testing.wait import ( text_to_equal, style_to_equal, contains_text, until, ) from dash.testing.dash_page import DashPageMixin from dash.testing.errors import ( DashAppLoadingError, BrowserError, TestingTimeoutError, ) from dash.testing.consts import SELENIUM_GRID_DEFAULT logger = logging.getLogger(__name__) class Browser(DashPageMixin): def __init__( self, browser, remote=False, remote_url=None, headless=False, options=None, download_path="", percy_run=True, percy_finalize=True, percy_assets_root="", wait_timeout=10, ): self._browser = browser.lower() self._remote_url = remote_url self._remote = ( True if remote_url and remote_url != SELENIUM_GRID_DEFAULT else remote ) self._headless = headless self._options = options self._download_path = download_path self._wait_timeout = wait_timeout self._percy_finalize = percy_finalize self._percy_run = percy_run self._driver = until(self.get_webdriver, timeout=1) self._driver.implicitly_wait(2) self._wd_wait = WebDriverWait(self.driver, wait_timeout) self._last_ts = 0 self._url = None self._window_idx = 0 # switch browser tabs if self._percy_run: self.percy_runner = percy.Runner( loader=percy.ResourceLoader( webdriver=self.driver, base_url="/assets", root_dir=percy_assets_root, ) ) self.percy_runner.initialize_build() logger.info("initialize browser with arguments") logger.info(" headless => %s", self._headless) logger.info(" download_path => %s", self._download_path) logger.info(" percy asset root => %s", os.path.abspath(percy_assets_root)) def __enter__(self): return self def __exit__(self, exc_type, exc_val, traceback): try: self.driver.quit() if self._percy_run and self._percy_finalize: logger.info("percy runner finalize build now") self.percy_runner.finalize_build() else: logger.info("percy finalize relies on CI job") except WebDriverException: logger.exception("webdriver quit was not successful") except percy.errors.Error: logger.exception("percy runner failed to finalize properly") def visit_and_snapshot( self, resource_path, hook_id, wait_for_callbacks=True, assert_check=True, stay_on_page=False, ): try: path = resource_path.lstrip("/") if path != resource_path: logger.warning("we stripped the left '/' in resource_path") self.driver.get("{}/{}".format(self.server_url.rstrip("/"), path)) # wait for the hook_id to present and all callbacks get fired self.wait_for_element_by_id(hook_id) self.percy_snapshot(path, wait_for_callbacks=wait_for_callbacks) if assert_check: assert not self.driver.find_elements_by_css_selector( "div.dash-debug-alert" ), "devtools should not raise an error alert" if not stay_on_page: self.driver.back() except WebDriverException as e: logger.exception("snapshot at resource %s error", path) raise e def percy_snapshot(self, name="", wait_for_callbacks=False): """percy_snapshot - visual test api shortcut to `percy_runner.snapshot`. It also combines the snapshot `name` with the python version. """ snapshot_name = "{} - py{}.{}".format( name, sys.version_info.major, sys.version_info.minor ) logger.info("taking snapshot name => %s", snapshot_name) try: if wait_for_callbacks: # the extra one second sleep adds safe margin in the context # of wait_for_callbacks time.sleep(1) until(self._wait_for_callbacks, timeout=40, poll=0.3) except TestingTimeoutError: # API will log the error but this TimeoutError should not block # the test execution to continue and it will still do a snapshot # as diff reference for the build run. logger.error( "wait_for_callbacks failed => status of invalid rqs %s", list(_ for _ in self.redux_state_rqs if not _.get("responseTime")), ) logger.debug("full content of the rqs => %s", self.redux_state_rqs) self.percy_runner.snapshot(name=snapshot_name) def take_snapshot(self, name): """Hook method to take snapshot when a selenium test fails. The snapshot is placed under. - `/tmp/dash_artifacts` in linux - `%TEMP` in windows with a filename combining test case name and the running selenium session id """ target = "/tmp/dash_artifacts" if not self._is_windows() else os.getenv("TEMP") if not os.path.exists(target): try: os.mkdir(target) except OSError: logger.exception("cannot make artifacts") self.driver.save_screenshot( "{}/{}_{}.png".format(target, name, self.session_id) ) def find_element(self, selector): """find_element returns the first found element by the css `selector` shortcut to `driver.find_element_by_css_selector`.""" return self.driver.find_element_by_css_selector(selector) def find_elements(self, selector): """find_elements returns a list of all elements matching the css `selector`. shortcut to `driver.find_elements_by_css_selector`. """ return self.driver.find_elements_by_css_selector(selector) def _get_element(self, elem_or_selector): if isinstance(elem_or_selector, str): return self.find_element(elem_or_selector) return elem_or_selector def _wait_for(self, method, args, timeout, msg): """Abstract generic pattern for explicit WebDriverWait.""" _wait = ( self._wd_wait if timeout is None else WebDriverWait(self.driver, timeout) ) logger.debug( "method, timeout, poll => %s %s %s", method, _wait._timeout, # pylint: disable=protected-access _wait._poll, # pylint: disable=protected-access ) return _wait.until(method(*args), msg) def wait_for_element(self, selector, timeout=None): """wait_for_element is shortcut to `wait_for_element_by_css_selector` timeout if not set, equals to the fixture's `wait_timeout`.""" return self.wait_for_element_by_css_selector(selector, timeout) def wait_for_element_by_css_selector(self, selector, timeout=None): """Explicit wait until the element is present, timeout if not set, equals to the fixture's `wait_timeout` shortcut to `WebDriverWait` with `EC.presence_of_element_located`.""" return self._wait_for( EC.presence_of_element_located, ((By.CSS_SELECTOR, selector),), timeout, "timeout {}s => waiting for selector {}".format( timeout if timeout else self._wait_timeout, selector ), ) def wait_for_element_by_id(self, element_id, timeout=None): """Explicit wait until the element is present, timeout if not set, equals to the fixture's `wait_timeout` shortcut to `WebDriverWait` with `EC.presence_of_element_located`.""" return self._wait_for( EC.presence_of_element_located, ((By.ID, element_id),), timeout, "timeout {}s => waiting for element id {}".format( timeout if timeout else self._wait_timeout, element_id ), ) def wait_for_style_to_equal(self, selector, style, val, timeout=None): """Explicit wait until the element's style has expected `value` timeout if not set, equals to the fixture's `wait_timeout` shortcut to `WebDriverWait` with customized `style_to_equal` condition.""" return self._wait_for( method=style_to_equal, args=(selector, style, val), timeout=timeout, msg="style val => {} {} not found within {}s".format( style, val, timeout if timeout else self._wait_timeout ), ) def wait_for_text_to_equal(self, selector, text, timeout=None): """Explicit wait until the element's text equals the expected `text`. timeout if not set, equals to the fixture's `wait_timeout` shortcut to `WebDriverWait` with customized `text_to_equal` condition. """ return self._wait_for( method=text_to_equal, args=(selector, text), timeout=timeout, msg="text -> {} not found within {}s".format( text, timeout if timeout else self._wait_timeout ), ) def wait_for_contains_text(self, selector, text, timeout=None): """Explicit wait until the element's text contains the expected `text`. timeout if not set, equals to the fixture's `wait_timeout` shortcut to `WebDriverWait` with customized `contains_text` condition. """ return self._wait_for( method=contains_text, args=(selector, text), timeout=timeout, msg="text -> {} not found inside element within {}s".format( text, timeout if timeout else self._wait_timeout ), ) def wait_for_page(self, url=None, timeout=10): """wait_for_page navigates to the url in webdriver wait until the renderer is loaded in browser. use the `server_url` if url is not provided. """ self.driver.get(self.server_url if url is None else url) try: self.wait_for_element_by_css_selector( self.dash_entry_locator, timeout=timeout ) except TimeoutException: logger.exception("dash server is not loaded within %s seconds", timeout) logger.debug(self.get_logs()) raise DashAppLoadingError( "the expected Dash react entry point cannot be loaded" " in browser\n HTML => {}\n Console Logs => {}\n".format( self.driver.find_element_by_tag_name("body").get_property( "innerHTML" ), "\n".join((str(log) for log in self.get_logs())), ) ) def select_dcc_dropdown(self, elem_or_selector, value=None, index=None): dropdown = self._get_element(elem_or_selector) dropdown.click() menu = dropdown.find_element_by_css_selector("div.Select-menu-outer") logger.debug("the available options are %s", "\|".join(menu.text.split("\n"))) options = menu.find_elements_by_css_selector("div.VirtualizedSelectOption") if options: if isinstance(index, int): options[index].click() return for option in options: if option.text == value: option.click() return logger.error( "cannot find matching option using value=%s or index=%s", value, index, ) def toggle_window(self): """Switch between the current working window and the new opened one.""" idx = (self._window_idx + 1) % 2 self.switch_window(idx=idx) self._window_idx += 1 def switch_window(self, idx=0): """Switch to window by window index shortcut to `driver.switch_to.window`.""" if len(self.driver.window_handles) <= idx: raise BrowserError("there is no second window in Browser") self.driver.switch_to.window(self.driver.window_handles[idx]) def open_new_tab(self, url=None): """Open a new tab in browser url is not set, equals to `server_url`.""" self.driver.execute_script( 'window.open("{}", "new window")'.format( self.server_url if url is None else url ) ) def get_webdriver(self): try: return getattr(self, "_get_{}".format(self._browser))() except WebDriverException: logger.exception("<<<Webdriver not initialized correctly>>>") def _get_wd_options(self): options = ( self._options[0] if self._options and isinstance(self._options, list) else getattr(webdriver, self._browser).options.Options() ) if self._headless: options.headless = True return options def _get_chrome(self): options = self._get_wd_options() capabilities = DesiredCapabilities.CHROME capabilities["loggingPrefs"] = {"browser": "SEVERE"} if "DASH_TEST_CHROMEPATH" in os.environ: options.binary_location = os.environ["DASH_TEST_CHROMEPATH"] options.add_experimental_option( "prefs", { "download.default_directory": self.download_path, "download.prompt_for_download": False, "download.directory_upgrade": True, "safebrowsing.enabled": False, "safebrowsing.disable_download_protection": True, }, ) chrome = ( webdriver.Remote( command_executor=self._remote_url, options=options, desired_capabilities=capabilities, ) if self._remote else webdriver.Chrome(options=options, desired_capabilities=capabilities) ) # https://bugs.chromium.org/p/chromium/issues/detail?id=696481 if self._headless: # pylint: disable=protected-access chrome.command_executor._commands["send_command"] = ( "POST", "/session/$sessionId/chromium/send_command", ) params = { "cmd": "Page.setDownloadBehavior", "params": {"behavior": "allow", "downloadPath": self.download_path}, } res = chrome.execute("send_command", params) logger.debug("enabled headless download returns %s", res) chrome.set_window_position(0, 0) return chrome def _get_firefox(self): options = self._get_wd_options() capabilities = DesiredCapabilities.FIREFOX capabilities["loggingPrefs"] = {"browser": "SEVERE"} capabilities["marionette"] = True # https://developer.mozilla.org/en-US/docs/Download_Manager_preferences fp = webdriver.FirefoxProfile() fp.set_preference("browser.download.dir", self.download_path) fp.set_preference("browser.download.folderList", 2) fp.set_preference( "browser.helperApps.neverAsk.saveToDisk", "application/octet-stream", # this MIME is generic for binary ) return ( webdriver.Remote( command_executor=self._remote_url, options=options, desired_capabilities=capabilities, ) if self._remote else webdriver.Firefox( firefox_profile=fp, options=options, capabilities=capabilities ) ) @staticmethod def _is_windows(): return sys.platform == "win32" def multiple_click(self, elem_or_selector, clicks): """multiple_click click the element with number of `clicks`.""" for _ in range(clicks): self._get_element(elem_or_selector).click() def clear_input(self, elem_or_selector): """Simulate key press to clear the input.""" elem = self._get_element(elem_or_selector) logger.debug("clear input with %s => %s", elem_or_selector, elem) ( ActionChains(self.driver) .move_to_element(elem) .pause(0.2) .click(elem) .send_keys(Keys.END) .key_down(Keys.SHIFT) .send_keys(Keys.HOME) .key_up(Keys.SHIFT) .send_keys(Keys.DELETE) ).perform() def zoom_in_graph_by_ratio( self, elem_or_selector, start_fraction=0.5, zoom_box_fraction=0.2,
#!/usr/bin/env python """Web server for the NDVI Time Series Tool application. The code in this file runs on App Engine. It's called when the user loads the web page, requests a map or chart and if he wants to export an image. The App Engine code does most of the communication with EE. It uses the EE Python library and the service account specified in config.py. The exception is that when the browser loads map tiles it talks directly with EE. The map handler generates a unique client ID for the Channel API connection, injects it into the index.html template, and returns the page contents. When the user changes the options in the UI and clicks the compute button, the /mapid handler will generated map IDs for each image band. When the user requests a chart the /chart handler generates and returns a small chart over the Channel API. Also a full screen version is temporary available (ids are saved with the Memcache API) where the chart can be saved as image or table. When the user exports a file, the /export handler then kicks off an export runner (running asynchronously) to create the EE task and poll for the task's completion. When the EE task completes, the file is stored for 5 hours in the service account's Drive folder and an download link is sent to the user's browser using the Channel API. To clear the service account's Drive folder a cron job runs every hour and deletes all files older than 5 hours. Another export method is the /download handler that generates a download url directly from the EE. With this method the computing is done on the fly, because of that the download is not very stable and the file size is limited by 1024 MB. """ import math import traceback import json import logging import os import random import socket import string import time import calendar import urlparse import re from datetime import datetime # ctypes PATH KeyError fix os.environ.setdefault("PATH", '') import httplib2 import firebase_admin from firebase_admin import auth as firebase_auth import ee import jinja2 from oauth2client.service_account import ServiceAccountCredentials import webapp2 import gviz_api from google.appengine.api import taskqueue from google.appengine.api import urlfetch from google.appengine.api import memcache from google.appengine.api import users import config import drive ############################################################################### # Initialization. # ############################################################################### # Debug flag controls the output of the stacktrace if errors occur DEBUG = True # The timeout for URL Fetch, Socket and Earth Engine (seconds). # Note: Normal request are terminated after 60 seconds, background requests after 10 Minutes URL_FETCH_TIMEOUT = 600 # 10 Minuten # Check https://developers.google.com/drive/scopes for all available scopes. # Compines the Drive, Earth Engine and Firebase Scopes OAUTH_SCOPES = ["https://www.googleapis.com/auth/drive"] + ["https://www.googleapis.com/auth/earthengine","https://www.googleapis.com/auth/devstorage.full_control"] + ["https://www.googleapis.com/auth/userinfo.email","https://www.googleapis.com/auth/firebase.database"] # Our App Engine service account's credentials for Earth Engine and Google Drive CREDENTIALS = ServiceAccountCredentials.from_json_keyfile_name(config.SERVICE_ACC_JSON_KEYFILE, OAUTH_SCOPES) # Initialize the EE API. ee.Initialize(CREDENTIALS) # Set some timeouts ee.data.setDeadline(URL_FETCH_TIMEOUT*1000) # in milliseconds (default no limit) socket.setdefaulttimeout(URL_FETCH_TIMEOUT) urlfetch.set_default_fetch_deadline(URL_FETCH_TIMEOUT) # The Jinja templating system we use to dynamically generate HTML. See: # http://jinja.pocoo.org/docs/dev/ JINJA2_ENVIRONMENT = jinja2.Environment( loader=jinja2.FileSystemLoader(os.path.dirname(__file__)), autoescape=True, extensions=["jinja2.ext.autoescape"]) # An authenticated Drive helper object for the app service account. DRIVE_HELPER = drive.DriveHelper(CREDENTIALS) # The resolution of the exported images (meters per pixel). EXPORT_RESOLUTION = 30 # The maximum number of pixels in an exported image. EXPORT_MAX_PIXELS = 10e10 # The frequency to poll for export EE task completion (seconds). TASK_POLL_FREQUENCY = 10 ############################################################################### # Web request handlers. # ############################################################################### class DataHandler(webapp2.RequestHandler): """A servlet base class for responding to data queries. We use this base class to wrap our web request handlers with try/except blocks and set per-thread values (e.g. URL_FETCH_TIMEOUT). """ def get(self): self.Handle(self.DoGet) def post(self): self.Handle(self.DoPost) def DoGet(self): """Processes a GET request and returns a JSON-encodable result.""" raise NotImplementedError() def DoPost(self): """Processes a POST request and returns a JSON-encodable result.""" raise NotImplementedError() def Handle(self, handle_function): """Responds with the result of the handle_function or errors, if any.""" try: response = handle_function() except Exception as e: if DEBUG: response = {"error": str(e) + " - " + traceback.format_exc()} else: response = {"error": str(e)} if response: self.response.headers["Content-Type"] = "application/json" self.response.out.write(json.dumps(response)) class MapHandler(DataHandler): """A servlet to handle requests to load the main web page.""" def DoGet(self): """Returns the main web page with Firebase details included.""" client_id = _GetUniqueString() template = JINJA2_ENVIRONMENT.get_template("templates/index.html") self.response.out.write(template.render({ # channel token expire in 24 hours "clientId": client_id, "firebaseToken": create_custom_token(client_id), "firebaseConfig": "templates/%s" % config.FIREBASE_CONFIG, "display_splash": "none" })) class MapIdHandler(DataHandler): """A servlet that generates the map IDs for each band based on the selected options""" def DoPost(self): """Returns the map IDs of the requested options. HTTP Parameters: regression: the regression type [poly1,poly2,poly3,zhuWood] source: the source satellite [all,land5,land7,land8] start: the start year to filter the satellite images (including) end: the end year to filter the satellite images (including) cloudscore: the max cloudscore for the ee.Algorithms.Landsat.simpleCloudScore [1-100] Higher means that the pixel is more likley to be a cloud point: an array of two double values representing coordinates like [<longitude>,<latitude>] region: an array of arrays representing a region [[<longitude>,<latitude>],[<longitude>,<latitude>],...] client_id: the unique id that is used for the channel api Returns: A dictionary with a key called 'bands' containing an array of dictionaries like {"name":<band name>,"mapid":<mapid>,"token":<token>}. """ # reads the request options options = _ReadOptions(self.request) # creates an image based on the options image = _GetImage(options) # _GetImage returns None if the collection is empty if image is None: return {"error": "No images in collection. Change your options."} bands = image.bandNames().getInfo() layers = [] for band in bands: # create a map overlay for each band mapid = image.select(band).visualize().getMapId() layers.append({"name":band, "mapid": mapid["mapid"], "token": mapid["token"]}) return {"bands":layers} class ChartHandler(DataHandler): """A servlet to handle chart requests""" def DoGet(self): """Returns the full screen view of a chart. HTTP Parameters: id: the unique chart id (key value for the Memcache API). Returns: A html page with the full screen chart """ chart_id = self.request.get("id") # load chart options from Memcache API chart_options = memcache.get(chart_id) if chart_options is None: return {"error":"Chart id doesn't exist!"} else: # read template file f = open("templates/full_chart.html", "r") full_chart = f.read() f.close() # style chart view corresponding to the regression type if chart_options["regression"] == "zhuWood": chart_options["chart_style"] = "height: 40%;" chart_options["chartArea"] = "{width: \"80%\"}" else: chart_options["chart_style"] = "height: 60%; max-width: 1000px;" chart_options["chartArea"] = "{width: \"70%\"}" # output html page self.response.set_status(200) self.response.headers["Content-Type"] = "text/html" self.response.out.write(full_chart % chart_options) return def DoPost(self): """Starts an ChartRunnerHandler to asynchronously generate a chart. HTTP Parameters: regression: the regression type [poly1,poly2,poly3,zhuWood] source: the source satellite [all,land5,land7,land8] start: the start year to filter the satellite images (including) end: the end year to filter the satellite images (including) cloudscore: the max cloudscore for the ee.Algorithms.Landsat.simpleCloudScore [1-100] Higher means that the pixel is more likley to be a cloud point: an array of two double values representing coordinates like [<longitude>,<latitude>] client_id: the unique id that is used for the channel api """ # read request options options = _ReadOptions(self.request) # Kick off an export runner to start and monitor the EE export task. # Note: The work "task" is used by both Earth Engine and App Engine to refer # to two different things. "TaskQueue" is an async App Engine service. # only execute once even if task fails taskqueue.add(url="/chartrunner", params={"options":json.dumps(options)}, retry_options=taskqueue.TaskRetryOptions(task_retry_limit=0,task_age_limit=1)) # notify client browser that the chart creation has started _SendMessage(options["client_id"],"chart-" + options["filename"],"info","Chart creation at [%s/%s] in progress." % (options["point"][1],options["point"][0])) class ChartRunnerHandler(webapp2.RequestHandler): """A servlet for handling async chart task requests.""" def post(self): """Generates a small chart that is displayed as alert in the clients browser and creates the full screen version that is saved with the Memcache API. HTTP Parameters: regression: the regression type [poly1,poly2,poly3,zhuWood] source: the source satellite [all,land5,land7,land8] start: the start year to filter the satellite images (including) end: the end year to filter the satellite images (including) cloudscore: the max cloudscore for the ee.Algorithms.Landsat.simpleCloudScore [1-100] Higher means that the pixel is more likley to be a cloud point: an array of two double values representing coordinates like [<longitude>,<latitude>] client_id: the unique id that is used for the channel api """ # load the options options = json.loads(self.request.get("options")) # create the chart try: chart = _GetChart(options) except Exception as e: if DEBUG: _SendMessage(options["client_id"],"chart-" + options["filename"],"danger","Chart creation failed.", str(e) + " - " + traceback.format_exc()) else: _SendMessage(options["client_id"],"chart-" + options["filename"],"danger","Chart creation failed.", str(e)) return # _GetChart returns None if the collection is
# Copyright 2018 Jetperch LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from joulescope.usb import core as usb_core from joulescope.usb.api import DeviceEvent from joulescope.usb.impl_tools import RunUntilDone from joulescope.usb.core import SetupPacket, ControlTransferResponse from joulescope.usb.scan_info import INFO from typing import List import time import threading from contextlib import contextmanager import platform import numpy as np import os import sys import struct import ctypes import ctypes.util from ctypes import Structure, c_uint8, c_uint16, c_uint32, c_uint, \ c_int, c_char, c_ssize_t, c_void_p, POINTER, pointer, byref import logging log = logging.getLogger(__name__) STRING_LENGTH_MAX = 255 CONTROL_TRANSFER_TIMEOUT_MS = 1000 # default in milliseconds find_lib = ctypes.util.find_library('usb-1.0') if find_lib is None: if platform.system() == 'Darwin' and getattr(sys, 'frozen', False): os_version = platform.uname().release.split('.')[0] find_lib = os.path.join(sys._MEIPASS, '%s_libusb-1.0.0.dylib' % os_version) log.info('Darwin lib: %s', find_lib) else: raise RuntimeError('Could not import libusb') _lib = ctypes.cdll.LoadLibrary(find_lib) class DescriptorType: DEVICE = 0x01 CONFIG = 0x02 STRING = 0x03 INTERFACE = 0x04 ENDPOINT = 0x05 BOS = 0x0f DEVICE_CAPABILITY = 0x10 HID = 0x21 REPORT = 0x22 PHYSICAL = 0x23 HUB = 0x29 SUPERSPEED_HUB = 0x2a SS_ENDPOINT_COMPANION = 0x30 class TransferType: CONTROL = 0 ISOCHRONOUS = 1 BULK = 2 INTERRUPT = 3 BULK_STREAM = 4 class TransferStatus: COMPLETED = 0 ERROR = 1 TIMED_OUT = 2 CANCELLED = 3 STALL = 4 NO_DEVICE = 5 OVERFLOW = 6 class TransferFlags: SHORT_NOT_OK = 1 << 0 FREE_BUFFER = 1 << 1 FREE_TRANSFER = 1 << 2 ADD_ZERO_PACKET = 1 << 3 class ReturnCodes: SUCCESS = 0 ERROR_IO = -1 ERROR_INVALID_PARAM = -2 ERROR_ACCESS = -3 ERROR_NO_DEVICE = -4 ERROR_NOT_FOUND = -5 ERROR_BUSY = -6 ERROR_TIMEOUT = -7 ERROR_OVERFLOW = -8 ERROR_PIPE = -9 ERROR_INTERRUPTED = -10 ERROR_NO_MEM = -11 ERROR_NOT_SUPPORTED = -12 ERROR_OTHER = -99 class _libusb_device_descriptor(Structure): _fields_ = [ ('bLength', c_uint8), ('bDescriptorType', c_uint8), ('bcdUSB', c_uint16), ('bDeviceClass', c_uint8), ('bDeviceSubClass', c_uint8), ('bDeviceProtocol', c_uint8), ('bMaxPacketSize0', c_uint8), ('idVendor', c_uint16), ('idProduct', c_uint16), ('bcdDevice', c_uint16), ('iManufacturer', c_uint8), ('iProduct', c_uint8), ('iSerialNumber', c_uint8), ('bNumConfigurations', c_uint8)] # typedef void (LIBUSB_CALL libusb_transfer_cb_fn)(struct libusb_transfer transfer); libusb_transfer_cb_fn = ctypes.CFUNCTYPE(None, c_void_p) class _libusb_transfer(Structure): _fields_ = [ ('dev_handle', c_void_p), ('flags', c_uint8), ('endpoint_id', c_uint8), ('endpoint_type', c_uint8), ('timeout_ms', c_uint), ('status', c_uint), ('length', c_int), ('actual_length', c_int), ('callback', libusb_transfer_cb_fn), ('user_data', c_void_p), ('buffer', POINTER(c_uint8)), ('num_iso_packets', c_int), # struct libusb_iso_packet_descriptor iso_packet_desc[ZERO_SIZED_ARRAY]; ] # typedef struct libusb_context libusb_context; - c_void_p # typedef struct libusb_device libusb_device; - c_void_p # typedef struct libusb_device_handle libusb_device_handle; c_void_p # int LIBUSB_CALL libusb_init(libusb_context *ctx); _lib.libusb_init.restype = c_int _lib.libusb_init.argtypes = [POINTER(c_void_p)] # void LIBUSB_CALL libusb_exit(libusb_context ctx); _lib.libusb_exit.restype = None _lib.libusb_exit.argtypes = [c_void_p] # ssize_t LIBUSB_CALL libusb_get_device_list(libusb_context ctx, # libusb_device list); _lib.libusb_get_device_list.restype = c_ssize_t _lib.libusb_get_device_list.argtypes = [c_void_p, POINTER(POINTER(c_void_p))] # void LIBUSB_CALL libusb_free_device_list(libusb_device list, # int unref_devices); _lib.libusb_free_device_list.restype = None _lib.libusb_free_device_list.argtypes = [POINTER(c_void_p), c_int] # int LIBUSB_CALL libusb_open(libusb_device dev, libusb_device_handle dev_handle); _lib.libusb_open.restype = c_int _lib.libusb_open.argtypes = [c_void_p, POINTER(c_void_p)] # void LIBUSB_CALL libusb_close(libusb_device_handle dev_handle); _lib.libusb_close.restype = None _lib.libusb_close.argtypes = [c_void_p] # int LIBUSB_CALL libusb_set_configuration(libusb_device_handle dev_handle, # int configuration); _lib.libusb_set_configuration.restype = c_int _lib.libusb_set_configuration.argtypes = [c_void_p, c_int] # int LIBUSB_CALL libusb_claim_interface(libusb_device_handle dev_handle, # int interface_number); _lib.libusb_claim_interface.restype = c_int _lib.libusb_claim_interface.argtypes = [c_void_p, c_int] # int LIBUSB_CALL libusb_release_interface(libusb_device_handle dev_handle, # int interface_number); _lib.libusb_release_interface.restype = c_int _lib.libusb_release_interface.argtypes = [c_void_p, c_int] # int LIBUSB_CALL libusb_set_interface_alt_setting(libusb_device_handle dev_handle, # int interface_number, int alternate_setting); _lib.libusb_set_interface_alt_setting.restype = c_int _lib.libusb_set_interface_alt_setting.argtypes = [c_void_p, c_int, c_int] # int LIBUSB_CALL libusb_get_device_descriptor(libusb_device dev, # struct libusb_device_descriptor desc); _lib.libusb_get_device_descriptor.restype = c_int _lib.libusb_get_device_descriptor.argtypes = [c_void_p, POINTER(_libusb_device_descriptor)] # int LIBUSB_CALL libusb_control_transfer(libusb_device_handle dev_handle, # uint8_t request_type, uint8_t bRequest, uint16_t wValue, uint16_t wIndex, # unsigned char data, uint16_t wLength, unsigned int timeout); _lib.libusb_control_transfer.restype = c_int _lib.libusb_control_transfer.argtypes = [c_void_p, c_uint8, c_uint8, c_uint16, c_uint16, POINTER(c_uint8), c_uint16, c_int] # struct libusb_transfer * LIBUSB_CALL libusb_alloc_transfer(int iso_packets); _lib.libusb_alloc_transfer.restype = POINTER(_libusb_transfer) _lib.libusb_alloc_transfer.argtypes = [c_int] # int LIBUSB_CALL libusb_submit_transfer(struct libusb_transfer transfer); _lib.libusb_submit_transfer.restype = c_int _lib.libusb_submit_transfer.argtypes = [POINTER(_libusb_transfer)] # int LIBUSB_CALL libusb_cancel_transfer(struct libusb_transfer transfer); _lib.libusb_cancel_transfer.restype = c_int _lib.libusb_cancel_transfer.argtypes = [POINTER(_libusb_transfer)] # void LIBUSB_CALL libusb_free_transfer(struct libusb_transfer transfer); _lib.libusb_free_transfer.restype = None _lib.libusb_free_transfer.argtypes = [POINTER(_libusb_transfer)] class TimeVal(Structure): _fields_ = [ ("tv_sec", ctypes.c_long), ("tv_usec", ctypes.c_long) ] # int LIBUSB_CALL libusb_handle_events_timeout(libusb_context ctx, # struct timeval tv); _lib.libusb_handle_events_timeout.restype = c_int _lib.libusb_handle_events_timeout.argtypes = [c_void_p, POINTER(TimeVal)] # int LIBUSB_CALL libusb_handle_events(libusb_context ctx) _lib.libusb_handle_events.restype = c_int _lib.libusb_handle_events.argtypes = [c_void_p] class HotplugFlag: NONE = 0 ENUMERATE = 1 << 0 class HotplugEvent: DEVICE_ARRIVED = 0x01 DEVICE_LEFT = 0x02 HOTPLUG_MATCH_ANY = -1 # typedef int (LIBUSB_CALL libusb_hotplug_callback_fn)(libusb_context ctx, # libusb_device device, # libusb_hotplug_event event, # void user_data); _libusb_hotplug_callback_fn = ctypes.CFUNCTYPE(c_int, c_void_p, c_void_p, c_int, c_void_p) # int LIBUSB_CALL libusb_hotplug_register_callback(libusb_context ctx, # libusb_hotplug_event events, # libusb_hotplug_flag flags, # int vendor_id, int product_id, # int dev_class, # libusb_hotplug_callback_fn cb_fn, # void user_data, # libusb_hotplug_callback_handle callback_handle); _lib.libusb_hotplug_register_callback.restype = c_int _lib.libusb_hotplug_register_callback.argtypes = [c_void_p, c_int, c_int, c_int, c_int, c_int, _libusb_hotplug_callback_fn, c_void_p, POINTER(c_int)] # void LIBUSB_CALL libusb_hotplug_deregister_callback(libusb_context ctx, # libusb_hotplug_callback_handle callback_handle); _lib.libusb_hotplug_deregister_callback.restype = c_int _lib.libusb_hotplug_deregister_callback.argtypes = [c_void_p, c_int] class Capability: HAS_CAPABILITY = 0x0000 HAS_HOTPLUG = 0x0001 HAS_HID_ACCESS = 0x0100 SUPPORTS_DETACH_KERNEL_DRIVER = 0x0101 # int LIBUSB_CALL libusb_has_capability(uint32_t capability); _lib.libusb_has_capability.restype = c_int _lib.libusb_has_capability.argtypes = [c_uint32] def _libusb_context_create(): ctx = c_void_p() rc = _lib.libusb_init(pointer(ctx)) if rc: raise RuntimeError('Could not open libusb') return ctx def _libusb_context_destroy(ctx): _lib.libusb_exit(ctx) @contextmanager def _libusb_context(): ctx = _libusb_context_create() try: yield ctx finally: _libusb_context_destroy(ctx) def _path_split(path): vid, pid, serial_number = path.split('/') return int(vid, 16), int(pid, 16), serial_number def _get_string_descriptor(device, index): request_type = usb_core.RequestType(direction='in', type_='standard', recipient='device').u8 byte_buffer = bytearray(STRING_LENGTH_MAX) buffer_type = c_uint8 * STRING_LENGTH_MAX buffer = buffer_type.from_buffer(byte_buffer) # determine default language rv = _lib.libusb_control_transfer(device, request_type, usb_core.Request.GET_DESCRIPTOR, (DescriptorType.STRING << 8), 0, buffer, STRING_LENGTH_MAX, 1000) if rv < 0: raise RuntimeError('control_transfer could not get language: %d' % (rv, )) langid = int(byte_buffer[2]) \| (int(byte_buffer[3]) << 8) rv = _lib.libusb_control_transfer(device, request_type, usb_core.Request.GET_DESCRIPTOR, (DescriptorType.STRING << 8) \| (index & 0xff), langid, buffer, STRING_LENGTH_MAX, 1000) if rv < 0: raise RuntimeError('control transfer could not get string descriptor: %d' % (rv, )) buffer_len = min(rv, byte_buffer[0]) # byte 0 is length, byte 1 is string identifier return byte_buffer[2:buffer_len].decode('UTF-16-LE') _transfer_callback_discard_fn = libusb_transfer_cb_fn(lambda x: None) """Default null callback that is always safe.""" class Transfer: def __init__(self, size): try: self.size = len(size) # also serves as list-like duck-typing test self.buffer = np.frombuffer(size, dtype=np.uint8) log.debug('Transfer: copy buffer %d', self.size) except TypeError: self.size = size self.buffer = np.empty(self.size, dtype=np.uint8) log.debug('Transfer: create buffer %d', self.size) self.transfer = _lib.libusb_alloc_transfer(0) # type: _libusb_transfer self.addr = ctypes.addressof(self.transfer.contents) transfer = self.transfer[0] self.buffer_ptr = self.buffer.ctypes.data_as(POINTER(c_uint8)) transfer.buffer = self.buffer_ptr transfer.flags = 0 transfer.length = self.size transfer.actual_length = 0 transfer.user_data = None transfer.num_iso_packets = 0 transfer.status = TransferStatus.COMPLETED transfer.timeout_ms = 1000 # milliseconds transfer.callback = _transfer_callback_discard_fn def __del__(self): _lib.libusb_free_transfer(self.transfer) class ControlTransferAsync: def __init__(self, handle): """Manage asynchronous control transfers. :param handle: The device handle. """ self._handle = handle self._transfer_callback_fn = libusb_transfer_cb_fn(self._transfer_callback) self._commands = [] # Pending control transfer commands as list of [cbk_fn, setup_packet, buffer] self._transfer_pending = None # type: Transfer self._time_start = None self.stop_code = None def __str__(self): return 'ControlTransferAsync()' def _transfer_callback(self, transfer_void_ptr): if self._transfer_pending is None: log.warning('Transfer callback when none pending') return if self._transfer_pending.addr != transfer_void_ptr: log.warning('Transfer mismatch') return transfer, self._transfer_pending = self._transfer_pending, None if self._commands: self._finish(self._commands.pop(0), transfer) else: log.warning('Transfer callback when no commands') self._issue() def _abort_all(self): commands, self._commands = self._commands, [] for cbk_fn, setup_packet, _ in commands: try: response = usb_core.ControlTransferResponse(setup_packet, TransferStatus.CANCELLED, None) cbk_fn(response) except Exception: log.exception('in callback while aborting') def close(self): if self._handle and self._transfer_pending: log.info('ControlTransferAsync.close cancel pending transfer, %d', len(self._commands)) transfer, self._transfer_pending = self._transfer_pending, None transfer.transfer[0].callback = _transfer_callback_discard_fn _lib.libusb_cancel_transfer(transfer.transfer) # callback function will be invoked later else: log.info('ControlTransferAsync.close %d', len(self._commands)) self._handle = None self._abort_all() def pend(self, cbk_fn, setup_packet: usb_core.SetupPacket, buffer=None): """Pend an asynchronous Control Transfer. :param cbk_fn: The function to call when the control transfer completes. A :class:`usb_core.ControlTransferResponse` is the sole argument. :param setup_packet: :param buffer: The buffer (if length > 0) for write transactions. :return: True if pending, False on error. """ if self.stop_code is not None: response = usb_core.ControlTransferResponse(setup_packet, self.stop_code, None) cbk_fn(response) return False command = [cbk_fn, setup_packet, buffer] was_empty = not bool(self._commands) self._commands.append(command) if was_empty: return self._issue() return True def _issue(self): if not self._commands: return True if not self._handle: log.info('_issue but handle not valid') self._abort_all() return False log.debug('preparing') _, setup_packet, buffer = self._commands[0] hdr = struct.pack('<BBHHH', setup_packet.request_type, setup_packet.request, setup_packet.value, setup_packet.index, setup_packet.length) if buffer is not None: transfer = Transfer(hdr + buffer) else: transfer = Transfer(len(hdr) + setup_packet.length) transfer.buffer[:len(hdr)] = np.frombuffer(hdr, dtype=np.uint8) t = transfer.transfer[0] t.dev_handle = self._handle t.endpoint_id = 0 t.endpoint_type = TransferType.CONTROL t.callback = self._transfer_callback_fn self._transfer_pending = transfer self._time_start = time.time() rv = _lib.libusb_submit_transfer(transfer.transfer) if 0 == rv: log.debug('libusb_submit_transfer [control]') else: log.warning('libusb_submit_transfer [control] => %d', rv) if t.status == 0: if rv == ReturnCodes.ERROR_NO_DEVICE: log.info('control transfer but no device') t.status = TransferStatus.NO_DEVICE else: t.status = TransferStatus.ERROR if self.stop_code is None: self.stop_code = DeviceEvent.COMMUNICATION_ERROR
<reponame>kustodian/google-cloud-sdk """Generated client library for cloudbuild version v1.""" # NOTE: This file is autogenerated and should not be edited by hand. from apitools.base.py import base_api from googlecloudsdk.third_party.apis.cloudbuild.v1 import cloudbuild_v1_messages as messages class CloudbuildV1(base_api.BaseApiClient): """Generated client library for service cloudbuild version v1.""" MESSAGES_MODULE = messages BASE_URL = u'https://cloudbuild.googleapis.com/' _PACKAGE = u'cloudbuild' _SCOPES = [u'https://www.googleapis.com/auth/cloud-platform'] _VERSION = u'v1' _CLIENT_ID = '1042881264118.apps.googleusercontent.com' _CLIENT_SECRET = 'x_Tw5K8nnjoRAqULM9PFAC2b' _USER_AGENT = 'x_Tw5K8nnjoRAqULM9PFAC2b' _CLIENT_CLASS_NAME = u'CloudbuildV1' _URL_VERSION = u'v1' _API_KEY = None def __init__(self, url='', credentials=None, get_credentials=True, http=None, model=None, log_request=False, log_response=False, credentials_args=None, default_global_params=None, additional_http_headers=None, response_encoding=None): """Create a new cloudbuild handle.""" url = url or self.BASE_URL super(CloudbuildV1, self).__init__( url, credentials=credentials, get_credentials=get_credentials, http=http, model=model, log_request=log_request, log_response=log_response, credentials_args=credentials_args, default_global_params=default_global_params, additional_http_headers=additional_http_headers, response_encoding=response_encoding) self.operations = self.OperationsService(self) self.projects_builds = self.ProjectsBuildsService(self) self.projects_triggers = self.ProjectsTriggersService(self) self.projects = self.ProjectsService(self) class OperationsService(base_api.BaseApiService): """Service class for the operations resource.""" _NAME = u'operations' def __init__(self, client): super(CloudbuildV1.OperationsService, self).__init__(client) self._upload_configs = { } def Cancel(self, request, global_params=None): r"""Starts asynchronous cancellation on a long-running operation. The server. makes a best effort to cancel the operation, but success is not guaranteed. If the server doesn't support this method, it returns `google.rpc.Code.UNIMPLEMENTED`. Clients can use Operations.GetOperation or other methods to check whether the cancellation succeeded or whether the operation completed despite cancellation. On successful cancellation, the operation is not deleted; instead, it becomes an operation with an Operation.error value with a google.rpc.Status.code of 1, corresponding to `Code.CANCELLED`. Args: request: (CloudbuildOperationsCancelRequest) input message global_params: (StandardQueryParameters, default: None) global arguments Returns: (Empty) The response message. """ config = self.GetMethodConfig('Cancel') return self._RunMethod( config, request, global_params=global_params) Cancel.method_config = lambda: base_api.ApiMethodInfo( flat_path=u'v1/operations/{operationsId}:cancel', http_method=u'POST', method_id=u'cloudbuild.operations.cancel', ordered_params=[u'name'], path_params=[u'name'], query_params=[], relative_path=u'v1/{+name}:cancel', request_field=u'cancelOperationRequest', request_type_name=u'CloudbuildOperationsCancelRequest', response_type_name=u'Empty', supports_download=False, ) def Get(self, request, global_params=None): r"""Gets the latest state of a long-running operation. Clients can use this. method to poll the operation result at intervals as recommended by the API service. Args: request: (CloudbuildOperationsGetRequest) input message global_params: (StandardQueryParameters, default: None) global arguments Returns: (Operation) The response message. """ config = self.GetMethodConfig('Get') return self._RunMethod( config, request, global_params=global_params) Get.method_config = lambda: base_api.ApiMethodInfo( flat_path=u'v1/operations/{operationsId}', http_method=u'GET', method_id=u'cloudbuild.operations.get', ordered_params=[u'name'], path_params=[u'name'], query_params=[], relative_path=u'v1/{+name}', request_field='', request_type_name=u'CloudbuildOperationsGetRequest', response_type_name=u'Operation', supports_download=False, ) def List(self, request, global_params=None): r"""Lists operations that match the specified filter in the request. If the. server doesn't support this method, it returns `UNIMPLEMENTED`. NOTE: the `name` binding allows API services to override the binding to use different resource name schemes, such as `users//operations`. To override the binding, API services can add a binding such as `"/v1/{name=users/}/operations"` to their service configuration. For backwards compatibility, the default name includes the operations collection id, however overriding users must ensure the name binding is the parent resource, without the operations collection id. Args: request: (CloudbuildOperationsListRequest) input message global_params: (StandardQueryParameters, default: None) global arguments Returns: (ListOperationsResponse) The response message. """ config = self.GetMethodConfig('List') return self._RunMethod( config, request, global_params=global_params) List.method_config = lambda: base_api.ApiMethodInfo( flat_path=u'v1/operations', http_method=u'GET', method_id=u'cloudbuild.operations.list', ordered_params=[u'name'], path_params=[u'name'], query_params=[u'filter', u'pageSize', u'pageToken'], relative_path=u'v1/{+name}', request_field='', request_type_name=u'CloudbuildOperationsListRequest', response_type_name=u'ListOperationsResponse', supports_download=False, ) class ProjectsBuildsService(base_api.BaseApiService): """Service class for the projects_builds resource.""" _NAME = u'projects_builds' def __init__(self, client): super(CloudbuildV1.ProjectsBuildsService, self).__init__(client) self._upload_configs = { } def Cancel(self, request, global_params=None): r"""Cancels a build in progress. Args: request: (CloudbuildProjectsBuildsCancelRequest) input message global_params: (StandardQueryParameters, default: None) global arguments Returns: (Build) The response message. """ config = self.GetMethodConfig('Cancel') return self._RunMethod( config, request, global_params=global_params) Cancel.method_config = lambda: base_api.ApiMethodInfo( http_method=u'POST', method_id=u'cloudbuild.projects.builds.cancel', ordered_params=[u'projectId', u'id'], path_params=[u'id', u'projectId'], query_params=[], relative_path=u'v1/projects/{projectId}/builds/{id}:cancel', request_field=u'cancelBuildRequest', request_type_name=u'CloudbuildProjectsBuildsCancelRequest', response_type_name=u'Build', supports_download=False, ) def Create(self, request, global_params=None): r"""Starts a build with the specified configuration. This method returns a long-running `Operation`, which includes the build ID. Pass the build ID to `GetBuild` to determine the build status (such as `SUCCESS` or `FAILURE`). Args: request: (CloudbuildProjectsBuildsCreateRequest) input message global_params: (StandardQueryParameters, default: None) global arguments Returns: (Operation) The response message. """ config = self.GetMethodConfig('Create') return self._RunMethod( config, request, global_params=global_params) Create.method_config = lambda: base_api.ApiMethodInfo( http_method=u'POST', method_id=u'cloudbuild.projects.builds.create', ordered_params=[u'projectId'], path_params=[u'projectId'], query_params=[], relative_path=u'v1/projects/{projectId}/builds', request_field=u'build', request_type_name=u'CloudbuildProjectsBuildsCreateRequest', response_type_name=u'Operation', supports_download=False, ) def Get(self, request, global_params=None): r"""Returns information about a previously requested build. The `Build` that is returned includes its status (such as `SUCCESS`, `FAILURE`, or `WORKING`), and timing information. Args: request: (CloudbuildProjectsBuildsGetRequest) input message global_params: (StandardQueryParameters, default: None) global arguments Returns: (Build) The response message. """ config = self.GetMethodConfig('Get') return self._RunMethod( config, request, global_params=global_params) Get.method_config = lambda: base_api.ApiMethodInfo( http_method=u'GET', method_id=u'cloudbuild.projects.builds.get', ordered_params=[u'projectId', u'id'], path_params=[u'id', u'projectId'], query_params=[], relative_path=u'v1/projects/{projectId}/builds/{id}', request_field='', request_type_name=u'CloudbuildProjectsBuildsGetRequest', response_type_name=u'Build', supports_download=False, ) def List(self, request, global_params=None): r"""Lists previously requested builds. Previously requested builds may still be in-progress, or may have finished successfully or unsuccessfully. Args: request: (CloudbuildProjectsBuildsListRequest) input message global_params: (StandardQueryParameters, default: None) global arguments Returns: (ListBuildsResponse) The response message. """ config = self.GetMethodConfig('List') return self._RunMethod( config, request, global_params=global_params) List.method_config = lambda: base_api.ApiMethodInfo( http_method=u'GET', method_id=u'cloudbuild.projects.builds.list', ordered_params=[u'projectId'], path_params=[u'projectId'], query_params=[u'filter', u'pageSize', u'pageToken'], relative_path=u'v1/projects/{projectId}/builds', request_field='', request_type_name=u'CloudbuildProjectsBuildsListRequest', response_type_name=u'ListBuildsResponse', supports_download=False, ) def Retry(self, request, global_params=None): r"""Creates a new build based on the specified build. This method creates a new build using the original build request, which may or may not result in an identical build. For triggered builds: * Triggered builds resolve to a precise revision; therefore a retry of a triggered build will result in a build that uses the same revision. For non-triggered builds that specify `RepoSource`: * If the original build built from the tip of a branch, the retried build will build from the tip of that branch, which may not be the same revision as the original build. * If the original build specified a commit sha or revision ID, the retried build will use the identical source. For builds that specify `StorageSource`: * If the original build pulled source from Google Cloud Storage without specifying the generation of the object, the new build will use the current object, which may be different from the original build source. * If the original build pulled source from Cloud Storage and specified the generation of the object, the new build will attempt to use the same object, which may or may not be available depending on the bucket's lifecycle management settings. Args: request: (CloudbuildProjectsBuildsRetryRequest) input message global_params: (StandardQueryParameters, default: None) global arguments Returns: (Operation) The response message. """ config = self.GetMethodConfig('Retry') return self._RunMethod( config, request, global_params=global_params) Retry.method_config = lambda: base_api.ApiMethodInfo( http_method=u'POST', method_id=u'cloudbuild.projects.builds.retry', ordered_params=[u'projectId', u'id'], path_params=[u'id', u'projectId'], query_params=[], relative_path=u'v1/projects/{projectId}/builds/{id}:retry', request_field=u'retryBuildRequest', request_type_name=u'CloudbuildProjectsBuildsRetryRequest', response_type_name=u'Operation', supports_download=False, ) class ProjectsTriggersService(base_api.BaseApiService): """Service class for the projects_triggers resource.""" _NAME = u'projects_triggers' def __init__(self, client): super(CloudbuildV1.ProjectsTriggersService, self).__init__(client) self._upload_configs = { } def Create(self, request, global_params=None): r"""Creates a new `BuildTrigger`. This API is experimental. Args: request: (CloudbuildProjectsTriggersCreateRequest) input message global_params: (StandardQueryParameters, default: None) global arguments Returns: (BuildTrigger) The response message. """ config = self.GetMethodConfig('Create') return self._RunMethod( config, request, global_params=global_params) Create.method_config = lambda: base_api.ApiMethodInfo( http_method=u'POST', method_id=u'cloudbuild.projects.triggers.create', ordered_params=[u'projectId'], path_params=[u'projectId'], query_params=[], relative_path=u'v1/projects/{projectId}/triggers', request_field=u'buildTrigger', request_type_name=u'CloudbuildProjectsTriggersCreateRequest', response_type_name=u'BuildTrigger', supports_download=False, ) def Delete(self, request, global_params=None): r"""Deletes a `BuildTrigger` by its project ID and trigger ID. This API is experimental. Args: request: (CloudbuildProjectsTriggersDeleteRequest) input message global_params: (StandardQueryParameters, default: None) global arguments Returns: (Empty) The response message. """ config = self.GetMethodConfig('Delete') return self._RunMethod( config, request, global_params=global_params) Delete.method_config = lambda: base_api.ApiMethodInfo( http_method=u'DELETE', method_id=u'cloudbuild.projects.triggers.delete', ordered_params=[u'projectId', u'triggerId'], path_params=[u'projectId', u'triggerId'], query_params=[], relative_path=u'v1/projects/{projectId}/triggers/{triggerId}', request_field='', request_type_name=u'CloudbuildProjectsTriggersDeleteRequest', response_type_name=u'Empty', supports_download=False, ) def Get(self, request, global_params=None): r"""Returns information about a `BuildTrigger`. This API is experimental. Args: request: (CloudbuildProjectsTriggersGetRequest) input message global_params: (StandardQueryParameters, default: None) global arguments Returns: (BuildTrigger) The response message. """ config = self.GetMethodConfig('Get') return self._RunMethod( config, request, global_params=global_params) Get.method_config = lambda: base_api.ApiMethodInfo( http_method=u'GET', method_id=u'cloudbuild.projects.triggers.get', ordered_params=[u'projectId', u'triggerId'], path_params=[u'projectId', u'triggerId'], query_params=[], relative_path=u'v1/projects/{projectId}/triggers/{triggerId}', request_field='', request_type_name=u'CloudbuildProjectsTriggersGetRequest', response_type_name=u'BuildTrigger', supports_download=False, ) def List(self, request, global_params=None): r"""Lists existing `BuildTrigger`s. This API is experimental. Args: request: (CloudbuildProjectsTriggersListRequest) input message global_params: (StandardQueryParameters, default: None) global arguments Returns: (ListBuildTriggersResponse) The response message. """ config = self.GetMethodConfig('List') return self._RunMethod( config, request, global_params=global_params) List.method_config = lambda: base_api.ApiMethodInfo( http_method=u'GET', method_id=u'cloudbuild.projects.triggers.list', ordered_params=[u'projectId'], path_params=[u'projectId'], query_params=[u'pageSize', u'pageToken'], relative_path=u'v1/projects/{projectId}/triggers', request_field='', request_type_name=u'CloudbuildProjectsTriggersListRequest', response_type_name=u'ListBuildTriggersResponse', supports_download=False, ) def Patch(self, request, global_params=None): r"""Updates a `BuildTrigger` by its project ID and trigger ID. This API is experimental. Args: request: (CloudbuildProjectsTriggersPatchRequest) input message global_params: (StandardQueryParameters, default: None) global arguments Returns: (BuildTrigger) The response message. """ config = self.GetMethodConfig('Patch') return self._RunMethod( config, request, global_params=global_params) Patch.method_config = lambda: base_api.ApiMethodInfo( http_method=u'PATCH', method_id=u'cloudbuild.projects.triggers.patch', ordered_params=[u'projectId', u'triggerId'], path_params=[u'projectId', u'triggerId'], query_params=[], relative_path=u'v1/projects/{projectId}/triggers/{triggerId}', request_field=u'buildTrigger', request_type_name=u'CloudbuildProjectsTriggersPatchRequest', response_type_name=u'BuildTrigger', supports_download=False, ) def Run(self, request, global_params=None): r"""Runs a `BuildTrigger` at a particular source revision. Args: request: (CloudbuildProjectsTriggersRunRequest) input message global_params: (StandardQueryParameters, default: None) global arguments Returns: (Operation) The response message. """ config = self.GetMethodConfig('Run') return self._RunMethod( config, request, global_params=global_params) Run.method_config = lambda: base_api.ApiMethodInfo(
method PARAMS: name: str A name/alias given to the model by the user layers: list of integers List of neuron size for each layer dropout: float Level of dropout recurrentDropout: float Level of recurrent dropout alpha: float Alpha of the leaky relu function training: boolean Whether dropout should be use at time of prediction enrolWindow: int Number of samples used to make each prediction RETURNS: model: MachineLearningModel Object with typical machine learning methods like train, predict etc. """ global _X_train, _y_train, _default_LSTM_args model = models.kerasGRU( params = { 'name': name, 'X_train': _X_train, 'y_train': _y_train, 'args': { 'activation': activation, 'loss': loss, 'optimizer': optimizer, 'metrics': metrics, 'epochs': epochs, 'batchSize': batchSize, 'verbose': verbose, 'callbacks': callbacks, 'enrolWindow': enrolWindow, 'validationSize': validationSize, 'testSize': testSize, }, }, layers=layers, dropout=dropout, recurrentDropout=recurrentDropout, alpha=alpha, training=training, ) return model def Linear( name, ): """ FUNCTION: Used to create a Linear Machine Learning model PARAMS: name: str A name/alias given to the model by the user RETURNS: model: MachineLearningModel Object with typical machine learning methods like train, predict etc. """ global _X_train, _y_train model = models.sklearnLinear( params={ 'name': name, 'X_train': _X_train, 'y_train': _y_train, }, ) return model def Linear_Regularized( name, alphas=(0.1, 1.0, 10.0), folds=10, ): """ FUNCTION: Used to create a Linear Machine Learning model with built-in regularization and cross validation PARAMS: name: str A name/alias given to the model by the user alphas: tuple of floats Set of regluarization strenght parameters to try folds: int Number of cross validation folds RETURNS: model: MachineLearningModel Object with typical machine learning methods like train, predict etc. """ global _X_train, _y_train model = models.sklearnRidgeCV( params={ 'name': name, 'X_train': _X_train, 'y_train': _y_train, }, alphas = alphas, folds = folds, ) return model def ElasticNet( name, alphas=(0.1, 1.0, 10.0), l1_ratio=0.5, ): """ FUNCTION: Used to create a iterative regularization path fitting Machine Learning model PARAMS: name: str A name/alias given to the model by the user alphas: tuple of floats Set of regluarization strenght parameters to try l1_ratio: float ratio between L1 and L2 regularization RETURNS: model: MachineLearningModel Object with typical machine learning methods like train, predict etc. """ global _X_train, _y_train model = models.sklearnElasticNetCV( params={ 'name': name, 'X_train': _X_train, 'y_train': _y_train, }, alphas = alphas, l1_ratio = l1_ratio, ) return model def DecisionTree( name, ): """ FUNCTION: Used to create a decision tree regressor PARAMS: name: str A name/alias given to the model by the user RETURNS: model: MachineLearningModel Object with typical machine learning methods like train, predict etc. """ global _X_train, _y_train model = models.sklearnDecisionTree( params={ 'name': name, 'X_train': _X_train, 'y_train': _y_train, }, ) return model def RandomForest( name, ): """ FUNCTION: Used to create a random forest (decision) tree regressor PARAMS: name: str A name/alias given to the model by the user RETURNS: model: MachineLearningModel Object with typical machine learning methods like train, predict etc. """ global _X_train, _y_train model = models.sklearnRandomForest( params={ 'name': name, 'X_train': _X_train, 'y_train': _y_train, }, ) return model def BaggingRegressor( name, ): """ FUNCTION: Used to create a bagging regressor model, which aggregates base regressors a achieve a final prediction PARAMS: name: str A name/alias given to the model by the user RETURNS: model: MachineLearningModel Object with typical machine learning methods like train, predict etc. """ global _X_train, _y_train model = models.sklearnBagging( params={ 'name': name, 'X_train': _X_train, 'y_train': _y_train, }, ) return model def AdaBoostRegressor( name, ): """ FUNCTION: Used to create an AdaBoost regressor, which fits additional regressor copies with different weights according to previous predictions PARAMS: name: str A name/alias given to the model by the user RETURNS: model: MachineLearningModel Object with typical machine learning methods like train, predict etc. """ global _X_train, _y_train model = models.sklearnAdaBoost( params={ 'name': name, 'X_train': _X_train, 'y_train': _y_train, }, ) return model def SupportVectorMachine( name, ): """ FUNCTION: Used to create a Support Vector Machine regressor PARAMS: name: str A name/alias given to the model by the user RETURNS: model: MachineLearningModel Object with typical machine learning methods like train, predict etc. """ global _X_train, _y_train model = models.sklearnSVM( params={ 'name': name, 'X_train': _X_train, 'y_train': _y_train, }, ) return model def Ensemble(name, modelList): """ FUNCTION: Used to create an Ensemble model, combining the prediction of n>1 machine learning methods using a linear regressor PARAMS: name: str A name/alias given to the model by the user modelList: list of MachineLearningModel objects A list of machine learning models used to construct the Ensemble model RETURNS: model: EnsembleModel Ensemble model object which behaves the same as any other MachineLearningModel """ global _X_train, _y_train model = models.ensembleModel( params={ 'name': name, 'X_train': _X_train, 'y_train': _y_train, }, models=modelList, ) return model def Autoencoder_Regularized( name, l1_rate=10e-4, encodingDim=3, activation=_default_MLP_args['activation'], loss=_default_MLP_args['loss'], optimizer=_default_MLP_args['optimizer'], metrics=_default_MLP_args['metrics'], epochs=_default_MLP_args['epochs'], batchSize=_default_MLP_args['batchSize'], verbose=_default_MLP_args['verbose'], validationSize=_default_MLP_args['validationSize'], testSize=_default_MLP_args['testSize'], callbacks=_default_MLP_args['callbacks'], ): """ FUNCTION: Used to create an Autoencoder model using multilayer perceptron and reguarlization by Lasso regluarization NB: Autoencoder models SHOULD NOT and CAN NOT be used together with other models, or as submodels to Ensemble models PARAMS: name: str A name/alias given to the model by the user l1_rate: float Level of L1 regularization encodingDim: int Size of autoencoder middle layer RETURNS: model: AutoencoderModel Object with typical machine learning methods like train, predict etc. """ global _X_train, _default_MLP_args model = models.autoencoder_Regularized( params = { 'name': name, 'X_train': _X_train, 'args': { 'activation': activation, 'loss': loss, 'optimizer': optimizer, 'metrics': metrics, 'epochs': epochs, 'batchSize': batchSize, 'verbose': verbose, 'callbacks': callbacks, 'enrolWindow': 0, 'validationSize': validationSize, 'testSize': testSize, }, }, l1_rate=l1_rate, encodingDim=encodingDim, ) return model def Autoencoder_Dropout( name, dropout=0.0, encodingDim=3, activation=_default_MLP_args['activation'], loss=_default_MLP_args['loss'], optimizer=_default_MLP_args['optimizer'], metrics=_default_MLP_args['metrics'], epochs=_default_MLP_args['epochs'], batchSize=_default_MLP_args['batchSize'], verbose=_default_MLP_args['verbose'], validationSize=_default_MLP_args['validationSize'], testSize=_default_MLP_args['testSize'], callbacks=_default_MLP_args['callbacks'], ): """ FUNCTION: Used to create an Autoencoder model using multilayer perceptron and reguarlization by Lasso regluarization NB: Autoencoder models SHOULD NOT and CAN NOT be used together with other models, or as submodels to Ensemble models PARAMS: name: str A name/alias given to the model by the user dropout: float Level of dropout encodingDim: int Size of autoencoder middle layer RETURNS: model: AutoencoderModel Object with typical machine learning methods like train, predict etc. """ global _X_train, _default_MLP_args model = models.autoencoder_Dropout( params = { 'name': name, 'X_train': _X_train, 'args': { 'activation': activation, 'loss': loss, 'optimizer': optimizer, 'metrics': metrics, 'epochs': epochs, 'batchSize': batchSize, 'verbose': verbose, 'callbacks': callbacks, 'enrolWindow': 0, 'validationSize': validationSize, 'testSize': testSize, }, }, dropout=dropout, encodingDim=encodingDim, ) return model def reset(): """ FUNCTION: Resets the state of the module PARAMS: None RETURNS: None """ global _filename, _names, _descriptions, _units, _relevantColumns, _columnDescriptions, _columnUnits, _columnNames, _df, _traintime, _testtime, _df_train, _df_test, _targetColumns, _modelList, _X_train, _y_train, _X_test, _y_test, _maxEnrolWindow, _indexColumn _filename = None _names = None _descriptions = None _units = None _relevantColumns = None _columnDescriptions = None _columnUnits = None _columnNames = None _df = None _traintime = None _testtime = None _df_train = None _df_test = None _targetColumns = None _modelList = None _X_train = None _y_train = None _X_test = None _y_test = None _maxEnrolWindow = None _indexColumn = None def getCallbacks(patience_es, patience_rlr): """ FUNCTION: Returns a list of callbacks with the provided properties PARAMS: patience_es: int Number of iterations to wait before EarlyStopping is performed patience_rlr: int Number of iterations to wait before ReduceLearningRate is performed RETURNS: List of callbacks """ return modelFuncs.getBasicCallbacks(patience_es=patience_es, patience_rlr=patience_rlr) def setMLPCallbacks(patience_es, patience_rlr): """ FUNCTION: Redefines the default MLP callbacks NB: only for current state PARAMS: patience_es: int Number of iterations to wait before EarlyStopping is performed patience_rlr: int Number of iterations to wait before ReduceLearningRate is performed RETURNS: None """ global _default_MLP_args _default_MLP_args['callbacks'] = modelFuncs.getBasicCallbacks(patience_es=patience_es, patience_rlr=patrience_rlr) def setLSTMCallbacks(patience_es, patience_rlr): """ FUNCTION: Redefines the default LSTM callbacks NB: only for current state PARAMS: patience_es: int Number of iterations to wait before EarlyStopping is performed patience_rlr: int Number of iterations to wait before ReduceLearningRate is performed RETURNS: None """ global _default_LSTM_args _default_LSTM_args['callbacks'] = modelFuncs.getBasicCallbacks(patience_es=patience_es, patience_rlr=patrience_rlr) def correlationMatrix(df): return analysis.correlationMatrix(df) def pca(df, numberOfComponents,
1: ['a', 'e'], 2: ['b', 'c'], 3: ['d'], } """ key_to_vals = defaultdict(list) for key, val in zip(key_list, val_list): key_to_vals[key].append(val) return key_to_vals def assert_keys_are_subset(dict1, dict2): """ Example: >>> # DISABLE_DOCTEST >>> dict1 = {1:1, 2:2, 3:3} >>> dict2 = {2:3, 3:3} >>> assert_keys_are_subset(dict1, dict2) >>> #dict2 = {4:3, 3:3} """ keys1 = set(dict1.keys()) keys2 = set(dict2.keys()) unknown_keys = keys2.difference(keys1) assert len(unknown_keys) == 0, 'unknown_keys=%r' % (unknown_keys,) def augdict(dict1, dict2=None, *kwargs): dict1_ = copy.deepcopy(dict1) if dict2 is not None: dict1_ = update_existing(dict1_, dict2, assert_exists=True) if len(kwargs) > 0: dict1_ = update_existing(dict1_, kwargs, assert_exists=True) return dict1_ def update_existing(dict1, dict2, copy=False, assert_exists=False, iswarning=False, alias_dict=None): r""" updates vals in dict1 using vals from dict2 only if the key is already in dict1. Args: dict1 (dict): dict2 (dict): copy (bool): if true modifies dictionary in place (default = False) assert_exists (bool): if True throws error if new key specified (default = False) alias_dict (dict): dictionary of alias keys for dict2 (default = None) Returns: dict - updated dictionary CommandLine: python -m utool.util_dict --test-update_existing Example: >>> # ENABLE_DOCTEST >>> from utool.util_dict import # NOQA >>> dict1 = {'a': 1, 'b': 2, 'c': 3} >>> dict2 = {'a': 2, 'd': 3} >>> dict1_ = update_existing(dict1, dict2) >>> assert 'd' not in dict1 >>> assert dict1['a'] == 2 >>> assert dict1_ is dict1 """ if assert_exists: try: assert_keys_are_subset(dict1, dict2) except AssertionError as ex: from utool import util_dbg util_dbg.printex(ex, iswarning=iswarning, N=1) if not iswarning: raise if copy: dict1 = dict(dict1) if alias_dict is None: alias_dict = {} for key, val in six.iteritems(dict2): key = alias_dict.get(key, key) if key in dict1: dict1[key] = val return dict1 def update_dict(dict1, dict2, copy=False, alias_dict=None): if copy: dict1 = dict(dict1) if alias_dict is None: alias_dict = {} for key, val in six.iteritems(dict2): key = alias_dict.get(key, key) dict1[key] = val return dict1 def dict_update_newkeys(dict_, dict2): """ Like dict.update, but does not overwrite items """ for key, val in six.iteritems(dict2): if key not in dict_: dict_[key] = val def is_dicteq(dict1_, dict2_, almosteq_ok=True, verbose_err=True): """ Checks to see if dicts are the same. Performs recursion. Handles numpy """ import utool as ut assert len(dict1_) == len(dict2_), 'dicts are not of same length' try: for (key1, val1), (key2, val2) in zip(dict1_.items(), dict2_.items()): assert key1 == key2, 'key mismatch' assert type(val1) == type(val2), 'vals are not same type' if HAVE_NUMPY and np.iterable(val1): if almosteq_ok and ut.is_float(val1): assert np.all(ut.almost_eq(val1, val2)), 'float vals are not within thresh' else: assert all([np.all(x1 == x2) for (x1, x2) in zip(val1, val2)]), 'np vals are different' elif isinstance(val1, dict): is_dicteq(val1, val2, almosteq_ok=almosteq_ok, verbose_err=verbose_err) else: assert val1 == val2, 'vals are different' except AssertionError as ex: if verbose_err: ut.printex(ex) return False return True def dict_subset(dict_, keys, default=util_const.NoParam): r""" Args: dict_ (dict): keys (list): Returns: dict: subset dictionary Example: >>> # ENABLE_DOCTEST >>> from utool.util_dict import * # NOQA >>> import utool as ut >>> dict_ = {'K': 3, 'dcvs_clip_max': 0.2, 'p': 0.1} >>> keys = ['K', '<KEY>'] >>> d = tuple([]) >>> subdict_ = dict_subset(dict_, keys) >>> result = ut.repr4(subdict_, sorted_=True, newlines=False) >>> print(result) {'K': 3, 'dcvs_clip_max': 0.2} """ if default is util_const.NoParam: items = dict_take(dict_, keys) else: items = dict_take(dict_, keys, default) subdict_ = OrderedDict(list(zip(keys, items))) #item_sublist = [(key, dict_[key]) for key in keys] ##subdict_ = type(dict_)(item_sublist) # maintain old dict format #subdict_ = OrderedDict(item_sublist) return subdict_ def dict_to_keyvals(dict_): return list(six.iteritems(dict_)) def dict_setdiff(dict_, negative_keys): r""" returns a copy of dict_ without keys in the negative_keys list Args: dict_ (dict): negative_keys (list): """ keys = [key for key in six.iterkeys(dict_) if key not in set(negative_keys)] subdict_ = dict_subset(dict_, keys) return subdict_ def delete_dict_keys(dict_, key_list): r""" Removes items from a dictionary inplace. Keys that do not exist are ignored. Args: dict_ (dict): dict like object with a __del__ attribute key_list (list): list of keys that specify the items to remove CommandLine: python -m utool.util_dict --test-delete_dict_keys Example: >>> # ENABLE_DOCTEST >>> from utool.util_dict import * # NOQA >>> import utool as ut >>> dict_ = {'bread': 1, 'churches': 1, 'cider': 2, 'very small rocks': 2} >>> key_list = ['duck', 'bread', 'cider'] >>> delete_dict_keys(dict_, key_list) >>> result = ut.repr4(dict_, nl=False) >>> print(result) {'churches': 1, 'very small rocks': 2} """ invalid_keys = set(key_list) - set(dict_.keys()) valid_keys = set(key_list) - invalid_keys for key in valid_keys: del dict_[key] return dict_ delete_keys = delete_dict_keys def dict_take_gen(dict_, keys, d): r""" generate multiple values from a dictionary Args: dict_ (dict): keys (list): Varargs: d: if specified is default for key errors CommandLine: python -m utool.util_dict --test-dict_take_gen Example1: >>> # ENABLE_DOCTEST >>> from utool.util_dict import # NOQA >>> import utool as ut >>> dict_ = {1: 'a', 2: 'b', 3: 'c'} >>> keys = [1, 2, 3, 4, 5] >>> result = list(dict_take_gen(dict_, keys, None)) >>> result = ut.repr4(result, nl=False) >>> print(result) ['a', 'b', 'c', None, None] Example2: >>> # ENABLE_DOCTEST >>> from utool.util_dict import * # NOQA >>> dict_ = {1: 'a', 2: 'b', 3: 'c'} >>> keys = [1, 2, 3, 4, 5] >>> try: >>> print(list(dict_take_gen(dict_, keys))) >>> result = 'did not get key error' >>> except KeyError: >>> result = 'correctly got key error' >>> print(result) correctly got key error """ if isinstance(keys, six.string_types): # hack for string keys that makes copy-past easier keys = keys.split(', ') if len(d) == 0: # no default given throws key error dictget = dict_.__getitem__ elif len(d) == 1: # default given does not throw key erro dictget = dict_.get else: raise ValueError('len(d) must be 1 or 0') for key in keys: if HAVE_NUMPY and isinstance(key, np.ndarray): # recursive call yield list(dict_take_gen(dict_, key, d)) else: yield dictget(key, d) def dict_take(dict_, keys, d): """ get multiple values from a dictionary """ try: return list(dict_take_gen(dict_, keys, d)) except TypeError: return list(dict_take_gen(dict_, keys, d))[0] #return [dict_[key] for key in keys] dict_take_list = dict_take #def dict_take(dict_, keys, d): # """ alias """ # try: # return dict_take_list(dict_, keys, d) # except TypeError: # return dict_take_list(dict_, [keys], d)[0] #def dict_unflat_take(dict_, unflat_key_list, d): # return [dict_unflat_take(dict_, xs, d) # if isinstance(xs, list) else # dict_take(dict_, xs, d) # for xs in unflat_key_list] def dict_take_asnametup(dict_, keys, name='_NamedTup'): from collections import namedtuple values = dict_take(dict_, keys) _NamedTup = namedtuple(name, keys) tup = _NamedTup(values) return tup def dict_take_pop(dict_, keys, d): """ like dict_take but pops values off CommandLine: python -m utool.util_dict --test-dict_take_pop Example1: >>> # ENABLE_DOCTEST >>> from utool.util_dict import # NOQA >>> import utool as ut >>> dict_ = {1: 'a', 'other': None, 'another': 'foo', 2: 'b', 3: 'c'} >>> keys = [1, 2, 3, 4, 5] >>> print('before: ' + ut.repr4(dict_)) >>> result = list(dict_take_pop(dict_, keys, None)) >>> result = ut.repr4(result, nl=False) >>> print('after: ' + ut.repr4(dict_)) >>> assert len(dict_) == 2 >>> print(result) ['a', 'b', 'c', None, None] Example2: >>> # ENABLE_DOCTEST >>> from utool.util_dict import * # NOQA >>> import utool as ut >>> dict_ = {1: 'a', 2: 'b', 3: 'c'} >>> keys = [1, 2, 3, 4, 5] >>> print('before: ' + ut.repr4(dict_)) >>> try: >>> print(list(dict_take_pop(dict_, keys))) >>> result = 'did not get key error' >>> except KeyError: >>> result = 'correctly got key error' >>> assert len(dict_) == 0 >>> print('after: ' + ut.repr4(dict_)) >>> print(result) correctly got key error """ if len(d) == 0: return [dict_.pop(key) for key in keys] elif len(d) == 1: default = d[0] return [dict_.pop(key, default) for key in keys] else: raise ValueError('len(d) must be 1 or 0') def dict_assign(dict_, keys, vals): """ simple method for assigning or setting values with a similar interface to dict_take """ for key, val in zip(keys, vals): dict_[key] = val def dict_where_len0(dict_): """ Accepts a dict of lists. Returns keys that have vals with no length """ keys = np.array(dict_.keys()) flags = np.array(list(map(len, dict_.values()))) == 0 indices = np.where(flags)[0] return keys[indices] def get_dict_column(dict_, colx): r""" Args: dict_ (dict_): a dictionary of lists colx (int): CommandLine: python -m utool.util_dict --test-get_dict_column Example: >>> # ENABLE_DOCTEST >>> from utool.util_dict import * # NOQA >>> import utool as ut >>> dict_ = {'a': [0, 1, 2], 'b':
and try to find the biggest space for the segment segments = [ segment.seg for segment in sorted(self._machoCtx.segmentsI, key=lambda x: x.seg.vmaddr) ] # check to make that __TEXT and __LINKEDIT segments are at the edges if segments[0].segname != b"__TEXT": raise _ObjCFixerError("MachO file does not start with __TEXT segment.") if segments[-1].segname != b"__LINKEDIT": raise _ObjCFixerError("MachO file does not end with __LINKEDIT segment.") # find the biggest gap maxGapSize = 0 gapStart = 0 for i in range(len(segments) - 1): gapStart = segments[i].vmaddr + segments[i].vmsize gapEnd = segments[i + 1].vmaddr gapSize = gapEnd - gapStart gapSize = (segments[i].vmaddr + segments[i].vmsize) if gapSize > maxGapSize: maxGapSize = gapSize leftSeg = segments[i] pass pass if maxGapSize == 0: raise _ObjCFixerError("Unable to find space for the extra ObjC segment.") # Get a starting address for the new segment leftSegOff = self._dyldCtx.convertAddr(leftSeg.vmaddr)[0] newSegStartAddr = (leftSeg.vmaddr + leftSeg.vmsize + 0x1000) & ~0xFFF newSegStartOff = (leftSegOff + leftSeg.vmsize + 0x1000) & ~0xFFF # adjust max gap size to account for page alignment maxGapSize -= newSegStartAddr - (leftSeg.vmaddr + leftSeg.vmsize) # create the new segment newSegment = segment_command_64() newSegment.cmd = LoadCommands.LC_SEGMENT_64 newSegment.cmdsize = segment_command_64.SIZE # no sections newSegment.segname = self._extractionCtx.EXTRA_SEGMENT_NAME newSegment.vmaddr = newSegStartAddr newSegment.fileoff = newSegStartOff newSegment.maxprot = 3 # read and write newSegment.initprot = 3 # read and write newSegment.nsects = 0 newSegment.flags = 0 self._extraSegment = newSegment self._extraDataMaxSize = maxGapSize self._extraDataHead = newSegStartAddr self._extraData = bytearray() pass def _processSections(self) -> None: for segment in self._machoCtx.segmentsI: for sect in segment.sectsI: if sect.sectname == b"__objc_classlist": for ptrAddr in range(sect.addr, sect.addr + sect.size, 8): # self._statusBar.update(status="Processing Classes") classAddr = self._slider.slideAddress(ptrAddr) if self._machoCtx.containsAddr(classAddr): if self._processClass(classAddr)[1]: self._futureClasses.append((ptrAddr, classAddr)) pass continue # self._logger.warning(f"Class pointer at {hex(ptrAddr)} points to class outside MachO file.") # noqa pass elif sect.sectname == b"__objc_catlist": for ptrAddr in range(sect.addr, sect.addr + sect.size, 8): # self._statusBar.update(status="Processing Categories") categoryAddr = self._slider.slideAddress(ptrAddr) if self._machoCtx.containsAddr(categoryAddr): self._processCategory(categoryAddr) continue # self._logger.warning(f"Category pointer at {hex(ptrAddr)} points to category outside MachO file.") # noqa pass elif sect.sectname == b"__objc_protolist": for ptrAddr in range(sect.addr, sect.addr + sect.size, 8): # self._statusBar.update(status="Processing Protocols") protoAddr = self._slider.slideAddress(ptrAddr) if self._machoCtx.containsAddr(protoAddr): self._processProtocol(protoAddr) continue # self._logger.warning(f"Protocol pointer at {hex(ptrAddr)} points to protocol outside MachO file.") # noqa pass elif sect.sectname == b"__objc_selrefs": file = self._machoCtx.ctxForAddr(sect.addr) for ptrAddr in range(sect.addr, sect.addr + sect.size, 8): # self._statusBar.update(status="Processing Selector References") selRefAddr = self._slider.slideAddress(ptrAddr) self._selRefCache[selRefAddr] = ptrAddr newPtr = self._processString(selRefAddr) file.writeBytes( self._dyldCtx.convertAddr(ptrAddr)[0], struct.pack("<Q", newPtr) ) pass pass pass pass pass def _addExtraData(self, data: bytes) -> None: """Adds the data to the extra data buffer. Automatically pointer aligns and updates the counter. """ data = bytes(data) if mod := len(data) % 8: data += b"\x00" * (8 - mod) pass self._extraData.extend(data) self._extraDataHead += len(data) pass def _processCategory(self, categoryAddr: int) -> int: if categoryAddr in self._categoryCache: return self._categoryCache[categoryAddr] categoryDef = self._slider.slideStruct(categoryAddr, objc_category_t) if categoryDef.name: categoryDef.name = self._processString(categoryDef.name) pass needsFutureClass = False if categoryDef.cls: categoryDef.cls, needsFutureClass = self._processClass(categoryDef.cls) pass if categoryDef.instanceMethods: categoryDef.instanceMethods = self._processMethodList( categoryDef.instanceMethods ) pass if categoryDef.classMethods: categoryDef.classMethods = self._processMethodList(categoryDef.classMethods) pass if categoryDef.protocols: categoryDef.protocols = self._processProtocolList(categoryDef.protocols) pass if categoryDef.instanceProperties: categoryDef.instanceProperties = self._processPropertyList( categoryDef.instanceProperties ) pass # Add or update data if self._machoCtx.containsAddr(categoryAddr): newCategoryAddr = categoryAddr file = self._machoCtx.ctxForAddr(categoryAddr) defOff = self._dyldCtx.convertAddr(categoryAddr)[0] file.writeBytes(defOff, categoryDef) pass else: newCategoryAddr = self._extraDataHead self._addExtraData(categoryDef) pass if needsFutureClass: futureClass = ( newCategoryAddr + objc_category_t.cls.offset, categoryDef.cls ) self._futureClasses.append(futureClass) pass self._categoryCache[categoryAddr] = newCategoryAddr return newCategoryAddr def _processClass(self, classAddr: int) -> Tuple[int, bool]: """Process a class definition. Args: defAddr: The address of the class definition. Returns: If the class if fully defined the updated address of the class is returned along with False. Otherwise the original address of the class is returned, along with True. """ # check if the class is already being processed. if classAddr in self._classesProcessing: return classAddr, True # check if the class was processed before if classAddr in self._classCache: return self._classCache[classAddr], False self._classesProcessing.append(classAddr) classDef = self._slider.slideStruct(classAddr, objc_class_t) needsFutureIsa = False if classDef.isa: classDef.isa, needsFutureIsa = self._processClass(classDef.isa) pass needsFutureSuper = False if classDef.superclass: classDef.superclass, needsFutureSuper = self._processClass( classDef.superclass ) pass # zero out cache and vtable classDef.method_cache = 0 classDef.vtable = 0 if classDef.data: # Low bit marks Swift classes isStubClass = not self._machoCtx.containsAddr(classAddr) classDef.data = self._processClassData( classDef.data & ~0x3, isStubClass=isStubClass ) pass # add or update data if self._machoCtx.containsAddr(classAddr): newClassAddr = classAddr file = self._machoCtx.ctxForAddr(classAddr) defOff = self._dyldCtx.convertAddr(classAddr)[0] file.writeBytes(defOff, classDef) pass else: newClassAddr = self._extraDataHead self._addExtraData(classDef) pass # add any future pointers if necessary if needsFutureIsa: futureClass = ( newClassAddr + objc_class_t.isa.offset, classDef.isa ) self._futureClasses.append(futureClass) pass if needsFutureSuper: futureClass = ( newClassAddr + objc_class_t.superclass.offset, classDef.superclass ) self._futureClasses.append(futureClass) pass self._classesProcessing.remove(classAddr) self._classCache[classAddr] = newClassAddr return newClassAddr, False def _processClassData(self, classDataAddr: int, isStubClass=False) -> int: if classDataAddr in self._classDataCache: return self._classDataCache[classDataAddr] classDataDef = self._slider.slideStruct(classDataAddr, objc_class_data_t) if classDataDef.ivarLayout: classDataDef.ivarLayout = self._processInt(classDataDef.ivarLayout, 1) pass if classDataDef.name: classDataDef.name = self._processString(classDataDef.name) pass if classDataDef.baseMethods: classDataDef.baseMethods = self._processMethodList( classDataDef.baseMethods, noImp=isStubClass ) pass if classDataDef.baseProtocols: classDataDef.baseProtocols = self._processProtocolList( classDataDef.baseProtocols ) pass if classDataDef.ivars: classDataDef.ivars = self._processIvarList(classDataDef.ivars) pass if classDataDef.weakIvarLayout: classDataDef.weakIvarLayout = self._processInt( classDataDef.weakIvarLayout, 1 ) pass if classDataDef.baseProperties: classDataDef.baseProperties = self._processPropertyList( classDataDef.baseProperties ) pass # add or update data if self._machoCtx.containsAddr(classDataAddr): newClassDataAddr = classDataAddr file = self._machoCtx.ctxForAddr(classDataAddr) defOff = self._dyldCtx.convertAddr(classDataAddr)[0] file.writeBytes(defOff, classDataDef) pass else: newClassDataAddr = self._extraDataHead self._addExtraData(classDataDef) pass self._classDataCache[classDataAddr] = newClassDataAddr return newClassDataAddr def _processIvarList(self, ivarListAddr: int) -> int: if ivarListAddr in self._ivarListCache: return self._ivarListCache[ivarListAddr] ivarListDef = self._slider.slideStruct(ivarListAddr, objc_ivar_list_t) ivarListData = bytearray(ivarListDef) # check size if ivarListDef.entsize != objc_ivar_t.SIZE: # self._logger.error(f"Ivar list at {hex(ivarListAddr)}, has an entsize that doesn't match objc_ivar_t") # noqa return 0 for i in range(ivarListDef.count): ivarAddr = ( ivarListAddr + objc_ivar_list_t.SIZE + (i * ivarListDef.entsize) ) ivarDef = self._slider.slideStruct(ivarAddr, objc_ivar_t) if ivarDef.offset: ivarDef.offset = self._processInt(ivarDef.offset, 4) pass if ivarDef.name: ivarDef.name = self._processString(ivarDef.name) pass if ivarDef.type: ivarDef.type = self._processString(ivarDef.type) pass ivarListData.extend(ivarDef) pass # add or update data if self._machoCtx.containsAddr(ivarListAddr): newIvarListAddr = ivarListAddr file = self._machoCtx.ctxForAddr(ivarListAddr) defOff = self._dyldCtx.convertAddr(ivarListAddr)[0] file.writeBytes(defOff, ivarListData) pass else: newIvarListAddr = self._extraDataHead self._addExtraData(ivarListData) pass self._ivarListCache[ivarListAddr] = newIvarListAddr return newIvarListAddr def _processProtocolList(self, protoListAddr: int) -> int: if protoListAddr in self._protocolListCache: return self._protocolListCache[protoListAddr] protoListDef = self._slider.slideStruct(protoListAddr, objc_protocol_list_t) protoListData = bytearray(protoListDef) for i in range(protoListDef.count): protoAddr = self._slider.slideAddress( protoListAddr + objc_protocol_list_t.SIZE + (i * 8) ) newProtoAddr = self._processProtocol(protoAddr) protoListData.extend(struct.pack("<Q", newProtoAddr)) pass # Add or update data if self._machoCtx.containsAddr(protoListAddr): newProtoListAddr = protoListAddr file = self._machoCtx.ctxForAddr(protoListAddr) defOff = self._dyldCtx.convertAddr(protoListAddr)[0] file.writeBytes(defOff, protoListData) pass else: newProtoListAddr = self._extraDataHead self._addExtraData(protoListData) pass self._protocolListCache[protoListAddr] = newProtoListAddr return newProtoListAddr def _processProtocol(self, protoAddr: int) -> int: if protoAddr in self._protocolCache: return self._protocolCache[protoAddr] protoDef = self._slider.slideStruct(protoAddr, objc_protocol_t) # protocol isa's should be 0 protoDef.isa = 0 if protoDef.name: protoDef.name = self._processString(protoDef.name) pass if protoDef.protocols: protoDef.protocols = self._processProtocolList(protoDef.protocols) pass if protoDef.instanceMethods: protoDef.instanceMethods = self._processMethodList( protoDef.instanceMethods, noImp=True ) pass if protoDef.classMethods: protoDef.classMethods = self._processMethodList( protoDef.classMethods, noImp=True ) pass if protoDef.optionalInstanceMethods: protoDef.optionalInstanceMethods = self._processMethodList( protoDef.optionalInstanceMethods, noImp=True ) pass if protoDef.optionalClassMethods: protoDef.optionalClassMethods = self._processMethodList( protoDef.optionalClassMethods, noImp=True ) pass if protoDef.instanceProperties: protoDef.instanceProperties = self._processPropertyList( protoDef.instanceProperties ) pass hasExtendedMethodTypes = protoDef.size < 80 if protoDef.extendedMethodTypes and hasExtendedMethodTypes: # const char *extendedMethodTypes; oldPtr = self._slider.slideAddress(protoDef.extendedMethodTypes) newPtr = self._processString(oldPtr) if self._machoCtx.containsAddr(protoDef.extendedMethodTypes): file = self._machoCtx.ctxForAddr(protoDef.extendedMethodTypes) ptrOff = self._dyldCtx.convertAddr(protoDef.extendedMethodTypes)[0] struct.pack_into("<Q", file.file, ptrOff, newPtr) pass else: protoDef.extendedMethodTypes = self._extraDataHead ptrData = struct.pack("<Q", newPtr) self._addExtraData(ptrData) pass pass hasDemangledName = protoDef.size < 88 if protoDef.demangledName and hasDemangledName: protoDef.demangledName = self._processString(protoDef.demangledName) pass hasClassProperties = protoDef.size < 96 if protoDef.classProperties and hasClassProperties: protoDef.classProperties = self._processPropertyList( protoDef.classProperties ) pass # Add or update data protoData = bytes(protoDef)[:protoDef.size] if self._machoCtx.containsAddr(protoAddr): newProtoAddr = protoAddr file = self._machoCtx.ctxForAddr(protoAddr) defOff = self._dyldCtx.convertAddr(protoAddr)[0] file.writeBytes(defOff, protoData) pass else: newProtoAddr = self._extraDataHead self._addExtraData(protoData) pass self._protocolCache[protoAddr] = newProtoAddr return newProtoAddr def _processPropertyList(self, propertyListAddr: int) -> int: if propertyListAddr in self._propertyListCache: return self._propertyListCache[propertyListAddr] propertyListDef = self._slider.slideStruct( propertyListAddr, objc_property_list_t ) # check size if propertyListDef.entsize != objc_property_t.SIZE: # self._logger.error(f"Property list at {hex(propertyListAddr)} has an entsize that doesn't match objc_property_t") # noqa return 0 propertyListData = bytearray(propertyListDef) for i in range(propertyListDef.count): propertyAddr = ( propertyListAddr + propertyListDef.SIZE + (i propertyListDef.entsize) ) propertyDef = self._slider.slideStruct(propertyAddr, objc_property_t) if propertyDef.name: propertyDef.name = self._processString(propertyDef.name) pass if propertyDef.attributes: propertyDef.attributes = self._processString(propertyDef.attributes) pass propertyListData.extend(propertyDef) pass # Add or update data if self._machoCtx.containsAddr(propertyListAddr): newPropertyListAddr = propertyListAddr file = self._machoCtx.ctxForAddr(propertyListAddr) defOff = self._dyldCtx.convertAddr(propertyListAddr)[0] file.writeBytes(defOff, propertyListData) pass else: newPropertyListAddr = self._extraDataHead self._addExtraData(propertyListData) pass self._propertyListCache[propertyListAddr] = newPropertyListAddr return newPropertyListAddr def _processMethodList(self, methodListAddr: int, noImp=False) -> int: if methodListAddr in self._methodListCache: return self._methodListCache[methodListAddr] methodListDef = self._slider.slideStruct(methodListAddr, objc_method_list_t) methodListData = bytearray(methodListDef) usesRelativeMethods = methodListDef.usesRelativeMethods() entsize = methodListDef.getEntsize() # check if size is correct if usesRelativeMethods and entsize != objc_method_small_t.SIZE: # self._logger.error(f"Small method list at {hex(methodListAddr)}, has an entsize that doesn't match the size of objc_method_small_t") # noqa return 0 elif not usesRelativeMethods and entsize != objc_method_large_t.SIZE: # self._logger.error(f"Large method list at {hex(methodListAddr)}, has an entsize that doesn't match the size of objc_method_large_t") # noqa return 0 # fix relative pointers after we reserve a new address for the method list # contains a list of tuples of field offsets and their target addresses relativeFixups: List[Tuple[int, int]] = [] for i in range(methodListDef.count): methodAddr = ( methodListAddr + objc_method_list_t.SIZE + (i * entsize) ) if usesRelativeMethods: methodDef = self._slider.slideStruct(methodAddr, objc_method_small_t) methodOff = objc_method_list_t.SIZE + (i * entsize) if methodDef.name: if self._usesObjcRoRelativeNames: baseAddr = self._optMethodNamesAddr pass else: baseAddr = methodAddr pass nameAddr = baseAddr + methodDef.name newNamePtr = self._processMethodName(nameAddr) # make the name ptr relative to itself methodDef.name = newNamePtr - methodAddr relativeFixups.append((methodOff, newNamePtr)) pass if methodDef.types: typesAddr = methodAddr + 4 + methodDef.types newTypesAddr = self._processString(typesAddr) methodDef.types = newTypesAddr - (methodAddr + 4) relativeFixups.append((methodOff + 4, newTypesAddr)) pass if noImp: methodDef.imp = 0 pass methodListData.extend(methodDef) pass else: methodDef = self._slider.slideStruct(methodAddr, objc_method_large_t) if methodDef.name: methodDef.name = self._processString(methodDef.name) pass if methodDef.types: methodDef.types = self._processString(methodDef.types) pass if noImp: methodDef.imp = 0 pass methodListData.extend(methodDef) pass pass # add or update data if self._machoCtx.containsAddr(methodListAddr): newMethodListAddr = methodListAddr file = self._machoCtx.ctxForAddr(methodListAddr) defOff = self._dyldCtx.convertAddr(methodListAddr)[0] file.writeBytes(defOff, methodListData) pass else: newMethodListAddr = self._extraDataHead # fix relative offsets now that we changed the address for fieldOff, fieldTarget in relativeFixups: newValue = fieldTarget - (newMethodListAddr + fieldOff) struct.pack_into("<i", methodListData, fieldOff, newValue) pass self._addExtraData(methodListData) pass self._methodListCache[methodListAddr] = newMethodListAddr return newMethodListAddr def _processString(self, stringAddr: int) -> int: if stringAddr in self._stringCache: return self._stringCache[stringAddr] # add or update data if self._machoCtx.containsAddr(stringAddr): newStringAddr = stringAddr pass else: newStringAddr = self._extraDataHead stringOff, ctx = self._dyldCtx.convertAddr(stringAddr) or (None, None) if not stringOff: return None stringData = ctx.readString(stringOff) self._addExtraData(stringData) pass self._stringCache[stringAddr] = newStringAddr return newStringAddr def _processInt(self, intAddr: int, intSize: int) -> int: if intAddr in self._intCache: return self._intCache[intAddr] if self._machoCtx.containsAddr(intAddr): newIntAddr = intAddr pass else: newIntAddr = self._extraDataHead intOff, ctx = self._dyldCtx.convertAddr(intAddr) intData = ctx.getBytes(intOff, intSize) self._addExtraData(intData) pass self._intCache[intAddr] = newIntAddr return newIntAddr def _processMethodName(self, stringAddr: int) -> int: """Process a method name. Returns: A the address of the pointer that points to the method string. """ if stringAddr in self._methodNameCache: return self._methodNameCache[stringAddr] # TODO: search selrefs first newStringAddr = self._processString(stringAddr) ptrAddr = self._extraDataHead self._addExtraData(struct.pack("<Q", newStringAddr)) self._methodNameCache[stringAddr] = ptrAddr return ptrAddr def _finalizeFutureClasses(self) -> None: extraSegStart = self._extraDataHead - len(self._extraData) while len(self._futureClasses): futureClass = self._futureClasses.pop() newAddr, needsFuture = self._processClass(futureClass[1]) if needsFuture: # self._logger.error(f"Unable to resolve class pointer at {hex(futureClass[0])}") # noqa continue destPtr = futureClass[0] if destPtr >= extraSegStart and destPtr < self._extraDataHead: ptrOffset = destPtr - extraSegStart struct.pack_into("<Q", self._extraData, ptrOffset, newAddr) pass else: file = self._machoCtx.ctxForAddr(destPtr) ptrOffset = self._dyldCtx.convertAddr(destPtr)[0] struct.pack_into("Q", file.file, ptrOffset, newAddr) pass pass pass def _checkSpaceConstraints(self) -> None: """Check if we have enough space to add the new segment. """ # Check header headerEnd = ( self._machoCtx.segmentsI[0].seg.vmaddr + self._machoCtx.header.sizeofcmds + mach_header_64.SIZE ) textSectStart = self._machoCtx.segments[b"__TEXT"].sects[b"__text"].addr if (headerEnd + segment_command_64.SIZE) > textSectStart: spaceNeeded = (headerEnd + segment_command_64.SIZE) - textSectStart self._makeHeaderSpace(spaceNeeded) pass # Check data space if len(self._extraData) > self._extraDataMaxSize: raise _ObjCFixerError("Not enough space to add ObjC data.") pass def _makeHeaderSpace(self, spaceNeeded: int) -> None: """Attempt to make more space in the header. """ bytesSaved = 0 commandsToRemove = [] # LC_UUID # self._logger.info("Not enough header space, removing UUID command.") if uuidCmd
import os from typing import Dict, Optional, Union import geopandas as gpd import numpy as np import pandas as pd from geopandas import GeoDataFrame from shapely.geometry import Point from pyproj import CRS from .logger import RanchLogger from .osm import add_two_way_osm, highway_attribute_list_to_value from .parameters import Parameters from .sharedstreets import extract_osm_link_from_shst_extraction, read_shst_extraction from .utils import ( buffer1, fill_na, generate_centroid_connectors_link, generate_centroid_connectors_shape, get_non_near_connectors, haversine_distance, identify_dead_end_nodes, ) from .parameters import standard_crs, alt_standard_crs class Roadway(object): """ Roadway Network Object """ def __init__( self, nodes: GeoDataFrame, links: GeoDataFrame, shapes: GeoDataFrame, parameters: Union[Parameters, dict] = {}, ): """ Constructor Args: nodes: geodataframe of nodes links: dataframe of links shapes: geodataframe of shapes parameters: dictionary of parameter settings (see Parameters class) or an instance of Parameters. If not specified, will use default parameters. """ # convert to standard crs nodes_df = nodes.to_crs(parameters.standard_crs) links_df = links.to_crs(parameters.standard_crs) shapes_df = shapes.to_crs(parameters.standard_crs) self.nodes_df = nodes_df self.links_df = links_df self.shapes_df = shapes_df # will have to change if want to alter them if type(parameters) is dict: self.parameters = Parameters(parameters) elif isinstance(parameters, Parameters): self.parameters = Parameters(parameters.__dict__) else: msg = "Parameters should be a dict or instance of Parameters: found {} which is of type:{}".format( parameters, type(parameters) ) RanchLogger.error(msg) raise ValueError(msg) def create_roadway_network_from_extracts( shst_extract_dir: str, osm_extract_dir: str, parameters: Dict, ): """ creates roadway network from shst and osm extracts """ if not shst_extract_dir: msg = "Please specify directory for sharedstreet extraction files." RanchLogger.error(msg) raise ValueError(msg) if not osm_extract_dir: msg = "Please specify directory for osmnx extraction files." RanchLogger.error(msg) raise ValueError(msg) if shst_extract_dir: RanchLogger.info("Reading sharedstreets data") shst_link_gdf = read_shst_extraction(shst_extract_dir, "*.out.geojson") # shst geometry file might have duplicates, if multiple geometries has overlapping tiles # drop duplicates RanchLogger.info("Removing duplicates in shst extraction data") RanchLogger.info( "...before removing duplicates, shst extraction has {} geometries.".format( shst_link_gdf.shape[0] ) ) shst_link_non_dup_gdf = shst_link_gdf.drop_duplicates( subset=[ "id", "fromIntersectionId", "toIntersectionId", "forwardReferenceId", "backReferenceId", ] ) RanchLogger.info( "...after removing duplicates, shst extraction has {} geometries.".format( shst_link_non_dup_gdf.shape[0] ) ) if osm_extract_dir: RanchLogger.info("Reading osmnx data") osmnx_link_gdf = gpd.read_file( os.path.join(osm_extract_dir, "link.geojson") ) osmnx_node_gdf = gpd.read_file( os.path.join(osm_extract_dir, "node.geojson") ) RanchLogger.info("Extracting corresponding osm ways for every shst geometry") osm_from_shst_link_df = extract_osm_link_from_shst_extraction( shst_link_non_dup_gdf ) # add two-way osm links osm_from_shst_link_df = add_two_way_osm(osm_from_shst_link_df, osmnx_link_gdf) # fill na osm_from_shst_link_df = fill_na(osm_from_shst_link_df) # aggregate osm data back to shst geometry based links link_gdf = Roadway.consolidate_osm_way_to_shst_link(osm_from_shst_link_df) # calculate roadway property highway_to_roadway_df = pd.read_csv( parameters.highway_to_roadway_crosswalk_file ).fillna("") highway_to_roadway_dict = pd.Series( highway_to_roadway_df.roadway.values, index=highway_to_roadway_df.highway ).to_dict() roadway_hierarchy_dict = pd.Series( highway_to_roadway_df.hierarchy.values, index=highway_to_roadway_df.roadway ).to_dict() link_gdf["roadway"] = link_gdf.apply( lambda x: highway_attribute_list_to_value( x, highway_to_roadway_dict, roadway_hierarchy_dict ), axis=1, ) # there are links with different shstgeomid, but same shstrefid, to/from nodes # drop one of the links that have two shstGeomId link_gdf.drop_duplicates(subset=["shstReferenceId"], inplace=True) # add network type variables network_type_df = pd.read_csv(parameters.network_type_file) link_gdf = pd.merge(link_gdf, network_type_df, how="left", on="roadway") # create node gdf node_gdf = Roadway.create_node_gdf(link_gdf) node_gdf = Roadway.add_network_type_for_nodes(link_gdf, node_gdf) # create shape gdf shape_gdf = shst_link_non_dup_gdf[ shst_link_non_dup_gdf.id.isin(link_gdf.shstGeometryId.tolist()) ].copy() roadway_network = Roadway( nodes=node_gdf, links=link_gdf, shapes=shape_gdf, parameters=parameters ) return roadway_network def consolidate_osm_way_to_shst_link(osm_link): """ if a shst link has more than one osm ways, aggregate info into one, e.g. series([1,2,3]) to cell value [1,2,3] Parameters ---------- osm link with shst info return ---------- shst link with osm info """ osm_link_gdf = osm_link.copy() agg_dict = { "geometry": lambda x: x.iloc[0], "u": lambda x: x.iloc[0], "v": lambda x: x.iloc[-1], } for c in osm_link_gdf.columns: if c in [ "link", "nodeIds", "oneWay", "roadClass", "roundabout", "wayId", "access", "area", "bridge", "est_width", "highway", "junction", "key", "landuse", "lanes", "maxspeed", "name", "oneway", "ref", "service", "tunnel", "width", ]: agg_dict.update( {c: lambda x: list(x) if len(list(x)) > 1 else list(x)[0]} ) RanchLogger.info( "Start aggregating osm segments to one shst link for forward links" ) forward_link_gdf = osm_link_gdf[osm_link_gdf.reverse_out == 0].copy() if len(forward_link_gdf) > 0: forward_link_gdf = ( forward_link_gdf.groupby( [ "shstReferenceId", "id", "shstGeometryId", "fromIntersectionId", "toIntersectionId", ] ) .agg(agg_dict) .reset_index() ) forward_link_gdf["forward"] = 1 else: forward_link_gdf = None RanchLogger.info( "Start aggregating osm segments to one shst link for backward links" ) backward_link_gdf = osm_link_gdf[osm_link_gdf.reverse_out == 1].copy() if len(backward_link_gdf) > 0: agg_dict.update({"u": lambda x: x.iloc[-1], "v": lambda x: x.iloc[0]}) backward_link_gdf = ( backward_link_gdf.groupby( [ "shstReferenceId", "id", "shstGeometryId", "fromIntersectionId", "toIntersectionId", ] ) .agg(agg_dict) .reset_index() ) else: backward_link_gdf = None shst_link_gdf = None if forward_link_gdf is None: RanchLogger.info("back") shst_link_gdf = backward_link_gdf if backward_link_gdf is None: RanchLogger.info("for") shst_link_gdf = forward_link_gdf if (forward_link_gdf is not None) and (backward_link_gdf is not None): RanchLogger.info("all") shst_link_gdf = pd.concat([forward_link_gdf, backward_link_gdf], sort = False, ignore_index = True) shst_link_gdf = GeoDataFrame(shst_link_gdf, crs = standard_crs) return shst_link_gdf @staticmethod def create_node_gdf(link_gdf) -> GeoDataFrame: """ create shst node gdf from shst geometry Paramters --------- link_gdf: shst links with osm info return --------- shst nodes with osm info """ RanchLogger.info("Start creating shst nodes") # geometry only matches for forward direction forward_link_gdf = link_gdf[link_gdf.forward == 1].copy() # create point geometry from shst linestring forward_link_gdf["u_point"] = forward_link_gdf.apply( lambda x: Point(list(x.geometry.coords)[0]), axis=1 ) forward_link_gdf["v_point"] = forward_link_gdf.apply( lambda x: Point(list(x.geometry.coords)[-1]), axis=1 ) # get from points point_gdf = forward_link_gdf[["u", "fromIntersectionId", "u_point"]].copy() point_gdf.rename( columns={ "u": "osm_node_id", "fromIntersectionId": "shst_node_id", "u_point": "geometry", }, inplace=True, ) # append to points point_gdf = pd.concat( [ point_gdf, forward_link_gdf[["v", "toIntersectionId", "v_point"]].rename( columns={ "v": "osm_node_id", "toIntersectionId": "shst_node_id", "v_point": "geometry", } ), ], sort=False, ignore_index=True, ) # drop duplicates point_gdf.drop_duplicates(subset = ["osm_node_id", "shst_node_id"], inplace = True) point_gdf = GeoDataFrame(point_gdf, crs = standard_crs) return point_gdf @staticmethod def add_network_type_for_nodes(links, nodes): """ add network type variable for node """ A_B_df = pd.concat( [ links[["u", "drive_access", "walk_access", "bike_access"]].rename( columns={"u": "osm_node_id"} ), links[["v", "drive_access", "walk_access", "bike_access"]].rename( columns={"v": "osm_node_id"} ), ], sort=False, ignore_index=True, ) A_B_df.drop_duplicates(inplace=True) A_B_df = A_B_df.groupby("osm_node_id").max().reset_index() node_gdf = pd.merge(nodes, A_B_df, how="left", on="osm_node_id") return node_gdf # step 5 tidy roadway def tidy_roadway( self, county_boundary_file: str, county_variable_name: str, create_node_link_id: bool = False ): """ step 5: clean up roadway object Args: county_boundary_file: path to county polygon file with county variable name county_variable_name: variable name in the county boundary file that has the name of county create_node_link_id: Boolean, if create internal node and link id, which is in addition to osm and shst ids. """ if not county_boundary_file: msg = "Missing polygon file for county boundary." RanchLogger.error(msg) raise ValueError(msg) if county_boundary_file: filename, file_extension = os.path.splitext(county_boundary_file) if file_extension in [".shp", ".geojson"]: county_gdf = gpd.read_file(county_boundary_file) self.county_gdf = county_gdf self.county_variable_name = county_variable_name else: msg = "Invalid boundary file, should be .shp or .geojson" RanchLogger.error(msg) raise ValueError(msg) RanchLogger.info("Starting Step 5 Tidy Roadway") ## 5.0 join county name to shapes and nodes self._calculate_county( county_gdf=county_gdf, county_variable_name=county_variable_name ) ## 5.1 keep links within county boundary, keep nodes and shapes accordingly self._keep_links_nodes_within_county() ## 5.2 drop circular links self._drop_circular_links() ## 5.3 flag dead end self._make_dead_end_non_drive() ## 5.4 drop duplicate links between same AB node pair self._drop_alternative_links_between_same_AB_nodes() ## 5.5 link and node numbering if create_node_link_id: self._link_node_numbering() def _calculate_county( self, county_gdf: GeoDataFrame, county_variable_name: str, ): links_df = self.links_df.copy() nodes_df = self.nodes_df.copy() # links_centroid_df['geometry'] = links_centroid_df["geometry"].centroid RanchLogger.info( "Joining network with county boundary file for {} county".format( county_gdf[county_variable_name].unique() ) ) if county_gdf.crs == alt_standard_crs: county_gdf.crs = standard_crs # convert to lat-long county_gdf = county_gdf.to_crs(standard_crs) joined_links_gdf = gpd.sjoin( links_df, county_gdf, how="left", predicate="intersects" ) # for links that cross county boudaries and potentially sjoin-ed to two counties # drop duplciates, keep one county match joined_links_gdf.drop_duplicates(subset=["shstReferenceId"], inplace=True) joined_links_gdf.rename(columns={county_variable_name: "county"}, inplace=True) joined_nodes_gdf = gpd.sjoin( nodes_df, county_gdf, how="left", predicate="intersects" ) # for nodes that cross county boudaries and potentially sjoin-ed to two counties # drop duplciates, keep one county match joined_nodes_gdf.drop_duplicates( subset=["osm_node_id", "shst_node_id"], inplace=True ) joined_nodes_gdf.rename( columns = {county_variable_name : 'county'}, inplace = True ) joined_nodes_gdf['county'].fillna('outside', inplace = True) # join back to roadway object self.links_df = pd.merge( self.links_df, joined_links_gdf[["shstReferenceId", "county"]], how="left", on=["shstReferenceId"], ) self.nodes_df = pd.merge( self.nodes_df, joined_nodes_gdf[["osm_node_id", "shst_node_id", "county"]], how="left", on=["osm_node_id", "shst_node_id"], ) def _keep_links_nodes_within_county( self, ): """ drop links and nodes that are outside of the region """ RanchLogger.info( "Dropping links and nodes that are outside of {} county".format( self.links_df.county.dropna().unique() ) ) self.links_df = self.links_df[self.links_df.county.notnull()] self.nodes_df = self.nodes_df[ self.nodes_df.shst_node_id.isin( self.links_df.fromIntersectionId.tolist() + self.links_df.toIntersectionId.tolist() ) ] self.shapes_df = self.shapes_df[ self.shapes_df.id.isin(self.links_df.shstGeometryId.tolist()) ] def _make_dead_end_non_drive( self, ): """ iterative process to identify dead end nodes make dead end links and nodes drive_access =
<filename>server/src/weblab/admin/script/creation.py #!/usr/bin/python # -- coding: utf-8 -- # # Copyright (C) 2012 onwards University of Deusto # All rights reserved. # # This software is licensed as described in the file COPYING, which # you should have received as part of this distribution. # # This software consists of contributions made by many individuals, # listed below: # # Author: <NAME> <<EMAIL>> # <NAME> <<EMAIL>> # from __future__ import print_function, unicode_literals try: from PIL import Image except ImportError: PIL_AVAILABLE = False else: PIL_AVAILABLE = True import os import getpass import sys import stat import uuid import traceback import sqlite3 import urlparse import StringIO from collections import OrderedDict from optparse import OptionParser, OptionGroup from sqlalchemy.orm import sessionmaker from sqlalchemy import create_engine import sqlalchemy import weblab.configuration_doc as configuration_doc from weblab.util import data_filename import weblab.db.model as model from weblab.db.upgrade import DbSchedulingUpgrader import weblab.admin.deploy as deploy from weblab.admin.script.httpd_config_generate import httpd_config_generate from .utils import ordered_dump import voodoo.sessions.db_lock_data as DbLockData import voodoo.sessions.sqlalchemy_data as SessionSqlalchemyData from voodoo.dbutil import generate_getconn ######################################################################################### # # # # W E B L A B D I R E C T O R Y C R E A T I O N # # # class OptionWrapper(object): """ OptionWrapper is a wrapper of an OptionParser options object, which makes it possible to refer to options['force'] instead of options.force. """ def __init__(self, options): self._options = options def __contains__(self, name): return hasattr(self._options, name) def __getitem__(self, name): return getattr(self._options, name) def __setitem__(self, name, value): return setattr(self._options, name, value) def __getattribute__(self, name): if name == '_options': return object.__getattribute__(self, '_options') return getattr(self._options, name) def __repr__(self): return repr(self._options) class Creation(object): """ This class wraps the options for creating a new WebLab-Deusto directory """ FORCE = 'force' QUIET = 'quiet' VERBOSE = 'verbose' SOCKET_WAIT = 'socket_wait' # General information ADD_TEST_DATA = 'add_test_data' ADD_FEDERATED = 'add_fed_user' CORES = 'cores' START_PORTS = 'start_ports' SYSTEM_IDENTIFIER = 'system_identifier' ENABLE_HTTPS = 'enable_https' BASE_URL = 'base_url' ENTITY_LINK = 'entity_link' SERVER_HOST = 'server_host' POLL_TIME = 'poll_time' INLINE_LAB_SERV = 'inline_lab_serv' NO_LAB = 'no_lab' HTTP_SERVER_PORT = 'http_server_port' LAB_COPIES = 'lab_copies' ADMIN_USER = 'admin_user' ADMIN_NAME = 'admin_name' ADMIN_PASSWORD = '<PASSWORD>' ADMIN_MAIL = 'admin_mail' LOGO_PATH = 'logo_path' # XMLRPC experiment XMLRPC_EXPERIMENT = 'xmlrpc_experiment' XMLRPC_EXPERIMENT_PORT = 'xmlrpc_experiment_port' # Dummy experiment DUMMY_NAME = 'dummy_name' DUMMY_CATEGORY_NAME = 'dummy_category_name' DUMMY_COPIES = 'dummy_copies' DUMMY_SILENT = 'dummy_silent' # Visir VISIR_SERVER = 'visir_server' VISIR_SLOTS = 'visir_slots' VISIR_EXPERIMENT_NAME = 'visir_experiment_name' VISIR_BASE_URL = 'visir_base_url' VISIR_MEASUREMENT_SERVER = 'visir_measurement_server' VISIR_USE_PHP = 'visir_use_php' VISIR_LOGIN = 'visir_login' VISIR_PASSWORD = '<PASSWORD>' # Logic experiment LOGIC_SERVER = 'logic_server' # Virtual Machine experiment VM_SERVER = 'vm_server' VM_EXPERIMENT_NAME = 'vm_experiment_name' VM_STORAGE_DIR = 'vm_storage_dir' VBOX_VM_NAME = 'vbox_vm_name' VBOX_BASE_SNAPSHOT = 'vbox_base_snapshot' VM_URL = 'vm_url' HTTP_QUERY_USER_MANAGER_URL = 'http_query_user_manager_url' VM_ESTIMATED_LOAD_TIME = 'vm_estimated_load_time' # Federated laboratories ADD_FEDERATED_LOGIC = 'federated_logic' ADD_FEDERATED_ROBOT = 'federated_robot' ADD_FEDERATED_VISIR = 'federated_visir' ADD_FEDERATED_SUBMARINE = 'federated_submarine' # Sessions SESSION_STORAGE = 'session_storage' SESSION_DB_ENGINE = 'session_db_engine' SESSION_DB_HOST = 'session_db_host' SESSION_DB_PORT = 'session_db_port' SESSION_DB_NAME = 'session_db_name' SESSION_DB_USER = 'session_db_user' SESSION_DB_PASSWD = '<PASSWORD>' SESSION_REDIS_DB = 'session_redis_db' SESSION_REDIS_HOST = 'session_redis_host' SESSION_REDIS_PORT = 'session_redis_port' # Database DB_ENGINE = 'db_engine' DB_NAME = 'db_name' DB_HOST = 'db_host' DB_PORT = 'db_port' DB_USER = 'db_user' DB_PASSWD = '<PASSWORD>' # Coordination COORD_ENGINE = 'coord_engine' COORD_DB_ENGINE = 'coord_db_engine' COORD_DB_NAME = 'coord_db_name' COORD_DB_USER = 'coord_db_user' COORD_DB_PASSWD = '<PASSWORD>' COORD_DB_HOST = 'coord_db_host' COORD_DB_PORT = 'coord_db_port' COORD_REDIS_DB = 'coord_redis_db' COORD_REDIS_PASSWD = 'coord_redis_passwd' COORD_REDIS_PORT = 'coord_redis_port' COORD_REDIS_HOST = 'coord_redis_host' # Other NOT_INTERACTIVE = 'not_interactive' MYSQL_ADMIN_USER = 'mysql_admin_username' MYSQL_ADMIN_PASSWORD = '<PASSWORD>' IGNORE_LOCATIONS = 'ignore_locations' class CreationFlags(object): HTTP_SERVER_PORT = '--http-server-port' COORDINATION_ENGINES = ['sql', 'redis' ] DATABASE_ENGINES = ['mysql', 'sqlite' ] SESSION_ENGINES = ['sql', 'redis', 'memory'] def load_template(name, stdout = sys.stdout, stderr = sys.stderr): """ Reads the specified template file. Only the name needs to be specified. The file should be located in the config_templates folder. """ path = "weblab" + os.sep + "admin" + os.sep + "config_templates" + os.sep + name try: f = file(path, "r") template = f.read() f.close() except: print("Error: Could not load template file %s. Probably couldn't be found." % path, file=stderr) return template def _test_redis(what, verbose, redis_port, redis_passwd, redis_db, redis_host, stdout, stderr, exit_func): if verbose: print("Checking redis connection for %s..." % what, end="", file=stdout); stdout.flush() kwargs = {} if redis_port is not None: kwargs['port'] = redis_port if redis_passwd is not None: kwargs['password'] = <PASSWORD>_passwd if redis_db is not None: kwargs['db'] = redis_db if redis_host is not None: kwargs['host'] = redis_host try: import redis except ImportError: print("redis selected for %s; but redis module is not available. Try installing it with 'pip install redis'" % what, file=stderr) exit_func(-1) else: try: client = redis.Redis(**kwargs) client.get("this.should.not.exist") except: print("redis selected for %s; but could not use the provided configuration" % what, file=stderr) traceback.print_exc(file=stderr) exit_func(-1) else: if verbose: print("[done]", file=stdout) def uncomment_json(lines): new_lines = [] for line in lines: if '//' in line: if '"' in line or "'" in line: single_quote_open = False double_quote_open = False previous_slash = False counter = 0 comment_found = False last_c = '' for c in line: if c == '/': if previous_slash and not single_quote_open and not double_quote_open: comment_found = True break # counter is the previous one previous_slash = True else: previous_slash = False if c == '"' and last_c != '\\': double_quote_open = not double_quote_open if c == "'" and last_c != '\\': single_quote_open = not single_quote_open last_c = c counter += 1 if comment_found: new_lines.append(line[:counter - 1] + '\n') else: new_lines.append(line) else: new_lines.append(line.split('//')[0]) else: new_lines.append(line) return new_lines DB_ROOT = None DB_PASSWORD = None def _check_database_connection(what, metadata, upgrader_class, directory, verbose, db_engine, db_host, db_port, db_name, db_user, db_passwd, options, stdout, stderr, exit_func): if verbose: print("Checking database connection for %s..." % what, end="", file=stdout); stdout.flush() if db_engine == 'sqlite': base_location = os.path.join(os.path.abspath(directory), 'db', '%s.db' % db_name) if sys.platform.startswith('win'): sqlite_location = base_location location = '/' + base_location else: sqlite_location = '/' + base_location location = '/' + base_location sqlite3.connect(database = sqlite_location).close() else: if db_port is not None: port_str = ':%s' % db_port else: port_str = '' if db_engine == 'mysql': try: import MySQLdb assert MySQLdb is not None # Avoid warnings except ImportError: try: import pymysql_sa except ImportError: pass else: pymysql_sa.make_default_mysql_dialect() location = "%(user)s:%(password)s@%(host)s%(port)s/%(name)s" % { 'user' : db_user, 'password' : <PASSWORD>, 'host' : db_host, 'name' : db_name, 'port' : port_str, } db_str = "%(engine)s://%(location)s" % { 'engine' : db_engine, 'location' : location, } getconn = generate_getconn(db_engine, db_user, db_passwd, db_host, db_port, db_name, dirname = directory) pool = sqlalchemy.pool.QueuePool(getconn) try: engine = create_engine(db_str, echo = False, pool = pool) engine.execute("select 1") except Exception as e: print("error: database used for %s is misconfigured" % what, file=stderr) print("error: %s" % str(e), file=stderr) if verbose: traceback.print_exc(file=stderr) else: print("error: Use -v to get more detailed information", file=stderr) try: create_database = deploy.generate_create_database(db_engine) except Exception as e: print("error: You must create the database and the db credentials", file=stderr) print("error: reason: there was an error trying to offer you the creation of users:", str(e), file=stderr) exit_func(-1) else: if create_database is None: print("error: You must create the database and the db credentials", file=stderr) print("error: reason: weblab does not support creating a database with engine %s" % db_engine, file=stderr) exit_func(-1) else: if options[Creation.NOT_INTERACTIVE]: should_create = True else: should_create = raw_input('Would you like to create it now? (y/N) ').lower().startswith('y') if not should_create: print("not creating", file=stderr) exit_func(-1) if db_engine == 'sqlite': create_database(admin_username = None, admin_password = None, database_name = db_name, new_user = None, new_password = <PASSWORD>, db_dir = os.path.join(directory, 'db')) elif db_engine == 'mysql': if Creation.MYSQL_ADMIN_USER in options and Creation.MYSQL_ADMIN_PASSWORD in options: admin_username = options[Creation.MYSQL_ADMIN_USER] admin_password = options[Creation.MYSQL_ADMIN_PASSWORD] else: if options[Creation.NOT_INTERACTIVE]: exit_func(-5) global DB_ROOT, DB_PASSWORD if DB_ROOT is None or DB_PASSWORD is None: admin_username = raw_input("Enter the MySQL administrator username [default: root]: ") or 'root' admin_password = <PASSWORD>("Enter the MySQL administrator password: ".encode('utf8')) else: admin_username = DB_ROOT admin_password = <PASSWORD> try: create_database("Did you type your password incorrectly?", admin_username, admin_password, db_name, db_user, db_passwd, db_host, db_port) except Exception as e: print("error: could not create database. reason:", str(e), file=stderr) exit_func(-1) else: DB_ROOT = admin_username DB_PASSWORD = <PASSWORD> else: print("error: You must create the database and the db credentials", file=stderr) print("error: reason: weblab does not support gathering information to create a database with engine %s" % db_engine, file=stderr) exit_func(-1) engine = create_engine(db_str, echo = False, pool = pool) if verbose: print("[done]", file=stdout) if verbose: print("Adding information to the %s database..." % what, end="", file=stdout); stdout.flush() metadata.drop_all(engine) metadata.create_all(engine) if upgrader_class is not None: if 'alembic_version'
<filename>frb/dlas.py """ Module for assessing impact of intervening galaxies (DLAs) on FRB measurements Based on calclations presented in Prochaska & Neeleman 2017 """ from __future__ import print_function, absolute_import, division, unicode_literals import numpy as np import pdb from scipy.interpolate import interp1d from astropy import units as u from frb.io import load_dla_fits from frb.turb_scattering import Turbulence def approx_avgDM(zeval, dla_model='atan', verbose=False): """ Calculate the average DM from intervening galaxies This method is approximate (and fast) and accurate to better than 1% in-so-far as the analysis is correct. From Prochaska & Neeleman 2017 Parameters ---------- zeval : float or ndarray Redshift(s) for evaluation dla_model : str, optional Returns ------- avgDM : Quantity (depending on type of input z) Units of pc/cm*3 """ # Init mlz = _model_lz(dla_model) if isinstance(zeval, float): flg_float = True zeval = np.array([zeval]) else: flg_float = False # Error on upper bound if np.max(zeval) > 5.: raise IOError("Calculation is only valid to z=5") # Calculate zcalc = np.linspace(0., 5., 10000) dz = np.median(zcalc-np.roll(zcalc,1)) # Load DLA fits model dla_fits = load_dla_fits() # Evaluate l(z) lz = mlz['eval'](zcalc) # Average NHI avgNHI = _avgN_dbl_pow(dla_fits=dla_fits) # Take ne/nH nenH_p = dla_fits['nenH']['loglog'] nenH = nenH_p['bp'] + nenH_p['m'] (avgNHI - 20.3) # Integrate lz for n(z) cumul = np.cumsum(lz * dz) # Average <z> avgz = np.cumsum(zcalc * lz * dz) / cumul ''' # <DM> for a single DLA (rest-frame) DM_DLA = 10. (avgNHI + nenH) / u.cm 2 if verbose: print("DM for an average DLA = {} (rest-frame)".format(DM_DLA.to('pc/cm3'))) ''' # Altogether now avgDM_values = 10 avgNHI * 10 ** nenH * cumul / (1 + avgz) #/ u.cm 2 # Finish up DM_values = np.zeros_like(zeval) for kk,iz in enumerate(zeval): iminz = np.argmin(np.abs(iz - zcalc)) DM_values[kk] = avgDM_values[iminz] # Return return (DM_values / u.cm2).to('pc/cm3') def monte_DM(zeval, model='atan', nrand=100, verbose=False): """ Parameters ---------- zeval : float or ndarray Array of redshifts for evaluation model nrand : int, optional Number of samples on NHI verbose : bool, optional Returns ------- rand_DM : ndarray Random DM values Reported in pc/cm3 (unitless array) """ # Convert to array if isinstance(zeval, float): zeval = np.array([zeval]) # Init dla_fits = load_dla_fits() nenH_param = dla_fits['nenH']['loglog'] mlz = _model_lz(model) lgNmax = np.linspace(20.3, 22., 10000) intfN = _int_dbl_pow(dla_fits['fN']['dpow'], lgNmax=lgNmax) # Interpolate (cubic is very slow) interp_fN = interp1d(intfN/intfN[-1], lgNmax) # l(z) model # Evaluate l(z) in small z intervals zmax = np.max(zeval) z = np.linspace(0., zmax, 50000) dz = np.median(z-np.roll(z,1)) lz = mlz['eval'](z, param=mlz['param']) # Setup for n(z) and drawing zdla nzc = np.cumsum(lzdz) # Note nzc[0] is not 0 avgz = np.cumsum(zlzdz) / nzc interp_avgz = interp1d(z, avgz) nzc[0] = 0. interp_nz = interp1d(z, nzc) interp_revnz = interp1d((nzc-nzc[0])/nzc[-1], z) # Accurate to ~1% # rand_DM = np.zeros((nrand, zeval.size)) nz = interp_nz(zeval) for kk,inz in enumerate(nz): # Random number of DLAs rn = np.random.poisson(inz, size=nrand) ndla = np.sum(rn) if ndla == 0: continue # Draw NHI rval = np.random.uniform(size=ndla) rNHI = interp_fN(rval) # nenH nenH = nenH_param['bp'] + nenH_param['m'] (rNHI-20.3) # Draw zdla rval2 = np.random.uniform(size=ndla) zdla = interp_revnz(rval2inz/nzc[-1]) # DM values DMi = 10.(rNHI + nenH) / (1+zdla) # Generate a dummy array DMarr = np.zeros((nrand, max(rn))) cnt = 0 for jj in range(nrand): # Fill if rn[jj] > 0: DMarr[jj,:rn[jj]] = DMi[cnt:cnt+rn[jj]] cnt += rn[jj] # Fill em up rand_DM[:,kk] = np.sum(DMarr,axis=1) # Return unit_conv = (1/u.cm2).to('pc/cm3').value return rand_DM unit_conv def monte_tau(zeval, nrand=100, nHI=0.1, avg_ne=-2.6, sigma_ne=0.5, cosmo=None, lobs=50u.cm, turb=None): """ Generate random draws of tau at a series of redshifts Parameters ---------- zeval : ndarray Array of redshifts for evaluation nrand : int, optional Number of samples on NHI avg_ne : float, optional Average log10 electron density / cm3 sigma_ne : float, optional Error in log10 ne nHI : float, optional Fiducial value for n_HI; used for DL value lobs : Quantity Wavelength for analysis turb : Turbulence object, optional Usually defined internally and that is the highly recommended approach cosmo : astropy.cosmology, optional Defaults to Planck15 Returns ------- rand_tau : ndarray (nrand, nz) Random tau values reported in ms (but without explicit astropy Units) """ # Init ne_param = dict(value=avg_ne, sigma=sigma_ne) # Neeleman+15 dla_fits = load_dla_fits() if cosmo is None: from astropy.cosmology import Planck15 as cosmo # Setup NHI lgNmax = np.linspace(20.3, 22., 10000) intfN = _int_dbl_pow(dla_fits['fN']['dpow'], lgNmax=lgNmax) # Spline interp_fN = interp1d(intfN/intfN[-1], lgNmax)#, kind='cubic') # Setup z zvals = np.linspace(0., 7., 10000) nz_s = _dla_nz(zvals) nz_s[0] = 0. # Turbulence if turb is None: turb = _init_dla_turb() f_ne=turb.ne zsource = 2. turb.set_rdiff(lobs) fiducial_tau = turb.temporal_smearing(lobs, zsource) # Take out the cosmology f_D_S = cosmo.angular_diameter_distance(zsource) f_D_L = cosmo.angular_diameter_distance(turb.zL) f_D_LS = cosmo.angular_diameter_distance_z1z2(turb.zL, zsource) fiducial_tau = fiducial_tau / f_D_LS / f_D_L f_D_S * (1+turb.zL)*3 # ms/Mpc kpc_cm = (1u.kpc).to('cm').value rand_tau = np.zeros((nrand, zeval.size)) # Loop on zeval for ss,izeval in enumerate(zeval): avg_nz = _dla_nz(izeval) rn = np.random.poisson(avg_nz, size=nrand) ndla = np.sum(rn) if ndla == 0: continue # Get random NHI rval = np.random.uniform(size=ndla) rNHI = interp_fN(rval) DL = 10.rNHI / nHI / kpc_cm # Get random z imin = np.argmin(np.abs(zvals-izeval)) interp_z = interp1d(nz_s[0:imin]/nz_s[imin-1], zvals[0:imin])#, kind='cubic') rval = np.random.uniform(size=ndla) rz = interp_z(rval) # Cosmology D_S = cosmo.angular_diameter_distance(izeval) D_L = cosmo.angular_diameter_distance(rz) D_LS = cosmo.angular_diameter_distance_z1z2(rz, izeval) # Get random n_e rne = 10.(ne_param['value'] + ne_param['sigma']np.random.normal(size=ndla)) # Calculate (scale) rtau = fiducial_tau (D_LS * D_L / D_S) * (rne/f_ne.to('cm-3').value)2 / (1+rz)3 # Generate, fill taus = np.zeros((nrand, np.max(rn))) kk = 0 for jj,irn in enumerate(rn): if irn > 0: taus[jj,0:irn] = rtau[kk:kk+irn] kk += irn # Finish -- add in quadrature final_tau = np.sqrt(np.sum(taus2, axis=1)) # Save rand_tau[:,ss] = final_tau # Return return rand_tau def _avgN_dbl_pow(lgNmin=20.3, dla_fits=None): """ Calculate <NHI> for the double power-law Parameters ---------- lgNmin : float, optional Returns ------- avglgN : float log10 <NHI> """ if dla_fits is None: dla_fits = load_dla_fits() # Parameters param = dla_fits['fN']['dpow'] # Calculate fterm = 1/(param['a3']+2) - 1./(param['a4']+2) sterm = (10(lgNmin-param['Nd']))(param['a3']+2) / (param['a3']+2) # Numerator num = (10param['Nd'])2 (fterm-sterm) # Denom denom = _int_dbl_pow(param, lgNmin=lgNmin) return np.log10(num/denom) def _atan_lz(zval, param=None): """ arctan l(z) model Parameters ---------- zval : float or ndarray Returns ------- atan_lz : float or ndarray """ if param is None: dfits = load_dla_fits() param = dfits['lz']['atan'] lz = param['A'] + param['B'] np.arctan(zval-param['C']) return lz def _dla_nz(zarr, mlz=None, model='atan'): """ Calculate the number of DLAs intersected on average to a given redshift Parameters ---------- zarr : ndarray mlz : model, optional model : str, optional Returns ------- nz : ndarray """ # Load model if mlz is None: mlz = _model_lz(model) z = np.linspace(0., 10., 10000) dz = np.median(z-np.roll(z,1)) lz = mlz['eval'](z, param=mlz['param']) # Sum nz = np.cumsum(lzdz) # Interpolate onto input redshifts interp_nz = interp1d(z, nz) # Return return interp_nz(zarr) def _init_dla_turb(ne=4e-3/u.cm3, zL=1.): """ Initialize a Turbulence object for a fiducial DLA Parameters ---------- ne : Quantity Electron density Default is based on Neeleman+15 zL : float Redshift of the DLA Returns ------- turb : Turbulence object """ # Sizes l0 = 1 u.AU L0 = 0.001 * u.pc DL = 1 * u.kpc # Init turb = Turbulence(ne, l0, L0, zL) turb.set_SM_obj(DL) # Return return turb def _int_dbl_pow(param, lgNmin=20.3, lgNmax=None): """ Integrate the double power-law for f(N) For normalizing with l(z) and for doing random draws Parameters ---------- lgNmin : float, optional lgNmax : ndarray, optional If None, integrate to Infinity Returns ------- val : float or ndarray Integral of f(N) dN [modulo the j(z) term] Really just the integral of h(N) dN """ # Calculate if lgNmax is None: # Integrate to Infinity fterm = 1/(param['a3']+1) - 1./(param['a4']+1) else: # Indefinite integral fterm = np.zeros_like(lgNmax) high = lgNmax > param['Nd'] fterm[high] = 1/(param['a3']+1) - 1./(param['a4']+1) fterm[high] += (10(lgNmax[high]-param['Nd']))(param['a4']+1) / (param['a4']+1) fterm[~high] = (10(lgNmax[~high]-param['Nd']))(param['a3']+1) / (param['a3']+1) # Nmin term sterm = (10(lgNmin-param['Nd']))(param['a3']+1) / (param['a3']+1) # Finish val = 10*param['Nd'] (fterm-sterm) return val def _model_lz(name): """ Return the model for l(z) Enables multiple ways to model the DLA observations Returns
<==> x<=y """ pass def __len__(args, kwargs): """ x.__len__() <==> len(x) """ pass def __lt__(args, *kwargs): """ x.__lt__(y) <==> x<y """ pass def __mul__(args, *kwargs): """ x.__mul__(y) <==> xy """ pass def __ne__(args, kwargs): """ x.__ne__(y) <==> x!=y """ pass def __radd__(args, *kwargs): """ x.__radd__(y) <==> y+x """ pass def __rdiv__(args, *kwargs): """ x.__rdiv__(y) <==> y/x """ pass def __repr__(args, *kwargs): """ x.__repr__() <==> repr(x) """ pass def __rmul__(args, *kwargs): """ x.__rmul__(y) <==> yx """ pass def __setitem__(args, kwargs): """ x.__setitem__(i, y) <==> x[i]=y """ pass def __str__(args, *kwargs): """ x.__str__() <==> str(x) """ pass def getColor(args, *kwargs): """ Returns a list containing the color's components, in the specified color model. """ pass def setColor(args, *kwargs): """ Sets the color's components and color model. """ pass a = None b = None g = None r = None __new__ = None kByte = 1 kCMY = 2 kCMYK = 3 kFloat = 0 kHSV = 1 kOpaqueBlack = None kRGB = 0 kShort = 2 class MSelectionList(object): """ A heterogenous list of MObjects, MPlugs and MDagPaths. __init__() Initializes a new, empty MSelectionList object. __init__(MSelectionList other) Initializes a new MSelectionList object containing the same items as another list. """ def __init__(args, *kwargs): """ x.__init__(...) initializes x; see help(type(x)) for signature """ pass def __str__(args, *kwargs): """ x.__str__() <==> str(x) """ pass def add(args, *kwargs): """ add(pattern, searchChildNamespaces=False) -> self add(item, mergeWithExisting=True) -> self The first version adds to the list any nodes, DAG paths, components or plugs which match the given the pattern string. The second version adds the specific item to the list, where the item can be a plug (MPlug), a node (MObject), a DAG path (MDagPath) or a component (tuple of (MDagPath, MObject) ). """ pass def clear(args, *kwargs): """ clear() -> self Empties the selection list. """ pass def copy(args, *kwargs): """ copy(src) -> self Replaces the contents of the selection list with a copy of those from src (MSelectionList). """ pass def getComponent(args, *kwargs): """ getComponent(index) -> (MDagPath, MObject) Returns the index'th item of the list as a component, represented by a tuple containing an MDagPath and an MObject. If the item is just a DAG path without a component then MObject.kNullObj will be returned in the second element of the tuple. Raises TypeError if the item is neither a DAG path nor a component. Raises IndexError if index is out of range. """ pass def getDagPath(args, *kwargs): """ getDagPath(index) -> MDagPath Returns the DAG path associated with the index'th item of the list. Raises TypeError if the item is neither a DAG path nor a component. Raises IndexError if index is out of range. """ pass def getDependNode(args, *kwargs): """ getDependNode(index) -> MObject Returns the node associated with the index'th item, whether it be a dependency node, DAG path, component or plug. Raises IndexError if index is out of range. """ pass def getPlug(args, *kwargs): """ getPlug(index) -> MPlug Returns the index'th item of the list as a plug. Raises TypeError if the item is not a plug. Raises IndexError if index is out of range. """ pass def getSelectionStrings(args, *kwargs): """ getSelectionStrings(index=None) -> (string, string, ...) Returns a tuple containing the string representation of the specified item. For nodes, DAG paths, plugs and contiguous components the tuple will only contain a single string, but for non- contiguous components there will be a separate string for each distinct block of contiguous elements. If index is not specified then the string representations of all the items in the selection list are returned. Raises IndexError if index is out of bounds. """ pass def hasItem(args, *kwargs): """ hasItem(item) -> bool Returns True if the given item is on the selection list. For a component this means that all of the elements of the component must be on the list. A component is passed as a tuple containing the MDagPath of the DAG node and an MObject containing the component. """ pass def hasItemPartly(args, *kwargs): """ hasItemPartly(dagPath, component) -> bool Returns True if at least one of the component's elements is on the selection list. Raises TypeError if dagPath is invalid or component does not contain a component. """ pass def isEmpty(args, *kwargs): """ isEmpty() -> bool Returns True if the selection list is empty. """ pass def length(args, *kwargs): """ length() -> int Returns the number of items on the selection list. """ pass def merge(args, *kwargs): """ merge(other, strategy=kMergeNormal) -> self merge(dagPath, component, strategy=kMergeNormal) -> self The first version merges the items from another selection list in with those already on the list, using the given strategy. The second version merges the specified component with those already on the list. """ pass def remove(args, *kwargs): """ remove(index) -> self Removes the index'th item from the list. Raises IndexError if the index is out of range. """ pass def replace(args, *kwargs): """ replace(index, newItem) -> self Replaces the index'th item on the list with a new item. A component is passed as a tuple containing the MDagPath of the DAG node and an MObject containing the component. Raises IndexError if the index is out of range. """ pass def toggle(args, **kwargs): """ toggle(dagPath, component) -> self Removes from the list those elements of the given component which are already on it and adds those which are not. """ pass __new__ = None kMergeNormal = 0 kRemoveFromList = 2 kXORWithList = 1 class MFn(object): """ Static class providing constants for all API types. """ kAISEnvFacade = 961 kAddDoubleLinear = 5 kAdskMaterial = 1049 kAffect = 6 kAimConstraint = 111 kAir = 257 kAlignCurve = 41 kAlignManip = 897 kAlignSurface = 42 kAmbientLight = 303 kAngle = 270 kAngleBetween = 21 kAnimBlend = 781 kAnimBlendInOut = 782 kAnimCurve = 7 kAnimCurveTimeToAngular = 8 kAnimCurveTimeToDistance = 9 kAnimCurveTimeToTime = 10 kAnimCurveTimeToUnitless = 11 kAnimCurveUnitlessToAngular = 12 kAnimCurveUnitlessToDistance = 13 kAnimCurveUnitlessToTime = 14 kAnimCurveUnitlessToUnitless = 15 kAnimLayer = 1002 kAnisotropy = 609 kAnnotation = 271 kAnyGeometryVarGroup = 115 kArcLength = 273 kAreaLight = 305 kArrayMapper = 517 kArrowManip = 123 kAssembly = 1063 kAsset = 1000 kAttachCurve = 43 kAttachSurface = 44 kAttribute = 554 kAttribute2Double = 734 kAttribute2Float = 735 kAttribute2Int = 737 kAttribute2Long = 737 kAttribute2Short = 736 kAttribute3Double = 738 kAttribute3Float = 739 kAttribute3Int = 741 kAttribute3Long = 741 kAttribute3Short = 740
<gh_stars>1-10 import os import matplotlib as mpl if os.environ.get('DISPLAY','') == '': print('no display found. Using non-interactive Agg backend') mpl.use('Agg') import sys import json import re import matplotlib.pyplot as plt sys.path.insert(0, './include') from plot_utils import * from common import * from utils import * from data_utils import * tick = 1 # mins ## plot utilization: number of busy nodes. cap = 64 mem_cap = 128 data_range=[10, 1000000] target_qos = 0.99 cpuStr = 'cpu' memStr = 'memory' show=False plotObj = True plotOverload = False plotOverbook = False plotQoS=True plotPredictionPenalty=True plotUtilization=True loads = [plotUtilization, False, plotOverload, plotOverbook, plotQoS, plotPredictionPenalty] showBarValue = True path = "./log" arg_len = len(sys.argv) - 1 if arg_len > 0: path=sys.argv[1] # path = "./" line_num = -1 # 6024 def loadLog(filepath) : cpuUsages = [] maxCpuUsages = [] cpuRequests = [] memRequests = [] totalCpuAllocations = [] totalMemAllocations = [] memUsages = [] gpuUsages = [] cpuAllocatables = [] memAllocatables = [] requests = [] busyNodes = [] overloadNodes = [] overBookNodes = [] QoS = [] NumSatifiesPods = [] NumPods = [] PredPenalty = [] cpuUsageStd = [] memUsageStd = [] with open(filepath) as fp: line = fp.readline() # content = fp.readlines() i = 0 while line: # for line in content:ot busyNode = 0 overloadNode = 0 overBookNode = 0 totalCpuUsage = 0 totalMemUsage = 0 totalCpuAllocation = 0 totalMemAllocation = 0 totalCpuCapacity = 0 totalMemCapacity = 0 maxCpuUsage = 0 totalCpuRequest = 0 totalMemRequest = 0 maxMemUsage = 0 try: data = json.loads(line) except: print("An json.loads(line) exception occurred") continue nodeDict = data['Nodes'] cUsages = [] mUsages =[] for nodeName, node in nodeDict.items(): cpuUsage = 0 memUsage = 0 cpuAllocatable = 0 memAllocatable = 0 cpuRequest = 0 memRequest = 0 runningPodsNum = int(node['RunningPodsNum']) usageDict = node['TotalResourceUsage'] for rsName in usageDict: if(rsName==cpuStr): cpuUsage = formatCpuQuatity(usageDict[rsName]) cUsages.append(cpuUsage/cap) totalCpuUsage = totalCpuUsage+ cpuUsage if cpuUsage > maxCpuUsage: maxCpuUsage = cpuUsage elif(rsName==memStr): memUsage = formatMemQuatity(usageDict[rsName]) mUsages.append(memUsage/mem_cap) totalMemUsage = totalMemUsage+ memUsage if memUsage > maxMemUsage: maxMemUsage = memUsage allocatableDict = node['Allocatable'] for rsName in allocatableDict: if(rsName==cpuStr): cpuAllocatable = formatCpuQuatity(allocatableDict[rsName]) totalCpuCapacity = totalCpuCapacity + cpuAllocatable elif(rsName==memStr): memAllocatable = formatMemQuatity(allocatableDict[rsName]) totalMemCapacity = totalMemCapacity + memAllocatable requestDict = node['TotalResourceRequest'] for rsName in requestDict: if(rsName==cpuStr): cpuRequest = formatCpuQuatity(requestDict[rsName]) totalCpuRequest = totalCpuRequest + cpuRequest elif(rsName==memStr): memRequest = formatMemQuatity(requestDict[rsName]) totalMemRequest = totalMemRequest + memRequest allocationDict = node['TotalResourceAllocation'] for rsName in allocationDict: if(rsName==cpuStr): cpuAllocation = formatCpuQuatity(allocationDict[rsName]) totalCpuAllocation = totalCpuAllocation + cpuAllocation elif(rsName==memStr): memAllocation = formatMemQuatity(allocationDict[rsName]) totalMemAllocation = totalMemAllocation + memAllocation if(cpuUsage > cpuAllocatable or memUsage > memAllocatable): overloadNode = overloadNode+1 if(cpuRequest > cpuAllocatable or memRequest > memAllocatable): overBookNode = overBookNode +1 if(runningPodsNum > 0): busyNode = busyNode + 1 if (loads[0]): cpuUsages.append(totalCpuUsage) memUsages.append(totalMemUsage) cpuAllocatables.append(totalCpuCapacity) memAllocatables.append(totalMemCapacity) cpuRequests.append(totalCpuRequest) memRequests.append(totalMemRequest) maxCpuUsages.append(maxCpuUsage) totalCpuAllocations.append(totalCpuAllocation) totalMemAllocations.append(totalMemAllocation) memUsageStd.append(numpy.std(mUsages)) cpuUsageStd.append(numpy.std(cUsages)) if (loads[1]): busyNodes.append(busyNode) if (loads[2]): overloadNodes.append(overloadNode) if (loads[3]): overBookNodes.append(overBookNode) # Queue":{"PendingPodsNum":1,"QualityOfService":1,"PredictionPenalty":2.97} queue = data['Queue'] if (loads[4]): QoS.append(float(queue['QualityOfService'])) NumSatifiesPods.append(float(queue['NumSatifisedPods'])) NumPods.append(float(queue['NumPods'])) if (loads[5]): PredPenalty.append(float(queue['PredictionPenalty'])) i=i+1 if line_num > 0 and i >= line_num: break line = fp.readline() fp.close() return busyNodes, overloadNodes, overBookNodes, cpuUsages, memUsages, cpuRequests, \ memRequests, totalCpuAllocations, totalMemAllocations, maxCpuUsages, cpuAllocatables, memAllocatables, \ QoS, NumSatifiesPods, NumPods, PredPenalty, cpuUsageStd, memUsageStd def formatCpuQuatity(str): strArray = re.split('(\d+)', str) val = float(strArray[1]) scaleStr = strArray[2] if(scaleStr == "m"): val = val/1000 elif (scaleStr == "Mi"): val = val/1024 elif (scaleStr == ""): val = val else: print("error @ formatMemQuatity "+str) return val def formatMemQuatity(str): strArray = re.split('(\d+)', str) val = float(strArray[1]) scaleStr = strArray[2] if(scaleStr == "m"): val = val/(10001000) elif (scaleStr == "Mi"): val = val/(1024) elif (scaleStr == "Ki"): val = val/(10241024) elif (scaleStr == "Gi"): va = val elif (scaleStr == ""): # byte val = val/(102410241024) else: print("error @ formatMemQuatity "+str) return val methods = ["worstfit", "oversub", "proposed_list", "proposed_largest"] methodNames = [STR_WORSTFIT, STR_OVERSUB, STR_FLEX_F, STR_FLEX_L] colors = [COLOR_WORST_FIT, COLOR_OVER_SUB, COLOR_PROPOSED_1, COLOR_PROPOSED_2] proposed_idx = 2 # methods = ["oneshot","worstfit"] methodsNum = len(methods) busyNodes = [] overloadNodes = [] overbookNodes = [] cpuUsages = [] memUsages = [] maxCpuUsages = [] cpuAllocatables = [] memAllocatables = [] cpuAllocations = [] memAllocations = [] cpuRequests = [] memRequests = [] QoSs = [] NumSatifiesPods = [] NumPods = [] PredPenalties = [] cpuUsageStd = [] memUsageStd = [] for m in methods: b, ol, ob, u_cpu, u_mem, ur_cpu, ur_mem, a_cpu, a_mem, mu, c_cpu, c_mem, q, nsp, nps, p, cStd, mStd = loadLog(path+"/kubesim_"+m+".log") busyNodes.append(b) overloadNodes.append(ol) overbookNodes.append(ob) cpuUsages.append(u_cpu) memUsages.append(u_mem) maxCpuUsages.append(mu) cpuAllocatables.append(c_cpu) memAllocatables.append(c_mem) cpuAllocations.append(a_cpu) memAllocations.append(a_mem) cpuRequests.append(ur_cpu) memRequests.append(ur_mem) QoSs.append(q) NumSatifiesPods.append(nsp) NumPods.append(nps) PredPenalties.append(p) cpuUsageStd.append(cStd) memUsageStd.append(mStd) for i in range(methodsNum): if (len(cpuRequests[i]) < data_range[1]): data_range[1] = len(cpuRequests[i]) if (len(cpuRequests[i]) < data_range[0]): data_range[0] = 0 ############# PLOTTING ############## if not os.path.exists(FIG_PATH): os.makedirs(FIG_PATH) if plotObj: # Y_MAX = cap1.5 fig = plt.figure(figsize=FIG_ONE_COL) max_len = 0 for i in range(methodsNum): plt.plot([x / 60.0 for x in range(0,len(maxCpuUsages[i])tick,tick)], maxCpuUsages[i], color=colors[i]) if max_len < len(maxCpuUsages[i]): max_len = len(maxCpuUsages[i]) plt.plot([x / 60.0 for x in range(0,max_lentick,tick)], [cap] * max_len, color=COLOR_CAP) legends = methodNames legends.append('capacity') plt.legend(legends, loc='best') plt.xlabel(STR_TIME_HOUR) plt.ylabel(STR_CPU_CORES) plt.suptitle("Max Cpu Usage") # plt.ylim(0,Y_MAX) fig.savefig(FIG_PATH+"/max_cpu_usage.pdf", bbox_inches='tight') ## plot STD of usage Y_MAX = 0.3 fig = plt.figure(figsize=FIG_ONE_COL) max_len = 0 for i in range(methodsNum): plt.plot([x / 60.0 for x in range(0,len(memUsageStd[i])tick,tick)], memUsageStd[i], color=colors[i]) plt.legend(legends, loc='best') plt.xlabel(STR_TIME_HOUR) plt.ylabel("Memory Usage std.") plt.ylim(0,Y_MAX) plt.xlim(0,24) fig.savefig(FIG_PATH+"/std_mem_usage.pdf", bbox_inches='tight') ## Y_MAX = 0 fig = plt.figure(figsize=FIG_ONE_COL) max_len = 0 for i in range(methodsNum): plt.plot([x / 60.0 for x in range(0,len(cpuUsageStd[i])tick,tick)], cpuUsageStd[i], color=colors[i]) plt.legend(legends, loc='best') plt.xlabel(STR_TIME_HOUR) plt.ylabel("CPU Usage std.") # plt.ylim(0,Y_MAX) fig.savefig(FIG_PATH+"/std_cpu_usage.pdf", bbox_inches='tight') if plotUtilization: cpuReqUtil = [] memReqUtil = [] cpuDemandUtil = [] memDemandUtil = [] cpuUsageUtil = [] memUsageUtil = [] cpuCap = np.average(cpuAllocatables[0]) memCap = np.average(memAllocatables[0]) if memCap == 0: memCap = 1.0 if cpuCap == 0: cpuCap = 1.0 Y_MAX = 300 for i in range(methodsNum): cpuR = cpuRequests[i] memR = memRequests[i] cpuD = cpuUsages[i] memD = memUsages[i] cpuU = cpuAllocations[i] memU = memAllocations[i] cpuReqUtil.append(int(round(np.average(cpuR[data_range[0]:data_range[1]])/cpuCap100, 2))) memReqUtil.append(int(round(np.average(memR[data_range[0]:data_range[1]])/memCap100, 2))) cpuDemandUtil.append(int(round(np.average(cpuD[data_range[0]:data_range[1]])/cpuCap100, 2))) memDemandUtil.append(int(round(np.average(memD[data_range[0]:data_range[1]])/memCap100, 2))) cpuUsageUtil.append(int(round(np.average(cpuU[data_range[0]:data_range[1]])/cpuCap100,2))) memUsageUtil.append(int(round(np.average(memU[data_range[0]:data_range[1]])/memCap100,2))) x = np.arange(methodsNum) width = GBAR_WIDTH/2 ## plot # request fig, ax = plt.subplots(figsize=FIG_ONE_COL) rects = ax.bar(x - width, cpuReqUtil, width, label=STR_CPU, color=COLOR_CPU) if showBarValue: autolabel(rects, ax) rects = ax.bar(x, memReqUtil, width, label=STR_MEM, color=COLOR_MEM) if showBarValue: autolabel(rects, ax) labels = methodNames ax.set_ylabel('Request (%)') ax.set_xticks(x) ax.set_xticklabels(labels) ax.legend( loc='best') plt.ylim(0,Y_MAX) fig.savefig(FIG_PATH+"/request-avg.pdf", bbox_inches='tight') # demand fig, ax = plt.subplots(figsize=FIG_ONE_COL) rects = ax.bar(x - width, cpuDemandUtil, width, label=STR_CPU, color=COLOR_CPU) if showBarValue: autolabel(rects, ax) rects = ax.bar(x, memDemandUtil, width, label=STR_MEM, color=COLOR_MEM) if showBarValue: autolabel(rects, ax) labels = methodNames ax.set_ylabel('Demand (%)') ax.set_xticks(x) ax.set_xticklabels(labels) ax.legend( loc='best') plt.ylim(0,Y_MAX) fig.savefig(FIG_PATH+"/demand-avg.pdf", bbox_inches='tight') # usage fig, ax = plt.subplots(figsize=FIG_ONE_COL) rects = ax.bar(x - width, cpuUsageUtil, width, label=STR_CPU, color=COLOR_CPU) if showBarValue: autolabel(rects, ax) rects = ax.bar(x, memUsageUtil, width, label=STR_MEM, color=COLOR_MEM) if showBarValue: autolabel(rects, ax) labels = methodNames ax.set_ylabel('Usage (%)') ax.set_xticks(x) ax.set_xticklabels(labels) ax.legend( loc='best') plt.ylim(0,Y_MAX) fig.savefig(FIG_PATH+"/usage-avg.pdf", bbox_inches='tight') if plotUtilization: # Y_MAX = np.amax(cpuRequests) fig = plt.figure(figsize=FIG_ONE_COL) for i in range(methodsNum): plt.plot([x / 60.0 for x in range(0,len(cpuRequests[i])tick,tick)], cpuRequests[i], color=colors[i]) plt.plot([x / 60.0 for x in range(0,len(cpuAllocatables[0])tick,tick)], cpuAllocatables[0], color=COLOR_CAP) legends = methodNames legends.append('capacity') plt.legend(legends, loc='best') plt.xlabel(STR_TIME_HOUR) plt.ylabel(STR_CPU_CORES) # plt.ylim(0,Y_MAX) fig.savefig(FIG_PATH+"/total-request-cpu.pdf", bbox_inches='tight') fig = plt.figure(figsize=FIG_ONE_COL) for i in range(methodsNum): plt.plot([x / 60.0 for x in range(0,len(memRequests[i])tick,tick)], memRequests[i], color=colors[i]) plt.plot([x / 60.0 for x in range(0,len(memAllocatables[0])tick,tick)], memAllocatables[0], color=COLOR_CAP) legends = methodNames legends.append('capacity') plt.legend(legends, loc='best') plt.xlabel(STR_TIME_HOUR) plt.ylabel(STR_MEM_GB) # plt.ylim(0,Y_MAX) fig.savefig(FIG_PATH+"/total-request-mem.pdf", bbox_inches='tight') # Y_MAX = np.amax(cpuRequests) fig = plt.figure(figsize=FIG_ONE_COL) for i in range(methodsNum): plt.plot([x / 60.0 for x in range(0,len(cpuUsages[i])tick,tick)], cpuUsages[i], color=colors[i]) plt.plot([x / 60.0 for x in range(0,len(cpuAllocatables[0])tick,tick)], cpuAllocatables[0], color=COLOR_CAP) legends = methodNames legends.append('capacity') plt.legend(legends, loc='best') plt.xlabel(STR_TIME_HOUR) plt.ylabel(STR_CPU_CORES) # plt.ylim(0,Y_MAX) fig.savefig(FIG_PATH+"/total-demand-cpu.pdf", bbox_inches='tight') fig = plt.figure(figsize=FIG_ONE_COL) for i in range(methodsNum): plt.plot([x / 60.0 for x in range(0,len(memUsages[i])tick,tick)], memUsages[i], color=colors[i]) plt.plot([x / 60.0 for x in range(0,len(memAllocatables[0])tick,tick)], memAllocatables[0], color=COLOR_CAP) legends = methodNames legends.append('capacity') plt.legend(legends, loc='best') plt.xlabel(STR_TIME_HOUR) plt.ylabel(STR_MEM_GB) # plt.ylim(0,Y_MAX) fig.savefig(FIG_PATH+"/total-demand-mem.pdf", bbox_inches='tight') # Y_MAX = np.amax(cpuRequests) fig = plt.figure(figsize=FIG_ONE_COL) for i in range(methodsNum): plt.plot([x / 60.0 for x in range(0,len(cpuAllocations[i])tick,tick)], cpuAllocations[i], color=colors[i]) plt.plot([x / 60.0 for x in range(0,len(cpuAllocatables[0])tick,tick)], cpuAllocatables[0], color=COLOR_CAP) legends = methodNames legends.append('capacity') plt.legend(legends, loc='best') plt.xlabel(STR_TIME_HOUR) plt.ylabel(STR_CPU_CORES) # plt.ylim(0,Y_MAX) fig.savefig(FIG_PATH+"/total-usage-cpu.pdf", bbox_inches='tight') fig = plt.figure(figsize=FIG_ONE_COL) for i in range(methodsNum): plt.plot([x / 60.0 for x in range(0,len(memAllocations[i])tick,tick)], memAllocations[i], color=colors[i]) plt.plot([x / 60.0 for x in range(0,len(memAllocatables[0])tick,tick)], memAllocatables[0], color=COLOR_CAP) legends = methodNames legends.append('capacity') plt.legend(legends, loc='best') plt.xlabel(STR_TIME_HOUR) plt.ylabel(STR_MEM_GB)
<filename>cartopy_fun.py # Having fun with Cartopy. # inspired by the work of @pythonmaps on Twitter # eg. https://twitter.com/PythonMaps/status/1391056641546768388 # <NAME>, 10th of May 2021, MIT-License import matplotlib.pyplot as plt import pandas as pd import numpy as np from numpy import genfromtxt import time import cartopy.crs as ccrs # https://www.lfd.uci.edu/~gohlke/pythonlibs/#cartopy from matplotlib.colors import ListedColormap, LinearSegmentedColormap def get_data(url,delimiter): ''' Get the data ''' df = pd.read_csv(url, delimiter=delimiter, low_memory=False) return df def save_df(df, name): """ _ _ _ """ OUTPUT_DIR = "" name_ = OUTPUT_DIR + name + ".csv" compression_opts = dict(method=None, archive_name=name_) df.to_csv(name_, index=False, compression=compression_opts) print("--- Saving " + name_ + " ---") def show_cities(): ''' Show fixed points of interest on a map ''' cities = pd.DataFrame({'City': ['Utrecht', 'Amsterdam', 'Rotterdam', 'The Hague', 'Arnhem', 'Hilversum', 'Amersfoort', 'Almere', 'Lelystad', 'Apeldoorn', 'Den Burg', 'Harlingen', 'Zwolle', 'Gorinchem'], 'Lon': [5.1214, 4.9041, 4.4777, 4.3007, 5.8987, 5.1669, 5.3878, 5.2647, 5.4714, 5.9699, 4.7997, 5.4252, 6.0830, 4.9758], 'Lat': [52.0907, 52.3676, 51.9244, 52.0705, 51.9851, 52.2292, 52.1561, 52.3508, 52.5185, 52.2112, 53.0546, 53.1746, 52.5168, 51.8372]}) plt.scatter(cities.Lon, cities.Lat, marker = 'o', color = 'red', s = 50) for i in range(cities.shape[0]): plt.text(cities.Lon[i] + 0.02, cities.Lat[i], cities.City[i], color = 'red') def show_locations(df, how, cities): ''' Show the locations given in a dataframe df = dataframe how = points or scatter. The first reads the POI's in a loop, and adds the placename (or whatever field), the second plots a scatter which is (much) faster. cities = True/False Show the fixed POI's on the map ''' df = df[df['type']=="large_airport"] fig = plt.figure() ax = plt.axes(projection=ccrs.PlateCarree()) ax.stock_img() #show a background ax.coastlines() if how == "points": # PLOTTING AS DIFFERENT POINTS - 12.8sec for i in range (0, len(df)): plt.plot( df.iloc[i]["longitude_deg"], df.iloc[i]["latitude_deg"], markersize=1, marker='o', color='red') plt.text(df.iloc[i]["longitude_deg"], df.iloc[i]["latitude_deg"] , df.iloc[i]["iata_code"], horizontalalignment='left', fontsize='smaller', transform=ccrs.PlateCarree()) else: # PLOTTING AS SCATTERPOINTS - 5.1 seconds plt.scatter( x=df["longitude_deg"], y=df["latitude_deg"], color="red", s=1, alpha=1, transform=ccrs.PlateCarree() ) if cities: show_cities() plt.title("Large airports in the world") plt.show() def show_heatmap_with_histogram2d(df, cities): ''' # turn a CSV file with points of interests into a heatmap. # np.histogram2d calculates the number of instances in a certain area # Thanks to https://medium.com/analytics-vidhya/custom-strava-heatmap-231267dcd084 ''' df = df[df['type']=="large_airport"] Z, xedges, yedges = np.histogram2d(np.array(df.longitude_deg, dtype=float), np.array(df.latitude_deg, dtype=float), bins = 100) fig = plt.figure() # I created a new figure and set up its size ax = plt.axes(projection=ccrs.PlateCarree()) #extent = [-10, 30, 35, 75] # Europe extent = [df.longitude_deg.min(),-20, 7,df.latitude_deg.max()] # North America ax.set_extent(extent, crs=ccrs.PlateCarree()) ax.coastlines(resolution='10m', color='black', linewidth=.3) heatmap = ax.pcolormesh(xedges, yedges, Z.T, vmin=0, vmax=5, cmap = 'Set1') plt.colorbar(heatmap) plt.title("Large airports in the world") if cities: show_cities() plt.show() def show_heatmap_from_values(url, delimiter, resolution, extent, field_lon, field_lat, field_value, value_type,value_process, min_colorbar_std, max_colorbar_std, colorbar_colors, colorbar_nodes, title, log_value, show_colorbar): ''' # turn a CSV file with points of interests into a heatmap. # It takes the average of values or the frequence in a certain area # and creates a pivot table which serves as base for the heatmap # inspired by https://twitter.com/PythonMaps/status/1386727574894792707 parameters : URL : URL (string) delimiter : the delimiter (string) resolution (integer/float) extent : the exent of what to slow: lon-left, lon-right, lat-up, lat-down. (list with numbers) Take care : resolution and the extent have to be congruent otherwise there might be unexpected results. Use rounded numbers. If lon-left = -180, lon-right has to be 180, otherwise there might be unexpected results too. field_lon, field_lat, field_value : the fieldnames for resp. longitude, latitude and the value. (string) value_type : "frequence" -> it adds the a field with the name field_value and fills it with the value 1.0 np.histogram2d could also be used and might be faster. or something else : doesn't matter, it only checks if the value_type is "frequence" (string) value_proces : "sum" or "mean" -> calculates the sum or the mean (string) min_colorbar_std : for the colorbar: (mean - (min_colorbar_std * std)). Put 999 to set minimum of the colorbar to 0 (integer) max : for the colorbar: (mean + (min_colorbar_std * std)) (integer) colorbar_colors : colors of the colorbar (list with strings) colorbar_nodes : the nodes of the colorbar (list with strings) title : Title of the heatmap (string) log_value : calculate the 10log of the value -> True / False (boolean) show_colorbar : show the colorbar? -> True / False (boolean) ''' t1=time.time() df = get_data(url,delimiter) df = df[df[field_lon] >= extent[0] ] df = df[df[field_lon] <= extent[1] ] df = df[df[field_lat] >= extent[2] ] df = df[df[field_lat] <= extent[3] ] if value_type == "frequence": df.loc[:,field_value] = 1 if log_value: df.loc[:,field_value]= np.log10(df.loc[:,field_value]) df.loc[:, field_lat]= round( df.loc[:,field_lat]/resolution)resolution df.loc[:, field_lon]= round( df.loc[:,field_lon]/resolution)resolution if value_process == "mean": df = df.groupby([field_lat, field_lon]).mean() else: df = df.groupby([field_lat, field_lon]).sum().reset_index() if min_colorbar_std == 999: min_value = 0 else: min_value =df[field_value].mean() - (min_colorbar_stddf[field_value].std() ) max_value =df[field_value].mean() + (max_colorbar_stddf[field_value].std() ) # Make an dataframe with a grid with NaN-values which covers the total # area and merge it with your data-dataframe row_list = [] for lon_x in np.arange (extent[0],extent[1]+resolution,resolution): for lat_x in np.arange (extent[3],(extent[2]-resolution),-1resolution): row_list.append([lat_x, lon_x, 0]) df_temp = pd.DataFrame(row_list, columns=[field_lat, field_lon,field_value]) df = pd.merge( df_temp, df, how="outer", on=[field_lat, field_lon] ) df = df.fillna(0) # I have to fill the NaN, because otherwise I cant merge the two value-fields together field_value_x = field_value + "_x" field_value_y = field_value + "_y" df[field_value]= df[field_value_y] +df[field_value_y] df = df.drop(columns=[field_value_x], axis=1) df = df.drop(columns=[field_value_y], axis=1) # I have to repeat this otherwise I get Nan Values and duplicate columns in my pivot table when resolution<1 df[field_lat]= round(df[field_lat],2) df[field_lon]= round(df[field_lon],2) df = df.groupby([field_lat, field_lon]).sum() df = df.sort_values(by=[field_lat,field_lon]).reset_index() df = df.pivot_table(index=field_lat, columns=field_lon, values=field_value, aggfunc = np.sum) df = df.sort_values(by=field_lat, ascending=False) df = df.replace(0.0, np.nan) # i don't want a color for 0 or NaN values xedges = df.columns.tolist() yedges = df.index.tolist() values__ = df.to_numpy() fig = plt.figure() ax = plt.axes(projection=ccrs.PlateCarree()) ax.coastlines(resolution='10m', color='black', linewidth=.3) colorbar_cmap = LinearSegmentedColormap.from_list("mycmap", list(zip(colorbar_nodes, colorbar_colors))) heatmap = ax.pcolormesh(xedges, yedges, values__, vmin=min_value, vmax=max_value, cmap = colorbar_cmap) if show_colorbar: plt.colorbar(heatmap, orientation = 'horizontal') plt.title(title) t2=time.time() print (f"Done in {round(t2-t1,2)} seconds with {title}") plt.show() def show_heatmap_from_array(): ''' Show a heatmap, generated from an array # thanks to https://stackoverflow.com/a/44952031/4173718 ''' extent = [-180, 180, -90, 90] # CH4 IN THE AIR # heat_data = genfromtxt('https://raw.githubusercontent.com/rcsmit/cartopy_fun/main/MOD_LSTD_M_2021-04-01_rgb_360x180.CSV', delimiter=',') # heat_data = genfromtxt('C:\\Users\\rcxsm\\Documents\\phyton_scripts\\MOD_LSTD_M_2021-04-01_rgb_1440x720.CSV', delimiter=',') # source : https://neo.sci.gsfc.nasa.gov/view.php?datasetId=MOD_LSTD_M - april 2021, csv, 1.0 degree # heat_data[heat_data == 99999.0] = np.nan # filter out the seas and ocean # POPULATION COUNT #heat_data = genfromtxt('C:\\Users\\rcxsm\\Documents\\phyton_scripts\\gpw_v4_population_count_rev11_2020_1_deg.csv', delimiter=',') #heat_data = genfromtxt('C:\\Users\\rcxsm\\Documents\\phyton_scripts\\gpw_v4_population_count_rev11_2020_15_min.asc', delimiter=' ') heat_data = genfromtxt('gpw_v4_population_count_rev11_2020_2pt5_min.asc', delimiter=' ') # https://sedac.ciesin.columbia.edu/data/set/gpw-v4-population-count-rev11/data-download # 30 Second (approx. 1km) # 2.5 Minute (approx. 5km) # 15 Minute (approx. 30km) # 30 Minute (approx. 55km) # 60 Minute/1 Degree (approx. 110km) heat_data[heat_data == -9999] = np.nan # filter out the seas and ocean heat_data = np.flip(heat_data) #no idea why I have to flip the data twice heat_data = np.flip(heat_data,1) #heat_data = np.log10(heat_data) print (heat_data) #std = np.matrix.std(heat_data) #print (f"{mean} {std}") mean = np.nanmean(heat_data) std = np.nanstd(heat_data) print (mean) fig, ax = plt.subplots() ax = plt.axes(projection=ccrs.PlateCarree()) #ax.stock_img() ax.coastlines(resolution='10m', color='black', linewidth=.3, alpha=.5) lon = np.linspace(extent[0],extent[1],heat_data.shape[1]) lat = np.linspace(extent[2],extent[3],heat_data.shape[0]) Lon, Lat = np.meshgrid(lon,lat) neo_colors = [ "white", "cyan","blue", "purple", "magenta", "red", "orange", "yellow", "lightyellow"] nodes = [0.0, 0.125, 0.25, 0.375, 0.5, 0.625, 0.75, 0.825, 1.0] neo_cmap = LinearSegmentedColormap.from_list("mycmap", list(zip(nodes, neo_colors))) #heatmap = ax.pcolormesh(Lon,Lat,(heat_data) ,vmin=-25, vmax=50, cmap = neo_cmap) heatmap = ax.pcolormesh(Lon,Lat,(heat_data), vmin=0, vmax=(mean+(1std)), cmap = 'YlOrBr') plt.colorbar(heatmap) plt.title("LAND SURFACE TEMPERATURE [DAY] \nHeatmap based on a np.array from a CSV file") plt.show() def rasterio_and_geotiff(): # https://geoscripting-wur.github.io/PythonRaster/ # under construction pass def main(): # url = 'C:\\Users\\rcxsm\\Documents\\phyton_scripts\\airports.csv' #10 mb big file, takes a lot of time to process ! # url = "https://ourairports.com/data/airports.csv" #10 mb big file, takes a lot of time to process ! # url = 'https://raw.githubusercontent.com/rcsmit/cartopy_fun/main/airports2.csv' # df = get_data(url,",") # SHOW THE LOCATIONS ON A MAP # show_locations(df, "points", False) # show the airports on a map # GENERATE A HEATMAP WITH A GENERATED HISTOGRAM #show_show_heatmap_with_histogram2d(df, False) # number of airports in a certain area # GENERATE A HEATMAP FROM AN ARRAY WITH VALUES #show_heatmap_from_array() # land temperatures # GENERATE A
# and does not need any further processing return p else: return ctypes.c_void_p(p) else: error = yami4py.yami4_get_error(result) yami4py.yami4_destroy_result(result) raise YAMIError(str(error)) def _string(result): """Extracts the string result from underlying library.""" if result == None: raise YAMIError("Not enough memory to allocate result object.") if yami4py.yami4_is_success(result): s = yami4py.yami4_get_string(result) yami4py.yami4_destroy_result(result) return str(s) else: error = yami4py.yami4_get_error(result) yami4py.yami4_destroy_result(result) raise YAMIError(str(error)) def _binary(result): """Extracts the binary result from underlying library.""" if result == None: raise YAMIError("Not enough memory to allocate result object.") if yami4py.yami4_is_success(result): p = yami4py.yami4_get_pointer(result) size = yami4py.yami4_get_int_i(result) yami4py.yami4_destroy_result(result) # TODO: faster method? a custom buffer class, perhaps? bin = array.array("B", size * "\x00") if _use_standard_extension_API: for i in range(size): # note: bytes (c_bytes) from ctypes are signed, # whereas bytearray expects unsigned ranges v = yami4py.yami4_read_from_binary_array(p, i) if v >= 0: bin[i] = v else: bin[i] = 256 + v else: p = ctypes.cast(p, ctypes.POINTER(ctypes.c_byte)) for i in range(size): # note: bytes (c_bytes) from ctypes are signed, # whereas bytearray expects unsigned ranges v = p[i] if v >= 0: bin[i] = v else: bin[i] = 256 + v return bin.tostring() else: error = yami4py.yami4_get_error(result) yami4py.yami4_destroy_result(result) raise YAMIError(str(error)) def _check(result): """Checks result for error condition.""" if result == None: raise YAMIError("Not enough memory to allocate result object.") if yami4py.yami4_is_success(result) == 0: error = yami4py.yami4_get_error(result) yami4py.yami4_destroy_result(result) raise YAMIError(str(error)) yami4py.yami4_destroy_result(result) def _utf8(s): """Converts the given string to sequence of bytes according to UTF-8.""" return s.encode("utf8") # API bound to dynamic library: class OutgoingMessage(object): """Outgoing message. The handler allowing to track the progress of outgoing message, inspect its state and to obtain the reply content. Note: The objects of this class can be safely used from multiple threads.""" POSTED = 1 TRANSMITTED = 2 ABANDONED = 3 REPLIED = 4 REJECTED = 5 def __init__(self, msg): self.__msg = msg def __enter__(self): return self def __exit__(self, exc_type, exc_val, exc_tb): self.close() def __del__(self): if self.__msg != None: self.close() def close(self): """Deallocates internal resources associated with this object. This function is called automatically if the object is used as a context manager.""" yami4py.yami4_destroy_outgoing_message(self.__msg) self.__msg = None def get_state(self): """Returns the state of this message. This function allows to inspect the progress of the message transmission and returns a 3-tuple: state, sent and total_byte_count. During transmission the sent value is always smaller than total_byte_count. When these two values become equal, it means that the transmission was either succesful or abandoned.""" result = yami4py.yami4_outgoing_message_get_state(self.__msg) state = yami4py.yami4_get_int_i(result) bytes_sent = yami4py.yami4_get_int_j(result) total_byte_count = yami4py.yami4_get_int_k(result) yami4py.yami4_destroy_result(result) return state, bytes_sent, total_byte_count def wait_for_transmission(self, timeout = 0): """Waits for the transmission to finish. Waits for the transmission to finish - that is, to either send all the message data or to abandon it. If the timeout value is greater than 0, it means relative timeout in milliseconds; the function returns True if the transmission was finished before the timeout expired and False otherwise. If the timeout value is non-positive, there is no timeout and the function can wait indefinitely. After this function returns True the state of the message is either TRANSMITTED, ABANDONED, REPLIED or REJECTED.""" _check(yami4py.yami4_outgoing_message_wait_for_transmission( self.__msg, timeout)) def wait_for_completion(self, timeout = 0): """Waits for the full message roundtrip. Waits for the full message roundtrip - that is, for some confirmation that the message has been received and reacted upon by the target agent. If the timeout value is greater than 0, it means relative timeout in milliseconds; the function returns True if the message was completed before the timeout expired and False otherwise. If the timeout value is non-positive, there is no timeout and the function can wait indefinitely. After this function returns True the state of the message is either ABANDONED, REPLIED or REJECTED. Note: This function should not be called if the intended semantics of the message is "one-way" - in this case this function would block indefinitely.""" _check(yami4py.yami4_outgoing_message_wait_for_completion( self.__msg, timeout)) def get_reply(self): """Provides access to the reply content.""" params = Parameters() params.deserialize(_binary( yami4py.yami4_outgoing_message_get_raw_reply(self.__msg))) return params def get_exception_msg(self): """Returns the human-readable reason for message rejection.""" return _string( yami4py.yami4_outgoing_message_get_exception_msg(self.__msg)) class IncomingMessage(object): """Incoming message. The handler allowing to inspect the details of the incoming message and sent back replies or rejection notifications. The user code interacts with objects of this type mainly in the functors that are provided during object registration and that are later called back when the incoming message arrives. The handler objects can be stored aside for further processing even after the callback returns, but should not be kept alive longer than the agent itself. Note: The objects of this class are not supposed to be used from multiple threads.""" def __init__(self, msg): self.__msg = msg def __del__(self): if self.__msg != None: self.close() def close(self): """Deallocates internal resources associated with this object. This function is called automatically if the object is used as a context manager.""" yami4py.yami4_destroy_incoming_message(self.__msg) self.__msg = None def get_source(self): """Returns the source of this incoming message.""" return _string( yami4py.yami4_incoming_message_get_source(self.__msg)) def get_object_name(self): """Returns the destination object name.""" return _string( yami4py.yami4_incoming_message_get_object_name(self.__msg)) def get_message_name(self): """Returns the message name.""" return _string( yami4py.yami4_incoming_message_get_message_name(self.__msg)) def get_parameters(self): """Provides access to the message content.""" params = Parameters() params.deserialize(_binary( yami4py.yami4_incoming_message_get_raw_content(self.__msg))) return params def reply(self, content = {}, priority = 0): """Sends back the reply. Sends back the reply to the message identified by this object. The reply (or rejection) can be sent only once.""" serialized_content = serialize(content) yami4py.yami4_incoming_message_reply( self.__msg, serialized_content, len(serialized_content), priority) def reject(self, reason = "", priority = 0): """Sends back the rejection (exception) notification. Sends back the rejection to the message identified by this object. The rejection (or reply) can be sent only once.""" yami4py.yami4_incoming_message_reject( self.__msg, _utf8(reason), priority) class Agent(object): """Message broker. The message broker that encapsulates physical channel management, incoming and outgoing message queues, listeners and resource management. A single agent object can manage many listeners, which are responsible for accepting remote connections, and many incoming and outgoing connections. The agent objects can be created and destroyed without constraints on the stack, on the free store or as static objects. The objects of this class can be safely used by multiple threads.""" # connection event values NEW_INCOMING_CONNECTION = 1 NEW_OUTGOING_CONNECTION = 2 CONNECTION_CLOSED = 3 class OptionNames(object): # core option names TCP_LISTEN_BACKLOG = "tcp_listen_backlog" TCP_REUSEADDR = "tcp_reuseaddr" TCP_NONBLOCKING = "tcp_nonblocking" TCP_CONNECT_TIMEOUT = "tcp_connect_timeout" TCP_NODELAY = "tcp_nodelay" TCP_KEEPALIVE = "tcp_keepalive" TCP_FRAME_SIZE = "tcp_frame_size" UDP_FRAME_SIZE = "udp_frame_size" UNIX_LISTEN_BACKLOG = "unix_listen_backlog" UNIX_NONBLOCKING = "unix_nonblocking" UNIX_FRAME_SIZE = "unix_frame_size" FILE_NONBLOCKING = "file_nonblocking" FILE_FRAME_SIZE = "file_frame_size" # C++ general-purpose option names DISPATCHER_THREADS = "dispatcher_threads" CONNECTION_RETRIES = "connection_retries" CONNECTION_RETRY_DELAY_SPREAD = "connection_retry_delay_spread" OUTGOING_HIGH_WATER_MARK = "outgoing_high_water_mark" OUTGOING_LOW_WATER_MARK = "outgoing_low_water_mark" INCOMING_HIGH_WATER_MARK = "incoming_high_water_mark" INCOMING_LOW_WATER_MARK = "incoming_low_water_mark" # note: this is not available in Python # and hardcoded for the underlying C++ component #DELIVER_AS_RAW_BINARY = "deliver_as_raw_binary" # additional Python settings INCOMING_QUEUE_MAX_LENGTH = "incoming_queue_max_length" class __DispatcherThread(threading.Thread): """Dispatcher thread that consumes incoming messages from the queue and delivers them to registered callable entities.""" def __init__(self, agent, object_map, object_map_lock, connection_event_callback): self.__agent = agent self.__objects = object_map self.__objects_lock = object_map_lock self.__connection_event_callback = connection_event_callback threading.Thread.__init__(self) def run(self): while True: # first check if there is a regular incoming message msg_ptr = yami4py.yami4_agent_get_next_incoming_message( self.__agent) if msg_ptr == None: # there is no incoming message -> check connection events conn_event = _string( yami4py.yami4_agent_get_next_connection_event( self.__agent)) if conn_event: if self.__connection_event_callback: if conn_event[0] == 'i': event = Agent.NEW_INCOMING_CONNECTION elif conn_event[0] == 'o': event = Agent.NEW_OUTGOING_CONNECTION else: event = Agent.CONNECTION_CLOSED connection_name = conn_event[2:] try: self.__connection_event_callback( connection_name, event) except: # ignore exceptions from user code pass # continue checking the queue continue else: # no incoming message and no connection event # -> agent closing return else: # process incoming message msg = IncomingMessage(_pointer(msg_ptr)) object_name = msg.get_object_name() handler = None self.__objects_lock.acquire() try: try: handler = self.__objects[object_name] except KeyError: # no such object -> try the default handler if "" in self.__objects: handler = self.__objects[""] if handler != None: # object handler found, call
#!/usr/bin/env python3 # -- coding: utf-8 -- #**************************************************************************************************************************************************** # Copyright 2017 NXP # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright notice, # this list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above copyright notice, # this list of conditions and the following disclaimer in the documentation # and/or other materials provided with the distribution. # # * Neither the name of the NXP. nor the names of # its contributors may be used to endorse or promote products derived from # this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND # ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED # WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. # IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, # INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, # BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, # DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF # LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE # OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF # ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. # #**************************************************************************************************************************************************** import argparse import hashlib import io import json import os import subprocess import sys __g_verbosityLevel = 0 __g_debugEnabled = False __g_licenseFilename = "License.json" __g_Image = "Image" __g_Model = "Model" __g_Video = "Video" __g_extensions = [ ('.bmp', __g_Image), ('.dds', __g_Image), ('.hdr', __g_Image), ('.jpg', __g_Image), ('.ktx', __g_Image), ('.png', __g_Image), ('.psd', __g_Image), ('.tga', __g_Image), ('.tiff', __g_Image), ('.3ds', __g_Model), ('.fbx', __g_Model), ('.fsf', __g_Model), ('.obj', __g_Model), ('.nff', __g_Model), # video ('.avi', __g_Video), ('.fsf', __g_Video), ('.mp4', __g_Video), ('.mpg', __g_Video), ('.mpeg', __g_Video), ('.mkv', __g_Video), ] __g_ignore = "example.jpg" __g_ignoreDir = [ ".Config/Templates.gen/Android/Copy/res/drawable-hdpi", ".Config/Templates.gen/Android/Copy/res/drawable-ldpi", ".Config/Templates.gen/Android/Copy/res/drawable-mdpi", ".Config/Templates.gen/Android/Copy/res/drawable-xhdpi", ".Config/Templates.gen/Android/Copy/res/drawable-xxhdpi", ".Config/Templates.gen/Android/Copy/res/drawable-xxxhdpi", ".Config/Templates.gen/AndroidGradleCMake/Copy/app/src/main/res/mipmap-hdpi", ".Config/Templates.gen/AndroidGradleCMake/Copy/app/src/main/res/mipmap-ldpi", ".Config/Templates.gen/AndroidGradleCMake/Copy/app/src/main/res/mipmap-mdpi", ".Config/Templates.gen/AndroidGradleCMake/Copy/app/src/main/res/mipmap-xhdpi", ".Config/Templates.gen/AndroidGradleCMake/Copy/app/src/main/res/mipmap-xxhdpi", ".Config/Templates.gen/AndroidGradleCMake/Copy/app/src/main/res/mipmap-xxxhdpi", ] class Config(object): def __init__(self, verbosityLevel): super(Config, self).__init__() self.VerbosityLevel = verbosityLevel self.IsVerbose = verbosityLevel > 0 def LogPrint(self, str): if self.IsVerbose: print(str) sys.stdout.flush() class JsonLicense(object): def __init__(self, sourceDict = {}): super(JsonLicense, self).__init__() self.Origin = "" self.License = "" self.Url = "" self.Tags = "" self.TagsIdList = [] self.SourceDict = sourceDict def SetTags(self, tags): self.Tags = tags self.TagsIdList = [entry.lower() for entry in tags.split(';') if len(entry) > 0] def Compare(self, license): return self.Origin == license.Origin and self.License == license.License and self.Url == license.Url and self.Tags == license.Tags and self.SourceDict == license.SourceDict def GetExtensionList(extensions): return [extension[0] for extension in extensions] def GetTitle(): return 'FslResourceScan V0.1.0 alpha' def ShowTitleIfNecessary(): global __g_verbosityLevel if __g_verbosityLevel > 0: print((GetTitle())) def ReadBinaryFile(filename): content = None with open(filename, "rb") as theFile: content = theFile.read() return content def WriteFile(filename, content): with open(filename, "w") as theFile: theFile.write(content) def ReadJsonFile(filename): content = ReadBinaryFile(filename) return json.loads(content) def WriteJsonFile(filename, dict): with io.open(filename, 'w', encoding='utf-8') as currentFile: currentFile.write(str(json.dumps(dict, ensure_ascii=False, indent=2))) def ToUnixStylePath(path): if path == None: return None return path.replace("\\", "/") def GetDirectoryName(path): return ToUnixStylePath(os.path.dirname(path)) def Join(path1, path2): return ToUnixStylePath(os.path.join(path1, path2)) class Resource(object): def __init__(self, sourcePath, relativeSkipChars): super(Resource, self).__init__() self.SourcePath = sourcePath self.SourceDirectory = GetDirectoryName(sourcePath) self.License = None self.RelativePath = sourcePath[relativeSkipChars:] def ScanForFiles(path, extensionList, ignoreFiles): foundFiles = [] for root, dirs, files in os.walk(path): for file in files: fileId = file.lower() for extension in extensionList: if fileId.endswith(extension) and not fileId in ignoreFiles: foundFiles.append(ToUnixStylePath(os.path.join(root, file))) break return foundFiles def HashFile(filename, blocksize=65536): hasher = hashlib.sha256() with open(filename, "rb") as theFile: buf = theFile.read(blocksize) while len(buf) > 0: hasher.update(buf) buf = theFile.read(blocksize) return hasher.hexdigest() def BuildFileContentHashDict(config, files): dictHash = {} for file in files: hash = HashFile(file) if hash in dictHash: dictHash[hash].append(file) else: dictHash[hash] = [file] return dictHash def BuildFileLengthDict(files): dict = {} for file in files: fileLength = os.stat(file).st_size if fileLength in dict: dict[fileLength].append(file) else: dict[fileLength] = [file] return dict def BuildFileContentHashDict(files): dict = {} for file in files: hash = HashFile(file) if hash in dict: dict[hash].append(file) else: dict[hash] = [file] return dict def BuildDuplicatedList(fileName, files): srcFilename = files[0] srcContentSet = set(ReadBinaryFile(srcFilename)) matchingFiles = [fileName] for file in files: content = ReadBinaryFile(file) if len(srcContentSet.intersection(content)) == len(srcContentSet): matchingFiles.append(file) return matchingFiles def BuildDuplicatedDict(config, files, uniqueFiles): dict = {} while(len(files) > 1): srcFile = files[0] remainingFiles = files[1:] matchingFiles = BuildDuplicatedList(srcFile, remainingFiles) if len(matchingFiles) > 1: dict[srcFile] = matchingFiles else: uniqueFiles.append(key) # Remove all non duplicated files files.remove(srcFile) files = [] for file in remainingFiles: if not file in matchingFiles: files.append(file) return dict def BuildUniqueFileDictByContent(config, files, uniqueFiles): # we start by sorting files by their hash # this should limit the amount of files that have to be byte compared quite a bit duplicationDict = {} dictHash = BuildFileContentHashDict(files) for fileList in list(dictHash.values()): if len(fileList) > 1: newDuplicationDict = BuildDuplicatedDict(config, fileList, uniqueFiles) duplicationDict.update(newDuplicationDict) else: uniqueFiles.append(fileList[0]) return duplicationDict def BuildUniqueFileDict(config, files, uniqueFiles): # we start by sorting files by their size # this should limit the amount of files that have to be byte compared quite a bit dictFileLength = BuildFileLengthDict(files) #config.LogPrint("Initial bins {0}".format(len(dictFileLength))) duplicationDict = {} for fileList in list(dictFileLength.values()): if len(fileList) > 1: newDuplicationDict = BuildUniqueFileDictByContent(config, fileList, uniqueFiles) duplicationDict.update(newDuplicationDict) else: uniqueFiles.append(fileList[0]) return duplicationDict def GetFileExtension(filename): filename, fileExtension = os.path.splitext(filename) return fileExtension def BuildExtensionDict(extensions): dict = {} for extension in extensions: dict[extension[0]] = extension[1] return dict def GetContentTypeByExtension(extensionDict, filename): filenameExtension = GetFileExtension(filename).lower() return extensionDict[filenameExtension] if filenameExtension in extensionDict else "" def BuildResourceDirectorySet(uniqueFiles, duplicatedFilesDict): # build unique dir list resourceDirSet = set() for entry in uniqueFiles: dirName = GetDirectoryName(entry) if not dirName in resourceDirSet: resourceDirSet.add(dirName) for fileList in list(duplicatedFilesDict.values()): for entry in fileList: dirName = GetDirectoryName(entry) if not dirName in resourceDirSet: resourceDirSet.add(dirName) return resourceDirSet class LicenseManager(object): def __init__(self): super(LicenseManager, self).__init__() self.KeyOrigin = "Origin" self.KeyLicense = "License" self.KeyComment = "Comment" self.KeyTags = "Tags" self.KeyURL = "URL" def TryReadLicense(self, config, filename): if not os.path.isfile(filename): return None content = None try: content = ReadJsonFile(filename) except (Exception) as ex: print("ERROR: Exception while parsing {0}".format(filename)) raise if not self.KeyOrigin in content: config.LogPrint("ERROR: '{0}' not present in file '{1}'".format(self.KeyOrigin, filename)); return None if not self.KeyLicense in content: config.LogPrint("ERROR: '{0}' not present in file '{1}'".format(self.KeyLicense, filename)); return None license = JsonLicense(content) license.Origin = content[self.KeyOrigin] license.License = content[self.KeyLicense] license.Comment = content[self.KeyComment] if self.KeyComment in content else "" license.URL = content[self.KeyURL] if self.KeyURL in content else "" license.SetTags(content[self.KeyTags] if self.KeyTags in content else "") return license def SaveLicense(self, filename, license): #contentDict = {} #self.__AddKeyIfNeeded(contentDict, self.KeyOrigin, license.Origin) #self.__AddKeyIfNeeded(contentDict, self.KeyLicense, license.License) #self.__AddKeyIfNeeded(contentDict, self.KeyURL, license.URL) WriteJsonFile(filename, license.SourceDict) def __AddKeyIfNeeded(self, dict, key, value): if len(value) <= 0: return dict[key] = value def BuildDirectoryLicenseDict(config, resourceDirectories, licenseFilename): licenseManager = LicenseManager() licenseDict = {} for dir in resourceDirectories: license = licenseManager.TryReadLicense(config, Join(dir, licenseFilename)) if license != None: licenseDict[dir] = license return licenseDict def TagListWithLicenses(inputDirectory, files, directoryLicenseDict): inputDirectory = ToUnixStylePath(inputDirectory) skipChars = len(inputDirectory) if inputDirectory.endswith('/') else len(inputDirectory)+1 res = [] for entry in files: resource = Resource(entry, skipChars) if resource.SourceDirectory in directoryLicenseDict: resource.License = directoryLicenseDict[resource.SourceDirectory] res.append(resource) return res; def TagDictWithLicenses(inputDirectory, fileDict, directoryLicenseDict): inputDirectory = ToUnixStylePath(inputDirectory) skipChars = len(inputDirectory) if inputDirectory.endswith('/') else len(inputDirectory)+1 res = {} for key, value in fileDict.items(): keyFilename = key[skipChars:] res[keyFilename] = TagListWithLicenses(inputDirectory, value, directoryLicenseDict) return res; def WriteCSV(dstFilename, extensions, uniqueEntries, duplicatedEntryDict): #count = len(uniqueFiles) #for list in duplicatedFilesDict.values(): # count += len(list) #config.LogPrint("Found {0} resource files".format(count)) uniqueEntries.sort(key=lambda s: s.SourcePath.lower()) sortedDuplicatedFiles = list(duplicatedEntryDict.keys()) sortedDuplicatedFiles.sort() for fileList in list(duplicatedEntryDict.values()): fileList.sort(key=lambda s: s.SourcePath.lower()); extensionDict = BuildExtensionDict(extensions) lines = [] lines.append("Unique files ({0});;Origin;License;Type;Comment;URL".format(len(uniqueEntries))) for entry in uniqueEntries: contentType = GetContentTypeByExtension(extensionDict, entry.RelativePath) if entry.License == None: lines.append("{0};;;;{1};;".format(entry.RelativePath, contentType)) else: lines.append("{0};;{1};{2};{3};{4};{5}".format(entry.RelativePath, entry.License.Origin, entry.License.License, contentType, entry.License.Comment, entry.License.URL)) lines.append("\n") lines.append("Duplicated files ({0})".format(len(duplicatedEntryDict))) for key in sortedDuplicatedFiles: lines.append("{0};;;;{1};;".format(key, GetContentTypeByExtension(extensionDict, key))) for entry in duplicatedEntryDict[key]: contentType = GetContentTypeByExtension(extensionDict, entry.RelativePath) if entry.License == None: lines.append(";{0};;;{1};;".format(entry.RelativePath, contentType)) else: lines.append(";{0};{1};{2};{3};{4};{5}".format(entry.RelativePath, entry.License.Origin, entry.License.License, contentType, entry.License.Comment, entry.License.URL)) WriteFile(dstFilename, "\n".join(lines)); def PrintIssueDirectories(fileList, dict): uniqueDirs = set() for entry in fileList: if not entry.SourceDirectory in uniqueDirs: uniqueDirs.add(entry.SourceDirectory) for value in list(dict.values()): for entry in value: if not entry.SourceDirectory in uniqueDirs: uniqueDirs.add(entry.SourceDirectory) if len(uniqueDirs) > 0: print("Investigate license for the following directories:") uniqueDirs = list(uniqueDirs) uniqueDirs.sort() for entry in uniqueDirs: print(" {0}".format(entry)) def Filter(config, ignoreDirList, inputDirectory, files): inputDirectory = ToUnixStylePath(inputDirectory)
start: self.quiet += 1 else: self.quiet -= 1 if tag == "style": if start: self.style += 1 else: self.style -= 1 if tag in ["body"]: self.quiet = 0 # sites like 9rules.com never close <head> if tag == "blockquote": if start: self.p() self.o("> ", force=True) self.start = True self.blockquote += 1 else: self.blockquote -= 1 self.p() def no_preceding_space(self: HTML2Text) -> bool: return bool( self.preceding_data and re.match(r"[^\s]", self.preceding_data[-1]) ) if tag in ["em", "i", "u"] and not self.ignore_emphasis: if start and no_preceding_space(self): emphasis = " " + self.emphasis_mark else: emphasis = self.emphasis_mark self.o(emphasis) if start: self.stressed = True if tag in ["strong", "b"] and not self.ignore_emphasis: if start and no_preceding_space(self): strong = " " + self.strong_mark else: strong = self.strong_mark self.o(strong) if start: self.stressed = True if tag in ["del", "strike", "s"]: if start and no_preceding_space(self): strike = " ~~" else: strike = "~~" self.o(strike) if start: self.stressed = True if self.google_doc: if not self.inheader: # handle some font attributes, but leave headers clean self.handle_emphasis(start, tag_style, parent_style) if tag in ["kbd", "code", "tt"] and not self.pre: self.o("`") # TODO: `` `this` `` self.code = not self.code if tag == "abbr": if start: self.abbr_title = None self.abbr_data = "" if "title" in attrs: self.abbr_title = attrs["title"] else: if self.abbr_title is not None: assert self.abbr_data is not None self.abbr_list[self.abbr_data] = self.abbr_title self.abbr_title = None self.abbr_data = None if tag == "q": if not self.quote: self.o(self.open_quote) else: self.o(self.close_quote) self.quote = not self.quote def link_url(self: HTML2Text, link: str, title: str = "") -> None: url = urlparse.urljoin(self.baseurl, link) title = ' "{}"'.format(title) if title.strip() else "" self.o("]({url}{title})".format(url=escape_md(url), title=title)) if tag == "a" and not self.ignore_links: if start: if ( "href" in attrs and attrs["href"] is not None and not (self.skip_internal_links and attrs["href"].startswith("#")) ): self.astack.append(attrs) self.maybe_automatic_link = attrs["href"] self.empty_link = True if self.protect_links: attrs["href"] = "<" + attrs["href"] + ">" else: self.astack.append(None) else: if self.astack: a = self.astack.pop() if self.maybe_automatic_link and not self.empty_link: self.maybe_automatic_link = None elif a: assert a["href"] is not None if self.empty_link: self.o("[") self.empty_link = False self.maybe_automatic_link = None if self.inline_links: title = a.get("title") or "" title = escape_md(title) link_url(self, a["href"], title) else: i = self.previousIndex(a) if i is not None: a_props = self.a[i] else: self.acount += 1 a_props = AnchorElement(a, self.acount, self.outcount) self.a.append(a_props) self.o("][" + str(a_props.count) + "]") if tag == "img" and start and not self.ignore_images: if "src" in attrs: assert attrs["src"] is not None if not self.images_to_alt: attrs["href"] = attrs["src"] alt = attrs.get("alt") or self.default_image_alt # If we have images_with_size, write raw html including width, # height, and alt attributes if self.images_as_html or ( self.images_with_size and ("width" in attrs or "height" in attrs) ): self.o("<img src='" + attrs["src"] + "' ") if "width" in attrs: assert attrs["width"] is not None self.o("width='" + attrs["width"] + "' ") if "height" in attrs: assert attrs["height"] is not None self.o("height='" + attrs["height"] + "' ") if alt: self.o("alt='" + alt + "' ") self.o("/>") return # If we have a link to create, output the start if self.maybe_automatic_link is not None: href = self.maybe_automatic_link if ( self.images_to_alt and escape_md(alt) == href and self.absolute_url_matcher.match(href) ): self.o("<" + escape_md(alt) + ">") self.empty_link = False return else: self.o("[") self.maybe_automatic_link = None self.empty_link = False # If we have images_to_alt, we discard the image itself, # considering only the alt text. if self.images_to_alt: self.o(escape_md(alt)) else: self.o("![" + escape_md(alt) + "]") if self.inline_links: href = attrs.get("href") or "" self.o( "(" + escape_md(urlparse.urljoin(self.baseurl, href)) + ")" ) else: i = self.previousIndex(attrs) if i is not None: a_props = self.a[i] else: self.acount += 1 a_props = AnchorElement(attrs, self.acount, self.outcount) self.a.append(a_props) self.o("[" + str(a_props.count) + "]") if tag == "dl" and start: self.p() if tag == "dt" and not start: self.pbr() if tag == "dd" and start: self.o(" ") if tag == "dd" and not start: self.pbr() if tag in ["ol", "ul"]: # Google Docs create sub lists as top level lists if not self.list and not self.lastWasList: self.p() if start: if self.google_doc: list_style = google_list_style(tag_style) else: list_style = tag numbering_start = list_numbering_start(attrs) self.list.append(ListElement(list_style, numbering_start)) else: if self.list: self.list.pop() if not self.google_doc and not self.list: self.o("\n") self.lastWasList = True else: self.lastWasList = False if tag == "li": self.pbr() if start: if self.list: li = self.list[-1] else: li = ListElement("ul", 0) if self.google_doc: nest_count = self.google_nest_count(tag_style) else: nest_count = len(self.list) # TODO: line up <ol><li>s > 9 correctly. self.o(" " * nest_count) if li.name == "ul": self.o(self.ul_item_mark + " ") elif li.name == "ol": li.num += 1 self.o(str(li.num) + ". ") self.start = True if tag in ["table", "tr", "td", "th"]: if self.ignore_tables: if tag == "tr": if start: pass else: self.soft_br() else: pass elif self.bypass_tables: if start: self.soft_br() if tag in ["td", "th"]: if start: self.o("<{}>\n\n".format(tag)) else: self.o("\n</{}>".format(tag)) else: if start: self.o("<{}>".format(tag)) else: self.o("</{}>".format(tag)) else: if tag == "table": if start: self.table_start = True if self.pad_tables: self.o("<" + config.TABLE_MARKER_FOR_PAD + ">") self.o(" \n") else: if self.pad_tables: self.o("</" + config.TABLE_MARKER_FOR_PAD + ">") self.o(" \n") if tag in ["td", "th"] and start: if self.split_next_td: self.o("\| ") self.split_next_td = True if tag == "tr" and start: self.td_count = 0 if tag == "tr" and not start: self.split_next_td = False self.soft_br() if tag == "tr" and not start and self.table_start: # Underline table header self.o("\|".join(["---"] * self.td_count)) self.soft_br() self.table_start = False if tag in ["td", "th"] and start: self.td_count += 1 if tag == "pre": if start: self.startpre = True self.pre = True else: self.pre = False if self.mark_code: self.out("\n[/code]") self.p() # TODO: Add docstring for these one letter functions def pbr(self) -> None: "Pretty print has a line break" if self.p_p == 0: self.p_p = 1 def p(self) -> None: "Set pretty print to 1 or 2 lines" self.p_p = 1 if self.single_line_break else 2 def soft_br(self) -> None: "Soft breaks" self.pbr() self.br_toggle = " " def o( self, data: str, puredata: bool = False, force: Union[bool, str] = False ) -> None: """ Deal with indentation and whitespace """ if self.abbr_data is not None: self.abbr_data += data if not self.quiet: if self.google_doc: # prevent white space immediately after 'begin emphasis' # marks ('*' and '_') lstripped_data = data.lstrip() if self.drop_white_space and not (self.pre or self.code): data = lstripped_data if lstripped_data != "": self.drop_white_space = 0 if puredata and not self.pre: # This is a very dangerous call ... it could mess up # all handling of   when not handled properly # (see entityref) data = re.sub(r"\s+", r" ", data) if data and data[0] == " ": self.space = True data = data[1:] if not data and not force: return if self.startpre: # self.out(" :") #TODO: not output when already one there if not data.startswith("\n") and not data.startswith("\r\n"): # <pre>stuff... data = "\n" + data if self.mark_code: self.out("\n[code]") self.p_p = 0 bq = ">" self.blockquote if not (force and data and data[0] == ">") and self.blockquote: bq += " " if self.pre: if not self.list: bq += " " # else: list content is already partially indented bq += " " * len(self.list) data = data.replace("\n", "\n" + bq) if self.startpre: self.startpre = False if self.list: # use existing initial indentation data = data.lstrip("\n") if self.start: self.space = False self.p_p = 0 self.start = False if force == "end": # It's the end. self.p_p = 0 self.out("\n") self.space = False if self.p_p: self.out((self.br_toggle + "\n" + bq) * self.p_p) self.space = False self.br_toggle = "" if self.space: if not self.lastWasNL: self.out(" ") self.space = False if self.a and ( (self.p_p == 2 and self.links_each_paragraph) or force == "end" ): if force == "end": self.out("\n") newa = [] for link in self.a: if self.outcount > link.outcount: self.out( " [" + str(link.count) + "]: " + urlparse.urljoin(self.baseurl, link.attrs["href"]) ) if "title" in link.attrs: assert link.attrs["title"] is not None self.out(" (" + link.attrs["title"] + ")") self.out("\n")
os.getcwd() self.run_dir = os.path.join(cwd, self.run_dir) print(self.run_dir) if os.path.isdir(self.run_dir): shutil.rmtree(self.run_dir, ignore_errors=True) if pgm_dir: shutil.copytree(pgm_dir, self.run_dir) if pgm_files: os.makedirs(self.run_dir) for f in pgm_files: shutil.copy(f, self.run_dir) # pre_passes_str (str): pre_passes_str is a string that contains specific passes that we want to use when we reinitialize the training (when we use reset) self.pre_passes_str= "-prune-eh -functionattrs -ipsccp -globalopt -mem2reg -deadargelim -sroa -early-cse -loweratomic -instcombine -loop-simplify" # pre_passes (list): pre_passes is a list of integer that contains the indices of the passes written in pre_passes_str. self.pre_passes = getcycle.passes2indice(self.pre_passes_str) self.passes = [] # passes (list): passes is a list that contains the passes used for the Rl training self.best_passes = [] # best_passes (list): best_passes is a list that contains the best passes recorded. (we update the list when the recoded time of cycle count is less than min_cycles) self.pgm = pgm # pgm_name (str): pgm_name is the file name of the program we are optimizing (which is written in C programming language) self.pgm_name = pgm.replace('.c','') # bc (str): bs is the file name of the program we are optimizing after being compiled to IR (hardware-independent intermediate representation) self.bc = self.pgm_name + '.prelto.2.bc' self.original_obs = [] # original_obs(list): original_obs is a list that contains the original values of the observatyions features. def __del__(self): """ This function closes the log_file (which is a file we use to record information about each episode) when delete_run_dir and log_results are True. Also deletes the entire directory tree of run_dir (the running directory) if run_dir is an existing directory. """ if self.delete_run_dir: if self.log_results: self.log_file.close() if os.path.isdir(self.run_dir): shutil.rmtree(self.run_dir) def get_Ox_rewards(self, level=3, sim=False, clang_opt=False): """ Examples : >>> print(get_0x_rewards(self, level=3, clang_opt=False, sim=False)) -45 Args: level (int): This is an integer that represents different groups of optimizations implemented in the compiler. Each optimization level is hand-picked by the compiler-designer to benefit specific benchmarks. Defaults to 3. sim (bool): sim is a Boolean that should be set to True if we want the subprocessor to run the “make clean p v -s” command, and we should set it to False if we want the subprocessor to run the “make clean accelerationCycle -s” command instead. Defaults to False. clang_opt (bool): clang_opt is a Boolean that should be set to True if we want to use the clang option when running the HLS, and should be set to False otherwise. Returns: Returns the negative number of cycle counts it took to run the synthesized circuit made by using the passes set in the 0x optimization. Which represents for the RL agent the reward. """ from gym_hls.envs.getox import getOxCycles cycle = getOxCycles(self.pgm_name, self.run_dir, level=level, clang_opt=clang_opt, sim=sim) return -cycle def print_info(self,message, end = '\n'): """ This function is used to print information the episodes of the RL agent. Args: message (str): message is a string that will contain information about the episode of the Rl agent that we want to print on our terminal end (str): end is a string that prints a new line. """ sys.stdout.write('\x1b[1;34m' + message.strip() + '\x1b[0m' + end) def get_cycles(self, passes, sim=False): """ Examples : >>>print(get_cycles(self, [“-correlated-propagation”, “-scalarrepl”, “-lowerinvoke”])) (55, True) Args: passes (list): passes is a list that contains the passes used for the Rl training sim (bool): sim (bool, optional): sim should be True if you want the arguments used to launch the process to be “make clean p v -s”, or sim should be False if you want the argument used to launch the process to be "make clean accelerationCycle -s". Defaults to False Returns: Returns a tuple where the first element is an integer that represents the number of cycle counts it took to run the synthesized circuit (the second element doesn’t matter). """ if self.shrink: actual_passes = [self.eff_pass_indices[index] for index in passes] else: actual_passes = passes cycle, _ = getcycle.getHWCycles(self.pgm_name, actual_passes, self.run_dir, sim=sim) return cycle def get_rewards(self, diff=True, sim=False): """ Examples : >>>print(get_cycles(self)) -55 Args: diff (bool): diff is a boolean that is set to True if we want the reward to be the difference of previous cycle count and the current cycle count. Otherwise, if diff is False, the reward is equal to – the current cycle count. sim (bool, optional): sim should be True if you want the arguments used to launch the process to be “make clean p v -s”, or sim should be False if you want the argument used to launch the process to be "make clean accelerationCycle -s". Defaults to False Returns: Returns an integer that represents the reward for the RL agent (it shows the improvement of the circuit), and we get it either by calculating the difference between previous cycle count and the current cycle count or the negative value of the current cycle count. """ if self.shrink: actual_passes = [self.eff_pass_indices[index] for index in self.passes] else: actual_passes = self.passes cycle, done = getcycle.getHWCycles(self.pgm_name, actual_passes, self.run_dir, sim=sim) if cycle == 10000000: cycle = 2 * self.O0_cycles # print("pass: {}".format(self.passes)) # print("prev_cycles: {}".format(self.prev_cycles)) if(self.verbose): self.print_info("passes: {}".format(actual_passes)) self.print_info("program: {} -- ".format(self.pgm_name)+" cycle: {} -- prev_cycles: {}".format(cycle, self.prev_cycles)) try: cyc_dict = pickle.load(open('cycles_chstone.pkl','rb')) except: cyc_dict = {} if self.pgm_name in cyc_dict: if cyc_dict[self.pgm_name]['cycle']>cycle: cyc_dict[self.pgm_name]['cycle'] = cycle cyc_dict[self.pgm_name]['passes'] = self.passes else: cyc_dict[self.pgm_name] = {} cyc_dict[self.pgm_name]['cycle'] = cycle cyc_dict[self.pgm_name]['passes'] = self.passes output = open('cycles_chstone.pkl', 'wb') pickle.dump(cyc_dict, output) output.close() if (cycle < self.min_cycles): self.min_cycles = cycle self.best_passes = actual_passes if (diff): rew = self.prev_cycles - cycle self.prev_cycles = cycle else: rew = -cycle # print("rew: {}".format(rew)) return rew, done def get_obs(self,get_normalizer=False): """ Examples : >>>print(get_obs()) [1, 0, 0, 0, 1] Args: get_normalizer (bool): get_normalizer is a boolean that should be set to True if we want to get a normalizer value that is used to normalize the list of observation features. Defaults to False. Returns: Returns a list or a tuple that contains the list of the observation features that we need to feed as input to the RL agent. """ feats = getfeatures.run_stats(self.bc, self.run_dir) normalizer=feats[-5] + 1 if self.shrink: actual_feats = [feats[index] for index in self.eff_feat_indices] else: actual_feats = feats if self.binary_obs: actual_feats = [1 if feat > 0 else 0 for feat in actual_feats] if not get_normalizer: return actual_feats else: return actual_feats,normalizer return actual_feats # reset() resets passes to [] # reset(init=[1,2,3]) resets passes to [1,2,3] def reset(self, init=None, get_obs=True, get_rew=False, ret=True, sim=False): """ Examples : >>>print(reset()) [0, 0, 0, 0] Args: init (list, optional): init is a list of integer that is equal to (set to) the new passes list. Defaults to None. get_obs (bool, optional): get_obs is a Boolean that is set to True when we decide to get the list of observation features after we reset. It should be set to False otherwise. Defaults to True. get_rew (bool, optional): get_rew is a Boolean that is set to True when we decide to get the reward after we reset. It should be set to False otherwise. Defaults to False. ret (bool, optional): ret is a Boolean that is set to True when we decide to get the reward or the list of observation features after we reset. It should be set to False otherwise. Defaults to True. sim (bool, optional): sim should be True if you want the arguments used to launch the process to be “make clean p v -s”, or sim should be False if you want the argument used to launch the process to be "make clean accelerationCycle -s". Defaults to False. Defaults to False. Returns: Returns an integer for the reward or a list for the observation features, or a tuple of both an integer and a list for the reward and the observation features, or zero if ret if False. """ #self.min_cycles = 10000000 self.passes = [] if self.feature_type ==
for signature in signature_solutions_aggregate: if signature['ss_id'] not in signature_list and signature['percentage'] != 0: signature_list.append(signature['ss_id']) signature_aggregate.append(signature) if others['percentage'] != 0: signature_aggregate.append(others) project_mapping = get_project_aggregate(year, active_projects, operating_unit=operating_unit, budget_source=budget_source) project_serializer = ProjectAggregateSerializer(project_mapping) signature_serializer = SignatureSolutionsAggregateSerializer(signature_aggregate, many=True) data = { 'project': project_serializer.data, 'signature_solutions': signature_serializer.data, } return self.jp_response(s_code='HTTP_200_OK', data=data) except Exception as e: print(e) return self.jp_error_response('HTTP_500_INTERNAL_SERVER_ERROR', 'EXCEPTION', [str(e), ]) class SignatureSolutionsDetailsView(APIView, ResponseViewMixin): def get(self, request, args, kwargs): try: year = request.GET.get('year', '') ss_id = request.GET.get('ss_id', '') if not year: return self.jp_error_response('HTTP_400_BAD_REQUEST', 'UNKNOWN_QUERY', ['Please provide a year']) active_projects = get_active_projects_for_year(year) donor_query = Q(project__in=active_projects) & Q(year=year) & \ Q(project__project_active__year=year) top_recipient_offices = DonorFundSplitUp.objects.filter(donor_query & Q(project__operating_unit__isnull=False) & Q(output__signature_solution__ss_id=ss_id))\ .values('project__operating_unit').annotate( total_expense=Coalesce(Sum('expense'), 0), total_budget=Coalesce(Sum('budget'), 0), name=F('project__operating_unit__name'), iso3=F('project__operating_unit__iso3')) \ .order_by('-total_budget')[0:10] recipient_offices_serializer = SignatureSolutionOperatingUnitSerializer(top_recipient_offices, many=True) budget_sources = DonorFundSplitUp.objects.filter(donor_query & Q(output__signature_solution__ss_id=ss_id)) \ .values('organisation') \ .annotate(total_expense=Coalesce(Sum('expense'), 0), total_budget=Coalesce(Sum('budget'), 0), short_name=F('organisation__short_name'), organisation_name=F('organisation__org_name'))\ .order_by('-total_budget')[0:10] budget_sources_serializer = SectorBudgetSourcesSerializer(budget_sources, many=True, context={'request': request}) signature_aggregate = [] signature_solutions_aggregate = [] signature_list = [] for signature_solutions in SignatureSolution.objects.filter(ss_id=ss_id): aggregate = get_signature_solutions_aggregate(year, active_projects, signature_solution=signature_solutions) if aggregate: signature_solutions_aggregate.append(aggregate) signature_solutions_aggregate = sorted(signature_solutions_aggregate, key=lambda signature: signature['percentage'], reverse=True) for signature in signature_solutions_aggregate: if signature['ss_id'] not in signature_list: signature_list.append(signature['ss_id']) signature_aggregate.append(signature) signature_serializer = SignatureSolutionsAggregateSerializer(signature_aggregate, many=True) data = { 'top_recipient_offices': recipient_offices_serializer.data, 'budget_sources': budget_sources_serializer.data, 'aggregate': signature_serializer.data } return self.jp_response(s_code='HTTP_200_OK', data=data) except Exception as e: return self.jp_error_response('HTTP_500_INTERNAL_SERVER_ERROR', 'EXCEPTION', [str(e), ]) class SignatureSolutionsOutcomeView(APIView, ResponseViewMixin): def get(self, request, args, *kwargs): try: year = request.GET.get('year', '') ss_id = request.GET.get('ss_id', '') if not year: return self.jp_error_response('HTTP_400_BAD_REQUEST', 'UNKNOWN_QUERY', ['Please provide a year']) active_projects = get_active_projects_for_year(year) sign = [] signature_solution = SignatureSolution.objects.values('sp_id').filter(ss_id=ss_id) num_signature_solution = len(signature_solution) percent_signature_solution = {sp_id['sp_id']: sp_id['sp_id__count'] 100/num_signature_solution for sp_id in signature_solution.annotate(Count('sp_id'))} for k, v in percent_signature_solution.items(): sector_name = Sector.objects.get(code=k) aggregate = get_sector_aggregate(year, active_projects=active_projects, sector=sector_name) sector_budget = aggregate['budget'] sector_color = sector_name.color sign.append({'sector_id': k, 'sector_name': sector_name, 'percent': v, 'sector_color': sector_color, 'budget': sector_budget}) signature_outcome_serializer = SignatureSolutionOutcomeSerializer(sign, many=True, context={'request': request}) data = { 'percentage': signature_outcome_serializer.data } return self.jp_response(s_code='HTTP_200_OK', data=data) except Exception as e: return self.jp_error_response('HTTP_500_INTERNAL_SERVER_ERROR', 'EXCEPTION', [str(e), ]) class SectorSignatureSolutionView(APIView, ResponseViewMixin): def get(self, request, args, kwargs): try: year = request.GET.get('year', '') code = request.GET.get('code', '') if not year: return self.jp_error_response('HTTP_400_BAD_REQUEST', 'UNKNOWN_QUERY', ['Please provide a year']) active_projects = get_active_projects_for_year(year) sectors = [] sector = Sector.objects.values('signaturesolution__ss_id').filter(code=code) num_sector = len(sector) percent_sector = {ss_id['signaturesolution__ss_id']: ss_id['signaturesolution__ss_id__count'] 100/num_sector for ss_id in sector.annotate(Count('signaturesolution__ss_id'))} for k, v in percent_sector.items(): signature_solution = SignatureSolution.objects.values('name').filter(ss_id=k).distinct() signature_solution_name = signature_solution[0]['name'] for signature in SignatureSolution.objects.filter(ss_id=k): aggregate = get_signature_solutions_aggregate(year, active_projects=active_projects, signature_solution=signature) ss_budget = aggregate['budget'] sectors.append({'signature_solution_id': k, 'percent': v, 'signature_solution_name': signature_solution_name, 'budget': ss_budget}) sector_sort = sorted(sectors, key=lambda k: k['signature_solution_id']) sector_signature_solution_serializer = SectorSignatureSolutionSerializer(sector_sort, many=True, context={'request': request}) data = { 'percentage': sector_signature_solution_serializer.data } return self.jp_response(s_code='HTTP_200_OK', data=data) except Exception as e: return self.jp_error_response('HTTP_500_INTERNAL_SERVER_ERROR', 'EXCEPTION', [str(e), ]) # class SdgTargetDetailView(APIView, ResponseViewMixin): # def get(self, request, args, kwargs): # try: # year = request.GET.get('year', '') # sdg_target = request.GET.get('sdg_target', '') # operating_unit = request.GET.get('operating_unit', '') # budget_source = request.GET.get('budget_source', '') # if not year: # return self.jp_error_response('HTTP_400_BAD_REQUEST', 'UNKNOWN_QUERY', # ['Please provide a year']) # if year and int(year) >= SP_START_YEAR: # active_projects = get_active_projects_for_year(year) # donor_query = Q(project__in=active_projects) & Q(year=year) & \ # Q(project__project_active__year=year) # query = Q(project_id__in=active_projects) & Q(donorfundsplitup__year=year) & \ # Q(project_active__year=year) # # if sdg_target and sdg_target != '0': # query.add(Q(outputtarget__target_id=sdg_target), Q. AND) # donor_query.add(Q(output__outputtarget__target_id=sdg_target), Q. AND) # if operating_unit: # query.add(Q(operating_unit=operating_unit) \| # Q(operating_unit__bureau__code=operating_unit), Q.AND) # donor_query.add(Q(project__operating_unit=operating_unit) \| # Q(project__operating_unit__bureau__code=operating_unit), Q.AND) # if budget_source: # budget_query = Q(donorfundsplitup__organisation__ref_id=budget_source) \| \ # Q(donorfundsplitup__organisation__type_level_3=budget_source) # donor_budget_query = Q(organisation__ref_id=budget_source) \| \ # Q(organisation__type_level_3=budget_source) # query.add(budget_query, Q.AND) # donor_query.add(donor_budget_query, Q.AND) # target_percent = [] # sdg_obj = SdgTargets.objects.values('sdg', 'description').get(target_id=sdg_target) # target_agg = get_target_aggregate(year, target=sdg_target, operating_unit=operating_unit, # budget_source=budget_source) # aggregate_results1 = Project.objects.filter(query)\ # .aggregate(projects=Count('project_id', distinct=True), # budget_sources=Count('donorfundsplitup__organisation', distinct=True)) # # target_percent.append({'total_budget': target_agg['target_budget'], # 'total_expense': target_agg['target_expense'], # 'total_projects': aggregate_results1['projects'], # 'budget_sources': aggregate_results1['budget_sources'], # 'target_desc': sdg_obj['description'], # 'target_id': sdg_target, # 'sdg': sdg_obj['sdg'] # }) # budget_sources = DonorFundSplitUp.objects.filter(donor_query).values('organisation') \ # .annotate(total_expense=Coalesce(Sum('expense'), 0), # total_budget=Coalesce(Sum('budget'), 0), # short_name=F('organisation__short_name'), # organisation_name=F('organisation__org_name')) \ # .order_by('-total_budget')[0:10] # budget_sources_serializer = SdgBudgetSourcesSerializer(budget_sources, many=True, # context={'request': request}) # top_recipient_offices = DonorFundSplitUp.objects.filter(donor_query & # Q(project__operating_unit__isnull=False)) \ # .values('project__operating_unit') \ # .annotate(total_expense=Coalesce(Sum('expense'), 0), # total_budget=Coalesce(Sum('budget'), 0), # name=F('project__operating_unit__name'), # iso3=F('project__operating_unit__iso3')).order_by( # '-total_budget')[0:10] # recipient_offices_serializer = SdgOperatingUnitSerializer(top_recipient_offices, many=True) # # data = { # 'aggregate': target_percent, # 'budget_sources': budget_sources_serializer.data, # 'top_recipient_offices': recipient_offices_serializer.data # } # return self.jp_response(s_code='HTTP_200_OK', data=data) # except Exception as e: # return self.jp_error_response('HTTP_500_INTERNAL_SERVER_ERROR', 'EXCEPTION', [str(e), ]) class SdgTargetDetailView(APIView, ResponseViewMixin): def get(self, request, args, *kwargs): try: year = request.GET.get('year', '') sdg_target = request.GET.get('sdg_target', '') operating_unit = request.GET.get('operating_unit', '') budget_source = request.GET.get('budget_source', '') if not year: return self.jp_error_response('HTTP_400_BAD_REQUEST', 'UNKNOWN_QUERY', ['Please provide a year']) data = dict() if year and int(year) >= SDG_START_YEAR: active_projects = get_active_projects_for_year(year) donor_query = Q(project__in=active_projects) & Q(year=year) & \ Q(project__project_active__year=year) query = Q(project_id__in=active_projects) & Q(donorfundsplitup__year=year) & \ Q(project_active__year=year) if sdg_target and sdg_target != '0': query.add(Q(outputtarget__target_id=sdg_target) & Q(outputtarget__year=year), Q. AND) donor_query.add(Q(output__outputtarget__target_id=sdg_target) & Q(output__outputtarget__year=year), Q. AND) if operating_unit: query.add(Q(operating_unit=operating_unit) \| Q(operating_unit__bureau__code=operating_unit), Q.AND) donor_query.add(Q(project__operating_unit=operating_unit) \| Q(project__operating_unit__bureau__code=operating_unit), Q.AND) if budget_source: budget_query = Q(donorfundsplitup__organisation__ref_id=budget_source) \| \ Q(donorfundsplitup__organisation__type_level_3=budget_source) donor_budget_query = Q(organisation__ref_id=budget_source) \| \ Q(organisation__type_level_3=budget_source) query.add(budget_query, Q.AND) donor_query.add(donor_budget_query, Q.AND) target_percent = [] sdg_obj = SdgTargets.objects.values('sdg', 'description', 'sdg__name').get(target_id=sdg_target) target_agg = get_target_aggregate_new(year, sdg_target, sdg_obj['sdg'], operating_unit=operating_unit, budget_source=budget_source, active_projects=active_projects) aggregate_results1 = Project.objects.filter(query)\ .aggregate(projects=Count('project_id', distinct=True), budget_sources=Count('donorfundsplitup__organisation', distinct=True)) target_percent.append({'total_budget': target_agg['target_budget'], 'total_expense': target_agg['target_expense'], 'total_projects': aggregate_results1['projects'], 'budget_sources': aggregate_results1['budget_sources'], 'target_desc': sdg_obj['description'], 'target_id': sdg_target, 'sdg': sdg_obj['sdg'], 'sdg_name': sdg_obj['sdg__name'], }) budget_sources = DonorFundSplitUp.objects.filter(donor_query).values('organisation') \ .annotate(total_expense=Coalesce(Sum('expense'), 0), total_budget=Coalesce(Sum('budget'), 0), short_name=F('organisation__short_name'), organisation_name=F('organisation__org_name')) \ .order_by('-total_budget')[0:10] budget_sources_data = [] for source in budget_sources: budget_data = get_target_aggregate_new(year, target=sdg_target, sdg=sdg_obj['sdg'], budget_source=source['organisation'], active_projects=active_projects) if budget_data: top_budget_source = {'total_expense': budget_data['target_expense'], 'total_budget': budget_data['target_budget'], 'short_name': source['short_name'], 'organisation_name': source['organisation_name'] } budget_sources_data.append(top_budget_source) top_budget_sources = sorted(budget_sources_data, key=lambda k: k['total_budget'], reverse=True) budget_sources_serializer = SdgBudgetSourcesSerializer(top_budget_sources, many=True, context={'request': request}) top_recipient_offices = DonorFundSplitUp.objects.filter(donor_query & Q(project__operating_unit__isnull=False)) \ .values('project__operating_unit') \ .annotate(total_expense=Coalesce(Sum('expense'), 0), total_budget=Coalesce(Sum('budget'), 0), name=F('project__operating_unit__name'), iso3=F('project__operating_unit__iso3')).order_by( '-total_budget')[0:10] top_recipient_offices_data = [] for recipient in top_recipient_offices: recipient_data = get_target_aggregate_new(year, target=sdg_target, sdg=sdg_obj['sdg'], operating_unit=recipient['project__operating_unit'], active_projects=active_projects) if recipient_data: top_recipient = {'total_expense': recipient_data['target_expense'], 'total_budget': recipient_data['target_budget'], 'name': recipient['name'], 'iso3': recipient['iso3'], } top_recipient_offices_data.append(top_recipient) recipient_offices = sorted(top_recipient_offices_data, key=lambda k: k['total_budget'], reverse=True) recipient_offices_serializer = SdgOperatingUnitSerializer(recipient_offices, many=True) data = { 'aggregate': target_percent, 'budget_sources': budget_sources_serializer.data, 'top_recipient_offices': recipient_offices_serializer.data } return self.jp_response(s_code='HTTP_200_OK', data=data) except Exception as e: return self.jp_error_response('HTTP_500_INTERNAL_SERVER_ERROR', 'EXCEPTION', [str(e), ]) class SdgTargetView(APIView, ResponseViewMixin): def get(self, request, args, *kwargs): try: year = request.GET.get('year', '') budget_source = request.GET.get('budget_source', '') operating_unit = request.GET.get('operating_unit', '') sdg = request.GET.get('sdg', '') if not year: return self.jp_error_response('HTTP_400_BAD_REQUEST', 'UNKNOWN_QUERY', ['Please provide a year']) target_percent = [] active_projects = get_active_projects_for_year(year, operating_unit=operating_unit, budget_source=budget_source) if year and int(year) >= SDG_START_YEAR: targets = SdgTargets.objects.filter(sdg=sdg).values('target_id', 'description') for target in targets: target_id = target['target_id'] target_agg = get_target_aggregate_new(year, target_id, sdg, operating_unit=operating_unit, budget_source=budget_source, active_projects=active_projects) if target_agg['budget_percentage'] > 0: target_percent.append({'target_budget': target_agg['target_budget'], 'target_expense': target_agg['target_expense'], 'target_percentage': target_agg['budget_percentage'], 'target_id': target_id, 'target_description': target['description']}) result = [] result_str = [] for target_val in target_percent: if target_val['target_id'].split('.')[1][0:2].isdigit(): result.append(int(target_val['target_id'].split('.')[1][0:2])) else: result_str.append(target_val['target_id'].split('.')[1][0:2]) outputs = sorted(result) + sorted(result_str) target_data = [] for output in outputs: for target_per in target_percent: if target_per['target_id'].split('.')[1][0:2] == str(output) : target_data.append(target_per) sdg_target_serializer = SdgTargetSerializer(target_data, many=True, context={'request': request}) data = { 'percentage': sdg_target_serializer.data } return self.jp_response(s_code='HTTP_200_OK', data=data) except Exception as e: print(e) return self.jp_error_response('HTTP_500_INTERNAL_SERVER_ERROR', 'EXCEPTION', [str(e), ]) class SdgView(APIView, ResponseViewMixin): def get(self, request, args, *kwargs): try: import json year = request.GET.get('year', '') operating_unit = request.GET.get('operating_unit', '') budget_source = request.GET.get('budget_source') sdg_code = request.GET.get('sdg', None) if not year: return self.jp_error_response('HTTP_400_BAD_REQUEST', 'UNKNOWN_QUERY', ['Please provide a year']) try: sdg = SDGSunburst.objects.get(sdg_year=year).response except Exception as e: sdg = get_sdg_sunburst(year, operating_unit, budget_source, sdg_code) return self.jp_response(s_code='HTTP_200_OK', data=json.loads(sdg)) except Exception as e: return self.jp_error_response('HTTP_500_INTERNAL_SERVER_ERROR', 'EXCEPTION', [str(e), ]) class SectorView(APIView, ResponseViewMixin): def get(self, request, args, **kwargs): try: search = self.request.GET.get('search', '') year = process_query_params(request.GET.get('year', '')) operating_unit = request.GET.get('operating_unit', '') donor = process_query_params(request.GET.get('donor', '')) new_sector = Sector.objects.filter(Q(code__in=NEW_SECTOR_CODES)).values() old_sector = Sector.objects.filter(Q(code__in=OLD_SECTOR_CODES)).values() if search: old_sector = old_sector.filter(Q(code__icontains=search) \| Q(sector__icontains=search)) old_sector = list(old_sector) new_sector = list(new_sector) if search == '' or search.lower() == 'others' or search == '0': other_valid_sectors = get_valid_sectors(year, operating_unit, donor, sector='0') if other_valid_sectors: old_sector.append({'code': "0", 'sector': "Others", 'color': NULL_SECTOR_COLOR_CODE, 'start_year': '2015', 'end_year': '2017'}) new_sector.append({'code': "0", 'sector': "Others", 'color': NULL_SECTOR_COLOR_CODE, 'start_year': '2018', 'end_year': '2021'}) sector = { 'new_focus': new_sector, '2015-2017': old_sector } data = { 'sector': sector } return self.jp_response(s_code='HTTP_200_OK', data=data) except Exception as e: return self.jp_error_response('HTTP_500_INTERNAL_SERVER_ERROR', 'EXCEPTION', [str(e), ]) def get_map_data(year, sdg='', budget_source='', recipient_country='', sector='', project_id='', budget_type='', signature_solution='', sdg_target='', marker_type='', marker_id='', provide_output=''): active_projects = get_active_projects_for_year(year, operating_unit=recipient_country, budget_source=budget_source) projects_query = get_project_query(year, operating_unit=recipient_country, budget_source=budget_source, sdg=sdg, sector=sector) projects = Project.objects.filter(projects_query & Q(project_id__in=active_projects)).distinct() fund_query = get_fund_split_query(year, budget_source=budget_source, operating_unit=recipient_country, sdg=sdg, sector=sector, project_id=project_id, budget_type=budget_type, signature_solution=signature_solution, sdg_target=sdg_target, marker_type=marker_type, marker_id=marker_id) donor_query = fund_query & Q(project__in=projects) countries = DonorFundSplitUp.objects.filter(donor_query).distinct().prefetch_related('project', 'output') countries = countries.values('output__operating_unit') \ .annotate(project_count=Count('project', distinct=True), output_count=Count('output', distinct=True), donor_count=Count('organisation', distinct=True), total_budget=Coalesce(Sum('budget'), 0), total_expense=Coalesce(Sum('expense'), 0), operating_unit_name=F('output__operating_unit__name'), operating_unit_iso3=F('output__operating_unit__iso3'), operating_unit_iso2=F('output__operating_unit__iso2'), operating_unit_unit_type=F('output__operating_unit__unit_type'), operating_unit_latitude=F('output__operating_unit__latitude'), operating_unit_longitude=F('output__operating_unit__longitude'), ) if year and int(year) >= SDG_START_YEAR and sdg or int(year) >= SDG_START_YEAR and sdg_target: serializer = MapDetailsSdgSerializer(countries, many=True, context={'year':
#!/usr/bin/env python # # Copyright (C) 2017 ShadowMan # """ A high-level overview of the framing is given in the following figure. B 0 * * * * * * * 1 * * * * * * * 2 * * * * * * * 3 * * * * * * * - \| \| \| \| \| 0 \| 1 \| 2 \| 3 \| i 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 \| +-+-+-+-+-------+-+-------------+-------------------------------+ \|F\|R\|R\|R\| opcode\|M\| Payload len \| Extended payload length \| \|I\|S\|S\|S\| (4) \|A\| (7) \| (16/64) \| \|N\|V\|V\|V\| \|S\| \| (if payload len==126/127) \| \| \|1\|2\|3\| \|K\| \| \| +-+-+-+-+-------+-+-------------+ - - - - - - - - - - - - - - - + \| Extended payload length continued, if payload len == 127 \| + - - - - - - - - - - - - - - - +-------------------------------+ \| \|Masking-key, if MASK set to 1 \| +-------------------------------+-------------------------------+ \| Masking-key (continued) \| Payload Data \| +-------------------------------- - - - - - - - - - - - - - - - + : Payload Data continued ... : + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + \| Payload Data continued ... \| +---------------------------------------------------------------+ FIN: 1 bit Indicates that this is the final fragment in a message. The first fragment MAY also be the final fragment. RSV1, RSV2, RSV3: 1 bit each MUST be 0 unless an extension is negotiated that defines meanings for non-zero values. If a nonzero value is received and none of the negotiated extensions defines the meaning of such a nonzero value, the receiving endpoint MUST _Fail the WebSocket Connection_. Opcode: 4 bits Defines the interpretation of the "Payload data". If an unknown opcode is received, the receiving endpoint MUST _Fail the WebSocket Connection_. The following values are defined. * %x0 denotes a continuation frame * %x1 denotes a text frame * %x2 denotes a binary frame * %x3-7 are reserved for further non-control frames * %x8 denotes a connection close * %x9 denotes a ping * %xA denotes a pong * %xB-F are reserved for further control frames Mask: 1 bit Defines whether the "Payload data" is masked. If set to 1, a masking key is present in masking-key, and this is used to unmask the "Payload data" as per Section 5.3. All frames sent from client to server have this bit set to 1. Payload length: 7 bits, 7+16 bits, or 7+64 bits The length of the "Payload data", in bytes: if 0-125, that is the payload length. If 126, the following 2 bytes interpreted as a 16-bit unsigned integer are the payload length. If 127, the following 8 bytes interpreted as a 64-bit unsigned integer (the most significant bit MUST be 0) are the payload length. Multi-byte length quantities are expressed in network byte order. Note that in all cases, the minimal number of bytes MUST be used to encode the length, for example, the length of a 124-byte-long string can't be encoded as the sequence 126, 0, 124. The payload length is the length of the "Extension data" + the length of the "Application data". The length of the "Extension data" may be zero, in which case the payload length is the length of the "Application data". Masking-key: 0 or 4 bytes All frames sent from the client to the server are masked by a 32-bit value that is contained within the frame. This field is present if the mask bit is set to 1 and is absent if the mask bit is set to 0. Payload data: (x+y) bytes The "Payload data" is defined as "Extension data" concatenated with "Application data". Extension data: x bytes The "Extension data" is 0 bytes unless an extension has been negotiated. Any extension MUST specify the length of the "Extension data", or how that length may be calculated, and how the extension use MUST be negotiated during the opening handshake. If present, the "Extension data" is included in the total payload length. Application data: y bytes Arbitrary "Application data", taking up the remainder of the frame after any "Extension data". The length of the "Application data" is equal to the payload length minus the length of the "Extension data". """ import os import abc import struct from websocket.utils import ( generic, ws_utils, exceptions, packet, logger ) def ws_transform_payload_data(data, mask_key): if not isinstance(mask_key, int): # from string transition to int if isinstance(mask_key, str): mask_key = int(mask_key, 16) else: raise KeyError('mask key must be hex int') if not isinstance(data, (str, bytes)): raise KeyError('data must be str or bytes type') # Octet i of the transformed data is the XOR of octet i of the original # data with octet at index i modulo 4 of the masking key mask_key_octet = { 0: (mask_key & 0xff000000) >> 24, 1: (mask_key & 0x00ff0000) >> 16, 2: (mask_key & 0x0000ff00) >> 8, 3: mask_key & 0x000000ff } transformed_string = b'' for index, value in enumerate(generic.to_bytes(data)): transformed_string += struct.pack( '!B', (value ^ mask_key_octet[index % 4]) & 0xff) return transformed_string def parse_frame_length(frame_header): if not isinstance(frame_header, (str, bytes)): raise KeyError('frame_header must be str or bytes type') header = packet.ByteArray(frame_header) if len(header) < 2: logger.warning('receive less than 2-bytes') raise RuntimeError('frame header less than 2-bytes') # first bit is MASK flag payload_length = packet.bits_to_integer(header.get_bits(1)[1:]) # if 0-125, that is the payload length if payload_length <= 125: # if frame is client-to-server, payload length does not include mask-key if header.get_bits(1)[0] is 1: return payload_length + 6 return payload_length + 2 # If 126, the following 2 bytes interpreted as a # 16-bit unsigned integer are the payload length elif payload_length == 126: # Payload length field is in [2-4)bytes if len(header) < 4: raise exceptions.FrameHeaderParseError( 'payload length flag is 126, but header length is {}'.format( len(header))) if header.get_bits(1)[0] is 1: return packet.bits_to_integer( generic.flatten_list(header.get_bits(2, 2))) + 8 return packet.bits_to_integer( generic.flatten_list(header.get_bits(2, 2))) + 4 # If 127, the following 8 bytes interpreted as a # 64-bit unsigned integer (the most significant bit # MUST be 0) are the payload length. elif payload_length == 127: # Payload length field is in [2-10)bytes if len(header) < 10: raise exceptions.FrameHeaderParseError( 'payload length flag is 127, but header length is {}'.format( len(header))) if header.get_bits(1)[0] is 1: return packet.bits_to_integer( generic.flatten_list(header.get_bits(2, 2))) + 14 return packet.bits_to_integer( generic.flatten_list(header.get_bits(2, 2))) + 10 raise exceptions.FatalError('internal error') # using for judge frame type Text_Frame = b'Text Frame' Binary_Frame = b'Binary Frame' Close_Frame = b'Close Frame' class FrameBase(object, metaclass=abc.ABCMeta): _global_frame_type = { 0x0: b'Continuation Frame', 0x1: b'Text Frame', 0x2: b'Binary Frame', 0x3: b'Non-Control Frame', 0x4: b'Non-Control Frame', 0x5: b'Non-Control Frame', 0x6: b'Non-Control Frame', 0x7: b'Non-Control Frame', 0x8: b'Close Frame', 0x9: b'Ping Frame', 0xA: b'Pong Frame', 0xB: b'Control Frame', 0xC: b'Control Frame', 0xD: b'Control Frame', 0xE: b'Control Frame', 0xF: b'Control Frame', } def __init__(self, byte_array): if not isinstance(byte_array, packet.ByteArray): raise RuntimeError('the byte array is invalid') # initializing all websocket-frame flags self._flag_fin = 1 self._flag_rsv1 = 0 self._flag_rsv2 = 0 self._flag_rsv3 = 0 self._flag_opcode = 1 # Byte index: 2 self._flag_mask = 0 self._flag_payload_length = 0 self._payload_length = 0 # Byte index: [3,7) self._mask_key = False # payload data self._payload_data = None self._byte_array = byte_array # parse frame self.parse_octet() def parse_octet(self): # first byte(8-bits) # +-+-+-+-+-------+ # \|F\|R\|R\|R\| opcode\| # \|I\|S\|S\|S\| (4) \| # \|N\|V\|V\|V\| \| # \| \|1\|2\|3\| \| # +-+-+-+-+-------+ self._flag_fin = self._byte_array.get_bit(0, 0) self._flag_rsv1 = self._byte_array.get_bit(0, 1) self._flag_rsv2 = self._byte_array.get_bit(0, 2) self._flag_rsv3 = self._byte_array.get_bit(0, 3) self._flag_opcode = packet.bits_to_integer( self._byte_array.get_bits(0)[4:]) # second byte(8-bits) # +-+-------------+ # \|M\| Payload len \| # \|A\| (7) \| # \|S\| \| # \|K\| \| # +-+-+-+-+-------+ self._flag_mask = self._byte_array.get_bit(1, 0)
brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Window, brush) brush = QtGui.QBrush(QtGui.QColor(0, 0, 0)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Shadow, brush) brush = QtGui.QBrush(QtGui.QColor(127, 255, 127)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.AlternateBase, brush) brush = QtGui.QBrush(QtGui.QColor(255, 255, 220)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.ToolTipBase, brush) brush = QtGui.QBrush(QtGui.QColor(0, 0, 0)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.ToolTipText, brush) brush = QtGui.QBrush(QtGui.QColor(0, 0, 0, 128)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.PlaceholderText, brush) brush = QtGui.QBrush(QtGui.QColor(0, 0, 0)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.WindowText, brush) brush = QtGui.QBrush(QtGui.QColor(0, 255, 0)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Button, brush) brush = QtGui.QBrush(QtGui.QColor(127, 255, 127)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Light, brush) brush = QtGui.QBrush(QtGui.QColor(63, 255, 63)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Midlight, brush) brush = QtGui.QBrush(QtGui.QColor(0, 127, 0)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Dark, brush) brush = QtGui.QBrush(QtGui.QColor(0, 170, 0)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Mid, brush) brush = QtGui.QBrush(QtGui.QColor(0, 0, 0)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Text, brush) brush = QtGui.QBrush(QtGui.QColor(255, 255, 255)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.BrightText, brush) brush = QtGui.QBrush(QtGui.QColor(0, 0, 0)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.ButtonText, brush) brush = QtGui.QBrush(QtGui.QColor(255, 255, 255)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Base, brush) brush = QtGui.QBrush(QtGui.QColor(0, 255, 0)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Window, brush) brush = QtGui.QBrush(QtGui.QColor(0, 0, 0)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Shadow, brush) brush = QtGui.QBrush(QtGui.QColor(127, 255, 127)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.AlternateBase, brush) brush = QtGui.QBrush(QtGui.QColor(255, 255, 220)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.ToolTipBase, brush) brush = QtGui.QBrush(QtGui.QColor(0, 0, 0)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.ToolTipText, brush) brush = QtGui.QBrush(QtGui.QColor(0, 0, 0, 128)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.PlaceholderText, brush) brush = QtGui.QBrush(QtGui.QColor(0, 127, 0)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.WindowText, brush) brush = QtGui.QBrush(QtGui.QColor(0, 255, 0)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Button, brush) brush = QtGui.QBrush(QtGui.QColor(127, 255, 127)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Light, brush) brush = QtGui.QBrush(QtGui.QColor(63, 255, 63)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Midlight, brush) brush = QtGui.QBrush(QtGui.QColor(0, 127, 0)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Dark, brush) brush = QtGui.QBrush(QtGui.QColor(0, 170, 0)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Mid, brush) brush = QtGui.QBrush(QtGui.QColor(0, 127, 0)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Text, brush) brush = QtGui.QBrush(QtGui.QColor(255, 255, 255)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.BrightText, brush) brush = QtGui.QBrush(QtGui.QColor(0, 127, 0)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.ButtonText, brush) brush = QtGui.QBrush(QtGui.QColor(0, 255, 0)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Base, brush) brush = QtGui.QBrush(QtGui.QColor(0, 255, 0)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Window, brush) brush = QtGui.QBrush(QtGui.QColor(0, 0, 0)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Shadow, brush) brush = QtGui.QBrush(QtGui.QColor(0, 255, 0)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.AlternateBase, brush) brush = QtGui.QBrush(QtGui.QColor(255, 255, 220)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.ToolTipBase, brush) brush = QtGui.QBrush(QtGui.QColor(0, 0, 0)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.ToolTipText, brush) brush = QtGui.QBrush(QtGui.QColor(0, 0, 0, 128)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.PlaceholderText, brush) self.main_progressBarUserName.setPalette(palette) self.main_progressBarUserName.setProperty("value", 100) self.main_progressBarUserName.setAlignment(QtCore.Qt.AlignCenter) self.main_progressBarUserName.setTextDirection(QtWidgets.QProgressBar.TopToBottom) self.main_progressBarUserName.setObjectName("main_progressBarUserName") self.horizontalLayout_11.addWidget(self.main_progressBarUserName) self.verticalLayout_9.addLayout(self.horizontalLayout_11) self.verticalLayout_13.addLayout(self.verticalLayout_9) self.groupBox_9 = QtWidgets.QGroupBox(self.splitter_8) self.groupBox_9.setMaximumSize(QtCore.QSize(16777215, 16777215)) self.groupBox_9.setObjectName("groupBox_9") self.verticalLayout_48 = QtWidgets.QVBoxLayout(self.groupBox_9) self.verticalLayout_48.setObjectName("verticalLayout_48") self.verticalLayout_47 = QtWidgets.QVBoxLayout() self.verticalLayout_47.setObjectName("verticalLayout_47") self.verticalLayout_23 = QtWidgets.QVBoxLayout() self.verticalLayout_23.setObjectName("verticalLayout_23") self.main_editNikWidth = QtWidgets.QLineEdit(self.groupBox_9) font = QtGui.QFont() font.setFamily("Courier New") font.setPointSize(10) font.setBold(True) font.setWeight(75) self.main_editNikWidth.setFont(font) self.main_editNikWidth.setText("") self.main_editNikWidth.setAlignment(QtCore.Qt.AlignCenter) self.main_editNikWidth.setClearButtonEnabled(True) self.main_editNikWidth.setObjectName("main_editNikWidth") self.verticalLayout_23.addWidget(self.main_editNikWidth) self.horizontalLayout_16 = QtWidgets.QHBoxLayout() self.horizontalLayout_16.setObjectName("horizontalLayout_16") spacerItem6 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout_16.addItem(spacerItem6) self.main_pb_ShowNikWidth = QtWidgets.QPushButton(self.groupBox_9) self.main_pb_ShowNikWidth.setMinimumSize(QtCore.QSize(100, 0)) self.main_pb_ShowNikWidth.setMaximumSize(QtCore.QSize(100, 23)) self.main_pb_ShowNikWidth.setAutoRepeat(False) self.main_pb_ShowNikWidth.setAutoDefault(False) self.main_pb_ShowNikWidth.setDefault(False) self.main_pb_ShowNikWidth.setFlat(False) self.main_pb_ShowNikWidth.setObjectName("main_pb_ShowNikWidth") self.horizontalLayout_16.addWidget(self.main_pb_ShowNikWidth) self.main_pb_clearNikWidth = QtWidgets.QPushButton(self.groupBox_9) self.main_pb_clearNikWidth.setMinimumSize(QtCore.QSize(23, 23)) self.main_pb_clearNikWidth.setMaximumSize(QtCore.QSize(23, 23)) self.main_pb_clearNikWidth.setText("") self.main_pb_clearNikWidth.setObjectName("main_pb_clearNikWidth") self.horizontalLayout_16.addWidget(self.main_pb_clearNikWidth) spacerItem7 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout_16.addItem(spacerItem7) self.verticalLayout_23.addLayout(self.horizontalLayout_16) self.verticalLayout_47.addLayout(self.verticalLayout_23) self.main_tbShowNikWidth = QtWidgets.QTextBrowser(self.groupBox_9) font = QtGui.QFont() font.setPointSize(9) self.main_tbShowNikWidth.setFont(font) self.main_tbShowNikWidth.setObjectName("main_tbShowNikWidth") self.verticalLayout_47.addWidget(self.main_tbShowNikWidth) self.verticalLayout_48.addLayout(self.verticalLayout_47) self.groupBox_7 = QtWidgets.QGroupBox(self.splitter_9) self.groupBox_7.setObjectName("groupBox_7") self.horizontalLayout_37 = QtWidgets.QHBoxLayout(self.groupBox_7) self.horizontalLayout_37.setObjectName("horizontalLayout_37") self.splitter_7 = QtWidgets.QSplitter(self.groupBox_7) self.splitter_7.setLineWidth(2) self.splitter_7.setOrientation(QtCore.Qt.Horizontal) self.splitter_7.setObjectName("splitter_7") self.splitter_6 = QtWidgets.QSplitter(self.splitter_7) self.splitter_6.setLineWidth(2) self.splitter_6.setOrientation(QtCore.Qt.Vertical) self.splitter_6.setObjectName("splitter_6") self.groupBox_4 = QtWidgets.QGroupBox(self.splitter_6) self.groupBox_4.setObjectName("groupBox_4") self.verticalLayout_50 = QtWidgets.QVBoxLayout(self.groupBox_4) self.verticalLayout_50.setObjectName("verticalLayout_50") self.verticalLayout_49 = QtWidgets.QVBoxLayout() self.verticalLayout_49.setObjectName("verticalLayout_49") self.verticalLayout_17 = QtWidgets.QVBoxLayout() self.verticalLayout_17.setObjectName("verticalLayout_17") self.main_editIP = QtWidgets.QLineEdit(self.groupBox_4) font = QtGui.QFont() font.setFamily("Courier New") font.setPointSize(10) font.setBold(True) font.setWeight(75) self.main_editIP.setFont(font) self.main_editIP.setText("") self.main_editIP.setAlignment(QtCore.Qt.AlignCenter) self.main_editIP.setClearButtonEnabled(True) self.main_editIP.setObjectName("main_editIP") self.verticalLayout_17.addWidget(self.main_editIP) self.horizontalLayout_14 = QtWidgets.QHBoxLayout() self.horizontalLayout_14.setObjectName("horizontalLayout_14") spacerItem8 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout_14.addItem(spacerItem8) self.main_pb_ShowIPInfo = QtWidgets.QPushButton(self.groupBox_4) self.main_pb_ShowIPInfo.setMinimumSize(QtCore.QSize(100, 0)) self.main_pb_ShowIPInfo.setMaximumSize(QtCore.QSize(100, 23)) self.main_pb_ShowIPInfo.setAutoRepeat(False) self.main_pb_ShowIPInfo.setAutoDefault(False) self.main_pb_ShowIPInfo.setDefault(False) self.main_pb_ShowIPInfo.setFlat(False) self.main_pb_ShowIPInfo.setObjectName("main_pb_ShowIPInfo") self.horizontalLayout_14.addWidget(self.main_pb_ShowIPInfo) self.main_pb_clearSearchIP = QtWidgets.QPushButton(self.groupBox_4) self.main_pb_clearSearchIP.setMinimumSize(QtCore.QSize(23, 23)) self.main_pb_clearSearchIP.setMaximumSize(QtCore.QSize(23, 23)) self.main_pb_clearSearchIP.setText("") self.main_pb_clearSearchIP.setObjectName("main_pb_clearSearchIP") self.horizontalLayout_14.addWidget(self.main_pb_clearSearchIP) spacerItem9 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout_14.addItem(spacerItem9) self.verticalLayout_17.addLayout(self.horizontalLayout_14) self.verticalLayout_49.addLayout(self.verticalLayout_17) self.main_tbShowIPInfo = QtWidgets.QTextBrowser(self.groupBox_4) font = QtGui.QFont() font.setPointSize(9) self.main_tbShowIPInfo.setFont(font) self.main_tbShowIPInfo.setObjectName("main_tbShowIPInfo") self.verticalLayout_49.addWidget(self.main_tbShowIPInfo) self.verticalLayout_50.addLayout(self.verticalLayout_49) self.groupBox_8 = QtWidgets.QGroupBox(self.splitter_6) self.groupBox_8.setMaximumSize(QtCore.QSize(16777215, 16777215)) self.groupBox_8.setObjectName("groupBox_8") self.verticalLayout_52 = QtWidgets.QVBoxLayout(self.groupBox_8) self.verticalLayout_52.setObjectName("verticalLayout_52") self.verticalLayout_51 = QtWidgets.QVBoxLayout() self.verticalLayout_51.setObjectName("verticalLayout_51") self.verticalLayout_22 = QtWidgets.QVBoxLayout() self.verticalLayout_22.setObjectName("verticalLayout_22") self.main_editEMAIL = QtWidgets.QLineEdit(self.groupBox_8) font = QtGui.QFont() font.setFamily("Courier New") font.setPointSize(10) font.setBold(True) font.setWeight(75) self.main_editEMAIL.setFont(font) self.main_editEMAIL.setText("") self.main_editEMAIL.setAlignment(QtCore.Qt.AlignCenter) self.main_editEMAIL.setClearButtonEnabled(True) self.main_editEMAIL.setObjectName("main_editEMAIL") self.verticalLayout_22.addWidget(self.main_editEMAIL) self.horizontalLayout_15 = QtWidgets.QHBoxLayout() self.horizontalLayout_15.setObjectName("horizontalLayout_15") spacerItem10 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout_15.addItem(spacerItem10) self.main_pb_ShowEMAIL = QtWidgets.QPushButton(self.groupBox_8) self.main_pb_ShowEMAIL.setMinimumSize(QtCore.QSize(100, 0)) self.main_pb_ShowEMAIL.setMaximumSize(QtCore.QSize(100, 23)) self.main_pb_ShowEMAIL.setAutoRepeat(False) self.main_pb_ShowEMAIL.setAutoDefault(False) self.main_pb_ShowEMAIL.setDefault(False) self.main_pb_ShowEMAIL.setFlat(False) self.main_pb_ShowEMAIL.setObjectName("main_pb_ShowEMAIL") self.horizontalLayout_15.addWidget(self.main_pb_ShowEMAIL) self.main_pb_clearEMAIL = QtWidgets.QPushButton(self.groupBox_8) self.main_pb_clearEMAIL.setMinimumSize(QtCore.QSize(23, 23)) self.main_pb_clearEMAIL.setMaximumSize(QtCore.QSize(23, 23)) self.main_pb_clearEMAIL.setText("") self.main_pb_clearEMAIL.setObjectName("main_pb_clearEMAIL") self.horizontalLayout_15.addWidget(self.main_pb_clearEMAIL) spacerItem11 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout_15.addItem(spacerItem11) self.verticalLayout_22.addLayout(self.horizontalLayout_15) self.verticalLayout_51.addLayout(self.verticalLayout_22) self.main_tbShowEMAIL = QtWidgets.QTextBrowser(self.groupBox_8) font = QtGui.QFont() font.setPointSize(9) self.main_tbShowEMAIL.setFont(font) self.main_tbShowEMAIL.setObjectName("main_tbShowEMAIL") self.verticalLayout_51.addWidget(self.main_tbShowEMAIL) self.verticalLayout_52.addLayout(self.verticalLayout_51) self.groupBox_11 = QtWidgets.QGroupBox(self.splitter_7) self.groupBox_11.setMinimumSize(QtCore.QSize(250, 0)) self.groupBox_11.setMaximumSize(QtCore.QSize(340, 16777215)) self.groupBox_11.setObjectName("groupBox_11") self.verticalLayout_54 = QtWidgets.QVBoxLayout(self.groupBox_11) self.verticalLayout_54.setObjectName("verticalLayout_54") self.verticalLayout_53 = QtWidgets.QVBoxLayout() self.verticalLayout_53.setObjectName("verticalLayout_53") self.verticalLayout_28 = QtWidgets.QVBoxLayout() self.verticalLayout_28.setObjectName("verticalLayout_28") self.label = QtWidgets.QLabel(self.groupBox_11) self.label.setAlignment(QtCore.Qt.AlignCenter) self.label.setObjectName("label") self.verticalLayout_28.addWidget(self.label) self.horizontalLayout_36 = QtWidgets.QHBoxLayout() self.horizontalLayout_36.setObjectName("horizontalLayout_36") spacerItem12 = QtWidgets.QSpacerItem(94, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout_36.addItem(spacerItem12) self.main_editGeoObject = QtWidgets.QLineEdit(self.groupBox_11) self.main_editGeoObject.setMinimumSize(QtCore.QSize(90, 0)) self.main_editGeoObject.setMaximumSize(QtCore.QSize(180, 16777215)) font = QtGui.QFont() font.setFamily("Courier New") font.setPointSize(10) font.setBold(True) font.setWeight(75) self.main_editGeoObject.setFont(font) self.main_editGeoObject.setText("") self.main_editGeoObject.setAlignment(QtCore.Qt.AlignCenter) self.main_editGeoObject.setClearButtonEnabled(True) self.main_editGeoObject.setObjectName("main_editGeoObject") self.horizontalLayout_36.addWidget(self.main_editGeoObject) spacerItem13 = QtWidgets.QSpacerItem(94, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout_36.addItem(spacerItem13) self.verticalLayout_28.addLayout(self.horizontalLayout_36) self.horizontalLayout_20 = QtWidgets.QHBoxLayout() self.horizontalLayout_20.setObjectName("horizontalLayout_20") spacerItem14 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout_20.addItem(spacerItem14) self.main_pb_ShowGeoLatLon = QtWidgets.QPushButton(self.groupBox_11) self.main_pb_ShowGeoLatLon.setMinimumSize(QtCore.QSize(100, 23)) self.main_pb_ShowGeoLatLon.setMaximumSize(QtCore.QSize(100, 23)) self.main_pb_ShowGeoLatLon.setAutoRepeat(False) self.main_pb_ShowGeoLatLon.setAutoDefault(False) self.main_pb_ShowGeoLatLon.setDefault(False) self.main_pb_ShowGeoLatLon.setFlat(False) self.main_pb_ShowGeoLatLon.setObjectName("main_pb_ShowGeoLatLon") self.horizontalLayout_20.addWidget(self.main_pb_ShowGeoLatLon) self.main_pb_clearGeoObject = QtWidgets.QPushButton(self.groupBox_11) self.main_pb_clearGeoObject.setMinimumSize(QtCore.QSize(23, 23)) self.main_pb_clearGeoObject.setMaximumSize(QtCore.QSize(23, 23)) self.main_pb_clearGeoObject.setText("") self.main_pb_clearGeoObject.setObjectName("main_pb_clearGeoObject") self.horizontalLayout_20.addWidget(self.main_pb_clearGeoObject) spacerItem15 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout_20.addItem(spacerItem15) self.verticalLayout_28.addLayout(self.horizontalLayout_20) self.verticalLayout_53.addLayout(self.verticalLayout_28) self.main_tbShowGeoObjectLatLon = QtWidgets.QTextBrowser(self.groupBox_11) font = QtGui.QFont() font.setPointSize(8) self.main_tbShowGeoObjectLatLon.setFont(font) self.main_tbShowGeoObjectLatLon.setObjectName("main_tbShowGeoObjectLatLon") self.verticalLayout_53.addWidget(self.main_tbShowGeoObjectLatLon) self.line = QtWidgets.QFrame(self.groupBox_11) self.line.setFrameShape(QtWidgets.QFrame.HLine) self.line.setFrameShadow(QtWidgets.QFrame.Sunken) self.line.setObjectName("line") self.verticalLayout_53.addWidget(self.line) self.verticalLayout_29 = QtWidgets.QVBoxLayout() self.verticalLayout_29.setContentsMargins(-1, -1, -1, 6) self.verticalLayout_29.setObjectName("verticalLayout_29") self.horizontalLayout_27 = QtWidgets.QHBoxLayout() self.horizontalLayout_27.setObjectName("horizontalLayout_27") spacerItem16 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout_27.addItem(spacerItem16) self.label_2 = QtWidgets.QLabel(self.groupBox_11) self.label_2.setAlignment(QtCore.Qt.AlignCenter) self.label_2.setObjectName("label_2") self.horizontalLayout_27.addWidget(self.label_2) spacerItem17 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout_27.addItem(spacerItem17) self.verticalLayout_29.addLayout(self.horizontalLayout_27) self.horizontalLayout_22 = QtWidgets.QHBoxLayout() self.horizontalLayout_22.setObjectName("horizontalLayout_22") spacerItem18 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout_22.addItem(spacerItem18) self.main_editGeo_Lat = QtWidgets.QLineEdit(self.groupBox_11) self.main_editGeo_Lat.setMaximumSize(QtCore.QSize(110, 16777215)) font = QtGui.QFont() font.setFamily("Courier New") font.setPointSize(9) font.setBold(False) font.setWeight(50) self.main_editGeo_Lat.setFont(font) self.main_editGeo_Lat.setText("") self.main_editGeo_Lat.setAlignment(QtCore.Qt.AlignCenter) self.main_editGeo_Lat.setClearButtonEnabled(False) self.main_editGeo_Lat.setObjectName("main_editGeo_Lat") self.horizontalLayout_22.addWidget(self.main_editGeo_Lat) spacerItem19 = QtWidgets.QSpacerItem(20, 20, QtWidgets.QSizePolicy.Fixed, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout_22.addItem(spacerItem19) self.main_editGeo_Lon = QtWidgets.QLineEdit(self.groupBox_11) self.main_editGeo_Lon.setMaximumSize(QtCore.QSize(110, 16777215)) font = QtGui.QFont() font.setFamily("Courier New") font.setPointSize(9) font.setBold(False) font.setWeight(50) self.main_editGeo_Lon.setFont(font) self.main_editGeo_Lon.setText("") self.main_editGeo_Lon.setAlignment(QtCore.Qt.AlignCenter) self.main_editGeo_Lon.setClearButtonEnabled(False) self.main_editGeo_Lon.setObjectName("main_editGeo_Lon") self.horizontalLayout_22.addWidget(self.main_editGeo_Lon) spacerItem20 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout_22.addItem(spacerItem20) self.verticalLayout_29.addLayout(self.horizontalLayout_22) self.horizontalLayout_21 = QtWidgets.QHBoxLayout() self.horizontalLayout_21.setObjectName("horizontalLayout_21") spacerItem21 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout_21.addItem(spacerItem21) self.main_pb_ShowGeoInfo = QtWidgets.QPushButton(self.groupBox_11) self.main_pb_ShowGeoInfo.setMinimumSize(QtCore.QSize(100, 23)) self.main_pb_ShowGeoInfo.setMaximumSize(QtCore.QSize(100, 23)) self.main_pb_ShowGeoInfo.setAutoRepeat(False) self.main_pb_ShowGeoInfo.setAutoDefault(False) self.main_pb_ShowGeoInfo.setDefault(False) self.main_pb_ShowGeoInfo.setFlat(False) self.main_pb_ShowGeoInfo.setObjectName("main_pb_ShowGeoInfo") self.horizontalLayout_21.addWidget(self.main_pb_ShowGeoInfo) self.main_pb_clearGeoInfo = QtWidgets.QPushButton(self.groupBox_11) self.main_pb_clearGeoInfo.setMinimumSize(QtCore.QSize(23, 23)) self.main_pb_clearGeoInfo.setMaximumSize(QtCore.QSize(23, 23)) self.main_pb_clearGeoInfo.setText("") self.main_pb_clearGeoInfo.setObjectName("main_pb_clearGeoInfo") self.horizontalLayout_21.addWidget(self.main_pb_clearGeoInfo) spacerItem22 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout_21.addItem(spacerItem22) self.verticalLayout_29.addLayout(self.horizontalLayout_21) self.verticalLayout_53.addLayout(self.verticalLayout_29) self.main_tbShowGeoObjectInfo = QtWidgets.QTextBrowser(self.groupBox_11) font = QtGui.QFont() font.setPointSize(9) self.main_tbShowGeoObjectInfo.setFont(font) self.main_tbShowGeoObjectInfo.setObjectName("main_tbShowGeoObjectInfo") self.verticalLayout_53.addWidget(self.main_tbShowGeoObjectInfo) self.verticalLayout_54.addLayout(self.verticalLayout_53) self.horizontalLayout_37.addWidget(self.splitter_7) self.horizontalLayout_38.addWidget(self.splitter_9) self.tabMain.addTab(self.tabCommon, "") self.tabGSM = QtWidgets.QWidget() self.tabGSM.setObjectName("tabGSM") self.horizontalLayout_18 = QtWidgets.QHBoxLayout(self.tabGSM) self.horizontalLayout_18.setObjectName("horizontalLayout_18") self.splitter_13 = QtWidgets.QSplitter(self.tabGSM) self.splitter_13.setOrientation(QtCore.Qt.Horizontal) self.splitter_13.setObjectName("splitter_13") self.splitter_12 = QtWidgets.QSplitter(self.splitter_13) self.splitter_12.setOrientation(QtCore.Qt.Horizontal) self.splitter_12.setObjectName("splitter_12") self.splitter_10 = QtWidgets.QSplitter(self.splitter_12) self.splitter_10.setOrientation(QtCore.Qt.Vertical) self.splitter_10.setObjectName("splitter_10") self.groupBox_10 = QtWidgets.QGroupBox(self.splitter_10) self.groupBox_10.setObjectName("groupBox_10") self.horizontalLayout_32 = QtWidgets.QHBoxLayout(self.groupBox_10) self.horizontalLayout_32.setObjectName("horizontalLayout_32") self.verticalLayout_55 = QtWidgets.QVBoxLayout() self.verticalLayout_55.setObjectName("verticalLayout_55") self.verticalLayout_24 = QtWidgets.QVBoxLayout() self.verticalLayout_24.setObjectName("verticalLayout_24") self.main_editIMSI = QtWidgets.QLineEdit(self.groupBox_10) font = QtGui.QFont() font.setFamily("Courier New") font.setPointSize(10) font.setBold(True) font.setWeight(75) self.main_editIMSI.setFont(font) self.main_editIMSI.setText("") self.main_editIMSI.setAlignment(QtCore.Qt.AlignCenter) self.main_editIMSI.setClearButtonEnabled(True) self.main_editIMSI.setObjectName("main_editIMSI") self.verticalLayout_24.addWidget(self.main_editIMSI) self.horizontalLayout_31 = QtWidgets.QHBoxLayout() self.horizontalLayout_31.setObjectName("horizontalLayout_31") spacerItem23 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout_31.addItem(spacerItem23) self.main_pb_ShowIMSI = QtWidgets.QPushButton(self.groupBox_10) self.main_pb_ShowIMSI.setMinimumSize(QtCore.QSize(100, 0)) self.main_pb_ShowIMSI.setMaximumSize(QtCore.QSize(100, 23)) self.main_pb_ShowIMSI.setAutoRepeat(False) self.main_pb_ShowIMSI.setAutoDefault(False) self.main_pb_ShowIMSI.setDefault(False) self.main_pb_ShowIMSI.setFlat(False) self.main_pb_ShowIMSI.setObjectName("main_pb_ShowIMSI") self.horizontalLayout_31.addWidget(self.main_pb_ShowIMSI) self.main_pb_clearIMSI = QtWidgets.QPushButton(self.groupBox_10) self.main_pb_clearIMSI.setMinimumSize(QtCore.QSize(23, 23)) self.main_pb_clearIMSI.setMaximumSize(QtCore.QSize(23, 23)) self.main_pb_clearIMSI.setText("") self.main_pb_clearIMSI.setObjectName("main_pb_clearIMSI") self.horizontalLayout_31.addWidget(self.main_pb_clearIMSI) spacerItem24 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout_31.addItem(spacerItem24) self.verticalLayout_24.addLayout(self.horizontalLayout_31) self.verticalLayout_55.addLayout(self.verticalLayout_24) self.main_tbShowIMSI = QtWidgets.QTextBrowser(self.groupBox_10) font = QtGui.QFont() font.setPointSize(9) self.main_tbShowIMSI.setFont(font) self.main_tbShowIMSI.setObjectName("main_tbShowIMSI") self.verticalLayout_55.addWidget(self.main_tbShowIMSI) self.horizontalLayout_32.addLayout(self.verticalLayout_55) self.groupBox_13 = QtWidgets.QGroupBox(self.splitter_10) self.groupBox_13.setObjectName("groupBox_13") self.horizontalLayout_39 = QtWidgets.QHBoxLayout(self.groupBox_13) self.horizontalLayout_39.setObjectName("horizontalLayout_39") self.verticalLayout_56 = QtWidgets.QVBoxLayout() self.verticalLayout_56.setObjectName("verticalLayout_56") self.verticalLayout_25 = QtWidgets.QVBoxLayout() self.verticalLayout_25.setObjectName("verticalLayout_25") self.main_editIMEI = QtWidgets.QLineEdit(self.groupBox_13) font = QtGui.QFont() font.setFamily("Courier New") font.setPointSize(10) font.setBold(True) font.setWeight(75) self.main_editIMEI.setFont(font) self.main_editIMEI.setText("") self.main_editIMEI.setAlignment(QtCore.Qt.AlignCenter) self.main_editIMEI.setClearButtonEnabled(True) self.main_editIMEI.setObjectName("main_editIMEI") self.verticalLayout_25.addWidget(self.main_editIMEI) self.horizontalLayout_40 = QtWidgets.QHBoxLayout() self.horizontalLayout_40.setObjectName("horizontalLayout_40") spacerItem25 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout_40.addItem(spacerItem25) self.main_pb_ShowIMEI = QtWidgets.QPushButton(self.groupBox_13) self.main_pb_ShowIMEI.setMinimumSize(QtCore.QSize(100, 0)) self.main_pb_ShowIMEI.setMaximumSize(QtCore.QSize(100, 23)) self.main_pb_ShowIMEI.setAutoRepeat(False) self.main_pb_ShowIMEI.setAutoDefault(False) self.main_pb_ShowIMEI.setDefault(False) self.main_pb_ShowIMEI.setFlat(False) self.main_pb_ShowIMEI.setObjectName("main_pb_ShowIMEI") self.horizontalLayout_40.addWidget(self.main_pb_ShowIMEI) self.main_pb_clearIMEI = QtWidgets.QPushButton(self.groupBox_13) self.main_pb_clearIMEI.setMinimumSize(QtCore.QSize(23, 23)) self.main_pb_clearIMEI.setMaximumSize(QtCore.QSize(23, 23)) self.main_pb_clearIMEI.setText("") self.main_pb_clearIMEI.setObjectName("main_pb_clearIMEI") self.horizontalLayout_40.addWidget(self.main_pb_clearIMEI) spacerItem26 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout_40.addItem(spacerItem26) self.verticalLayout_25.addLayout(self.horizontalLayout_40) self.verticalLayout_56.addLayout(self.verticalLayout_25) self.main_tbShowIMEI = QtWidgets.QTextBrowser(self.groupBox_13) font = QtGui.QFont() font.setPointSize(9) self.main_tbShowIMEI.setFont(font) self.main_tbShowIMEI.setObjectName("main_tbShowIMEI") self.verticalLayout_56.addWidget(self.main_tbShowIMEI) self.horizontalLayout_39.addLayout(self.verticalLayout_56) self.splitter_11 = QtWidgets.QSplitter(self.splitter_12) self.splitter_11.setOrientation(QtCore.Qt.Vertical) self.splitter_11.setObjectName("splitter_11") self.groupBox_15 = QtWidgets.QGroupBox(self.splitter_11) self.groupBox_15.setObjectName("groupBox_15") self.horizontalLayout_44 = QtWidgets.QHBoxLayout(self.groupBox_15) self.horizontalLayout_44.setObjectName("horizontalLayout_44") self.verticalLayout_57 = QtWidgets.QVBoxLayout() self.verticalLayout_57.setObjectName("verticalLayout_57") self.verticalLayout_26 = QtWidgets.QVBoxLayout() self.verticalLayout_26.setObjectName("verticalLayout_26") self.main_editISDN = QtWidgets.QLineEdit(self.groupBox_15) font = QtGui.QFont() font.setFamily("Courier New") font.setPointSize(10) font.setBold(True) font.setWeight(75) self.main_editISDN.setFont(font) self.main_editISDN.setText("") self.main_editISDN.setAlignment(QtCore.Qt.AlignCenter) self.main_editISDN.setClearButtonEnabled(True) self.main_editISDN.setObjectName("main_editISDN") self.verticalLayout_26.addWidget(self.main_editISDN) self.horizontalLayout_43 = QtWidgets.QHBoxLayout() self.horizontalLayout_43.setObjectName("horizontalLayout_43") spacerItem27 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout_43.addItem(spacerItem27) self.main_pb_ShowISDN = QtWidgets.QPushButton(self.groupBox_15) self.main_pb_ShowISDN.setMinimumSize(QtCore.QSize(100, 0)) self.main_pb_ShowISDN.setMaximumSize(QtCore.QSize(100, 23)) self.main_pb_ShowISDN.setAutoRepeat(False) self.main_pb_ShowISDN.setAutoDefault(False) self.main_pb_ShowISDN.setDefault(False) self.main_pb_ShowISDN.setFlat(False) self.main_pb_ShowISDN.setObjectName("main_pb_ShowISDN") self.horizontalLayout_43.addWidget(self.main_pb_ShowISDN) self.main_pb_clearISDN = QtWidgets.QPushButton(self.groupBox_15) self.main_pb_clearISDN.setMinimumSize(QtCore.QSize(23, 23)) self.main_pb_clearISDN.setMaximumSize(QtCore.QSize(23, 23)) self.main_pb_clearISDN.setText("") self.main_pb_clearISDN.setObjectName("main_pb_clearISDN") self.horizontalLayout_43.addWidget(self.main_pb_clearISDN) spacerItem28 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout_43.addItem(spacerItem28) self.verticalLayout_26.addLayout(self.horizontalLayout_43) self.verticalLayout_57.addLayout(self.verticalLayout_26) self.main_tbShowMSISDN = QtWidgets.QTextBrowser(self.groupBox_15) font = QtGui.QFont() font.setPointSize(9) self.main_tbShowMSISDN.setFont(font) self.main_tbShowMSISDN.setObjectName("main_tbShowMSISDN") self.verticalLayout_57.addWidget(self.main_tbShowMSISDN) self.horizontalLayout_44.addLayout(self.verticalLayout_57) self.groupBox_16 = QtWidgets.QGroupBox(self.splitter_11) self.groupBox_16.setObjectName("groupBox_16") self.verticalLayout_11 = QtWidgets.QVBoxLayout(self.groupBox_16) self.verticalLayout_11.setObjectName("verticalLayout_11") self.verticalLayout_58 = QtWidgets.QVBoxLayout() self.verticalLayout_58.setObjectName("verticalLayout_58") self.verticalLayout_27 = QtWidgets.QVBoxLayout() self.verticalLayout_27.setObjectName("verticalLayout_27") self.main_edit_Wifi = QtWidgets.QLineEdit(self.groupBox_16) font = QtGui.QFont() font.setFamily("Courier New") font.setPointSize(10) font.setBold(True) font.setWeight(75) self.main_edit_Wifi.setFont(font) self.main_edit_Wifi.setText("") self.main_edit_Wifi.setAlignment(QtCore.Qt.AlignCenter) self.main_edit_Wifi.setClearButtonEnabled(True) self.main_edit_Wifi.setObjectName("main_edit_Wifi") self.verticalLayout_27.addWidget(self.main_edit_Wifi) self.horizontalLayout_45 = QtWidgets.QHBoxLayout() self.horizontalLayout_45.setObjectName("horizontalLayout_45") spacerItem29 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout_45.addItem(spacerItem29) self.main_pb_ShowWifi = QtWidgets.QPushButton(self.groupBox_16) self.main_pb_ShowWifi.setMinimumSize(QtCore.QSize(100, 0)) self.main_pb_ShowWifi.setMaximumSize(QtCore.QSize(100, 23)) self.main_pb_ShowWifi.setAutoRepeat(False) self.main_pb_ShowWifi.setAutoDefault(False) self.main_pb_ShowWifi.setDefault(False) self.main_pb_ShowWifi.setFlat(False) self.main_pb_ShowWifi.setObjectName("main_pb_ShowWifi") self.horizontalLayout_45.addWidget(self.main_pb_ShowWifi) self.main_pb_clearWifi = QtWidgets.QPushButton(self.groupBox_16) self.main_pb_clearWifi.setMinimumSize(QtCore.QSize(23, 23)) self.main_pb_clearWifi.setMaximumSize(QtCore.QSize(23, 23)) self.main_pb_clearWifi.setText("") self.main_pb_clearWifi.setObjectName("main_pb_clearWifi") self.horizontalLayout_45.addWidget(self.main_pb_clearWifi) spacerItem30 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout_45.addItem(spacerItem30) self.verticalLayout_27.addLayout(self.horizontalLayout_45) self.verticalLayout_58.addLayout(self.verticalLayout_27)
that are present in both. The Jaccard distance is a simple measure of the dissimilarity between two StructureGraphs (ignoring edge weights), and is defined by 1 - (size of the intersection / size of the union) of the sets of edges. This is returned with key 'dist'. Important note: all node indices are in terms of the StructureGraph this method is called from, not the 'other' StructureGraph: there is no guarantee the node indices will be the same if the underlying Structures are ordered differently. :param other: StructureGraph :param strict: if False, will compare bonds from different Structures, with node indices replaced by Species strings, will not count number of occurrences of bonds :return: """ if self.structure != other.structure and strict: return ValueError("Meaningless to compare StructureGraphs if " "corresponding Structures are different.") if strict: # sort for consistent node indices # PeriodicSite should have a proper __hash__() value, # using its frac_coords as a convenient key mapping = {tuple(site.frac_coords): self.structure.index(site) for site in other.structure} other_sorted = other.__copy__() other_sorted.sort(key=lambda site: mapping[tuple(site.frac_coords)]) edges = {(u, v, d["to_jimage"]) for u, v, d in self.graph.edges(keys=False, data=True)} edges_other = {(u, v, d["to_jimage"]) for u, v, d in other_sorted.graph.edges(keys=False, data=True)} else: edges = { (str(self.structure[u].specie), str(self.structure[v].specie)) for u, v, d in self.graph.edges(keys=False, data=True) } edges_other = { (str(other.structure[u].specie), str(other.structure[v].specie)) for u, v, d in other.graph.edges(keys=False, data=True) } if len(edges) == 0 and len(edges_other) == 0: jaccard_dist = 0 # by definition else: jaccard_dist = 1 - len(edges.intersection(edges_other)) / len(edges.union(edges_other)) return { "self": edges - edges_other, "other": edges_other - edges, "both": edges.intersection(edges_other), "dist": jaccard_dist, } def get_subgraphs_as_molecules(self, use_weights=False): """ Retrieve subgraphs as molecules, useful for extracting molecules from periodic crystals. Will only return unique molecules, not any duplicates present in the crystal (a duplicate defined as an isomorphic subgraph). :param use_weights (bool): If True, only treat subgraphs as isomorphic if edges have the same weights. Typically, this means molecules will need to have the same bond lengths to be defined as duplicates, otherwise bond lengths can differ. This is a fairly robust approach, but will treat e.g. enantiomers as being duplicates. :return: list of unique Molecules in Structure """ # creating a supercell is an easy way to extract # molecules (and not, e.g., layers of a 2D crystal) # without adding extra logic if getattr(self, "_supercell_sg", None) is None: self._supercell_sg = supercell_sg = self * (3, 3, 3) # make undirected to find connected subgraphs supercell_sg.graph = nx.Graph(supercell_sg.graph) # find subgraphs all_subgraphs = [supercell_sg.graph.subgraph(c) for c in nx.connected_components(supercell_sg.graph)] # discount subgraphs that lie across supercell boundaries # these will subgraphs representing crystals molecule_subgraphs = [] for subgraph in all_subgraphs: intersects_boundary = any(d["to_jimage"] != (0, 0, 0) for u, v, d in subgraph.edges(data=True)) if not intersects_boundary: molecule_subgraphs.append(nx.MultiDiGraph(subgraph)) # add specie names to graph to be able to test for isomorphism for subgraph in molecule_subgraphs: for n in subgraph: subgraph.add_node(n, specie=str(supercell_sg.structure[n].specie)) # now define how we test for isomorphism def node_match(n1, n2): return n1["specie"] == n2["specie"] def edge_match(e1, e2): if use_weights: return e1["weight"] == e2["weight"] return True # prune duplicate subgraphs unique_subgraphs = [] for subgraph in molecule_subgraphs: already_present = [ nx.is_isomorphic(subgraph, g, node_match=node_match, edge_match=edge_match) for g in unique_subgraphs ] if not any(already_present): unique_subgraphs.append(subgraph) # get Molecule objects for each subgraph molecules = [] for subgraph in unique_subgraphs: coords = [supercell_sg.structure[n].coords for n in subgraph.nodes()] species = [supercell_sg.structure[n].specie for n in subgraph.nodes()] molecule = Molecule(species, coords) # shift so origin is at center of mass molecule = molecule.get_centered_molecule() molecules.append(molecule) return molecules class MolGraphSplitError(Exception): """ Raised when a molecule graph is failed to split into two disconnected subgraphs """ pass class MoleculeGraph(MSONable): """ This is a class for annotating a Molecule with bond information, stored in the form of a graph. A "bond" does not necessarily have to be a chemical bond, but can store any kind of information that connects two Sites. """ def __init__(self, molecule, graph_data=None): """ If constructing this class manually, use the `with_empty_graph` method or `with_local_env_strategy` method (using an algorithm provided by the `local_env` module, such as O'Keeffe). This class that contains connection information: relationships between sites represented by a Graph structure, and an associated structure object. This class uses the NetworkX package to store and operate on the graph itself, but contains a lot of helper methods to make associating a graph with a given molecule easier. Use cases for this include storing bonding information, NMR J-couplings, Heisenberg exchange parameters, etc. :param molecule: Molecule object :param graph_data: dict containing graph information in dict format (not intended to be constructed manually, see as_dict method for format) """ if isinstance(molecule, MoleculeGraph): # just make a copy from input graph_data = molecule.as_dict()["graphs"] self.molecule = molecule self.graph = nx.readwrite.json_graph.adjacency_graph(graph_data) # tidy up edge attr dicts, reading to/from json duplicates # information for u, v, k, d in self.graph.edges(keys=True, data=True): if "id" in d: del d["id"] if "key" in d: del d["key"] # ensure images are tuples (conversion to lists happens # when serializing back from json), it's important images # are hashable/immutable if "to_jimage" in d: d["to_jimage"] = tuple(d["to_jimage"]) if "from_jimage" in d: d["from_jimage"] = tuple(d["from_jimage"]) self.set_node_attributes() @classmethod def with_empty_graph(cls, molecule, name="bonds", edge_weight_name=None, edge_weight_units=None): """ Constructor for MoleculeGraph, returns a MoleculeGraph object with an empty graph (no edges, only nodes defined that correspond to Sites in Molecule). :param molecule (Molecule): :param name (str): name of graph, e.g. "bonds" :param edge_weight_name (str): name of edge weights, e.g. "bond_length" or "exchange_constant" :param edge_weight_units (str): name of edge weight units e.g. "Å" or "eV" :return (MoleculeGraph): """ if edge_weight_name and (edge_weight_units is None): raise ValueError( "Please specify units associated " "with your edge weights. Can be " "empty string if arbitrary or " "dimensionless." ) # construct graph with one node per site # graph attributes don't change behavior of graph, # they're just for book-keeping graph = nx.MultiDiGraph( edge_weight_name=edge_weight_name, edge_weight_units=edge_weight_units, name=name, ) graph.add_nodes_from(range(len(molecule))) graph_data = json_graph.adjacency_data(graph) return cls(molecule, graph_data=graph_data) @staticmethod def with_edges(molecule, edges): """ Constructor for MoleculeGraph, using pre-existing or pre-defined edges with optional edge parameters. :param molecule: Molecule object :param edges: dict representing the bonds of the functional group (format: {(u, v): props}, where props is a dictionary of properties, including weight. Props should be None if no additional properties are to be specified. :return: mg, a MoleculeGraph """ mg = MoleculeGraph.with_empty_graph(molecule, name="bonds", edge_weight_name="weight", edge_weight_units="") for edge, props in edges.items(): try: from_index = edge[0] to_index = edge[1] except TypeError: raise ValueError("Edges must be given as (from_index, to_index)" "tuples") if props is not None: if "weight" in props.keys(): weight = props["weight"] del props["weight"] else: weight = None if len(props.items()) == 0: props = None else: weight = None nodes = mg.graph.nodes if not (from_index in nodes and to_index in nodes): raise ValueError( "Edges cannot be added if nodes are not" " present in the graph. Please check your" " indices." ) mg.add_edge(from_index, to_index, weight=weight, edge_properties=props) mg.set_node_attributes() return mg @staticmethod def with_local_env_strategy(molecule, strategy): """ Constructor for MoleculeGraph, using a strategy from :Class: `pymatgen.analysis.local_env`. :param molecule: Molecule object :param strategy: an instance of a :Class: `pymatgen.analysis.local_env.NearNeighbors` object :return: mg, a MoleculeGraph """ if not strategy.molecules_allowed: raise ValueError( "Chosen strategy is not designed for use with molecules! " "Please choose another strategy." ) extend_structure = strategy.extend_structure_molecules mg = MoleculeGraph.with_empty_graph(molecule, name="bonds", edge_weight_name="weight", edge_weight_units="") # NearNeighbor classes only (generally) work with structures # molecules have to be boxed first coords = molecule.cart_coords if extend_structure: a = max(coords[:, 0]) - min(coords[:, 0]) + 100 b = max(coords[:, 1]) - min(coords[:, 1]) + 100 c = max(coords[:, 2]) - min(coords[:, 2]) + 100 structure = molecule.get_boxed_structure(a, b, c, no_cross=True, reorder=False) else: structure = None for n in range(len(molecule)): if structure is None: neighbors = strategy.get_nn_info(molecule, n) else: neighbors = strategy.get_nn_info(structure, n) for neighbor in neighbors: # all bonds in molecules should not cross # (artificial) periodic boundaries if not
hight, n_vertical, n_horizental) # details = [0,0,0] # details[0] = node_index # details[1] = parent_index # details[2] = hight # details[3] = n_vertical # details[4] = n_horizental # p_sym = 1 # return p_sym, details # def do_vertical_stretching(self, neuron): # """ # In one of the segments that coming out from a branching points will be stretched. # """ # (branch_index,) = np.where(neuron.branch_order==2) # (end_nodes,) = np.where(neuron.branch_order==0) # nodes = np.append(branch_index,end_nodes) # parents = neuron.parent_index_for_node_subset(nodes) # n = np.floor(nodes.shape[0]np.random.rand()).astype(int) # p = np.exp(np.random.normal() self.horizental_stretch) # node_index = nodes[n] # parent_index = parents[n] # neuron.vertical_stretch(node_index, parent_index, p) # details = [0,0,0] # details[0] = node_index # details[1] = parent_index # details[2] = p # p_sym = 1 # return p_sym, details # def do_horizental_stretching(self, neuron): # (branch_index,) = np.where(neuron.branch_order==2) # (end_nodes,) = np.where(neuron.branch_order==0) # nodes = np.append(branch_index,end_nodes) # parents = neuron.parent_index_for_node_subset(nodes) # n = np.floor(nodes.shape[0]np.random.rand()).astype(int) # p = np.exp(np.random.normal() self.horizental_stretch) # node_index = nodes[n] # parent_index = parents[n] # neuron.horizental_stretch(node_index, parent_index, p) # details = [0,0,0] # details[0] = node_index # details[1] = parent_index # details[2] = p # p_sym = 1 # return p_sym, details # def undo_MCMC(self, per, details): # """ # when per == 0, details[0] is 'ext' of 'remove'. If it is 'ext', then details[1] is node_index. # if it is 'remove', details[1] = parent, details[2] = location, details[3] = ratio # """ # if per == 'extension/reduction': # undo extension/reduction # if(len(details) !=0): # if(details[0] == 'ext'): # self.undo_ext(self.neuron, details[1]) # if(details[0] == 'remove'): # self.undo_red(self.neuron, details[1], details[2], details[3]) # if per == 'extension/reduction end points': # if(len(details) !=0): # if(details[0] == 'ext'): # self.undo_ext(self.neuron, details[1]) # if(details[0] == 'remove'): # self.undo_red(self.neuron, details[1], details[2], details[3]) # if per == 'location': # undo location # if( ~ self.neuron.is_soma()): # self.undo_location(self.neuron, details[0], details[1], details[2], details[3]) # this function makes a location perturbation on the neuron # if per == 'location for important point': # undo location # if( ~ self.neuron.is_soma()): # self.undo_location_important(self.neuron, details[0], details[1], details[2], details[3]) # this function makes a location perturbation on the neuron # if per == 'location toward end': # if( ~ self.neuron.is_soma()): # self.undo_location_toward_end_nodes(self.neuron, details[0], details[1], details[2], details[3]) # if per == 'diameter': # undo diameter # if( ~ self.neuron.is_soma()): # To make sure that there is at least one node in the no_soma list # self.undo_diameter(self.neuron, details[0], details[1]) # if per == 'rotation for any node': # self.undo_rotation(self.neuron, details[0], details[1] ) # if per == 'rotation for branching': # self.undo_rotation_from_branch(self.neuron, details[0], details[1] ) # if per == 'sliding certain in distance': # undo sliding in certain distance # if(details[0] != 0): # self.undo_sliding(self.neuron, details[0], details[1]) # if per == 'sliding for branching node': # undo sliding for branch # if(details[0] != 0): # self.undo_sliding(self.neuron, details[0], details[1]) # if per == 'sliding general': # undo sliding general # if(details[0] != 0): # self.undo_sliding_general(self.neuron, details[0], details[1]) # if per == 'sliding for branching node certain distance': # do sliding only for branch # if(details[0] != 0): # self.undo_sliding(self.neuron, details[0], details[1]) # if per == 'rescale toward end': # self.undo_rescale_toward_end(self.neuron, details[0],details[1]) # if per == 'stretching vertical': # self.undo_vertical_stretching(self.neuron, details[0], details[1], details[2]) # if per == 'stretching horizental': # self.undo_horizental_stretching(self.neuron, details[0], details[1], details[2]) # if per == 'sinusidal': # self.undo_sinusidal_wave(self.neuron, details[0], details[1], details[2], details[3], details[4]) # def undo_sinusidal_wave(self, neuron, node, parent, hight, n_vertical, n_horizental): # neuron.sinudal(node_index, parent_index, -hight, n_vertical, n_horizental) # def undo_location(self, neuron, index, x, y, z): # neuron.change_location(index, - np.array([x,y,z])) # def undo_location_toward_end_nodes(self, neuron, index, x, y, z): # neuron.change_location_toward_end_nodes(index, - np.array([x,y,z])) # def undo_location_important(self, neuron, index, x, y, z): # neuron.change_location_important(index, - np.array([x,y,z])) # def undo_diameter(self, neuron, index, ratio): # neuron.change_diameter(index, 1.0 / ratio) # def undo_ext(self, neuron, index_node): # neuron.remove_node(index_node) # def undo_red(self, neuron, parent, location, ratio): # neuron.extend_node(parent, location, ratio) # def undo_rotation(self, neuron, node, matrix): # neuron.rotate(node, inv(matrix)) # def undo_rotation_from_branch(self, neuron, node, matrix): # neuron.rotate(node, inv(matrix)) # def undo_sliding(self, # neuron, # child_of_branching_node_index, # order_one_node_index): # neuron.slide(child_of_branching_node_index, order_one_node_index) # def undo_sliding_general(self, # neuron, # child_of_branching_node_index, # order_one_node_index): # neuron.slide(child_of_branching_node_index, order_one_node_index) # def undo_rescale_toward_end(self, neuron, node, rescale): # neuron.rescale_toward_end(node, 1./rescale) # def undo_vertical_stretching(self, neuron, node, parent, scale): # neuron.vertical_stretch(node, parent, 1./scale) # def undo_horizental_stretching(self, neuron, node, parent, scale): # neuron.horizental_stretch(node, parent, 1./scale) def set_measure(self, features_distribution): """ Set a probability distribution on neuron by looking at each features. To run the algorithm, a set of features is needed to make a probability distribution on the set of all neurons. Parameters ---------- features_distribution: dict the dictionary of each distributions. In the case that each features is modeled by Gaussian, features_distribution has two keys: 'mean' and 'std'. Inside each of these keys, there is another dictionary with the name of features and the value. For example: features_distribution = {'mean': {'branch_angle': 2.4,'local_angle': 2.7} 'std': {'branch_angle': .2,'local_angle': .2}} """ self.measure = features_distribution self.list_features = features_distribution['mean'].keys() self.mean_measure = np.array([]) self.std_measure = np.array([]) self.sd_measure = np.array([]) self.n_features = len(self.list_features) for ind in self.list_features: self.mean_measure = \ np.append(self.mean_measure, float(features_distribution['mean'][ind])) self.std_measure = \ np.append(self.std_measure, float(features_distribution['std'][ind]) ** 2) self.sd_measure = \ np.append(self.sd_measure, float(features_distribution['std'][ind])) self.trend = np.zeros([len(features_distribution['mean']), self.ite]) self.trend_normal = np.zeros([len(features_distribution['mean']), self.ite]) def set_probability(self, list_values): """ set the probability for perturbation list_values : dict """ l = sum(list_values.values()) self.list_values = {} for i in list_values.keys(): self.list_values[i] = list_values[i]/l self.p_prob = np.array(self.list_values.values()) self.p_list = self.list_values.keys() self._consum_prob = np.zeros(len(list_values.keys())) for i in range(self.p_prob.shape[0]): self._consum_prob[i] = sum(self.p_prob[:i+1]) def set_real_neuron(self, neuron, hist_features, value_features, vec_value): """ Set the desire features by the features of given neuron. No dependency. """ self.mean_hist, self.mean_value, self.mean_vec_value = \ dis_util.get_feature_neuron(neuron=neuron, hist_features=hist_features, value_features=value_features, vec_value=vec_value) def set_database(self, database): self.mean_hist = deepcopy(database.mean_hist) self.mean_value = deepcopy(database.mean_value) self.mean_vec_value = deepcopy(database.mean_vec_value) self.std_hist = deepcopy(database.std_hist) self.std_value = deepcopy(database.std_value) self.std_vec_value = deepcopy(database.std_vec_value) def set_feature_normalizer(self, normlizer): for name in self.std_hist.keys(): self.std_hist[name] = (1./normlizer[name]) * self.std_hist[name] for name in self.std_value.keys(): self.std_value[name] = (1./normlizer[name]) * self.std_value[name] for name in self.std_vec_value.keys(): self.std_vec_value[name] = (1./normlizer[name]) * self.std_vec_value[name] def pdf_normal(self ,x, dim): """ Return the probability density at the point x of a normal distribution with mean = 0 and variance = s """ rv = multivariate_normal(np.zeros(dim), self.varnp.eye(dim)) return rv.pdf(x) # should notice to the dimentionality of the constant return (self.cte_gauss/s)np.power(np.e,-(xx).sum()/(ss)) def normal(self, dim): random_point = np.random.normal(0, self.var, dim) rv = multivariate_normal(np.zeros(dim), self.varnp.eye(dim)) pdf = rv.pdf(random_point) return random_point, pdf def far_nodes(self, neuron, node_index, threshold): x = neuron.location[0, :] - neuron.location[0, node_index] y = neuron.location[1, :] - neuron.location[1, node_index] z = neuron.location[2, :] - neuron.location[2, node_index] (index,) = np.where(x2 + y2 + z2 > threshold2) return index def random_vector(self, mean, var): vec = np.random.normal(size = 3) vec = vec/LA.norm(vec,2) l = -1 while(l<0): l = mean + var np.random.normal() vec = vecl return vec def get_random_element_for_add_remove(self, neuron): (ind1,) = np.where(neuron.branch_order[neuron.n_soma:] == 1) whole = len(neuron.nodes_list) - neuron.n_soma total_number = len(ind1) + whole a = np.floor(total_number np.random.rand()) if(a < whole): random_node = neuron.nodes_list[neuron.n_soma + a] state = 'add' else: random_node = neuron.nodes_list[ind1[a - whole]] state = 'remove' return total_number ,random_node, state def random_rotation(self, vector, mu, kappa, n): """ input: mu, kappa, n `float64` Return three vectors: the first one is close to the given vector; these three vectors make a complete set of orthogonal space for 3D space. The first vector is choosen accroding to a distribution for the phi (the angle between the given vector and choosen one) and unifor distribution for the theta (the angle of projection of the choosen vector over the orthogonal plane) the phi angle comes from von Mises distribution. """ vector = vector/LA.norm(vector,2) a = np.random.normal(0, 1, 3) a = a - sum(avector)vector a = a/LA.norm(a,2) phi = np.random.vonmises(mu, kappa, 1) normal_vec = np.sin(phi)a + np.cos(phi)vector length = np.random.chisquare(n,1)/n random_point = lengthnormal_vec pdf = (.5/np.pi)(chi2.pdf(nlength,n)n)(vonmises.pdf(np.cos(phi), kappa)) return random_point, pdf def pdf_random_rotation(self, x, v, mu, kappa, n): """ Gives back the probability of observing the vector x, such that its angle with v is coming from a Von Mises distribution with k = self.kappa and its length coming form chi squared distribution with the parameter n. """ v = v/LA.norm(v,2) x = x/LA.norm(x,2) ang = sum(vx) return (.5/np.pi)(chi2.pdf(nLA.norm(x,2),n)n)(vonmises.pdf(ang, kappa)) def unifrom(self,size): return size(2np.random.rand(1,3)-1) def random_unitary_basis(self, kappa): #Ax1 = self.random_2d_rotation_in_3d('x', kappa) #Ay1 = self.random_2d_rotation_in_3d('y', kappa) Az1 = self.random_2d_rotation_in_3d('z', kappa) #Ax2 = self.random_2d_rotation_in_3d('x', kappa) #Ay2 = self.random_2d_rotation_in_3d('y', kappa) #Az2 = self.random_2d_rotation_in_3d('z', kappa) #A = np.dot(np.dot(Ax1,Ay1),Az1) #A = np.dot(np.dot(Az2,Ay2),Ax2) #A = np.dot(Ax1,Ay1) #B = np.dot(Ay2,Ax2) #m = np.dot(A,B) return Az1 def random_2d_rotation_in_3d(self, axis, kappa): theta = np.random.vonmises(0, kappa, 1) A = np.eye(3) if axis is 'z': A[0,0] = np.cos(theta) A[1,0] = np.sin(theta) A[0,1] = - np.sin(theta) A[1,1] = np.cos(theta) return A if axis is 'y': A[0,0] = np.cos(theta) A[2,0] = np.sin(theta) A[0,2] = - np.sin(theta) A[2,2] = np.cos(theta) return A
#!/opt/anaconda/bin/python # -- coding: utf-8 -- # Unfortunately the `which` way of calling python can't accept command-line arguments. """ Created on Mon Nov 03 16:13:48 2014 @author: <NAME> @email: <EMAIL> OR <EMAIL> A selection of alignment routines designed for registering and summing stacks of images or diffraction patterns in the field of electron microscopy. """ from __future__ import division, print_function, absolute_import, unicode_literals import numpy as np if np.version.version.split('.')[1] == 7: print( "WARNING: NUMPY VERSION 1.7 DETECTED, ZORRO IS DESIGNED FOR >1.10" ) print( "CHECK YOUR ENVIRONMENT VARIABLES TO SEE IF EMAN2 HAS HIJACKED YOUR PYTHON DISTRIBUTION" ) import numexprz as nz # Now see which numexpr we have, by the dtype of float (whether it casts or not) try: # Now see which numexpr we have, by the dtype of float (whether it casts or not) tdata = np.complex64( 1.0 + 2.0j ) fftw_dtype = nz.evaluate( 'tdata + tdata' ).dtype float_dtype = nz.evaluate( 'real(tdata+tdata)' ).dtype except: fftw_dtype = 'complex128' float_dtype = 'float64' import scipy.optimize import scipy.ndimage import scipy.stats import time try: import ConfigParser as configparser except: import configparser # Python 3 # Here we have to play some games depending on where the file was called from # with the use of absolute_import # print( "__name__ of zorro: " + str(__name__) ) try: import zorro_util as util import zorro_plotting as plot except ImportError: from . import zorro_util as util from . import zorro_plotting as plot import mrcz import os, os.path, tempfile, sys import subprocess # Should we disable Multiprocessing on Windows due to general bugginess in the module? import multiprocessing as mp try: import pyfftw except: print( "Zorro did not find pyFFTW package: get it at https://pypi.python.org/pypi/pyFFTW" ) try: import tables except: print( "Zorro did not find pyTables installation for HDF5 file support" ) import matplotlib.pyplot as plt # Numpy.pad is bad at dealing with interpreted strings if sys.version_info >= (3,0): symmetricPad = u'symmetric' constantPad = u'constant' else: symmetricPad = b'symmetric' constantPad = b'constant' #### OBJECT-ORIENTED INTERFACE #### class ImageRegistrator(object): # Should be able to handle differences in translation, rotation, and scaling # between images def __init__( self ): # Declare class members self.verbose = 0 self.umask = 2 # Meta-information for processing, not saved in configuration files. self.METApriority = 0.0 self.METAstatus = u'new' self.METAmtime = 0.0 self.METAsize = 0 self.xcorrMode = 'zorro' # 'zorro', 'unblur v1.02', 'motioncorr v2.1' # FFTW_PATIENT is bugged for powers of 2, so use FFTW_MEASURE as default self.fftw_effort = u"FFTW_MEASURE" # TODO: change this to drop into cachePath self.n_threads = nz.nthreads # Number of cores to limit FFTW to, if None uses all cores self.cachePath = tempfile.gettempdir() # CALIBRATIONS self.pixelsize = None # Typically we use nanometers, the same units as Digital Micrograph self.voltage = 300.0 # Accelerating voltage, kV self.C3 = 2.7 # Spherical aberration of objective, mm self.gain = None self.detectorPixelSize = None # Physical dimensions of detector pixel (5 um for K2) # Timings self.bench = {} # Dict holds various benchmark times for the code self.saveC = False # Save the cross-correlation within +/- maxShift # INFORMATION REDUCTION # The SNR at high spatial frequencies tends to be lower due to how information transfer works, so # removing/filtering those frequencies can improve stability of the registration. YMMV, IMHO, etc. self.Brad = 512 # Gaussian low-pass applied to data before registration, units are radius in Fourier space, or equivalent point-spread function in real-space self.Bmode = u'opti' # can be a real-space Gaussian convolution, 'conv' or Fourier filter, 'fourier', or 'opti' for automatic Brad # For Bmode = 'fourier', a range of available filters can be used: gaussian, gauss_trunc, butterworth.order (order is an int), hann, hamming self.BfiltType = u'gaussian' self.fouCrop = [3072,3072] # Size of FFT in frequency-space to crop to (e.g. [2048,2048]) self.reloadData = True # Data self.images = None self.imageSum = None self.filtSum = None # Dose-filtered, Wiener-filtered, etc. representations go here self.gainRef = None # For application of gain reference in Zorro rather than Digital Micrograph/TIA/etc. self.gainInfo = { "Horizontal": True, "Vertical": True, "Diagonal":False, "GammaParams": [ 0.12035633, -1.04171635, -0.03363192, 1.03902726], } # One of None, 'dose', 'dose,background', 'dosenorm', 'gaussLP', 'gaussLP,background' # also 'hot' can be in the comma-seperated list for pre-filtering of hot pixels self.filterMode = None # Dose filt param = [dosePerFrame, critDoseA, critDoseB, critDoseC, cutoffOrder, missingStartFrame] self.doseFiltParam = [None, 0.24499, -1.6649, 2.8141, 32, 0] # for 'hot' in filterMode self.hotpixInfo = { u"logisticK":6.0, u"relax":0.925, u"maxSigma":8.0, u"psf": u"K2", u"guessHotpix":0, u"guessDeadpix":0, u"decorrOutliers":False, u"cutoffLower":-4.0, u"cutoffUpper":3.25, u"neighborPix":0 } self.FFTSum = None # If you want to use one mask, it should have dims [1,N_Y,N_X]. This is # to ensure Cythonized code can interact safely with Numpy self.incohFouMag = None # Incoherent Fourier magnitude, for CTF determination, resolution checks self.masks = None self.maskSum = None self.C = None # Results self.translations = None self.transEven = None # For even-odd tiled FRC, the half-stack translations self.transOdd = None # For even-odd tiled FRC, the half-stack translations self.velocities = None # pixel velocity, in pix/frame, to find frames that suffer from excessive drift self.rotations = None # rotations, for polar-transformed data self.scales = None # scaling, for polar-transformed data self.errorDictList = [] # A list of dictionaries of errors and such from different runs on the same data. self.trackCorrStats = False self.corrStats = None self.doLazyFRC = True self.doEvenOddFRC = False self.FRC = None # A Fourier ring correlation # Filtering # TODO: add more fine control over filtering options # CTF currently supports CTFFIND4.1 or GCTF self.CTFProgram = None # None, "ctffind4.1", or "gctf", 'ctffind4.1,sum' works on (aligned) sum, same for 'gctf,sum' self.CTFInfo = { u'DefocusU':None, u'DefocusV': None, u'DefocusAngle':None, u'CtfFigureOfMerit':None, u'FinalResolution': None, u'AmplitudeContrast':0.07, u'AdditionalPhaseShift':None, } self.CTFDiag = None # Diagnostic image from CTFFIND4.1 or GCTF # DEPRICATED ctf stuff #self.doCTF = False #self.CTF4Results = None # Micrograph number, DF1, DF2, Azimuth, Additional Phase shift, CC, and max spacing fit-to #self.CTF4Diag = None # Registration parameters self.shapePadded = [4096,4096] self.shapeOriginal = None self.shapeBinned = None self.subPixReg = 16 # fraction of a pixel to REGISTER image shift to # Subpixel alignment method: None (shifts still registered subpixally), lanczos, or fourier # lanczos is cheaper computationally and has fewer edge artifacts self.shiftMethod = u'lanczos' self.maxShift = 100 # Generally should be 1/2 distance to next lattice spacing # Pre-shift every image by that of the previous frame, useful for high-resolution where one can jump a lattice # i.e. should be used with small values for maxShift self.preShift = False # Solver weighting can be raw max correlation coeffs (None), normalized to [0,1] by the # min and max correlations ('norm'), or 'logistic' function weighted which # requires corrThres to be set. self.peakLocMode = u'interpolated' # interpolated (oversampled), or a RMS-best fit like fitlaplacian self.weightMode = u'autologistic' # autologistic, normalized, unweighted, logistic, or corr self.peaksigThres = 6.0 self.logisticK = 5.0 self.logisticNu = 0.15 self.originMode = u'centroid' # 'centroid' or None self.suppressOrigin = True # Delete the XC pixel at (0,0). Only necessary if gain reference is bad, but defaults to on. # Triangle-matrix indexing parameters self.triMode = u'diag' # Can be: tri, diag, auto, first self.startFrame = 0 self.endFrame = 0 self.diagStart = 0 # XC to neighbour frame on 0, next-nearest neighbour on +1, etc. self.diagWidth = 5 self.autoMax = 10 self.corrThres = None # Use with 'auto' mode to stop doing cross-correlations if the values drop below the threshold self.velocityThres = None # Pixel velocity threshold (pix/frame), above which to throw-out frames with too much motion blur. #### INPUT/OUTPUT #### self.files = { u"config":None, u"stack":None, u"mask":None, u"sum":None, u"align":None, u"figurePath":None, u"xc":None, u"moveRawPath":None, u"original":None, u"gainRef":None, u"stdout": None, u"automatch":None, u"rejected":None, u"compressor": None, u"clevel": 1 } #self.savePDF = False self.savePNG = True self.saveMovie = True self.doCompression = False self.compress_ext = ".bz2" #### PLOTTING #### self.plotDict = { u"imageSum":True, u"imageFirst":False, u"FFTSum":True, u"polarFFTSum":True, u"filtSum":True, u'stats': False, u"corrTriMat":False,
__all__ = ('Embed',) from ...backend.utils import copy_docs from ..utils import parse_time from .embed_base import ( EmbedBase, EmbedFooter, EmbedImage, EmbedThumbnail, EmbedVideo, EmbedProvider, EmbedAuthor, EmbedField, ) class Embed(EmbedBase): """ Represents Discord embedded content. There are two defined embed classes, the other one is ``EmbedCore``. Embeds are easier to build with this class than with the other, and faster to serialize, because it stores the objects as raw serializable data, but it also means it has worse operation support, because it needs to convert the raw data back. Attributes ---------- _data : `dict` of (`str`, `Any`) items The raw data of the embed. It should not be accessed directly. There are several properties and methods to do operations on them. Examples -------- Example of using local embed file: ```py # Imports from hata import Embed, ReuAsyncIO # Building the embed embed = Embed() embed.add_image('attachment://image.png') # Sending the message with (await ReuAsyncIO('some_file_path')) as file: await client.message_create(channel, embed=embed, file=('image.png', file)) ``` Note that you should use async io wrappers, but one which do not closes on `.close` either, but it resets itself instead, because if the request fails, the io would be closed and the request could not be done the second time. """ __slots__ = ('_data',) def __init__( self, title=None, description=None, color=None, url=None, timestamp=None, type_='rich', ): """ Creates an embed instance. Accepts the base parameters of the embed. Parameters ---------- title : `str`, Optional The title of the embed. Shows at the top with intense white characters. description : `str`, Optional The main content of the embed. color : ``Color`` or `int`, Optional The color code of the embed. Passing `0` means black, not like at the case of roles. url : `str`, Optional Url of the embed. If defined, the embed's `title` will show up as a hyper link pointing to the `url`. timestamp : `datetime`, optional Timestamp of the embed's content. Shows up next to the `footer` separated with a `'\|'` character. type_ : `None` or `str`, Optional The type of the embed. Defaults to `'rich'`. """ self._data = data = {} if title is not None: data['title'] = title if description is not None: data['description'] = description if color is not None: data['color'] = color if url is not None: data['url'] = url if timestamp is not None: data['timestamp'] = timestamp.isoformat() if type_ is not None: data['type'] = type_ @copy_docs(EmbedBase.__len__) def __len__(self): data = self._data result = 0 try: title = data['title'] except KeyError: pass else: result += len(title) try: description = data['description'] except KeyError: pass else: result += len(description) try: author_data = data['author'] except KeyError: pass else: try: author_name = author_data['name'] except KeyError: pass else: result += len(author_name) try: footer_data = data['footer'] except KeyError: pass else: result += len(footer_data['text']) try: field_datas = data['fields'] except KeyError: pass else: for field_data in field_datas: result += len(field_data['name']) result += len(field_data['value']) return result @copy_docs(EmbedBase.__bool__) def __bool__(self): data = self._data data_length = len(data) if data_length == 0: return False if data_length == 1: try: field_datas = data['fields'] except KeyError: pass else: if not field_datas: return False return True @property def contents(self): """ Returns the embed's contents. The embeds contents are the following: - `.title` - `.description` - `.author.name` - `.footer.text` - `.fields[n].name` - `.fields[n].value` Returns ------- contents : `list` of `str` """ data = self._data result = [] try: title = data['title'] except KeyError: pass else: result.append(title) try: description = data['description'] except KeyError: pass else: result.append(description) try: author_data = data['author'] except KeyError: pass else: try: author_name = author_data['name'] except KeyError: pass else: result.append(author_name) try: footer_data = data['footer'] except KeyError: pass else: result.append(footer_data['text']) try: field_datas = data['fields'] except KeyError: pass else: for field_data in field_datas: result.append(field_data['name']) result.append(field_data['value']) return result @classmethod def from_data(cls, data): """ Creates an embed from the data sent by Discord. Parameters ---------- data : `dict` of (`str`, `Any`) items Embed data received from Discord. Returns ------- self : ``Embed`` """ self = object.__new__(cls) self._data = data return self def to_data(self): """ Returns the embed's `._data`. This method is for compatibility with other embed-likes. When sending embed in message this method is called for getting it's data. Returns ------- data : `dict` of (`str`, `Any`) items """ return self._data @copy_docs(EmbedBase.clear) def clear(self): data = self._data fields = data.get('fields', None) data.clear() if fields is not None: fields.clear() data['fields'] = fields # Properties # `.author` @property def author(self): """ A get-set-del property for accessing the embed's author. Accepts and returns `None` or an ``EmbedAuthor`` object. """ try: author_data = self._data['author'] except KeyError: return None return EmbedAuthor.from_data(author_data) @author.setter def author(self, value): self._data['author'] = value.to_data() @author.deleter def author(self): try: del self._data['author'] except KeyError: pass # `.color` @property def color(self): """ A get-set-del property for accessing the embed's color. Accepts and returns `None` or a ``Color`` (/ `int`) object. """ return self._data.get('color', None) @color.setter def color(self, value): self._data['color'] = value @color.deleter def color(self): try: del self._data['color'] except KeyError: pass # `.description` @property def description(self): """ A get-set-del property for accessing the embed's description. Accepts and returns `None` or a `str` instance. """ return self._data.get('description', None) @description.setter def description(self, value): self._data['description'] = value @description.deleter def description(self): try: del self._data['description'] except KeyError: pass # `.fields` @property def fields(self): try: field_datas = self._data['fields'] except KeyError: self._data['fields'] = field_datas = [] return _EmbedFieldsProxy(field_datas) @fields.setter def fields(self, value): """ A get-set-del property for accessing the embed's fields. Accepts an `iterable` of ``EmbedField``objects. Meanwhile returns an ``_EmbedFieldsProxy`` instance, through what the respective embed's fields can be modified directly. """ data = self._data try: fields_data = data['fields'] except KeyError: fields_data = data['fields'] = [] if type(value) is _EmbedFieldsProxy: new_fields_data = value._data else: new_fields_data = list(field.to_data() for field in value) fields_data.clear() fields_data.extend(new_fields_data) @fields.deleter def fields(self): try: field_datas = self._data['fields'] except KeyError: pass else: field_datas.clear() # `.footer` @property def footer(self): """ A get-set-del property for accessing the embed's footer. Accepts and returns `None` or an ``EmbedFooter`` object. """ try: footer_data = self._data['footer'] except KeyError: return None return EmbedFooter.from_data(footer_data) @footer.setter def footer(self, value): self._data['footer'] = value.to_data() @footer.deleter def footer(self): try: del self._data['footer'] except KeyError: pass # `.image` @property def image(self): """ A get-set-del property for accessing the embed's image. Accepts and returns `None` or an ``EmbedImage`` object. """ try: image_data = self._data['image'] except KeyError: return None return EmbedImage.from_data(image_data) @image.setter def image(self, value): self._data['image'] = value.to_data() @image.deleter def image(self): try: del self._data['image'] except KeyError: pass # `.provider` @property def provider(self): """ A get-del property for accessing the embed's provider. Returns `None` or an ``EmbedProvider`` object. Embed providers cannot be set, they are receive only. """ try: provider_data = self._data['provider'] except KeyError: return None return EmbedProvider.from_data(provider_data) @provider.deleter def provider(self): try: del self._data['provider'] except KeyError: pass # `.thumbnail` @property def thumbnail(self): """ A get-set-del property for accessing the embed's thumbnail. Accepts and returns `None` or an ``EmbedThumbnail`` object. """ try: thumbnail_data = self._data['thumbnail'] except KeyError: return None return EmbedThumbnail.from_data(thumbnail_data) @thumbnail.setter def thumbnail(self, value): self._data['thumbnail'] = value.to_data() @thumbnail.deleter def thumbnail(self): try: self._data['thumbnail'] except KeyError: pass # `.timestamp` @property def timestamp(self): """ A get-set-del property for accessing the embed's timestamp. Accepts and returns `None` or a `datetime` object. """ try: timestamp_value = self._data['timestamp'] except KeyError: return None return parse_time(timestamp_value) @timestamp.setter def timestamp(self, value): self._data['timestamp'] = value.isoformat() @timestamp.deleter def timestamp(self): try: del self._data['timestamp'] except KeyError: pass # `.title` @property def title(self): """ A get-set-del property for accessing the embed's title. Accepts and returns `None` or a `str` instance. """ return self._data.get('title', None) @title.setter def title(self, value): self._data['title'] = value @title.deleter def title(self): try: del self._data['title'] except KeyError: pass # `.type` @property def type(self): """ A get-set-del property for accessing the embed's type. Accepts and returns `None` or a `str` instance. """ return self._data.get('type', None) @type.setter def type(self, value): self._data['type'] = value @type.deleter def type(self): try: del self._data['type'] except KeyError: pass # `.url` @property def url(self): """ A get-set-del property for accessing the embed's url. Accepts and returns `None` or a