我们从Python开源项目中，提取了以下50个代码示例，用于说明如何使用numpy.in1d()。
def take_slice_of_analogsignalarray_by_channelindex(self, channel_indexes=None): ''' Return slices of the :class:`AnalogSignalArrays` in the :class:`Segment` that correspond to the :attr:`channel_indexes` provided. ''' if channel_indexes is None: return [] sliced_sigarrays = [] for sigarr in self.analogsignals: if sigarr.get_channel_index() is not None: ind = np.in1d(sigarr.get_channel_index(), channel_indexes) sliced_sigarrays.append(sigarr[:, ind]) return sliced_sigarrays
def get_1000G_snps(sumstats, out_file): sf = np.loadtxt(sumstats,dtype=str,skiprows=1) h5f = h5py.File('ref/Misc/1000G_SNP_info.h5','r') rf = h5f['snp_chr'][:] h5f.close() ind1 = np.in1d(sf[:,1],rf[:,2]) ind2 = np.in1d(rf[:,2],sf[:,1]) sf1 = sf[ind1] rf1 = rf[ind2] ### check order ### if sum(sf1[:,1]==rf1[:,2])==len(rf1[:,2]): print 'Good!' else: print 'Shit happens, sorting sf1 to have the same order as rf1' O1 = np.argsort(sf1[:,1]) O2 = np.argsort(rf1[:,2]) O3 = np.argsort(O2) sf1 = sf1[O1][O3] out = ['hg19chrc snpid a1 a2 bp or p'+'\n'] for i in range(len(sf1[:,1])): out.append(sf1[:,0][i]+' '+sf1[:,1][i]+' '+sf1[:,2][i]+' '+sf1[:,3][i]+' '+rf1[:,1][i]+' '+sf1[:,5][i]+' '+sf1[:,6][i]+'\n') ff = open(out_file,"w") ff.writelines(out) ff.close()
def test_multicollinearity(df, target_name, r2_threshold = 0.89): '''Tests if any of the features could be predicted from others with R2 >= 0.89 input: dataframe, name of target (to exclude) ''' r2s = pd.DataFrame() for feature in df.columns.difference([target_name]): model = sk.linear_model.Ridge() model.fit(df[df.columns.difference([target_name,feature])], df[feature]) pos = np.in1d(model.coef_, np.sort(model.coef_)[-5:]) r2s = r2s.append(pd.DataFrame({'r2':sk.metrics.r2_score(df[feature],\ model.predict(df[df.columns.difference([target_name, feature])])),\ 'predictors' : str(df.columns.difference([target_name, feature])[np.ravel(np.argwhere(pos == True))].tolist())}, index = [feature])) print('Testing', feature) print('-----------------') if len(r2s[r2s['r2'] >= r2_threshold]) > 0: print('Multicollinearity detected') print(r2s[r2s['r2'] >= r2_threshold]) else: print('No multicollinearity')
def __init__(self, **kwargs): logging.info('Crossword __init__: Initializing crossword...') logging.debug('kwargs:', kwargs) # Reading kwargs self.setup = kwargs self.rows = int(kwargs.get('n', 5)) self.cols = int(kwargs.get('m', 5)) self.words_file = str(kwargs.get('word_file', 'lemma.num.txt')) self.sort = bool(kwargs.get('sort', False)) self.maximize_len = bool(kwargs.get('maximize_len', False)) self.repeat_words = bool(kwargs.get('repeat_words', False)) logging.debug('Crossword __init__: n={}, m={}, fname={}'.format(self.rows, self.cols, self.words_file)) # Loading words logging.debug('Crossword __init__: Started loading words from {}'.format(self.words_file)) arr = np.genfromtxt(self.words_file, dtype='str', delimiter=' ') self.words = arr[np.in1d(arr[:, 3], ['v', 'n', 'adv', 'a'])][:, 2].tolist() # Number of words loaded logging.debug('Crossword __init__: Number of words loaded: {}'.format(len(self.words))) self.words = list(set(x for x in self.words if len(x) <= self.rows and len(x) <= self.cols)) if self.sort: self.words = sorted(self.words, key=len, reverse=self.maximize_len) # After filter logging logging.debug('Crossword __init__: Number of words after filter: {}, maxlen = {}'.format(len(self.words), len( max(self.words, key=len))))
def test_df_col_or_idx_equivalence(df1, df2, col=None): '''check whether two dataframes contain the same elements (but not necessarily in the same order) in either the indexes or a selected column inputs df1, df2 the dataframes to check col if not None, test this dataframe column for equivalency, otherwise test the dataframe indexes Returns True or False ''' if not col: result = all(np.in1d(df1.index, df2.index, assume_unique=True, invert=False)) else: result = all(np.in1d(df1[col], df2[col], assume_unique=False, invert=False)) return result
def make_classifier(self, name, ids, labels): """Entrenar un clasificador SVM sobre los textos cargados. Crea un clasificador que se guarda en el objeto bajo el nombre `name`. Args: name (str): Nombre para el clasidicador. ids (list): Se espera una lista de N ids de textos ya almacenados en el TextClassifier. labels (list): Se espera una lista de N etiquetas. Una por cada id de texto presente en ids. Nota: Usa el clasificador de `Scikit-learn <http://scikit-learn.org/>`_ """ if not all(np.in1d(ids, self.ids)): raise ValueError("Hay ids de textos que no se encuentran \ almacenados.") setattr(self, name, SGDClassifier()) classifier = getattr(self, name) indices = np.searchsorted(self.ids, ids) classifier.fit(self.tfidf_mat[indices, :], labels)
def retrain(self, name, ids, labels): """Reentrenar parcialmente un clasificador SVM. Args: name (str): Nombre para el clasidicador. ids (list): Se espera una lista de N ids de textos ya almacenados en el TextClassifier. labels (list): Se espera una lista de N etiquetas. Una por cada id de texto presente en ids. Nota: Usa el clasificador de `Scikit-learn <http://scikit-learn.org/>`_ """ if not all(np.in1d(ids, self.ids)): raise ValueError("Hay ids de textos que no se encuentran \ almacenados.") try: classifier = getattr(self, name) except AttributeError: raise AttributeError("No hay ningun clasificador con ese nombre.") indices = np.in1d(self.ids, ids) if isinstance(labels, str): labels = [labels] classifier.partial_fit(self.tfidf_mat[indices, :], labels)
def get_feedback_data(self, on_level=None): feedback = self.data.fields.feedback eval_data = self.data.test.evalset[feedback].values holdout = self.data.holdout_size feedback_data = eval_data.reshape(-1, holdout) if on_level is not None: try: iter(on_level) except TypeError: feedback_data = np.ma.masked_not_equal(feedback_data, on_level) else: mask_level = np.in1d(feedback_data.ravel(), on_level, invert=True).reshape(feedback_data.shape) feedback_data = np.ma.masked_where(mask_level, feedback_data) return feedback_data
def _find_optimal_clustering(self,clusterings): max_score = float('-inf') max_clustering = None for clustering in clusterings: labeled_vectors = [(node.vector,cluster_idx) for cluster_idx in range(len(clustering)) for node in _get_cluster_nodes(clustering[cluster_idx][1]) ] vectors,labels = [np.array(x) for x in zip(*labeled_vectors)] if np.in1d([1],labels)[0]: score = silhouette_score(vectors,labels,metric='cosine') else: continue # silhouette doesn't work with just one cluster if score > max_score: max_score = score max_clustering = clustering return zip(*max_clustering)[1] if max_clustering else zip(*clusterings[0])[1]
def remove_rare_elements(data, min_user_activity, min_item_popularity): '''Removes user and items that appears in too few interactions. min_user_activity is the minimum number of interaction that a user should have. min_item_popularity is the minimum number of interaction that an item should have. NB: the constraint on item might not be strictly satisfied because rare users and items are removed in alternance, and the last removal of inactive users might create new rare items. ''' print('Remove inactive users and rare items...') #Remove inactive users a first time user_activity = data.groupby('u').size() data = data[np.in1d(data.u, user_activity[user_activity >= min_user_activity].index)] #Remove unpopular items item_popularity = data.groupby('i').size() data = data[np.in1d(data.i, item_popularity[item_popularity >= min_item_popularity].index)] #Remove users that might have passed below the activity threshold due to the removal of rare items user_activity = data.groupby('u').size() data = data[np.in1d(data.u, user_activity[user_activity >= min_user_activity].index)] return data
def reconstruct_goal(world): # pdb.set_trace() world = world.copy() ## indices for grass and puddle background_inds = [obj['index'] for (name, obj) in library.objects.iteritems() if obj['background']] ## background mask background = np.in1d(world, background_inds) background = background.reshape( (world.shape) ) ## set backgronud to 0 world[background] = 0 ## subtract largest background ind ## so indices of objects begin at 1 world[~background] -= max(background_inds) world = np.expand_dims(np.expand_dims(world, 0), 0) # pdb.set_trace() return world
def check_multiplication_dims(dims, N, M, vidx=False, without=False): dims = array(dims, ndmin=1) if len(dims) == 0: dims = arange(N) if without: dims = setdiff1d(range(N), dims) if not np.in1d(dims, arange(N)).all(): raise ValueError('Invalid dimensions') P = len(dims) sidx = np.argsort(dims) sdims = dims[sidx] if vidx: if M > N: raise ValueError('More multiplicants than dimensions') if M != N and M != P: raise ValueError('Invalid number of multiplicants') if P == M: vidx = sidx else: vidx = sdims return sdims, vidx else: return sdims
def particle_mask(self): # Dynamically create the masking array for particles, and get # the data using standard yt methods. if self._particle_mask is not None: return self._particle_mask # This is from disk. pid = self.__getitem__('particle_index') # This is from the sphere. if self._name == "RockstarHalo": ds = self.ds.sphere(self.CoM, self._radjust * self.max_radius) elif self._name == "LoadedHalo": ds = self.ds.sphere(self.CoM, np.maximum(self._radjust * \ self.ds.quan(self.max_radius, 'code_length'), \ self.ds.index.get_smallest_dx())) sp_pid = ds['particle_index'] self._ds_sort = sp_pid.argsort() sp_pid = sp_pid[self._ds_sort] # This matches them up. self._particle_mask = np.in1d(sp_pid, pid) return self._particle_mask
def has_approx_support(m, m_hat, prob=0.01): """Returns 1 if model selection error is less than or equal to prob rate, 0 else. NOTE: why does np.nonzero/np.flatnonzero create so much problems? """ m_nz = np.flatnonzero(np.triu(m, 1)) m_hat_nz = np.flatnonzero(np.triu(m_hat, 1)) upper_diagonal_mask = np.flatnonzero(np.triu(np.ones(m.shape), 1)) not_m_nz = np.setdiff1d(upper_diagonal_mask, m_nz) intersection = np.in1d(m_hat_nz, m_nz) # true positives not_intersection = np.in1d(m_hat_nz, not_m_nz) # false positives true_positive_rate = 0.0 if len(m_nz): true_positive_rate = 1. * np.sum(intersection) / len(m_nz) true_negative_rate = 1. - true_positive_rate false_positive_rate = 0.0 if len(not_m_nz): false_positive_rate = 1. * np.sum(not_intersection) / len(not_m_nz) return int(np.less_equal(true_negative_rate + false_positive_rate, prob))
def get_membership_mask(self, labels, rows_or_columns): from .util import array_tostring assert rows_or_columns in ['rows', 'columns'] assert isinstance(labels, np.ndarray) assert labels.size > 0 if rows_or_columns == "rows": filter_labels = self.rowlabels else: filter_labels = self.columnlabels labels = array_tostring(labels) filter_labels = array_tostring(filter_labels) return np.in1d(filter_labels.ravel(), labels).reshape(filter_labels.shape)
def discrete(self, x, bin=5): #res = np.array([0] * x.shape[-1], dtype=int) #?????????????????????WOE?????????????<=?WOE?? x_copy = pd.Series.copy(x) x_copy = x_copy.astype(str) #x_copy = x_copy.astype(np.str_) #x_copy = x x_gt0 = x[x>=0] #if x.name == 'TD_PLTF_CNT_1M': #bin = 5 #x_gt0 = x[(x>=0) & (x<=24)] for i in range(bin): point1 = stats.scoreatpercentile(x_gt0, i * (100.0/bin)) point2 = stats.scoreatpercentile(x_gt0, (i + 1) * (100.0/bin)) x1 = x[(x >= point1) & (x <= point2)] mask = np.in1d(x, x1) #x_copy[mask] = i + 1 x_copy[mask] = '%s-%s' % (point1,point2) #x_copy[mask] = point1 #print x_copy[mask] #print x #print x return x_copy
def grade(self, x, bin=5): #res = np.array([0] * x.shape[-1], dtype=int) #?????????????????????WOE?????????????<=?WOE?? x_copy = np.copy(x) #x_copy = x_copy.astype(str) #x_copy = x_copy.astype(np.str_) #x_copy = x x_gt0 = x[x>=0] for i in range(bin): point1 = stats.scoreatpercentile(x_gt0, i * (100.0/bin)) point2 = stats.scoreatpercentile(x_gt0, (i + 1) * (100.0/bin)) x1 = x[(x >= point1) & (x <= point2)] mask = np.in1d(x, x1) #x_copy[mask] = i + 1 x_copy[mask] = i + 1 #x_copy[mask] = point1 #print x_copy[mask] #print x print point1,point2 #print x return x_copy
def map_values(values, pos, target_pos, dtype=None, nan=dat.CPG_NAN): """Maps `values` array at positions `pos` to `target_pos`. Inserts `nan` for uncovered positions. """ assert len(values) == len(pos) assert np.all(pos == np.sort(pos)) assert np.all(target_pos == np.sort(target_pos)) values = values.ravel() pos = pos.ravel() target_pos = target_pos.ravel() idx = np.in1d(pos, target_pos) pos = pos[idx] values = values[idx] if not dtype: dtype = values.dtype target_values = np.empty(len(target_pos), dtype=dtype) target_values.fill(nan) idx = np.in1d(target_pos, pos).nonzero()[0] assert len(idx) == len(values) assert np.all(target_pos[idx] == pos) target_values[idx] = values return target_values
def test_learn_codes(): """Test learning of codes.""" thresh = 0.25 X, ds, z = simulate_data(n_trials, n_times, n_times_atom, n_atoms) for solver in ('l_bfgs', 'ista', 'fista'): z_hat = update_z(X, ds, reg, n_times_atom, solver=solver, solver_kwargs=dict(factr=1e11, max_iter=50)) X_hat = construct_X(z_hat, ds) assert_true(np.corrcoef(X.ravel(), X_hat.ravel())[1, 1] > 0.99) assert_true(np.max(X - X_hat) < 0.1) # Find position of non-zero entries idx = np.ravel_multi_index(z[0].nonzero(), z[0].shape) loc_x, loc_y = np.where(z_hat[0] > thresh) # shift position by half the length of atom idx_hat = np.ravel_multi_index((loc_x, loc_y), z_hat[0].shape) # make sure that the positions are a subset of the positions # in the original z mask = np.in1d(idx_hat, idx) assert_equal(np.sum(mask), len(mask))
def __init__(self, topology, selstr=None, deg=False, cossin=False, periodic=True): indices = indices_phi(topology) if not selstr: self._phi_inds = indices else: self._phi_inds = indices[np.in1d(indices[:, 1], topology.select(selstr), assume_unique=True)] indices = indices_psi(topology) if not selstr: self._psi_inds = indices else: self._psi_inds = indices[np.in1d(indices[:, 1], topology.select(selstr), assume_unique=True)] # alternate phi, psi pairs (phi_1, psi_1, ..., phi_n, psi_n) dih_indexes = np.array(list(phi_psi for phi_psi in zip(self._phi_inds, self._psi_inds))).reshape(-1, 4) super(BackboneTorsionFeature, self).__init__(topology, dih_indexes, deg=deg, cossin=cossin, periodic=periodic)
def test_ttv_array_like_data_source(self): dummy_data_source = DummyDataSource() subject_info_dir = os.path.join('test', 'dummy_data', 'metadata') ttv = yaml_to_dict(os.path.join(subject_info_dir, 'dummy_ttv.yaml')) array_ds = TTVArrayLikeDataSource(dummy_data_source, ttv) self.assertEqual(len(array_ds), 3) all_values = np.fromiter((x for x in array_ds[:]), dtype='int16') self.assertTrue( np.all( np.in1d( all_values, np.array([1, 2, 3]) ) ) )
def get_data(self, dataset, event_list=None): # Load Basics for this dataset and shift it by 1 data = hax.minitrees.load_single_minitree(dataset, 'Basics') df = data.shift(1) # Add previous_ prefix to all columns df = df.rename(columns=lambda x: 'previous_' + x) # Add (unshifted) event number and run number, to support merging df['event_number'] = data['event_number'] df['run_number'] = data['run_number'] # Support for event list (lame) if event_list is not None: df = df[np.in1d(df['event_number'].values, event_list)] return df
def extract_from_volume(vol_data, vox_ijk): """Extract data values (broadcasting across time if relevant).""" i, j, k = vox_ijk.T ii, jj, kk = vol_data.shape[:3] fov = (np.in1d(i, np.arange(ii)) & np.in1d(j, np.arange(jj)) & np.in1d(k, np.arange(kk))) if len(vol_data.shape) == 3: ntp = 1 else: ntp = vol_data.shape[-1] roi_data = np.empty((len(i), ntp)) roi_data[:] = np.nan roi_data[fov] = vol_data[i[fov], j[fov], k[fov]] return roi_data
def clip_catalog(self): # ROI-specific catalog logger.debug("Clipping full catalog...") cut_observable = self.mask.restrictCatalogToObservableSpace(self.catalog_full) # All objects within disk ROI logger.debug("Creating roi catalog...") self.catalog_roi = self.catalog_full.applyCut(cut_observable) self.catalog_roi.project(self.roi.projector) self.catalog_roi.spatialBin(self.roi) # All objects interior to the background annulus logger.debug("Creating interior catalog...") cut_interior = numpy.in1d(ang2pix(self.config['coords']['nside_pixel'], self.catalog_roi.lon, self.catalog_roi.lat), self.roi.pixels_interior) #cut_interior = self.roi.inInterior(self.catalog_roi.lon,self.catalog_roi.lat) self.catalog_interior = self.catalog_roi.applyCut(cut_interior) self.catalog_interior.project(self.roi.projector) self.catalog_interior.spatialBin(self.roi) # Set the default catalog #logger.info("Using interior ROI for likelihood calculation") self.catalog = self.catalog_interior #self.pixel_roi_cut = self.roi.pixel_interior_cut
def inFootprint(self, pixels, nside=None): """ Open each valid filename for the set of pixels and determine the set of subpixels with valid data. """ if numpy.isscalar(pixels): pixels = numpy.array([pixels]) if nside is None: nside = self.nside_likelihood inside = numpy.zeros( len(pixels), dtype='bool') if not self.nside_catalog: catalog_pix = [0] else: catalog_pix = superpixel(pixels,nside,self.nside_catalog) catalog_pix = numpy.intersect1d(catalog_pix,self.catalog_pixels) for filenames in self.filenames[catalog_pix]: #logger.debug("Loading %s"%filenames['mask_1']) subpix_1,val_1 = ugali.utils.skymap.readSparseHealpixMap(filenames['mask_1'],'MAGLIM',construct_map=False) #logger.debug("Loading %s"%filenames['mask_2']) subpix_2,val_2 = ugali.utils.skymap.readSparseHealpixMap(filenames['mask_2'],'MAGLIM',construct_map=False) subpix = numpy.intersect1d(subpix_1,subpix_2) superpix = numpy.unique(ugali.utils.skymap.superpixel(subpix,self.nside_pixel,nside)) inside |= numpy.in1d(pixels, superpix) return inside
def index_pixels(lon,lat,pixels,nside): """ Find the index for object amoung a subset of healpix pixels. Set index of objects outside the pixel subset to -1 # ADW: Not really safe to set index = -1 (accesses last entry); # -np.inf would be better, but breaks other code... """ pix = ang2pix(nside,lon,lat) # pixels should be pre-sorted, otherwise...??? index = np.searchsorted(pixels,pix) if np.isscalar(index): if not np.in1d(pix,pixels).any(): index = -1 else: # Find objects that are outside the roi #index[np.take(pixels,index,mode='clip')!=pix] = -1 index[~np.in1d(pix,pixels)] = -1 return index ############################################################
def get(self, names=None, burn=None, clip=None): if names is None: names = list(self.dtype.names) names = np.array(names,ndmin=1) missing = names[~np.in1d(names,self.dtype.names)] if len(missing): msg = "field(s) named %s not found"%(missing) raise ValueError(msg) #idx = np.where(np.in1d(self.dtype.names,names))[0] idx = np.array([self.dtype.names.index(n) for n in names]) # Remove zero entries zsel = ~np.all(self.ndarray==0,axis=1) # Remove burn entries bsel = np.zeros(len(self),dtype=bool) bsel[slice(burn,None)] = 1 data = self.ndarray[:,idx][bsel&zsel] if clip is not None: from astropy.stats import sigma_clip mask = sigma_clip(data,sig=clip,copy=False,axis=0).mask data = data[np.where(~mask.any(axis=1))] return data
def _setup_subpix(self,nside=2**16): """ Subpixels for random position generation. """ # Only setup once... if hasattr(self,'subpix'): return # Simulate over full ROI self.roi_radius = self.config['coords']['roi_radius'] # Setup background spatial stuff logger.info("Setup subpixels...") self.nside_pixel = self.config['coords']['nside_pixel'] self.nside_subpixel = self.nside_pixel * 2**4 # Could be config parameter epsilon = np.degrees(healpy.max_pixrad(self.nside_pixel)) # Pad roi radius to cover edge healpix subpix = ugali.utils.healpix.query_disc(self.nside_subpixel,self.roi.vec,self.roi_radius+epsilon) superpix = ugali.utils.healpix.superpixel(subpix,self.nside_subpixel,self.nside_pixel) self.subpix = subpix[np.in1d(superpix,self.roi.pixels)]
def iterate_minibatches(self, batchsize, shuffle=True, train=True): indices = [] if train: indices = np.argwhere(np.in1d(data.labels, data.train_classes)) else: indices = np.argwhere(np.logical_not(np.in1d(data.labels, data.train_classes))) if shuffle: np.random.shuffle(indices) for start_idx in range(0, len(self.img_paths) - batchsize + 1, batchsize): excerpt = indices[start_idx:start_idx + batchsize] images = [self._load_preprocess_img(self.img_paths[int(i)]) for i in excerpt] if len(images) == batchsize: yield np.concatenate(images), np.array(self.labels[excerpt]).astype(np.int32).T else: raise StopIteration
def GetEdgeMask(self, angle): """ Returns a mask of the points of a surface mesh that have a surface angle greater than angle Parameters ---------- angle : float Angle to consider an edge. """ featureEdges = vtk.vtkFeatureEdges() featureEdges.SetInputData(self) featureEdges.FeatureEdgesOn() featureEdges.BoundaryEdgesOff() featureEdges.NonManifoldEdgesOff() featureEdges.ManifoldEdgesOff() featureEdges.SetFeatureAngle(angle) featureEdges.Update() edges = featureEdges.GetOutput() origID = vtkInterface.GetPointScalars(edges, 'vtkOriginalPointIds') return np.in1d(self.GetPointScalars('vtkOriginalPointIds'), origID, assume_unique=True)
def RaDec2region(ra, dec, nside): SCP_indx, NES_indx, GP_indx, WFD_indx = mutually_exclusive_regions(nside) indices = _raDec2Hpid(nside, np.radians(ra), np.radians(dec)) result = np.empty(np.size(indices), dtype = object) SCP = np.in1d(indices, SCP_indx) NES = np.in1d(indices,NES_indx) GP = np.in1d(indices,GP_indx) WFD = np.in1d(indices,WFD_indx) result[SCP] = 'SCP' result[NES] = 'NES' result[GP] = 'GP' result[WFD] = 'WFD' return result
def __getitem__(self, thing: Any) -> sparse.coo_matrix: if type(thing) is slice or type(thing) is np.ndarray or type(thing) is int: gm = GraphManager(None, axis=self.axis) for key, g in self.items(): # Slice the graph matrix properly without making it dense (a, b, w) = (g.row, g.col, g.data) indices = np.arange(g.shape[0])[thing] mask = np.logical_and(np.in1d(a, indices), np.in1d(b, indices)) a = a[mask] b = b[mask] w = w[mask] d = dict(zip(np.sort(indices), np.arange(indices.shape[0]))) a = np.array([d[x] for x in a]) b = np.array([d[x] for x in b]) gm[key] = sparse.coo_matrix((w, (a, b)), shape=(len(indices), len(indices))) return gm else: return self.__getattr__(thing)
def get_data_by_id(self, ids): """ Helper for getting current data values from stored identifiers :param float|list ids: ids for which data are requested :return: the stored ids :rtype: np.ndarray """ if self.ids is None: raise ValueError("IDs not stored in node {}".format(self.name)) if self.data is None: raise ValueError("No data in node {}".format(self.name)) ids = np.array(ids, ndmin=1, copy=False) found_items = np.in1d(ids, self.ids) if not np.all(found_items): raise ValueError("Cannot find {} among {}".format(ids[np.logical_not(found_items)], self.name)) idx = np.empty(len(ids), dtype='int') for k, this_id in enumerate(ids): if self.ids.ndim > 1: idx[k] = np.flatnonzero(np.all(self.ids == this_id, axis=1))[0] else: idx[k] = np.flatnonzero(self.ids == this_id)[0] return np.array(self.data, ndmin=1)[idx]
def split_data(data, num_folds, seed=0): """ Split all interactions into K-fold sets of training and test dataframes. Splitting is done by assigning student ids to the training or test sets. :param pd.DataFrame data: all interactions :param int num_folds: number of folds :param int seed: seed for the splitting :return: a generator over (train dataframe, test dataframe) tuples :rtype: generator[(pd.DataFrame, pd.DataFrame)] """ # break up students into folds fold_student_idx = _get_fold_student_idx(np.unique(data[USER_IDX_KEY]), num_folds=num_folds, seed=seed) for fold_test_student_idx in fold_student_idx: test_idx = np.in1d(data[USER_IDX_KEY], fold_test_student_idx) train_idx = np.logical_not(test_idx) yield (data[train_idx].copy(), data[test_idx].copy())
def eval_loop(data_loader, model, base_classes, novel_classes): model = model.eval() top1 = None top5 = None all_labels = None for i, (x,y) in enumerate(data_loader): x = Variable(x.cuda()) scores = model(x) top1_this, top5_this = perelement_accuracy(scores.data, y) top1 = top1_this if top1 is None else np.concatenate((top1, top1_this)) top5 = top5_this if top5 is None else np.concatenate((top5, top5_this)) all_labels = y.numpy() if all_labels is None else np.concatenate((all_labels, y.numpy())) is_novel = np.in1d(all_labels, novel_classes) is_base = np.in1d(all_labels, base_classes) is_either = is_novel | is_base top1_novel = np.mean(top1[is_novel]) top1_base = np.mean(top1[is_base]) top1_all = np.mean(top1[is_either]) top5_novel = np.mean(top5[is_novel]) top5_base = np.mean(top5[is_base]) top5_all = np.mean(top5[is_either]) return np.array([top1_novel, top5_novel, top1_base, top5_base, top1_all, top5_all])
def _mask_edges_weights(mask, edges, weights=None): """Apply a mask to edges (weighted or not)""" inds = np.arange(mask.size) inds = inds[mask.ravel()] ind_mask = np.logical_and(np.in1d(edges[0], inds), np.in1d(edges[1], inds)) edges = edges[:, ind_mask] if weights is not None: weights = weights[ind_mask] if len(edges.ravel()): maxval = edges.max() else: maxval = 0 order = np.searchsorted(np.unique(edges.ravel()), np.arange(maxval + 1)) edges = order[edges] if weights is None: return edges else: return edges, weights
def map_2D_hist_to_ima(imaSlc2volHistMap, volHistMask): """Volume histogram to image mapping for slices (uses np.ind1). Parameters ---------- imaSlc2volHistMap : TODO volHistMask : TODO Returns ------- imaSlcMask : TODO """ imaSlcMask = np.zeros(imaSlc2volHistMap.flatten().shape) idxUnique = np.unique(volHistMask) for idx in idxUnique: linIndices = np.where(volHistMask.flatten() == idx)[0] # return logical array with length equal to nr of voxels voxMask = np.in1d(imaSlc2volHistMap.flatten(), linIndices) # reset mask and apply logical indexing imaSlcMask[voxMask] = idx imaSlcMask = imaSlcMask.reshape(imaSlc2volHistMap.shape) return imaSlcMask
def detect_input(cls, values, sample_size=200): """ Return first "from_" method that in more than 50% matches values, or None. """ assert isinstance(values, pd.Series) values = values.drop_duplicates().dropna() if len(values) > sample_size: values = values.sample(sample_size) strlen = values.str.len().dropna().unique() for method, *cond in ((cls.from_cc2, len(strlen) == 1 and strlen[0] == 2), (cls.from_cc3, len(strlen) == 1 and strlen[0] == 3), (cls.from_cc_name,), (cls.from_us_state,), (cls.from_city_eu,), (cls.from_city_us,), (cls.from_city_world,), (cls.from_region,), (cls.from_fips,), (cls.from_hasc, np.in1d(strlen, [2, 5, 8]).all())): if cond and not cond[0]: continue if sum(map(bool, method(values))) >= len(values) / 2: return method return None
def init_snapshots(self): """Initialize snapshots for model variables given in attributes of Dataset. """ self.snapshot_vars = self.dataset.xsimlab.snapshot_vars self.snapshot_values = {} for vars in self.snapshot_vars.values(): self.snapshot_values.update({v: [] for v in vars}) self.snapshot_save = { clock: np.in1d(self.dataset[self.master_clock_dim].values, self.dataset[clock].values) for clock in self.snapshot_vars if clock is not None }
def crossGenotypeWindows(commonSNPsCHR, commonSNPsPOS, snpsP1, snpsP2, inFile, binLen, outFile, logDebug = True): ## inFile are the SNPs of the sample (snpCHR, snpPOS, snpGT, snpWEI, DPmean) = snpmatch.parseInput(inFile = inFile, logDebug = logDebug) # identifying the segregating SNPs between the accessions # only selecting 0 or 1 segSNPsind = np.where((snpsP1 != snpsP2) & (snpsP1 >= 0) & (snpsP2 >= 0) & (snpsP1 < 2) & (snpsP2 < 2))[0] log.info("number of segregating snps between parents: %s", len(segSNPsind)) (ChrBins, PosBins) = getBinsSNPs(commonSNPsCHR, commonSNPsPOS, binLen) log.info("number of bins: %s", len(ChrBins)) outfile = open(outFile, 'w') for i in range(len(PosBins)): start = np.sum(PosBins[0:i]) end = start + PosBins[i] # first snp positions which are segregating and are in this window reqPOSind = segSNPsind[np.where((segSNPsind < end) & (segSNPsind >= start))[0]] reqPOS = commonSNPsPOS[reqPOSind] perchrTarPosind = np.where(snpCHR == ChrBins[i])[0] perchrTarPos = snpPOS[perchrTarPosind] matchedAccInd = reqPOSind[np.where(np.in1d(reqPOS, perchrTarPos))[0]] matchedTarInd = perchrTarPosind[np.where(np.in1d(perchrTarPos, reqPOS))[0]] matchedTarGTs = snpGT[matchedTarInd] try: TarGTBinary = snpmatch.parseGT(matchedTarGTs) TarGTBinary[np.where(TarGTBinary == 2)[0]] = 4 genP1 = np.subtract(TarGTBinary, snpsP1[matchedAccInd]) genP1no = len(np.where(genP1 == 0)[0]) (geno, pval) = getWindowGenotype(genP1no, len(genP1)) outfile.write("%s\t%s\t%s\t%s\t%s\n" % (i+1, genP1no, len(genP1), geno, pval)) except: outfile.write("%s\tNA\tNA\tNA\tNA\n" % (i+1)) if i % 40 == 0: log.info("progress: %s windows", i+10) log.info("done!") outfile.close()
def intersect_and_sort_samples(sample_metadata, feature_table): '''Return input tables retaining only shared samples, row order equivalent. Parameters ---------- sample_metadata : pd.DataFrame Contingency table with rows, columns = samples, metadata. feature_table : pd.DataFrame Contingency table with rows, columns = samples, features. Returns ------- sample_metadata, feature_table : pd.DataFrame, pd.DataFrame Input tables with unshared samples removed and ordered equivalently. Raises ------ ValueError If no shared samples are found. ''' shared_samples = np.intersect1d(sample_metadata.index, feature_table.index) if shared_samples.size == 0: raise ValueError('There are no shared samples between the feature ' 'table and the sample metadata. Ensure that you have ' 'passed the correct files.') elif (shared_samples.size == sample_metadata.shape[0] == feature_table.shape[0]): s_metadata = sample_metadata.copy() s_features = feature_table.copy() else: s_metadata = sample_metadata.loc[np.in1d(sample_metadata.index, shared_samples), :].copy() s_features = feature_table.loc[np.in1d(feature_table.index, shared_samples), :].copy() return s_metadata, s_features.loc[s_metadata.index, :]
def prepare_input(d,q): f = np.zeros(d.shape[:2]).astype('int32') for i in range(d.shape[0]): f[i,:] = np.in1d(d[i,:,0],q[i,:,0]) return f
def get_piece_bool(num, dict): '''Uses a vertex number to find the right bool array as created by divide_garment()''' count = 0 nums = dict['garment_pieces']['numbers_array'] for i in nums: if np.in1d(num, i): return count count += 1
def find_linked(ob, vert, per_face='empty'): '''Takes a vert and returns an array of linked face indices''' the_coffee_is_hot = True fidx = np.arange(len(ob.data.polygons)) eidx = np.arange(len(ob.data.edges)) f_set = np.array([]) e_set = np.array([]) verts = ob.data.vertices verts[vert].select = True v_p_f_count = [len(p.vertices) for p in ob.data.polygons] max_count = np.max(v_p_f_count) if per_face == 'empty': per_face = [[i for i in poly.vertices] for poly in ob.data.polygons] for i in per_face: for j in range(max_count-len(i)): i.append(i[0]) verts_per_face = np.array(per_face) vert=np.array([vert]) while the_coffee_is_hot: booly = np.any(np.in1d(verts_per_face, vert).reshape(verts_per_face.shape), axis=1) f_set = np.append(f_set, fidx[booly]) new_verts = verts_per_face[booly].ravel() if len(new_verts) == 0: return np.array(f_set, dtype=np.int64) cull = np.in1d(new_verts, vert) vert = new_verts[-cull] verts_per_face = verts_per_face[-booly] fidx = fidx[-booly]
def divide_garment(ob, dict): '''Creates a set of bool arrays and a set of number arrays for indexing a sub set of the uv coords. The nuber arrays can be used to look up wich bool array to use based on a vertex number''' if ob == 'empty': ob = bpy.context.object #----------------------------------- v_count = len(ob.data.vertices) idx = np.arange(v_count) full_set = np.array([]) dict['islands'] = [] v_list = [[i for i in poly.vertices] for poly in ob.data.polygons] v_in_faces = np.hstack(v_list) dict['v_in_faces'] = v_in_faces remaining = [1] vert = 0 while len(remaining) > 0: linked = find_linked(ob, vert, v_list) selected = np.unique(np.hstack(np.array(v_list)[linked]).ravel()) dict['islands'].append(selected) full_set = np.append(full_set, selected) remain_bool = np.in1d(idx, full_set, invert=True) remaining = idx[remain_bool] if len(remaining) == 0: break vert = remaining[0] #################################
def setdiff1d(ar1, ar2, assume_unique=False): """ Find the set difference of two arrays. Return the sorted, unique values in `ar1` that are not in `ar2`. Parameters ---------- ar1 : array_like Input array. ar2 : array_like Input comparison array. assume_unique : bool If True, the input arrays are both assumed to be unique, which can speed up the calculation. Default is False. Returns ------- setdiff1d : ndarray Sorted 1D array of values in `ar1` that are not in `ar2`. See Also -------- numpy.lib.arraysetops : Module with a number of other functions for performing set operations on arrays. Examples -------- >>> a = np.array([1, 2, 3, 2, 4, 1]) >>> b = np.array([3, 4, 5, 6]) >>> np.setdiff1d(a, b) array([1, 2]) """ if assume_unique: ar1 = np.asarray(ar1).ravel() else: ar1 = unique(ar1) ar2 = unique(ar2) return ar1[in1d(ar1, ar2, assume_unique=True, invert=True)]
def set_snapshot_weights(ratio_dict, orig_rng, eg_range): '''Determine the job distribution ratios to carry forward during the ratio condition application period using actual jobs held ratios. likely called at implementation month by main job assignment function Count the number of jobs held by each of the ratio groups for each of the affected job level numbers. Set the weightings in the distribute function accordingly. inputs ratio_dict (dictionary) dictionary containing job levels as keys and ratio groups, weightings, month_start and month end as values. orig_rng (numpy array) month slice of original job array eg_range (numpy array) month slice of employee group code array ''' ratio_dict = copy.deepcopy(ratio_dict) job_nums = list(ratio_dict.keys()) for job in job_nums: wgt_list = [] for ratio_group in ratio_dict[job][0]: wgt_list.append(np.count_nonzero((orig_rng == job) & (np.in1d(eg_range, ratio_group)))) ratio_dict[job][1] = tuple(wgt_list) return ratio_dict # ASSIGN JOBS BY RATIO CONDITION
