Python scipy.delete函数代码示例

  def scanSound(self, source, minnotel):
    binarized = source
    scale = 60. / self.wavetempo * (binarized[0].size / self.duration)
    noise_length = scale*minnotel

    antinoised = sp.zeros_like(binarized)

    for i in range(sp.shape(binarized)[0]):
      new_line = binarized[i, :].copy()
      diffed = sp.diff(new_line)
      ones_keys = sp.where(diffed == 1)[0]
      minus_keys = sp.where(diffed == -1)[0]
      
      if(ones_keys.size != 0 and minus_keys.size != 0):
        if(ones_keys[0] > minus_keys[0]):
          new_line = self.cutNoise(
              (0, minus_keys[0]), noise_length, new_line)
          minus_keys = sp.delete(minus_keys, 0)

        if(ones_keys[-1] > minus_keys[-1]):
          new_line = self.cutNoise(
              (ones_keys[-1], new_line.size-1), noise_length, new_line)
          ones_keys = sp.delete(ones_keys, -1)

        for j in range(sp.size(ones_keys)):
          new_line = self.cutNoise(
              (ones_keys[j], minus_keys[j]), noise_length, new_line)

        antinoised[i, :] = new_line

    return antinoised

    def generateNodesAdaptive(self):
        innerDomainSize = self.innerDomainSize
        innerMeshSize   = self.innerMeshSize
        numberElementsInnerDomain = innerDomainSize/innerMeshSize
	assert(numberElementsInnerDomain < self.numberElements)
        domainCenter = (self.domainStart+self.domainEnd)/2
        nodes0 = np.linspace(domainCenter,innerDomainSize/2.0,(numberElementsInnerDomain/2.0)+1.0)
        nodes0 = np.delete(nodes0,-1)
        numberOuterIntervalsFromDomainCenter = (self.numberElements - numberElementsInnerDomain)/2.0
        const = np.log2(innerDomainSize/2.0)/0.5
        exp = np.linspace(const,np.log2(self.domainEnd*self.domainEnd),numberOuterIntervalsFromDomainCenter+1)
        nodes1 = np.power(np.sqrt(2),exp)
        nodesp = np.concatenate((nodes0,nodes1))
        nodesn = -nodesp[::-1]
        nodesn = np.delete(nodesn,-1)
        linNodalCoordinates = np.concatenate((nodesn,nodesp))
        nodalCoordinates = 0

        #Introduce higher order nodes
        if self.elementType == "quadratic" or self.elementType == "cubic":
           if self.elementType == "quadratic":
              numberNodesPerElement = 3 
           elif self.elementType == "cubic":
              numberNodesPerElement = 4

           for i in range(0,len(linNodalCoordinates)-1):
              newnodes = np.linspace(linNodalCoordinates[i],linNodalCoordinates[i+1],numberNodesPerElement)
              nodalCoordinates = np.delete(nodalCoordinates,-1)
              nodalCoordinates = np.concatenate((nodalCoordinates,newnodes))

        else:
           nodalCoordinates = linNodalCoordinates
    
        return nodalCoordinates

    def _csv2m(self, csv_link):
        '''
        Import the csv as an array, clipping out strings for bars
        ...

        Arguments
        ---------
        csv_link        : str
                          Path to csv file to be converted into a map

        Returns
        -------
        m               : array
                          Array of floats to be plotted as map
        rows            : list
                          List of tuples (row, color) to locate horizontal bars
        cols            : list
                          List of tuples (col, color) to locate vertical bars    
        '''
        csv = [line.strip('\n').strip('\r').split(',') for line in open(csv_link).readlines()]
        a = np.array(csv)
        rows, cols = [], []
        for i, row in enumerate(a):
            color =  [row[0], row[-1]]
            if 'w' in color or 'b' in color:
                rows.append((i, color[0]))
        for i, col in enumerate(a.T):
            color =  [col[0], col[-1]]
            if 'w' in color or 'b' in color:
                cols.append((i, color[0]))
        m = scipy.delete(a, [i[0] for i in rows], 0)
        m = scipy.delete(m, [i[0] for i in cols], 1)
        return np.array(m, dtype=float), rows, cols

    def gstamp(self, ports_v, time=0, reduced=True):
        """Returns the differential (trans)conductance wrt the port specified by port_index
        when the element has the voltages specified in ports_v across its ports,
        at (simulation) time.

        ports_v: a list in the form: [voltage_across_port0, voltage_across_port1, ...]
        port_index: an integer, 0 <= port_index < len(self.get_ports())
        time: the simulation time at which the evaluation is performed. Set it to
        None during DC analysis.
        """
        indices = ([self.n1 - 1]*2 + [self.n2 - 1]*2,
                   [self.n1 - 1, self.n2 - 1]*2)
        gm = self.model.get_gm(self.model, 0, utilities.tuplinator(ports_v), 0, self.device)
        if gm == 0:
            gm = options.gmin*2
        stamp = np.array(((gm, -gm),
                          (-gm, gm)), dtype=np.float64)
        if reduced:
            zap_rc = [pos for pos, i in enumerate(indices[1][:2]) if i == -1]
            stamp = np.delete(stamp, zap_rc, axis=0)
            stamp = np.delete(stamp, zap_rc, axis=1)
            indices = tuple(zip(*[(i, y) for i, y in zip(*indices) if (i != -1 and y != -1)]))
            stamp_flat = stamp.reshape(-1)
            stamp_folded = []
            indices_folded = []
            for ix, it in enumerate([(i, y) for i, y in zip(*indices)]):
                if it not in indices_folded:
                    indices_folded.append(it)
                    stamp_folded.append(stamp_flat[ix])
                else:
                    w = indices_folded.index(it)
                    stamp_folded[w] += stamp_flat[ix]
            indices = tuple(zip(*indices_folded))
            stamp = np.array(stamp_folded)
        return indices, stamp

def calc_coh(subject, conditions, task, meg_electordes_names, meg_electrodes_data, tmin=0, tmax=2.5, sfreq=1000, fmin=55, fmax=110, bw=15, n_jobs=6):
    input_file = op.join(ELECTRODES_DIR, subject, task, 'electrodes_data_trials.mat')
    output_file = op.join(ELECTRODES_DIR, subject, task, 'electrodes_coh.npy')
    d = sio.loadmat(input_file)
    # Remove and sort the electrodes according to the meg_electordes_names
    electrodes = get_electrodes_names(subject, task)
    electrodes_to_remove = set(electrodes) - set(meg_electordes_names)
    indices_to_remove = [electrodes.index(e) for e in electrodes_to_remove]
    electrodes = scipy.delete(electrodes, indices_to_remove).tolist()
    electrodes_indices = np.array([electrodes.index(e) for e in meg_electordes_names])
    electrodes = np.array(electrodes)[electrodes_indices].tolist()
    assert(np.all(electrodes==meg_electordes_names))

    for cond, data in enumerate([d[conditions[0]], d[conditions[1]]]):
        data = scipy.delete(data, indices_to_remove, 1)
        data = data[:, electrodes_indices, :]
        data = downsample_data(data)
        data = data[:, :, :meg_electrodes_data.shape[2]]
        if cond == 0:
            coh_mat = np.zeros((data.shape[1], data.shape[1], 2))

        con_cnd, _, _, _, _ = spectral_connectivity(
            data, method='coh', mode='multitaper', sfreq=sfreq,
            fmin=fmin, fmax=fmax, mt_adaptive=True, n_jobs=n_jobs, mt_bandwidth=bw, mt_low_bias=True,
            tmin=tmin, tmax=tmax)
        con_cnd = np.mean(con_cnd, axis=2)
        coh_mat[:, :, cond] = con_cnd
    np.save(output_file[:-4], coh_mat)
    return con_cnd

def main(filename,metric,opts):
  reader = csv.reader(open(filename,'r'),delimiter=',')
  reader.next() # ignore first line
  header = reader.next()
  origModels = header[1:]
  students = origModels[:-4]
  if opts.useHC:
    models = list(origModels)
  else:
    models = list(students)

  results = numpy.zeros([len(students),len(models)])
  for i,row in enumerate(reader):
    if len(origModels) != len(row) - 1:
      print >>sys.stderr,'Bad Size:',len(origModels),len(row)-1
      sys.exit(2)
    for j,v in enumerate(row[1:len(models)+1]):
      results[i,j] = float(v)
  
  # get the arguments we want to call
  args = []
  args.append((results,models,opts.numStudentsToChoose,metric))
  for i,student in enumerate(students):
    tempResults = scipy.delete(results,i,0)
    tempResults = scipy.delete(tempResults,i,1)
    tempModels = list(models)
    del tempModels[i]
    args.append((tempResults,tempModels,opts.numStudentsToChoose,metric))
  if opts.multi:
    pool = Pool()
    res = pool.map(calcBestWrapper,args)
  else:
    res = map(calcBestWrapper,args)
  for student,(bestVal,bestInds,bestModels) in zip(['Overall'] + students,res):
    print '%s,%s' % (student,','.join(bestModels))

def marginalize(dist_vars,marg_vars):
    #Initialize marginal dict, same for all dists
    margdist_vars={}
    margdist_vars['dist']=dist_vars['dist']
    #Gaussian
    if dist_vars['dist']=='gaussian':
        N_k=len(dist_vars['w'])#Number of gaussians
        N_D=len(dist_vars['mu'][0])#Dim of orgiginal parameter space
        
        #Initialize remaining components of marg dict, before any marginalization        
        margdist_vars['mu']=dist_vars['mu'][:]
        margdist_vars['cov']=dist_vars['cov'][:]
        margdist_vars['w']=dist_vars['w'][:]
        margdist_vars['vars']=dist_vars['vars'][:]
        
        for marg_var in marg_vars:
            #Get indices of marginalized var in current gaussian
            i_m=margdist_vars['vars'].index(marg_var)
            #Create list of current indices
            i_old=list(range(N_D))
            #remove index of marg_var
            i_old.remove(i_m)
            
            
            #remove marg_var from list of vars
            margdist_vars['vars'].remove(marg_var)
        
            margdist_vars['mu']=[sp.delete(margdist_vars['mu'][i],i_m,0) for i in range(len(margdist_vars['w']))]
            
            #For testing
#            for i in range(N_k):
#                margdist_vars['w'][i]=dist_vars['w'][i]
#                margdist_vars['cov'][i]=sp.delete(sp.delete(margdist_vars['cov'][i],i_m,0),i_m,1)
            
            #Loop over components in mixture
            #marg cov:T_M=L_m-T_m
            #marg weight:w_m=sp.sqrt(2*pi/L_mm)
            for i in range(N_k):
                #invert original covariance matrix
                Lambda=inv(sp.matrix(margdist_vars['cov'][i]))
                #Store marg compononent of 
                L_mm=Lambda[i_m,i_m]
                #Remove marginal component from Lambda
                L_m=sp.delete(sp.delete(Lambda,i_m,0),i_m,1)
                #Construct skew matrix
                l_m=sp.matrix(Lambda[i_m,i_old]+Lambda[i_old,i_m])
                T_m=l_m.T*l_m/(4*L_mm)
                #Construct marginalized covariance matrix
                margdist_vars['cov'][i]=sp.asarray(inv(L_m-T_m))
                #Scale weight
                margdist_vars['w'][i]=sp.sqrt(2*sp.pi/L_mm)*dist_vars['w'][i]
            
            #Update dimensions of marginalized parameter space
            N_D=N_D-1
         
        return margdist_vars
            
                
                

 def lab_reduce(y_true, y_score):
     empty_indices = scan_empty(y_true)
     i = 0
     for k in empty_indices:
         y_true = scipy.delete(y_true, k-i, 1)
         y_score = scipy.delete(y_score, k-i, 1)
         i += 1
     return y_true, y_score

def load_structural(fname):
    data = np.genfromtxt(fname, delimiter=',')
    # removing first column and first row, because they're headers
    data = scipy.delete(data, 0, 1)
    data = scipy.delete(data, 0, 0)
    # format it to be subjects x variables
    data = data.T
    return data

def main():
    '''
    Breast Cancer data set
    '''
    # Get the breast cancer data
    cancer_data = np.loadtxt("breast-cancer-wisconsin.data", delimiter=',', dtype=str)
    # All the missing values are subsitutes to 0.0
    cancer_data[cancer_data == "?"] = 0.0
    # Extract the cancer ids from the given input
    cancer_id = cancer_data[:, :1]
    # Extract the features from the given input
    input_matrix = cancer_data[:, 1:-1]
    # Extract the output labels
    labels = cancer_data[:, -1]
    # Instantiation of Logistic Regression
    # Regularization to avoid overfitting
    logistic_classifier = LogisticRegression(C=0.5, max_iter = 900)
    # Splitting the datas into training and testing
    # Could have split into training , test and cross-valdation to avoid overfitting.
    train_set, test_set, train_class_label, test_class_label = train_test_split(input_matrix, labels, train_size = 0.5, test_size=0.5, random_state=10)
    # To ease, all the values are converted to float
    train_set=np.array(train_set,dtype=float)
    test_set=np.array(test_set,dtype=float)
    train_class_label=np.array(train_class_label,dtype=float)
    test_class_label=np.array(test_class_label,dtype=float)
    '''Train a machine learning model with the given training set'''
    logistic_classifier.fit(train_set, train_class_label)
    '''
    Titanic Data set
    '''
    titanic_data = np.loadtxt("train.csv", delimiter=',', dtype=str)
    titanic_data[titanic_data == "?"] = 0.0
    titanic_data[titanic_data == ""] = 0.0
    labels = titanic_data[1:, 1]
    # To Ease, all the string columns are removed so that the logistic regression model can be built easily
    # Columns removed are : Passenger Id, Name, Pclass, Embarkment, Sex, Cabin
    # Traveller info contains the information of the passenger's name, id and sex
    titanic_data = titanic_data[1:, 2:-1]
    titanic_data = scipy.delete(titanic_data, [1,2,3,7,9], 1)
    titanic_data=np.array(titanic_data,dtype=float)
    titanic_logistic_classifier = LogisticRegression(C=0.5, max_iter = 900)
    titanic_logistic_classifier.fit(titanic_data, labels)
    
    # Test set of titanic data set
    titanic_test_set = np.loadtxt("test.csv", delimiter=',', dtype=str)
    titanic_test_set[titanic_test_set == "?"] = 0.0
    titanic_test_set[titanic_test_set == ""] = 0.0
    
    # Slice the features from the input
    # To Ease, all the string columns are removed so that the logistic regression model can be built easily
    # Columns removed are : Passenger Id, Name, Pclass, Embarkment, Sex, Cabin
    # Traveller info contains the information of the passenger's name, id and sex
    traveller_info = titanic_test_set[1:, :5]
    titanic_test_set = titanic_test_set[1:, 1:]
    titanic_test_set = scipy.delete(titanic_test_set, [1,2,3,7,9], 1)
    titanic_test_set=np.array(titanic_test_set,dtype=float)
    # Calling the function correlate date
    correlate_data_sets(test_set, logistic_classifier, titanic_test_set, titanic_logistic_classifier, traveller_info, cancer_id)

    def __init__(self, opts):
        self.train_file = opts["train_file"]
        self.test_file = opts["test_file"]
        self.out_file = opts["out_file"]

        self.learning_rate = opts["learning_rate"]
        self.decay_rate = opts["decay_rate"]
        self.batch_size = opts["batch_size"]
        self.n_iter = opts["n_iter"]
        self.shuffle = opts["shuffle"]
        self.holdout_size = opts["holdout_size"]
        self.l2 = opts["l2"]
        self.standardization = opts["standardize"]
        self.loss_method = opts["loss"]
        self.use_adagrad = opts["adagrad"]
        self.use_rmsprop = opts["rmsprop"]
        self.hash_trick_mod = opts["hash"]

        print opts

        train_data = read_data(self.train_file)
        test_data = read_data(self.test_file)

        self.test_input = np.ones((test_data.shape[0], test_data.shape[1] + 1), dtype=np.float)
        self.test_input[:, 1:] = test_data[:, :]
        self.test_initial = np.array(self.test_input)
        self.test_output = np.zeros(test_data.shape[0])

        self.input = np.ones(train_data.shape, dtype=np.float)
        self.input[:, 1:] = train_data[:, :-1]
        self.output = train_data[:, -1:].transpose(1, 0)[0]

        self.validation_input = np.array([])
        self.validation_output = np.array([])
        if self.holdout_size:
            holdout_part = int(self.holdout_size * self.input.shape[0])
            random_rows = random.sample(range(self.input.shape[0]), holdout_part)
            self.validation_input = self.input[random_rows, :]
            self.validation_output = self.output[random_rows]
            self.input = scipy.delete(self.input, random_rows, 0)
            self.output = scipy.delete(self.output, random_rows)

        self.learning_input = np.array(self.input)
        self.learning_output = np.array(self.output)

        if self.hash_trick_mod != 0:
            self.learning_input = hash_trick(self.learning_input, self.hash_trick_mod)
            self.validation_input = hash_trick(self.validation_input, self.hash_trick_mod)
            self.test_input = hash_trick(self.test_input, self.hash_trick_mod)

        if self.standardization:
            standardize(self.learning_input)
            standardize(self.validation_input)
            standardize(self.test_input)

        self.w = np.zeros(self.learning_input.shape[1], dtype=np.float)
        self.adagrad_cache = np.zeros(len(self.w))
        self.rmsprop_cache = np.zeros(len(self.w))

    def condenseMatrix(self,H):
        
        # applyBoundaryConditions on Hx Hy Hz
        H = np.delete(H,0,0)
        H = np.delete(H,-1,0)
        H = np.delete(H,0,1)
        H = np.delete(H,-1,1)

        return H

 def delete_invalid_data(self,value=0.0):
     r"""
     .. todo:: The explicite dependency on cathode current needs to be removed
     """
     rows=sp.where(self._data[self._objectives[0]]==value)
     self._logger.warning('Deleting invalid data rows: '+str(rows))
     sp._data=sp.delete(self._data,rows,axis=0)
     for key in self._datadict.keys():
         self._datadict[key] = sp.delete(self._datadict[key],rows,axis=0)

def rankUsingPCA(fileName):
        fp = open(fileName)
        line = fp.readline()
        firstLine = line.strip().split(',')
        fp.close()
        #names = numpy.array(firstLine[1:-1])
        names = numpy.array(firstLine[1:])

        #print names.shape
        print names
        dataMat = loadDataSet(fileName)
        #print dataMat
        meanVals = mean(dataMat, axis=0)
        meanRemoved = dataMat - meanVals
        covMat = cov(meanRemoved, rowvar=0)
        eigVals,eigVects = linalg.eig(mat(covMat))
        eigValInd = argsort(eigVals)
        eigValInd = eigValInd[:-(999999+1):-1]
        redEigVects = eigVects[:,eigValInd]
        #lowDMat, reconMat = pca(dataMat)
        lowDDataMat = meanRemoved * redEigVects
        T = redEigVects.getA()
        print T
        # calculate the variance covered by each components in PCA
        percentagePCA = calculateFractionOfVarianceExplainedByPCA(lowDDataMat)
        for d in range(T.shape[0]):
                T[:,d] = T[:,d] * percentagePCA[d]

        #print T
        rankMatrix = {}
        rank = 0
        while(T.shape[0] > 1 and T.shape[1] > 1):
                rowMax = -99999
                index = 0
                maxIndex = -1
                for r in T:
                  valMax = numpy.amax(r)
                  if (valMax > rowMax):
                    rowMax = valMax
                    maxIndex = index
                  #endif
                  index = index + 1
                #endfor
                print names[maxIndex]
                rankMatrix[names[maxIndex]] = rank
                rank = rank + 1
                T = scipy.delete(T,maxIndex,0)
                #print T
                names = scipy.delete(names,maxIndex,0)
                #print names
        #end while
        print names[0]
        rankMatrix[names[0]] = rank
        return rankMatrix

def remove_from_hierarchy(obj, remove_half_orphans=True):
    """ Removes a Neo object from the hierarchy it is embedded in. Mostly
    downward links are removed (except for possible links in
    :class:`neo.core.Spike` or :class:`neo.core.SpikeTrain` objects).
    For example, when ``obj`` is a :class:`neo.core.Segment`, the link from
    its parent :class:`neo.core.Block` will be severed. Also, all links to
    the segment from its spikes and spike trains will be severed.

    :param obj: The object to be removed.
    :type obj: Neo object
    :param bool remove_half_orphans: When True, :class:`neo.core.Spike`
        and :class:`neo.core.SpikeTrain` belonging to a
        :class:`neo.core.Segment` or :class:`neo.core.Unit` removed by
        this function will be removed from the hierarchy as well, even
        if they are still linked from a :class:`neo.core.Unit` or
        :class:`neo.core.Segment`, respectively. In this case, their
        links to the hierarchy defined by ``obj`` will be kept intact.
    """
    classname = type(obj).__name__

    # Parent for arbitrary object
    if classname in neo.description.many_to_one_relationship:
        for n in neo.description.many_to_one_relationship[classname]:
            p = getattr(obj, n.lower())
            if p is None:
                continue
            l = getattr(p, classname.lower() + 's', ())
            try:
                l.remove(obj)
            except ValueError:
                pass

    # Many-to-many relationships
    if isinstance(obj, neo.RecordingChannel):
        for rcg in obj.recordingchannelgroups:
            try:
                idx = rcg.recordingchannels.index(obj)
                if rcg.channel_indexes.shape[0] == len(rcg.recordingchannels):
                    rcg.channel_indexes = sp.delete(rcg.channel_indexes, idx)
                if rcg.channel_names.shape[0] == len(rcg.recordingchannels):
                    rcg.channel_names = sp.delete(rcg.channel_names, idx)
                rcg.recordingchannels.remove(obj)
            except ValueError:
                pass

    if isinstance(obj, neo.RecordingChannelGroup):
        for rc in obj.recordingchannels:
            try:
                rc.recordingchannelgroups.remove(obj)
            except ValueError:
                pass

    _handle_orphans(obj, remove_half_orphans)