Python numpy.union1d函数代码示例

def cr_reject2(fl, er, nsig=10.0, fwhm=2, grow=1, debug=True):
    """ interpolate across features that have widths smaller than the
    expected fwhm resolution.

    Parameters
    ----------
    fwhm: int
      Resolution fwhm in pixels
    fl : array of floats, shape (N,)
      Flux
    er : array of floats, shape (N,)
      Error

    Returns the interpolated flux and error arrays.
    """
    fl, er = (np.array(a, dtype=float) for a in (fl, er))
    # interpolate over bad pixels 
    fl1 = convolve_psf(fl, fwhm)
    ibad = np.where(np.abs(fl1 - fl) > nsig*er)[0]
    if debug: print(len(ibad))
    extras1 = np.concatenate([ibad + 1 + i for i in range(grow)]) 
    extras2 = np.concatenate([ibad - 1 - i for i in range(grow)]) 
    ibad = np.union1d(ibad, np.union1d(extras1, extras2))
    ibad = ibad[(ibad > -1) & (ibad < len(fl))]
    igood = np.setdiff1d(np.arange(len(fl1)), ibad)
    fl[ibad] = np.interp(ibad, igood, fl[igood])
    er[ibad] = np.nan
    return fl,er

    def get_obstList(self,X,Y,Z):
        """
   Define areas external to pipe.
        """
       #Pipe in - find all points exterior of small
	pipe_in = np.array(np.where((X - 1)**2 + (Y - 1)**2 > (self.diam_in/2)**2)).flatten()
	pipe_in_stop = np.array(np.where(Z <= 3 + 0.5*(self.diam_out - self.diam_in))).flatten()
	pipe_in = np.intersect1d(pipe_in[:],pipe_in_stop[:])

	#Expansion - find all points exterior of expansion
	r_cone = self.diam_in
	h_cone = self.diam_in	
	expansion = np.array(np.where((X - 1)**2 + (Y - 1)**2 > (r_cone/h_cone)**2*(Z - 3)**2)).flatten()
	expansion_start = np.array(np.where(Z >= 3 + 0.5*(self.diam_out - self.diam_in)))
	#expansion_stop = np.array(np.where(Z <= 4)).flatten()
	expansion = np.intersect1d(expansion[:],expansion_start[:])
	#expansion = np.intersect1d(expansion[:],expansion_stop[:])

	#Pipe out - final all points exterior of smaller pipe
	pipe_out = np.array(np.where((X - 1)**2 + (Y - 1)**2 > (self.diam_out/2)**2)).flatten()
	pipe_out_start = np.array(np.where(Z >= 3 + 0.5*(self.diam_in - self.diam_out))).flatten()
	pipe_out = np.intersect1d(pipe_out[:],pipe_out_start[:])


	#Put the pieces together

	pipe = expansion[:]
	pipe = np.union1d(expansion[:],pipe_in[:])
	pipe = np.union1d(pipe[:],pipe_out[:])

	obst_list = pipe[:]

       
        return list(obst_list[:])

    def fit(self, X, y):
        self._X_colcount = X.shape[1]
        #self.learner.fit(self._transform(X), y)
        self.get_learner(X, y)
        classifier_features = self.getClassifierFeatures()

        fe = SecondLayerFeatureEvaluator()
        local_excluded_features = np.union1d(self.excluded_features,
                                             classifier_features)
        local_X = utilities.exclude_cols(X,
                                         local_excluded_features)
        scores = fe.evaluate(local_X, X[:,classifier_features],
                             n_jobs = self.n_jobs)
        i = 0
        for feature in classifier_features:
            fc = sklearn.base.clone(
                self.feature_confidence_estimator).set_params(
                    **self.feature_confidence_estimator.get_params())
            fc.fit(X, feature,
                   scores[i],
                   local_excluded_features)
            self.setFeatureConfidenceEstimator(feature, fc)
            self._second_layer_features = np.union1d(
                self._second_layer_features, fc.getFeatures())
            i += 1
        return(self)

    def set_cavity_walls(self,walls=['left','right','bottom','west','east']): 
        """
         set up to 5 walls as solid walls for the simulation
        """
        solid_list_a = np.empty(0).flatten()
        solid_list_b = np.empty(0).flatten()
        solid_list_c = np.empty(0).flatten()
        solid_list_d = np.empty(0).flatten()
        solid_list_e = np.empty(0).flatten()

        for w in walls:
            if w=='right':
                solid_list_a = np.array(np.where((self.x==0.))).flatten()
            elif w=='left':
                solid_list_b = np.array(np.where((self.x > (self.Lx_p-self.dx/2.)))).flatten()
            elif w=='west':
                solid_list_d = np.array(np.where((self.z == 0.))).flatten()
            elif w=='bottom':
                solid_list_c = np.array(np.where((self.y == 0.))).flatten()
            elif w=='east':
                solid_list_e = np.array(np.where((self.z > (self.Lz_p - self.dx/2.)))).flatten()

        solid_list = np.array(np.union1d(solid_list_a,solid_list_b)); 
        solid_list = np.array(np.union1d(solid_list,solid_list_c));
        solid_list = np.array(np.union1d(solid_list,solid_list_e));
        self.solid_list = np.array(np.union1d(solid_list,solid_list_d))
        
        self.lid_list = np.array(np.where((self.y > (self.Ly_p-self.dx/2.)))).flatten()

def dDCR_moments(SED1, SED2, bandpass):
    zenith_angle = np.pi/4.0 * galsim.radians
    R500 = galsim.dcr.get_refraction(500, zenith_angle)

    # analytic first moment differences
    R = lambda w:(galsim.dcr.get_refraction(w, zenith_angle) - R500) / galsim.arcsec
    x1 = np.union1d(bandpass.wave_list, SED1.wave_list)
    x1 = x1[(x1 >= bandpass.blue_limit) & (x1 <= bandpass.red_limit)]
    x2 = np.union1d(bandpass.wave_list, SED2.wave_list)
    x2 = x2[(x2 >= bandpass.blue_limit) & (x2 <= bandpass.red_limit)]
    numR1 = np.trapz(R(x1) * bandpass(x1) * SED1(x1), x1)
    numR2 = np.trapz(R(x2) * bandpass(x2) * SED2(x2), x2)
    den1 = SED1.calculateFlux(bandpass)
    den2 = SED2.calculateFlux(bandpass)

    R1 = numR1/den1
    R2 = numR2/den2
    dR_analytic = R1 - R2

    # analytic second moment differences
    V1_kernel = lambda w:(R(w) - R1)**2
    V2_kernel = lambda w:(R(w) - R2)**2
    numV1 = np.trapz(V1_kernel(x1) * bandpass(x1) * SED1(x1), x1)
    numV2 = np.trapz(V2_kernel(x2) * bandpass(x2) * SED2(x2), x2)
    V1 = numV1/den1
    V2 = numV2/den2
    dV_analytic = V1 - V2

    return dR_analytic, dV_analytic, len(x2)

    def averageHopDistance(transmissions):
        """
        Take a list of numpy arrays which has rows as an information transmission.
        Outputs the total number of hops of information divided by the total
        number of original senders (those that did not receive from another person).
        A measure of the average spread of information from each source. 
        """
        numIterations = len(transmissions)
        originalInfoSenders = numpy.array([])
        infoReceivers = numpy.array([])
        totalHops = 0

        #Assume transmissions are unique 
        for i in range(0, numIterations):
            currentAlters = transmissions[i][:, 1]
            infoReceivers = numpy.union1d(infoReceivers, currentAlters)
            totalHops += transmissions[i].shape[0]

            currentEgos = transmissions[i][:, 0]
            originalInfoSenders = numpy.union1d(originalInfoSenders, currentEgos)
            originalInfoSenders = numpy.setdiff1d(originalInfoSenders, infoReceivers)

        #Number of path ends is infoReceivers.shape[0]
        if originalInfoSenders.shape[0] != 0:
            return float(totalHops)/originalInfoSenders.shape[0]
        else:
            return 0 

    def chi(self, customattribute):
        """
        计算其卡方值.
        """
        attributeDict = dict()
        classAttributeDict = dict()
        for piece in self.chunks:
            for (attribute, classAttribute), arrays in piece.groupby([customattribute, self.classAttribute]).studentID.unique().iteritems():
                attributeDict.setdefault((attribute, classAttribute), np.array([]))
                attributeDict[(attribute, classAttribute)] = np.union1d(attributeDict[(attribute, classAttribute)], arrays)

            for classAttribute, arrays in piece.groupby(self.classAttribute).studentID.unique().iteritems():
                classAttributeDict.setdefault(classAttribute, np.array([]))
                classAttributeDict[classAttribute] = np.union1d(classAttributeDict[classAttribute], arrays)

        #各个类别的毕业去向群体中所占的比例.
        classSeries = Series(classAttributeDict).apply(lambda x:len(x))
        classSeries /= classSeries.sum()

        #在各个attribute上的实际观测值.
        attributeObs = Series(attributeDict).apply(lambda x:len(x)).unstack(fill_value=0)

        attributeExp = DataFrame(index=attributeObs.index, columns=attributeObs.columns)

        #设置初始值.
        for index in attributeExp.index:
            attributeExp.ix[index] = attributeObs.ix[index].sum()
        #根据各个目标类别中的比例来获得其期望值.
        attributeExp = attributeExp.mul(classSeries).fillna(0)
        #根据实际观测值与期望值来计算其卡方值，并返回p-value值.
        return chisquare(attributeObs.stack(), attributeExp.stack()), attributeObs

def reference_naive_aggregation(C):
    S = np.array_split(C.indices, C.indptr[1:-1])
    n = C.shape[0]
    aggregates = np.empty(n, dtype=C.indices.dtype)
    aggregates[:] = -1  # aggregates[j] denotes the aggregate j is in
    R = np.zeros((0,))     # R stores already aggregated nodes
    j = 0               # j is the aggregate counter
    Cpts = []

    # Only one aggregation pass
    for i, row in enumerate(S):

        # if i isn't already aggregated, grab all his neighbors
        if aggregates[i] == -1:
            unaggregated_neighbors = np.setdiff1d(row, R)
            aggregates[unaggregated_neighbors] = j
            aggregates[i] = j
            j += 1
            R = np.union1d(R, unaggregated_neighbors)
            R = np.union1d(R, np.array([i]))
            Cpts.append(i)
        else:
            pass

    assert(np.unique(R).shape[0] == n)

    Pj = aggregates
    Pp = np.arange(n+1)
    Px = np.ones(n)

    return csr_matrix((Px, Pj, Pp)), np.array(Cpts)

 def __call__(self, s, *pargs, **kargs):
     if len(kargs) == 0 and len(kargs) == 0:
         if s in self.a:
             at = np.searchsorted(self.a, s)
             return self.m[at]
         else:
             if s <= self.a[0]:
                 return self.m[0]
             elif s >= self.a[-1]:
                 return self.m[-1]
             else:
                 at = np.searchsorted(self.a, s)
                 d = (s - self.a[at-1]) / (self.a[at] - self.a[at-1])
                 r1 = self.m[at-1]
                 r2 = self.m[at]
                 # if isinstance(r1, mesh2d):
                 #    # X= np.union1d(r1.X, r2.X)
                 #    # Y = [res for res in r1(X)]
                 #    # print Y
                 if isinstance(r1, mesh3d):
                     X = np.union1d(r1.X, r2.X)
                     Y = np.union1d(r1.X, r2.X)
                     res = np.zeros((X.size, Y.size))
                     for ix, vx in enumerate(X):
                         for iy, vy in enumerate(Y):
                             a = r1(vx, vy)
                             b = r2(vx, vy)
                             res[ix, iy] = a + d * (b - a)
                     return mesh3d(X=X, Y=Y, Z=res)
                 else:
                     print u"Pas dans la liste"

 def update_dimensions(self, test_cases):
     for tc in test_cases:
         self.x_range = num.union1d(self.x_range, 
                         tc.test_parameters.items()[0].values())
         self.y_range = num.union1d(self.y_range, 
                         tc.test_parameters.items()[1].values())
         self.z_range = num.union1d(self.z_range, 
                         tc.test_parameters.items()[2].values())

    def change(self):
        '''
        suggests a potential change to state
        return a changed state
        '''
        ratio=self.params['transRotRatio']
        position_idx=int(np.random.rand()*(self.params['m']))
        noChangeFlag=1
        if self.params['TtoDIsPhysical'] and np.random.rand()<=self.params['probFormChange'] and self.state[2][position_idx]==0:
            changedForm=self.state[2].copy()
            changedForm[position_idx]=1
            changedState=(self.state[0],self.state[1],changedForm,self.state[3])
            noChangeFlag=0
        elif np.random.rand()<=ratio:
            noChangeFlag=0
            #translation.
            changedPosition=self.state[0].copy()
            if self.params['isAlt']:
                #Alternative dynamics, where proteins only move if nearby position is open
                u=np.random.uniform()
                #Perform change of position
                if u<= self.params['altProb'] and (self.state[0][position_idx]-1)%self.params['N'] not in self.state[0]:
                    changedPosition[position_idx]=changedPosition[position_idx]-1
                    
                elif u> self.params['altProb'] and (self.state[0][position_idx]+1)%self.params['N'] not in self.state[0]:
                    changedPosition[position_idx]=changedPosition[position_idx]+1
                    
            else:
                #where jumping through proteins is allowed.
                # to be more physical, we force proteins to get into empty slots
                # not connected to any other proteins before they can get back
                # into a slot next to other proteins.
                
                # flag of whether chosen protein is currently connected to 
                # other proteins
                if (self.state[0][position_idx]+1)%self.params['N'] in self.state[0] or (self.state[0][position_idx]-1)%self.params['N'] in self.state[0]:
                    # protein is currently connected to other proteins, 
                    # so we force it to go into empty slots not connected to proteins
                    connectedEmptySlots=np.union1d(np.union1d(self.state[0],(self.state[0]+1)%self.params['N']),(self.state[0]-1)%self.params['N'])
                    slotsToChoose=np.array([x for x in xrange(self.params['N']) if x not in connectedEmptySlots])
                else:
                    # if protein is currently not conencted to proteins, any slot not occupied can be taken
                    slotsToChoose = np.array([x for x in xrange(self.params['N']) if x not in self.state[0]])
                if len(slotsToChoose)!=0:
                    changedPosition[position_idx]=random.choice(slotsToChoose)
            
            changedState=(changedPosition,self.state[1],self.state[2],self.state[3])
        else:
            #Rotation
            changedType=self.state[1].copy()
            changedType[position_idx]=changedType[position_idx][::-1]
            changedM=self.state[3].copy()
            changedM[self.state[1][position_idx][1],self.state[1][position_idx][0]]-=1
            changedM[changedType[position_idx][1],changedType[position_idx][0]]+=1
            #note the above changedM, idx 1 is for row, while idx 0 is for column in the Type array. This is due to np.ravel of np.indices
            changedState=(self.state[0],changedType,self.state[2],changedM)
#        changedState=self.checkATP(changedState)
        return changedState

    def CONSTRUCT_TREE(df_nodes, df_edges,source=-1,sources=[-1,-2]):
        
        ids = np.union1d(df_edges.Pred_ID.unique(),df_edges.Prey_ID.unique())
        
        df_tree = pd.DataFrame(columns=df_edges.columns)


        for node_id in ids:
            if node_id in sources: continue
            df = df_edges[(df_edges.Pred_ID==node_id) & (df_edges.Prey_ID.isin(sources))].sort('BiomassIngested')
            if len(df) > 0:
                row = df.irow(-1)
            else:
                try:
                    row = df_edges[(df_edges.Pred_ID==node_id)].sort('BiomassIngested').irow(-1)
                except:
                    print 'Discarding node without prey...'
                    print node_id
                    print df_edges[(df_edges.Pred_ID==node_id)].sort('BiomassIngested')
                    #continue                    
                    raise Exception('nodes without prey')
                
            
            df_tree = df_tree.append(row)
                
                
        df_tree[['Pred_ID', 'Prey_ID']] = df_tree[['Pred_ID', 'Prey_ID']].astype(int)
        df_tree[['Biomass_Assimilated','BiomassIngested']] = df_tree[['Biomass_Assimilated','BiomassIngested']].astype(float)
        
        ids = np.union1d(df_tree.Pred_ID.unique(),df_tree.Prey_ID.unique())        
        df_nodes = df_nodes[df_nodes.ID.isin(ids)]                
        
        a_values = {ii:0 for ii in ids}
        c_values = {ii:0 for ii in ids}
        print len(a_values)
        print len(df_nodes)
        print a_values
        print df_tree.Prey_ID.unique()        
        
        df_nodes['A_value'] = a_values.values()
        df_nodes['C_value'] = a_values.values()     
        
        #return df_nodes,df_tree        
        
        Tree.GET_A(df_tree,source,a_values,c_values)
        
        df_nodes = df_nodes.set_index('ID',drop = False)
        
        for key,value in a_values.iteritems():
            #print key,value
            df_nodes.loc[key,'A_value'] = value
        for key,value in c_values.iteritems():
            df_nodes.loc[key,'C_value'] = value

        df_nodes[['A_value','C_value']] = df_nodes[['A_value','C_value']].astype(int)
        return df_nodes,df_tree

def create_error_matrix(obs_data, prd_data, compact=True, classes=None):
    """
    Create an error (confusion) matrix from observed and predicted data.
    The data is assumed to represent classes rather than continuous data.

    Parameters
    ----------
    obs_data : array-like
        Observed classes

    prd_data : array-like
        Predicted classes

    compact : bool
        Flag for whether or not to return error matrix in compact form.  If
        True, only the classes that are represented in the data will be
        returned.  If False, all classes in classes keyword should be
        returned.  Defaults to True.

    classes : array-like
        If compact is False, return error matrix for all classes.  Defaults
        to None.

    Returns
    -------
    err_mat : np.array
        Error matrix of classes

    class_xwalk : dict
        Dictionary of class value to row or column number
    """

    if compact == True:
        # Find all classes present in either the observed or predicted data
        classes = np.union1d(np.unique(obs_data), np.unique(prd_data))
    else:
        if classes == None:
            # No classes given - default to those present as above
            classes = np.union1d(np.unique(obs_data), np.unique(prd_data))
        else:
            # Use the user-defined classes
            classes = np.array(classes)

    n = classes.size

    # One liner for calculating error matrix
    # http://stackoverflow.com/questions/10958702/
    # python-one-liner-for-a-confusion-contingency-matrix-needed
    err_mat = np.array([zip(obs_data, prd_data).count(x) for x in
        itertools.product(classes, repeat=2)]).reshape(n, n)

    # Create the dictionary of class value to row/column number
    class_xwalk = dict((c, i) for (c, i) in zip(classes, xrange(n)))

    return err_mat, class_xwalk

def num_bipartisan_donors(df):
    """Find the number of people that have donated to more than one parties.
    
    Args:
        df: A DataFrame generated from the campaign finance data csv file
            with the column "party" added.

    Returns:
        An integer count of the number of people that have donated to more than
        one parties.
    
    Initial Approach:
        Select only contb amts > 0 (i.e. it is a donation)
        Select only people, parties.
        Group by people.
        aggregate number of parties
        select > 1
        Count

        
        '''
        bipartyAgg= df[df['contb_receipt_amt']>0][['contbr_nm','party']].drop_duplicates(['contbr_nm','party']).groupby('contbr_nm', as_index=False).agg(lambda x: x.count())
        bipartySet= bipartyAgg[bipartyAgg['party']>1]
        return bipartySet['contbr_nm'].count()
        # TODO: Implement this function.
        '''

    """
    
    # Obtain the set of unique contributor, party pairs
    pplSet= df[df['contb_receipt_amt']>0][['contbr_nm','party']].drop_duplicates(['contbr_nm','party']) 
    
    # Obtain all of the democrat info
    democ_d = np.array(pplSet[pplSet['party']=="Democrat"]["contbr_nm"])
    
    # Obtain the republican subset
    repub_d = np.array(pplSet[pplSet['party']=="Republican"]["contbr_nm"])
    
    # OBtain the libertarian subset
    liber_d = np.array(pplSet[pplSet['party']=="Libertarian"]["contbr_nm"])
    
    # Intersect democrat set with republican
    dr = np.intersect1d(democ_d, repub_d)
    
    # Intersect republican set with libertarian
    rl = np.intersect1d(repub_d, liber_d)
    
    # Intersect democrat set with libertarian
    ld = np.intersect1d(liber_d, democ_d)

    # Return the union of all three intersections
    retVal = np.union1d(dr, np.union1d(rl, ld))    
    return retVal
    
    pass

def process_eyelid_traces(traces,time_vect,idx_CS_US,idx_US,idx_CS,thresh_CR=.1,time_CR_on=-.1,time_US_on=.05):
    """ 
    preprocess traces output of get_behavior_traces 

    Parameters:
    ----------

    traces: ndarray (N trials X t time points)
        eyelid traces output of get_behavior_traces. 

    thresh_CR: float
        fraction of eyelid closure considered a CR

    time_CR_on: float
        time of alleged beginning of CRs

    time_US_on: float
        time when US is considered to induce have a UR


    Returns:
    -------
    eye_traces: ndarray 
        normalized eyelid traces

    trigs: dict
        dictionary containing various subdivision of the triggers according to behavioral responses

        'idxCSUSCR': index of trials with  CS+US with CR
        'idxCSUSNOCR': index of trials with  CS+US without CR
        'idxCSCR':   
        'idxCSNOCR':
        'idxNOCR': index of trials with no CRs
        'idxCR': index of trials with CRs
        'idxUS':

    """
    #normalize by max amplitudes at US    

    eye_traces=old_div(traces,np.nanmax(np.nanmedian(traces[np.hstack([idx_CS_US,idx_US])][:,np.logical_and(time_vect>time_US_on,time_vect<time_US_on +.4 )],0)))

    amplitudes_at_US=np.mean(eye_traces[:,np.logical_and( time_vect > time_CR_on , time_vect <= time_US_on )],1)

    trigs=dict()

    trigs['idxCSUSCR']=idx_CS_US[np.where(amplitudes_at_US[idx_CS_US]>thresh_CR)[-1]]
    trigs['idxCSUSNOCR']=idx_CS_US[np.where(amplitudes_at_US[idx_CS_US]<thresh_CR)[-1]]
    trigs['idxCSCR']=idx_CS[np.where(amplitudes_at_US[idx_CS]>thresh_CR)[-1]]
    trigs['idxCSNOCR']=idx_CS[np.where(amplitudes_at_US[idx_CS]<thresh_CR)[-1]]
    trigs['idxNOCR']=np.union1d(trigs['idxCSUSNOCR'],trigs['idxCSNOCR'])
    trigs['idxCR']=np.union1d(trigs['idxCSUSCR'],trigs['idxCSCR'])
    trigs['idxUS']=idx_US

    return eye_traces,amplitudes_at_US, trigs