python类sum()的实例源码

def _neighbors_from_list_with_mask(self, labels, min_contact_area=None, real_area=True):
        if (not self._neighbors is None) and (sum([i in self._neighbors.keys() for i in labels])==len(labels)):
            result = dict([(i,self._neighbors[i]) for i in labels])
            if  min_contact_area is None:
                return result
            else:
                return self._filter_with_area(result, min_contact_area, real_area)

        edges = {}
        for label in labels:
            try:
                slices = self.boundingbox(label)
                ex_slices = dilation(slices)
                mask_img = self.image[ex_slices]
            except:
                mask_img = self.image
            neigh = list(contact_surface(mask_img,label))
            if min_contact_area is not None:
                neigh = self._neighbors_filtering_by_contact_area(label, neigh, min_contact_area, real_area)
            edges[label] = neigh

        return edges

def _add_provenance(in_file, settings, air_msk, rot_msk):
    from mriqc import __version__ as version
    from niworkflows.nipype.utils.filemanip import hash_infile
    import nibabel as nb
    import numpy as np

    air_msk_size = nb.load(air_msk).get_data().astype(
        np.uint8).sum()
    rot_msk_size = nb.load(rot_msk).get_data().astype(
        np.uint8).sum()

    out_prov = {
        'md5sum': hash_infile(in_file),
        'version': version,
        'software': 'mriqc',
        'warnings': {
            'small_air_mask': bool(air_msk_size < 5e5),
            'large_rot_frame': bool(rot_msk_size > 500),
        }
    }

    if settings:
        out_prov['settings'] = settings

    return out_prov

def property_topomesh_gaussian_smoothing_force(topomesh,gaussian_sigma=10.0):

    compute_topomesh_property(topomesh,'vertices',degree=1)
    edge_vertices = topomesh.wisp_property('vertices',degree=1).values()

    reversed_edge_vertices = np.transpose([edge_vertices[:,1],edge_vertices[:,0]])
    edge_vertices = np.append(edge_vertices,reversed_edge_vertices,axis=0)


    edge_vectors = topomesh.wisp_property('barycenter',degree=0).values(edge_vertices[:,1]) - topomesh.wisp_property('barycenter',degree=0).values(edge_vertices[:,0])
    edge_lengths = np.linalg.norm(edge_vectors,axis=1)
    gaussian_edge_lengths = np.exp(-np.power(edge_lengths,2.0)/np.power(gaussian_sigma,2.0))
    gaussian_edge_vectors = gaussian_edge_lengths[:,np.newaxis]*edge_vectors

    gaussian_force = np.transpose([nd.sum(gaussian_edge_vectors[:,d],edge_vertices[:,0],index=list(topomesh.wisps(0))) for d in [0,1,2]])
    vertices_weights = 1.+nd.sum(gaussian_edge_lengths,edge_vertices[:,0],index=list(topomesh.wisps(0)))
    gaussian_force = gaussian_force/vertices_weights[:,np.newaxis]

    return gaussian_force

def histo_plot(figure,X,color,xlabel="",ylabel="",cumul=False,bar=True,n_points=400,smooth_factor=0.1,spline_order=3,linewidth=3,alpha=1.0,label=""):
    if '%' in xlabel:
        magnitude = 100
        X_values = np.array(np.minimum(np.around(X),n_points+1),int)
    else:
        # magnitude = np.power(10,np.around(4*np.log10(X.mean()))/4+0.5)
        magnitude = np.power(10,np.around(4*np.log10(np.nanmean(X)+np.nanstd(X)+1e-7))/4+1)
        magnitude = np.around(magnitude,int(-np.log10(magnitude))+1)
        # print magnitude
        #magnitude = X.mean()+5.0*X.std()
        X_values = np.array(np.minimum(np.around(n_points*X[True-np.isnan(X)]/magnitude),n_points+1),int)
    X_histo = np.zeros(n_points+1,float)
    for x in np.linspace(0,n_points,n_points+1):
        X_histo[x] = nd.sum(np.ones_like(X_values,float),X_values,index=x)
        if '%' in ylabel:
            X_histo[x] /= X_values.size/100.0
        if cumul:
            X_histo[x] += X_histo[x-1]

    if bar:
        bar_plot(figure,np.linspace(0,magnitude,n_points+1),X_histo,np.array([1,1,1]),color,xlabel,ylabel,label=label)
    else:
        smooth_plot(figure,np.linspace(0,magnitude,n_points+1),X_histo,color,color,xlabel,ylabel,n_points=n_points,smooth_factor=smooth_factor,spline_order=spline_order,linewidth=linewidth,alpha=alpha,label=label)

def com(A, d1, d2):
    """Calculation of the center of mass for spatial components
     Inputs:
     A:   np.ndarray
          matrix of spatial components (d x K)
     d1:  int
          number of pixels in x-direction
     d2:  int
          number of pixels in y-direction

     Output:
     cm:  np.ndarray
          center of mass for spatial components (K x 2)
    """
    nr = np.shape(A)[-1]
    Coor = dict()
    Coor['x'] = np.kron(np.ones((d2, 1)), np.expand_dims(range(d1), axis=1))
    Coor['y'] = np.kron(np.expand_dims(range(d2), axis=1), np.ones((d1, 1)))
    cm = np.zeros((nr, 2))        # vector for center of mass
    cm[:, 0] = np.dot(Coor['x'].T, A) / A.sum(axis=0)
    cm[:, 1] = np.dot(Coor['y'].T, A) / A.sum(axis=0)

    return cm

def stellar_luminosity2(self, steps=10000):
        """
        DEPRECATED: ADW 2017-09-20

        Compute the stellar luminosity (L_Sol; average per star).
        Uses "sample" to generate mass sample and pdf.  The range of
        integration only covers the input isochrone data (no
        extrapolation used), but this seems like a sub-percent effect
        if the isochrone goes to 0.15 Msun for the old and metal-poor
        stellar populations of interest.

        Note that the stellar luminosity is very sensitive to the
        post-AGB population.
        """
        msg = "'%s.stellar_luminosity2': ADW 2017-09-20"%self.__class__.__name__
        DeprecationWarning(msg)
        mass_init, mass_pdf, mass_act, mag_1, mag_2 = self.sample(mass_steps=steps)
        luminosity_interpolation = scipy.interpolate.interp1d(self.mass_init, self.luminosity,fill_value=0,bounds_error=False)
        luminosity = luminosity_interpolation(mass_init)
        return np.sum(luminosity * mass_pdf)

    # ADW: For temporary backward compatibility

def filter_isolated_cells(array, struct):

    filtered_array = np.copy(array)
    id_regions, num_ids = ndimage.label(filtered_array, structure=struct)
    id_sizes = np.array(ndimage.sum(array, id_regions, range(num_ids + 1)))
    area_mask = (id_sizes == 1)
    filtered_array[area_mask[id_regions]] = 0

    return filtered_array

#Decide if given spectrum shows bird sounds or noise only

def get_largest_component(self, closing_shape=None):
        mask, bounds = self._get_bounded_mask(closing_shape)

        label_im, num_labels = ndimage.label(mask)
        label_sizes = ndimage.sum(mask, label_im, range(num_labels + 1))
        label_im[(label_sizes < label_sizes.max())[label_im]] = 0
        label_im = np.minimum(label_im, 1)

        if label_im[tuple(self.seed - bounds[0])] == 0:
            logging.warning('Seed voxel ({}) is not in connected component.'.format(np.array_str(self.seed)))

        return label_im, bounds

def _run_interface(self, runtime):
        in_file = nb.load(self.inputs.in_file)
        data = in_file.get_data()
        mask = np.zeros_like(data, dtype=np.uint8)
        mask[data <= 0] = 1

        # Pad one pixel to control behavior on borders of binary_opening
        mask = np.pad(mask, pad_width=(1,), mode='constant', constant_values=1)

        # Remove noise
        struc = nd.generate_binary_structure(3, 2)
        mask = nd.binary_opening(mask, structure=struc).astype(
            np.uint8)

        # Remove small objects
        label_im, nb_labels = nd.label(mask)
        if nb_labels > 2:
            sizes = nd.sum(mask, label_im, list(range(nb_labels + 1)))
            ordered = list(reversed(sorted(zip(sizes, list(range(nb_labels + 1))))))
            for _, label in ordered[2:]:
                mask[label_im == label] = 0

        # Un-pad
        mask = mask[1:-1, 1:-1, 1:-1]

        # If mask is small, clean-up
        if mask.sum() < 500:
            mask = np.zeros_like(mask, dtype=np.uint8)

        out_img = in_file.__class__(mask, in_file.affine, in_file.header)
        out_img.header.set_data_dtype(np.uint8)

        out_file = fname_presuffix(self.inputs.in_file,
                                   suffix='_rotmask', newpath='.')
        out_img.to_filename(out_file)
        self._results['out_file'] = out_file
        return runtime

def artifact_mask(imdata, airdata, distance, zscore=10.):
    """Computes a mask of artifacts found in the air region"""
    from statsmodels.robust.scale import mad

    if not np.issubdtype(airdata.dtype, np.integer):
        airdata[airdata < .95] = 0
        airdata[airdata > 0.] = 1

    bg_img = imdata * airdata
    if np.sum((bg_img > 0).astype(np.uint8)) < 100:
        return np.zeros_like(airdata)

    # Find the background threshold (the most frequently occurring value
    # excluding 0)
    bg_location = np.median(bg_img[bg_img > 0])
    bg_spread = mad(bg_img[bg_img > 0])
    bg_img[bg_img > 0] -= bg_location
    bg_img[bg_img > 0] /= bg_spread

    # Apply this threshold to the background voxels to identify voxels
    # contributing artifacts.
    qi1_img = np.zeros_like(bg_img)
    qi1_img[bg_img > zscore] = 1
    qi1_img[distance < .10] = 0

    # Create a structural element to be used in an opening operation.
    struc = nd.generate_binary_structure(3, 1)
    qi1_img = nd.binary_opening(qi1_img, struc).astype(np.uint8)
    qi1_img[airdata <= 0] = 0

    return qi1_img

def fuzzy_jaccard(in_tpms, in_mni_tpms):
    overlaps = []
    for tpm, mni_tpm in zip(in_tpms, in_mni_tpms):
        tpm = tpm.reshape(-1)
        mni_tpm = mni_tpm.reshape(-1)

        num = np.min([tpm, mni_tpm], axis=0).sum()
        den = np.max([tpm, mni_tpm], axis=0).sum()
        overlaps.append(float(num / den))
    return overlaps

def makeMask(self):
        #Narrow down to pixels which measured something every frame
        counts = np.sum(self.Zs > 0, 2)
        self.Mask = (counts == self.Zs.shape[2])
        #Find biggest connected component out of remaining pixels
        ILabel, NLabels = ndimage.label(self.Mask)
        idx = np.argmax(ndimage.sum(self.Mask, ILabel, range(NLabels+1)))
        self.Mask = (ILabel == idx)
        plt.imshow(self.Mask)
        plt.show()

    #Actually sets up all of the vertex, face, and adjacency structures in the 
    #PolyMeshObject

def property_topomesh_area_smoothing_force(topomesh,target_areas=None):

    compute_topomesh_property(topomesh,'vertices',degree=2)
    compute_topomesh_property(topomesh,'length',degree=1)

    triangle_vertices = topomesh.wisp_property('vertices',degree=2).values()
    rotated_triangle_vertices = np.transpose([triangle_vertices[:,2],triangle_vertices[:,0],triangle_vertices[:,1]])
    antirotated_triangle_vertices = np.transpose([triangle_vertices[:,1],triangle_vertices[:,2],triangle_vertices[:,0]])
    triangle_vertices = np.append(np.append(triangle_vertices,rotated_triangle_vertices,axis=0),antirotated_triangle_vertices,axis=0)

    edge_index_list = np.array([[1, 2],[0, 1],[0, 2]])
    triangle_edge_vertices = triangle_vertices[:,edge_index_list]

    triangle_edge_vectors = topomesh.wisp_property('barycenter',degree=0).values(triangle_edge_vertices[...,1]) - topomesh.wisp_property('barycenter',degree=0).values(triangle_edge_vertices[...,0])
    #triangle_edge_lengths = np.power(np.sum(np.power(triangle_edge_vectors,2.0),axis=2),0.5)
    triangle_edge_lengths = np.linalg.norm(triangle_edge_vectors,axis=2)
    triangle_edge_directions = triangle_edge_vectors/triangle_edge_lengths[...,np.newaxis]

    triangle_perimeters = np.sum(triangle_edge_lengths,axis=1)
    triangle_areas = np.sqrt((triangle_perimeters/2.0)*(triangle_perimeters/2.0-triangle_edge_lengths[:,0])*(triangle_perimeters/2.0-triangle_edge_lengths[:,1])*(triangle_perimeters/2.0-triangle_edge_lengths[:,2]))
    triangle_areas[np.where(triangle_areas==0.0)] = 0.001

    if target_areas is None:
        target_areas = triangle_areas.mean()*np.ones(len(triangle_areas))
    else:
        target_areas = np.tile(target_areas,(3))

    area_edge_1_force = (target_areas-triangle_areas)*(triangle_edge_lengths[:,0]**2+triangle_edge_lengths[:,2]**2-triangle_edge_lengths[:,1]**2)*triangle_edge_lengths[:,1]
    area_edge_2_force = (target_areas-triangle_areas)*(triangle_edge_lengths[:,0]**2+triangle_edge_lengths[:,1]**2-triangle_edge_lengths[:,2]**2)*triangle_edge_lengths[:,2]

    area_unitary_force = -(area_edge_1_force[:,np.newaxis]*triangle_edge_directions[:,1] + area_edge_2_force[:,np.newaxis]*triangle_edge_directions[:,2])

    triangle_force = np.transpose([nd.sum(area_unitary_force[:,d],triangle_vertices[:,0],index=list(topomesh.wisps(0))) for d in xrange(3)])

    return triangle_force

def property_topomesh_laplacian_smoothing_force(topomesh,cellwise_smoothing=False):

    if not topomesh.has_wisp_property('vertices',degree=1,is_computed=True):
        compute_topomesh_property(topomesh,'vertices',degree=1)
    edge_vertices = topomesh.wisp_property('vertices',degree=1).values()
    reversed_edge_vertices = np.transpose([edge_vertices[:,1],edge_vertices[:,0]])
    edge_vertices = np.append(edge_vertices,reversed_edge_vertices,axis=0)

    if not topomesh.has_wisp_property('valence',degree=0,is_computed=True):
        compute_topomesh_property(topomesh,'valence',degree=0)
    vertices_degrees = topomesh.wisp_property('valence',degree=0).values()

    if cellwise_smoothing:

        laplacian_force = np.zeros_like(topomesh.wisp_property('barycenter',degree=0).values(),np.float32)

        for c in topomesh.wisps(3):

            if not topomesh.has_wisp_property('vertices',degree=3,is_computed=True):
                compute_topomesh_property(topomesh,'vertices',degree=3)
            cell_vertices = topomesh.wisp_property('vertices',degree=3)[c]

            cell_edges = np.sum(nd.sum(np.ones_like(cell_vertices),cell_vertices,index=edge_vertices),axis=1)
            cell_edge_vertices = edge_vertices[np.where(cell_edges==2)[0]]
            cell_edge_vectors = topomesh.wisp_property('barycenter',degree=0).values(cell_edge_vertices[:,1]) - topomesh.wisp_property('barycenter',degree=0).values(cell_edge_vertices[:,0])

            cell_laplacian_force = np.transpose([nd.sum(cell_edge_vectors[:,0],cell_edge_vertices[:,0],index=cell_vertices),
                                                 nd.sum(cell_edge_vectors[:,1],cell_edge_vertices[:,0],index=cell_vertices),
                                                 nd.sum(cell_edge_vectors[:,2],cell_edge_vertices[:,0],index=cell_vertices)])
            laplacian_force[cell_vertices] += cell_laplacian_force/vertices_degrees[cell_vertices,np.newaxis]

    else:
        edge_vectors = topomesh.wisp_property('barycenter',degree=0).values(edge_vertices[:,1]) - topomesh.wisp_property('barycenter',degree=0).values(edge_vertices[:,0])

        laplacian_force = np.transpose([nd.sum(edge_vectors[:,d],edge_vertices[:,0],index=list(topomesh.wisps(0))) for d in [0,1,2]])
        laplacian_force = laplacian_force/vertices_degrees[:,np.newaxis]

    return laplacian_force

def where_list(array,values):
    mask = nd.sum(np.ones_like(values),values,index=array)
    return np.where(mask>0)

def density_plot(figure,X,Y,color,xlabel="",ylabel="",n_points=10,linewidth=1,marker_size=40.,alpha=1.0,label=""):
    font = fm.FontProperties(family = 'Trebuchet', weight ='light')
    #font = fm.FontProperties(family = 'CenturyGothic',fname = '/Library/Fonts/Microsoft/Century Gothic', weight ='light')
    figure.patch.set_facecolor('white')
    axes = figure.add_subplot(111)
    # axes.plot(X,Y,linewidth=1,color=tuple(color2),alpha=0.2)
    # ratios = (Y-Y.min())/(Y.max()-Y.min())
    # X_min = X.mean()-3*X.std()
    # X_max = X.mean()+3*X.std()
    X_min = np.percentile(X,100/n_points)
    X_max = np.percentile(X,100 - 100/n_points)
    Y_min = np.percentile(Y,100/n_points)
    # Y_min = Y.mean()-3*Y.std()
    Y_max = np.percentile(Y,100 - 100/n_points)

    X_grid = np.linspace(X_min,X_max,n_points)
    Y_grid = np.linspace(Y_min,Y_max,n_points)

    X_sampled = X_grid[vq(X,X_grid)[0]]
    Y_sampled = Y_grid[vq(Y,Y_grid)[0]]

    point_density = {}
    for x in np.unique(X_sampled):
        point_count = nd.sum(np.ones_like(np.where(X_sampled==x)),Y_sampled[np.where(X_sampled==x)],index=np.unique(Y_sampled))
        for i,y in enumerate(np.unique(Y_sampled)):
            point_density[(x,y)] = point_count[i]/len(Y)

    point_area = np.array([np.pi*10.0*marker_size*point_density[(x,y)]/np.array(point_density.values()).max() for x,y in zip(X_sampled,Y_sampled)])
    #colors = np.random.rand(len(X))
    colors = np.array([point_density[(x,y)]/np.array(point_density.values()).max() * color for x,y in zip(X_sampled,Y_sampled)])
    colors += np.array([(1-point_density[(x,y)]/np.array(point_density.values()).max()) * np.ones(3) for x,y in zip(X_sampled,Y_sampled)])

    axes.scatter(X_sampled,Y_sampled,s=point_area,c=colors,linewidth=linewidth,alpha=alpha,label=label)
    axes.set_xlim(X_min,X_max)
    axes.set_xlabel(xlabel,fontproperties=font, size=10, style='italic')
    axes.set_xticklabels(axes.get_xticks(),fontproperties=font, size=12)
    axes.set_ylim(Y_min,Y_max)
    axes.set_ylabel(ylabel, fontproperties=font, size=10, style='italic')
    axes.set_yticklabels(axes.get_yticks(),fontproperties=font, size=12)

def order_components(A, C):
    """Order components based on their maximum temporal value and size

    Parameters
    -----------
    A:   sparse matrix (d x K)
         spatial components
    C:   matrix or np.ndarray (K x T)
         temporal components

    Returns
    -------
    A_or:  np.ndarray
        ordered spatial components
    C_or:  np.ndarray
        ordered temporal components
    srt:   np.ndarray
        sorting mapping

    """
    A = np.array(A.todense())
    nA2 = np.sqrt(np.sum(A**2, axis=0))
    K = len(nA2)
    A = np.array(np.matrix(A) * spdiags(1 / nA2, 0, K, K))
    nA4 = np.sum(A**4, axis=0)**0.25
    C = np.array(spdiags(nA2, 0, K, K) * np.matrix(C))
    mC = np.ndarray.max(np.array(C), axis=1)
    srt = np.argsort(nA4 * mC)[::-1]
    A_or = A[:, srt] * spdiags(nA2[srt], 0, K, K)
    C_or = spdiags(1. / nA2[srt], 0, K, K) * (C[srt, :])

    return A_or, C_or, srt

def extract_DF_F(Y, A, C, i=None):
    """Extract DF/F values from spatial/temporal components and background

     Parameters
     -----------
     Y: np.ndarray
           input data (d x T)
     A: sparse matrix of np.ndarray
           Set of spatial including spatial background (d x K)
     C: matrix
           Set of temporal components including background (K x T)

     Returns
     -----------
     C_df: matrix
          temporal components in the DF/F domain
     Df:  np.ndarray
          vector with baseline values for each trace
    """
    A2 = A.copy()
    A2.data **= 2
    nA2 = np.squeeze(np.array(A2.sum(axis=0)))
    A = A * diags(1 / nA2, 0)
    C = diags(nA2, 0) * C

    # if i is None:
    #    i = np.argmin(np.max(A,axis=0))

    Y = np.matrix(Y)
    Yf = A.transpose() * (Y - A * C)  # + A[:,i]*C[i,:])
    Df = np.median(np.array(Yf), axis=1)
    C_df = diags(1 / Df, 0) * C

    return C_df, Df

def find_matches(D_s, print_assignment=False):

    matches=[]
    costs=[]
    t_start=time.time()
    for ii,D in enumerate(D_s):
        DD=D.copy()    
        if np.sum(np.where(np.isnan(DD)))>0:
            raise Exception('Distance Matrix contains NaN, not allowed!')


    #    indexes = m.compute(DD)
#        indexes = linear_assignment(DD)
        indexes = linear_sum_assignment(DD)
        indexes2=[(ind1,ind2) for ind1,ind2 in zip(indexes[0],indexes[1])]
        matches.append(indexes)
        DD=D.copy()   
        total = []
        for row, column in indexes2:
            value = DD[row,column]
            if print_assignment:
                print '(%d, %d) -> %f' % (row, column, value)
            total.append(value)      
        print  'FOV: %d, shape: %d,%d total cost: %f' % (ii, DD.shape[0],DD.shape[1], np.sum(total))
        print time.time()-t_start
        costs.append(total)      

    return matches,costs

#%%

def run(self, ips, snap, img, para = None):
        intenimg = WindowsManager.get(para['inten']).ips.img
        strc = ndimage.generate_binary_structure(2, 1 if para['con']=='4-connect' else 2)
        buf, n = ndimage.label(snap, strc, output=np.uint16)
        index = range(1, n+1)
        idx = (np.ones(n+1)*para['front']).astype(np.uint8)
        msk = np.ones(n, dtype=np.bool)

        if para['mean']>0: msk *= ndimage.mean(intenimg, buf, index)>=para['mean']
        if para['mean']<0: msk *= ndimage.mean(intenimg, buf, index)<-para['mean']
        if para['max']>0: msk *= ndimage.maximum(intenimg, buf, index)>=para['max']
        if para['max']<0: msk *= ndimage.maximum(intenimg, buf, index)<-para['max']
        if para['min']>0: msk *= ndimage.minimum(intenimg, buf, index)>=para['min']
        if para['min']<0: msk *= ndimage.minimum(intenimg, buf, index)<-para['min']
        if para['sum']>0: msk *= ndimage.sum(intenimg, buf, index)>=para['sum']
        if para['sum']<0: msk *= ndimage.sum(intenimg, buf, index)<-para['sum']
        if para['std']>0: msk *= ndimage.standard_deviation(intenimg, buf, index)>=para['std']
        if para['std']<0: msk *= ndimage.standard_deviation(intenimg, buf, index)<-para['std']


        xy = ndimage.center_of_mass(intenimg, buf, index)
        xy = np.array(xy).round(2).T

        idx[1:][~msk] = para['back']
        idx[0] = 0
        img[:] = idx[buf]

        WindowsManager.get(para['inten']).ips.mark = RGMark((xy.T, msk))
        WindowsManager.get(para['inten']).ips.update = True

def run(self, ips, snap, img, para = None):
        ips.lut = self.buflut
        lab, n = ndimg.label(snap>para['thr2'], np.ones((3,3)), output=np.uint16)
        sta = ndimg.sum(snap>para['thr1'], lab, index=range(n+1)) > 0
        img[:] = (sta*255)[lab]

def _centroid(x, y, density):
    """
    Computes weighted centroid of one bin.
    Equation (4) of Cappellari & Copin (2003)

    """
    mass = np.sum(density)
    xBar = x.dot(density)/mass
    yBar = y.dot(density)/mass

    return xBar, yBar

#----------------------------------------------------------------------

def makeMask(self):
        #Narrow down to pixels which measured something every frame
        counts = np.sum(self.Zs > 0, 2)
        self.Mask = (counts == self.Zs.shape[2])
        #Find biggest connected component out of remaining pixels
        ILabel, NLabels = ndimage.label(self.Mask)
        idx = np.argmax(ndimage.sum(self.Mask, ILabel, range(NLabels+1)))
        self.Mask = (ILabel == idx)
        plt.imshow(self.Mask)
        plt.show()

    #Actually sets up all of the vertex, face, and adjacency structures in the 
    #PolyMeshObject

def stellar_mass(self, mass_min=0.1, steps=10000):
        """
        Compute the stellar mass (Msun; average per star). PDF comes
        from IMF, but weight by actual stellar mass.

        Parameters:
        -----------
        mass_min : Minimum mass to integrate the IMF
        steps    : Number of steps to sample the isochrone

        Returns:
        --------
        mass     : Stellar mass [Msun]
        """
        mass_max = self.mass_init_upper_bound

        d_log_mass = (np.log10(mass_max) - np.log10(mass_min)) / float(steps)
        log_mass = np.linspace(np.log10(mass_min), np.log10(mass_max), steps)
        mass = 10.**log_mass

        if mass_min < np.min(self.mass_init):
            mass_act_interpolation = scipy.interpolate.interp1d(np.insert(self.mass_init, 0, mass_min),
                                                                np.insert(self.mass_act, 0, mass_min))
        else:
           mass_act_interpolation = scipy.interpolate.interp1d(self.mass_init, self.mass_act) 

        mass_act = mass_act_interpolation(mass)
        return np.sum(mass_act * d_log_mass * self.imf.pdf(mass, log_mode=True))

def stellar_luminosity(self, steps=10000):
        """
        Compute the stellar luminosity (Lsun; average per star). PDF
        comes from IMF.  The range of integration only covers the
        input isochrone data (no extrapolation used), but this seems
        like a sub-percent effect if the isochrone goes to 0.15 Msun
        for the old and metal-poor stellar populations of interest.

        Note that the stellar luminosity is very sensitive to the
        post-AGB population.

        Parameters:
        -----------
        steps : Number of steps to sample the isochrone.

        Returns:
        --------
        lum   : The stellar luminosity [Lsun]
        """
        mass_min = np.min(self.mass_init)
        mass_max = self.mass_init_upper_bound

        d_log_mass = (np.log10(mass_max) - np.log10(mass_min)) / float(steps)
        log_mass = np.linspace(np.log10(mass_min), np.log10(mass_max), steps)
        mass = 10.**log_mass

        luminosity_interpolation = scipy.interpolate.interp1d(self.mass_init, self.luminosity,fill_value=0,bounds_error=False)
        luminosity = luminosity_interpolation(mass)

        return np.sum(luminosity * d_log_mass * self.imf.pdf(mass, log_mode=True))

def absolute_magnitude(self, richness=1, steps=1e4):
        """
        Calculate the absolute magnitude (Mv) by integrating the
        stellar luminosity.

        Parameters:
        -----------
        richness : isochrone normalization parameter
        steps    : number of isochrone sampling steps

        Returns:
        --------
        abs_mag : Absolute magnitude (Mv)
        """
        # Using the SDSS g,r -> V from Jester 2005 [arXiv:0506022]
        # for stars with R-I < 1.15
        # V = g_sdss - 0.59(g_sdss-r_sdss) - 0.01
        # g_des = g_sdss - 0.104(g_sdss - r_sdss) + 0.01
        # r_des = r_sdss - 0.102(g_sdss - r_sdss) + 0.02
        if self.survey.lower() != 'des':
            raise Exception('Only valid for DES')
        if 'g' not in [self.band_1,self.band_2]:
            msg = "Need g-band for absolute magnitude"
            raise Exception(msg)    
        if 'r' not in [self.band_1,self.band_2]:
            msg = "Need r-band for absolute magnitude"
            raise Exception(msg)    

        mass_init,mass_pdf,mass_act,mag_1,mag_2=self.sample(mass_steps = steps)
        g,r = (mag_1,mag_2) if self.band_1 == 'g' else (mag_2,mag_1)

        V = g - 0.487*(g - r) - 0.0249
        flux = np.sum(mass_pdf*10**(-V/2.5))
        Mv = -2.5*np.log10(richness*flux)
        return Mv

def observableFractionCMDX(self, mask, distance_modulus, mass_min=0.1):
        """
        Compute observable fraction of stars with masses greater than mass_min in each 
        pixel in the interior region of the mask.

        ADW: Careful, this function is fragile! The selection here should
             be the same as mask.restrictCatalogToObservable space. However,
             for technical reasons it is faster to do the calculation with
             broadcasting here.
        ADW: Could this function be even faster / more readable?
        ADW: Should this include magnitude error leakage?
        """
        mass_init_array,mass_pdf_array,mass_act_array,mag_1_array,mag_2_array = self.sample(mass_min=mass_min,full_data_range=False)
        mag = mag_1_array if self.band_1_detection else mag_2_array
        color = mag_1_array - mag_2_array

        # ADW: Only calculate observable fraction over interior pixels...
        pixels = mask.roi.pixels_interior
        mag_1_mask = mask.mask_1.mask_roi_sparse[mask.roi.pixel_interior_cut]
        mag_2_mask = mask.mask_2.mask_roi_sparse[mask.roi.pixel_interior_cut]

        # ADW: Restrict mag and color to range of mask with sufficient solid angle
        cmd_cut = ugali.utils.binning.take2D(mask.solid_angle_cmd,color,mag+distance_modulus,
                                             mask.roi.bins_color, mask.roi.bins_mag) > 0
        # Pre-apply these cuts to the 1D mass_pdf_array to save time
        mass_pdf_cut = mass_pdf_array*cmd_cut

        # Create 2D arrays of cuts for each pixel
        mask_1_cut = (mag_1_array+distance_modulus)[:,np.newaxis] < mag_1_mask
        mask_2_cut = (mag_2_array+distance_modulus)[:,np.newaxis] < mag_2_mask
        mask_cut_repeat = mask_1_cut & mask_2_cut

        observable_fraction = (mass_pdf_cut[:,np.newaxis]*mask_cut_repeat).sum(axis=0)
        return observable_fraction

def observableFractionMMD(self, mask, distance_modulus, mass_min=0.1):
        # This can be done faster...
        logger.info('Calculating observable fraction from MMD')

        mmd = self.signalMMD(mask,distance_modulus)
        obs_frac = mmd.sum(axis=-1).sum(axis=-1)[mask.mask_roi_digi[mask.roi.pixel_interior_cut]]
        return obs_frac

def absolute_magnitude(distance_modulus,g,r,prob=None):
    """ Calculate the absolute magnitude from a set of bands """
    V = g - 0.487*(g - r) - 0.0249

    flux = np.sum(10**(-(V-distance_modulus)/2.5))
    Mv = -2.5*np.log10(flux)
    return Mv

def compute_image_property(self, property_name, min_contact_area=None, sub_factor=8.):
        """
        """
        computable_properties = ['barycenter','volume','neighborhood_size','layer','mean_curvature','gaussian_curvature']
        try:
            assert property_name in computable_properties
        except:
            print "Property \""+property_name+"\" can not be computed on image"
            print "Try with one of the following :"
            for p in computable_properties:
                print "  * "+p
        else:
            if self._image is not None:
                if property_name in ['barycenter','volume']:
                    graph = graph_from_image(self._image,background=self.background,spatio_temporal_properties=[property_name],ignore_cells_at_stack_margins=self.ignore_cells_at_stack_margins)
                    property_dict = graph.vertex_property(property_name)
                elif property_name == 'neighborhood_size':
                    neighbors = [self.image_graph.neighbors(l) for l in self.labels]
                    property_dict = dict(zip(self.labels,map(len,neighbors)))
                elif property_name == 'layer':
                    if min_contact_area is None:
                        min_contact_area = self.min_contact_area
                    graph = graph_from_image(self._image,background=self.background,spatio_temporal_properties=['L1'],ignore_cells_at_stack_margins=self.ignore_cells_at_stack_margins, min_contact_area=min_contact_area)
                    first_layer = graph.vertex_property('L1')
                    second_layer_cells = [v for v in graph.vertices() if np.any([first_layer[n] for n in graph.neighbors(v)]) and not first_layer[v]]
                    second_layer = dict(zip(list(graph.vertices()),[v in second_layer_cells for v in graph.vertices()]))
                    property_dict = dict(zip(self.labels,[1 if first_layer[l] else 2 if second_layer[l] else 3 for l in self.labels]))
                elif property_name in ['mean_curvature','gaussian_curvature']:
                    if not self.has_image_property('barycenter'):
                        self.compute_image_property('barycenter')
                    if not self.has_image_property('layer'):
                        print "--> Computing layer property"
                        self.compute_image_property('layer')

                    cell_centers = self.image_property('barycenter')
                    L1_cells = self.labels[self.image_property('layer').values()==1]

                    from openalea.cellcomplex.property_topomesh.utils.implicit_surfaces import implicit_surface_topomesh
                    from openalea.cellcomplex.property_topomesh.property_topomesh_analysis import compute_topomesh_property, compute_topomesh_vertex_property_from_faces
                    from openalea.cellcomplex.property_topomesh.property_topomesh_optimization import property_topomesh_vertices_deformation, topomesh_triangle_split

                    sub_binary_image = (self._image!=self.background).astype(float)[::sub_factor,::sub_factor,::sub_factor]
                    surface_topomesh = implicit_surface_topomesh(sub_binary_image,np.array(sub_binary_image.shape),sub_factor*np.array(self._image.voxelsize),center=False)
                    property_topomesh_vertices_deformation(surface_topomesh,iterations=10)

                    compute_topomesh_property(surface_topomesh,'barycenter',2)
                    compute_topomesh_property(surface_topomesh,'normal',2,normal_method='orientation')

                    compute_topomesh_vertex_property_from_faces(surface_topomesh,'normal',adjacency_sigma=2,neighborhood=5)
                    compute_topomesh_property(surface_topomesh,'mean_curvature',2)
                    compute_topomesh_vertex_property_from_faces(surface_topomesh,property_name,adjacency_sigma=2,neighborhood=5)

                    surface_cells = L1_cells[vq(surface_topomesh.wisp_property('barycenter',0).values(),cell_centers.values(L1_cells))[0]]
                    surface_topomesh.update_wisp_property('label',0,array_dict(surface_cells,list(surface_topomesh.wisps(0))))

                    L1_cell_property = nd.sum(surface_topomesh.wisp_property(property_name,0).values(),surface_cells,index=L1_cells)/nd.sum(np.ones_like(surface_cells),surface_cells,index=L1_cells)
                    L1_cell_property = array_dict(L1_cell_property,L1_cells)    
                    property_dict = array_dict([L1_cell_property[l] if (l in L1_cells) and (not np.isnan(L1_cell_property[l])) else 0 for l in self.labels],self.labels)

                property_data = [property_dict[l] for l in self.labels]
                self.update_image_property(property_name,property_data)