remove=REMOVE,dataType='networkx')
Nsa = N.split_graph(N.A,single)
Nsc = N.split_graph(N.C,single)
Nse = N.split_graph(N.E,single)
N.split_left_right(M.left,M.right)
N.map_right_graphs(M.lrmap)
J = from_db('JSH',adjacency=True,chemical=True,
electrical=True,dataType='networkx')
Jsa = J.split_graph(J.A,single)
Jsc = J.split_graph(J.C,single)
Jse = J.split_graph(J.E,single)
J.split_left_right(M.left,M.right)
J.map_right_graphs(M.lrmap)
e = Matrix(M.cam,params.matrix)
e.load_genes()
e.load_cells(nodes)
e.assign_expression()
e.clean_expression()
e.binarize()
A = nx.Graph()
C = nx.DiGraph()
E = nx.Graph()
A = consensus_graph(A,[N.Al,N.Ar,J.Al,J.Ar],deg,M.left)
A = consensus_graph(A,[Nsa,Jsa],min(deg,2),single)
C = consensus_graph(C,[N.Cl,N.Cr,J.Cl,J.Cr],deg,M.left)
C = consensus_graph(C,[Nsc,Jsc],min(deg,2),single)
E = consensus_graph(E,[N.El,N.Er,J.El,J.Er],deg,M.left)
E = consensus_graph(E,[Nse,Jse],min(deg,2),single)
parser.add_argument('matrix', action='store', help='Path to matrix file')
parser.add_argument('db', action='store', help='Database')
#parser.add_argument('cell',action='store',help='Cell name')
params = parser.parse_args()
#cell = params.cell
ML = MatLoader()
ML.load_lrmap()
nodes = sorted(ML.load_reduced_nodes())
C = from_db(params.db, adjacency=True, dataType='networkx', remove=REMOVE)
e = Matrix(ML.cam, params.matrix)
e.load_genes()
e.load_cells(nodes)
e.assign_expression()
e.binarize()
#np.random.shuffle(e.M)
M = e.E[:, idx_gene]
#np.random.shuffle(M)
if METHOD == 1:
data = defaultdict(list)
comb = combinations(nodes, 2)
for (u, v) in comb:
l = nx.shortest_path_length(C.A, source=u, target=v)
required=False,
default=None,
help='Output file')
params = parser.parse_args()
ML = MatLoader()
ML.load_lrmap()
#nodes = sorted(ML.load_reduced_nodes())
nodes = sorted(ML.load_all_tissue())
neurons = sorted(ML.load_reduced_nodes()) + NODE_SCREEN
clusters = aux.read.into_dict(TOP_CLUSTERS)
cluster_color = aux.read.into_dict(CLUSTER_COLOR)
e = Matrix(ML.cam, params.matrix)
e.load_genes()
e.load_cells(nodes)
e.assign_expression()
e.clean_expression()
e.binarize()
#M = e.E[:,idx_gene]
#for i in range(M.shape[0]): np.random.shuffle(M[i,:])
#for (i,g) in e.gene_idx.items(): print(i,g)
D = e.distance_matrix(gdx=idx_gene[params.camtype], metric=params.metric)
Y = sch.linkage(D, method='ward')
Z = sch.dendrogram(Y, orientation='right', no_plot=True)
type= int,
help = 'Conserved degree')
parser.add_argument('cell',action='store',help='Cell name')
params = parser.parse_args()
M = MatLoader()
M.load_left()
C = M.load_consensus_graphs(params.deg)
S = M.load_consensus_chemical_synapse(params.deg)
S1 = M.load_consensus_chemical_synapse(1)
C1 = M.load_consensus_graphs(1)
print(C.C.number_of_edges(),C1.C.number_of_edges())
e = Matrix(cam,params.matrix)
e.load_genes()
e.load_cells(sorted(C.A.nodes()))
e.assign_expression()
e.binarize()
print(len(e.gene_idx),len(e.cells_idx),e.E.shape)
syn,neigh,cneigh = predict.get_synapse_data(S[params.cell],e,cpartners=set(C.C.neighbors(params.cell)))
gene_sig = predict.gene_differential(e.E,syn,neigh)
_remove = ['VC01', 'VD01', 'VB01', 'VB02']
C = from_db(params.db,
adjacency=True,
chemical=True,
electrical=True,
remove=_remove)
C.remove_self_loops()
C.reduce_to_adjacency()
vertices = sorted(C.neurons)
vdict = dict([(vertices[i], i) for i in range(len(vertices))])
D = degree_coefficients(C, vertices=vertices)
N = len(vertices)
e = Matrix(params.matrix, cells=vertices)
e.binarize()
e.E = e.E.T
E = np.copy(e.E)
idx = vertices.index(TCELL)
alpha = float(-(D[idx, 0] - D[idx, 1]))
beta = float(D[idx, 1])
con = db.connect.default(params.db)
cur = con.cursor()
contins = db.mine.get_presynapse_contins(cur, TCELL, end=END)
G = np.zeros((E.shape[0], 1))
Gp = np.zeros((E.shape[0], E.shape[0]))
Gn = np.zeros((E.shape[0], E.shape[0]))
Mp, Mn = 0., 0.
import sys
sys.path.append('./volumetric_analysis')
import argparse
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from cam.expression import Matrix
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description=__doc__,
formatter_class=argparse.RawDescriptionHelpFormatter)
parser.add_argument('matrix',
action='store',
help="Path to file with expression matrix")
params = parser.parse_args()
M = Matrix(params.matrix)
print(M.cells)
print(M.genes)
print(M.M.shape)
M.M[M.M < 500] = 0
M.M[M.M > 0] = 1
cmap = ListedColormap(['w', 'k'])
plt.matshow(M.M, cmap=cmap)
plt.show()
def __init__(self, fgenes, fexp):
Matrix.__init__(self, fgenes, fexp)