Python Network.write_json_to_file方法代码示例

# 需要导入模块: from clustergrammer import Network [as 别名]
# 或者: from clustergrammer.Network import write_json_to_file [as 别名]
def make_enr_vect_clust():
  import enrichr_functions as enr_fun 
  from clustergrammer import Network

  net = Network()

  g2e_post = net.load_json_to_dict('json/g2e_enr_vect.json')

  net = enr_fun.make_enr_vect_clust(g2e_post, 0.001, 1)

  net.write_json_to_file('viz','json/enr_vect_example.json')

# 需要导入模块: from clustergrammer import Network [as 别名]
# 或者: from clustergrammer.Network import write_json_to_file [as 别名]
def make_viz_json(inst_df, name):
  from clustergrammer import Network
  net = Network()

  filename = 'json/'+name
  load_df = {}
  load_df['mat'] = inst_df
  net.df_to_dat(load_df)
  net.swap_nan_for_zero()
  net.make_clust(views=[])
  net.write_json_to_file('viz', filename, 'no-indent')

# 需要导入模块: from clustergrammer import Network [as 别名]
# 或者: from clustergrammer.Network import write_json_to_file [as 别名]
def cluster():
  from clustergrammer import Network

  net = Network()

  vect_post = net.load_json_to_dict('fake_vect_post.json')  

  net.load_vect_post_to_net(vect_post)

  net.swap_nan_for_zero()
  
  # net.N_top_views()
  net.make_clust(dist_type='cos',views=['N_row_sum','N_row_var'], dendro=True)

  net.write_json_to_file('viz','json/large_vect_post_example.json','indent')  

# 需要导入模块: from clustergrammer import Network [as 别名]
# 或者: from clustergrammer.Network import write_json_to_file [as 别名]
def clustergrammer_load():
  # import network class from Network.py
  from clustergrammer import Network

  net = Network()

  net.pandas_load_file('mat_cats.tsv')  

  net.make_clust(dist_type='cos',views=['N_row_sum','N_row_var'])

  net.write_json_to_file('viz','json/mult_cats.json','indent')  

  print('\n**********************')
  print(net.dat['node_info']['row'].keys())

  print('\n\n')

# 需要导入模块: from clustergrammer import Network [as 别名]
# 或者: from clustergrammer.Network import write_json_to_file [as 别名]
def main():

  import time
  start_time = time.time()
  import pandas as pd
  import StringIO

  # import network class from Network.py
  from clustergrammer import Network

  net = Network()

  # load data to dataframe 
  # net.load_tsv_to_net('txt/example_tsv_network.txt')
  # net.load_tsv_to_net('txt/mat_1mb.txt')

  # choose file 
  ################
  # file_buffer = open('txt/col_categories.txt')
  file_buffer = open('txt/example_tsv_network.txt'  )


  buff = StringIO.StringIO( file_buffer.read() )
  net.pandas_load_tsv_to_net(buff)

  # filter rows 
  views = ['filter_row_sum','N_row_sum']

  # distance metric 
  dist_type = 'cosine'

  # linkage type 
  linkage_type = 'average'


  net.make_clust(dist_type=dist_type, views=views, calc_col_cats=True,\
  linkage_type=linkage_type)

  net.write_json_to_file('viz', 'json/mult_view.json', 'no-indent')

  elapsed_time = time.time() - start_time
  print('\n\n\nelapsed time: '+str(elapsed_time))

# 需要导入模块: from clustergrammer import Network [as 别名]
# 或者: from clustergrammer.Network import write_json_to_file [as 别名]
def prepare_heatmap(matrix_input, html_file, html_dir, tools_dir, categories, distance, linkage):
    # prepare directory and html
    os.mkdir(html_dir)

    env = Environment(loader=FileSystemLoader(tools_dir + "/templates"))
    template = env.get_template("clustergrammer.template")
    overview = template.render()
    with open(html_file, "w") as outf:
        outf.write(overview)

    json_output = html_dir + "/mult_view.json"

    net = Network()
    net.load_file(matrix_input)
    if (categories['row']):
        net.add_cats('row', categories['row'])
    if (categories['col']):
        net.add_cats('col', categories['col'])
    net.cluster(dist_type=distance, linkage_type=linkage)
    net.write_json_to_file('viz', json_output)

# 需要导入模块: from clustergrammer import Network [as 别名]
# 或者: from clustergrammer.Network import write_json_to_file [as 别名]
def make_json_from_tsv(name):
  '''
  make a clustergrammer json from a tsv file
  '''
  from clustergrammer import Network

  print('\n' + name)

  net = Network()

  filename = 'txt/'+ name + '.txt'

  net.load_file(filename)

  df = net.dat_to_df()

  net.swap_nan_for_zero()

  # zscore first to get the columns distributions to be similar
  net.normalize(axis='col', norm_type='zscore', keep_orig=True)

  # filter the rows to keep the perts with the largest normalizes values
  net.filter_N_top('row', 1000)

  num_rows = net.dat['mat'].shape[0]
  num_cols = net.dat['mat'].shape[1]

  print('num_rows ' + str(num_rows))
  print('num_cols ' + str(num_cols))

  if num_cols < 50 or num_rows < 1000:

    views = ['N_row_sum']
    net.make_clust(dist_type='cos', views=views)
    export_filename = 'json/' + name + '.json'
    net.write_json_to_file('viz', export_filename)

  else:
    print('did not cluster, too many columns ')

# 需要导入模块: from clustergrammer import Network [as 别名]
# 或者: from clustergrammer.Network import write_json_to_file [as 别名]
def make_viz_from_df(df, filename):
  from clustergrammer import Network

  net = Network()

  net.df_to_dat(df)
  net.swap_nan_for_zero()

  # zscore first to get the columns distributions to be similar
  net.normalize(axis='col', norm_type='zscore', keep_orig=True)

  # filter the rows to keep the perts with the largest normalizes values
  net.filter_N_top('row', 2000)

  num_coluns = net.dat['mat'].shape[1]

  if num_coluns < 50:
    # views = ['N_row_sum', 'N_row_var']
    views = ['N_row_sum']
    net.make_clust(dist_type='cos', views=views)

    filename = 'json/' + filename.split('/')[1].replace('.gct','') + '.json'

    net.write_json_to_file('viz', filename)

# 需要导入模块: from clustergrammer import Network [as 别名]
# 或者: from clustergrammer.Network import write_json_to_file [as 别名]
# make network object and load file
from clustergrammer import Network
net = Network()
net.load_file('mult_view.tsv')




# Z-score normalize the rows
#net.normalize(axis='row', norm_type='zscore', keep_orig=True)





# calculate clustering using default parameters
net.cluster()

# save visualization JSON to file for use by front end
net.write_json_to_file('viz', 'mult_view.json')



#	needs pandas and sklearn as well
#	pip install --user --upgrade clustergrammer pandas sklearn

# 需要导入模块: from clustergrammer import Network [as 别名]
# 或者: from clustergrammer.Network import write_json_to_file [as 别名]
net.load_file('txt/rc_two_cats.txt')
# net.load_file('txt/example_tsv.txt')
# net.load_file('txt/col_categories.txt')
# net.load_file('txt/mat_cats.tsv')
# net.load_file('txt/mat_1mb.Txt')
# net.load_file('txt/mnist.txt')
# net.load_file('txt/sim_mat_4_cats.txt')

views = ['N_row_sum','N_row_var']

# # filtering rows and cols by sum 
# net.filter_sum('row', threshold=20)
# net.filter_sum('col', threshold=30)
  
# # keep top rows based on sum 
# net.filter_N_top('row', 10, 'sum')

net.make_clust(dist_type='cos',views=views , dendro=True,
               sim_mat=True, filter_sim=0.1)

# net.produce_view({'N_row_sum':10,'dist':'euclidean'})

net.write_json_to_file('viz', 'json/mult_view.json', 'no-indent')
net.write_json_to_file('sim_row', 'json/mult_view_sim_row.json', 'no-indent')
net.write_json_to_file('sim_col', 'json/mult_view_sim_col.json', 'no-indent')

elapsed_time = time.time() - start_time

print('\n\nelapsed time')
print(elapsed_time)

# 需要导入模块: from clustergrammer import Network [as 别名]
# 或者: from clustergrammer.Network import write_json_to_file [as 别名]
from clustergrammer import Network
net = Network()

# choose tsv file
####################
inst_name = 'Tyrosine'
# net.load_file('txt/phos_ratios_all_treat_no_geld_ST.txt')
net.load_file('txt/phos_ratios_all_treat_no_geld_Tyrosine.txt')


net.swap_nan_for_zero()

# net.normalize(axis='row', norm_type='zscore', keep_orig=True)

print(net.dat.keys())

views = ['N_row_sum', 'N_row_var']

net.make_clust(dist_type='cos',views=views , dendro=True,
               sim_mat=True, filter_sim=0.1, calc_cat_pval=False)
               # run_enrichr=['KEA_2015'])
               # run_enrichr=['ENCODE_TF_ChIP-seq_2014'])
               # run_enrichr=['GO_Biological_Process_2015'])

net.write_json_to_file('viz', 'json/'+inst_name+'.json', 'no-indent')
net.write_json_to_file('sim_row', 'json/'+inst_name+'_sim_row.json', 'no-indent')
net.write_json_to_file('sim_col', 'json/'+inst_name+'_sim_col.json', 'no-indent')

elapsed_time = time.time() - start_time
print('\n\nelapsed time: '+str(elapsed_time))

# 需要导入模块: from clustergrammer import Network [as 别名]
# 或者: from clustergrammer.Network import write_json_to_file [as 别名]
import time
start_time = time.time()

# import network class from Network.py
from clustergrammer import Network

net = Network()

net.load_tsv_to_net('txt/example_tsv.txt')

net.make_filtered_views(dist_type='cos',views=['N_row_sum','pct_row_sum'])

net.write_json_to_file('viz', 'json/mult_view.json', 'indent')

# your code
elapsed_time = time.time() - start_time

print('\n\n\nelapsed time')
print(elapsed_time)

# 需要导入模块: from clustergrammer import Network [as 别名]
# 或者: from clustergrammer.Network import write_json_to_file [as 别名]
# import network class from Network.py
from clustergrammer import Network

# get instance of Network
net = Network()
print(net.__doc__)
print('make tsv clustergram')

# load network from tsv file
##############################
net.load_tsv_to_net('txt/example_tsv_network.txt')

inst_filt = 0.001
inst_meet = 1
net.filter_network_thresh(inst_filt,inst_meet)

# cluster
#############
net.cluster_row_and_col('cos')

# export data visualization to file
######################################
net.write_json_to_file('viz', 'json/default_example.json', 'indent')