Python Network.swap_nan_for_zero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 swap_nan_for_zero [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 swap_nan_for_zero [as 别名]
def main( buff, inst_filename, mongo_address, viz_id):
  import numpy as np
  import flask
  from bson.objectid import ObjectId
  from pymongo import MongoClient
  from flask import request
  from clustergrammer import Network
  import StringIO

  client = MongoClient(mongo_address)
  db = client.clustergrammer

  viz_id = ObjectId(viz_id)
  found_viz = db.networks.find_one({'_id':viz_id})

  try:

    net = Network()
    net.load_tsv_to_net(buff)

    net.swap_nan_for_zero()

    views = ['N_row_sum', 'N_row_var']

    net.make_clust(dist_type='cosine', dendro=True, views=views, \
                   linkage_type='average')

    export_dat = {}
    export_dat['name'] = inst_filename
    export_dat['dat'] = net.export_net_json('dat')
    export_dat['source'] = 'user_upload'

    dat_id = db.network_data.insert(export_dat)

    update_viz = net.viz 
    update_dat = dat_id

  except:
    print('\n-----------------------')
    print('error in clustering')
    print('-----------------------\n')
    update_viz = 'error'
    update_dat = 'error'

  found_viz['viz'] = update_viz
  found_viz['dat'] = update_dat

  db.networks.update_one( {'_id':viz_id}, {'$set': found_viz} )

  client.close()


  

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

  filename = 'gcts/LDS-1003.gct'
  print('exporting processed GCT as tsv file')

  df = load_file(filename)

  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', 200)

  net.write_matrix_to_tsv('txt/example_gct_export.txt')

# 需要导入模块: from clustergrammer import Network [as 别名]
# 或者: from clustergrammer.Network import swap_nan_for_zero [as 别名]
def proc_locally():
  from clustergrammer import Network
  # import run_g2e_background

  net = Network()

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

  print(vect_post.keys())

  # mongo_address = '10.125.161.139'


  net.load_vect_post_to_net(vect_post)

  net.swap_nan_for_zero()

  net.N_top_views()  

  print(net.viz.keys())

# 需要导入模块: from clustergrammer import Network [as 别名]
# 或者: from clustergrammer.Network import swap_nan_for_zero [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 swap_nan_for_zero [as 别名]
def reproduce_Mark_correlation_matrix():
  import pandas as pd
  from scipy.spatial.distance import squareform
  from clustergrammer import Network
  from copy import deepcopy

  dist_vect = calc_custom_dist(data_type='ptm_none', dist_metric='correlation',
                              pairwise='True')


  dist_mat = squareform(dist_vect)

  # make similarity matrix
  dist_mat = 1 - dist_mat

  net = Network()

  data_type = 'ptm_none'

  filename = '../lung_cellline_3_1_16/lung_cl_all_ptm/precalc_processed/' + \
             data_type + '.txt'

  # load file and export dataframe
  net = deepcopy(Network())
  net.load_file(filename)
  net.swap_nan_for_zero()
  tmp_df = net.dat_to_df()
  df = tmp_df['mat']

  cols = df.columns.tolist()
  rows = cols

  mark_df = pd.DataFrame(data=dist_mat, columns=cols, index=rows)

  save_filename = '../lung_cellline_3_1_16/lung_cl_all_ptm/precalc_processed/' \
             + 'Mark_corr_sim_mat' + '.txt'
  mark_df.to_csv(save_filename, sep='\t')

# 需要导入模块: from clustergrammer import Network [as 别名]
# 或者: from clustergrammer.Network import swap_nan_for_zero [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 swap_nan_for_zero [as 别名]
# possible filtering and normalization
##########################################
# net.filter_sum('row', threshold=20)
# net.filter_sum('col', threshold=30)

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

# net.filter_N_top('row', 100, rank_type='var')
# net.filter_N_top('col', 3, rank_type='var')

# net.filter_threShold('col', threshold=2, num_occur=3
# net.filter_threshold('row', threshold=3.0, num_occur=4)

net.swap_nan_for_zero()

# df = net.dat_to_df()

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=['ChEA_2015'])
               # run_enrichr=['ENCODE_TF_ChIP-seq_2014'])
               # run_enrichr=['KEA_2015'])
               # run_enrichr=['GO_Biological_Process_2015'])

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')

# 需要导入模块: from clustergrammer import Network [as 别名]
# 或者: from clustergrammer.Network import swap_nan_for_zero [as 别名]
def main(mongo_address, viz_id, vect_post):
  from bson.objectid import ObjectId
  from pymongo import MongoClient
  from clustergrammer import Network

  # set up database connection 
  client = MongoClient(mongo_address)
  db = client.clustergrammer 
  viz_id = ObjectId(viz_id)
  # get placeholder viz data 
  found_viz = db.networks.find_one({'_id': viz_id })

  # initialize export_dat 
  export_dat = {}
  export_viz = {}

  # try to make clustegram using vect_post 
  try:

    # ini network obj 
    net = Network()
    
    # vector endpoint 
    net.load_vect_post_to_net(vect_post)

    # swap nans for zeros
    net.swap_nan_for_zero()

    # deprecated clustering modules 
    ####################################
    # cluster g2e using pandas
    # net.fast_mult_views()

    # # calculate top views rather than percentage views
    # net.N_top_views()
    ####################################

    net.make_filtered_views(dist_type='cosine', dendro=True, \
      views=['N_row_sum'], linkage_type='average')

    # export dat 
    try:

      # convert data to list 
      net.dat['mat'] = net.dat['mat'].tolist()
      net.dat['mat_up'] = net.dat['mat_up'].tolist()
      net.dat['mat_dn'] = net.dat['mat_dn'].tolist()

      export_dat['dat'] = net.export_net_json('dat')
      export_dat['source'] = 'g2e_enr_vect'
      dat_id = db.network_data.insert( export_dat )
      print('G2E: network data successfully uploaded')
    
    except:
      export_dat['dat'] = 'data-too-large'
      export_dat['source'] = 'g2e_enr_vect'
      dat_id = db.network_data.insert( export_dat )
      print('G2E: network data too large to be uploaded')

    update_viz = net.viz 
    update_dat = dat_id

  # if there is an error update json with error 
  except:

    print('\n--------------------------------')
    print('G2E clustering error')
    print('----------------------------------\n')
    update_viz = 'error'
    update_dat = 'error'


  # export vix to database 

  found_viz['viz'] = update_viz
  found_viz['dat'] = update_dat

   # update the viz data 
  try:
    db.networks.update_one( {"_id":viz_id}, {"$set": found_viz} )
    print('\n\n---------------------------------------------------')
    print( 'G2E Successfully made and uploaded clustergram')
    print('---------------------------------------------------\n\n')
  except:
    print('\n--------------------------------')
    print('G2E error in loading viz into database')
    print('----------------------------------\n')

  # close database connection 
  client.close() 

# 需要导入模块: from clustergrammer import Network [as 别名]
# 或者: from clustergrammer.Network import swap_nan_for_zero [as 别名]
def main( buff, inst_filename, mongo_address, viz_id):
  import numpy as np
  import flask
  from bson.objectid import ObjectId
  from pymongo import MongoClient
  from flask import request
  from clustergrammer import Network
  import StringIO

  ##############################
  # set up database connection 
  ##############################
  # set up connection 
  client = MongoClient(mongo_address)
  db = client.clustergrammer

  # get placeholder viz data 
  viz_id = ObjectId(viz_id)
  found_viz = db.networks.find_one({'_id':viz_id})

  try:
    ########################
    # load and cluster 
    ########################

    # initiate class network 
    net = Network()
    # net.load_lines_from_tsv_to_net(file_lines)  
    net.pandas_load_tsv_to_net(buff)

    # swap nans for zero 
    net.swap_nan_for_zero()

    # deprecated clustering module
    ####################################

    # # fast mult views takes care of pre-filtering
    # net.fast_mult_views()

    ####################################

    net.make_filtered_views(dist_type='cosine', dendro=True, \
      views=['filter_row_sum'], linkage_type='average')

    ###############################
    # save to database 
    ###############################

    export_dat = {}
    export_dat['name'] = inst_filename
    export_dat['dat'] = net.export_net_json('dat')
    export_dat['source'] = 'user_upload'
    # save dat to separate document 
    dat_id = db.network_data.insert(export_dat)

    update_viz = net.viz 
    update_dat = dat_id

  except:
    print('\n-----------------------')
    print('error in clustering')
    print('-----------------------\n')
    update_viz = 'error'
    update_dat = 'error'

  # update found_viz 
  found_viz['viz'] = update_viz
  found_viz['dat'] = update_dat

  # update found_viz in database 
  db.networks.update_one( {'_id':viz_id}, {'$set': found_viz} )

  ############################
  # end database connection 
  ############################
  client.close()