本文整理汇总了Python中clustergrammer.Network.load_vect_post_to_net方法的典型用法代码示例。如果您正苦于以下问题：Python Network.load_vect_post_to_net方法的具体用法？Python Network.load_vect_post_to_net怎么用？Python Network.load_vect_post_to_net使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类clustergrammer.Network的用法示例。
在下文中一共展示了Network.load_vect_post_to_net方法的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: cluster
# 需要导入模块: from clustergrammer import Network [as 别名]
# 或者: from clustergrammer.Network import load_vect_post_to_net [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') 示例2: proc_locally
# 需要导入模块: from clustergrammer import Network [as 别名]
# 或者: from clustergrammer.Network import load_vect_post_to_net [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())示例3: main
# 需要导入模块: from clustergrammer import Network [as 别名]
# 或者: from clustergrammer.Network import load_vect_post_to_net [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()