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_filtered_views(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')
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_filtered_views(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")
def post_request():
from clustergrammer_old import Network
import requests
import json
net = Network()
vect_post = net.load_json_to_dict('fake_vect_post.json')
upload_url = 'http://localhost:9000/clustergrammer/vector_upload/'
r = requests.post(upload_url, json.dumps(vect_post))
response_json = json.loads(r.text)
link = response_json['link']
print(link.split('viz/')[1])
def post_request():
from clustergrammer_old import Network
import requests
import json
print('vector upload')
net = Network()
vect_post = net.load_json_to_dict('fake_vect_post.json')
# upload_url = 'http://localhost:9000/clustergrammer/vector_upload/'
# upload_url = 'http://0.0.0.0:8087/clustergrammer/vector_upload/'
upload_url = 'http://amp.pharm.mssm.edu/clustergrammer/vector_upload/'
print('making request')
r = requests.post(upload_url, json.dumps(vect_post))
response_json = json.loads(r.text)
link = response_json['link']
print(link.split('clustergrammer/')[1])
def post_request():
from clustergrammer_old import Network
import requests
import json
print("vector upload")
net = Network()
vect_post = net.load_json_to_dict("fake_vect_post.json")
# upload_url = 'http://localhost:9000/clustergrammer/vector_upload/'
# upload_url = 'http://0.0.0.0:8087/clustergrammer/vector_upload/'
upload_url = "http://amp.pharm.mssm.edu/clustergrammer/vector_upload/"
print("making request")
r = requests.post(upload_url, json.dumps(vect_post))
response_json = json.loads(r.text)
link = response_json["link"]
print(link.split("clustergrammer/")[1])
def post_request():
from clustergrammer_old import Network
import requests
import json
from flask import current_app
print('vector upload')
net = Network()
vect_post = net.load_json_to_dict('fake_vect_post.json')
# upload_url = 'http://localhost:9000/clustergrammer/vector_upload/'
# upload_url = 'http://0.0.0.0:8087/clustergrammer/vector_upload/'
upload_url = current_app.config['ORIGIN'] + current_app.config['ENTRY_POINT'] + '/vector_upload/'
print('making request')
r = requests.post(upload_url, json.dumps(vect_post))
response_json = json.loads(r.text)
link = response_json['link']
print(link.split('clustergrammer/')[1])
def l1000cds2_upload():
import requests
import json
from clustergrammer_old import Network
from pymongo import MongoClient
from bson.objectid import ObjectId
l1000cds2 = json.loads( request.form.get('signatures') )
net = Network()
net.load_l1000cds2(l1000cds2)
cutoff_comp = 0
min_num_comp = 2
net.cluster_row_and_col(dist_type='cosine', dendro=True)
net.dat['node_info']['row']['ini'] = net.sort_rank_node_values('row')
net.dat['node_info']['col']['ini'] = net.sort_rank_node_values('col')
net.viz = {}
net.viz['row_nodes'] = []
net.viz['col_nodes'] = []
net.viz['links'] = []
net.viz_json()
export_dict = {}
export_dict['name'] = 'l1000cds2'
export_dict['dat'] = net.export_net_json('dat')
export_dict['viz'] = net.viz
export_dict['_id'] = ObjectId(l1000cds2['_id'])
client = MongoClient(mongo_address)
db = client.clustergrammer
tmp = db.networks.find_one({'_id': ObjectId(l1000cds2['_id']) })
if tmp is None:
tmp_id = db.networks.insert( export_dict )
client.close()
return redirect('/clustergrammer/l1000cds2/'+l1000cds2['_id'])
def make_json():
from clustergrammer_old import Network
net = Network()
row_num = 50
num_columns = 10
# make up all names for all data
row_names = make_up_names(row_num)
# initialize vect_post
vect_post = {}
vect_post['title'] = 'Some-Clustergram'
vect_post['link'] = 'some-link'
vect_post['views'] = ['N_row_var']
vect_post['is_up_down'] = True
vect_post['columns'] = []
# the user can request a link or a json
vect_post['response_type'] = 'json'
split = True
# fraction of rows in each column - 1 means all columns have all rows
inst_prob = 1
# make column data
for col_num in range(num_columns):
inst_col = {}
col_name = 'Col-' + str( col_num+1 ) + ' make name longer'
inst_col['col_name'] = col_name
inst_col['link'] = 'col-link'
if col_num < 5:
inst_col['cat'] = 'brain'
else:
inst_col['cat'] = 'lung'
# save to columns
inst_col['data'] = [] #vector
# get random subset of row_names
vect_rows = get_subset_rows(row_names, inst_prob)
# generate vectors
for inst_row in vect_rows:
# genrate values
##################
# add positive/negative values
if random.random() > 0.5:
value_up = 10*random.random()
else:
value_up = 0
if random.random() > 0.5:
value_dn = -10*random.random()
else:
value_dn = 0
value = value_up + value_dn
# # generate vector component
# #############################
# vector.append([ inst_row, value ])
# vector_up.append([ inst_row, value_up ])
# vector_dn.append([ inst_row, value_dn ])
# define row object - within column
row_obj = {}
row_obj['row_name'] = inst_row
row_obj['val'] = value
row_obj['val_up'] = value_up
row_obj['val_dn'] = value_dn
inst_col['data'].append(row_obj)
# if split:
# inst_col['vector_up'] = vector_up
# inst_col['vector_dn'] = vector_dn
# save columns to vect_post
vect_post['columns'].append(inst_col)
net.save_dict_to_json(vect_post, 'fake_vect_post.json', indent='indent')
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_filtered_views(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')
def make_json():
from clustergrammer_old import Network
net = Network()
row_num = 15
num_columns = 10
# make up all names for all data
row_names = make_up_names(row_num)
# initialize vect_post
vect_post = {}
vect_post['title'] = 'Some-Clustergram'
vect_post['link'] = 'some-link'
vect_post['filter'] = 'N_row_sum'
vect_post['is_up_down'] = True
vect_post['columns'] = []
# the user can request a link or a json
vect_post['response_type'] = 'json'
split = True
# fraction of rows in each column - 1 means all columns have all rows
inst_prob = 1
# make column data
for col_num in range(num_columns):
inst_col = {}
col_name = 'Col-' + str( col_num+1 ) + ' make name longer'
inst_col['col_name'] = col_name
inst_col['link'] = 'col-link'
if col_num < 5:
inst_col['cat'] = 'brain'
else:
inst_col['cat'] = 'lung'
# save to columns
inst_col['data'] = [] #vector
# get random subset of row_names
vect_rows = get_subset_rows(row_names, inst_prob)
# generate vectors
for inst_row in vect_rows:
# genrate values
##################
# add positive/negative values
if random.random() > 0.5:
value_up = 10*random.random()
else:
value_up = 0
if random.random() > 0.5:
value_dn = -10*random.random()
else:
value_dn = 0
value = value_up + value_dn
# # generate vector component
# #############################
# vector.append([ inst_row, value ])
# vector_up.append([ inst_row, value_up ])
# vector_dn.append([ inst_row, value_dn ])
# define row object - within column
row_obj = {}
row_obj['row_name'] = inst_row
row_obj['val'] = value
row_obj['val_up'] = value_up
row_obj['val_dn'] = value_dn
inst_col['data'].append(row_obj)
# if split:
# inst_col['vector_up'] = vector_up
# inst_col['vector_dn'] = vector_dn
# save columns to vect_post
vect_post['columns'].append(inst_col)
net.save_dict_to_json(vect_post, 'fake_vect_post.json', indent='indent')
def make_json():
from clustergrammer_old import Network
net = Network()
row_num = 50
num_columns = 10
# make up all names for all data
row_names = make_up_names(row_num)
# initialize vect_post
vect_post = {}
vect_post["title"] = "Some-Clustergram"
vect_post["link"] = "some-link"
vect_post["views"] = ["N_row_var"]
vect_post["is_up_down"] = True
vect_post["columns"] = []
# the user can request a link or a json
vect_post["response_type"] = "json"
split = True
# fraction of rows in each column - 1 means all columns have all rows
inst_prob = 1
# make column data
for col_num in range(num_columns):
inst_col = {}
col_name = "Col-" + str(col_num + 1) + " make name longer"
inst_col["col_name"] = col_name
inst_col["link"] = "col-link"
if col_num < 5:
inst_col["cat"] = "brain"
else:
inst_col["cat"] = "lung"
# save to columns
inst_col["data"] = [] # vector
# get random subset of row_names
vect_rows = get_subset_rows(row_names, inst_prob)
# generate vectors
for inst_row in vect_rows:
# genrate values
##################
# add positive/negative values
if random.random() > 0.5:
value_up = 10 * random.random()
else:
value_up = 0
if random.random() > 0.5:
value_dn = -10 * random.random()
else:
value_dn = 0
value = value_up + value_dn
# # generate vector component
# #############################
# vector.append([ inst_row, value ])
# vector_up.append([ inst_row, value_up ])
# vector_dn.append([ inst_row, value_dn ])
# define row object - within column
row_obj = {}
row_obj["row_name"] = inst_row
row_obj["val"] = value
row_obj["val_up"] = value_up
row_obj["val_dn"] = value_dn
inst_col["data"].append(row_obj)
# if split:
# inst_col['vector_up'] = vector_up
# inst_col['vector_dn'] = vector_dn
# save columns to vect_post
vect_post["columns"].append(inst_col)
net.save_dict_to_json(vect_post, "fake_vect_post.json", indent="indent")
def l1000cds2_upload():
'''
l1000cds2 is using a old version of clustergrammer.py
'''
import requests
import json
from clustergrammer_old import Network
from pymongo import MongoClient
from bson.objectid import ObjectId
from flask import request
l1000cds2 = json.loads(request.form.get('signatures'))
net = Network()
net.load_l1000cds2(l1000cds2)
cutoff_comp = 0
min_num_comp = 2
net.cluster_row_and_col(dist_type='cosine', dendro=True)
net.dat['node_info']['row']['ini'] = net.sort_rank_node_values('row')
net.dat['node_info']['col']['ini'] = net.sort_rank_node_values('col')
net.viz = {}
net.viz['row_nodes'] = []
net.viz['col_nodes'] = []
net.viz['links'] = []
net.viz_json()
export_dict = {}
export_dict['name'] = 'l1000cds2'
export_dict['dat'] = net.export_net_json('dat')
export_dict['viz'] = net.viz
export_dict['_id'] = ObjectId(l1000cds2['_id'])
client = MongoClient(mongo_address)
db = client.clustergrammer
tmp = db.networks.find_one({'_id': ObjectId(l1000cds2['_id'])})
if tmp is None:
tmp_id = db.networks.insert(export_dict)
client.close()
return redirect('/clustergrammer/l1000cds2/' + l1000cds2['_id'])
