#!/usr/bin/env python
# coding: utf-8

# # tmp-API-check
# The `clustergrammer_widget` class is now being loaded into the `Network` class. The class and widget instance are saved in th `Network` instance, `net`. This allows us to load data, cluster, and finally produce a new widget instance using the `widget` method. The instance of the widget is saved in `net` and can be used to grab the data from the clustergram as a Pandas DataFrame using the `widget_df` method. The exported DataFrame will reflect any filtering or imported categories that were added on the front end.
# 
# In these examples, we will filter the matrix using the brush crop tool, export the filtered matrix as a DataFrame, and finally visualize this as a new clustergram widget. 

# In[1]:


import numpy as np
import pandas as pd
from clustergrammer_widget import *
net = Network(clustergrammer_widget)


# # Make widget using new API

# In[2]:


net.load_file('rc_two_cats.txt')
net.cluster()
net.widget()


# Above, we have filtered the matrix to a region of interest using the brush cropping tool. Below we will get export this region of interest, defined on the front end, to a DataFrame, `df_genes`. This demonstrates the two-way communication capabilities of widgets. 

# In[3]:


df_genes = net.widget_df()
df_genes.shape


# In[4]:


net.load_df(df_genes)
net.cluster()
net.widget()


# Above, we made a new widget visualizing this region of interest. 

# # Generate random DataFrame
# Here we will genrate a DataFrame with random data and visualize it using the widget.

# In[5]:


# generate random matrix
num_rows = 500
num_cols = 10
np.random.seed(seed=100)
mat = np.random.rand(num_rows, num_cols)

# make row and col labels
rows = range(num_rows)
cols = range(num_cols)
rows = [str(i) for i in rows]
cols = [str(i) for i in cols]

# make dataframe 
df = pd.DataFrame(data=mat, columns=cols, index=rows)


# In[6]:


net.load_df(df)
net.cluster()
net.widget()


# Above, we selected a region of interest using the front-end brush crop tool and export to DataFrame, df_random. Below we will visualize it using a new widget.

# In[7]:


df_random = net.widget_df()
df_random.shape


# In[8]:


net.load_df(df_random)
net.cluster()
net.widget()


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