import brewer2mpl
import random
import pandas as pd
import numpy as np

np.random.seed(10)

df = pd.DataFrame(np.random.randn(8, 4), columns=['A', 'B', 'C', 'D'])

set1 = brewer2mpl.get_map('Set1', 'qualitative', 7).mpl_colors

fig = plt.figure()
ax = fig.add_subplot(111)

bp = ax.boxplot(df.values)

import random
import pandas as pd
import numpy as np

np.random.seed(10)

df = pd.DataFrame(np.random.randn(8, 4), columns=['A', 'B', 'C', 'D'])

fig = plt.figure()
ax = fig.add_subplot(111)

bp = ax.boxplot(df.values)

ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['bottom'].set_visible(False)

import random
import pandas as pd
import numpy as np

np.random.seed(10)

df = pd.DataFrame(np.random.randn(8, 4), columns=['A', 'B', 'C', 'D'])

fig = plt.figure()
ax = fig.add_subplot(111)

bp = ax.boxplot(df.values)

ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['bottom'].set_visible(False)

ax.xaxis.set_ticks_position('none')
ax.yaxis.set_ticks_position('none')

import random
import pandas as pd
import numpy as np

np.random.seed(10)

df = pd.DataFrame(np.random.randn(8, 4), columns=['A', 'B', 'C', 'D'])

fig = plt.figure()
ax = fig.add_subplot(111)

bp = ax.boxplot(df.values)

ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['bottom'].set_visible(False)

ax.xaxis.set_ticks_position('none')
ax.yaxis.set_ticks_position('none')

ax.xaxis.set_ticklabels(df.columns)

import brewer2mpl
import random
import pandas as pd
import numpy as np

np.random.seed(10)

df = pd.DataFrame(np.random.randn(8, 4), columns=['A', 'B', 'C', 'D'])

fig = plt.figure()
ax = fig.add_subplot(111)

bp = ax.boxplot(df.values)

ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['bottom'].set_visible(False)

ax.xaxis.set_ticks_position('none')
ax.yaxis.set_ticks_position('none')

ax.xaxis.set_ticklabels(df.columns)

set1 = brewer2mpl.get_map('Set1', 'qualitative', 7).mpl_colors
plt.setp(bp['boxes'], color=set1[1])
plt.setp(bp['medians'], color=set1[0])
plt.setp(bp['whiskers'], color=set1[1])
plt.setp(bp['fliers'], color=set1[1])
plt.setp(bp['caps'], color=set1[1])

import brewer2mpl
import random
import pandas as pd
import numpy as np

np.random.seed(10)

df = pd.DataFrame(np.random.randn(8, 4), columns=['A', 'B', 'C', 'D'])

set1 = brewer2mpl.get_map('Set1', 'qualitative', 7).mpl_colors

fig = plt.figure()
ax = fig.add_subplot(111)

bp = ax.boxplot(df.values, widths=0.15)


ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['bottom'].set_visible(False)

ax.xaxis.set_ticks_position('none')
ax.yaxis.set_ticks_position('none')

ax.xaxis.set_ticklabels(df.columns)

set1 = brewer2mpl.get_map('Set1', 'qualitative', 7).mpl_colors
plt.setp(bp['boxes'], color=set1[1])
plt.setp(bp['medians'], color=set1[0])
plt.setp(bp['whiskers'], color=set1[1])
plt.setp(bp['fliers'], color=set1[1])
plt.setp(bp['caps'], color=set1[1])

import brewer2mpl
import random
import pandas as pd
import numpy as np

np.random.seed(10)

df = pd.DataFrame(np.random.randn(8, 4), columns=['A', 'B', 'C', 'D'])

set1 = brewer2mpl.get_map('Set1', 'qualitative', 7).mpl_colors

fig = plt.figure()
ax = fig.add_subplot(111)

bp = ax.boxplot(df.values, widths=0.15)

ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['bottom'].set_visible(False)

ax.xaxis.set_ticks_position('none')
ax.yaxis.set_ticks_position('none')

ax.xaxis.set_ticklabels(df.columns)

set1 = brewer2mpl.get_map('Set1', 'qualitative', 7).mpl_colors
plt.setp(bp['boxes'], color=set1[1])
plt.setp(bp['medians'], color=set1[0])
plt.setp(bp['whiskers'], color=set1[1], linestyle='solid', linewidth=0.5)
plt.setp(bp['fliers'], color=set1[1])
plt.setp(bp['caps'], color=set1[1])

import brewer2mpl
import random
import pandas as pd
import numpy as np

np.random.seed(10)

df = pd.DataFrame(np.random.randn(8, 4), columns=['A', 'B', 'C', 'D'])

set1 = brewer2mpl.get_map('Set1', 'qualitative', 7).mpl_colors

fig = plt.figure()
ax = fig.add_subplot(111)

bp = ax.boxplot(df.values, widths=0.15)

ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['bottom'].set_visible(False)

ax.xaxis.set_ticks_position('none')
ax.yaxis.set_ticks_position('none')

ax.xaxis.set_ticklabels(df.columns)

set1 = brewer2mpl.get_map('Set1', 'qualitative', 7).mpl_colors
plt.setp(bp['boxes'], color=set1[1], linewidth=0.5)
plt.setp(bp['medians'], color=set1[0])
plt.setp(bp['whiskers'], color=set1[1], linestyle='solid', linewidth=0.5)
plt.setp(bp['fliers'], color=set1[1])
plt.setp(bp['caps'], color=set1[1])

import brewer2mpl
import random
import pandas as pd
import numpy as np

np.random.seed(10)


df = pd.DataFrame(np.random.randn(8, 4), columns=['A', 'B', 'C', 'D'])

set1 = brewer2mpl.get_map('Set1', 'qualitative', 7).mpl_colors

fig = plt.figure()
ax = fig.add_subplot(111)

bp = ax.boxplot(df.values, widths=0.15)

ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['bottom'].set_visible(False)

ax.xaxis.set_ticks_position('none')
ax.yaxis.set_ticks_position('none')

ax.xaxis.set_ticklabels(df.columns)

set1 = brewer2mpl.get_map('Set1', 'qualitative', 7).mpl_colors
plt.setp(bp['boxes'], color=set1[1], linewidth=0.5)
plt.setp(bp['medians'], color=set1[0])
plt.setp(bp['whiskers'], color=set1[1], linestyle='solid', linewidth=0.5)
plt.setp(bp['fliers'], color=set1[1])
plt.setp(bp['caps'], color='none')

import brewer2mpl
import random
import pandas as pd
import numpy as np

np.random.seed(10)

df = pd.DataFrame(np.random.randn(8, 4), columns=['A', 'B', 'C', 'D'])

set1 = brewer2mpl.get_map('Set1', 'qualitative', 7).mpl_colors

fig = plt.figure()
ax = fig.add_subplot(111)

bp = ax.boxplot(df.values, widths=0.15)
ax.xaxis.set_ticklabels(df.columns)

ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['bottom'].set_visible(False)
ax.xaxis.set_ticks_position('none')
ax.yaxis.set_ticks_position('none')

set1 = brewer2mpl.get_map('Set1', 'qualitative', 7).mpl_colors
plt.setp(bp['boxes'], color=set1[1], linewidth=0.5)
plt.setp(bp['medians'], color=set1[0])
plt.setp(bp['whiskers'], color=set1[1], linestyle='solid', linewidth=0.5)
plt.setp(bp['fliers'], color=set1[1])
plt.setp(bp['caps'], color='none')

ax.spines['left']._linewidth = 0.5

%run /Users/olga/Dropbox/Docs/UCSD/rotations/q1-fall-ideker/src/YeastTFs.py

fig = plt.figure()
ax = fig.add_subplot(111)

# Maximum value we see for fraction common bound
xlim = [0,0.25]

# Maximum value we see for alignment scores
ylim = [0,800]

feature='score'
alignment = 'local'

for i, tf in enumerate(yeast_tfs.pombe_tfs):
#    ax = axes[i]
    ind = tf.binary_ind
    x = tf.cerev_common_bound_fraction[tf.cerev_common_ind[
                        tf.cerev_common_ind != False]]
    y = tf.__dict__['df_'+alignment].ix[ind,feature]
            
    # Linear regression
            
    ax.scatter(x, y, c=tf.color, edgecolor='black',
                            lw=0.2)
    ax.set_ylim(ylim)
    ax.set_xlim(xlim)
    
    # Remove top and right axes and ticks
    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)
    ax.yaxis.set_ticks_position('none')
    ax.xaxis.set_ticks_position('none')
            
    ax.set_title('Local alignment score vs Fraction common bound')
            
    ax.set_xlabel('fraction common bound')
    ax.set_ylabel('local alignment score')
      
plt.tight_layout()

import math
num_plots = len(yeast_tfs.pombe_tfs)
n = int(math.ceil(math.sqrt(num_plots)))

fig = plt.figure(figsize=(10,10))

# Maximum value we see for fraction common bound
xlim = [0,0.25]

# Maximum value we see for alignment scores
ylim = [0,800]

axes = [plt.subplot(n,n,i) for i in range(1,num_plots+1)]
feature='score'
alignment = 'local'

for i, tf in enumerate(yeast_tfs.pombe_tfs):
    ax = axes[i]
    ind = tf.binary_ind
    x = tf.cerev_common_bound_fraction[tf.cerev_common_ind[
                        tf.cerev_common_ind != False]]
    y = tf.__dict__['df_'+alignment].ix[ind,feature]
            
    ax.scatter(x, y, c=tf.color, edgecolor='black',
                            lw=0.2)
    ax.set_ylim(ylim)
    ax.set_xlim(xlim)
    
    # Remove top and right axes and ticks
    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)
    ax.yaxis.set_ticks_position('none')
    ax.xaxis.set_ticks_position('none')
            
    ax.set_title(tf.id)
            
    ax.set_xlabel('fraction common bound')
    ax.set_ylabel('local alignment score')

import math
num_plots = len(yeast_tfs.pombe_tfs)
n = int(math.ceil(math.sqrt(num_plots)))

import brewer2mpl
set2 = brewer2mpl.get_map('set2', 'qualitative', 3).mpl_colors
color = set2[0]

fig = plt.figure(figsize=(10,10))

# Maximum value we see for fraction common bound
xlim = [0,0.25]

# Maximum value we see for alignment scores
ylim = [0,800]

axes = [plt.subplot(n,n,i) for i in range(1,num_plots+1)]
alignments = ['local', 'global']
features = ['identity', 'similarity', 'score']

feature='score'

alignment = 'local'
for i, tf in enumerate(yeast_tfs.pombe_tfs):
    ax = axes[i]
    ind = tf.binary_ind
    x = tf.cerev_common_bound_fraction[tf.cerev_common_ind[
                        tf.cerev_common_ind != False]]
    y = tf.__dict__['df_'+alignment].ix[ind,feature]
            
    ax.scatter(x, y, c=color, edgecolor='black',
                            lw=0.2)
    ax.set_ylim(ylim)
    ax.set_xlim(xlim)
    
    # Remove top and right axes and ticks
    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)
    ax.yaxis.set_ticks_position('none')
    ax.xaxis.set_ticks_position('none')
            
    ax.set_title(tf.id)
            
    ax.set_xlabel('fraction common bound')
    ax.set_ylabel('local alignment score')
      
plt.tight_layout()

import math
num_plots = len(yeast_tfs.pombe_tfs)
n = int(math.ceil(math.sqrt(num_plots)))

from scipy import stats

import brewer2mpl
set2 = brewer2mpl.get_map('set2', 'qualitative', 3).mpl_colors
color = set2[0]

fig = plt.figure(figsize=(10,10))

# Maximum value we see for fraction common bound
xlim = [0,0.25]

# Maximum value we see for alignment scores
ylim = [0,800]

axes = [plt.subplot(n,n,i) for i in range(1,num_plots+1)]
alignments = ['local', 'global']
features = ['identity', 'similarity', 'score']

feature='score'

alignment = 'local'
for i, tf in enumerate(yeast_tfs.pombe_tfs):
    ax = axes[i]
    ind = tf.binary_ind
    x = tf.cerev_common_bound_fraction[tf.cerev_common_ind[
                        tf.cerev_common_ind != False]]
    y = tf.__dict__['df_'+alignment].ix[ind,feature]
            
    ax.scatter(x, y, c=color, edgecolor='black',
                            lw=0.2)
    ax.set_ylim(ylim)
    ax.set_xlim(xlim)
    
    # Remove top and right axes and ticks
    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)
    ax.yaxis.set_ticks_position('none')
    ax.xaxis.set_ticks_position('none')
            
    ax.set_title(tf.id)
            
    ax.set_xlabel('fraction common bound')
    ax.set_ylabel('local alignment score')
    
    # Linear regression
    slope, intercept, r_value, p_value, std_err = stats.linregress(x,y)
    x_fit = np.arange(xlim[0], xlim[1], (xlim[1]-xlim[0])/100.)
    y_fit = np.multiply(x_fit, slope) + intercept
    
    # Plot linear regression line
    ax.plot(x_fit,y_fit, color='grey')
    
    # Write 'R' value onto plot
    ax.text(xlim[1], ylim[1],'$R^2$ = %.4f' % r_value,fontsize=12,
        ha='right',va='top')
    
plt.tight_layout()

import math
num_plots = len(yeast_tfs.pombe_tfs)
n = int(math.ceil(math.sqrt(num_plots)))

from scipy import stats

import brewer2mpl
set2 = brewer2mpl.get_map('set2', 'qualitative', 3).mpl_colors
color = set2[0]

fig = plt.figure(figsize=(10,10))

# Maximum value we see for fraction common bound
xlim = [0,0.25]

# Maximum value we see for alignment scores
ylim = [0,800]

axes = [plt.subplot(n,n,i) for i in range(1,num_plots+1)]
alignments = ['local', 'global']
features = ['identity', 'similarity', 'score']

feature='score'

alignment = 'local'
for i, tf in enumerate(yeast_tfs.pombe_tfs):
    ax = axes[i]
    ind = tf.binary_ind
    x = tf.cerev_common_bound_fraction[tf.cerev_common_ind[
                        tf.cerev_common_ind != False]]
    y = tf.__dict__['df_'+alignment].ix[ind,feature]
            
    ax.scatter(x, y, c=color, edgecolor='black',
                            lw=0.2)
    ax.set_ylim(ylim)
    ax.set_xlim(xlim)
    
    # Remove top and right axes and ticks
    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)
    ax.yaxis.set_ticks_position('none')
    ax.xaxis.set_ticks_position('none')
    
    ax.set_title(tf.id)
            
    ax.set_xlabel('fraction common bound')
    ax.set_ylabel('local alignment score')

    # --- Linear regression --- #
    slope, intercept, r_value, p_value, std_err = stats.linregress(x,y)
    x_fit = np.arange(xlim[0], xlim[1], (xlim[1]-xlim[0])/100.)
    y_fit = np.multiply(x_fit, slope) + intercept
    
    # Plot linear regression line
    ax.plot(x_fit,y_fit, color='grey')
    
    # Write 'R' value onto plot
    ax.text(xlim[1], ylim[1],'$R^2$ = %.4f' % r_value,fontsize=12,
        ha='right',va='top')
    
    # --- Thin out the axis lines --- #
    ax.spines['left']._linewidth = 0.5
    ax.spines['bottom']._linewidth = 0.5
    
plt.tight_layout()

import math
num_plots = len(yeast_tfs.pombe_tfs)
n = int(math.ceil(math.sqrt(num_plots)))

from scipy import stats

import brewer2mpl
set2 = brewer2mpl.get_map('set2', 'qualitative', 3).mpl_colors
color = set2[0]

fig = plt.figure(figsize=(10,10))

# Maximum value we see for fraction common bound
xlim = [0,0.25]

# Maximum value we see for alignment scores
ylim = [0,800]

axes = [plt.subplot(n,n,i) for i in range(1,num_plots+1)]
alignments = ['local', 'global']
features = ['identity', 'similarity', 'score']

feature='score'

alignment = 'local'
for i, tf in enumerate(yeast_tfs.pombe_tfs):
    ax = axes[i]
    ind = tf.binary_ind
    x = tf.cerev_common_bound_fraction[tf.cerev_common_ind[
                        tf.cerev_common_ind != False]]
    y = tf.__dict__['df_'+alignment].ix[ind,feature]
            
    ax.scatter(x, y, c=color, edgecolor='black',
                            lw=0.2)
    ax.set_ylim(ylim)
    ax.set_xlim(xlim)
    
    ax.set_title(tf.id)
    
    # Remove top and right axes and ticks
    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)
    ax.yaxis.set_ticks_position('none')
    ax.xaxis.set_ticks_position('none')
    
    # --- Linear regression --- #
    slope, intercept, r_value, p_value, std_err = stats.linregress(x,y)
    x_fit = np.arange(xlim[0], xlim[1], (xlim[1]-xlim[0])/100.)
    y_fit = np.multiply(x_fit, slope) + intercept
    
    # Plot linear regression line
    ax.plot(x_fit,y_fit, color='grey')
    
    # Write 'R' value onto plot
    ax.text(xlim[1], ylim[1],'$R^2$ = %.4f' % r_value,fontsize=12,
        ha='right',va='top')
    
    # --- Thin out the axis lines --- #
    ax.spines['left']._linewidth = 0.5
    ax.spines['bottom']._linewidth = 0.5
            
    # Only label the x and y axis of the first plot, since they are all the same
    if i == 0:
        ax.set_xlabel('fraction common bound')
        ax.set_ylabel('local alignment score')
      
plt.tight_layout()

import math
num_plots = len(yeast_tfs.pombe_tfs)
n = int(math.ceil(math.sqrt(num_plots)))

from scipy import stats

import brewer2mpl
set2 = brewer2mpl.get_map('set2', 'qualitative', 3).mpl_colors
color = set2[0]

fig = plt.figure(figsize=(10,10))

# Maximum value we see for fraction common bound
xlim = [0,0.25]

# Maximum value we see for alignment scores
ylim = [0,800]

axes = [plt.subplot(n,n,i) for i in range(1,num_plots+1)]
alignments = ['local', 'global']
features = ['identity', 'similarity', 'score']

feature='score'

alignment = 'local'
for i, tf in enumerate(yeast_tfs.pombe_tfs):
    ax = axes[i]
    ind = tf.binary_ind
    x = tf.cerev_common_bound_fraction[tf.cerev_common_ind[
                        tf.cerev_common_ind != False]]
    y = tf.__dict__['df_'+alignment].ix[ind,feature]
            
    ax.scatter(x, y, c=color, edgecolor='black',
                            lw=0.2)
    ax.set_ylim(ylim)
    ax.set_xlim(xlim)
            
    ax.set_title(tf.id)
    
    # Remove top and right axes and ticks
    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)
    ax.yaxis.set_ticks_position('none')
    ax.xaxis.set_ticks_position('none')
    
    ax.spines['left']._linewidth = 0.5
    ax.spines['bottom']._linewidth = 0.5

    # --- Linear regression --- #
    slope, intercept, r_value, p_value, std_err = stats.linregress(x,y)
    x_fit = np.arange(xlim[0], xlim[1], (xlim[1]-xlim[0])/100.)
    y_fit = np.multiply(x_fit, slope) + intercept
    
    # Plot linear regression line
    ax.plot(x_fit,y_fit, color='grey')
    
    # Write 'R' value onto plot
    ax.text(xlim[1], ylim[1],'$R^2$ = %.4f' % r_value,fontsize=12,
        ha='right',va='top')
    
    # --- Thin out the axis lines --- #
    ax.spines['left']._linewidth = 0.5
    ax.spines['bottom']._linewidth = 0.5
            
    # Only label the x and y axis of the first plot, since they are all the same
    if i == 0:
        ax.set_xlabel('fraction common bound')
        ax.set_ylabel('local alignment score')
      
    # Python's built-in range is fine for making a sequence of integers
    ax.yaxis.set_ticks(range(ylim[1]/4,ylim[1]+ylim[1]/4,ylim[1]/4))
    
    # Need to use np.arange to make a sequence of floats
    ax.xaxis.set_ticks(np.arange(xlim[1]/5, xlim[1]+2*xlim[1]/5, 2*xlim[1]/5))
    
plt.tight_layout()