jupyter_notebook_header.py

%matplotlib inline

# proxies for internet access on the cluster
%env http_proxy=http://proxy-default:3128
%env https_proxy=http://proxy-default:3128

import numpy as np
import pandas as pd
from pandas import DataFrame, Series
import warnings
from scipy import signal, stats
from statsmodels.nonparametric.smoothers_lowess import lowess
from sklearn.decomposition import PCA

# Make inline plots vector graphics instead of raster graphics
from matplotlib_inline.backend_inline import set_matplotlib_formats
set_matplotlib_formats('retina', 'png')
#set_matplotlib_formats('pdf', 'svg')

import matplotlib
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from matplotlib.patches import Rectangle
from matplotlib.lines import Line2D

#matplotlib.rcParams['figure.figsize'] = (20.0, 10.0)

import seaborn as sns
sns.set() # sets seaborn default "prettyness:
sns.set_style("ticks")

# scale down size of default plots
sns.set_context("paper")
import matplotlib as mpl
scale = 0.8
d = dict([(k, v*scale) for (k, v) in sns.plotting_context('paper').items()])
d['figure.figsize'] = [5.4, 3.5]
mpl.rcParams.update(d)


def abline(slope, intercept, ax=None):
    "Add a straight line through the plot"
    if ax is None:
        ax = plt.gca()
    x_vals = np.array(ax.get_xlim())
    y_vals = intercept + slope * x_vals
    ax.plot(x_vals, y_vals, '--', color='grey')
    
def add_lowess(x, y, ax=None, color=None, is_sorted=True, frac=0.005, it=0, **kwargs):
    "Add a lowess curve to the plot"
    if ax is None:
        ax = plt.gca() 
    filtered = lowess(y, x, is_sorted=is_sorted, frac=frac, it=it, **kwargs)
    ax.plot(filtered[:,0], filtered[:,1])

def add_band(x_low, x_high, y_low=None, y_high=None, ax=None, color='gray', linewidth=0, alpha=0.5, zorder=0, **kwargs):
    "Plot a gray block on x interval"
    if ax is None:
        ax = plt.gca()
    if y_low is None:
        y_low, _ = ax.get_ylim()
    if y_high is None:
        _, y_high = ax.get_ylim()
    g = ax.add_patch(Rectangle((x_low, y_low), x_high-x_low, y_high-y_low, 
                 facecolor=color,
                 linewidth=linewidth,
                 alpha=alpha,
                 zorder=zorder,
                 **kwargs))

def stairs(df, start='start', end='end', pos='pos', endtrim=0):
    "Turn a df with start, end into one with pos to plot as stairs"
    df1 = df.copy(deep=True)
    df2 = df.copy(deep=True)
    df1[pos] = df1[start]
    df2[pos] = df2[end] - endtrim
    return pd.concat([df1, df2]).sort_values([start, end])

# My own paired palette replacing the last brown pair with violets
sns.color_palette('Paired').as_hex()
Paired = sns.color_palette(['#a6cee3', '#1f78b4', '#b2df8a', '#33a02c', '#fb9a99', '#e31a1c',
                            '#fdbf6f', '#ff7f00', '#cab2d6','#6a3d9a', '#e585cf', '#ad009d'])
#sns.palplot(Paired)
Infographics = sns.color_palette(['#e8615d', '#f49436', '#2d9de5', '#3bbdbd', '#634792'])
#sns.palplot(Infographics)

def pca(matrix, labels=None, n_components=2):
    sklearn_pca = PCA(n_components=n_components)
    sklearn_pca.fit(matrix)
    transform = sklearn_pca.transform(matrix)
    df = pd.DataFrame()
    for i in range(n_components):
        df[f'PC{i+1}'] = transform[:, i]
    if labels is not None:
        df['labels'] = labels
    return df

class left:
    def __rlshift__(self, df):
        "Left align columns of data frame: df << left()"
        left_aligned_df = df.style.set_properties(**{'text-align': 'left'})
        left_aligned_df = left_aligned_df.set_table_styles(
        [dict(selector = 'th', props=[('text-align', 'left')])])
        display(left_aligned_df)