preamble.py

from IPython.display import set_matplotlib_formats, display, HTML
from IPython.core.interactiveshell import InteractiveShell
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import openml as oml
import mglearn
import os
from cycler import cycler
from pprint import pprint

set_matplotlib_formats('pdf', 'png')
plt.rcParams['savefig.dpi'] = 300
plt.rcParams['image.cmap'] = "viridis"
plt.rcParams['image.interpolation'] = "none"
plt.rcParams['savefig.bbox'] = "tight"
#plt.rcParams['lines.linewidth'] = 1
plt.rcParams['legend.numpoints'] = 1
plt.rc('axes', prop_cycle=(cycler('color', mglearn.plot_helpers.cm_cycle.colors) +
                           cycler('linestyle', ['-', '--', ':',
                                                '-.', '--'])
                           )
       )

np.set_printoptions(precision=3, suppress=True)

pd.set_option("display.max_columns", 8)
pd.set_option('precision', 2)

np, mglearn

# Prints outputs in cells so that we don't have to write print() every time 
#InteractiveShell.ast_node_interactivity = "all"

# Matplotlib tweaks for presentations
plt.rcParams["figure.figsize"] = (5, 3)
plt.rcParams["figure.max_open_warning"] = -1
plt.rcParams['font.size'] = 8; 
plt.rcParams['lines.linewidth'] = 0.5


# Presentations
from notebook.services.config import ConfigManager
cm = ConfigManager()
cm.update('livereveal', {'width': '95%', 'height': 786, 'scroll': True, 'theme': 'solarized', 'transition': 'fade', 'overflow': 'visible', 'start_slideshow_at': 'selected'})

# Silence warnings
import warnings
warnings.simplefilter(action="ignore", category=FutureWarning)
warnings.simplefilter(action="ignore", category=UserWarning)
warnings.simplefilter(action="ignore", category=RuntimeWarning)