Clone the slideshow template to where you will be making your IPython notebook slideshows. You need style.css and talktools.py specifically.
Clone this repo to your equivalent of:
~/.ipython/nbextensions/
then, for whichever profile you use, in my case "customcss," edit the file:
~/.ipython/profile_customcss/static/custom/custom.js
| from sklearn.decomposition import PCA | |
| from sklearn.decomposition import RandomizedPCA | |
| def pcaplot(df, sample_id_to_label, pivot_kw=None, x_pc=1, y_pc=2, distance='L1', | |
| whiten=True, num_vectors=10, label_to_color=None, | |
| title='PCA', show_vectors=True, | |
| show_vector_labels=True, T=False, | |
| fillna=False, dropna=False, | |
| pca_type='PCA', s=100, text_group=None, | |
| three_d=False): |
| def single_track(track): | |
| within_track = False | |
| lines = [] | |
| with open(track) as f: | |
| for line in f: | |
| # print line, | |
| if line.startswith('track') and within_track: | |
| within_track = False | |
| yield lines | |
| lines = [line] |
| import pandas as pd | |
| mini_summary = pd.read_csv('https://www.dropbox.com/s/elv636x7wma51zd/mini_summary.csv?dl=1') | |
| test_groupby = {'P4_11': 'test1', | |
| 'P8_10': 'test1'} | |
| index_cols = ['million_reads', 'splice_type', 'event_name'] | |
| for pair_name, df1 in mini_summary.groupby(test_groupby): |