tmp_data.index
# <codecell>
from PlottingTools import make_heatmap_df
order = ['R5-1', 'R5-2', 'R5-3', 'R5-4', 'R5-P', 'X4-P', 'X4']
tmp_data = pdata['Pval'].copy()
tmp_data.columns= pd.Index(order)
tmp_data[tmp_data<2] = np.nan
order = ['CSF', 'brain', 'meninges',
'PBMC', 'monocyte', 'plasma/serum/blood', 'resting CD4+ T cells',
'lymph node', 'spleen',
'GALT', 'colon',
'BAL', 'lung', 'liver', 'cervix/v****a',
's***n/testis', 'urethra']
fig = make_heatmap_df(tmp_data.ix[order], colormap='copper_r', figsize=(10,10))
cbar = plt.colorbar()
cbar.set_clim([0, 40])
cbar.set_ticks(range(0, 40, 5))
plt.savefig('final_figures/bining_tissue_figure_shortened.png', dpi=1000)
# <codecell>
mask = ((trim_lanl['PSSMScore'] > -2.98) & (trim_lanl['STissue'] == 'brain'))
wanted_acc = set(trim_lanl['Accession'][mask])
tdata = trim_lanl[['Accession','PSSMScore']][mask].groupby('Accession').first()
out_seqs = []
for name, seq in aa_seq_list:
if name in wanted_acc:
out_seqs.append({
'Accession':name,
import pandas as pd
grade_df = pd.DataFrame(tmp_data, columns = ['BW_id', 'Prob', 'Size'])
# <codecell>
pdata = pd.pivot_table(grade_df, rows = ['BW_id', 'Prob'],
values = 'Size', aggfunc = [np.min, len]).dropna()
pdata.head()
# <codecell>
best_scores = pd.pivot_table(pdata.reset_index(), rows = 'BW_id',
cols = 'Prob', values = 'amin')
fig = make_heatmap_df(best_scores.T, figsize = (10,10))
plt.colorbar().set_label('Best Solution Nodesize')
# <codecell>
num_tries = pd.pivot_table(pdata.reset_index(), rows = 'BW_id',
cols = 'Prob', values = 'len',
aggfunc = np.sum)
fig = make_heatmap_df(num_tries.applymap(np.log10).T, figsize = (10,10))
cb = plt.colorbar()
tickpos = [5, 10, 15, 20, 30, 40, 50, 60, 100]
cb.set_label('#Tries')
cb.set_ticks([np.log10(x) for x in tickpos])
cb.set_ticklabels(map(str, tickpos))
# <codecell>
