for p in ['VN1203', 'Indo05']:
amp.plot_median_responses_by_exp_groups(group_medians, ['D21', 'D42'], prot_str=p,
fig_path=fig_path, fig_prefix=None, fig_size=(18,11), y_lims=[0, 10000])
"""
2. Raw responses by group:
Plot responses by groups for each protein separately - groups can be prot_strs, or any subset of these:
the second list is prot_strs for figure titles.
you can use this for all proteins:
for p, s in zip(prot_names, prot_strs):
"""
for p in ['VN1203', 'Indo05']:
amp.plot_responses_by_exp_groups(arr_df=raw_arr_df, antigen_inds = ind_dict[p],
exp_groups=exp_groups, fig_path=fig_path, fig_prefix=p + '_Raw', y_lims=y_lims)
for p in ['VN1203', 'Indo05']:
amp.plot_responses_by_exp_groups(arr_df=arr_df, antigen_inds = ind_dict[p],
exp_groups=['D21', 'D42'], fig_path=fig_path, fig_prefix=p, y_lims=y_lims)
"""
3. Boxplot of summary stats by group:
plot boxplots of all groups - magnitude and breadth for each protein
"""
amp.plot_summary_stat_boxplots_by_exp_groups(raw_arr_df, arr_summary_stats, sample_inds=None, fig_path=fig_path)
"""Clustering plots"""
""" 2. Raw responses by group: Plot responses by groups for each protein separately - groups can be prot_strs, or any subset of these: the second list is prot_strs for figure titles. you can use this for all proteins: for p, s in zip(prot_names, prot_strs): """ # for p in ['EBOV-GP']: # amp.plot_responses_by_exp_groups(arr_df=raw_arr_df, antigen_inds = ind_dict[p], # exp_groups=exp_groups, fig_path=fig_path, fig_prefix=p + '_Raw', y_lims=y_lims) for p in ['EBOV-GP']: amp.plot_responses_by_exp_groups(arr_df=arr_df, antigen_inds = ind_dict[p], exp_groups=exp_groups, fig_path=fig_path, fig_prefix=p, y_lims=[0, 30000]) """ 3. Boxplot of summary stats by group: plot boxplots of all groups - magnitude and breadth for each protein """ amp.plot_summary_stat_boxplots_by_exp_groups(arr_df, arr_summary_stats, sample_inds=None, fig_path=fig_path) """Clustering plots""" """ 1. Dendrograms: """ #amp.plot_clustering_dendrograms(Z_struct=Z_struct, prot_names=prot_names, labels=arr_df.index[time_dict['D21']], fig_path=None)
