diff options
| -rw-r--r-- | gn3/api/heatmaps.py | 8 | ||||
| -rw-r--r-- | gn3/heatmaps.py | 27 |
2 files changed, 15 insertions, 20 deletions
diff --git a/gn3/api/heatmaps.py b/gn3/api/heatmaps.py index 43ac580..0493f8a 100644 --- a/gn3/api/heatmaps.py +++ b/gn3/api/heatmaps.py @@ -1,3 +1,4 @@ +import io from flask import jsonify from flask import request from flask import Blueprint @@ -20,4 +21,9 @@ def clustered_heatmaps(): return "{dataset_name}::{trait_name}".format( dataset_name=name_parts[1], trait_name=name_parts[0]) traits_fullnames = [parse_trait_fullname(trait) for trait in traits_names] - return jsonify(build_heatmap(traits_fullnames, conn)), 200 + + with io.StringIO() as io_str: + _filename, figure = build_heatmap(traits_fullnames, conn) + figure.write_json(io_str) + fig_json = io_str.getvalue() + return fig_json, 200 diff --git a/gn3/heatmaps.py b/gn3/heatmaps.py index 205a3b3..cd93b3f 100644 --- a/gn3/heatmaps.py +++ b/gn3/heatmaps.py @@ -187,38 +187,27 @@ def build_heatmap(traits_names, conn: Any): genotype_filename, traits_filename, separate_nperm_output=True) qtlresults = parse_reaper_main_results(main_output) - # permudata = parse_reaper_permutation_results(permutations_output) organised = organise_reaper_main_results(qtlresults) traits_ids = [# sort numerically, but retain the ids as strings str(i) for i in sorted({int(row["ID"]) for row in qtlresults})] chromosome_names = sorted( {row["Chr"] for row in qtlresults}, key=chromosome_sorter_key_fn) - # loci_names = sorted({row["Locus"] for row in qtlresults}) ordered_traits_names = dict( zip(traits_ids, [traits[idx]["trait_fullname"] for idx in traits_order])) - # return generate_clustered_heatmap( - # process_traits_data_for_heatmap( - # organised, traits_ids, chromosome_names), - # clustered, - # "single_heatmap_{}".format(random_string(10)), - # y_axis=tuple( - # ordered_traits_names[traits_ids[order]] - # for order in traits_order), - # y_label="Traits", - # x_axis=chromosome_names, - # x_label="Chromosomes") - return { - "clustering_data": clustered, - "heatmap_data": process_traits_data_for_heatmap( + return generate_clustered_heatmap( + process_traits_data_for_heatmap( organised, traits_ids, chromosome_names), - "traits": tuple( + clustered, + "single_heatmap_{}".format(random_string(10)), + y_axis=tuple( ordered_traits_names[traits_ids[order]] for order in traits_order), - "chromosomes": chromosome_names - } + y_label="Traits", + x_axis=chromosome_names, + x_label="Chromosomes") def compute_traits_order(slink_data, neworder: tuple = tuple()): """ |