aboutsummaryrefslogtreecommitdiff
path: root/gn3
diff options
context:
space:
mode:
authorFrederick Muriuki Muriithi2021-09-17 11:20:16 +0300
committerFrederick Muriuki Muriithi2021-09-17 11:20:16 +0300
commit1e2357049adc72808fbf8eaac3da9411d3c78c66 (patch)
tree778d22e2f021fddf6dfbd4abc9ec312c4043f22d /gn3
parent8ac3194f06084dfe5d0cfb141f178d83d937fcc3 (diff)
downloadgenenetwork3-1e2357049adc72808fbf8eaac3da9411d3c78c66.tar.gz
Fix a number of linting issues
Issue: https://github.com/genenetwork/gn-gemtext-threads/blob/main/topics/gn1-migration-to-gn2/clustering.gmi
Diffstat (limited to 'gn3')
-rw-r--r--gn3/computations/qtlreaper.py7
-rw-r--r--gn3/db/genotypes.py2
-rw-r--r--gn3/heatmaps.py54
3 files changed, 26 insertions, 37 deletions
diff --git a/gn3/computations/qtlreaper.py b/gn3/computations/qtlreaper.py
index 5180853..377db9b 100644
--- a/gn3/computations/qtlreaper.py
+++ b/gn3/computations/qtlreaper.py
@@ -110,9 +110,10 @@ def organise_reaper_main_results(parsed_results):
unique_chromosomes = {item["Chr"] for item in id_items}
return {
"ID": identifier,
- "chromosomes": {_chr["Chr"]: _chr for _chr in [
- __organise_by_chromosome(chromo, id_items)
- for chromo in sorted(
+ "chromosomes": {
+ _chr["Chr"]: _chr for _chr in [
+ __organise_by_chromosome(chromo, id_items)
+ for chromo in sorted(
unique_chromosomes, key=chromosome_sorter_key_fn)]}}
unique_ids = {res["ID"] for res in parsed_results}
diff --git a/gn3/db/genotypes.py b/gn3/db/genotypes.py
index b03d55c..9d052d9 100644
--- a/gn3/db/genotypes.py
+++ b/gn3/db/genotypes.py
@@ -174,7 +174,7 @@ def parse_genotype_file(filename: str, parlist: tuple = tuple()):
geno_obj = dict(labels + header)
markers = tuple(
[parse_genotype_marker(line, geno_obj, parlist)
- for line in data_lines[1:]])
+ for line in data_lines[1:]])
chromosomes = tuple(
dict(chromosome) for chromosome in
build_genotype_chromosomes(geno_obj, markers))
diff --git a/gn3/heatmaps.py b/gn3/heatmaps.py
index 2859dde..c4fc67d 100644
--- a/gn3/heatmaps.py
+++ b/gn3/heatmaps.py
@@ -3,13 +3,13 @@ This module will contain functions to be used in computation of the data used to
generate various kinds of heatmaps.
"""
+from typing import Any, Dict, Sequence
import numpy as np
from functools import reduce
from gn3.settings import TMPDIR
import plotly.graph_objects as go
import plotly.figure_factory as ff
from gn3.random import random_string
-from typing import Any, Dict, Sequence
from gn3.computations.slink import slink
from plotly.subplots import make_subplots
from gn3.computations.correlations2 import compute_correlation
@@ -165,7 +165,7 @@ def build_heatmap(traits_names, conn: Any):
for fullname in traits_names]
traits_data_list = [retrieve_trait_data(t, conn) for t in traits]
genotype_filename = build_genotype_file(traits[0]["riset"])
- genotype = parse_genotype_file(genotype_filename)
+ # genotype = parse_genotype_file(genotype_filename)
strains = load_genotype_samples(genotype_filename)
exported_traits_data_list = [
export_trait_data(td, strains) for td in traits_data_list]
@@ -183,22 +183,21 @@ def build_heatmap(traits_names, conn: Any):
[t[2] for t in strains_and_values],
traits_filename)
- main_output, permutations_output = run_reaper(
+ main_output, _permutations_output = run_reaper(
genotype_filename, traits_filename, separate_nperm_output=True)
qtlresults = parse_reaper_main_results(main_output)
- permudata = parse_reaper_permutation_results(permutations_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 = {
- res_id: trait for res_id, trait in
+ {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])}
+ [traits[idx]["trait_fullname"] for idx in traits_order]))
return generate_clustered_heatmap(
process_traits_data_for_heatmap(
@@ -207,22 +206,11 @@ def build_heatmap(traits_names, conn: Any):
"single_heatmap_{}".format(random_string(10)),
y_axis=tuple(
ordered_traits_names[traits_ids[order]]
- for order in traits_order),
+ for order in traits_order),
y_label="Traits",
- x_axis=[chromo for chromo in chromosome_names],
+ x_axis=chromosome_names,
x_label="Chromosomes")
- return {
- "slink_data": slink_data,
- "ordering_data": ordering_data,
- "strainlist": strainlist,
- "genotype_filename": genotype_filename,
- "traits_list": traits_list,
- "traits_data_list": traits_data_list,
- "exported_traits_data_list": exported_traits_data_list,
- "traits_filename": traits_filename
- }
-
def compute_traits_order(slink_data, neworder: tuple = tuple()):
"""
Compute the order of the traits for clustering from `slink_data`.
@@ -314,7 +302,7 @@ def get_nearest_marker(traits_list, genotype):
https://github.com/genenetwork/genenetwork1/blob/master/web/webqtl/heatmap/Heatmap.py#L419-L438
"""
if not genotype["Mbmap"]:
- return [None] * len(trait_list)
+ return [None] * len(traits_list)
marker_finder = nearest_marker_finder(genotype)
return [marker_finder(trait) for trait in traits_list]
@@ -340,10 +328,10 @@ def process_traits_data_for_heatmap(data, trait_names, chromosome_names):
return hdata
def generate_clustered_heatmap(
- data, clustering_data, image_filename_prefix, x_axis = None,
- x_label: str = "", y_axis = None, y_label: str = "",
+ data, clustering_data, image_filename_prefix, x_axis=None,
+ x_label: str = "", y_axis=None, y_label: str = "",
output_dir: str = TMPDIR,
- colorscale = (
+ colorscale=(
(0.0, '#5D5D5D'), (0.4999999999999999, '#ABABAB'),
(0.5, '#F5DE11'), (1.0, '#FF0D00'))):
"""
@@ -357,15 +345,15 @@ def generate_clustered_heatmap(
shared_yaxes="rows",
horizontal_spacing=0.001,
subplot_titles=["distance"] + x_axis,
- figure = ff.create_dendrogram(
+ figure=ff.create_dendrogram(
np.array(clustering_data), orientation="right", labels=y_axis))
hms = [go.Heatmap(
name=chromo,
- y = y_axis,
- z = data_array,
+ y=y_axis,
+ z=data_array,
showscale=False) for chromo, data_array in zip(x_axis, data)]
- for i, hm in enumerate(hms):
- fig.add_trace(hm, row=1, col=(i + 2))
+ for i, heatmap in enumerate(hms):
+ fig.add_trace(heatmap, row=1, col=(i + 2))
fig.update_layout(
{
@@ -380,8 +368,8 @@ def generate_clustered_heatmap(
x_axes_layouts = {
"xaxis{}".format(i+1 if i > 0 else ""): {
"mirror": False,
- "showticklabels": True if i==0 else False,
- "ticks": "outside" if i==0 else ""
+ "showticklabels": True if i == 0 else False,
+ "ticks": "outside" if i == 0 else ""
}
for i in range(num_cols)}