diff --git a/jass/controllers/default_controller.py b/jass/controllers/default_controller.py
index af5997fbb22d9264ad3b15368b4fd3386ee36bc8..6bbdb3d77484ac10d82353b2ebc2c87c4e16212d 100644
--- a/jass/controllers/default_controller.py
+++ b/jass/controllers/default_controller.py
@@ -1,13 +1,17 @@
-from typing import List, Dict
-import os
+# -*- coding: utf-8 -*-
-from six import iteritems
-from flask import send_file, abort
-import connexion
+"""
+default_controller ensures the connection between the web interface and the Python JASS-analysis module
+"""
-from jass.models.phenotype import Phenotype, get_available_phenotypes
-from jass.models.project import Project, create_project
from jass.config import config
+from jass.models.project import Project, create_project
+from jass.models.phenotype import Phenotype, get_available_phenotypes
+import connexion
+from flask import send_file, abort
+from six import iteritems
+import os
+from typing import List, Dict
PHENOTYPES = get_available_phenotypes(
os.path.join(config["DATA_DIR"], "initTable.hdf5")
@@ -57,6 +61,10 @@ def projects_project_id_genome_get(projectID, threshold=None):
def projects_project_id_global_manhattan_plot_get(projectID):
+ """
+ projects_project_id_global_manhattan_plot_get
+ Gets the global Manhattan plot stored in the Project folder to display it on the Web interface
+ """
try:
return send_file(
Project(id=projectID).get_global_manhattan_plot_path(), mimetype="image/png"
@@ -76,6 +84,10 @@ def projects_project_id_global_manhattan_plot_get(projectID):
def projects_project_id_quadrant_plot_get(projectID):
+ """
+ projects_project_id_quadrant_plot_get
+ Gets the quadrant plot stored in the Project folder to display it on the Web interface
+ """
try:
return send_file(
Project(id=projectID).get_quadrant_plot_path(), mimetype="image/png"
@@ -95,9 +107,16 @@ def projects_project_id_quadrant_plot_get(projectID):
def projects_project_id_genome_full_get(projectID):
+ """
+ projects_project_id_genome_full_get
+ Downloads the file genome_full.csv stored in the Project folder
+ """
try:
return send_file(
- Project(id=projectID).get_csv_path(), mimetype="text/csv"
+ Project(id=projectID).get_csv_path(),
+ mimetype="text/csv",
+ as_attachment=True,
+ attachment_filename="genome_full.csv"
)
except FileNotFoundError:
status = Project(id=projectID).status
@@ -114,10 +133,18 @@ def projects_project_id_genome_full_get(projectID):
def projects_project_id_local_manhattan_data_get(projectID, chromosome, region):
+ """
+ projects_project_id_local_manhattan_data_get
+ Return the SumStatTab dataframe of the Project for a given chromosome and region for the Manhattan plot
+ """
return Project(id=projectID).get_project_local_manhattan_data(chromosome, region)
def projects_project_id_local_heatmap_data_get(projectID, chromosome, region):
+ """
+ projects_project_id_local_heatmap_data_get
+ Return the SumStatTab dataframe of the Project for a given chromosome and region for the Heatmap plot
+ """
return Project(id=projectID).get_project_local_heatmap_data(chromosome, region)
diff --git a/jass/models/plots.py b/jass/models/plots.py
index 4e46050cb37ca981af7bb5586446f09edb664bc4..719f606351c87acf37585825bb30106e4a8aab03 100755
--- a/jass/models/plots.py
+++ b/jass/models/plots.py
@@ -1,33 +1,43 @@
# -*- coding: utf-8 -*-
"""
-Created on Tue Mar 28 09:57:33 2017
+This software allows to plot and store graphs which can be displayed on the web interface.
-@author: vguillem
-@author: hmenager
-@author: hjulienn
-@author: clasry
+@author: vguillem, hmenager, hjulienn, clasry
"""
-from pandas import DataFrame, read_hdf
-# create (or open) an hdf5 file and opens in append mode
+import logging
import numpy as np
-import matplotlib
+# Keep the following order: 1) importing matplotlib, (2) configuring it to use AGG, (3) importing matplotlib submodules
+import matplotlib
matplotlib.use("AGG")
import matplotlib.pyplot as plt
from matplotlib import colors
import matplotlib.patches as mpatches
+import os
+from pandas import DataFrame, read_hdf
+
+
def replaceZeroes(df):
- ids = np.where((df !=0) & np.isfinite(df))
+ """
+ replaceZeroes
+ replace null values of df with the smallest non-zero value
+ """
+ ids = np.where((df != 0) & np.isfinite(df))
min_nonzero = np.min(df.values[ids])
df.values[df.values == 0] = min_nonzero
return df
def create_global_plot(work_file_path: str, global_plot_path: str):
+ """
+ create_global_plot
+ generate genome-wide manhattan plot for a given set of phenotypes
+ """
df = read_hdf(work_file_path, "SumStatTab")
- df[['JASS_PVAL', 'UNIVARIATE_MIN_PVAL']] = replaceZeroes(df[['JASS_PVAL', 'UNIVARIATE_MIN_PVAL']])
+ df[['JASS_PVAL', 'UNIVARIATE_MIN_PVAL']] = replaceZeroes(
+ df[['JASS_PVAL', 'UNIVARIATE_MIN_PVAL']])
df["-log10(Joint p-value)"] = -np.log10(df.JASS_PVAL)
df["ind"] = range(len(df))
@@ -63,8 +73,9 @@ def create_global_plot(work_file_path: str, global_plot_path: str):
"#bcbd22",
"#17becf",
"#1f77b4",
- "#ff7f0e",
+ "#ff7f0e"
]
+
for num, (name, group) in enumerate(df_grouped):
group = DataFrame(group)
group["absolute_position"] = group["position"] + m
@@ -99,21 +110,33 @@ def create_global_plot(work_file_path: str, global_plot_path: str):
fig.savefig(global_plot_path, dpi=300)
fig.clf()
+ # Update Jass_progress
+ progress_path = os.path.join(os.path.dirname(
+ work_file_path), "JASS_progress.txt")
+ JASS_progress = 99
+ file_progress = open(progress_path, "w")
+ file_progress.write(str(JASS_progress))
+ file_progress.close()
+ print("------ progress -----")
def create_quadrant_plot(work_file_path: str,
- quadrant_plot_path: str, significance_treshold=5*10**-8):
+ quadrant_plot_path: str, significance_treshold=5*10**-8):
"""
- Create a "quadrant" plot that represent the joint test pvalue versus the univariate test pvalue for the most significant SNPs by genomic region. The plot use a logarithmic scale.
+ create_quadrant_plot
+ Create a "quadrant" plot that represent the joint test pvalue versus the univariate \
+ test pvalue for the most significant SNPs by genomic region. The plot use a logarithmic scale.
:param work_file_path: path to the worktable
:type work_file_path: str
+
:param quadrant_plot_path: path to the file where to store the plot results
:type quadrant_plot_path: str
"""
df = read_hdf(work_file_path, "Regions")
- df[['JASS_PVAL', 'UNIVARIATE_MIN_PVAL']] = replaceZeroes(df[['JASS_PVAL', 'UNIVARIATE_MIN_PVAL']])
+ df[['JASS_PVAL', 'UNIVARIATE_MIN_PVAL']] = replaceZeroes(
+ df[['JASS_PVAL', 'UNIVARIATE_MIN_PVAL']])
#df["-log10(Joint p-value)"] = -np.log10(df.JASS_PVAL)
@@ -126,26 +149,27 @@ def create_quadrant_plot(work_file_path: str,
pv_t["color"] = "grey"
# blue: significant pvalues for and univariate tests
- cond = df.signif_status=="Both"
+ cond = df.signif_status == "Both"
pv_t.loc[cond.values, "color"] = "#3ba3ec"
b = cond.sum()
# red: significant pvalues for test only
- cond = df.signif_status=="Joint"
+ cond = df.signif_status == "Joint"
pv_t.loc[cond.values, "color"] = "#f77189"
r = cond.sum()
# green: significant pvalues for univariate test only
- cond = df.signif_status=="Univariate"
+ cond = df.signif_status == "Univariate"
pv_t.loc[cond.values, "color"] = "#50b131"
c = cond.sum()
# grey: non significant pvalues
- cond = df.signif_status=="None"
+ cond = df.signif_status == "None"
a = cond.sum()
fig, ax = plt.subplots(figsize=(10, 5))
plt.subplot(121)
- plt.scatter(pv_t.UNIVARIATE_MIN_PVAL, pv_t.JASS_PVAL, c=pv_t.color.tolist(), alpha=0.6, s=10)
+ plt.scatter(pv_t.UNIVARIATE_MIN_PVAL, pv_t.JASS_PVAL,
+ c=pv_t.color.tolist(), alpha=0.6, s=10)
plt.axis([0, pv_t.UNIVARIATE_MIN_PVAL.max(), 0, pv_t.JASS_PVAL.max()])
# axes abcisse et ordonnée à 8
@@ -168,13 +192,16 @@ def create_quadrant_plot(work_file_path: str,
red_patch = mpatches.Patch(
color='#f77189', label='{} Significant pvalues for joint test only'.format(r))
# grey: non significant pvalues
- grey_patch = mpatches.Patch(color='grey', label='{} Non significant pvalues'.format(a))
+ grey_patch = mpatches.Patch(
+ color='grey', label='{} Non significant pvalues'.format(a))
lgd = plt.legend(handles=[red_patch, blue_patch, green_patch, grey_patch],
- bbox_to_anchor=(0.95, -0.25), loc='lower center', ncol=2, mode="expand", borderaxespad=0.)
+ bbox_to_anchor=(0.95, -0.25), loc='lower center',
+ ncol=2, mode="expand", borderaxespad=0.)
plt.subplot(122)
- plt.scatter(pv_t.UNIVARIATE_MIN_PVAL, pv_t.JASS_PVAL, c=pv_t.color.tolist(), alpha=0.6, s=10)
+ plt.scatter(pv_t.UNIVARIATE_MIN_PVAL, pv_t.JASS_PVAL,
+ c=pv_t.color.tolist(), alpha=0.6, s=10)
# axes abcisse et ordonnee à 8
plt.axvline(treshold, color="grey", linestyle="--")
plt.axhline(treshold, color="grey", linestyle="--")
@@ -185,24 +212,35 @@ def create_quadrant_plot(work_file_path: str,
plt.axis([0, alim, 0, alim])
# légendes abcisse et ordonnee
plt.xlabel('-log10(P) for univariate tests', fontsize=12)
- #plt.show()
- plt.savefig(quadrant_plot_path, dpi=600, bbox_extra_artists=(lgd,), bbox_inches='tight')
+ # plt.show()
+ plt.savefig(quadrant_plot_path, dpi=600,
+ bbox_extra_artists=(lgd,), bbox_inches='tight')
plt.clf()
nb_omnibus = r
nb_total = r + b + c
+ # Update Jass_progress
+ progress_path = os.path.join(os.path.dirname(
+ work_file_path), "JASS_progress.txt")
+ JASS_progress = 100
+ file_progress = open(progress_path, "w")
+ file_progress.write(str(JASS_progress))
+ file_progress.close()
+ print("------ progress -----")
+
return (nb_omnibus, nb_total)
def create_qq_plot(work_file_path: str, qq_plot_path: str):
df = read_hdf(work_file_path, "SumStatTab")
- df[['JASS_PVAL', 'UNIVARIATE_MIN_PVAL']] = replaceZeroes(df[['JASS_PVAL', 'UNIVARIATE_MIN_PVAL']])
+ df[['JASS_PVAL', 'UNIVARIATE_MIN_PVAL']] = replaceZeroes(
+ df[['JASS_PVAL', 'UNIVARIATE_MIN_PVAL']])
pvalue = -np.log10(df.JASS_PVAL)
# Cast values between 0 and 1, 0 and 1 excluded
x = -np.log10(np.arange(1, pvalue.shape[0] + 1) / (pvalue.shape[0] + 2))
- y = np.sort(pvalue[:, 0])
+ y = pvalue.sort_values()
plt.scatter(x[::-1], y, s=5)
lambda_value = np.median(y) / np.median(x)
x_1 = np.linspace(0, 6)
diff --git a/jass/models/project.py b/jass/models/project.py
index 775c40e457df2c5ff3344cb53b6d638526ade1a3..7fa11a5c2e026be3cecdacb06c5ae42ad5332607 100644
--- a/jass/models/project.py
+++ b/jass/models/project.py
@@ -1,5 +1,7 @@
-# coding: utf-8
-
+# -*- coding: utf-8 -*-
+"""
+compute joint statistics and generate plots for a given set of phenotypes
+"""
from __future__ import absolute_import
from typing import List, Dict
import os, sys
@@ -8,17 +10,17 @@ import traceback
from celery import Celery
-from .base_model_ import Model
-from ..util import deserialize_model
-from .phenotype import Phenotype
-from .worktable import (
+from jass.models.base_model_ import Model
+from jass.util import deserialize_model
+from jass.models.phenotype import Phenotype
+from jass.models.worktable import (
create_worktable_file,
get_worktable_summary,
get_worktable_genomedata,
get_worktable_local_manhattan_data,
get_worktable_local_heatmap_data,
)
-from .plots import create_global_plot, create_quadrant_plot
+from jass.models.plots import create_global_plot, create_quadrant_plot
from jass.config import config
app = Celery("tasks", broker="pyamqp://guest@localhost//")
@@ -41,12 +43,13 @@ class Project(Model):
:param id: project ID.
:type id: str
"""
- self.swagger_types = {"id": str, "status": str, "phenotypes": List[Phenotype]}
+ self.swagger_types = {"id": str, "status": str, "phenotypes": List[Phenotype], "progress": str}
self.attribute_map = {
"id": "id",
"status": "status",
"phenotypes": "phenotypes",
+ "progress": "progress"
}
self._id = id
@@ -109,14 +112,34 @@ class Project(Model):
self._phenotypes = phenotypes
def get_folder_path(self):
+ """
+ get_folder_path
+ Gets the path of the folder where the project data are stored
+ """
return os.path.join(config["DATA_DIR"], "project_{}".format(self.id))
def get_worktable_path(self):
+ """
+ get_worktable_path
+ Gets the path of the file workTable.hdf5
+ """
return os.path.join(self.get_folder_path(), "workTable.hdf5")
def get_csv_path(self):
+ """
+ get_csv_path
+ Gets the path of the file genome_full.csv
+ """
return os.path.join(self.get_folder_path(), "workTable.csv")
+ def get_progress_path(self):
+ """
+ get_progress_path
+ Gets the path of the file containing the current progress percentage of \
+ the analysis performed within the project
+ """
+ return os.path.join(self.get_folder_path(), "JASS_progress.txt")
+
def get_project_summary_statistics(self):
return get_worktable_summary(self.get_worktable_path())
@@ -147,6 +170,10 @@ class Project(Model):
@property
def status(self):
+ """
+ status
+ Gets the status of the project
+ """
if not os.path.exists(self.get_folder_path()):
return Project.DOES_NOT_EXIST
else:
@@ -166,6 +193,19 @@ class Project(Model):
"quadrant_plot_status": quadrant_plot_status,
}
+ @property
+ def progress(self):
+ """
+ progress
+ Gets the percentage of completion of the phenotype analysis
+ """
+ JASS_progress = 0
+ progress_path = self.get_progress_path()
+ if os.path.exists(progress_path):
+ file_progress = open(progress_path, "r")
+ JASS_progress = file_progress.read()
+ file_progress.close()
+ return JASS_progress
def get_file_building_tb_path(file_path):
return file_path + ".log"
diff --git a/jass/models/worktable.py b/jass/models/worktable.py
index 15fab78b71f7d440e593b418bbf59f0ec1362959..e83ce2300edf6c5ac3eef162d4b960bc91f3ed95 100755
--- a/jass/models/worktable.py
+++ b/jass/models/worktable.py
@@ -2,18 +2,24 @@
"""
This contains all functions for accessing the "worktable" hdf5 file.
All functions either create or read a worktable at a specific path location.
+
@author: vguillem, hmenager, hjulienne
"""
+import math
+from jass.models.stats import (
+ make_stat_computer_nopattern,
+ make_stat_computer_pattern,
+ make_stat_computer_nan_dumb,
+)
+from jass.config import config
import logging
import os
import importlib
from typing import List
-# from dask.dataframe import read_hdf as dask_read_hdf
from pandas import HDFStore, DataFrame, concat, read_hdf, read_csv, Series, Index
-# create (or open) an hdf5 file and opens in append mode
import numpy as np
import scipy.stats as spst
import tables
@@ -21,81 +27,123 @@ import warnings
warnings.filterwarnings("ignore", category=tables.NaturalNameWarning)
-from ..config import config
-from .stats import (
- make_stat_computer_nopattern,
- make_stat_computer_pattern,
- make_stat_computer_nan_dumb,
-)
+
+def signif(x, digit):
+ """
+ signif
+ Round a number x to represent it with <digit> digits
+
+ :param x: the number to round
+ :type x: float
+
+ :param digit: the number of digits
+ :type digit: int
+
+ :return: the rounded number
+ :rtype: float
+
+ example:
+ >>> signif(1.2345678, 1)
+ 1.0
+
+ >>> signif(1.2345678, 3)
+ 1.23
+
+ >>> signif(1.2345678, 5)
+ 1.2346
+ """
+ if x == 0:
+ return 0
+
+ return round(x, digit - int(math.floor(math.log10(abs(x)))) - 1)
+
def choose_stat_function(smart_na_computation, optim_na, function_name, stat_function, sub_cov, **kwargs):
if smart_na_computation:
- #If stat is sumz use normal computer even with na
+ # If stat is sumz use normal computer even with na
if function_name == "omnibus_stat":
if optim_na:
- stat_compute = make_stat_computer_pattern(sub_cov, stat_function)
+ stat_compute = make_stat_computer_pattern(
+ sub_cov, stat_function)
else:
- stat_compute = make_stat_computer_nan_dumb(sub_cov, stat_function)
+ stat_compute = make_stat_computer_nan_dumb(
+ sub_cov, stat_function)
else:
if function_name == "meta_analysis":
- stat_compute = make_stat_computer_nopattern(sub_cov, stat_function, **kwargs)
- elif function_name=="sumz_stat":
+ stat_compute = make_stat_computer_nopattern(
+ sub_cov, stat_function, **kwargs)
+ elif function_name == "sumz_stat":
loading_file = kwargs.get('loadings', None)
if loading_file is None:
- #Default loadings would be one for every phenotypes
- stat_compute = make_stat_computer_nopattern(sub_cov, stat_function)
+ # Default loadings would be one for every phenotypes
+ stat_compute = make_stat_computer_nopattern(
+ sub_cov, stat_function)
else:
loadings = read_csv(loading_file, index_col=0)
- loadings = loadings.iloc[:,0]
- stat_compute = make_stat_computer_nopattern(sub_cov, stat_function,loadings=loadings )
+ loadings = loadings.iloc[:, 0]
+ stat_compute = make_stat_computer_nopattern(
+ sub_cov, stat_function, loadings=loadings)
else:
- stat_compute = make_stat_computer_nopattern(sub_cov, stat_function)
+ stat_compute = make_stat_computer_nopattern(
+ sub_cov, stat_function)
else:
stat_compute = make_stat_computer_nopattern(sub_cov, stat_function)
-
return stat_compute
-def add_signif_status_column(region_sub_tab, significance_treshold=5*10**-8):
+def add_signif_status_column(region_sub_tab, significance_treshold=5*10**-8):
region_sub_tab["signif_status"] = ""
+
# blue: significant pvalues for omnibus and univariate tests
- cond = np.where((region_sub_tab.JASS_PVAL < significance_treshold) & (region_sub_tab.UNIVARIATE_MIN_PVAL < significance_treshold))[0]
+ cond = np.where((region_sub_tab.JASS_PVAL < significance_treshold) & (
+ region_sub_tab.UNIVARIATE_MIN_PVAL < significance_treshold))[0]
region_sub_tab.loc[region_sub_tab.index[cond], "signif_status"] = "Both"
# red: significant pvalues for omnibus test only
- cond = np.where((region_sub_tab.JASS_PVAL < significance_treshold ) & (region_sub_tab.UNIVARIATE_MIN_PVAL > significance_treshold))[0]
+ cond = np.where((region_sub_tab.JASS_PVAL < significance_treshold) & (
+ region_sub_tab.UNIVARIATE_MIN_PVAL > significance_treshold))[0]
region_sub_tab.loc[region_sub_tab.index[cond], "signif_status"] = "Joint"
# green: significant pvalues for univariate test only
- cond = np.where((region_sub_tab.JASS_PVAL > significance_treshold) & (region_sub_tab.UNIVARIATE_MIN_PVAL < significance_treshold))[0]
- region_sub_tab.loc[region_sub_tab.index[cond], "signif_status"] = "Univariate"
+ cond = np.where((region_sub_tab.JASS_PVAL > significance_treshold) & (
+ region_sub_tab.UNIVARIATE_MIN_PVAL < significance_treshold))[0]
+ region_sub_tab.loc[region_sub_tab.index[cond],
+ "signif_status"] = "Univariate"
# grey: non significant pvalues
- cond = np.where((region_sub_tab.JASS_PVAL > significance_treshold) & (region_sub_tab.UNIVARIATE_MIN_PVAL > significance_treshold))[0]
+ cond = np.where((region_sub_tab.JASS_PVAL > significance_treshold) & (
+ region_sub_tab.UNIVARIATE_MIN_PVAL > significance_treshold))[0]
region_sub_tab.loc[region_sub_tab.index[cond], "signif_status"] = "None"
return region_sub_tab
+
def get_region_summary(sum_stat_tab, phenotype_ids, significance_treshold=5*10**-8):
# Select the most significant SNP for the joint test for each region
- region_sub_tab = sum_stat_tab.sort_values("JASS_PVAL").groupby("Region").first()#.reset_index()
+ region_sub_tab = sum_stat_tab.sort_values(
+ "JASS_PVAL").groupby("Region").first() # .reset_index()
# add minimum univariate p-value
univar = sum_stat_tab.groupby("Region").min().UNIVARIATE_MIN_PVAL
region_sub_tab.loc[univar.index, "UNIVARIATE_MIN_PVAL"] = univar.values
+
# Tag SNPs depending on which test is significant
region_sub_tab.reset_index(inplace=True)
- region_sub_tab = add_signif_status_column(region_sub_tab, significance_treshold)
+ region_sub_tab = add_signif_status_column(
+ region_sub_tab, significance_treshold)
# reorder columns
- region_sub_tab = region_sub_tab[['Region', "MiddlePosition", "snp_ids","CHR", "position", "Ref_allele", "Alt_allele", "JASS_PVAL", "UNIVARIATE_MIN_PVAL", "signif_status"] + phenotype_ids]
+ region_sub_tab = region_sub_tab[['Region', "MiddlePosition", "snp_ids", "CHR", "position",
+ "Ref_allele", "Alt_allele", "JASS_PVAL", "UNIVARIATE_MIN_PVAL",
+ "signif_status"] + phenotype_ids]
return region_sub_tab
-def post_computation_filtering(worktable_chunk, significant_treshold = 5*10**-8):
+
+def post_computation_filtering(worktable_chunk, significant_treshold=5*10**-8):
"""
Remove SNPs that seems aberrant: SNPs with a very low p-value that are isolated
in their region
@@ -113,23 +161,27 @@ def post_computation_filtering(worktable_chunk, significant_treshold = 5*10**-8)
# select region with only one SNP that is significant which is
#Â suspect
- reg = res.loc[res==1].index
+ reg = res.loc[res == 1].index
for reg_aberant in reg:
- aberant_SNP = worktable_chunk.loc[worktable_chunk.Region==reg_aberant].sort_values("JASS_PVAL").index[0]
+ aberant_SNP = worktable_chunk.loc[worktable_chunk.Region == reg_aberant].sort_values(
+ "JASS_PVAL").index[0]
worktable_chunk.drop(aberant_SNP, inplace=True)
return worktable_chunk
-def compute_pleiotropy_index(W,significance_treshold):
- N_significatif = (2.0 * spst.norm.sf(W.fillna(0, inplace=False).abs())<significance_treshold).sum(1)
+def compute_pleiotropy_index(W, significance_treshold):
+
+ N_significatif = (2.0 * spst.norm.sf(W.fillna(0,
+ inplace=False).abs()) < significance_treshold).sum(1)
N_pheno = (~W.isnull()).sum(1)
- #pleiotropy index is not meaningful for too few phenotype
+ # pleiotropy index is not meaningful for too few phenotype
S = N_significatif/N_pheno
S.loc[N_pheno < 4] = np.nan
return S
+
def create_worktable_file(
phenotype_ids: List[str],
init_file_path: str,
@@ -139,7 +191,7 @@ def create_worktable_file(
optim_na: bool = True,
csv_file: str = None,
chunk_size: int = 50,
- significance_treshold = 5*10**-8,
+ significance_treshold=5*10**-8,
post_filtering=True,
**kwargs
):
@@ -171,18 +223,30 @@ def create_worktable_file(
:type significant_treshold: float
:
"""
+ # Initialization of Jass_progress
+ progress_path = os.path.join(os.path.dirname(
+ project_hdf_path), "JASS_progress.txt")
+ JASS_progress = 0
+ file_progress = open(progress_path, "w")
+ file_progress.write(str(JASS_progress))
+ file_progress.close()
+ print("------ progress -----")
+
# read data by chunks to optimize memory usage
# select only rows (SNPs) where there are no missing data
how_dropna = "any" if remove_nan else "all"
if os.path.exists(project_hdf_path):
os.remove(project_hdf_path)
hdf_work = HDFStore(project_hdf_path)
+ if csv_file is not None:
+ if os.path.exists(csv_file):
+ os.remove(csv_file)
# subset of phenotypes that have been selected
phenolist = read_hdf(init_file_path, "PhenoList")
phenolist = phenolist.loc[phenotype_ids]
hdf_work.put(
- "PhenoList", phenolist
+ "PhenoList", phenolist
)
# subset of covariance matrix for the selected phenotypes
@@ -194,15 +258,16 @@ def create_worktable_file(
regions = read_hdf(init_file_path, "Regions").index.tolist()
sum_stat_tab_min_itemsizes = {"snp_ids": 50, "Region": 10, "CHR": 5}
- #['Region', "MiddlePosition", "snp_ids","CHR", "position", "Ref_allele", "Alt_allele", "JASS_PVAL", "UNIVARIATE_MIN_PVAL", "signif_status"]
- region_sub_table_min_itemsizes = {"Region": 10, "index": 10, "CHR": 5, "snp_ids": 50, "signif_status":20}
+ region_sub_table_min_itemsizes = {
+ "Region": 10, "index": 10, "CHR": 5, "snp_ids": 50, "signif_status": 20}
smart_na_computation = not (remove_nan)
module_name, function_name = stat.split(":")
stat_module = importlib.import_module(module_name)
stat_fn = getattr(stat_module, function_name)
- stat_compute = choose_stat_function(smart_na_computation, optim_na, function_name, stat_fn, sub_cov, samp_size=phenolist['Effective_sample_size'], **kwargs)
+ stat_compute = choose_stat_function(smart_na_computation, optim_na, function_name,
+ stat_fn, sub_cov, samp_size=phenolist['Effective_sample_size'], **kwargs)
Nchunk = len(regions) // chunk_size + 1
@@ -210,11 +275,17 @@ def create_worktable_file(
Nsnp_jassed = 0
for chunk in range(Nchunk):
+
+ # the chunk index starts at zero and we take into account the 2 plot stages
+ JASS_progress = round((chunk + 1) * 100 / (Nchunk + 2))
+
binf = chunk * chunk_size
bsup = (chunk+1) * chunk_size
+
sum_stat_tab = read_hdf(init_file_path, 'SumStatTab', columns=[
- 'Region', 'CHR', 'position', 'snp_ids', 'Ref_allele', 'Alt_allele', 'MiddlePosition'] + phenotype_ids,
- where='Region >= {0} and Region < {1}'.format(binf, bsup))
+ 'Region', 'CHR', 'position', 'snp_ids', 'Ref_allele', 'Alt_allele', 'MiddlePosition'] + phenotype_ids,
+ where='Region >= {0} and Region < {1}'.format(binf, bsup))
+
print("Regions {0} to {1}\r".format(binf, bsup))
# Remake row index unique: IMPORTANT for assignation with .loc at line 98
@@ -223,18 +294,21 @@ def create_worktable_file(
)
sum_stat_tab.reset_index(drop=True, inplace=True)
- if sum_stat_tab.shape[0]==0:
- print("No data available for region {0} to region {1}".format(binf, bsup))
- continue # skip region if no data are available
+ if sum_stat_tab.shape[0] == 0:
+ print(
+ "No data available for region {0} to region {1}".format(binf, bsup))
+ continue # skip region if no data are available
N_pheno = len(phenotype_ids)
Nsnp_total = Nsnp_total + sum_stat_tab.shape[0]
if remove_nan or stat.split(":")[-1] != "omnibus_stat":
- sum_stat_tab['JASS_PVAL'] = stat_compute(sum_stat_tab[phenotype_ids])
+ sum_stat_tab['JASS_PVAL'] = stat_compute(
+ sum_stat_tab[phenotype_ids])
else:
# Sort SumStatTab by missing patterns
- patterns_missing, frequent_pattern = compute_frequent_missing_pattern(sum_stat_tab[phenotype_ids])
+ patterns_missing, frequent_pattern = compute_frequent_missing_pattern(
+ sum_stat_tab[phenotype_ids])
# Apply the statistic computation by missing patterns
for pattern in frequent_pattern:
@@ -256,18 +330,24 @@ def create_worktable_file(
sum_stat_tab.sort_values(by=["Region", "CHR"], inplace=True)
sum_stat_tab["UNIVARIATE_MIN_PVAL"] = DataFrame(
- 2.0 * spst.norm.sf(sum_stat_tab[phenotype_ids].fillna(0, inplace=False).abs()),
- index=sum_stat_tab.index,
- ).min(axis=1)
+ 2.0 *
+ spst.norm.sf(sum_stat_tab[phenotype_ids].fillna(
+ 0, inplace=False).abs()),
+ index=sum_stat_tab.index,
+ ).min(axis=1)
- sum_stat_tab["UNIVARIATE_MIN_QVAL"] = sum_stat_tab["UNIVARIATE_MIN_PVAL"]*(1-np.isnan(sum_stat_tab[phenotype_ids]).astype(int)).sum(1)
- sum_stat_tab.loc[sum_stat_tab.UNIVARIATE_MIN_QVAL>1 , "UNIVARIATE_MIN_QVAL"] = 1
+ sum_stat_tab["UNIVARIATE_MIN_QVAL"] = sum_stat_tab["UNIVARIATE_MIN_PVAL"] * \
+ (1-np.isnan(sum_stat_tab[phenotype_ids]).astype(int)).sum(1)
+ sum_stat_tab.loc[sum_stat_tab.UNIVARIATE_MIN_QVAL >
+ 1, "UNIVARIATE_MIN_QVAL"] = 1
- #Computing pleiotropy
- sum_stat_tab["PLEIOTROPY_INDEX"] = compute_pleiotropy_index(sum_stat_tab[phenotype_ids], significance_treshold)
+ # Computing pleiotropy
+ sum_stat_tab["PLEIOTROPY_INDEX"] = compute_pleiotropy_index(
+ sum_stat_tab[phenotype_ids], significance_treshold)
sum_stat_tab = sum_stat_tab[
- ["Region", "CHR", "snp_ids", "position", 'Ref_allele', 'Alt_allele', "MiddlePosition", "JASS_PVAL", "UNIVARIATE_MIN_PVAL", "UNIVARIATE_MIN_QVAL","PLEIOTROPY_INDEX" ]
+ ["Region", "CHR", "snp_ids", "position", 'Ref_allele', 'Alt_allele', "MiddlePosition",
+ "JASS_PVAL", "UNIVARIATE_MIN_PVAL", "UNIVARIATE_MIN_QVAL", "PLEIOTROPY_INDEX"]
+ phenotype_ids]
if post_filtering:
@@ -279,15 +359,21 @@ def create_worktable_file(
if csv_file is not None:
with open(csv_file, 'a') as f:
- sum_stat_tab.to_csv(f, header=f.tell()==0)
+ sum_stat_tab.to_csv(f, header=f.tell() == 0)
- region_sub_table = get_region_summary(sum_stat_tab, phenotype_ids, significance_treshold=significance_treshold)
+ region_sub_table = get_region_summary(
+ sum_stat_tab, phenotype_ids, significance_treshold=significance_treshold)
hdf_work.append(
"Regions",
- region_sub_table
- ,min_itemsize=region_sub_table_min_itemsizes
+ region_sub_table, min_itemsize=region_sub_table_min_itemsizes
)
+
+ file_progress = open(progress_path, "w")
+ file_progress.write(str(JASS_progress))
+ file_progress.close()
+ print("------ progress -----")
+
hdf_work.close()
print("{1} SNPs treated on {0} SNPs".format(Nsnp_jassed, Nsnp_total))
@@ -300,12 +386,16 @@ def create_worktable_file(
np.array(
[
[
- sum((jost_min < significance_treshold) & (pval_min < significance_treshold)),
- sum((jost_min < significance_treshold) & (pval_min > significance_treshold)),
+ sum((jost_min < significance_treshold) &
+ (pval_min < significance_treshold)),
+ sum((jost_min < significance_treshold) &
+ (pval_min > significance_treshold)),
],
[
- sum((jost_min > significance_treshold) & (pval_min < significance_treshold)),
- sum((jost_min > significance_treshold) & (pval_min > significance_treshold)),
+ sum((jost_min > significance_treshold) &
+ (pval_min < significance_treshold)),
+ sum((jost_min > significance_treshold) &
+ (pval_min > significance_treshold)),
],
]
)
@@ -327,13 +417,15 @@ def compute_frequent_missing_pattern(sum_stat_tab):
cover 99 percent of the snps
"""
N_pheno = sum_stat_tab.shape[1]
- patterns_missing = Series(np.dot((1- sum_stat_tab.isnull()), 10**np.arange((N_pheno-1), -1, -1)))
+ patterns_missing = Series(
+ np.dot((1 - sum_stat_tab.isnull()), 10**np.arange((N_pheno-1), -1, -1)))
pattern_frequency = patterns_missing.value_counts() / len(patterns_missing)
- n_pattern = pattern_frequency.shape[0]#(pattern_frequency.cumsum() < 0.99).sum() + 1
+ n_pattern = pattern_frequency.shape[0]
+ # (pattern_frequency.cumsum() < 0.99).sum() + 1
#n_pattern = min(5000, n_pattern)
print("Number of pattern {}".format(n_pattern))
#print("Covering {}/1 of snps ".format(pattern_frequency.iloc[:n_pattern].sum()))
- frequent_pattern = pattern_frequency.index.tolist() #[:n_pattern]
+ frequent_pattern = pattern_frequency.index.tolist() # [:n_pattern]
return patterns_missing, frequent_pattern
@@ -350,8 +442,12 @@ def stringize_dataframe_region_chr(dataframe: DataFrame):
:return: The dataframe with converted Region and CHR columns
:rtype: pandas.DataFrame
"""
- dataframe["Region"] = dataframe["Region"].apply(lambda x: "Region" + str(x))
+ dataframe["Region"] = dataframe["Region"].apply(
+ lambda x: "Region" + str(x))
dataframe["CHR"] = dataframe["CHR"].apply(lambda x: "chr" + str(x))
+ dataframe["JASS_PVAL"] = dataframe["JASS_PVAL"].apply(
+ lambda x: str(signif(x, 4)))
+
return dataframe
@@ -383,8 +479,16 @@ def get_worktable_genomedata(project_hdf_path: str):
:return: The dataframe as a CSV formatted text
:rtype: str
"""
- region_subtable = stringize_dataframe_region_chr(read_hdf(project_hdf_path, "Regions"))
- region_subtable.rename(index=str, columns={'JASS_PVAL':'JOSTmin'}, inplace=True)
+ region_subtable = stringize_dataframe_region_chr(
+ read_hdf(project_hdf_path, "Regions"))
+
+ region_subtable.rename(index=str, columns={
+ 'JASS_PVAL': 'JOSTmin'}, inplace=True)
+
+ region_subtable['PVALmin'] = region_subtable['UNIVARIATE_MIN_PVAL']
+ region_subtable['PVALmin'] = region_subtable['PVALmin']. apply(
+ lambda x: str(signif(x, 4)))
+
return region_subtable.to_csv(index=False)
@@ -407,8 +511,9 @@ def get_worktable_local_manhattan_data(
region_int = region[6:]
chromosome_int = chromosome[3:]
dataframe = read_hdf(project_hdf_path, "SumStatTab",
- columns=["Region", "CHR", "position", "snp_ids", "JASS_PVAL"],
- where=['Region='+str(region_int), 'CHR='+str(chromosome_int)])
+ columns=["Region", "CHR", "position",
+ "snp_ids", "JASS_PVAL"],
+ where=['Region='+str(region_int), 'CHR='+str(chromosome_int)])
dataframe = stringize_dataframe_region_chr(dataframe)
dataframe = dataframe.sort_values("position")
return dataframe.to_csv(index=False)
@@ -434,13 +539,14 @@ def get_worktable_local_heatmap_data(
region_int = region[6:]
chromosome_int = chromosome[3:]
dataframe = read_hdf(project_hdf_path, "SumStatTab",
- where=['Region='+str(region_int), 'CHR='+str(chromosome_int)])
+ where=['Region='+str(region_int), 'CHR='+str(chromosome_int)])
dataframe = stringize_dataframe_region_chr(dataframe)
dataframe = dataframe.sort_values("position")
- dataframe.drop( ["Region", "CHR", "position", "JASS_PVAL", "MiddlePosition", "UNIVARIATE_MIN_PVAL", "UNIVARIATE_MIN_QVAL", "PLEIOTROPY_INDEX"],
- axis=1,
- inplace=True,
- )
+ dataframe.drop(["Region", "CHR", "position", "JASS_PVAL", "MiddlePosition", "UNIVARIATE_MIN_PVAL",
+ "UNIVARIATE_MIN_QVAL", "PLEIOTROPY_INDEX"],
+ axis=1,
+ inplace=True,
+ )
dataframe.rename(columns={"snp_ids": "ID"}, inplace=True)
column_order = list(dataframe.ID)
pivoted_dataframe = dataframe.pivot_table(columns="ID")
diff --git a/jass/static/export.html b/jass/static/export.html
index 25a6b90f3f58f5c12ece45243c3bcc0e9e1e1d7a..23f4666b72eb6bf6d26d8426d51d766dd5402908 100644
--- a/jass/static/export.html
+++ b/jass/static/export.html
@@ -124,22 +124,11 @@
var tr = document.createElement("tr");
//var val = data[i]['ID'];
//var linkRef = data[i]['linkRef'];
+
for (var j=0; j<columns.length; j++) {
-
var td = document.createElement("td");
td.innerHTML=data[i][columns[j]];
- if(columns[j] == "JASS_PVAL"){
- var val= -Math.log10(data[i][columns[j]]);
- td.innerHTML= val;
- //idCounter ++;
- tr.append(td);
- }
-
- else {
- td.innerHTML=data[i][columns[j]];
- tr.append(td);
- }
-
+ tr.append(td);
}
//var td = document.createElement("td");
//td.innerHTML='-';
diff --git a/jass/static/index.html b/jass/static/index.html
index cb851f2ed187eae57156675b5f228c87f83d8ea3..376dd7668e2d284ba7027f377ac7b656c3e52f08 100644
--- a/jass/static/index.html
+++ b/jass/static/index.html
@@ -53,7 +53,12 @@
<p>Jass is devellopped by the <a href="https://research.pasteur.fr/en/team/statistical-genetics/" target="_blank">Statistical Genetics</a> group in collaboration with the <a href="https://research.pasteur.fr/en/team/bioinformatics-and-biostatistics-hub/">HUB</a> </p>
</div>
<div id="tabs-3">
- <p>Publication pending !</p>
+ <p><b>JASS: command line and web interface for the joint analysis of GWAS results</b><br />
+ Hanna Julienne, Pierre Lechat, Vincent Guillemot, Carla Lasry, Chunzi Yao, Robinson Araud, Vincent Laville, Bjarni Vilhjalmsson, Hervé Ménager, Hugues Aschard<br />
+ in: NAR Genomics and Bioinformatics, Volume 2, Issue 1, March 2020, lqaa003, <a href="https://doi.org/10.1093/nargab/lqaa003"> <FONT color=#0000FF>https://doi.org/10.1093/nargab/lqaa003</FONT></a></p>
+ <p><b>Multitrait genetic-phenotype associations to connect disease variants and biological mechanisms</b><br />
+ Hanna Julienne, Vincent Laville, Zachary R. McCaw, Zihuai He, Vincent Guillemot, Carla Lasry, Andrey Ziyatdinov, Amaury Vaysse, Pierre Lechat, Hervé Ménager, Wilfried Le Goff, Marie-Pierre Dube, Peter Kraft, Iuliana Ionita-Laza, Bjarni J. Vilhjálmsson, Hugues Aschard<br />
+ preprint in: biorxiv, <a href=https://www.biorxiv.org/content/10.1101/2020.06.26.172999v1.full> <FONT color=#0000FF>https://www.biorxiv.org/content/10.1101/2020.06.26.172999v1.full</FONT></a></p>
</div>
</div>
</body>
diff --git a/jass/static/selectPhenotypes.html b/jass/static/selectPhenotypes.html
index ed804854b747480f7146737064d8825f60af0bc4..243fd59485a5e532e7411ca611a636fdf4a8939a 100644
--- a/jass/static/selectPhenotypes.html
+++ b/jass/static/selectPhenotypes.html
@@ -21,6 +21,10 @@
background-repeat:no-repeat;
}
#image-top {height:80!important}
+
+ div.blockMe { padding: 30px; margin: 30px; border: 10px solid #ccc; background-color: #ffd }
+ #question { background-color: #ffc; padding: 10px; }
+ #question input { width: 4em }
</style>
<script>
@@ -97,70 +101,104 @@
}
$(function(){
+ function avancement() {
+ var ava = document.getElementById("avancement");
+ var prc = document.getElementById("pourcentage");
+ prc.innerHTML = ava.value + "%";
+ }
+
+ function modif(val) {
+ var ava = document.getElementById("avancement");
+ if((ava.value+val)<=ava.max && (ava.value+val)>0) {
+ ava.value += val;
+ }
+ avancement();
+ }
+
+
// Variable to get ids for the checkboxes
var idCounter=1;
$("#btn1").click(function(){
- var selectedString = getSelected().join(',');
- if(selectedString != ''){
- var phe = {};
- phe['phenotypeID'] = selectedString;
- console.log("!!! "+selectedString);
- console.log("!!! "+phe['phenotypeID']);
- $.blockUI({ css: {
- border: 'none',
- padding: '15px',
- backgroundColor: '#000',
- '-webkit-border-radius': '10px',
- '-moz-border-radius': '10px',
- opacity: .5,
- color: '#fff'} });
+ var selectedNumber = getSelected().length;
+ var toApply = 1;
+ if (selectedNumber == 1){
+ // A confirmation window is displayed
+ var r = confirm("ð–ð€ð‘ððˆðð†: ð²ð¨ð® ð¡ðšð¯ðž ð¬ðžð¥ðžðœððžð ð¨ð§ð¥ð² ð¨ð§ðž ð©ð¡ðžð§ð¨ðð²ð©ðž!\nIt isn't the way JASS normally works.\nDo you want to continue?");
+ if (r == true) { // Button OK is selected
+ toApply = 1;
+ } else { // Button CANCEL is selected
+ toApply = 0;
+ }
+ }
+ if (toApply == 1){
+ var selectedString = getSelected().join(',');
+ if(selectedString != ''){
+ var phe = {};
+ phe['phenotypeID'] = selectedString;
+ console.log("!!! "+selectedString);
+ console.log("!!! "+phe['phenotypeID']);
+
+ $.blockUI({ message: $('#question'), css: { width: '275px' }});
+ avancement(); //Initialisation
+
- var status="-1";
- var getProjectStatus = function(){
- $.post( "/api/projects",phe).done(function( dataJson ) {
- let data = JSON.parse(JSON.stringify(dataJson));
- status = data.status.worktable;
- console.log("!! status "+status);
- if(status =="READY"){
- $.unblockUI();
- console.log( data );
- sessionStorage.setItem("id",data.id);
- console.log(data.phenotypes);
- //var monobjet_json = JSON.stringify(data.phenotypes[0]);
- var monobjet_json = JSON.stringify(data.phenotypes);
- sessionStorage.setItem("phenotypes",monobjet_json);
- console.log(data.phenotypes[0]["cohort"]);
- //location.href = 'http://hub17.hosting.pasteur.fr/getVar.html';
- location.href = 'chromo_heatmap_manhattan.html';
- }
- else if(status =="CREATING"){
- console.log("CREATING");
- setTimeout(getProjectStatus, 10000);
- }
- });
+ var status="-1";
+ var JASS_progress = 0;
+ var Old_progress = 0;
+ var getProjectStatus = function(){
+ $.post( "/api/projects",phe).done(function( data ) {
+ status = data.status.worktable;
+ console.log("!! status "+status);
+ JASS_progress = data.progress;
+ console.log(">>>>>>> progress "+JASS_progress);
+ var deltaProgress = JASS_progress - Old_progress;
+ Old_progress = JASS_progress;
+ modif(deltaProgress);
+
+
+ if(status =="READY"){
+ $.unblockUI();
+ console.log( data );
+ sessionStorage.setItem("id",data.id);
+ console.log(data.phenotypes);
+ //var monobjet_json = JSON.stringify(data.phenotypes[0]);
+ var monobjet_json = JSON.stringify(data.phenotypes);
+ sessionStorage.setItem("phenotypes",monobjet_json);
+ console.log(data.phenotypes[0]["cohort"]);
+ //location.href = 'http://hub17.hosting.pasteur.fr/getVar.html';
+ location.href = 'chromo_heatmap_manhattan.html';
+ }
+ else if(status =="CREATING"){
+ console.log("CREATING");
+
+
+ setTimeout(getProjectStatus, 10000);
+ }
+ });
- };
- getProjectStatus();
-
-
- }
- else{
- console.log( "rien");
- $.blockUI({ message: '<h2><img src="img/busy.gif" /> Please choose an array of Phenotypes...</h2>' ,
- css: {
- border: 'none',
- padding: '15px',
- backgroundColor: '#000',
- '-webkit-border-radius': '10px',
- '-moz-border-radius': '10px',
- opacity: .5,
- color: '#fff'}});
- setTimeout($.unblockUI, 2000);
- }
- });
+ };
+ getProjectStatus();
- });
+
+ }
+ else{
+ console.log( "rien");
+ $.blockUI({ message: '<h2><img src="img/busy.gif" /> Please choose an array of Phenotypes...</h2>' ,
+ css: {
+ border: 'none',
+ padding: '15px',
+ backgroundColor: '#000',
+ '-webkit-border-radius': '10px',
+ '-moz-border-radius': '10px',
+ opacity: .5,
+ color: '#fff'}});
+ setTimeout($.unblockUI, 2000);
+ }
+ }
+ });
+
+ });
@@ -176,6 +214,13 @@
<div id='divContainer'>
<table id ="pheTable" style="width: 100%" class="display dataTable"></table>
</div>
+
+<div id="question" style="display:none; cursor: default">
+ <H3>Analysis in progress ...</H3>
+ <progress id="avancement" value="0" max="100"></progress>
+ <span id="pourcentage"></span>
+</div>
+
<button id="btn1">Select Phenotypes</button>
</body>
diff --git a/jass/swagger/swagger.yaml b/jass/swagger/swagger.yaml
index e0a2a9f0e3ccd651a900c86c429d60fc5f2b55ad..ed2d8ff239af35abe4da0466a081b19d09fa8098 100644
--- a/jass/swagger/swagger.yaml
+++ b/jass/swagger/swagger.yaml
@@ -89,6 +89,8 @@ paths:
"global_manhattan": "CREATING"
"quadrant_plot_status": "CREATING"
"worktable": "CREATING"
+ progress":
+ "progress": "0"
Ready:
value:
id: "bca9d414e0f9a67b9e0d2131a47c316c"
@@ -113,6 +115,8 @@ paths:
"global_manhattan": "READY"
"quadrant_plot_status": "READY"
"worktable": "READY"
+ progress":
+ "progress": "100"
x-openapi-router-controller: jass.controllers.default_controller
"/projects/{projectID}":
get:
@@ -415,6 +419,8 @@ components:
type: string
status:
type: string
+ progress:
+ type: string
outcome:
type: array
items:
diff --git a/jass/test/test_plots.py b/jass/test/test_plots.py
new file mode 100644
index 0000000000000000000000000000000000000000..9bdbdf0fb7dd42ff4e24493dd763c2ae0a1ad3f9
--- /dev/null
+++ b/jass/test/test_plots.py
@@ -0,0 +1,38 @@
+# coding: utf-8
+
+from __future__ import absolute_import
+import os, shutil, tempfile
+
+from pandas import read_hdf
+from pandas.testing import assert_frame_equal
+
+from jass.models.plots import create_global_plot
+
+from . import JassTestCase
+
+
+class TestPlots(JassTestCase):
+
+ test_folder = "data_test1"
+
+ def setUp(self):
+ # Create a temporary directory
+ self.test_dir = tempfile.mkdtemp()
+ self.worktable_hdf_path = self.get_file_path_fn("worktable.hdf5")
+ self.global_plot_path = os.path.join(self.test_dir, "global_manhattan.png")
+
+ def tearDown(self):
+ # Remove the directory after the test
+ shutil.rmtree(self.test_dir)
+ pass
+
+ def test_create_global_plot(self):
+ """
+ Compare result and expected SumStatJostTab
+ """
+ create_global_plot(self.worktable_hdf_path, self.global_plot_path)
+
+if __name__ == "__main__":
+ import unittest
+
+ unittest.main()