delete unwanted correlation stuff (#5)

* delete unwanted correlation stuff

* Refactor/clean up correlations (#4)

* initial commit for Refactor/clean-up-correlation

* add python scipy dependency

* initial commit for sample correlation

* initial commit for sample correlation endpoint

* initial commit for integration and unittest

* initial commit for registering  correlation blueprint

* add and modify unittest and integration tests for correlation

* Add compute compute_all_sample_corr   method for correlation

* add scipy to requirement txt file

* add tissue correlation for trait list

* add unittest for tissue correlation

* add lit correlation for trait list

* add unittests for lit correlation for trait list

* modify lit correlarion for trait list

* add unittests for lit correlation for trait list

* add correlation metho  in dynamic url

* add file format for expected structure input  while doing sample correlation

* modify input data structure -> add  trait id

* update tests for sample r correlation

* add compute all lit correlation method

* add endpoint for computing lit_corr

* add unit and integration tests for computing lit corr

* add /api/correlation/tissue_corr/{corr_method} endpoint for tissue correlation

* add unittest and integration tests for tissue correlation

Co-authored-by: BonfaceKilz <bonfacemunyoki@gmail.com>

* update guix scm file

* fix pylint error for correlations api

Co-authored-by: BonfaceKilz <bonfacemunyoki@gmail.com>

5 files changed, 0 insertions, 885 deletions

diff --git a/gn3/correlation/__init__.py b/gn3/correlation/__init__.py
deleted file mode 100644
index e69de29..0000000
--- a/gn3/correlation/__init__.py
+++ /dev/null
diff --git a/gn3/correlation/correlation_computations.py b/gn3/correlation/correlation_computations.py
deleted file mode 100644
index 6a3f2bb..0000000
--- a/gn3/correlation/correlation_computations.py
+++ /dev/null
@@ -1,32 +0,0 @@
-"""module contains code for any computation in correlation"""
-
-import json
-from .show_corr_results import CorrelationResults
-
-def compute_correlation(correlation_input_data,
-                        correlation_results=CorrelationResults):
-    """function that does correlation .creates Correlation results instance
-
-    correlation_input_data structure is a dict with
-
-     {
-     "trait_id":"valid trait id",
-     "dataset":"",
-      "sample_vals":{},
-      "primary_samples":"",
-      "corr_type":"",
-      corr_dataset:"",
-      "corr_return_results":"",
-
-       
-     }
-
-    """
-
-    corr_object = correlation_results(
-        start_vars=correlation_input_data)
-
-    corr_results = corr_object.do_correlation(start_vars=correlation_input_data)
-    # possibility of file being so large cause of the not sure whether to return a file
-
-    return corr_results
diff --git a/gn3/correlation/correlation_functions.py b/gn3/correlation/correlation_functions.py
deleted file mode 100644
index be08c96..0000000
--- a/gn3/correlation/correlation_functions.py
+++ /dev/null
@@ -1,96 +0,0 @@
-
-"""
-# Copyright (C) University of Tennessee Health Science Center, Memphis, TN.
-#
-# This program is free software: you can redistribute it and/or modify it
-# under the terms of the GNU Affero General Public License
-# as published by the Free Software Foundation, either version 3 of the
-# License, or (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-# See the GNU Affero General Public License for more details.
-#
-# This program is available from Source Forge: at GeneNetwork Project
-# (sourceforge.net/projects/genenetwork/).
-#
-# Contact Drs. Robert W. Williams and Xiaodong Zhou (2010)
-# at rwilliams@uthsc.edu and xzhou15@uthsc.edu
-#
-#
-#
-# This module is used by GeneNetwork project (www.genenetwork.org)
-#
-# Created by GeneNetwork Core Team 2010/08/10
-#
-# Last updated by NL 2011/03/23
-
-
-"""
-
-import rpy2.robjects
-from gn3.base.mrna_assay_tissue_data import MrnaAssayTissueData
-
-
-#####################################################################################
-# Input: primaryValue(list): one list of expression values of one probeSet,
-#       targetValue(list): one list of expression values of one probeSet,
-#               method(string): indicate correlation method ('pearson' or 'spearman')
-# Output: corr_result(list): first item is Correlation Value, second item is tissue number,
-#                           third item is PValue
-# Function: get correlation value,Tissue quantity ,p value result by using R;
-# Note : This function is special case since both primaryValue and targetValue are from
-# the same dataset. So the length of these two parameters is the same. They are pairs.
-# Also, in the datatable TissueProbeSetData, all Tissue values are loaded based on
-# the same tissue order
-#####################################################################################
-
-def cal_zero_order_corr_for_tiss(primaryValue=[], targetValue=[], method='pearson'):
-    """refer above for info on the function"""
-    # pylint: disable = E, W, R, C
-
-    #nb disabled pylint until tests are written for this function
-
-    R_primary = rpy2.robjects.FloatVector(list(range(len(primaryValue))))
-    N = len(primaryValue)
-    for i in range(len(primaryValue)):
-        R_primary[i] = primaryValue[i]
-
-    R_target = rpy2.robjects.FloatVector(list(range(len(targetValue))))
-    for i in range(len(targetValue)):
-        R_target[i] = targetValue[i]
-
-    R_corr_test = rpy2.robjects.r['cor.test']
-    if method == 'spearman':
-        R_result = R_corr_test(R_primary, R_target, method='spearman')
-    else:
-        R_result = R_corr_test(R_primary, R_target)
-
-    corr_result = []
-    corr_result.append(R_result[3][0])
-    corr_result.append(N)
-    corr_result.append(R_result[2][0])
-
-    return corr_result
-
-
-####################################################
-####################################################
-# input: cursor, symbolList (list), dataIdDict(Dict): key is symbol
-# output: SymbolValuePairDict(dictionary):one dictionary of Symbol and Value Pair.
-#        key is symbol, value is one list of expression values of one probeSet.
-# function: wrapper function for getSymbolValuePairDict function
-#          build gene symbol list if necessary, cut it into small lists if necessary,
-#          then call getSymbolValuePairDict function and merge the results.
-###################################################
-#####################################################
-
-def get_trait_symbol_and_tissue_values(symbol_list=None):
-    """function to get trait symbol and tissues values refer above"""
-    tissue_data = MrnaAssayTissueData(gene_symbols=symbol_list)
-
-    if len(tissue_data.gene_symbols) >= 1:
-        return tissue_data.get_symbol_values_pairs()
-
-    return None
diff --git a/gn3/correlation/correlation_utility.py b/gn3/correlation/correlation_utility.py
deleted file mode 100644
index 7583bd7..0000000
--- a/gn3/correlation/correlation_utility.py
+++ /dev/null
@@ -1,22 +0,0 @@
-"""module contains utility functions for correlation"""
-
-
-class AttributeSetter:
-    """class for setting Attributes"""
-
-    def __init__(self, trait_obj):
-        for key, value in trait_obj.items():
-            setattr(self, key, value)
-
-    def __str__(self):
-        return self.__class__.__name__
-
-    def get_dict(self):
-        """dummy function  to get dict object"""
-        return self.__dict__
-
-
-def get_genofile_samplelist(dataset):
-    """mock function to get genofile samplelist"""
-
-    return ["C57BL/6J"]
diff --git a/gn3/correlation/show_corr_results.py b/gn3/correlation/show_corr_results.py
deleted file mode 100644
index 55d8366..0000000
--- a/gn3/correlation/show_corr_results.py
+++ /dev/null
@@ -1,735 +0,0 @@
-"""module contains code for doing correlation"""
-
-import json
-import collections
-import numpy
-import scipy.stats
-import rpy2.robjects as ro
-from flask import g
-from gn3.base.data_set import create_dataset
-from gn3.utility.db_tools import escape
-from gn3.utility.helper_functions import get_species_dataset_trait
-from gn3.utility.corr_result_helpers import normalize_values
-from gn3.base.trait import create_trait
-from gn3.utility import hmac
-from . import correlation_functions
-
-
-class CorrelationResults:
-    """class for computing correlation"""
-    # pylint: disable=too-many-instance-attributes
-    # pylint:disable=attribute-defined-outside-init
-
-    def __init__(self, start_vars):
-        self.assertion_for_start_vars(start_vars)
-
-    @staticmethod
-    def assertion_for_start_vars(start_vars):
-        # pylint: disable = E, W, R, C
-
-        # should better ways to assert the variables
-        # example includes sample
-        assert("corr_type" in start_vars)
-        assert(isinstance(start_vars['corr_type'], str))
-        # example includes pearson
-        assert('corr_sample_method' in start_vars)
-        assert('corr_dataset' in start_vars)
-        # means the  limit
-        assert('corr_return_results' in start_vars)
-
-        if "loc_chr" in start_vars:
-            assert('min_loc_mb' in start_vars)
-            assert('max_loc_mb' in start_vars)
-
-    def get_formatted_corr_type(self):
-        """method to formatt corr_types"""
-        self.formatted_corr_type = ""
-        if self.corr_type == "lit":
-            self.formatted_corr_type += "Literature Correlation "
-        elif self.corr_type == "tissue":
-            self.formatted_corr_type += "Tissue Correlation "
-        elif self.corr_type == "sample":
-            self.formatted_corr_type += "Genetic Correlation "
-
-        if self.corr_method == "pearson":
-            self.formatted_corr_type += "(Pearson's r)"
-        elif self.corr_method == "spearman":
-            self.formatted_corr_type += "(Spearman's rho)"
-        elif self.corr_method == "bicor":
-            self.formatted_corr_type += "(Biweight r)"
-
-    def process_samples(self, start_vars, sample_names, excluded_samples=None):
-        """method to process samples"""
-
-
-        if not excluded_samples:
-            excluded_samples = ()
-
-        sample_val_dict = json.loads(start_vars["sample_vals"])
-        print(sample_val_dict)
-        if sample_names is None:
-            raise  NotImplementedError
-
-        for sample in sample_names:
-            if sample not in excluded_samples:
-                value = sample_val_dict[sample]
-
-                if not value.strip().lower() == "x":
-                    self.sample_data[str(sample)] = float(value)
-
-    def do_tissue_correlation_for_trait_list(self, tissue_dataset_id=1):
-        """Given a list of correlation results (self.correlation_results),\
-        gets the tissue correlation value for each"""
-        # pylint: disable = E, W, R, C
-
-        # Gets tissue expression values for the primary trait
-        primary_trait_tissue_vals_dict = correlation_functions.get_trait_symbol_and_tissue_values(
-            symbol_list=[self.this_trait.symbol])
-
-        if self.this_trait.symbol.lower() in primary_trait_tissue_vals_dict:
-            primary_trait_tissue_values = primary_trait_tissue_vals_dict[self.this_trait.symbol.lower(
-            )]
-            gene_symbol_list = [
-                trait.symbol for trait in self.correlation_results if trait.symbol]
-
-            corr_result_tissue_vals_dict = correlation_functions.get_trait_symbol_and_tissue_values(
-                symbol_list=gene_symbol_list)
-
-            for trait in self.correlation_results:
-                if trait.symbol and trait.symbol.lower() in corr_result_tissue_vals_dict:
-                    this_trait_tissue_values = corr_result_tissue_vals_dict[trait.symbol.lower(
-                    )]
-
-                    result = correlation_functions.cal_zero_order_corr_for_tiss(primary_trait_tissue_values,
-                                                                                this_trait_tissue_values,
-                                                                                self.corr_method)
-
-                    trait.tissue_corr = result[0]
-                    trait.tissue_pvalue = result[2]
-
-    def do_lit_correlation_for_trait_list(self):
-        # pylint: disable = E, W, R, C
-
-        input_trait_mouse_gene_id = self.convert_to_mouse_gene_id(
-            self.dataset.group.species.lower(), self.this_trait.geneid)
-
-        for trait in self.correlation_results:
-
-            if trait.geneid:
-                trait.mouse_gene_id = self.convert_to_mouse_gene_id(
-                    self.dataset.group.species.lower(), trait.geneid)
-            else:
-                trait.mouse_gene_id = None
-
-            if trait.mouse_gene_id and str(trait.mouse_gene_id).find(";") == -1:
-                result = g.db.execute(
-                    """SELECT value
-                       FROM LCorrRamin3
-                       WHERE GeneId1='%s' and
-                             GeneId2='%s'
-                    """ % (escape(str(trait.mouse_gene_id)), escape(str(input_trait_mouse_gene_id)))
-                ).fetchone()
-                if not result:
-                    result = g.db.execute("""SELECT value
-                       FROM LCorrRamin3
-                       WHERE GeneId2='%s' and
-                             GeneId1='%s'
-                    """ % (escape(str(trait.mouse_gene_id)), escape(str(input_trait_mouse_gene_id)))
-                    ).fetchone()
-
-                if result:
-                    lit_corr = result.value
-                    trait.lit_corr = lit_corr
-                else:
-                    trait.lit_corr = 0
-            else:
-                trait.lit_corr = 0
-
-    def do_lit_correlation_for_all_traits(self):
-        """method for lit_correlation for all traits"""
-        # pylint: disable = E, W, R, C
-        input_trait_mouse_gene_id = self.convert_to_mouse_gene_id(
-            self.dataset.group.species.lower(), self.this_trait.geneid)
-
-        lit_corr_data = {}
-        for trait, gene_id in list(self.trait_geneid_dict.items()):
-            mouse_gene_id = self.convert_to_mouse_gene_id(
-                self.dataset.group.species.lower(), gene_id)
-
-            if mouse_gene_id and str(mouse_gene_id).find(";") == -1:
-                #print("gene_symbols:", input_trait_mouse_gene_id + " / " + mouse_gene_id)
-                result = g.db.execute(
-                    """SELECT value
-                       FROM LCorrRamin3
-                       WHERE GeneId1='%s' and
-                             GeneId2='%s'
-                    """ % (escape(mouse_gene_id), escape(input_trait_mouse_gene_id))
-                ).fetchone()
-                if not result:
-                    result = g.db.execute("""SELECT value
-                       FROM LCorrRamin3
-                       WHERE GeneId2='%s' and
-                             GeneId1='%s'
-                    """ % (escape(mouse_gene_id), escape(input_trait_mouse_gene_id))
-                    ).fetchone()
-                if result:
-                    #print("result:", result)
-                    lit_corr = result.value
-                    lit_corr_data[trait] = [gene_id, lit_corr]
-                else:
-                    lit_corr_data[trait] = [gene_id, 0]
-            else:
-                lit_corr_data[trait] = [gene_id, 0]
-
-        lit_corr_data = collections.OrderedDict(sorted(list(lit_corr_data.items()),
-                                                       key=lambda t: -abs(t[1][1])))
-
-        return lit_corr_data
-
-    def do_tissue_correlation_for_all_traits(self, tissue_dataset_id=1):
-        # Gets tissue expression values for the primary trait
-        # pylint: disable = E, W, R, C
-        primary_trait_tissue_vals_dict = correlation_functions.get_trait_symbol_and_tissue_values(
-            symbol_list=[self.this_trait.symbol])
-
-        if self.this_trait.symbol.lower() in primary_trait_tissue_vals_dict:
-            primary_trait_tissue_values = primary_trait_tissue_vals_dict[self.this_trait.symbol.lower(
-            )]
-
-            #print("trait_gene_symbols: ", pf(trait_gene_symbols.values()))
-            corr_result_tissue_vals_dict = correlation_functions.get_trait_symbol_and_tissue_values(
-                symbol_list=list(self.trait_symbol_dict.values()))
-
-            #print("corr_result_tissue_vals: ", pf(corr_result_tissue_vals_dict))
-
-            #print("trait_gene_symbols: ", pf(trait_gene_symbols))
-
-            tissue_corr_data = {}
-            for trait, symbol in list(self.trait_symbol_dict.items()):
-                if symbol and symbol.lower() in corr_result_tissue_vals_dict:
-                    this_trait_tissue_values = corr_result_tissue_vals_dict[symbol.lower(
-                    )]
-
-                    result = correlation_functions.cal_zero_order_corr_for_tiss(primary_trait_tissue_values,
-                                                                                this_trait_tissue_values,
-                                                                                self.corr_method)
-
-                    tissue_corr_data[trait] = [symbol, result[0], result[2]]
-
-            tissue_corr_data = collections.OrderedDict(sorted(list(tissue_corr_data.items()),
-                                                              key=lambda t: -abs(t[1][1])))
-
-    def get_sample_r_and_p_values(self, trait, target_samples):
-        """Calculates the sample r (or rho) and p-value
-
-        Given a primary trait and a target trait's sample values,
-        calculates either the pearson r or spearman rho and the p-value
-        using the corresponding scipy functions.
-
-        """
-        # pylint: disable = E, W, R, C
-        self.this_trait_vals = []
-        target_vals = []
-
-        for index, sample in enumerate(self.target_dataset.samplelist):
-            if sample in self.sample_data:
-                sample_value = self.sample_data[sample]
-                target_sample_value = target_samples[index]
-                self.this_trait_vals.append(sample_value)
-                target_vals.append(target_sample_value)
-
-        self.this_trait_vals, target_vals, num_overlap = normalize_values(
-            self.this_trait_vals, target_vals)
-
-        if num_overlap > 5:
-            # ZS: 2015 could add biweight correlation, see http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3465711/
-            if self.corr_method == 'bicor':
-                sample_r, sample_p = do_bicor(
-                    self.this_trait_vals, target_vals)
-
-            elif self.corr_method == 'pearson':
-                sample_r, sample_p = scipy.stats.pearsonr(
-                    self.this_trait_vals, target_vals)
-
-            else:
-                sample_r, sample_p = scipy.stats.spearmanr(
-                    self.this_trait_vals, target_vals)
-
-            if numpy.isnan(sample_r):
-                pass
-
-            else:
-
-                self.correlation_data[trait] = [
-                    sample_r, sample_p, num_overlap]
-
-    def convert_to_mouse_gene_id(self, species=None, gene_id=None):
-        """If the species is rat or human, translate the gene_id to the mouse geneid
-
-        If there is no input gene_id or there's no corresponding mouse gene_id, return None
-
-        """
-        if not gene_id:
-            return None
-
-        mouse_gene_id = None
-        if "species" == "mouse":
-            mouse_gene_id = gene_id
-
-        elif species == 'rat':
-            query = """SELECT mouse
-                   FROM GeneIDXRef
-                   WHERE rat='%s'""" % escape(gene_id)
-
-            result = g.db.execute(query).fetchone()
-            if result != None:
-                mouse_gene_id = result.mouse
-
-        elif species == "human":
-
-            query = """SELECT mouse
-                   FROM GeneIDXRef
-                   WHERE human='%s'""" % escape(gene_id)
-
-            result = g.db.execute(query).fetchone()
-            if result != None:
-                mouse_gene_id = result.mouse
-
-        return mouse_gene_id
-
-    def do_correlation(self, start_vars, create_dataset=create_dataset,
-                       create_trait=create_trait,
-                       get_species_dataset_trait=get_species_dataset_trait):
-        # pylint: disable = E, W, R, C
-        # probably refactor start_vars being passed twice
-        # this method  aims to replace the do_correlation but also add dependendency injection
-        # to enable testing
-
-        # should maybe refactor below code more or less works the same
-        if start_vars["dataset"] == "Temp":
-            self.dataset = create_dataset(
-                dataset_name="Temp", dataset_type="Temp", group_name=start_vars['group'])
-
-            self.trait_id = start_vars["trait_id"]
-
-            self.this_trait = create_trait(dataset=self.dataset,
-                                           name=self.trait_id,
-                                           cellid=None)
-
-        else:
-
-            get_species_dataset_trait(self, start_vars)
-
-        corr_samples_group = start_vars['corr_samples_group']
-        self.sample_data = {}
-        self.corr_type = start_vars['corr_type']
-        self.corr_method = start_vars['corr_sample_method']
-        self.min_expr = float(
-            start_vars["min_expr"]) if start_vars["min_expr"] != "" else None
-        self.p_range_lower = float(
-            start_vars["p_range_lower"]) if start_vars["p_range_lower"] != "" else -1.0
-        self.p_range_upper = float(
-            start_vars["p_range_upper"]) if start_vars["p_range_upper"] != "" else 1.0
-
-        if ("loc_chr" in start_vars and "min_loc_mb" in start_vars and "max_loc_mb" in start_vars):
-            self.location_type = str(start_vars['location_type'])
-            self.location_chr = str(start_vars['loc_chr'])
-
-            try:
-
-                # the code is below is basically a temporary fix
-                self.min_location_mb = int(start_vars['min_loc_mb'])
-                self.max_location_mb = int(start_vars['max_loc_mb'])
-            except Exception as e:
-                self.min_location_mb = None
-                self.max_location_mb = None
-
-        else:
-            self.location_type = self.location_chr = self.min_location_mb = self.max_location_mb = None
-
-        self.get_formatted_corr_type()
-
-        self.return_number = int(start_vars['corr_return_results'])
-
-        primary_samples = self.dataset.group.samplelist
-
-
-        # The two if statements below append samples to the sample list based upon whether the user
-        # rselected Primary Samples Only, Other Samples Only, or All Samples
-
-        if self.dataset.group.parlist != None:
-            primary_samples += self.dataset.group.parlist
-
-        if self.dataset.group.f1list != None:
-
-            primary_samples += self.dataset.group.f1list
-
-        # If either BXD/whatever Only or All Samples, append all of that group's samplelist
-
-        if corr_samples_group != 'samples_other':
-                
-            # print("primary samples are *****",primary_samples)
-
-            self.process_samples(start_vars, primary_samples)
-
-        if corr_samples_group != 'samples_primary':
-            if corr_samples_group == 'samples_other':
-                primary_samples = [x for x in primary_samples if x not in (
-                    self.dataset.group.parlist + self.dataset.group.f1list)]
-
-            self.process_samples(start_vars, list(self.this_trait.data.keys()), primary_samples)
-
-        self.target_dataset = create_dataset(start_vars['corr_dataset'])
-        # when you add code to retrieve the trait_data for target dataset got gets very slow
-        import time
-
-        init_time = time.time()
-        self.target_dataset.get_trait_data(list(self.sample_data.keys()))
-
-        aft_time = time.time() - init_time
-
-        self.header_fields = get_header_fields(
-            self.target_dataset.type, self.corr_method)
-
-        if self.target_dataset.type == "ProbeSet":
-            self.filter_cols = [7, 6]
-
-        elif self.target_dataset.type == "Publish":
-            self.filter_cols = [6, 0]
-
-        else:
-            self.filter_cols = [4, 0]
-
-        self.correlation_results = []
-
-        self.correlation_data = {}
-
-        if self.corr_type == "tissue":
-            self.trait_symbol_dict = self.dataset.retrieve_genes("Symbol")
-
-            tissue_corr_data = self.do_tissue_correlation_for_all_traits()
-            if tissue_corr_data != None:
-                for trait in list(tissue_corr_data.keys())[:self.return_number]:
-                    self.get_sample_r_and_p_values(
-                        trait, self.target_dataset.trait_data[trait])
-            else:
-                for trait, values in list(self.target_dataset.trait_data.items()):
-                    self.get_sample_r_and_p_values(trait, values)
-
-        elif self.corr_type == "lit":
-            self.trait_geneid_dict = self.dataset.retrieve_genes("GeneId")
-            lit_corr_data = self.do_lit_correlation_for_all_traits()
-
-            for trait in list(lit_corr_data.keys())[:self.return_number]:
-                self.get_sample_r_and_p_values(
-                    trait, self.target_dataset.trait_data[trait])
-
-        elif self.corr_type == "sample":
-            for trait, values in list(self.target_dataset.trait_data.items()):
-                self.get_sample_r_and_p_values(trait, values)
-
-        self.correlation_data = collections.OrderedDict(sorted(list(self.correlation_data.items()),
-                                                               key=lambda t: -abs(t[1][0])))
-
-        # ZS: Convert min/max chromosome to an int for the location range option
-
-        """
-        took 20.79 seconds took compute all the above majority of time taken on retrieving target dataset trait
-        info
-        """
-
-        initial_time_chr = time.time()
-
-        range_chr_as_int = None
-        for order_id, chr_info in list(self.dataset.species.chromosomes.chromosomes.items()):
-            if 'loc_chr' in start_vars:
-                if chr_info.name == self.location_chr:
-                    range_chr_as_int = order_id
-
-        for _trait_counter, trait in enumerate(list(self.correlation_data.keys())[:self.return_number]):
-            trait_object = create_trait(
-                dataset=self.target_dataset, name=trait, get_qtl_info=True, get_sample_info=False)
-            if not trait_object:
-                continue
-
-            chr_as_int = 0
-            for order_id, chr_info in list(self.dataset.species.chromosomes.chromosomes.items()):
-                if self.location_type == "highest_lod":
-                    if chr_info.name == trait_object.locus_chr:
-                        chr_as_int = order_id
-                else:
-                    if chr_info.name == trait_object.chr:
-                        chr_as_int = order_id
-
-            if (float(self.correlation_data[trait][0]) >= self.p_range_lower and
-                    float(self.correlation_data[trait][0]) <= self.p_range_upper):
-
-                if (self.target_dataset.type == "ProbeSet" or self.target_dataset.type == "Publish") and bool(trait_object.mean):
-                    if (self.min_expr != None) and (float(trait_object.mean) < self.min_expr):
-                        continue
-
-                if range_chr_as_int != None and (chr_as_int != range_chr_as_int):
-                    continue
-                if self.location_type == "highest_lod":
-                    if (self.min_location_mb != None) and (float(trait_object.locus_mb) < float(self.min_location_mb)):
-                        continue
-                    if (self.max_location_mb != None) and (float(trait_object.locus_mb) > float(self.max_location_mb)):
-                        continue
-                else:
-                    if (self.min_location_mb != None) and (float(trait_object.mb) < float(self.min_location_mb)):
-                        continue
-                    if (self.max_location_mb != None) and (float(trait_object.mb) > float(self.max_location_mb)):
-                        continue
-
-                (trait_object.sample_r,
-                 trait_object.sample_p,
-                 trait_object.num_overlap) = self.correlation_data[trait]
-
-                # Set some sane defaults
-                trait_object.tissue_corr = 0
-                trait_object.tissue_pvalue = 0
-                trait_object.lit_corr = 0
-                if self.corr_type == "tissue" and tissue_corr_data != None:
-                    trait_object.tissue_corr = tissue_corr_data[trait][1]
-                    trait_object.tissue_pvalue = tissue_corr_data[trait][2]
-                elif self.corr_type == "lit":
-                    trait_object.lit_corr = lit_corr_data[trait][1]
-
-                self.correlation_results.append(trait_object)
-
-        """
-        above takes time with respect to size of traits i.e n=100,500,.....t_size
-        """
-
-        if self.corr_type != "lit" and self.dataset.type == "ProbeSet" and self.target_dataset.type == "ProbeSet":
-            # self.do_lit_correlation_for_trait_list()
-            self.do_lit_correlation_for_trait_list()
-
-        if self.corr_type != "tissue" and self.dataset.type == "ProbeSet" and self.target_dataset.type == "ProbeSet":
-            self.do_tissue_correlation_for_trait_list()
-            # self.do_lit_correlation_for_trait_list()
-
-        self.json_results = generate_corr_json(
-            self.correlation_results, self.this_trait, self.dataset, self.target_dataset)
-
-        # org mode by bons
-
-        # DVORAKS
-        # klavaro for touch typing
-        # archwiki for documentation
-        # exwm for window manager ->13
-
-        # will fit perfectly with genenetwork 2 with change of anything if return self
-
-        # alternative for this
-        return self.json_results
-        # return {
-        #     # "Results": "succeess",
-        #     # "return_number": self.return_number,
-        #     # "primary_samples": primary_samples,
-        #     # "time_taken": 12,
-        #     # "correlation_data": self.correlation_data,
-        #     "correlation_json": self.json_results
-        # }
-
-
-def do_bicor(this_trait_vals, target_trait_vals):
-    # pylint: disable = E, W, R, C
-    r_library = ro.r["library"]             # Map the library function
-    r_options = ro.r["options"]             # Map the options function
-
-    r_library("WGCNA")
-    r_bicor = ro.r["bicorAndPvalue"]        # Map the bicorAndPvalue function
-
-    r_options(stringsAsFactors=False)
-
-    this_vals = ro.Vector(this_trait_vals)
-    target_vals = ro.Vector(target_trait_vals)
-
-    the_r, the_p, _fisher_transform, _the_t, _n_obs = [
-        numpy.asarray(x) for x in r_bicor(x=this_vals, y=target_vals)]
-
-    return the_r, the_p
-
-
-def get_header_fields(data_type, corr_method):
-    """function to get header fields when doing correlation"""
-    if data_type == "ProbeSet":
-        if corr_method == "spearman":
-
-            header_fields = ['Index',
-                             'Record',
-                             'Symbol',
-                             'Description',
-                             'Location',
-                             'Mean',
-                             'Sample rho',
-                             'N',
-                             'Sample p(rho)',
-                             'Lit rho',
-                             'Tissue rho',
-                             'Tissue p(rho)',
-                             'Max LRS',
-                             'Max LRS Location',
-                             'Additive Effect']
-
-        else:
-            header_fields = ['Index',
-                             'Record',
-                             'Abbreviation',
-                             'Description',
-                             'Mean',
-                             'Authors',
-                             'Year',
-                             'Sample r',
-                             'N',
-                             'Sample p(r)',
-                             'Max LRS',
-                             'Max LRS Location',
-                             'Additive Effect']
-
-    elif data_type == "Publish":
-        if corr_method == "spearman":
-
-            header_fields = ['Index',
-                             'Record',
-                             'Abbreviation',
-                             'Description',
-                             'Mean',
-                             'Authors',
-                             'Year',
-                             'Sample rho',
-                             'N',
-                             'Sample p(rho)',
-                             'Max LRS',
-                             'Max LRS Location',
-                             'Additive Effect']
-
-        else:
-            header_fields = ['Index',
-                             'Record',
-                             'Abbreviation',
-                             'Description',
-                             'Mean',
-                             'Authors',
-                             'Year',
-                             'Sample r',
-                             'N',
-                             'Sample p(r)',
-                             'Max LRS',
-                             'Max LRS Location',
-                             'Additive Effect']
-
-    else:
-        if corr_method == "spearman":
-            header_fields = ['Index',
-                             'ID',
-                             'Location',
-                             'Sample rho',
-                             'N',
-                             'Sample p(rho)']
-
-        else:
-            header_fields = ['Index',
-                             'ID',
-                             'Location',
-                             'Sample r',
-                             'N',
-                             'Sample p(r)']
-
-    return header_fields
-
-
-def generate_corr_json(corr_results, this_trait, dataset, target_dataset, for_api=False):
-    """function to generate corr json data"""
-    #todo refactor this function
-    results_list = []
-    for i, trait in enumerate(corr_results):
-        if trait.view == False:
-            continue
-        results_dict = {}
-        results_dict['index'] = i + 1
-        results_dict['trait_id'] = trait.name
-        results_dict['dataset'] = trait.dataset.name
-        results_dict['hmac'] = hmac.data_hmac(
-            '{}:{}'.format(trait.name, trait.dataset.name))
-        if target_dataset.type == "ProbeSet":
-            results_dict['symbol'] = trait.symbol
-            results_dict['description'] = "N/A"
-            results_dict['location'] = trait.location_repr
-            results_dict['mean'] = "N/A"
-            results_dict['additive'] = "N/A"
-            if bool(trait.description_display):
-                results_dict['description'] = trait.description_display
-            if bool(trait.mean):
-                results_dict['mean'] = f"{float(trait.mean):.3f}"
-            try:
-                results_dict['lod_score'] = f"{float(trait.LRS_score_repr) / 4.61:.1f}"
-            except:
-                results_dict['lod_score'] = "N/A"
-            results_dict['lrs_location'] = trait.LRS_location_repr
-            if bool(trait.additive):
-                results_dict['additive'] = f"{float(trait.additive):.3f}"
-            results_dict['sample_r'] = f"{float(trait.sample_r):.3f}"
-            results_dict['num_overlap'] = trait.num_overlap
-            results_dict['sample_p'] = f"{float(trait.sample_p):.3e}"
-            results_dict['lit_corr'] = "--"
-            results_dict['tissue_corr'] = "--"
-            results_dict['tissue_pvalue'] = "--"
-            if bool(trait.lit_corr):
-                results_dict['lit_corr'] = f"{float(trait.lit_corr):.3f}"
-            if bool(trait.tissue_corr):
-                results_dict['tissue_corr'] = f"{float(trait.tissue_corr):.3f}"
-                results_dict['tissue_pvalue'] = f"{float(trait.tissue_pvalue):.3e}"
-        elif target_dataset.type == "Publish":
-            results_dict['abbreviation_display'] = "N/A"
-            results_dict['description'] = "N/A"
-            results_dict['mean'] = "N/A"
-            results_dict['authors_display'] = "N/A"
-            results_dict['additive'] = "N/A"
-            if for_api:
-                results_dict['pubmed_id'] = "N/A"
-                results_dict['year'] = "N/A"
-            else:
-                results_dict['pubmed_link'] = "N/A"
-                results_dict['pubmed_text'] = "N/A"
-
-            if bool(trait.abbreviation):
-                results_dict['abbreviation_display'] = trait.abbreviation
-            if bool(trait.description_display):
-                results_dict['description'] = trait.description_display
-            if bool(trait.mean):
-                results_dict['mean'] = f"{float(trait.mean):.3f}"
-            if bool(trait.authors):
-                authors_list = trait.authors.split(',')
-                if len(authors_list) > 6:
-                    results_dict['authors_display'] = ", ".join(
-                        authors_list[:6]) + ", et al."
-                else:
-                    results_dict['authors_display'] = trait.authors
-            if bool(trait.pubmed_id):
-                if for_api:
-                    results_dict['pubmed_id'] = trait.pubmed_id
-                    results_dict['year'] = trait.pubmed_text
-                else:
-                    results_dict['pubmed_link'] = trait.pubmed_link
-                    results_dict['pubmed_text'] = trait.pubmed_text
-            try:
-                results_dict['lod_score'] = f"{float(trait.LRS_score_repr) / 4.61:.1f}"
-            except:
-                results_dict['lod_score'] = "N/A"
-            results_dict['lrs_location'] = trait.LRS_location_repr
-            if bool(trait.additive):
-                results_dict['additive'] = f"{float(trait.additive):.3f}"
-            results_dict['sample_r'] = f"{float(trait.sample_r):.3f}"
-            results_dict['num_overlap'] = trait.num_overlap
-            results_dict['sample_p'] = f"{float(trait.sample_p):.3e}"
-        else:
-            results_dict['location'] = trait.location_repr
-            results_dict['sample_r'] = f"{float(trait.sample_r):.3f}"
-            results_dict['num_overlap'] = trait.num_overlap
-            results_dict['sample_p'] = f"{float(trait.sample_p):.3e}"
-
-        results_list.append(results_dict)
-
-    return json.dumps(results_list)