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-rwxr-xr-xwqflask/base/data_set.py2
-rw-r--r--wqflask/utility/chunks.py96
-rw-r--r--wqflask/wqflask/heatmap/heatmap.py635
-rwxr-xr-xwqflask/wqflask/marker_regression/marker_regression.py4
4 files changed, 417 insertions, 320 deletions
diff --git a/wqflask/base/data_set.py b/wqflask/base/data_set.py
index 489bd374..9f805fc3 100755
--- a/wqflask/base/data_set.py
+++ b/wqflask/base/data_set.py
@@ -42,7 +42,7 @@ from base import species
from dbFunction import webqtlDatabaseFunction
from utility import webqtlUtil
from utility.benchmark import Bench
-from wqflask.my_pylmm.pyLMM import chunks
+from wqflask.utility import chunks
from maintenance import get_group_samplelists
diff --git a/wqflask/utility/chunks.py b/wqflask/utility/chunks.py
new file mode 100644
index 00000000..9565fb96
--- /dev/null
+++ b/wqflask/utility/chunks.py
@@ -0,0 +1,96 @@
+from __future__ import absolute_import, print_function, division
+
+import math
+import time
+
+
+def divide_into_chunks(the_list, number_chunks):
+ """Divides a list into approximately number_chunks smaller lists
+
+ >>> divide_into_chunks([1, 2, 7, 3, 22, 8, 5, 22, 333], 3)
+ [[1, 2, 7], [3, 22, 8], [5, 22, 333]]
+ >>> divide_into_chunks([1, 2, 7, 3, 22, 8, 5, 22, 333], 4)
+ [[1, 2, 7], [3, 22, 8], [5, 22, 333]]
+ >>> divide_into_chunks([1, 2, 7, 3, 22, 8, 5, 22, 333], 5)
+ [[1, 2], [7, 3], [22, 8], [5, 22], [333]]
+ >>>
+
+ """
+ length = len(the_list)
+
+ if length == 0:
+ return [[]]
+
+ if length <= number_chunks:
+ number_chunks = length
+
+ chunksize = int(math.ceil(length / number_chunks))
+
+ chunks = []
+ for counter in range(0, length, chunksize):
+ chunks.append(the_list[counter:counter+chunksize])
+
+ return chunks
+
+def _confirm_chunk(original, result):
+ all_chunked = []
+ for chunk in result:
+ all_chunked.extend(chunk)
+ print("length of all chunked:", len(all_chunked))
+ assert original == all_chunked, "You didn't chunk right"
+
+
+def _chunk_test(divide_func):
+ import random
+ random.seed(7)
+
+ number_exact = 0
+ total_amount_off = 0
+
+ for test in range(1, 1001):
+ print("\n\ntest:", test)
+ number_chunks = random.randint(1, 20)
+ number_elements = random.randint(0, 100)
+ the_list = list(range(1, number_elements))
+ result = divide_func(the_list, number_chunks)
+
+ print("Dividing list of length {} into approximately {} chunks - got {} chunks".format(
+ len(the_list), number_chunks, len(result)))
+ print("result:", result)
+
+ _confirm_chunk(the_list, result)
+
+ amount_off = abs(number_chunks - len(result))
+ if amount_off == 0:
+ number_exact += 1
+ else:
+ total_amount_off += amount_off
+
+
+ print("\n{} exact out of {} [Total amount off: {}]".format(number_exact,
+ test,
+ total_amount_off))
+ assert number_exact == 558
+ assert total_amount_off == 1580
+ return number_exact, total_amount_off
+
+
+def _main():
+ info = dict()
+ #funcs = (("sam", sam_divide_into_chunks), ("zach", zach_divide_into_chunks))
+ funcs = (("only one", divide_into_chunks),)
+ for name, func in funcs:
+ start = time.time()
+ number_exact, total_amount_off = _chunk_test(func)
+ took = time.time() - start
+ info[name] = dict(number_exact=number_exact,
+ total_amount_off=total_amount_off,
+ took=took)
+
+ print("info is:", info)
+
+if __name__ == '__main__':
+ _main()
+ print("\nConfirming doctests...")
+ import doctest
+ doctest.testmod()
diff --git a/wqflask/wqflask/heatmap/heatmap.py b/wqflask/wqflask/heatmap/heatmap.py
index 9b6b1b69..035736fd 100644
--- a/wqflask/wqflask/heatmap/heatmap.py
+++ b/wqflask/wqflask/heatmap/heatmap.py
@@ -1,317 +1,318 @@
-from __future__ import absolute_import, print_function, division
-
-import sys
-sys.path.append(".")
-
-import gc
-import string
-import cPickle
-import os
-import datetime
-import time
-import pp
-import math
-import collections
-import resource
-
-import scipy
-import numpy as np
-from scipy import linalg
-
-from pprint import pformat as pf
-
-from htmlgen import HTMLgen2 as HT
-import reaper
-
-from base.trait import GeneralTrait
-from base import data_set
-from base import species
-from base import webqtlConfig
-from utility import webqtlUtil
-from wqflask.my_pylmm.data import prep_data
-from wqflask.my_pylmm.pyLMM import lmm
-from wqflask.my_pylmm.pyLMM import input
-from utility import helper_functions
-from utility import Plot, Bunch
-from utility import temp_data
-
-from MySQLdb import escape_string as escape
-
-import cPickle as pickle
-import simplejson as json
-
-from pprint import pformat as pf
-
-from redis import Redis
-Redis = Redis()
-
-from flask import Flask, g
-
-class Heatmap(object):
-
- def __init__(self, start_vars, temp_uuid):
-
- trait_db_list = [trait.strip() for trait in start_vars['trait_list'].split(',')]
-
- helper_functions.get_trait_db_obs(self, trait_db_list)
-
- self.temp_uuid = temp_uuid
- self.num_permutations = 5000
- self.dataset = self.trait_list[0][1]
-
- self.json_data = {} #The dictionary that will be used to create the json object that contains all the data needed to create the figure
-
- self.all_sample_list = []
- self.traits = []
-
- chrnames = []
- self.species = species.TheSpecies(dataset=self.trait_list[0][1])
- for key in self.species.chromosomes.chromosomes.keys():
- chrnames.append([self.species.chromosomes.chromosomes[key].name, self.species.chromosomes.chromosomes[key].mb_length])
-
- for trait_db in self.trait_list:
-
- this_trait = trait_db[0]
- self.traits.append(this_trait.name)
- this_sample_data = this_trait.data
-
- for sample in this_sample_data:
- if sample not in self.all_sample_list:
- self.all_sample_list.append(sample)
-
- self.sample_data = []
- for trait_db in self.trait_list:
- this_trait = trait_db[0]
- this_sample_data = this_trait.data
-
- #self.sample_data[this_trait.name] = []
- this_trait_vals = []
- for sample in self.all_sample_list:
- if sample in this_sample_data:
- this_trait_vals.append(this_sample_data[sample].value)
- #self.sample_data[this_trait.name].append(this_sample_data[sample].value)
- else:
- this_trait_vals.append('')
- #self.sample_data[this_trait.name].append('')
- self.sample_data.append(this_trait_vals)
-
- self.gen_reaper_results()
- #self.gen_pylmm_results()
-
- #chrnames = []
- lodnames = []
- chr_pos = []
- pos = []
- markernames = []
-
- for trait in self.trait_results.keys():
- lodnames.append(trait)
-
- for marker in self.dataset.group.markers.markers:
- #if marker['chr'] not in chrnames:
- # chr_ob = [marker['chr'], "filler"]
- # chrnames.append(chr_ob)
- chr_pos.append(marker['chr'])
- pos.append(marker['Mb'])
- markernames.append(marker['name'])
-
- self.json_data['chrnames'] = chrnames
- self.json_data['lodnames'] = lodnames
- self.json_data['chr'] = chr_pos
- self.json_data['pos'] = pos
- self.json_data['markernames'] = markernames
-
- for trait in self.trait_results:
- self.json_data[trait] = self.trait_results[trait]
-
- self.js_data = dict(
- json_data = self.json_data
- )
-
- print("self.js_data:", self.js_data)
-
-
- def gen_reaper_results(self):
- self.trait_results = {}
- for trait_db in self.trait_list:
- self.dataset.group.get_markers()
- this_trait = trait_db[0]
- #this_db = trait_db[1]
- genotype = self.dataset.group.read_genotype_file()
- samples, values, variances = this_trait.export_informative()
-
- trimmed_samples = []
- trimmed_values = []
- for i in range(0, len(samples)):
- if samples[i] in self.dataset.group.samplelist:
- trimmed_samples.append(samples[i])
- trimmed_values.append(values[i])
-
- self.lrs_array = genotype.permutation(strains = trimmed_samples,
- trait = trimmed_values,
- nperm= self.num_permutations)
-
- #self.suggestive = self.lrs_array[int(self.num_permutations*0.37-1)]
- #self.significant = self.lrs_array[int(self.num_permutations*0.95-1)]
-
- reaper_results = genotype.regression(strains = trimmed_samples,
- trait = trimmed_values)
-
-
- lrs_values = [float(qtl.lrs) for qtl in reaper_results]
- print("lrs_values:", lrs_values)
- #self.dataset.group.markers.add_pvalues(p_values)
-
- self.trait_results[this_trait.name] = []
- for qtl in reaper_results:
- if qtl.additive > 0:
- self.trait_results[this_trait.name].append(-float(qtl.lrs))
- else:
- self.trait_results[this_trait.name].append(float(qtl.lrs))
- #for lrs in lrs_values:
- # if
- # self.trait_results[this_trait.name].append(lrs)
-
-
- #this_db_samples = self.dataset.group.samplelist
- #this_sample_data = this_trait.data
- ##print("this_sample_data", this_sample_data)
- #this_trait_vals = []
- #for index, sample in enumerate(this_db_samples):
- # if sample in this_sample_data:
- # sample_value = this_sample_data[sample].value
- # this_trait_vals.append(sample_value)
- # else:
- # this_trait_vals.append("x")
-
- #pheno_vector = np.array([val == "x" and np.nan or float(val) for val in this_trait_vals])
-
- #key = "pylmm:input:" + str(self.temp_uuid)
- #print("key is:", pf(key))
-
- #genotype_data = [marker['genotypes'] for marker in self.dataset.group.markers.markers]
-
- #no_val_samples = self.identify_empty_samples(this_trait_vals)
- #trimmed_genotype_data = self.trim_genotypes(genotype_data, no_val_samples)
-
- #genotype_matrix = np.array(trimmed_genotype_data).T
-
- #print("genotype_matrix:", str(genotype_matrix.tolist()))
- #print("pheno_vector:", str(pheno_vector.tolist()))
-
- #params = dict(pheno_vector = pheno_vector.tolist(),
- # genotype_matrix = genotype_matrix.tolist(),
- # restricted_max_likelihood = True,
- # refit = False,
- # temp_uuid = str(self.temp_uuid),
- #
- # # meta data
- # timestamp = datetime.datetime.now().isoformat(),
- # )
- #
- #json_params = json.dumps(params)
- ##print("json_params:", json_params)
- #Redis.set(key, json_params)
- #Redis.expire(key, 60*60)
- #print("before printing command")
- #
- #command = 'python /home/zas1024/gene/wqflask/wqflask/my_pylmm/pyLMM/lmm.py --key {} --species {}'.format(key,
- # "other")
- #print("command is:", command)
- #print("after printing command")
- #
- #os.system(command)
- #
- #json_results = Redis.blpop("pylmm:results:" + str(self.temp_uuid), 45*60)
-
- def gen_pylmm_results(self):
- self.trait_results = {}
- for trait_db in self.trait_list:
- this_trait = trait_db[0]
- #this_db = trait_db[1]
- self.dataset.group.get_markers()
-
- this_db_samples = self.dataset.group.samplelist
- this_sample_data = this_trait.data
- #print("this_sample_data", this_sample_data)
- this_trait_vals = []
- for index, sample in enumerate(this_db_samples):
- if sample in this_sample_data:
- sample_value = this_sample_data[sample].value
- this_trait_vals.append(sample_value)
- else:
- this_trait_vals.append("x")
-
- pheno_vector = np.array([val == "x" and np.nan or float(val) for val in this_trait_vals])
-
- key = "pylmm:input:" + str(self.temp_uuid)
- #print("key is:", pf(key))
-
- genotype_data = [marker['genotypes'] for marker in self.dataset.group.markers.markers]
-
- no_val_samples = self.identify_empty_samples(this_trait_vals)
- trimmed_genotype_data = self.trim_genotypes(genotype_data, no_val_samples)
-
- genotype_matrix = np.array(trimmed_genotype_data).T
-
- #print("genotype_matrix:", str(genotype_matrix.tolist()))
- #print("pheno_vector:", str(pheno_vector.tolist()))
-
- params = dict(pheno_vector = pheno_vector.tolist(),
- genotype_matrix = genotype_matrix.tolist(),
- restricted_max_likelihood = True,
- refit = False,
- temp_uuid = str(self.temp_uuid),
-
- # meta data
- timestamp = datetime.datetime.now().isoformat(),
- )
-
- json_params = json.dumps(params)
- #print("json_params:", json_params)
- Redis.set(key, json_params)
- Redis.expire(key, 60*60)
- print("before printing command")
-
- command = 'python /home/zas1024/gene/wqflask/wqflask/my_pylmm/pyLMM/lmm.py --key {} --species {}'.format(key,
- "other")
- print("command is:", command)
- print("after printing command")
-
- os.system(command)
-
- json_results = Redis.blpop("pylmm:results:" + str(self.temp_uuid), 45*60)
- results = json.loads(json_results[1])
- p_values = [float(result) for result in results['p_values']]
- #print("p_values:", p_values)
- self.dataset.group.markers.add_pvalues(p_values)
-
- self.trait_results[this_trait.name] = []
- for marker in self.dataset.group.markers.markers:
- self.trait_results[this_trait.name].append(marker['lod_score'])
-
-
- def identify_empty_samples(self, values):
- no_val_samples = []
- for sample_count, val in enumerate(values):
- if val == "x":
- no_val_samples.append(sample_count)
- return no_val_samples
-
- def trim_genotypes(self, genotype_data, no_value_samples):
- trimmed_genotype_data = []
- for marker in genotype_data:
- new_genotypes = []
- for item_count, genotype in enumerate(marker):
- if item_count in no_value_samples:
- continue
- try:
- genotype = float(genotype)
- except ValueError:
- genotype = np.nan
- pass
- new_genotypes.append(genotype)
- trimmed_genotype_data.append(new_genotypes)
- return trimmed_genotype_data
-
- \ No newline at end of file
+from __future__ import absolute_import, print_function, division
+
+import sys
+sys.path.append(".")
+
+import gc
+import string
+import cPickle
+import os
+import datetime
+import time
+import pp
+import math
+import collections
+import resource
+
+import scipy
+import numpy as np
+from scipy import linalg
+
+from pprint import pformat as pf
+
+from htmlgen import HTMLgen2 as HT
+import reaper
+
+from base.trait import GeneralTrait
+from base import data_set
+from base import species
+from base import webqtlConfig
+from utility import webqtlUtil
+from wqflask.my_pylmm.data import prep_data
+# from wqflask.my_pylmm.pyLMM import lmm
+# from wqflask.my_pylmm.pyLMM import input
+from utility import helper_functions
+from utility import Plot, Bunch
+from utility import temp_data
+
+from MySQLdb import escape_string as escape
+
+import cPickle as pickle
+import simplejson as json
+
+from pprint import pformat as pf
+
+from redis import Redis
+Redis = Redis()
+
+from flask import Flask, g
+
+class Heatmap(object):
+
+ def __init__(self, start_vars, temp_uuid):
+
+ trait_db_list = [trait.strip() for trait in start_vars['trait_list'].split(',')]
+
+ helper_functions.get_trait_db_obs(self, trait_db_list)
+
+ self.temp_uuid = temp_uuid
+ self.num_permutations = 5000
+ self.dataset = self.trait_list[0][1]
+
+ self.json_data = {} #The dictionary that will be used to create the json object that contains all the data needed to create the figure
+
+ self.all_sample_list = []
+ self.traits = []
+
+ chrnames = []
+ self.species = species.TheSpecies(dataset=self.trait_list[0][1])
+ for key in self.species.chromosomes.chromosomes.keys():
+ chrnames.append([self.species.chromosomes.chromosomes[key].name, self.species.chromosomes.chromosomes[key].mb_length])
+
+ for trait_db in self.trait_list:
+
+ this_trait = trait_db[0]
+ self.traits.append(this_trait.name)
+ this_sample_data = this_trait.data
+
+ for sample in this_sample_data:
+ if sample not in self.all_sample_list:
+ self.all_sample_list.append(sample)
+
+ self.sample_data = []
+ for trait_db in self.trait_list:
+ this_trait = trait_db[0]
+ this_sample_data = this_trait.data
+
+ #self.sample_data[this_trait.name] = []
+ this_trait_vals = []
+ for sample in self.all_sample_list:
+ if sample in this_sample_data:
+ this_trait_vals.append(this_sample_data[sample].value)
+ #self.sample_data[this_trait.name].append(this_sample_data[sample].value)
+ else:
+ this_trait_vals.append('')
+ #self.sample_data[this_trait.name].append('')
+ self.sample_data.append(this_trait_vals)
+
+ self.gen_reaper_results()
+ #self.gen_pylmm_results()
+
+ #chrnames = []
+ lodnames = []
+ chr_pos = []
+ pos = []
+ markernames = []
+
+ for trait in self.trait_results.keys():
+ lodnames.append(trait)
+
+ for marker in self.dataset.group.markers.markers:
+ #if marker['chr'] not in chrnames:
+ # chr_ob = [marker['chr'], "filler"]
+ # chrnames.append(chr_ob)
+ chr_pos.append(marker['chr'])
+ pos.append(marker['Mb'])
+ markernames.append(marker['name'])
+
+ self.json_data['chrnames'] = chrnames
+ self.json_data['lodnames'] = lodnames
+ self.json_data['chr'] = chr_pos
+ self.json_data['pos'] = pos
+ self.json_data['markernames'] = markernames
+
+ for trait in self.trait_results:
+ self.json_data[trait] = self.trait_results[trait]
+
+ self.js_data = dict(
+ json_data = self.json_data
+ )
+
+ print("self.js_data:", self.js_data)
+
+
+ def gen_reaper_results(self):
+ self.trait_results = {}
+ for trait_db in self.trait_list:
+ self.dataset.group.get_markers()
+ this_trait = trait_db[0]
+ #this_db = trait_db[1]
+ genotype = self.dataset.group.read_genotype_file()
+ samples, values, variances = this_trait.export_informative()
+
+ trimmed_samples = []
+ trimmed_values = []
+ for i in range(0, len(samples)):
+ if samples[i] in self.dataset.group.samplelist:
+ trimmed_samples.append(samples[i])
+ trimmed_values.append(values[i])
+
+ self.lrs_array = genotype.permutation(strains = trimmed_samples,
+ trait = trimmed_values,
+ nperm= self.num_permutations)
+
+ #self.suggestive = self.lrs_array[int(self.num_permutations*0.37-1)]
+ #self.significant = self.lrs_array[int(self.num_permutations*0.95-1)]
+
+ reaper_results = genotype.regression(strains = trimmed_samples,
+ trait = trimmed_values)
+
+
+ lrs_values = [float(qtl.lrs) for qtl in reaper_results]
+ print("lrs_values:", lrs_values)
+ #self.dataset.group.markers.add_pvalues(p_values)
+
+ self.trait_results[this_trait.name] = []
+ for qtl in reaper_results:
+ if qtl.additive > 0:
+ self.trait_results[this_trait.name].append(-float(qtl.lrs))
+ else:
+ self.trait_results[this_trait.name].append(float(qtl.lrs))
+ #for lrs in lrs_values:
+ # if
+ # self.trait_results[this_trait.name].append(lrs)
+
+
+ #this_db_samples = self.dataset.group.samplelist
+ #this_sample_data = this_trait.data
+ ##print("this_sample_data", this_sample_data)
+ #this_trait_vals = []
+ #for index, sample in enumerate(this_db_samples):
+ # if sample in this_sample_data:
+ # sample_value = this_sample_data[sample].value
+ # this_trait_vals.append(sample_value)
+ # else:
+ # this_trait_vals.append("x")
+
+ #pheno_vector = np.array([val == "x" and np.nan or float(val) for val in this_trait_vals])
+
+ #key = "pylmm:input:" + str(self.temp_uuid)
+ #print("key is:", pf(key))
+
+ #genotype_data = [marker['genotypes'] for marker in self.dataset.group.markers.markers]
+
+ #no_val_samples = self.identify_empty_samples(this_trait_vals)
+ #trimmed_genotype_data = self.trim_genotypes(genotype_data, no_val_samples)
+
+ #genotype_matrix = np.array(trimmed_genotype_data).T
+
+ #print("genotype_matrix:", str(genotype_matrix.tolist()))
+ #print("pheno_vector:", str(pheno_vector.tolist()))
+
+ #params = dict(pheno_vector = pheno_vector.tolist(),
+ # genotype_matrix = genotype_matrix.tolist(),
+ # restricted_max_likelihood = True,
+ # refit = False,
+ # temp_uuid = str(self.temp_uuid),
+ #
+ # # meta data
+ # timestamp = datetime.datetime.now().isoformat(),
+ # )
+ #
+ #json_params = json.dumps(params)
+ ##print("json_params:", json_params)
+ #Redis.set(key, json_params)
+ #Redis.expire(key, 60*60)
+ #print("before printing command")
+ #
+ #command = 'python /home/zas1024/gene/wqflask/wqflask/my_pylmm/pyLMM/lmm.py --key {} --species {}'.format(key,
+ # "other")
+ #print("command is:", command)
+ #print("after printing command")
+ #
+ #os.system(command)
+ #
+ #json_results = Redis.blpop("pylmm:results:" + str(self.temp_uuid), 45*60)
+
+ def gen_pylmm_results(self):
+ # This function is NOT used. If it is, we should use a shared function with marker_regression.py
+ self.trait_results = {}
+ for trait_db in self.trait_list:
+ this_trait = trait_db[0]
+ #this_db = trait_db[1]
+ self.dataset.group.get_markers()
+
+ this_db_samples = self.dataset.group.samplelist
+ this_sample_data = this_trait.data
+ #print("this_sample_data", this_sample_data)
+ this_trait_vals = []
+ for index, sample in enumerate(this_db_samples):
+ if sample in this_sample_data:
+ sample_value = this_sample_data[sample].value
+ this_trait_vals.append(sample_value)
+ else:
+ this_trait_vals.append("x")
+
+ pheno_vector = np.array([val == "x" and np.nan or float(val) for val in this_trait_vals])
+
+ key = "pylmm:input:" + str(self.temp_uuid)
+ #print("key is:", pf(key))
+
+ genotype_data = [marker['genotypes'] for marker in self.dataset.group.markers.markers]
+
+ no_val_samples = self.identify_empty_samples(this_trait_vals)
+ trimmed_genotype_data = self.trim_genotypes(genotype_data, no_val_samples)
+
+ genotype_matrix = np.array(trimmed_genotype_data).T
+
+ #print("genotype_matrix:", str(genotype_matrix.tolist()))
+ #print("pheno_vector:", str(pheno_vector.tolist()))
+
+ params = dict(pheno_vector = pheno_vector.tolist(),
+ genotype_matrix = genotype_matrix.tolist(),
+ restricted_max_likelihood = True,
+ refit = False,
+ temp_uuid = str(self.temp_uuid),
+
+ # meta data
+ timestamp = datetime.datetime.now().isoformat(),
+ )
+
+ json_params = json.dumps(params)
+ #print("json_params:", json_params)
+ Redis.set(key, json_params)
+ Redis.expire(key, 60*60)
+ print("before printing command")
+
+ command = 'python /home/zas1024/gene/wqflask/wqflask/my_pylmm/pyLMM/lmm.py --key {} --species {}'.format(key,
+ "other")
+ print("command is:", command)
+ print("after printing command")
+
+ os.system(command)
+
+ json_results = Redis.blpop("pylmm:results:" + str(self.temp_uuid), 45*60)
+ results = json.loads(json_results[1])
+ p_values = [float(result) for result in results['p_values']]
+ #print("p_values:", p_values)
+ self.dataset.group.markers.add_pvalues(p_values)
+
+ self.trait_results[this_trait.name] = []
+ for marker in self.dataset.group.markers.markers:
+ self.trait_results[this_trait.name].append(marker['lod_score'])
+
+
+ def identify_empty_samples(self, values):
+ no_val_samples = []
+ for sample_count, val in enumerate(values):
+ if val == "x":
+ no_val_samples.append(sample_count)
+ return no_val_samples
+
+ def trim_genotypes(self, genotype_data, no_value_samples):
+ trimmed_genotype_data = []
+ for marker in genotype_data:
+ new_genotypes = []
+ for item_count, genotype in enumerate(marker):
+ if item_count in no_value_samples:
+ continue
+ try:
+ genotype = float(genotype)
+ except ValueError:
+ genotype = np.nan
+ pass
+ new_genotypes.append(genotype)
+ trimmed_genotype_data.append(new_genotypes)
+ return trimmed_genotype_data
+
+
diff --git a/wqflask/wqflask/marker_regression/marker_regression.py b/wqflask/wqflask/marker_regression/marker_regression.py
index 49521bd6..c5fab4ee 100755
--- a/wqflask/wqflask/marker_regression/marker_regression.py
+++ b/wqflask/wqflask/marker_regression/marker_regression.py
@@ -37,8 +37,8 @@ from utility import webqtlUtil
from wqflask.marker_regression import gemma_mapping
#from wqflask.marker_regression import rqtl_mapping
from wqflask.my_pylmm.data import prep_data
-from wqflask.my_pylmm.pyLMM import lmm
-from wqflask.my_pylmm.pyLMM import input
+# from wqflask.my_pylmm.pyLMM import lmm
+# from wqflask.my_pylmm.pyLMM import input
from utility import helper_functions
from utility import Plot, Bunch
from utility import temp_data