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from __future__ import absolute_import, print_function, division
from flask import Flask, g
from base import webqtlCaseData
from utility import webqtlUtil, Plot, Bunch
from base.trait import GeneralTrait
import numpy as np
from scipy import stats
from pprint import pformat as pf
class SampleList(object):
def __init__(self,
dataset,
sample_names,
this_trait,
sample_group_type,
header):
self.dataset = dataset
self.this_trait = this_trait
self.sample_group_type = sample_group_type # primary or other
self.header = header
self.sample_list = [] # The actual list
try:
self.get_attributes()
except Exception:
print("failed to get attributes")
self.attributes = {}
print("camera: attributes are:", pf(self.attributes))
for counter, sample_name in enumerate(sample_names, 1):
sample_name = sample_name.replace("_2nd_", "")
#ZS - If there's no value for the sample/strain, create the sample object (so samples with no value are still displayed in the table)
try:
sample = self.this_trait.data[sample_name]
except KeyError:
print("No sample %s, let's create it now" % sample_name)
sample = webqtlCaseData.webqtlCaseData(sample_name)
#sampleNameAdd = ''
#if fd.RISet == 'AXBXA' and sampleName in ('AXB18/19/20','AXB13/14','BXA8/17'):
# sampleNameAdd = HT.Href(url='/mouseCross.html#AXB/BXA', text=HT.Sup('#'), Class='fs12', target="_blank")
sample.extra_info = {}
if self.dataset.group.name == 'AXBXA' and sample_name in ('AXB18/19/20','AXB13/14','BXA8/17'):
sample.extra_info['url'] = "/mouseCross.html#AXB/BXA"
sample.extra_info['css_class'] = "fs12"
print(" type of sample:", type(sample))
if sample_group_type == 'primary':
sample.this_id = "Primary_" + str(counter)
else:
sample.this_id = "Other_" + str(counter)
#### For extra attribute columns; currently only used by several datasets - Zach
if self.this_trait and self.dataset and self.dataset.type == 'ProbeSet':
sample.extra_attributes = self.get_extra_attribute_values(sample_name)
print("sample.extra_attributes is", pf(sample.extra_attributes))
self.sample_list.append(sample)
print("self.attributes is", pf(self.attributes))
self.get_z_scores()
self.do_outliers()
#do_outliers(the_samples)
print("*the_samples are [%i]: %s" % (len(self.sample_list), pf(self.sample_list)))
for sample in self.sample_list:
print("apple:", type(sample), sample)
#return the_samples
def __repr__(self):
return "<SampleList> --> %s" % (pf(self.__dict__))
#def get_z_scores(self):
# values = [sample.value for sample in self.sample_list if sample.value != None]
# dataX = values[:]
# dataX.sort(webqtlUtil.cmpOrder)
# dataY=webqtlUtil.U(len(dataX))
# z_scores=map(webqtlUtil.inverseCumul, dataY)
#
# print("self.sample_list:", [sample for sample in self.sample_list if sample.value != None])
# print("z_scores:", len(z_scores))
# for i, sample in enumerate([sample for sample in self.sample_list if sample.value != None]):
# print("sample is:", sample)
# sample.z_score = z_scores[i]
#def get_z_scores(self):
# values = [sample.value for sample in self.sample_list if sample.value != None]
# z_scores = z_score(values)
#
# print("self.sample_list:", [sample for sample in self.sample_list if sample.value != None])
# print("z_scores:", len(z_scores))
# for i, sample in enumerate([sample for sample in self.sample_list if sample.value != None]):
# print("sample is:", sample)
# sample.z_score = z_scores[i]
def get_z_scores(self):
values = [sample.value for sample in self.sample_list if sample.value != None]
numpy_array = np.array(values)
prob_plot = stats.probplot(numpy_array)[0]
print("prob_plot:", prob_plot)
values = prob_plot[1]
z_scores = prob_plot[0]
print("z_scores:", z_scores)
print("self.sample_list:", [sample for sample in self.sample_list if sample.value != None])
for i, sample in enumerate([sample for sample in self.sample_list if sample.value != None]):
print("sample is:", sample)
sample.z_score = z_scores[i]
sample.prob_plot_value = values[i]
def do_outliers(self):
values = [sample.value for sample in self.sample_list if sample.value != None]
upper_bound, lower_bound = Plot.find_outliers(values)
for sample in self.sample_list:
if sample.value:
if upper_bound and sample.value > upper_bound:
sample.outlier = True
elif lower_bound and sample.value < lower_bound:
sample.outlier = True
else:
sample.outlier = False
def get_attributes(self):
"""Finds which extra attributes apply to this dataset"""
#ZS: Id and name values for this trait's extra attributes
case_attributes = g.db.execute('''SELECT CaseAttribute.Id, CaseAttribute.Name
FROM CaseAttribute, CaseAttributeXRef
WHERE CaseAttributeXRef.ProbeSetFreezeId = %s AND
CaseAttribute.Id = CaseAttributeXRef.CaseAttributeId
group by CaseAttributeXRef.CaseAttributeId''',
(str(self.dataset.id),))
self.attributes = {}
for key, value in case_attributes.fetchall():
print("radish: %s - %s" % (key, value))
self.attributes[key] = Bunch()
self.attributes[key].name = value
attribute_values = g.db.execute('''SELECT DISTINCT CaseAttributeXRef.Value
FROM CaseAttribute, CaseAttributeXRef
WHERE CaseAttribute.Name = %s AND
CaseAttributeXRef.CaseAttributeId = CaseAttribute.Id''', (value,))
self.attributes[key].distinct_values = [item[0] for item in attribute_values.fetchall()]
self.attributes[key].distinct_values.sort(key=natural_sort_key)
def get_extra_attribute_values(self, sample_name):
attribute_values = {}
if self.attributes:
#ZS: Get StrainId value for the next query
result = g.db.execute("""SELECT Strain.Id
FROM Strain, StrainXRef, InbredSet
WHERE Strain.Name = %s and
StrainXRef.StrainId = Strain.Id and
InbredSet.Id = StrainXRef.InbredSetId and
InbredSet.Name = %s""", (sample_name,
self.dataset.group.name))
sample_id = result.fetchone().Id
for attribute in self.attributes:
#ZS: Add extra case attribute values (if any)
result = g.db.execute("""SELECT Value
FROM CaseAttributeXRef
WHERE ProbeSetFreezeId = %s AND
StrainId = %s AND
CaseAttributeId = %s
group by CaseAttributeXRef.CaseAttributeId""", (
self.this_trait.dataset.id, sample_id, str(attribute)))
attribute_value = result.fetchone().Value #Trait-specific attributes, if any
#ZS: If it's an int, turn it into one for sorting
#(for example, 101 would be lower than 80 if they're strings instead of ints)
try:
attribute_value = int(attribute_value)
except ValueError:
pass
attribute_values[self.attributes[attribute].name] = attribute_value
return attribute_values
def se_exists(self):
"""Returns true if SE values exist for any samples, otherwise false"""
return any(sample.variance for sample in self.sample_list)
#def z_score(vals):
# vals_array = np.array(vals)
# mean = np.mean(vals_array)
# stdv = np.std(vals_array)
#
# z_scores = []
# for val in vals_array:
# z_score = (val - mean)/stdv
# z_scores.append(z_score)
#
#
#
# return z_scores
#def z_score(row):
# L = [n for n in row if not np.isnan(n)]
# m = np.mean(L)
# s = np.std(L)
# zL = [1.0 * (n - m) / s for n in L]
# if len(L) == len(row): return zL
# # deal with nan
# retL = list()
# for n in row:
# if np.isnan(n):
# retL.append(nan)
# else:
# retL.append(zL.pop(0))
# assert len(zL) == 0
# return retL
def natural_sort_key(x):
"""Get expected results when using as a key for sort - ints or strings are sorted properly"""
try:
x = int(x)
except ValueError:
pass
return x
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