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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
import itertools
import utility.logger
logger = utility.logger.getLogger(__name__ )
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
self.sample_attribute_values = {}
self.get_attributes()
logger.debug("camera: attributes are:", pf(self.attributes))
if self.this_trait and self.dataset and self.dataset.type == 'ProbeSet':
self.get_extra_attribute_values()
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:
logger.debug("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"
logger.debug(" 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.sample_attribute_values:
sample.extra_attributes = self.sample_attribute_values.get(sample_name, {})
logger.debug("sample.extra_attributes is", pf(sample.extra_attributes))
self.sample_list.append(sample)
logger.debug("self.attributes is", pf(self.attributes))
self.do_outliers()
#do_outliers(the_samples)
logger.debug("*the_samples are [%i]: %s" % (len(self.sample_list), pf(self.sample_list)))
for sample in self.sample_list:
logger.debug("apple:", type(sample), sample)
#return the_samples
def __repr__(self):
return "<SampleList> --> %s" % (pf(self.__dict__))
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"""
# Get attribute names and distinct values for each attribute
results = g.db.execute('''
SELECT DISTINCT CaseAttribute.Id, CaseAttribute.Name, CaseAttributeXRef.Value
FROM CaseAttribute, CaseAttributeXRef
WHERE CaseAttributeXRef.CaseAttributeId = CaseAttribute.Id
AND CaseAttributeXRef.ProbeSetFreezeId = %s
ORDER BY CaseAttribute.Name''', (str(self.dataset.id),))
self.attributes = {}
for attr, values in itertools.groupby(results.fetchall(), lambda row: (row.Id, row.Name)):
key, name = attr
logger.debug("radish: %s - %s" % (key, name))
self.attributes[key] = Bunch()
self.attributes[key].name = name
self.attributes[key].distinct_values = [item.Value for item in values]
self.attributes[key].distinct_values.sort(key=natural_sort_key)
def get_extra_attribute_values(self):
if self.attributes:
results = g.db.execute('''
SELECT Strain.Name AS SampleName, CaseAttributeId AS Id, CaseAttributeXRef.Value
FROM Strain, StrainXRef, InbredSet, CaseAttributeXRef
WHERE StrainXRef.StrainId = Strain.Id
AND InbredSet.Id = StrainXRef.InbredSetId
AND CaseAttributeXRef.StrainId = Strain.Id
AND InbredSet.Name = %s
AND CaseAttributeXRef.ProbeSetFreezeId = %s
ORDER BY SampleName''',
(self.dataset.group.name, self.this_trait.dataset.id))
for sample_name, items in itertools.groupby(results.fetchall(), lambda row: row.SampleName):
attribute_values = {}
for item in items:
attribute_value = item.Value
#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[item.Id].name] = attribute_value
self.sample_attribute_values[sample_name] = 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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