path: root/wqflask/base/data_set.py

# 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)

from __future__ import absolute_import, print_function, division
import os
import math
import string
import collections
import codecs

import json
import gzip
import cPickle as pickle
import itertools
from operator import itemgetter

from redis import Redis
Redis = Redis()

from flask import Flask, g

import reaper

from base import webqtlConfig
from base import species
from dbFunction import webqtlDatabaseFunction
from utility import webqtlUtil
from utility.benchmark import Bench
from utility import chunks
from utility.tools import locate, locate_ignore_error

from maintenance import get_group_samplelists

from MySQLdb import escape_string as escape
from pprint import pformat as pf

# Used by create_database to instantiate objects
# Each subclass will add to this
DS_NAME_MAP = {}

def create_dataset(dataset_name, dataset_type = None, get_samplelist = True):
    if not dataset_type:
        dataset_type = Dataset_Getter(dataset_name)
        #dataset_type = get_dataset_type_from_json(dataset_name)

        print("dataset_type is:", dataset_type)
        #query = """
        #    SELECT DBType.Name
        #    FROM DBList, DBType
        #    WHERE DBList.Name = '{}' and
        #          DBType.Id = DBList.DBTypeId
        #    """.format(escape(dataset_name))
        #dataset_type = g.db.execute(query).fetchone().Name


    dataset_ob = DS_NAME_MAP[dataset_type]
    dataset_class = globals()[dataset_ob]
    return dataset_class(dataset_name, get_samplelist)


#def get_dataset_type_from_json(dataset_name):

class Dataset_Types(object):

    def __init__(self):
        self.datasets = {}
        file_name = "wqflask/static/new/javascript/dataset_menu_structure.json"
        with open(file_name, 'r') as fh:
            data = json.load(fh)

        print("*" * 70)
        for species in data['datasets']:
            for group in data['datasets'][species]:
                for dataset_type in data['datasets'][species][group]:
                    for dataset in data['datasets'][species][group][dataset_type]:
                        #print("dataset is:", dataset)

                        short_dataset_name = dataset[1]
                        if dataset_type == "Phenotypes":
                            new_type = "Publish"
                        elif dataset_type == "Genotypes":
                            new_type = "Geno"
                        else:
                            new_type = "ProbeSet"
                        self.datasets[short_dataset_name] = new_type

    def __call__(self, name):
        return self.datasets[name]

# Do the intensive work at startup one time only
Dataset_Getter = Dataset_Types()

#
#print("Running at startup:", get_dataset_type_from_json("HBTRC-MLPFC_0611"))


def create_datasets_list():
    key = "all_datasets"
    result = Redis.get(key)

    if result:
        print("Cache hit!!!")
        datasets = pickle.loads(result)

    else:
        datasets = list()
        with Bench("Creating DataSets object"):
            type_dict = {'Publish': 'PublishFreeze',
                         'ProbeSet': 'ProbeSetFreeze',
                         'Geno': 'GenoFreeze'}

            for dataset_type in type_dict:
                query = "SELECT Name FROM {}".format(type_dict[dataset_type])
                for result in g.db.execute(query).fetchall():
                    #The query at the beginning of this function isn't necessary here, but still would
                    #rather just reuse it
                    #print("type: {}\tname: {}".format(dataset_type, result.Name))
                    dataset = create_dataset(result.Name, dataset_type)
                    datasets.append(dataset)

        Redis.set(key, pickle.dumps(datasets, pickle.HIGHEST_PROTOCOL))
        Redis.expire(key, 60*60)

    return datasets


def create_in_clause(items):
    """Create an in clause for mysql"""
    in_clause = ', '.join("'{}'".format(x) for x in mescape(*items))
    in_clause = '( {} )'.format(in_clause)
    return in_clause


def mescape(*items):
    """Multiple escape"""
    escaped = [escape(str(item)) for item in items]
    #print("escaped is:", escaped)
    return escaped


class Markers(object):
    """Todo: Build in cacheing so it saves us reading the same file more than once"""
    def __init__(self, name):
        json_data_fh = open(locate(name + '.json','genotype/json'))
        try:
            markers = json.load(json_data_fh)
        except:
            markers = []

        for marker in markers:
            if (marker['chr'] != "X") and (marker['chr'] != "Y"):
                marker['chr'] = int(marker['chr'])
            marker['Mb'] = float(marker['Mb'])

        self.markers = markers
        #print("self.markers:", self.markers)


    def add_pvalues(self, p_values):
        print("length of self.markers:", len(self.markers))
        print("length of p_values:", len(p_values))

        if type(p_values) is list:
            # THIS IS only needed for the case when we are limiting the number of p-values calculated
            #if len(self.markers) > len(p_values):
            #    self.markers = self.markers[:len(p_values)]

            for marker, p_value in itertools.izip(self.markers, p_values):
                if not p_value:
                    continue
                marker['p_value'] = float(p_value)
                if math.isnan(marker['p_value']) or marker['p_value'] <= 0:
                    marker['lod_score'] = 0
                    marker['lrs_value'] = 0
                else:
                    marker['lod_score'] = -math.log10(marker['p_value'])
                    #Using -log(p) for the LRS; need to ask Rob how he wants to get LRS from p-values
                    marker['lrs_value'] = -math.log10(marker['p_value']) * 4.61
        elif type(p_values) is dict:
            filtered_markers = []
            for marker in self.markers:
                #print("marker[name]", marker['name'])
                #print("p_values:", p_values)
                if marker['name'] in p_values:
                    #print("marker {} IS in p_values".format(i))
                    marker['p_value'] = p_values[marker['name']]
                    if math.isnan(marker['p_value']) or (marker['p_value'] <= 0):
                        marker['lod_score'] = 0
                        marker['lrs_value'] = 0
                    else:
                        marker['lod_score'] = -math.log10(marker['p_value'])
                        #Using -log(p) for the LRS; need to ask Rob how he wants to get LRS from p-values
                        marker['lrs_value'] = -math.log10(marker['p_value']) * 4.61
                    filtered_markers.append(marker)
                #else:
                    #print("marker {} NOT in p_values".format(i))
                    #self.markers.remove(marker)
                    #del self.markers[i]
            self.markers = filtered_markers


        #for i, marker in enumerate(self.markers):
        #    if not 'p_value' in marker:
        #        #print("self.markers[i]", self.markers[i])
        #        del self.markers[i]
        #        #self.markers.remove(self.markers[i])

class HumanMarkers(Markers):

    def __init__(self, name, specified_markers = []):
        marker_data_fh = open(locate('genotype') + '/' + name + '.bim')
        self.markers = []
        for line in marker_data_fh:
            splat = line.strip().split()
            #print("splat:", splat)
            if len(specified_markers) > 0:
                if splat[1] in specified_markers:
                    marker = {}
                    marker['chr'] = int(splat[0])
                    marker['name'] = splat[1]
                    marker['Mb'] = float(splat[3]) / 1000000
                else:
                    continue
            else:
                marker = {}
                marker['chr'] = int(splat[0])
                marker['name'] = splat[1]
                marker['Mb'] = float(splat[3]) / 1000000
            self.markers.append(marker)

        #print("markers is: ", pf(self.markers))


    def add_pvalues(self, p_values):
        #for marker, p_value in itertools.izip(self.markers, p_values):
        #    if marker['Mb'] <= 0 and marker['chr'] == 0:
        #        continue
        #    marker['p_value'] = p_value
        #    print("p_value is:", marker['p_value'])
        #    marker['lod_score'] = -math.log10(marker['p_value'])
        #    #Using -log(p) for the LRS; need to ask Rob how he wants to get LRS from p-values
        #    marker['lrs_value'] = -math.log10(marker['p_value']) * 4.61

        #print("p_values2:", pf(p_values))
        super(HumanMarkers, self).add_pvalues(p_values)

        #with Bench("deleting markers"):
        #    markers = []
        #    for marker in self.markers:
        #        if not marker['Mb'] <= 0 and not marker['chr'] == 0:
        #            markers.append(marker)
        #    self.markers = markers



class DatasetGroup(object):
    """
    Each group has multiple datasets; each species has multiple groups.

    For example, Mouse has multiple groups (BXD, BXA, etc), and each group
    has multiple datasets associated with it.

    """
    def __init__(self, dataset):
        """This sets self.group and self.group_id"""
        #print("DATASET NAME2:", dataset.name)
        self.name, self.id = g.db.execute(dataset.query_for_group).fetchone()
        if self.name == 'BXD300':
            self.name = "BXD"

        self.f1list = None
        self.parlist = None
        self.get_f1_parent_strains()
        #print("parents/f1s: {}:{}".format(self.parlist, self.f1list))

        self.species = webqtlDatabaseFunction.retrieve_species(self.name)

        self.incparentsf1 = False
        self.allsamples = None
        self._datasets = None

    def get_specified_markers(self, markers = []):
        self.markers = HumanMarkers(self.name, markers)

    def get_markers(self):
        #print("self.species is:", self.species)
        if self.species == "human":
            marker_class = HumanMarkers
        else:
            marker_class = Markers

        self.markers = marker_class(self.name)

    def datasets(self):
        key = "group_dataset_menu:v2:" + self.name
        print("key is2:", key)
        #with Bench("Loading cache"):
        #    result = Redis.get(key)
        #if result:
        #    self._datasets = pickle.loads(result)
        #    return self._datasets

        dataset_menu = []
        print("[tape4] webqtlConfig.PUBLICTHRESH:", webqtlConfig.PUBLICTHRESH)
        print("[tape4] type webqtlConfig.PUBLICTHRESH:", type(webqtlConfig.PUBLICTHRESH))
        results = g.db.execute('''
             (SELECT '#PublishFreeze',PublishFreeze.FullName,PublishFreeze.Name
              FROM PublishFreeze,InbredSet
              WHERE PublishFreeze.InbredSetId = InbredSet.Id
                and InbredSet.Name = %s
                and PublishFreeze.public > %s)
             UNION
             (SELECT '#GenoFreeze',GenoFreeze.FullName,GenoFreeze.Name
              FROM GenoFreeze, InbredSet
              WHERE GenoFreeze.InbredSetId = InbredSet.Id
                and InbredSet.Name = %s
                and GenoFreeze.public > %s)
             UNION
             (SELECT Tissue.Name, ProbeSetFreeze.FullName,ProbeSetFreeze.Name
              FROM ProbeSetFreeze, ProbeFreeze, InbredSet, Tissue
              WHERE ProbeSetFreeze.ProbeFreezeId = ProbeFreeze.Id
                and ProbeFreeze.TissueId = Tissue.Id
                and ProbeFreeze.InbredSetId = InbredSet.Id
                and InbredSet.Name like %s
                and ProbeSetFreeze.public > %s
              ORDER BY Tissue.Name, ProbeSetFreeze.CreateTime desc, ProbeSetFreeze.AvgId)
            ''', (self.name, webqtlConfig.PUBLICTHRESH,
                  self.name, webqtlConfig.PUBLICTHRESH,
                  "%" + self.name + "%", webqtlConfig.PUBLICTHRESH))

        the_results = results.fetchall()

        #for tissue_name, dataset in itertools.groupby(the_results, itemgetter(0)):
        for dataset_item in the_results:
            tissue_name = dataset_item[0]
            dataset = dataset_item[1]
            dataset_short = dataset_item[2]
            if tissue_name in ['#PublishFreeze', '#GenoFreeze']:
                dataset_menu.append(dict(tissue=None, datasets=[(dataset, dataset_short)]))
            else:
                dataset_sub_menu = [item[1:] for item in dataset]

                tissue_already_exists = False
                tissue_position = None
                for i, tissue_dict in enumerate(dataset_menu):
                    if tissue_dict['tissue'] == tissue_name:
                        tissue_already_exists = True
                        tissue_position = i
                        break

                if tissue_already_exists:
                    #print("dataset_menu:", dataset_menu[i]['datasets'])
                    dataset_menu[i]['datasets'].append((dataset, dataset_short))
                else:
                    dataset_menu.append(dict(tissue=tissue_name,
                                        datasets=[(dataset, dataset_short)]))

        Redis.set(key, pickle.dumps(dataset_menu, pickle.HIGHEST_PROTOCOL))
        Redis.expire(key, 60*5)
        self._datasets = dataset_menu

        return self._datasets

    def get_f1_parent_strains(self):
        try:
            # NL, 07/27/2010. ParInfo has been moved from webqtlForm.py to webqtlUtil.py;
            f1, f12, maternal, paternal = webqtlUtil.ParInfo[self.name]
        except KeyError:
            f1 = f12 = maternal = paternal = None

        if f1 and f12:
            self.f1list = [f1, f12]
        if maternal and paternal:
            self.parlist = [maternal, paternal]

    def get_samplelist(self):
        key = "samplelist:v2:" + self.name
        #print("key is:", key)
        #with Bench("Loading cache"):
        result = Redis.get(key)

        if result:
            #print("Sample List Cache hit!!!")
            #print("Before unjsonifying {}: {}".format(type(result), result))
            self.samplelist = json.loads(result)
            #print("  type: ", type(self.samplelist))
            #print("  self.samplelist: ", self.samplelist)
        else:
            print("Cache not hit")

            genotype_fn = locate_ignore_error(self.name+".geno",'genotype')
            mapping_fn = locate_ignore_error(self.name+".fam",'mapping')
            if mapping_fn:
                self.samplelist = get_group_samplelists.get_samplelist("plink", mapping_fn)
            elif genotype_fn:
                self.samplelist = get_group_samplelists.get_samplelist("geno", genotype_fn)
            else:
                self.samplelist = None
            print("Sample list: ",self.samplelist)
            Redis.set(key, json.dumps(self.samplelist))
            Redis.expire(key, 60*5)

    def all_samples_ordered(self):
        result = []
        lists = (self.parlist, self.f1list, self.samplelist)
        [result.extend(l) for l in lists if l]
        return result

    def read_genotype_file(self):
        '''Read genotype from .geno file instead of database'''
        #if self.group == 'BXD300':
        #    self.group = 'BXD'
        #
        #assert self.group, "self.group needs to be set"

        #genotype_1 is Dataset Object without parents and f1
        #genotype_2 is Dataset Object with parents and f1 (not for intercross)

        genotype_1 = reaper.Dataset()

        # reaper barfs on unicode filenames, so here we ensure it's a string
        full_filename = str(locate(self.name+'.geno','genotype'))
        genotype_1.read(full_filename)

        if genotype_1.type == "group" and self.parlist:
            genotype_2 = genotype_1.add(Mat=self.parlist[0], Pat=self.parlist[1])       #, F1=_f1)
        else:
            genotype_2 = genotype_1

        #determine default genotype object
        if self.incparentsf1 and genotype_1.type != "intercross":
            #self.genotype = genotype_2
            genotype = genotype_2
        else:
            self.incparentsf1 = 0
            #self.genotype = genotype_1
            genotype = genotype_1

        #self.samplelist = list(self.genotype.prgy)
        self.samplelist = list(genotype.prgy)

        return genotype


#class DataSets(object):
#    """Builds a list of DataSets"""
#
#    def __init__(self):
#        self.datasets = list()
#


        #query = """SELECT Name FROM ProbeSetFreeze
        #           UNION
        #           SELECT Name From PublishFreeze
        #           UNION
        #           SELECT Name From GenoFreeze"""
        #
        #for result in g.db.execute(query).fetchall():
        #    dataset = DataSet(result.Name)
        #    self.datasets.append(dataset)

#ds = DataSets()
#print("[orange] ds:", ds.datasets)

class DataSet(object):
    """
    DataSet class defines a dataset in webqtl, can be either Microarray,
    Published phenotype, genotype, or user input dataset(temp)

    """

    def __init__(self, name, get_samplelist = True):

        assert name, "Need a name"
        self.name = name
        self.id = None
        self.shortname = None
        self.fullname = None
        self.type = None

        self.setup()

        self.check_confidentiality()

        self.retrieve_other_names()

        self.group = DatasetGroup(self)   # sets self.group and self.group_id and gets genotype
        if get_samplelist == True:
             self.group.get_samplelist()
        self.species = species.TheSpecies(self)


    def get_desc(self):
        """Gets overridden later, at least for Temp...used by trait's get_given_name"""
        return None

    #@staticmethod
    #def get_by_trait_id(trait_id):
    #    """Gets the dataset object given the trait id"""
    #
    #
    #
    #    name = g.db.execute(""" SELECT
    #
    #                        """)
    #
    #    return DataSet(name)

    # Delete this eventually
    @property
    def riset():
        Weve_Renamed_This_As_Group


    #@property
    #def group(self):
    #    if not self._group:
    #        self.get_group()
    #
    #    return self._group


    def retrieve_other_names(self):
        """
        If the data set name parameter is not found in the 'Name' field of the data set table,
        check if it is actually the FullName or ShortName instead.

        This is not meant to retrieve the data set info if no name at all is passed.

        """

        try:
            if self.type == "ProbeSet":
                query_args = tuple(escape(x) for x in (
                    str(webqtlConfig.PUBLICTHRESH),
                    self.name,
                    self.name,
                    self.name))

                self.id, self.name, self.fullname, self.shortname, self.tissue = g.db.execute("""
                        SELECT ProbeSetFreeze.Id, ProbeSetFreeze.Name, ProbeSetFreeze.FullName, ProbeSetFreeze.ShortName, Tissue.Name
                        FROM ProbeSetFreeze, ProbeFreeze, Tissue
                        WHERE ProbeSetFreeze.public > %s AND
                              ProbeSetFreeze.ProbeFreezeId = ProbeFreeze.Id AND
                              ProbeFreeze.TissueId = Tissue.Id AND
                             (ProbeSetFreeze.Name = '%s' OR ProbeSetFreeze.FullName = '%s' OR ProbeSetFreeze.ShortName = '%s')
                  """ % (query_args)).fetchone()
            else:
                query_args = tuple(escape(x) for x in (
                    (self.type + "Freeze"),
                    str(webqtlConfig.PUBLICTHRESH),
                    self.name,
                    self.name,
                    self.name))

                self.tissue = "N/A"
                self.id, self.name, self.fullname, self.shortname = g.db.execute("""
                        SELECT Id, Name, FullName, ShortName
                        FROM %s
                        WHERE public > %s AND
                             (Name = '%s' OR FullName = '%s' OR ShortName = '%s')
                  """ % (query_args)).fetchone()

        except TypeError:
            print("Dataset {} is not yet available in GeneNetwork.".format(self.name))
            pass

    def get_trait_data(self, sample_list=None):
        if sample_list:
            self.samplelist = sample_list
        else:
            self.samplelist = self.group.samplelist

        if self.group.parlist != None and self.group.f1list != None:
            if (self.group.parlist + self.group.f1list) in self.samplelist:
                self.samplelist += self.group.parlist + self.group.f1list

        query = """
            SELECT Strain.Name, Strain.Id FROM Strain, Species
            WHERE Strain.Name IN {}
            and Strain.SpeciesId=Species.Id
            and Species.name = '{}'
            """.format(create_in_clause(self.samplelist), *mescape(self.group.species))
        results = dict(g.db.execute(query).fetchall())
        sample_ids = [results[item] for item in self.samplelist]

        # MySQL limits the number of tables that can be used in a join to 61,
        # so we break the sample ids into smaller chunks
        # Postgres doesn't have that limit, so we can get rid of this after we transition
        chunk_size = 50
        number_chunks = int(math.ceil(len(sample_ids) / chunk_size))
        trait_sample_data = []
        for sample_ids_step in chunks.divide_into_chunks(sample_ids, number_chunks):

        #XZ, 09/24/2008: build one temporary table that only contains the records associated with the input GeneId
        #tempTable = None
        #if GeneId and db.type == "ProbeSet":
        #    if method == "3":
        #        tempTable = self.getTempLiteratureTable(species=species,
        #                                                input_species_geneid=GeneId,
        #                                                returnNumber=returnNumber)
        #
        #    if method == "4" or method == "5":
        #        tempTable = self.getTempTissueCorrTable(primaryTraitSymbol=GeneSymbol,
        #                                        TissueProbeSetFreezeId=tissueProbeSetFreezeId,
        #                                        method=method,
        #                                        returnNumber=returnNumber)

            if self.type == "Publish":
                dataset_type = "Phenotype"
            else:
                dataset_type = self.type
            temp = ['T%s.value' % item for item in sample_ids_step]
            if self.type == "Publish":
                query = "SELECT {}XRef.Id,".format(escape(self.type))
            else:
                query = "SELECT {}.Name,".format(escape(dataset_type))
            data_start_pos = 1
            query += string.join(temp, ', ')
            query += ' FROM ({}, {}XRef, {}Freeze) '.format(*mescape(dataset_type,
                                                                     self.type,
                                                                     self.type))

            for item in sample_ids_step:
                query += """
                        left join {}Data as T{} on T{}.Id = {}XRef.DataId
                        and T{}.StrainId={}\n
                        """.format(*mescape(self.type, item, item, self.type, item, item))

            if self.type == "Publish":
                query += """
                        WHERE {}XRef.InbredSetId = {}Freeze.InbredSetId
                        and {}Freeze.Name = '{}'
                        and {}.Id = {}XRef.{}Id
                        order by {}.Id
                        """.format(*mescape(self.type, self.type, self.type, self.name,
                                    dataset_type, self.type, dataset_type, dataset_type))
            else:
                query += """
                        WHERE {}XRef.{}FreezeId = {}Freeze.Id
                        and {}Freeze.Name = '{}'
                        and {}.Id = {}XRef.{}Id
                        order by {}.Id
                        """.format(*mescape(self.type, self.type, self.type, self.type,
                                   self.name, dataset_type, self.type, self.type, dataset_type))

            #print("trait data query: ", query)

            results = g.db.execute(query).fetchall()
            #print("query results:", results)
            trait_sample_data.append(results)

        trait_count = len(trait_sample_data[0])
        self.trait_data = collections.defaultdict(list)

        # put all of the separate data together into a dictionary where the keys are
        # trait names and values are lists of sample values
        for trait_counter in range(trait_count):
            trait_name = trait_sample_data[0][trait_counter][0]
            for chunk_counter in range(int(number_chunks)):
                self.trait_data[trait_name] += (
                    trait_sample_data[chunk_counter][trait_counter][data_start_pos:])

class PhenotypeDataSet(DataSet):
    DS_NAME_MAP['Publish'] = 'PhenotypeDataSet'

    def setup(self):

        #print("IS A PHENOTYPEDATASET")

        # Fields in the database table
        self.search_fields = ['Phenotype.Post_publication_description',
                            'Phenotype.Pre_publication_description',
                            'Phenotype.Pre_publication_abbreviation',
                            'Phenotype.Post_publication_abbreviation',
                            'Phenotype.Lab_code',
                            'Publication.PubMed_ID',
                            'Publication.Abstract',
                            'Publication.Title',
                            'Publication.Authors',
                            'PublishXRef.Id']

        # Figure out what display_fields is
        self.display_fields = ['name',
                               'pubmed_id',
                               'pre_publication_description',
                               'post_publication_description',
                               'original_description',
                               'pre_publication_abbreviation',
                               'post_publication_abbreviation',
                               'lab_code',
                               'submitter', 'owner',
                               'authorized_users',
                               'authors', 'title',
                               'abstract', 'journal',
                               'volume', 'pages',
                               'month', 'year',
                               'sequence', 'units', 'comments']

        # Fields displayed in the search results table header
        self.header_fields = ['Index',
                            'Record',
                            'Description',
                            'Authors',
                            'Year',
                            'Max LRS',
                            'Max LRS Location',
                            'Additive Effect']

        self.type = 'Publish'

        self.query_for_group = '''
                            SELECT
                                    InbredSet.Name, InbredSet.Id
                            FROM
                                    InbredSet, PublishFreeze
                            WHERE
                                    PublishFreeze.InbredSetId = InbredSet.Id AND
                                    PublishFreeze.Name = "%s"
                    ''' % escape(self.name)

    def check_confidentiality(self):
        # (Urgently?) Need to write this
        pass

    def get_trait_list(self):
        query = """
            select PublishXRef.Id
            from PublishXRef, PublishFreeze
            where PublishFreeze.InbredSetId=PublishXRef.InbredSetId
            and PublishFreeze.Id = {}
            """.format(escape(str(self.id)))
        results = g.db.execute(query).fetchall()
        trait_data = {}
        for trait in results:
            trait_data[trait[0]] = self.retrieve_sample_data(trait[0])
        return trait_data

    def get_trait_info(self, trait_list, species = ''):
        for this_trait in trait_list:

            if not this_trait.haveinfo:
                this_trait.retrieve_info(get_qtl_info=True)

            description = this_trait.post_publication_description

            #If the dataset is confidential and the user has access to confidential
            #phenotype traits, then display the pre-publication description instead
            #of the post-publication description
            if this_trait.confidential:
                this_trait.description_display = ""
                continue   # for now

                if not webqtlUtil.hasAccessToConfidentialPhenotypeTrait(
                        privilege=self.privilege,
                        userName=self.userName,
                        authorized_users=this_trait.authorized_users):

                    description = this_trait.pre_publication_description

            if len(description) > 0:
                this_trait.description_display = description.strip()
            else:
                this_trait.description_display = ""

            if not this_trait.year.isdigit():
                this_trait.pubmed_text = "N/A"
            else:
                this_trait.pubmed_text = this_trait.year

            if this_trait.pubmed_id:
                this_trait.pubmed_link = webqtlConfig.PUBMEDLINK_URL % this_trait.pubmed_id

            #LRS and its location
            this_trait.LRS_score_repr = "N/A"
            this_trait.LRS_score_value = 0
            this_trait.LRS_location_repr = "N/A"
            this_trait.LRS_location_value = 1000000

            if this_trait.lrs:
                result = g.db.execute("""
                    select Geno.Chr, Geno.Mb from Geno, Species
                    where Species.Name = %s and
                        Geno.Name = %s and
                        Geno.SpeciesId = Species.Id
                """, (species, this_trait.locus)).fetchone()

                if result:
                    if result[0] and result[1]:
                        LRS_Chr = result[0]
                        LRS_Mb = result[1]

                        #XZ: LRS_location_value is used for sorting
                        try:
                            LRS_location_value = int(LRS_Chr)*1000 + float(LRS_Mb)
                        except:
                            if LRS_Chr.upper() == 'X':
                                LRS_location_value = 20*1000 + float(LRS_Mb)
                            else:
                                LRS_location_value = ord(str(LRS_chr).upper()[0])*1000 + float(LRS_Mb)

                        this_trait.LRS_score_repr = LRS_score_repr = '%3.1f' % this_trait.lrs
                        this_trait.LRS_score_value = LRS_score_value = this_trait.lrs
                        this_trait.LRS_location_repr = LRS_location_repr = 'Chr%s: %.6f' % (LRS_Chr, float(LRS_Mb))

    def retrieve_sample_data(self, trait):
        query = """
                    SELECT
                            Strain.Name, PublishData.value, PublishSE.error, NStrain.count
                    FROM
                            (PublishData, Strain, PublishXRef, PublishFreeze)
                    left join PublishSE on
                            (PublishSE.DataId = PublishData.Id AND PublishSE.StrainId = PublishData.StrainId)
                    left join NStrain on
                            (NStrain.DataId = PublishData.Id AND
                            NStrain.StrainId = PublishData.StrainId)
                    WHERE
                            PublishXRef.InbredSetId = PublishFreeze.InbredSetId AND
                            PublishData.Id = PublishXRef.DataId AND PublishXRef.Id = %s AND
                            PublishFreeze.Id = %s AND PublishData.StrainId = Strain.Id
                    Order BY
                            Strain.Name
                    """
        results = g.db.execute(query, (trait, self.id)).fetchall()
        return results


class GenotypeDataSet(DataSet):
    DS_NAME_MAP['Geno'] = 'GenotypeDataSet'

    def setup(self):
        # Fields in the database table
        self.search_fields = ['Name',
                              'Chr']

        # Find out what display_fields is
        self.display_fields = ['name',
                               'chr',
                               'mb',
                               'source2',
                               'sequence']

        # Fields displayed in the search results table header
        self.header_fields = ['Index',
                              'ID',
                              'Location']

        # Todo: Obsolete or rename this field
        self.type = 'Geno'

        self.query_for_group = '''
                SELECT
                        InbredSet.Name, InbredSet.Id
                FROM
                        InbredSet, GenoFreeze
                WHERE
                        GenoFreeze.InbredSetId = InbredSet.Id AND
                        GenoFreeze.Name = "%s"
                ''' % escape(self.name)

    def check_confidentiality(self):
        return geno_mrna_confidentiality(self)

    def get_trait_list(self):
        query = """
            select Geno.Name
            from Geno, GenoXRef
            where GenoXRef.GenoId = Geno.Id
            and GenoFreezeId = {}
            """.format(escape(str(self.id)))
        results = g.db.execute(query).fetchall()
        trait_data = {}
        for trait in results:
            trait_data[trait[0]] = self.retrieve_sample_data(trait[0])
        return trait_data

    def get_trait_info(self, trait_list, species=None):
        for this_trait in trait_list:
            if not this_trait.haveinfo:
                this_trait.retrieveInfo()

            #XZ: trait_location_value is used for sorting
            trait_location_repr = 'N/A'
            trait_location_value = 1000000

            if this_trait.chr and this_trait.mb:
                try:
                    trait_location_value = int(this_trait.chr)*1000 + this_trait.mb
                except:
                    if this_trait.chr.upper() == 'X':
                        trait_location_value = 20*1000 + this_trait.mb
                    else:
                        trait_location_value = ord(str(this_trait.chr).upper()[0])*1000 + this_trait.mb

                this_trait.location_repr = 'Chr%s: %.6f' % (this_trait.chr, float(this_trait.mb) )
                this_trait.location_value = trait_location_value

    def retrieve_sample_data(self, trait):
        query = """
                    SELECT
                            Strain.Name, GenoData.value, GenoSE.error, GenoData.Id
                    FROM
                            (GenoData, GenoFreeze, Strain, Geno, GenoXRef)
                    left join GenoSE on
                            (GenoSE.DataId = GenoData.Id AND GenoSE.StrainId = GenoData.StrainId)
                    WHERE
                            Geno.SpeciesId = %s AND Geno.Name = %s AND GenoXRef.GenoId = Geno.Id AND
                            GenoXRef.GenoFreezeId = GenoFreeze.Id AND
                            GenoFreeze.Name = %s AND
                            GenoXRef.DataId = GenoData.Id AND
                            GenoData.StrainId = Strain.Id
                    Order BY
                            Strain.Name
                    """
        results = g.db.execute(query,
                               (webqtlDatabaseFunction.retrieve_species_id(self.group.name),
                                trait, self.name)).fetchall()
        return results


class MrnaAssayDataSet(DataSet):
    '''
    An mRNA Assay is a quantitative assessment (assay) associated with an mRNA trait

    This used to be called ProbeSet, but that term only refers specifically to the Affymetrix
    platform and is far too specific.

    '''
    DS_NAME_MAP['ProbeSet'] = 'MrnaAssayDataSet'

    def setup(self):
        # Fields in the database table
        self.search_fields = ['Name',
                              'Description',
                              'Probe_Target_Description',
                              'Symbol',
                              'Alias',
                              'GenbankId',
                              'UniGeneId',
                              'RefSeq_TranscriptId']

        # Find out what display_fields is
        self.display_fields = ['name', 'symbol',
                               'description', 'probe_target_description',
                               'chr', 'mb',
                               'alias', 'geneid',
                               'genbankid', 'unigeneid',
                               'omim', 'refseq_transcriptid',
                               'blatseq', 'targetseq',
                               'chipid', 'comments',
                               'strand_probe', 'strand_gene',
                               'probe_set_target_region',
                               'probe_set_specificity',
                               'probe_set_blat_score',
                               'probe_set_blat_mb_start',
                               'probe_set_blat_mb_end',
                               'probe_set_strand',
                               'probe_set_note_by_rw',
                               'flag']

        # Fields displayed in the search results table header
        self.header_fields = ['Index',
                             'Record',
                             'Symbol',
                             'Description',
                             'Location',
                             'Mean',
                             'Max LRS',
                             'Max LRS Location',
                             'Additive Effect']

        # Todo: Obsolete or rename this field
        self.type = 'ProbeSet'

        self.query_for_group = '''
                        SELECT
                                InbredSet.Name, InbredSet.Id
                        FROM
                                InbredSet, ProbeSetFreeze, ProbeFreeze
                        WHERE
                                ProbeFreeze.InbredSetId = InbredSet.Id AND
                                ProbeFreeze.Id = ProbeSetFreeze.ProbeFreezeId AND
                                ProbeSetFreeze.Name = "%s"
                ''' % escape(self.name)


    def check_confidentiality(self):
        return geno_mrna_confidentiality(self)

    def get_trait_list_1(self):
        query = """
            select ProbeSet.Name
            from ProbeSet, ProbeSetXRef
            where ProbeSetXRef.ProbeSetId = ProbeSet.Id
            and ProbeSetFreezeId = {}
            """.format(escape(str(self.id)))
        results = g.db.execute(query).fetchall()
        #print("After get_trait_list query")
        trait_data = {}
        for trait in results:
            #print("Retrieving sample_data for ", trait[0])
            trait_data[trait[0]] = self.retrieve_sample_data(trait[0])
        #print("After retrieve_sample_data")
        return trait_data

    #def get_trait_data(self):
    #    self.samplelist = self.group.samplelist + self.group.parlist + self.group.f1list
    #    query = """
    #        SELECT Strain.Name, Strain.Id FROM Strain, Species
    #        WHERE Strain.Name IN {}
    #        and Strain.SpeciesId=Species.Id
    #        and Species.name = '{}'
    #        """.format(create_in_clause(self.samplelist), *mescape(self.group.species))
    #    results = dict(g.db.execute(query).fetchall())
    #    sample_ids = [results[item] for item in self.samplelist]
    #
    #    # MySQL limits the number of tables that can be used in a join to 61,
    #    # so we break the sample ids into smaller chunks
    #    # Postgres doesn't have that limit, so we can get rid of this after we transition
    #    chunk_size = 50
    #    number_chunks = int(math.ceil(len(sample_ids) / chunk_size))
    #    trait_sample_data = []
    #    for sample_ids_step in chunks.divide_into_chunks(sample_ids, number_chunks):
    #
    #    #XZ, 09/24/2008: build one temporary table that only contains the records associated with the input GeneId
    #    #tempTable = None
    #    #if GeneId and db.type == "ProbeSet":
    #    #    if method == "3":
    #    #        tempTable = self.getTempLiteratureTable(species=species,
    #    #                                                input_species_geneid=GeneId,
    #    #                                                returnNumber=returnNumber)
    #    #
    #    #    if method == "4" or method == "5":
    #    #        tempTable = self.getTempTissueCorrTable(primaryTraitSymbol=GeneSymbol,
    #    #                                        TissueProbeSetFreezeId=tissueProbeSetFreezeId,
    #    #                                        method=method,
    #    #                                        returnNumber=returnNumber)
    #
    #        temp = ['T%s.value' % item for item in sample_ids_step]
    #        query = "SELECT {}.Name,".format(escape(self.type))
    #        data_start_pos = 1
    #        query += string.join(temp, ', ')
    #        query += ' FROM ({}, {}XRef, {}Freeze) '.format(*mescape(self.type,
    #                                                                 self.type,
    #                                                                 self.type))
    #
    #        for item in sample_ids_step:
    #            query += """
    #                    left join {}Data as T{} on T{}.Id = {}XRef.DataId
    #                    and T{}.StrainId={}\n
    #                    """.format(*mescape(self.type, item, item, self.type, item, item))
    #
    #        query += """
    #                WHERE {}XRef.{}FreezeId = {}Freeze.Id
    #                and {}Freeze.Name = '{}'
    #                and {}.Id = {}XRef.{}Id
    #                order by {}.Id
    #                """.format(*mescape(self.type, self.type, self.type, self.type,
    #                           self.name, self.type, self.type, self.type, self.type))
    #        results = g.db.execute(query).fetchall()
    #        trait_sample_data.append(results)
    #
    #    trait_count = len(trait_sample_data[0])
    #    self.trait_data = collections.defaultdict(list)
    #
    #    # put all of the separate data together into a dictionary where the keys are
    #    # trait names and values are lists of sample values
    #    for trait_counter in range(trait_count):
    #        trait_name = trait_sample_data[0][trait_counter][0]
    #        for chunk_counter in range(int(number_chunks)):
    #            self.trait_data[trait_name] += (
    #                trait_sample_data[chunk_counter][trait_counter][data_start_pos:])


    def get_trait_info(self, trait_list=None, species=''):

        #  Note: setting trait_list to [] is probably not a great idea.
        if not trait_list:
            trait_list = []

        for this_trait in trait_list:

            if not this_trait.haveinfo:
                this_trait.retrieveInfo(QTL=1)

            if not this_trait.symbol:
                this_trait.symbol = "N/A"

            #XZ, 12/08/2008: description
            #XZ, 06/05/2009: Rob asked to add probe target description
            description_string = unicode(str(this_trait.description).strip(codecs.BOM_UTF8), 'utf-8')
            target_string = unicode(str(this_trait.probe_target_description).strip(codecs.BOM_UTF8), 'utf-8')

            if len(description_string) > 1 and description_string != 'None':
                description_display = description_string
            else:
                description_display = this_trait.symbol

            if (len(description_display) > 1 and description_display != 'N/A' and
                    len(target_string) > 1 and target_string != 'None'):
                description_display = description_display + '; ' + target_string.strip()

            # Save it for the jinja2 template
            this_trait.description_display = description_display

            #XZ: trait_location_value is used for sorting
            trait_location_repr = 'N/A'
            trait_location_value = 1000000

            if this_trait.chr and this_trait.mb:
                #Checks if the chromosome number can be cast to an int (i.e. isn't "X" or "Y")
                #This is so we can convert the location to a number used for sorting
                trait_location_value = self.convert_location_to_value(this_trait.chr, this_trait.mb)
                #try:
                #    trait_location_value = int(this_trait.chr)*1000 + this_trait.mb
                #except ValueError:
                #    if this_trait.chr.upper() == 'X':
                #        trait_location_value = 20*1000 + this_trait.mb
                #    else:
                #        trait_location_value = (ord(str(this_trait.chr).upper()[0])*1000 +
                #                               this_trait.mb)

                #ZS: Put this in function currently called "convert_location_to_value"
                this_trait.location_repr = 'Chr%s: %.6f' % (this_trait.chr,
                                                                float(this_trait.mb))
                this_trait.location_value = trait_location_value

            #Get mean expression value
            query = (
            """select ProbeSetXRef.mean from ProbeSetXRef, ProbeSet
                where ProbeSetXRef.ProbeSetFreezeId = %s and
                ProbeSet.Id = ProbeSetXRef.ProbeSetId and
                ProbeSet.Name = '%s'
            """ % (escape(str(this_trait.dataset.id)),
                   escape(this_trait.name)))

            #print("query is:", pf(query))

            result = g.db.execute(query).fetchone()

            mean = result[0] if result else 0

            if mean:
                this_trait.mean = "%2.3f" % mean

            #LRS and its location
            this_trait.LRS_score_repr = 'N/A'
            this_trait.LRS_score_value = 0
            this_trait.LRS_location_repr = 'N/A'
            this_trait.LRS_location_value = 1000000

            #Max LRS and its Locus location
            if this_trait.lrs and this_trait.locus:
                query = """
                    select Geno.Chr, Geno.Mb from Geno, Species
                    where Species.Name = '{}' and
                        Geno.Name = '{}' and
                        Geno.SpeciesId = Species.Id
                """.format(species, this_trait.locus)
                result = g.db.execute(query).fetchone()

                if result:
                    #if result[0] and result[1]:
                    #    lrs_chr = result[0]
                    #    lrs_mb = result[1]
                    lrs_chr, lrs_mb = result
                    #XZ: LRS_location_value is used for sorting
                    lrs_location_value = self.convert_location_to_value(lrs_chr, lrs_mb)

                    #try:
                    #    lrs_location_value = int(lrs_chr)*1000 + float(lrs_mb)
                    #except:
                    #    if lrs_chr.upper() == 'X':
                    #        lrs_location_value = 20*1000 + float(lrs_mb)
                    #    else:
                    #        lrs_location_value = (ord(str(LRS_chr).upper()[0])*1000 +
                    #                              float(lrs_mb))

                    this_trait.LRS_score_repr = '%3.1f' % this_trait.lrs
                    this_trait.LRS_score_value = this_trait.lrs
                    this_trait.LRS_location_repr = 'Chr%s: %.6f' % (lrs_chr, float(lrs_mb))


    def convert_location_to_value(self, chromosome, mb):
        try:
            location_value = int(chromosome)*1000 + float(mb)
        except ValueError:
            if chromosome.upper() == 'X':
                location_value = 20*1000 + float(mb)
            else:
                location_value = (ord(str(chromosome).upper()[0])*1000 +
                                  float(mb))

        return location_value

    def get_sequence(self):
        query = """
                    SELECT
                            ProbeSet.BlatSeq
                    FROM
                            ProbeSet, ProbeSetFreeze, ProbeSetXRef
                    WHERE
                            ProbeSet.Id=ProbeSetXRef.ProbeSetId and
                            ProbeSetFreeze.Id = ProbeSetXRef.ProbSetFreezeId and
                            ProbeSet.Name = %s
                            ProbeSetFreeze.Name = %s
                """ % (escape(self.name), escape(self.dataset.name))
        results = g.db.execute(query).fetchone()
        return results[0]

    def retrieve_sample_data(self, trait):
        query = """
                    SELECT
                            Strain.Name, ProbeSetData.value, ProbeSetSE.error, ProbeSetData.Id
                    FROM
                            (ProbeSetData, ProbeSetFreeze, Strain, ProbeSet, ProbeSetXRef)
                    left join ProbeSetSE on
                            (ProbeSetSE.DataId = ProbeSetData.Id AND ProbeSetSE.StrainId = ProbeSetData.StrainId)
                    WHERE
                            ProbeSet.Name = '%s' AND ProbeSetXRef.ProbeSetId = ProbeSet.Id AND
                            ProbeSetXRef.ProbeSetFreezeId = ProbeSetFreeze.Id AND
                            ProbeSetFreeze.Name = '%s' AND
                            ProbeSetXRef.DataId = ProbeSetData.Id AND
                            ProbeSetData.StrainId = Strain.Id
                    Order BY
                            Strain.Name
                    """ % (escape(trait), escape(self.name))
        results = g.db.execute(query).fetchall()
        #print("RETRIEVED RESULTS HERE:", results)
        return results


    def retrieve_genes(self, column_name):
        query = """
                    select ProbeSet.Name, ProbeSet.%s
                    from ProbeSet,ProbeSetXRef
                    where ProbeSetXRef.ProbeSetFreezeId = %s and
                    ProbeSetXRef.ProbeSetId=ProbeSet.Id;
                """ % (column_name, escape(str(self.id)))
        results = g.db.execute(query).fetchall()

        return dict(results)

    #def retrieve_gene_symbols(self):
    #    query = """
    #                select ProbeSet.Name, ProbeSet.Symbol, ProbeSet.GeneId
    #                from ProbeSet,ProbeSetXRef
    #                where ProbeSetXRef.ProbeSetFreezeId = %s and
    #                ProbeSetXRef.ProbeSetId=ProbeSet.Id;
    #            """ % (self.id)
    #    results = g.db.execute(query).fetchall()
    #    symbol_dict = {}
    #    for item in results:
    #        symbol_dict[item[0]] = item[1]
    #    return symbol_dict
    #
    #def retrieve_gene_ids(self):
    #    query = """
    #                select ProbeSet.Name, ProbeSet.GeneId
    #                from ProbeSet,ProbeSetXRef
    #                where ProbeSetXRef.ProbeSetFreezeId = %s and
    #                ProbeSetXRef.ProbeSetId=ProbeSet.Id;
    #            """ % (self.id)
    #    return process_and_run_query(query)
    #    results = g.db.execute(query).fetchall()
    #    symbol_dict = {}
    #    for item in results:
    #        symbol_dict[item[0]] = item[1]
    #    return symbol_dict




class TempDataSet(DataSet):
    '''Temporary user-generated data set'''

    def setup(self):
        self.search_fields = ['name',
                              'description']

        self.display_fields = ['name',
                               'description']

        self.header_fields = ['Name',
                              'Description']

        self.type = 'Temp'

        # Need to double check later how these are used
        self.id = 1
        self.fullname = 'Temporary Storage'
        self.shortname = 'Temp'


    @staticmethod
    def handle_pca(desc):
        if 'PCA' in desc:
            # Todo: Modernize below lines
            desc = desc[desc.rindex(':')+1:].strip()
        else:
            desc = desc[:desc.index('entered')].strip()
        return desc

    def get_desc(self):
        g.db.execute('SELECT description FROM Temp WHERE Name=%s', self.name)
        desc = g.db.fetchone()[0]
        desc = self.handle_pca(desc)
        return desc

    def get_group(self):
        self.cursor.execute("""
                    SELECT
                            InbredSet.Name, InbredSet.Id
                    FROM
                            InbredSet, Temp
                    WHERE
                            Temp.InbredSetId = InbredSet.Id AND
                            Temp.Name = "%s"
            """, self.name)
        self.group, self.group_id = self.cursor.fetchone()
        #return self.group

    def retrieve_sample_data(self, trait):
        query = """
                SELECT
                        Strain.Name, TempData.value, TempData.SE, TempData.NStrain, TempData.Id
                FROM
                        TempData, Temp, Strain
                WHERE
                        TempData.StrainId = Strain.Id AND
                        TempData.Id = Temp.DataId AND
                        Temp.name = '%s'
                Order BY
                        Strain.Name
                """ % escape(trait.name)

        results = g.db.execute(query).fetchall()


def geno_mrna_confidentiality(ob):
    dataset_table = ob.type + "Freeze"
    #print("dataset_table [%s]: %s" % (type(dataset_table), dataset_table))

    query = '''SELECT Id, Name, FullName, confidentiality,
                        AuthorisedUsers FROM %s WHERE Name = %%s''' % (dataset_table)

    result = g.db.execute(query, ob.name)

    (dataset_id,
     name,
     full_name,
     confidential,
     authorized_users) = result.fetchall()[0]

    if confidential:
        return True