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 json
import gzip
import cPickle as pickle
import itertools

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 wqflask.my_pylmm.pyLMM import chunks

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):
    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)


#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[0]
                        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(os.path.join(webqtlConfig.NEWGENODIR + name + '.json'))
        self.markers = json.load(json_data_fh)
    
    def add_pvalues(self, p_values):
        #print("length of self.markers:", len(self.markers))
        #print("length of p_values:", len(p_values))
        
        # 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):
            marker['p_value'] = p_value
            if 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


class HumanMarkers(Markers):
    
    def __init__(self, name):
        marker_data_fh = open(os.path.join(webqtlConfig.PYLMM_PATH + name + '.bim'))
        self.markers = []
        for line in marker_data_fh:
            splat = line.strip().split()
            marker = {}
            marker['chr'] = int(splat[0])
            marker['name'] = splat[1]
            marker['Mb'] = float(splat[3]) / 1000000
            self.markers.append(marker)


    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
        
        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"""
        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


    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 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:v4:" + 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")
            try:
                self.samplelist = get_group_samplelists.get_samplelist(self.name + ".geno")
            except IOError:
                self.samplelist = None
            print("after get_samplelist")
            Redis.set(key, json.dumps(self.samplelist))
            Redis.expire(key, 60*5)

    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(os.path.join(webqtlConfig.GENODIR, self.name + '.geno'))
        if os.path.isfile(full_filename):
            print("Reading file: ", full_filename)
            genotype_1.read(full_filename)
            print("File read")
        else:
            try:
                full_filename = str(os.path.join(webqtlConfig.TMPDIR, self.name + '.geno'))
                #print("Reading file")
                genotype_1.read(full_filename)
                #print("File read")
            except IOError:
                print("File doesn't exist!")

        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":
            genotype = genotype_2
        else:
            self.incparentsf1 = 0
            genotype = genotype_1

        self.samplelist = list(genotype.prgy)


#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):

        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
        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.

        """

        query_args = tuple(escape(x) for x in (
            (self.type + "Freeze"),
            str(webqtlConfig.PUBLICTHRESH),
            self.name,
            self.name,
            self.name))
        print("query_args are:", query_args)

        #print("""
        #        SELECT Id, Name, FullName, ShortName
        #        FROM %s
        #        WHERE public > %s AND
        #             (Name = '%s' OR FullName = '%s' OR ShortName = '%s')
        #  """ % (query_args))
        
        try:
            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):
        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)
        
            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.PublicationId = {}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))
            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))
            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:])

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 = ['',
                            'ID',
                            'Description',
                            'Authors',
                            'Year',
                            'Max LRS',
                            'Max LRS Location']

        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:
                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
            
            this_trait.description_display = description

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

            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()
                #result = self.cursor.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: %.4f Mb' % (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 = %d AND PublishData.StrainId = Strain.Id
                    Order BY
                            Strain.Name
                    """ % (trait, self.id)
        results = g.db.execute(query).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 = ['',
                              '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: %.4f' % (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
                    """ % (webqtlDatabaseFunction.retrieve_species_id(self.group.name), trait, self.name)
        results = g.db.execute(query).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 = ['',
                             'ID',
                             'Symbol',
                             'Description',
                             'Location',
                             'Mean Expr',
                             'Max LRS',
                             'Max LRS Location']

        # 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 = str(this_trait.description).strip()
            target_string = str(this_trait.probe_target_description).strip()

            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: %.4f Mb' % (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

            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: %.4f Mb' % (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()
        return 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)
        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