Added option to export permutation results for mapping page

Added data_scale to dataset objects and basic stats table will now check data scale when calculating range

Made interval analyst results table work with datatables

Changed the appearance of the basic stats table some by giving it a border

1 files changed, 3 insertions, 71 deletions

diff --git a/wqflask/base/data_set.py b/wqflask/base/data_set.py
index e37a838f..379e5906 100755..100644
--- a/wqflask/base/data_set.py
+++ b/wqflask/base/data_set.py
@@ -509,6 +509,7 @@ class DataSet(object):
         self.shortname = None
         self.fullname = None
         self.type = None
+        self.data_scale = None #ZS: For example log2
 
         self.setup()
 
@@ -569,8 +570,8 @@ class DataSet(object):
                     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
+                self.id, self.name, self.fullname, self.shortname, self.data_scale, self.tissue = g.db.execute("""
+                        SELECT ProbeSetFreeze.Id, ProbeSetFreeze.Name, ProbeSetFreeze.FullName, ProbeSetFreeze.ShortName, ProbeSetFreeze.DataScale, Tissue.Name
                         FROM ProbeSetFreeze, ProbeFreeze, Tissue
                         WHERE ProbeSetFreeze.public > %s AND
                               ProbeSetFreeze.ProbeFreezeId = ProbeFreeze.Id AND
@@ -1035,75 +1036,6 @@ class MrnaAssayDataSet(DataSet):
         #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.