def getPlot(self):
"""
2010-1-22
add argument call_method_id when calling DrawSNPRegion.drawRegionAroundThisSNP()
2009-4-30
"""
chromosome = int(request.params.get('chromosome', 1))
start = int(request.params.get('start', 1))
stop = int(request.params.get('stop', 10))
if start>stop:
start, stop = stop, start
snps_id = '%s_%s'%(chromosome, start)
call_method_id = int(request.params.getone('call_method_id'))
phenotype_method_id = int(request.params.getone('phenotype_method_id'))
list_type_id = int(request.params.get('list_type_id', None))
plot_key = (chromosome, start, stop, call_method_id, phenotype_method_id, list_type_id)
if not hasattr(model, "plot_key2png_data"):
model.plot_key2png_data = {}
png_data = model.plot_key2png_data.get(plot_key)
if png_data is not None:
if hasattr(png_data, 'getvalue'): # png_data is StringIO
response.headers['Content-type'] = 'image/png'
return png_data.getvalue()
else:
return png_data
if not getattr(model, 'phenotype_id2analysis_method_id2gwr', None):
model.phenotype_id2analysis_method_id2gwr = {}
analysis_method_id2gwr = model.phenotype_id2analysis_method_id2gwr.get(phenotype_method_id)
if not analysis_method_id2gwr:
analysis_method_id2gwr = DrawSNPRegion.getSimilarGWResultsGivenResultsByGene(phenotype_method_id, call_method_id)
model.phenotype_id2analysis_method_id2gwr[phenotype_method_id] = analysis_method_id2gwr
if list_type_id>0: #2009-2-22
candidate_gene_set = GeneListRankTest.dealWithCandidateGeneList(list_type_id, return_set=True)
else:
candidate_gene_set = set()
gene_annotation = model.gene_annotation
snpData = hc.getSNPDataGivenCallMethodID(call_method_id)
pheno_data = hc.getPhenotypeDataInSNPDataOrder(snpData)
if h.ecotype_info is None: #2009-3-6 not used right now
h.ecotype_info = getEcotypeInfo(model.db)
snp_info = getattr(model, 'snp_info', None)
if snp_info is None:
snp_info = DrawSNPRegion.getSNPInfo(model.db)
model.snp_info = snp_info
LD_info = None
output_dir = '/tmp/' #not used at all, place holder
which_LD_statistic = 1
this_snp = SNPPassingData(chromosome=chromosome, position=start, stop=stop, snps_id='%s_%s'%(chromosome, start))
DrawSNPRegion.construct_chr_pos2index_forSNPData(snpData) #prerequisite
after_plot_data = DrawSNPRegion.drawRegionAroundThisSNP(phenotype_method_id, this_snp, candidate_gene_set, \
gene_annotation, snp_info, \
analysis_method_id2gwr, LD_info, output_dir, which_LD_statistic, \
min_distance=20000, list_type_id=list_type_id,
label_gene=True, \
draw_LD_relative_to_center_SNP=False,\
commit=True, snpData=snpData, phenData=pheno_data, \
ecotype_info=h.ecotype_info, snpData_before_impute=None,\
snp_matrix_data_type=1, call_method_id=call_method_id)
if getattr(after_plot_data, 'png_data', None):
response.headers['Content-type'] = 'image/png'
model.plot_key2png_data[plot_key] = after_plot_data.png_data
return after_plot_data.png_data.getvalue()
else:
model.plot_key2png_data[plot_key] = "No Plot"
return model.plot_key2png_data[plot_key]
def getMultiPhenotypeData(self):
"""
2009-5-28
present all phenotype data. no filter by phenotype_method_id_list
2009-5-24
add 'region' as a column into the data
2009-5-3
"""
call_method_id = request.params.get('call_method_id', None)
snpData = hc.getSNPDataGivenCallMethodID(call_method_id)
phenoData = hc.getPhenotypeDataInSNPDataOrder(snpData)
rows = model.Stock_250kDB.CallInfo.query.filter_by(method_id=call_method_id).all()
call_info_id2pcs = {}
for row in rows:
call_info_id2pcs[row.id] = row.pc_value_ls[:10]
column_name_type_ls = [("label", ("string","ID Name Phenotype")), ("date", ("date", "Date")), \
("lon",("number", "Longitude")), ("lat",("number", "Latitude")), \
("ecotypeid", ("number", "Ecotype ID")), ("name", ("string", "Native Name")), \
("country", ("string", "Country")), ("region", ("string", "Region")),\
("pc1", ("number","PC1")), ("pc2", ("number", "PC2")), ("pc3", ("number","PC3")), \
("pc4", ("number", "PC4")), ("pc5", ("number","PC5")), ("pc6", ("number", "PC6"))]
no_of_non_phenotype_cols = len(column_name_type_ls)
phenotype_id_included = []
#for phenotype_id in self.phenotype_method_id_list:
for phenotype_id in phenoData.col_id2col_index:
i = phenoData.col_id2col_index.get(phenotype_id)
if i is not None:
phenotype_name = phenoData.col_label_ls[i]
column_name_type_ls.append(("phenotype %s"%phenotype_id, ("number", phenotype_name)))
phenotype_id_included.append(phenotype_id)
description = dict(column_name_type_ls)
rows = model.db.metadata.bind.execute("select * from view_call where call_method_id=%s"%(call_method_id))
return_ls = []
phenoData_row_id_type = type(phenoData.row_id2row_index.keys()[0])
for row in rows:
pcs = call_info_id2pcs.get(row.call_info_id)
if pcs:
pc_value_ls = [getattr(call_info_pc, 'pc_value') for call_info_pc in pcs]
else:
pc_value_ls = [0]*10
label = '%s ID:%s'%(row.nativename, row.ecotype_id)
data = dict(date=datetime.date(2009,2,3), ecotypeid=row.ecotype_id, label=label, name=row.nativename, \
lat=row.latitude, lon=row.longitude,\
pc1=pc_value_ls[0], pc2=pc_value_ls[1], pc3=pc_value_ls[2], pc4=pc_value_ls[3], \
pc5=pc_value_ls[4], pc6=pc_value_ls[5], \
country=row.country, region=row.region)
# construct a tuple with (phenotype_name, phenotype_value) as entry, incorporated into data in the end
phenotype_name_value_tup_ls = [] #
for i in range(no_of_non_phenotype_cols, len(column_name_type_ls)):
phenotype_name = column_name_type_ls[i][0]
phenotype_id = phenotype_id_included[i-no_of_non_phenotype_cols]
pheno_row_index = phenoData.row_id2row_index.get(phenoData_row_id_type(row.ecotype_id))
pheno_col_index = phenoData.col_id2col_index.get(phenotype_id)
if pheno_row_index is not None and pheno_col_index is not None:
phenotype_value = phenoData.data_matrix[pheno_row_index][pheno_col_index]
if phenotype_value == 'NA' or numpy.isnan(phenotype_value):
phenotype_value = None
else:
phenotype_value = None #numpy.nan can't be recognized by ToJSon()
phenotype_name_value_tup_ls.append((phenotype_name, phenotype_value))
data.update(dict(phenotype_name_value_tup_ls))
return_ls.append(data)
data_table = gviz_api.DataTable(description)
data_table.LoadData(return_ls)
column_ls = [row[0] for row in column_name_type_ls]
json_result = data_table.ToJSon(columns_order=column_ls) #ToJSonResponse only works with google.visualization.Query
return json_result
