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query_database.py
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query_database.py
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# -*- coding: utf-8 -*-
"""
Created on Wed Apr 09 15:53:42 2014
@ author (C) Cristina Gallego, University of Toronto
"""
import sys, os
import string
import datetime
from numpy import *
from sqlalchemy import Column, Integer, String
from sqlalchemy.orm import sessionmaker
import database_atExam
import database_atlinkPatho
from base import Base, engine
import pandas as pd
#!/usr/bin/env python
class Query(object):
"""
USAGE:
=============
database = Query()
"""
def __call__(self):
""" Turn Class into a callable object """
Query()
def __init__(self):
""" initialize QueryDatabase """
self.QueryPatient = []
self.is_mass = []
self.is_nonmass = []
def queryRadiolinfo(self, fStudyID, redateID):
"""
run : Query by StudyID/AccesionN pair study to local folder NO GRAPICAL INTERFACE. default print to output
Inputs
======
fStudyID : (int) CAD fStudyID
redateID : (int) CAD StudyID Data of exam (format yyyy-mm-dd)
Output
======
"""
# Create the ORM’s “handle†to the database_atExam: the Session.
self.Session = sessionmaker()
self.Session.configure(bind=engine) # once engine is available
session = self.Session() #instantiate a Session
datainfo = [];
for cad, exam, in session.query(database_atExam.Cad_record, database_atExam.Exam_record).\
filter(database_atExam.Cad_record.pt_id==database_atExam.Exam_record.pt_id).\
filter(database_atExam.Cad_record.cad_pt_no_txt == str(fStudyID)).\
filter(database_atExam.Exam_record.exam_dt_datetime == redateID).all():
# print results
if not cad:
print "cad is empty"
if not exam:
print "exam is empty"
datainfo.append( [([cad.cad_pt_no_txt, cad.latest_mutation_status_int,
exam.exam_dt_datetime, exam.mri_cad_status_txt, exam.comment_txt,
exam.original_report_txt,
exam.sty_indicator_rout_screening_obsp_yn,
exam.sty_indicator_high_risk_yn,
exam.sty_indicator_high_risk_brca_1_yn,
exam.sty_indicator_high_risk_brca_2_yn,
exam.sty_indicator_high_risk_brca_1_or_2_yn,
exam.sty_indicator_high_risk_at_yn,
exam.sty_indicator_high_risk_other_gene_yn,
exam.sty_indicator_high_risk_prior_high_risk_marker_yn,
exam.sty_indicator_high_risk_prior_personal_can_hist_yn,
exam.sty_indicator_high_risk_hist_of_mantle_rad_yn,
exam.sty_indicator_high_risk_fam_hist_yn,
exam.sty_indicator_add_eval_as_folup_yn,
exam.sty_indicator_folup_after_pre_exam_yn,
exam.sty_indicator_pre_operative_extent_of_dis_yn,
exam.sty_indicator_post_operative_margin_yn,
exam.sty_indicator_pre_neoadj_trtmnt_yn,
exam.sty_indicator_prob_solv_diff_img_yn,
exam.sty_indicator_scar_vs_recurr_yn,
exam.sty_indicator_folup_recommend_yn,
exam.sty_indicator_prior_2_prophy_mast_yn])] )
################### Send to table display
# add main CAD record table
colLabels = ("cad.cad_pt_no_txt", "cad.latest_mutation", "exam.exam_dt_datetime", "exam.mri_cad_status_txt", "exam.comment_txt",
"exam.original_report_txt",
"exam.sty_indicator_rout_screening_obsp_yn",
"exam.sty_indicator_high_risk_yn",
"exam.sty_indicator_high_risk_brca_1_yn",
"exam.sty_indicator_high_risk_brca_2_yn",
"exam.sty_indicator_high_risk_brca_1_or_2_yn",
"exam.sty_indicator_high_risk_at_yn",
"exam.sty_indicator_high_risk_other_gene_yn",
"exam.sty_indicator_high_risk_prior_high_risk_marker_yn",
"exam.sty_indicator_high_risk_prior_personal_can_hist_yn",
"exam.sty_indicator_high_risk_hist_of_mantle_rad_yn",
"exam.sty_indicator_high_risk_fam_hist_yn",
"exam.sty_indicator_add_eval_as_folup_yn",
"exam.sty_indicator_folup_after_pre_exam_yn",
"exam.sty_indicator_pre_operative_extent_of_dis_yn",
"exam.sty_indicator_post_operative_margin_yn",
"exam.sty_indicator_pre_neoadj_trtmnt_yn",
"exam.sty_indicator_prob_solv_diff_img_yn",
"exam.sty_indicator_scar_vs_recurr_yn",
"exam.sty_indicator_folup_recommend_yn",
"exam.sty_indicator_prior_2_prophy_mast_yn")
# write output query to pandas frame.
print len(datainfo)
dinfo = pd.DataFrame(data=datainfo[0], columns=colLabels)
print(dinfo['exam.original_report_txt'][0])
return dinfo
def queryExamFindings(self, fStudyID, redateID):
"""
run : Query by StudyID/AccesionN pair study to local folder
Inputs
======
StudyID : (int) CAD StudyID
redateID : (int) CAD StudyID Data of exam (format yyyy-mm-dd)
Output
======
"""
# Create the database: the Session.
self.Session = sessionmaker()
self.Session.configure(bind=engine) # once engine is available
session = self.Session() #instantiate a Session
#for cad_case in session.query(Cad_record).order_by(Cad_record.pt_id):
# print cad_case.pt_id, cad_case.cad_pt_no_txt, cad_case.latest_mutation_status_int
datainfo = []; is_mass=[]; is_nonmass=[]; is_foci=[];
for pt, cad, exam, finding, in session.query(database_atExam.Pt_record, database_atExam.Cad_record, database_atExam.Exam_record, database_atExam.Exam_Finding).\
filter(database_atExam.Pt_record.pt_id==database_atExam.Cad_record.pt_id).\
filter(database_atExam.Cad_record.pt_id==database_atExam.Exam_record.pt_id).\
filter(database_atExam.Exam_record.pt_exam_id==database_atExam.Exam_Finding.pt_exam_id).\
filter(database_atExam.Cad_record.cad_pt_no_txt == str(fStudyID)).\
filter(database_atExam.Exam_record.exam_dt_datetime == redateID).\
filter(database_atExam.Exam_Finding.mri_nonmass_yn == True).all():
# print results
if not cad:
print "cad is empty"
if not exam:
print "exam is empty"
if not finding:
print "finding is empty"
datainfo.append([cad.cad_pt_no_txt, pt.anony_dob_datetime, cad.latest_mutation_status_int,
exam.exam_dt_datetime, exam.a_number_txt, exam.exam_img_dicom_txt, exam.exam_tp_mri_int, exam.mri_cad_status_txt, exam.comment_txt, exam.original_report_txt,
finding.all_birads_scr_int,
finding.mri_mass_yn, finding.mri_nonmass_yn, finding.mri_foci_yn])
# Find if it's mass or non-mass and process
if (finding.mri_mass_yn):
is_mass.append([finding.all_birads_scr_int, finding.side_int, finding.size_x_double, finding.size_y_double, finding.size_z_double, finding.mri_dce_init_enh_int, finding.mri_dce_delay_enh_int, finding.curve_int, finding.mri_mass_margin_int, finding.mammo_n_mri_mass_shape_int, finding.t2_signal_int, finding.mri_bk_enh_inten_int, finding.mri_o_find_cysts_yn, finding.lesion_mri_start_image_no_int, finding.lesion_mri_finish_image_no_int])
# add mass lesion record table
colLabelsmass = ("finding.all_birads_scr_int","finding.side_int", "finding.size_x_double", "finding.size_y_double", "finding.size_z_double", "finding.mri_dce_init_enh_int", "finding.mri_dce_delay_enh_int", "finding.curve_int", "finding.mri_mass_margin_int", "finding.mammo_n_mri_mass_shape_int", "finding.t2_signal_int", "finding.mri_bk_enh_inten_int", "finding.mri_o_find_cysts_yn", "finding.start_image_no_int", "finding.finish_image_no_int")
self.massreport = pd.DataFrame(data=array(is_mass), columns=list(colLabelsmass))
# Find if it's mass or non-mass and process
if (finding.mri_nonmass_yn):
is_nonmass.append([finding.all_birads_scr_int, finding.side_int, finding.size_x_double, finding.size_y_double, finding.size_z_double, finding.mri_dce_init_enh_int, finding.mri_dce_delay_enh_int, finding.curve_int, finding.mri_nonmass_dist_int, finding.mri_nonmass_int_enh_int, finding.t2_signal_int, finding.mri_bk_enh_inten_int, finding.mri_o_find_cysts_yn, finding.lesion_mri_start_image_no_int, finding.lesion_mri_finish_image_no_int ])
# add non-mass lesion record table
colLabelsnonmass = ("finding.all_birads_scr_int","finding.side_int", "finding.size_x_double", "finding.size_y_double", "finding.size_z_double", "finding.mri_dce_init_enh_int", "finding.mri_dce_delay_enh_int", "finding.curve_int", "finding.mri_nonmass_dist_int", "finding.mri_nonmass_int_enh_int", "finding.t2_signal_int", "finding.mri_bk_enh_inten_int", "finding.mri_o_find_cysts_yn", "finding.start_image_no_int", "finding.finish_image_no_int")
self.nonmassreport = pd.DataFrame(data=array(is_nonmass), columns=list(colLabelsnonmass))
# Find if it's mass or non-mass and process
if (finding.mri_foci_yn):
is_foci.append([finding.all_birads_scr_int, finding.side_int, finding.size_x_double, finding.size_y_double, finding.size_z_double, finding.mri_dce_init_enh_int, finding.mri_dce_delay_enh_int, finding.curve_int, finding.mri_foci_distr_int, finding.t2_signal_int, finding.mri_bk_enh_inten_int, finding.mri_o_find_cysts_yn, finding.lesion_mri_start_image_no_int, finding.lesion_mri_finish_image_no_int])
# add foci lesion record table
colLabelsfoci = ("finding.all_birads_scr_int","finding.side_int", "finding.size_x_double", "finding.size_y_double", "finding.size_z_double", "finding.mri_dce_init_enh_int", "finding.mri_dce_delay_enh_int", "finding.curve_int", "finding.mri_foci_distr_int", "finding.t2_signal_int", "finding.mri_bk_enh_inten_int", "finding.mri_o_find_cysts_yn", "finding.start_image_no_int", "finding.finish_image_no_int")
self.focireport = pd.DataFrame(data=array(is_foci), columns=list(colLabelsfoci))
####### finish finding masses and non-mass or foci
# add main CAD record
colLabels = ("cad.cad_pt_no_txt", "pt.anony_dob_datetime", "cad.latest_mutation", "exam.exam_dt_datetime","exam.a_number_txt","exam.exam_img_dicom_txt","exam.exam_tp_mri_int","exam.mri_cad_status_txt","exam.comment_txt","exam.original_report", "finding.all_birads_scr_int", "finding.mri_mass_yn", "finding.mri_nonmass_yn", "finding.mri_foci_yn")
self.findreport = pd.DataFrame(data=array(datainfo), columns=list(colLabels))
colLabelsmass = ("finding.all_birads_scr_int","finding.side_int", "finding.size_x_double", "finding.size_y_double", "finding.size_z_double", "finding.mri_dce_init_enh_int", "finding.mri_dce_delay_enh_int", "finding.curve_int", "finding.mri_mass_margin_int", "finding.mammo_n_mri_mass_shape_int", "finding.t2_signal_int", "finding.mri_bk_enh_inten_int", "finding.mri_o_find_cysts_yn")
colLabelsnonmass = ("finding.all_birads_scr_int","finding.side_int", "finding.size_x_double", "finding.size_y_double", "finding.size_z_double", "finding.mri_dce_init_enh_int", "finding.mri_dce_delay_enh_int", "finding.curve_int", "finding.mri_nonmass_dist_int", "finding.mri_nonmass_int_enh_int", "finding.t2_signal_int", "finding.mri_bk_enh_inten_int", "finding.mri_o_find_cysts_yn")
colLabelsfoci = ("finding.all_birads_scr_int","finding.side_int", "finding.size_x_double", "finding.size_y_double", "finding.size_z_double", "finding.mri_dce_init_enh_int", "finding.mri_dce_delay_enh_int", "finding.curve_int", "finding.mri_foci_distr_int", "finding.t2_signal_int", "finding.mri_bk_enh_inten_int", "finding.mri_o_find_cysts_yn")
return is_mass, colLabelsmass, is_nonmass, colLabelsnonmass, is_foci, colLabelsfoci
def queryifProcedure(self, fStudyID, redateID):
"""
run : Query by StudyID/AccesionN pair study to local folder
Inputs
======
StudyID : (int) CAD StudyID
redateID : (int) CAD StudyID Data of exam (format yyyy-mm-dd)
Output
======
"""
# Create the ORM’s “handle†to the database: the Session.
self.Session = sessionmaker()
self.Session.configure(bind=engine) # once engine is available
session = self.Session() #instantiate a Session
gtpathology=[]; dfgtpathology = [];
for cad, exam, finding, proc, patho in session.query(database_atExam.Cad_record, database_atExam.Exam_record, database_atExam.Exam_Finding, database_atlinkPatho.Procedure, database_atlinkPatho.Pathology).\
filter(database_atExam.Cad_record.pt_id==database_atExam.Exam_record.pt_id).\
filter(database_atExam.Exam_record.pt_exam_id==database_atExam.Exam_Finding.pt_exam_id).\
filter(database_atExam.Exam_record.pt_id==database_atlinkPatho.Procedure.pt_id).\
filter(database_atlinkPatho.Procedure.pt_procedure_id==database_atlinkPatho.Pathology.pt_procedure_id).\
filter(database_atExam.Cad_record.cad_pt_no_txt == str(fStudyID)).\
filter(database_atExam.Exam_record.exam_dt_datetime == str(redateID)).\
filter(database_atExam.Exam_Finding.mri_nonmass_yn == True).all():
# print results
if not proc:
print "proc is empty"
if not patho:
print "patho is empty"
gtpathology.append([proc.pt_procedure_id, proc.proc_dt_datetime, proc.proc_side_int, proc.proc_source_int, proc.proc_guid_int, proc.proc_tp_int, proc.original_report_txt,
patho.pt_path_id, patho.histop_core_biopsy_benign_yn, patho.histop_other_txt, patho.histop_benign_bst_parenchyma_yn,
patho.histop_tp_isc_ductal_yn, patho.histop_tp_isc_other_txt, patho.in_situ_nucl_grade_int,
patho.histop_tp_ic_yn, patho.histop_tp_ic_other_txt, patho.histop_other_2_txt])
gtpathologyLabels = ("proc.pt_procedure_id", "proc.proc_dt_datetime", "proc.proc_side_int", "proc.proc_source_int", "proc.proc_guid_int", "proc.proc_tp_int", "proc.original_report_txt",
"patho.pt_path_id", "patho.histop_core_biopsy_benign_yn", "patho.histop_other_txt", "patho.histop_benign_bst_parenchyma_yn",
"patho.histop_tp_isc_ductal_yn", "patho.histop_tp_isc_other_txt", "patho.in_situ_nucl_grade_int",
"patho.histop_tp_ic_yn", "patho.histop_tp_ic_other_txt", "patho.histop_other_2_txt")
dfgtpathology = pd.DataFrame(data=array(gtpathology), columns=list(gtpathologyLabels))
return dfgtpathology