dish_xls_exporter.py 17.9 KB
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import logging
import pyexcel
import hashlib
from os import fsencode
from .export_utils import get_partners_from_daisy, process_possible_date, process_yes_no_dontknow_answer, get_value_list_from_row, is_data, is_study, is_submission,  process_yes_no_answer, get_names_from_string


class DishXlsExporter:

    def __init__(self):
        logging.basicConfig(filename='export_dishxls.log', level=logging.DEBUG)
        institutions = get_partners_from_daisy()
        self.inst_dict = {}
        self.inst_ac_dict = {}
        for inst in institutions:
            self.inst_dict[inst.get('name').lower()] = inst.get('name')
        for inst in institutions:
            if inst.get('acronym'):
                self.inst_ac_dict[inst.get('acronym').lower()] = inst.get('name')

        self.h = hashlib.md5()
        self.predefined_data_types = set([
            "Omics data",
            "Genotype data",
            "Whole genome sequencing",
            "Exome sequencing",
            "Genomics variant array",
            "RNASeq",
            "Genetic and derived genetic data",
            "Transcriptome array",
            "Methylation array",
            "MicroRNA array",
            "Metabolomics",
            "Metagenomics",
            "Proteomics",
            "Other omics data",
            "Clinical Imaging",
            "Cell Imaging",
            "Human subject data",
            "Clinical data",
            "Lifestyle data",
            "Socio Economic Data",
            "Environmental Data",
            "Other Phenotype data",
            "Other"
        ])

    def export_submission(self, full_file_path):
        idx = 1
        logging.info('Processing start for ----> {}'.format(full_file_path))
        book = pyexcel.get_book(file_name=full_file_path)
        is_dish = any("_Help" in elem for elem in book.sheet_names())
        if is_dish:
            dataset_dict = {
                "source":   book.filename,
                "contacts": [],
                "data_declarations": [],
                "studies": [],
                "legal_bases": []
            }

            while idx < book.number_of_sheets():
                sheet = book.sheet_by_index(idx)
                logging.info('Processing sheet ----> {}'.format(book.sheet_names()[idx]))

                if is_study(sheet):
                    cohort_dict = {'name':  sheet[1, 1],
                                   'description' : sheet[2, 1] + ' ' + sheet[6, 1],
                                   'has_ethics_approval' : process_yes_no_answer(sheet[4, 1]),
                                   "ethics_approval_notes": sheet[5, 1],
                                   "url": sheet[3, 1],
                                   "contacts": [{"first_name": get_names_from_string(sheet[8,1])[0],
                                                 "last_name":get_names_from_string(sheet[8,1])[1],
                                                 "role": sheet[11,1],
                                                 "email":sheet[9,1],
                                                 "affiliations": [self.process_institution(sheet[10,1])]
                                                 }]
                                   }

                    if sheet[12, 1] and sheet[15, 1]:
                        cohort_dict["contacts"] = cohort_dict["contacts"].append({"first_name": get_names_from_string(sheet[12,1])[0],
                                                                                  "last_name": get_names_from_string(sheet[12,1])[1],
                                                                                  "role": sheet[15,1],
                                                                                  "email":sheet[13,1],
                                                                                  "affiliations": [self.process_institution(sheet[14,1])]
                                                                                  })
                    dataset_dict["studies"].append(cohort_dict)

                elif is_data(sheet):
                    datadec_dict = {'title' : sheet[1, 1],
                                    'source_study' : sheet[2, 1],
                                    "data_types":[]}
                    datadec_dict["data_type_notes"] = sheet[7, 1]

                    data_type_info = self.process_data_types(get_value_list_from_row(sheet, 6))
                    datadec_dict["data_types"].extend(data_type_info[0])
                    datadec_dict["data_type_notes"] = datadec_dict["data_type_notes"] +" "+ data_type_info[1]+ " Notes on samples: " + sheet[10, 1]

                    #if it involves samples add this as a datatype
                    if process_yes_no_answer(sheet[9, 1]):
                        datadec_dict["data_types"].append('Samples')

                    if sheet[8, 1]:
                        datadec_dict["de_identification"] = self.process_deidentification(sheet[8,1])
                    datadec_dict["consent_status"] = self.process_consent_status(sheet[32,1])
                    datadec_dict["consent_status_description"] = sheet[33, 1]

                    if sheet[20, 1]:
                        datadec_dict['subject_categories'] = sheet[20, 1].replace(' & ', '_and_')

                    if sheet[12, 1]:
                        lb_code = self.extract_lb_code(sheet[12, 1])
                        dataset_dict["legal_bases"].append({"data_declarations": [sheet[1, 1]],
                                                            "personal_data_codes":["Standard"],
                                                            "legal_basis_codes":[lb_code],
                                                            "legal_basis_notes": sheet[12, 0]
                                                            })

                    if sheet[13, 1]:
                        lb_code = self.extract_lb_code(sheet[13, 1])
                        dataset_dict["legal_bases"].append({"data_declarations": [sheet[1, 1]],
                                                            "personal_data_codes":["Standard"],
                                                            "legal_basis_codes":[lb_code],
                                                            "legal_basis_notes": sheet[13, 0]
                                                            })
                    if sheet[16, 1]:
                        lb_code = self.extract_lb_code(sheet[16, 1])
                        dataset_dict["legal_bases"].append({"data_declarations": [sheet[1, 1]],
                                                            "personal_data_codes":["Special"],
                                                            "legal_basis_codes":[lb_code],
                                                            "legal_basis_notes": sheet[16, 0]
                                                            })
                    if sheet[17, 1]:
                        lb_code = self.extract_lb_code(sheet[17, 1])
                        dataset_dict["legal_bases"].append({"data_declarations": [sheet[1, 1]],
                                                            "personal_data_codes":["Special"],
                                                            "legal_basis_codes":[lb_code],
                                                            "legal_basis_notes": sheet[17, 0]
                                                            })
                    if sheet[21, 1]:
                        datadec_dict['has_special_subjects'] = process_yes_no_dontknow_answer(
                            sheet[21, 1])
                        if datadec_dict.get('has_special_subjects') == True and sheet[22, 1]:
                            datadec_dict['special_subject_notes'] = sheet[22, 1]

                    use_restrictions = []
                    if process_yes_no_dontknow_answer(sheet[24, 1]):
                        use_restrictions.append({'use_class': 'RS[XX]',
                                                 'use_restriction_rule': "CONSTRAINTS",
                                                 'use_class_note': sheet[25, 1]})
                    elif process_yes_no_dontknow_answer(sheet[24, 1]) is not None:
                        use_restrictions.append({'use_class': 'RS[XX]',
                                                 'use_restriction_rule': "NO_CONSTRAINTS",
                                                 'use_class_note': sheet[25, 1]})
                    if process_yes_no_dontknow_answer(sheet[26, 1]):
                        use_restrictions.append({'use_class': 'GS[XX]',
                                                 'use_restriction_rule': "CONSTRAINTS",
                                                 'use_class_note': sheet[27, 1]})
                    elif process_yes_no_dontknow_answer(sheet[26, 1]) is not None:
                        use_restrictions.append({'use_class': 'GS[XX]',
                                                 'use_restriction_rule': "NO_CONSTRAINTS",
                                                 'use_class_note': sheet[27, 1]})

                    if process_yes_no_dontknow_answer(sheet[28, 1]):
                        use_restrictions.append({'use_class': 'IS',
                                                 'use_restriction_rule': "CONSTRAINTS",
                                                 'use_class_note': sheet[29, 1]})
                    elif process_yes_no_dontknow_answer(sheet[28, 1]) is not None:
                        use_restrictions.append({'use_class': 'IS',
                                                 'use_restriction_rule': "NO_CONSTRAINTS",
                                                 'use_class_note': sheet[29, 1]})

                    if process_yes_no_dontknow_answer(sheet[30, 1]):
                        use_restrictions.append({'use_class': 'TS-[XX]',
                                                 'use_restriction_rule': "CONSTRAINTS",
                                                 'use_class_note': sheet[31, 1]})
                    elif process_yes_no_dontknow_answer(sheet[30, 1]) is not None:
                        use_restrictions.append({'use_class': 'IS',
                                                 'use_restriction_rule': "NO_CONSTRAINTS",
                                                 'use_class_note': sheet[31, 1]})

                    if process_yes_no_answer(sheet[35, 1]):
                        use_restrictions.append({'use_class': 'PS',
                                                 'use_restriction_rule': "CONSTRAINTS",
                                                 'use_class_note': sheet[35, 0] + " PROJECT: "+dataset_dict["project"]})
                    else:
                        use_restrictions.append({'use_class': 'PS',
                                                 'use_restriction_rule': "NO_CONSTRAINTS",
                                                 'use_class_note': sheet[35, 0]})

                    if process_yes_no_answer(sheet[36, 1]):
                        datadec_dict["storage_end_date"] = process_possible_date(sheet[37, 1])
                        use_restrictions.append({'use_class': 'TS-[XX]',
                                                 'use_restriction_rule': "CONSTRAINTS",
                                                 'use_class_note': process_possible_date(sheet[37, 1])})
                    else:
                        use_restrictions.append({'use_class': 'TS-[XX]',
                                                 'use_restriction_rule': "NO_CONSTRAINTS",
                                                 'use_class_note': process_possible_date(sheet[37, 1])})

                    if process_yes_no_answer(sheet[38, 1]):
                        use_restrictions.append({'use_class': 'PUB',
                                                 'use_restriction_rule': "CONSTRAINTS",
                                                 'use_class_note': sheet[39, 1]})
                    else:
                        use_restrictions.append({'use_class': 'PUB',
                                                 'use_restriction_rule': "NO_CONSTRAINTS",
                                                 'use_class_note': sheet[39, 1]})

                    if process_yes_no_answer(sheet[42, 1]):
                        use_restrictions.append({'use_class': 'Other',
                                                 'use_restriction_rule': "CONSTRAINTS",
                                                 'use_class_note': sheet[42, 1]})

                    if process_yes_no_answer(sheet[47, 1]):
                        use_restrictions.append({'use_class': 'IP',
                                                 'use_restriction_rule': "CONSTRAINTS",
                                                 'use_class_note': sheet[48, 1]})
                    else:
                        use_restrictions.append({'use_class': 'IP',
                                                 'use_restriction_rule': "NO_CONSTRAINTS",
                                                 'use_class_note': sheet[48, 1]})
                    datadec_dict['use_restrictions'] = use_restrictions

                    datadec_dict["access_procedure"] = ""

                    if sheet[45, 1] and ('not' in sheet[45, 1]):
                        if sheet[44, 1] and ('no' in sheet[44, 1]):
                            datadec_dict["access_category"] = "open-access"
                            datadec_dict["access_procedure"] = datadec_dict["access_procedure"] + "Researchers need to sign an access request form."
                        else:
                            datadec_dict["access_category"] = "registered-access"
                    else:
                        datadec_dict["access_category"] = "controlled-access"
                        datadec_dict["access_procedure"] = datadec_dict["access_procedure"] +  sheet[46, 1]


                    dataset_dict["data_declarations"].append(datadec_dict)

                elif is_submission(sheet):
                    dataset_dict["name"] = sheet[2, 1]
                    dataset_dict["project"] = sheet[5, 1]
                    dataset_dict["contacts"].extend([{"first_name": get_names_from_string(sheet[9, 1])[0],
                                                      "last_name":get_names_from_string(sheet[9, 1])[1],
                                                      "role": sheet[11,1],
                                                      "email":sheet[10,1],
                                                      "affiliations": [self.process_institution(sheet[7,1])]
                                                      },
                                                     {"first_name": get_names_from_string(sheet[12, 1])[0],
                                                      "last_name":get_names_from_string(sheet[12, 1])[1],
                                                      "role": "Legal_Representative",
                                                      "email":sheet[10,1],
                                                      "affiliations": [self.process_institution(sheet[7,1])]
                                                      },
                                                     {"first_name": get_names_from_string(sheet[14, 1])[0],
                                                      "last_name":get_names_from_string(sheet[14, 1])[1],
                                                      "role": "Data_Protection_Officer",
                                                      "email":sheet[10,1],
                                                      "affiliations": [self.process_institution(sheet[7,1])]
                                                      }])
                    if sheet[16, 1] and sheet[18, 1]:
                        dataset_dict["contacts"].append({"first_name": get_names_from_string(sheet[14, 1])[0],
                                                         "last_name": get_names_from_string(sheet[14, 1])[1],
                                                         "role": sheet[18,1],
                                                         "email": sheet[10,1],
                                                         "affiliations": [self.process_institution(sheet[7,1])]
                                                         })

                else:
                    pass

                idx += 1

            logging.info('Processing end for ----> {}'.format(full_file_path))
            return dataset_dict
        else:
            raise ValueError("{} not a valid DISH Excel file".format(full_file_path))


    def get_hash_for_path(self, path):
        self.h.update(fsencode(path))
        return str(int(self.h.hexdigest(), 16))

    def process_data_types(self, xls_data_type_list):
        result = []
        data_type_notes = ''
        for type_name in xls_data_type_list:
            type_name = type_name.strip()
            if type_name:
                if type_name in self.predefined_data_types:
                    result.append(type_name.replace(" ", "_"))
                else:
                    data_type_notes += type_name + '\n'
        return (result, data_type_notes)

    def process_deidentification(self, deid_str):
        if 'seu' in deid_str:
            return 'pseudonymized'
        elif 'non' in deid_str:
            return 'anonymized'

    def process_consent_status(self, consent_str):
        if 'et' in consent_str:
            return 'heterogeneous'
        elif 'om' in consent_str:
            return 'homogeneous'

    def process_institution(self, institution_str):
        if institution_str:
            if self.inst_ac_dict.get(institution_str.lower()):
                return self.inst_ac_dict.get(institution_str.lower())
            elif self.inst_dict.get(institution_str.lower()):
                return self.inst_dict.get(institution_str.lower())
            else:
                logging.error('Unknown institution  -- > {}'.format(institution_str))
                return institution_str
        else:
            return ""

    def extract_lb_code(self, lb_value):
        op = lb_value.index('(')
        cp = lb_value.rindex(')')
        return lb_value[op+1:cp]