def __init__(self):
self.schemas = Schema(
[
Column("RA_Report #", [CanConvertValidation(int)]),
Column("RA_CAERS Created Date", [CanCallValidation(self.parse_date)]),
Column(
"AEC_Event Start Date",
[CanCallValidation(self.parse_date)],
allow_empty=True,
),
Column(
"PRI_Product Role", [InListValidation(["Suspect", "Concomitant"])]
),
Column("PRI_Reported Brand/Product Name"),
Column("PRI_FDA Industry Code"),
Column("PRI_FDA Industry Name"),
Column("CI_Age at Adverse Event"),
Column(
"CI_Age Unit",
[
InListValidation(
["Year(s)", "Decade(s)", "Month(s)", "Week(s)", "Day(s)"]
)
],
),
Column("CI_Gender", [InListValidation(["Female", "Male"])]),
Column("AEC_One Row Outcomes"),
Column("SYM_One Row Coded Symptoms"),
]
)
def create_schema(self) -> Schema:
""" Create Pandas schema with all the necessary validation rules read in from config """
col_list = []
for column in self.__spreadsheet_def.keys():
validators = [
LeadingWhitespaceValidation(),
TrailingWhitespaceValidation()
]
mandatory_field_flag = self.__spreadsheet_def.is_mandatory(column)
# Special cases for checking institutions/countries...
if column == 'submitting_institution':
validators.append(
InListValidation([i.name for i in self.__institutions]))
if column == 'country':
validators.append(
InListValidation([i.country for i in self.__institutions]))
else:
# Regex validation
if self.__spreadsheet_def.get_regex(column):
validators.append(
MatchesPatternValidation(
self.__spreadsheet_def.get_regex(column),
message=self.__spreadsheet_def.
get_regex_validation_message(column)))
# Validate allowed values
elif self.__spreadsheet_def.get_allowed_values(column):
validators.append(
InListValidation(
self.__spreadsheet_def.get_allowed_values(column),
case_sensitive=False))
# Field length validation
max_len = self.__spreadsheet_def.get_max_length(column)
if max_len and max_len > 0:
validators.append(
_StringLengthValidation(
'field length is greater than {} characters'.
format(str(max_len)), max_len))
# Mandatory field validation
col_list.append(
Column(self.__spreadsheet_def.get_column_name(column),
validators,
allow_empty=not mandatory_field_flag))
return Schema(col_list)
def defSchema():
print('Define expected Schema')
schema = Schema([
Column(name='id',
validations=[IsDtypeValidation(np.object_)],
allow_empty=False),
Column(name='comment_text',
validations=[IsDtypeValidation(np.object_)],
allow_empty=False),
Column(name='toxic',
validations=[InListValidation([0, 1])],
allow_empty=False),
Column(name='severe_toxic',
validations=[InListValidation([0, 1])],
allow_empty=False),
Column(name='obscene',
validations=[InListValidation([0, 1])],
allow_empty=False),
Column(name='threat',
validations=[InListValidation([0, 1])],
allow_empty=False),
Column(name='insult',
validations=[InListValidation([0, 1])],
allow_empty=False),
Column(name='identity_hate',
validations=[InListValidation([0, 1])],
allow_empty=False)
])
return schema
def __init__(self):
self.schemas = Schema(
[
Column(
"Given Name",
[LeadingWhitespaceValidation(), TrailingWhitespaceValidation()],
),
Column(
"Family Name",
[LeadingWhitespaceValidation(), TrailingWhitespaceValidation()],
),
Column("Age", [InRangeValidation(0, 120)]),
Column("Sex", [InListValidation(["Male", "Female", "Other"])]),
Column("Customer ID", [MatchesPatternValidation(r"\d{4}[A-Z]{4}")]),
]
)
def _validate(self, diagnosis_df):
schema = Schema([
Column('visit_dt', [
MatchesPatternValidation(r'^\d{4}-\d{2}-\d{2} \d{2}:\d{2}:00$')
]),
Column('sex', [InListValidation(['M', 'K'])]),
Column('icd10', [
MatchesPatternValidation(r'^[CDIJKMNRZ]{1}\d{1,2}.?\d{0,2}$')
])
])
errors = schema.validate(diagnosis_df)
for error in errors:
self.Logger.error(error)
if len(errors) > 0:
exit()
def __init__(self):
self.schemas = Schema(
[
Column("id"),
Column(
"payer_name",
[LeadingWhitespaceValidation(), TrailingWhitespaceValidation()],
),
Column("document_amount"),
Column("payed_amount"),
Column("payer_id_number"),
Column(
"payer_address",
[LeadingWhitespaceValidation(), TrailingWhitespaceValidation()],
),
Column("barcode"),
Column("typable_line"),
Column("number"),
Column(
"document_number",
[LeadingWhitespaceValidation(), TrailingWhitespaceValidation()],
),
Column("due_date", [DateFormatValidation("%m/%d/%y")]),
Column(
"city",
[LeadingWhitespaceValidation(), TrailingWhitespaceValidation()],
),
Column(
"state",
[LeadingWhitespaceValidation(), TrailingWhitespaceValidation()],
),
Column("zip_code"),
Column("bank_answer_date"),
Column("pdf_upload_date"),
Column(
"status", [InListValidation(["pending", "paid", "due", "error"])]
),
Column("callback"),
Column("object_id"),
Column("extra"),
]
)
BUILD_MAP = {'28': 'NCBI28',
'29': 'NCBI29',
'30': 'NCBI30',
'31': 'NCBI31',
'33': 'NCBI33',
'34': 'NCBI34',
'35': 'NCBI35',
'36': 'NCBI36',
'37': 'GRCh37',
'38': 'GRCh38'}
VALID_FILE_EXTENSIONS = [".txt", ".tsv", ".csv", ".tsv.gz", ".csv.gz", "gz", "gzip", ".tsv.gzip", ".csv.gzip"]
GENERIC_VALIDATORS = {
SNP_DSET: Column(SNP_DSET, [CanConvertValidation(DSET_TYPES[SNP_DSET]), MatchesPatternValidation(r'^rs[0-9]+$')], allow_empty=True),
CHR_DSET: Column(CHR_DSET, [InListValidation(VALID_CHROMOSOMES)], allow_empty=True),
BP_DSET: Column(BP_DSET, [CanConvertValidation(DSET_TYPES[BP_DSET]), InInclusiveRangeValidation(1, 999999999)], allow_empty=True),
EFFECT_WEIGHT_DSET: Column(EFFECT_WEIGHT_DSET, [CanConvertValidation(DSET_TYPES[EFFECT_WEIGHT_DSET])], allow_empty=True),
OR_DSET: Column(OR_DSET, [CanConvertValidation(DSET_TYPES[OR_DSET])], allow_empty=True),
HR_DSET: Column(HR_DSET, [CanConvertValidation(DSET_TYPES[HR_DSET])], allow_empty=True),
BETA_DSET: Column(BETA_DSET, [CanConvertValidation(DSET_TYPES[BETA_DSET])], allow_empty=True),
EFFECT_DSET: Column(EFFECT_DSET, [MatchesPatternValidation(r'^[ACTGN]+$')], allow_empty=False),
REF_DSET: Column(REF_DSET, [MatchesPatternValidation(r'^[ACTGN]+$')], allow_empty=True),
FREQ_DSET: Column(FREQ_DSET, [CanConvertValidation(DSET_TYPES[FREQ_DSET])], allow_empty=True),
LOCUS_DSET: Column(LOCUS_DSET, [CanConvertValidation(DSET_TYPES[LOCUS_DSET]), LeadingWhitespaceValidation(), TrailingWhitespaceValidation()], allow_empty=True)
}
SNP_VALIDATORS = {k:v for k,v in GENERIC_VALIDATORS.items()}
SNP_VALIDATORS[SNP_DSET] = Column(SNP_DSET, [CanConvertValidation(DSET_TYPES[SNP_DSET]), MatchesPatternValidation(r'^rs[0-9]+$')], allow_empty=False)
SNP_EMPTY_VALIDATORS = {k:v for k,v in GENERIC_VALIDATORS.items()}
def clean(spreadsheet, rules, tempfolder, progress=None):
if not os.path.exists(tempfolder):
os.makedirs(tempfolder)
filepath = tempfolder + '/temporary_file.xlsx'
results = SpreadCheck(filepath)
################################
# LOAD SPREADSHEET TO DF #
################################
try:
df = pd.read_csv(spreadsheet)
except:
try:
df = pd.read_excel(spreadsheet)
except:
results.spreadsheet = None
results.errors.append("ERROR: Unaccepted File Format")
return results
################################
# SCHEMAS VALIDATION #
################################
schemas_list = []
df.fillna("NULL", inplace=True)
for field, rule in rules.items():
schemas_list.append(
Column(field, [InListValidation(list(rule['allowed_value_set']))]))
schema = Schema(schemas_list)
errors = schema.validate(df, columns=schema.get_column_names())
# reset progress bar
if progress:
progress.config(value=0)
# increment progress bar #1
if progress:
progress.step(20)
################################
# BUILD SHEET W FORMATTING #
################################
writer = pd.ExcelWriter(filepath, engine='xlsxwriter')
# Skip row 1 headers so we can add manunally with formatting
df.to_excel(writer,
sheet_name='Sheet1',
startrow=1,
header=False,
index=False)
workbook = writer.book
worksheet = writer.sheets['Sheet1']
### WORKBOOK FORMATS ###
yellow_highlight = workbook.add_format({'bg_color': '#FFEB9C'})
header = workbook.add_format({
'bold': True,
'text_wrap': True,
'valign': 'top',
'fg_color': '#D7E4BC',
'border': 1
})
# increment progress bar #2
if progress:
progress.step(20)
# Set column widths
worksheet.set_column(0, len(df.columns) - 1, 22)
worksheet.set_default_row(hide_unused_rows=True)
# Write the column headers with the defined format.
for col_num, value in enumerate(df.columns.values):
worksheet.write(0, col_num, value, header)
# increment progress bar #3
if progress:
progress.step(20)
flag_rows = []
df_length = len(df)
for error in errors:
# Catch Key errors, like rules_file, data_file column name mismatch
try:
row = error.row + 1
column = df.columns.get_loc(error.column)
except:
results.spreadsheet = None
results.errors.append("ERROR: " + str(error))
return results
# Catch other errors. Not sure what yet.
try:
# If an autocorrect mapping exists for value, replace it.
if error.value in rules[error.column]['autocorrect_dict'].keys():
worksheet.write(
row, column,
rules[error.column]['autocorrect_dict'][error.value])
results.corrections += 1
else:
flag_rows.append(error.row)
# If no autocorrect mapping exists, highlight and annotate the entry
# Comments
worksheet.write_comment(row, column, error.message)
# Highlights
worksheet.conditional_format(row, column, row, column, {
'type': 'no_errors',
'format': yellow_highlight
})
results.flags += 1
except Exception as err:
results.spreadsheet = None
results.errors.append("ERROR: " + err)
return results
# increment progress bar #4
if progress:
progress.step(20)
# Hide Rows that don't contain annotations
for i in range(df_length + 1):
if i not in flag_rows:
worksheet.set_row(i + 1, None, None, {'hidden': True})
# increment progress bar #5
if progress:
progress.step(20)
writer.save()
results.messages.append("SUCCESS:")
results.messages.append("{} entries corrected".format(results.corrections))
results.messages.append("{} entries flagged for review".format(
results.flags))
return results
from collections import defaultdict
import re
from pandas_schema import Column, Schema
from pandas_schema.validation import (LeadingWhitespaceValidation, TrailingWhitespaceValidation,
CanConvertValidation, MatchesPatternValidation, CustomSeriesValidation,
InRangeValidation, InListValidation, DateFormatValidation)
study_schema = Schema([
Column('study_id', [CustomSeriesValidation(lambda x: ~x.isnull(), 'A value is required for the study_id column.') &
~InListValidation([''])]),
Column('pi_name', [CustomSeriesValidation(lambda x: ~x.isnull(), 'A value is required for the pi_name column.') &
~InListValidation([''])]),
Column('sample_type', [InListValidation(['wmgx', 'wmtx', '16S', 'other'])]),
Column('bioproject_accession', [InListValidation(['']) | MatchesPatternValidation(r'PRJ\w+\d+')]),
Column('geo_loc_name', [InListValidation(['']) | MatchesPatternValidation(r'\w+:\w+:\w+')]),
Column('analysis_desc', [InListValidation(['']) | CanConvertValidation(str)]),
Column('sequencing_facility', [LeadingWhitespaceValidation()]),
Column('env_biom', [MatchesPatternValidation(r'ENVO:\d+') | InListValidation([''])]),
Column('env_feature', [MatchesPatternValidation(r'ENVO:\d+') | InListValidation([''])]),
Column('env_material', [MatchesPatternValidation(r'ENVO:\d+') | InListValidation([''])]),
Column('host_tissue_sampled', [InListValidation(['']) | MatchesPatternValidation(r'BTO:\d+')]),
Column('animal_vendor', [LeadingWhitespaceValidation()]),
Column('paired', [InListValidation(['true', 'false'])]),
Column('paired_id', [InListValidation(['']) | MatchesPatternValidation(r'[a-zA-Z0-9_.]+')])
,
Column('pi_email', [CustomSeriesValidation(lambda x: ~x.isnull(), 'A value is required for the pi_email column.') &
~InListValidation([''])])
])
import pandas as pd
from pandas_schema import Column, Schema
from pandas_schema.validation import LeadingWhitespaceValidation, TrailingWhitespaceValidation, InRangeValidation, \
DateFormatValidation, InListValidation
schema = Schema([
Column('name',
[LeadingWhitespaceValidation(),
TrailingWhitespaceValidation()]),
Column('title',
[LeadingWhitespaceValidation(),
TrailingWhitespaceValidation()]),
Column('salary', [InRangeValidation(0, 33000)]),
Column('sex', [InListValidation(['F', 'M'])]),
Column('date', [DateFormatValidation('%Y-%m-%d')])
])
widths = [
9, # name
19, # title
6, # salary
4, # sex
11, # date
]
# read source data
test_data = pd.read_fwf("data/fixed_width.txt", widths=widths)
print('orig dataset')
print(test_data)
# data verification
schema = Schema([
Column('ID', []),
Column('Title', []),
Column('Authors', []),
Column('Publication Venue', []),
Column('Publication Date', [DateFormatValidation("%Y-%m-%d")]),
Column('Abstract', []),
Column('Link to PDF', []),
Column('Type', []),
Column('Keywords', []),
Column('Tags', []),
Column('Access', []),
Column('Area', []),
Column('Included in map', [InListValidation(["yes", "no"])]),
Column('Ready for publication?', [InListValidation(["yes", "no"])]),
Column('Validation', []),
Column('Comment 1', []),
Column('Author Comment 1', []),
Column('Comment 2', []),
Column('Author Comment 2', []),
Column('Comment 3', []),
Column('Author Comment 3', []),
Column('Comment 4', []),
Column('Author Comment 4', [])
])
def process_comments(row):
row = row.tolist()
import pandas as pd
from io import StringIO
from pandas_schema import Column, Schema
from pandas_schema.validation import LeadingWhitespaceValidation, TrailingWhitespaceValidation, CanConvertValidation, MatchesPatternValidation, InRangeValidation, InListValidation
schema = Schema([
Column('Given Name',
[LeadingWhitespaceValidation(),
TrailingWhitespaceValidation()]),
Column('Family Name',
[LeadingWhitespaceValidation(),
TrailingWhitespaceValidation()]),
Column('Age', [InRangeValidation(0, 120)]),
Column('Sex', [InListValidation(['Male', 'Female', 'Other'])]),
Column('Customer ID', [MatchesPatternValidation(r'\d{4}[A-Z]{4}')])
])
test_data = pd.read_csv(
StringIO('''Given Name,Family Name,Age,Sex,Customer ID
Gerald ,Hampton,82,Male,2582GABK
Yuuwa,Miyake,270,male,7951WVLW
Edyta,Majewska ,50,Female,775ANSID
'''))
errors = schema.validate(test_data)
for error in errors:
print(error)
import time
import pandas as pd
from pandas_schema import Column, Schema
from pandas_schema.validation import DateFormatValidation, MatchesPatternValidation, InListValidation
pattern_id = r'^-?\d{1,16}$' # Number / integer - up to 16
pattern_dec = r'^-?\d*\.\d{1,2}$'
pattern_geo = r'^-?\d*\.\d{1,20}$' # geo location / decimal with up to 18 decimal place
pattern_date = r'%Y-%m-%d %H:%M:%S' # Timestamp yyyy-MM-dd HH:mm:ss (in Zulu/UTC time zone) e.g. 2017-07-01 00:00:07
taxiRide_schema = Schema([
Column('rideId', [MatchesPatternValidation(pattern_id)]),
Column('isStart', [InListValidation(['START', 'END'])]),
Column('endTime', [DateFormatValidation(pattern_date)]),
Column('startTime', [DateFormatValidation(pattern_date)]),
Column('startLon', [MatchesPatternValidation(pattern_geo)]),
Column('startLat', [MatchesPatternValidation(pattern_geo)]),
Column('endLon', [MatchesPatternValidation(pattern_geo)]),
Column('endLat', [MatchesPatternValidation(pattern_geo)]),
Column('passengerCnt', [MatchesPatternValidation(pattern_id)])
], ordered=True)
taxiFare_schema = Schema([
Column('rideId', [MatchesPatternValidation(pattern_id)]),
Column('taxiId', [MatchesPatternValidation(pattern_id)]),
Column('driverId', [MatchesPatternValidation(pattern_id)]),
Column('startTime', [DateFormatValidation(pattern_date)]),
Column('paymentType', [InListValidation(['CSH', 'CRD', 'NOC', 'DIS', 'UNK'])]),
Column('tip', [MatchesPatternValidation(pattern_dec)]),
'atrialfib',
# ATCDs
'stroke_pre',
'tia_pre',
'ich_pre',
# Treatment
'treat_antipatelet',
'treat_anticoagulant',
'ivt_start', # needed to check if started before imaging
'treat_ivt',
'iat_start', # needed to check if started before imaging
]
validation_schema = Schema([
Column('age', [InRangeValidation(1, 120)]),
Column('sex', [InListValidation(['m', 'f'])]),
Column('height', [InRangeValidation(50, 300)]),
Column('weight', [InRangeValidation(10, 400)]),
Column('onset_known', [InListValidation(['yes', 'no', 'wake_up'])]),
Column('Firstimage_date', [DateFormatValidation('%Y-%m-%d %H:%M:%S')]),
Column('onset_time', [DateFormatValidation('%Y-%m-%d %H:%M:%S')]),
Column('NIH admission', [InRangeValidation(0, 43)]),
Column('bp_syst', [InRangeValidation(0, 300)]),
Column('bp_diast', [InRangeValidation(0, 300)]),
Column('glucose', [InRangeValidation(0.1, 30)]),
Column('créatinine', [InRangeValidation(0.1, 1000)]),
Column('hypertension', [InListValidation(['yes', 'no'])]),
Column('diabetes', [InListValidation(['yes', 'no'])]),
Column('hyperlipidemia', [InListValidation(['yes', 'no'])]),
Column('smoking', [InListValidation(['yes', 'no'])]),
Column('atrialfib', [InListValidation(['yes', 'no'])]),
def validate(df):
d_error = {}
list_bu = [x[0] for x in BU_CHOICES]
list_rd = [x[0] for x in RD_CHOICES]
list_dept = [x[0] for x in DEPT_CHOICES]
list_hplevel = [x[0] for x in HPLEVEL_CHOICES]
list_province = [x[0] for x in PROVINCE_CHOICES]
list_title = [x[0] for x in TITLE_CHOICES]
NullValidation = CustomElementValidation(lambda d: d is not np.nan,
"该字段不能为空")
schema = Schema([
Column("南北中国", [InListValidation(list_bu)]),
Column("区域", [InListValidation(list_rd)]),
Column("大区", [
LeadingWhitespaceValidation(),
TrailingWhitespaceValidation(), NullValidation
]),
Column("地区经理", [
LeadingWhitespaceValidation(),
TrailingWhitespaceValidation(), NullValidation
]),
Column("负责代表", [
LeadingWhitespaceValidation(),
TrailingWhitespaceValidation(), NullValidation
]),
Column(
"医院编码",
[
LeadingWhitespaceValidation(),
TrailingWhitespaceValidation(),
NullValidation,
MatchesPatternValidation(r"^[H]{1}(\d){9}$"),
],
),
Column("医院全称", [
LeadingWhitespaceValidation(),
TrailingWhitespaceValidation(), NullValidation
]),
Column("省/自治区/直辖市", [InListValidation(list_province)]),
Column("是否双call", [InListValidation(["是", "否"])]),
Column("医院级别", [InListValidation(list_hplevel)]),
Column("开户进展", [InListValidation(["已开户", "未开户"])]),
Column("客户姓名", [
LeadingWhitespaceValidation(),
TrailingWhitespaceValidation(),
IsDistinctValidation()
]),
Column("所在科室", [InListValidation(list_dept)]),
Column("职称", [InListValidation(list_title)]),
Column("月出诊次数（半天计）",
[CanConvertValidation(int),
InRangeValidation(0, 63)]),
Column("每半天\n门诊量", [CanConvertValidation(int),
InRangeValidation(0, )]),
Column("相关病人\n比例(%)\n建议比例：40%-80%",
[CanConvertValidation(int),
InRangeValidation(0, 101)]),
Column("备注"),
])
errors = schema.validate(df.loc[:, COL])
for error in errors:
str_warning = str(error)
for term in D_TRANSLATE:
str_warning = str_warning.replace(term, D_TRANSLATE[term])
findword = r": [0-9]\d*"
str_warning = re.sub(findword, row_refined, str_warning)
d_error[str_warning] = "<br>"
d_error = {**d_error, **check_inconsist(df, "医院编码", "医院全称", "both")}
d_error = {**d_error, **check_inconsist(df, "区域", "大区", "right")}
d_error = {**d_error, **check_inconsist(df, "大区", "地区经理", "right")}
d_error = {**d_error, **check_inconsist(df, "地区经理", "负责代表", "right")}
d_error = {**d_error, **check_inconsist(df, "医院编码", "省/自治区/直辖市", "left")}
d_error = {**d_error, **check_inconsist(df, "医院编码", "是否双call", "left")}
d_error = {**d_error, **check_inconsist(df, "医院编码", "医院级别", "left")}
d_error = {**d_error, **check_inconsist(df, "医院编码", "开户进展", "left")}
d_error = {**d_error, **check_inconsist(df, "医院全称", "省/自治区/直辖市", "left")}
d_error = {**d_error, **check_inconsist(df, "医院全称", "是否双call", "left")}
d_error = {**d_error, **check_inconsist(df, "医院全称", "医院级别", "left")}
d_error = {**d_error, **check_inconsist(df, "医院全称", "开户进展", "left")}
d_error = {**d_error, **check_hplevel_with_dept(df)} # 检查医院级别和所在科室是否出现矛盾
return d_error
Column("Chr15", [CanConvertValidation(float)]),
Column("Chr16", [CanConvertValidation(float)]),
Column("Chr17", [CanConvertValidation(float)]),
Column("Chr18", [CanConvertValidation(float)]),
Column("Chr19", [CanConvertValidation(float)]),
Column("Chr20", [CanConvertValidation(float)]),
Column("Chr21", [CanConvertValidation(float)]),
Column("Chr22", [CanConvertValidation(float)]),
Column("ChrX", [CanConvertValidation(float)]),
Column("ChrY", [CanConvertValidation(float)]),
Column("FF_Formatted", [CanConvertValidation(float)]),
Column("FFY", [CanConvertValidation(float)]),
Column("FFX", [CanConvertValidation(float)]),
Column("DuplicationRate", [CanConvertValidation(float)]),
Column("Bin2BinVariance", [CanConvertValidation(float)]),
Column("UnfilteredCNVcalls", [CanConvertValidation(float)]),
Column("CNVSegment", [InListValidation(["Found"])]),
Column("Flowcell", []),
Column("SequencingDate", []),
Column("Median_13", [CanConvertValidation(float)]),
Column("Median_18", [CanConvertValidation(float)]),
Column("Median_21", [CanConvertValidation(float)]),
Column("Median_X", [CanConvertValidation(float)]),
Column("Median_Y", [CanConvertValidation(float)]),
Column("Stdev_13", [CanConvertValidation(float)]),
Column("Stdev_18", [CanConvertValidation(float)]),
Column("Stdev_21", [CanConvertValidation(float)]),
Column("Stdev_X", [CanConvertValidation(float)]),
Column("Stdev_Y", [CanConvertValidation(float)]),
])
def main():
# Parse input arguments
parser = get_parser()
args = parser.parse_args()
data_path = args.path_in
path_tsv = os.path.join(data_path, 'participants.tsv')
tsv_file = pd.read_csv(path_tsv, sep='\t')
list_subj = [
name for name in os.listdir(data_path) if
os.path.isdir(os.path.join(data_path, name)) and name.startswith('sub')
]
df = pd.DataFrame(tsv_file)
list_tsv_participants = df['participant_id'].tolist()
missing_subjects_tsv = list(set(list_subj) - set(list_tsv_participants))
missing_subjects_folder = list(set(list_tsv_participants) - set(list_subj))
if missing_subjects_tsv:
# print ('Warning missing following subjects from participants.tsv : %s' %missing_subjects_tsv)
print('\nWarning missing following subjects from participants.tsv: ')
missing_subjects_tsv.sort()
pprint(missing_subjects_tsv)
if missing_subjects_folder:
# print ('\nWarning missing data for subjects listed in participants.tsv : %s' %missing_subjects_folder)
print(
'\nWarning missing data for subjects listed in participants.tsv: ')
missing_subjects_folder.sort()
pprint(missing_subjects_folder)
for dirName, subdirList, fileList in os.walk(data_path):
for file in fileList:
if file.endswith('.nii.gz'):
originalFilePath = os.path.join(dirName, file)
jsonSidecarPath = os.path.join(dirName,
file.split(".")[0] + '.json')
if os.path.exists(jsonSidecarPath) == False:
print("Missing jsonSidecar: " + jsonSidecarPath)
# Checking participants.tsv contents
schema = Schema([
Column('participant_id',
[LeadingWhitespaceValidation(),
TrailingWhitespaceValidation()]),
Column('sex', [InListValidation(['M', 'F'])]),
Column('age', [InRangeValidation(18, 60)]),
Column('height', [MatchesPatternValidation(r"[0-9]|-")]),
Column('weight', [MatchesPatternValidation(r"[0-9]|-")]),
Column('date_of_scan', [
DateFormatValidation('%Y-%m-%d') | MatchesPatternValidation(r"-")
]),
Column('institution_id',
[LeadingWhitespaceValidation(),
TrailingWhitespaceValidation()]),
Column('institution',
[LeadingWhitespaceValidation(),
TrailingWhitespaceValidation()]),
Column('manufacturer',
[LeadingWhitespaceValidation(),
TrailingWhitespaceValidation()]),
Column('manufacturers_model_name',
[LeadingWhitespaceValidation(),
TrailingWhitespaceValidation()]),
Column('receive_coil_name',
[LeadingWhitespaceValidation(),
TrailingWhitespaceValidation()]),
Column('software_versions',
[LeadingWhitespaceValidation(),
TrailingWhitespaceValidation()]),
Column('researcher',
[LeadingWhitespaceValidation(),
TrailingWhitespaceValidation()]),
])
errors = schema.validate(tsv_file)
print('\nChecking the contents of participants.tsv')
if not errors:
print("--> all good 👍")
else:
for error in errors:
print(error)
Column(PVAL_DSET, [
CanConvertValidation(DSET_TYPES[PVAL_DSET]),
InInclusiveRangeValidation(0, 1)
],
allow_empty=False),
BETA_DSET:
Column(BETA_DSET, [CanConvertValidation(float)], allow_empty=True),
SNP_DSET:
Column(SNP_DSET, [
CanConvertValidation(DSET_TYPES[SNP_DSET]),
MatchesPatternValidation(
r'^chr[0-9XY]+_[0-9]+_[ACTGNactgn]+_[ACTGNactgn]+|LONG_STRING$')
],
allow_empty=True),
CHR_DSET:
Column(CHR_DSET, [InListValidation(VALID_CHROMOSOMES)], allow_empty=False),
BP_DSET:
Column(BP_DSET, [
CanConvertValidation(DSET_TYPES[BP_DSET]),
InInclusiveRangeValidation(1, 999999999)
],
allow_empty=False),
EFFECT_DSET:
Column(EFFECT_DSET,
[MatchesPatternValidation(r'^[ACTGNactgn]+|LONG_STRING$')],
allow_empty=True),
OTHER_DSET:
Column(OTHER_DSET,
[MatchesPatternValidation(r'^[ACTGNactgn]+|LONG_STRING$')],
allow_empty=True),
FREQ_DSET:
def validate_and_annotate(self, file_path=''):
################################
# SCHEMAS VALIDATION #
################################
# FIX ME: this is a really janky way to do this. Passing empty string for file_path...
# is a temp fix for review_changes re-validate data button causing an infinite loop when calling this with an argument :(
if not file_path:
file_path = self.dirname + '/for_review/intermediate_hospital_data.xlsx'
try:
df = pd.read_csv(file_path)
except:
try:
df = pd.read_excel(file_path)
except:
print("UNACCEPTED FILE FORMAT")
df.fillna("NULL", inplace=True)
#~ print(file_path)
#~ print(df.info)
schema = Schema([
Column('N95PlanFitTested', [InListValidation(['Y', 'N', 'NULL'])]),
Column('PARPsPlanTrained', [InListValidation(['Y', 'N', 'NULL'])]),
])
errors = schema.validate(df, columns=schema.get_column_names())
#######################################
# Build excel worksheet w formatting
#######################################
save_path2 = self.dirname + '/for_review/'
if not os.path.exists(save_path2):
os.makedirs(save_path2)
writer = pd.ExcelWriter(save_path2 + 'intermediate_hospital_data.xlsx',
engine='xlsxwriter')
# Skip row 1 headers so we can add manunally with formatting
df.to_excel(writer,
sheet_name='Sheet1',
startrow=1,
header=False,
index=False)
workbook = writer.book
worksheet = writer.sheets['Sheet1']
### WORKBOOK FORMATS ###
yellow_highlight = workbook.add_format({'bg_color': '#FFEB9C'})
header = workbook.add_format({
'bold': True,
'text_wrap': True,
'valign': 'top',
'fg_color': '#D7E4BC',
'border': 1
})
########################
# Set column widths
worksheet.set_column('A:II', 30)
worksheet.set_default_row(hide_unused_rows=True)
# Write the column headers with the defined format.
for col_num, value in enumerate(df.columns.values):
worksheet.write(0, col_num, value, header)
# for storing error row numbers while we iterate thru error object
# will use for hiding rows
error_rows = []
df_length = len(df)
for error in errors:
error_rows.append(error.row)
row = error.row + 1
column = df.columns.get_loc(error.column)
# Comments
worksheet.write_comment(row, column, error.message)
# Highlights
worksheet.conditional_format(row, column, row, column, {
'type': 'no_errors',
'format': yellow_highlight
})
#~ print(error_rows);
# Hide Rows that don't contain errors
for i in range(df_length + 1):
if i not in error_rows:
worksheet.set_row(i + 1, None, None, {'hidden': True})
writer.save()
# Pop up
self.review_changes()
'smoking',
'atrialfib',
# ATCDs
'stroke_pre',
'tia_pre',
'ich_pre',
# general Treatment
'treat_antipatelet',
'treat_anticoagulant',
# acute treatment
'treat_ivt_before_ct', # ie ivt was started before imaging
]
validation_schema = Schema([
Column('age', [InRangeValidation(1, 120)]),
Column('sex', [InListValidation([0, 1])]),
Column('height', [InRangeValidation(50, 300)]),
Column('weight', [InRangeValidation(10, 400)]),
Column('onset_known_yes', [InListValidation([0, 1])]),
Column('onset_known_wake_up', [InListValidation([0, 1])]),
Column('onset_to_ct',
[InRangeValidation(0, 43800)]), # onset to CT time max is 1 month
Column('NIH admission', [InRangeValidation(0, 43)]),
Column('bp_syst', [InRangeValidation(0, 300)]),
Column('bp_diast', [InRangeValidation(0, 300)]),
Column('glucose', [InRangeValidation(0.1, 30)]),
Column('créatinine', [InRangeValidation(0.1, 1000)]),
Column('hypertension', [InListValidation([0, 1])]),
Column('diabetes', [InListValidation([0, 1])]),
Column('hyperlipidemia', [InListValidation([0, 1])]),
Column('smoking', [InListValidation([0, 1])]),
