diff --git a/README.md b/README.md
index 35c37969f2439ce38d0ec82797b6326350287516..c2dd4da72b941dfd878a9bcdaa319773d535e1f6 100644
--- a/README.md
+++ b/README.md
@@ -125,51 +125,53 @@ Phenotypes are defined in a JSON configuration file. The file describes how sour
An example concept set and code list for Abdominal Pain is show below:
```json
-
{
- "concept_sets": {
- "version": "3.2.10",
- "omop": {
- "vocabulary_id": "MELDB",
- "vocabulary_name": "Multidisciplinary Ecosystem to study Lifecourse Determinants and Prevention of Early-onset Burdensome Multimorbidity",
- "vocabulary_reference": "https://www.it-innovation.soton.ac.uk/projects/meldb"
- },
- "concept_set": [
- {
- "concept_set_name": "ABDO_PAIN",
- "concept_set_status": "AGREED",
- "metadata": {
- "#": "18",
- "CONCEPT DESCRIPTION": "Abdominal pain",
- "CONCEPT TYPE": "Workload indicator (symptom)",
- "DATE ADDED ": "2023-08-25",
- "REQUEST REASON ": "Clinician SF - requested by email - symptom example from Qualitative Evidence Synthesis",
- "SOURCE INFO": "YES",
- "FUNCTION": "QUERY BY CODING LIST",
- "FUNCTION.1": "https://clinicalcodes.rss.mhs.man.ac.uk/",
- "CODING LIST": "https://git.soton.ac.uk/meld/meldb-external/phenotype/-/tree/main/codes/ClinicalCodes.org%20from%20the%20University%20of%20Manchester/Symptom%20code%20lists/Abdominal%20pain/res176-abdominal-pain.csv ",
- "NOTES": "2023-09-08: Clinical SF confirmed that the clinical view would be that this would need to be recurrent or persistent.",
- }
- },
- }
- "codes": [
- {
- "folder": "codes/ClinicalCodes.org from the University of Manchester",
- "description": "SF's clinical codes - downloaded 16/11/23",
- "files": [
- {
- "file": "Symptom code lists/Abdominal pain/res176-abdominal-pain.csv",
- "columns": {
- "read2_code": "code",
- "metadata": [
- "description"
- ]
- },
- "concept_set": [
- "ABDO_PAIN"
- ]
- },
- }
+ "concept_sets": {
+ "version": "3.2.10",
+ "omop": {
+ "vocabulary_id": "MELDB",
+ "vocabulary_name": "Multidisciplinary Ecosystem to study Lifecourse Determinants and Prevention of Early-onset Burdensome Multimorbidity",
+ "vocabulary_reference": "https://www.it-innovation.soton.ac.uk/projects/meldb"
+ },
+ "concept_set": [
+ {
+ "concept_set_name": "ABDO_PAIN",
+ "concept_set_status": "AGREED",
+ "metadata": {
+ "#": "18",
+ "CONCEPT DESCRIPTION": "Abdominal pain",
+ "CONCEPT TYPE": "Workload indicator (symptom)",
+ "DATE ADDED ": "2023-08-25",
+ "REQUEST REASON ": "Clinician SF - requested by email - symptom example from Qualitative Evidence Synthesis",
+ "SOURCE INFO": "YES",
+ "FUNCTION": "QUERY BY CODING LIST",
+ "FUNCTION.1": "https://clinicalcodes.rss.mhs.man.ac.uk/",
+ "CODING LIST": "https://git.soton.ac.uk/meld/meldb-external/phenotype/-/tree/main/codes/ClinicalCodes.org%20from%20the%20University%20of%20Manchester/Symptom%20code%20lists/Abdominal%20pain/res176-abdominal-pain.csv ",
+ "NOTES": "2023-09-08: Clinical SF confirmed that the clinical view would be that this would need to be recurrent or persistent."
+ }
+ }
+ ]
+ },
+ "codes": [
+ {
+ "folder": "clinical-codes-org",
+ "description": "SF's clinical codes - downloaded 16/11/23",
+ "files": [
+ {
+ "file": "Symptom code lists/Abdominal pain/res176-abdominal-pain.csv",
+ "columns": {
+ "read2_code": "code",
+ "metadata": [
+ "description"
+ ]
+ },
+ "concept_set": [
+ "ABDO_PAIN"
+ ]
+ }
+ ]
+ }
+ ]
}
```
diff --git a/acmc.py b/acmc.py
index f7c581735bd87e0fc5df030e35fe4241c6ad03c7..ae4b051122bcf45cc4331b1f91f3394b1f29386d 100644
--- a/acmc.py
+++ b/acmc.py
@@ -4,6 +4,8 @@ import trud
import omop
import map
+from pathlib import Path
+
def trud_install(args):
"""Handle the `trud install` command."""
print(f"Installing TRUD")
@@ -31,7 +33,7 @@ def omop_delete(args):
def map_process(args):
"""Handle the `map process` command."""
print(f"Processing map with phenotype config file: {args.config_file}")
- print(f"Output directory: {args.output_dir}")
+ print(f"Output directory: {args.output_file}")
print(f"Target coding format: {args.target_coding}")
if args.translate:
print("Translating code types.")
@@ -51,8 +53,8 @@ def map_process(args):
args.target_coding,
args.translate,
args.verify,
- args.error_log,
- output_path="MELD_concepts_read.csv")
+ error_path=Path(args.error_log),
+ output_path=Path(args.output_file))
print(f"Phenotype processing completed")
@@ -95,15 +97,15 @@ def main():
map_process_parser = map_subparsers.add_parser("process", help="Process map configuration file")
map_process_parser.add_argument("-c", "--config-file", required=True, help="Phenotype configuration file")
map_process_parser.add_argument("-s", "--source-codes-dir", required=True, help="Source codes root directory")
- map_process_parser.add_argument("-o", "--output-dir", required=True, help="Output directory for CSV or OMOP database")
map_process_parser.add_argument("-t", "--target-coding", required=True, choices=['read2', 'read3', 'icd10', 'snomed', 'opcs4'], help="Specify the target coding (read2, read3, icd10, snomed, opcs4)")
+ map_process_parser.add_argument("-o", "--output-file", type=str, default=str(map.OUTPUT_PATH.resolve()), help="Output directory for CSV or OMOP database")
# Flags
map_process_parser.add_argument("-tr", "--translate", action="store_true", default=False, help="Do not translate code types")
map_process_parser.add_argument("-v", "--verify", action="store_true", default=False, help="Do not verify codes")
# Error log file
- map_process_parser.add_argument("-l", "--error-log", type=str, default='error.csv', help="Filepath to save error log to")
+ map_process_parser.add_argument("-l", "--error-log", type=str, default=str(map.ERROR_PATH.resolve()), help="Filepath to save error log to")
# Set the function to call when 'process' subcommand is used
map_process_parser.set_defaults(func=map_process)
diff --git a/map.py b/map.py
index c468dfaeca4c08c5b8960f1b115f964912ad3a7b..5456de729fe0c3a07f25af60b575d711f7370f69 100644
--- a/map.py
+++ b/map.py
@@ -24,6 +24,8 @@ from omop import setup
pd.set_option("mode.chained_assignment", None)
+OUTPUT_PATH = Path('build') / 'phenotype_mapping.csv'
+ERROR_PATH = Path('build') / 'errors.csv'
def read_table_file(path, excel_sheet=None):
"""
@@ -166,8 +168,7 @@ def sql_row_exist(conn, table, column, value):
return exists
-
-def process(config_file, source_codes_dir, target_code_type, translate=True, verify=True, log_errors_path="errors.csv", output_path="MELD_concepts_read.csv"):
+def process(config_file, source_codes_dir, target_code_type, translate=True, verify=True, error_path=ERROR_PATH, output_path=OUTPUT_PATH):
config_path = Path(config_file)
if not config_path.is_file():
raise FileNotFoundError(f"Error: phenotype configuration file '{config_path}' does not exist.")
@@ -196,27 +197,16 @@ def process(config_file, source_codes_dir, target_code_type, translate=True, ver
# Load Code File
if "excel_sheet" in file:
- df = read_table_file(
- path=file_path, excel_sheet=file["excel_sheet"]
- )
+ df = read_table_file(path=file_path, excel_sheet=file["excel_sheet"])
else:
df = read_table_file(path=file_path)
# Perform Structural Changes to file before preprocessing
# split column with multiple code types
- if (
- "actions" in file
- and "split_col" in file["actions"]
- and "codes_col" in file["actions"]
- ):
+ if ("actions" in file and "split_col" in file["actions"] and "codes_col" in file["actions"]):
split_col = file["actions"]["split_col"]
codes_col = file["actions"]["codes_col"]
- print(
- "Action: Splitting",
- split_col,
- "column into:",
- df[split_col].unique(),
- )
+ print("Action: Splitting", split_col, "column into:", df[split_col].unique(),)
codes = df[codes_col]
oh = pd.get_dummies(df[split_col], dtype=bool) # one hot encode
oh = oh.where((oh != True), codes, axis=0) # fill in 1s with codes
@@ -231,74 +221,36 @@ def process(config_file, source_codes_dir, target_code_type, translate=True, ver
# TODO: enable metacolumns to be outputted - problem with map_file appending
if "metadata" in file["columns"]:
meta_columns += file["columns"]["metadata"]
- df = preprocess(
- df,
- file["columns"],
- meta_columns=meta_columns,
- file_path=file_path,
- target_code_type=target_code_type,
- verify=verify,
- translate=translate,
- )
+ df = preprocess(df, file["columns"], meta_columns=meta_columns, file_path=file_path, target_code_type=target_code_type, verify=verify, translate=translate)
else:
raise Exception("No column format provided")
# partition table by categorical column
- if (
- "actions" in file
- and "divide_col" in file["actions"]
- and len(df) > 0
- ):
+ if ("actions" in file and "divide_col" in file["actions"] and len(df) > 0):
divide_col = file["actions"]["divide_col"]
- print(
- "Action: Dividing Table by",
- divide_col,
- "column into: ",
- df[divide_col].unique(),
- )
+ print("Action: Dividing Table by", divide_col, "column into: ", df[divide_col].unique(),)
df = df.groupby(divide_col)
# Map to MELDB Concept/Phenotype
if len(df) == 0:
pass
# out = df
- elif ("concept_set" in file) and isinstance(
- df, pd.core.frame.DataFrame
- ):
- out = map_file(
- df,
- target_code_type,
- out,
- concepts=file["concept_set"],
- meta_columns=meta_columns,
- translate=translate,
- )
- elif ("concept_set_categories" in file) and isinstance(
- df, pd.core.groupby.generic.DataFrameGroupBy
- ):
+ elif ("concept_set" in file) and isinstance(df, pd.core.frame.DataFrame):
+ out = map_file(df, target_code_type, out, concepts=file["concept_set"], meta_columns=meta_columns, translate=translate,)
+ elif ("concept_set_categories" in file) and isinstance(df, pd.core.groupby.generic.DataFrameGroupBy):
meta_columns.remove(divide_col) # delete categorical column
for cat, grp in df:
- if (
- cat in file["concept_set_categories"].keys()
- ): # check if category is mapped
- grp = grp.drop(
- columns=[divide_col]
- ) # delete categorical column
+ if (cat in file["concept_set_categories"].keys()): # check if category is mapped
+ grp = grp.drop(columns=[divide_col]) # delete categorical column
print("Category:", cat)
- out = map_file(
- grp,
- target_code_type,
- out,
- concepts=file["concept_set_categories"][cat],
- meta_columns=meta_columns,
- )
+ out = map_file(grp, target_code_type, out, concepts=file["concept_set_categories"][cat], meta_columns=meta_columns,)
else:
print("Folder is empty")
# check if out is empty
if len(out) <= 0:
- raise Exception("Output file is empty")
+ raise Exception("Output dataframe is empty")
# Final Processing
out = out.reset_index(drop=True)
@@ -340,11 +292,11 @@ def process(config_file, source_codes_dir, target_code_type, translate=True, ver
else:
# export as CSV to /output
out.to_csv(output_path, index=False)
- print("saved to", output_path)
+ print("Saved to", output_path)
# Save Error File
- if os.path.exists(log_errors_path):
- error_df = pd.read_csv(log_errors_path)
+ if error_path.exists():
+ error_df = pd.read_csv(error_path)
error_df = error_df.drop_duplicates() # Remove Duplicates from Error file
error_df = error_df.sort_values(by=["SOURCE", "VOCABULARY", "CONCEPT"])
- error_df.to_csv(log_errors_path, index=False)
+ error_df.to_csv(error_path, index=False)
diff --git a/parse.py b/parse.py
index 33907e6ef15ebe992f1ea9b3c2c9a3f296aa6d2f..ddf07484c68deb7100268de9c551ec71d31bc38d 100644
--- a/parse.py
+++ b/parse.py
@@ -68,13 +68,12 @@ class Proto_code():
class Read2_code(Proto_code):
def __init__(self, file_path=None):
super().__init__(file_path)
+
input_path = trud.MAPS_PROCESSED_DIR / 'read2_code.parquet'
if not input_path.is_file():
raise FileNotFoundError(f"Error: Read2 code file '{input_path}' does not exist. Please ensure you have installed TRUD correctly")
self.db = pd.read_parquet(input_path)
- self.arg_small = "-r2"
- self.arg_long = "--read2-code"
- self.arg_help = "Read V2 Codes Column name in Source File"
+
self.checks = [
(
"Not Empty",
@@ -115,9 +114,6 @@ class Read2_code(Proto_code):
class Read3_code(Proto_code):
def __init__(self, file_path=None):
super().__init__(file_path)
- self.arg_small = "-r3"
- self.arg_long = "--read3-code"
- self.arg_help = "Read V3 Codes Column name in Source File"
input_path = trud.MAPS_PROCESSED_DIR / 'read3_code.parquet'
if not input_path.is_file():
@@ -163,9 +159,6 @@ class Read3_code(Proto_code):
class Icd10_code(Proto_code):
def __init__(self, file_path=None):
super().__init__(file_path)
- self.arg_small = "-i"
- self.arg_long = "--icd10-code"
- self.arg_help = "ICD10 Codes Column name in Source File"
input_path = trud.MAPS_PROCESSED_DIR / 'icd10_code.parquet'
if not input_path.is_file():
@@ -229,9 +222,6 @@ class Icd10_code(Proto_code):
class Snomed_code(Proto_code):
def __init__(self, file_path=None):
super().__init__(file_path)
- self.arg_small = "-s"
- self.arg_long = "--snomed-code"
- self.arg_help = "SNOMED Codes Column name in Source File"
input_path = trud.MAPS_PROCESSED_DIR / 'snomed_code.parquet'
if not input_path.is_file():
@@ -289,9 +279,6 @@ class Snomed_code(Proto_code):
class Opcs4_code(Proto_code):
def __init__(self, file_path=None):
super().__init__(file_path)
- self.arg_small = "-o"
- self.arg_long = "--opcs4-code"
- self.arg_help = "OPCS4 Codes Column name in Source File"
input_path = trud.MAPS_PROCESSED_DIR / 'opcs4_code.parquet'
if not input_path.is_file():
@@ -317,9 +304,6 @@ class Opcs4_code(Proto_code):
class Atc_code(Proto_code):
def __init__(self, file_path=None):
super().__init__(file_path)
- self.arg_small = "-a"
- self.arg_long = "--atc-code"
- self.arg_help = "ATC Codes Column name in Source File"
self.checks = [
(
"Not Empty",
@@ -340,9 +324,6 @@ class Atc_code(Proto_code):
class Med_code(Proto_code):
def __init__(self, file_path=None):
super().__init__(file_path)
- self.arg_small = "-m"
- self.arg_long = "--med-code"
- self.arg_help = "Med Codes Column name in Source File"
self.checks = [
(
"Not Empty",
@@ -354,9 +335,6 @@ class Med_code(Proto_code):
class Cprd_code(Proto_code):
def __init__(self, file_path=None):
super().__init__(file_path)
- self.arg_small = "-c"
- self.arg_long = "--cprd-code"
- self.arg_help = "CPRD Product Codes Column name in Source File"
self.checks = [
(
"Not Empty",
@@ -366,14 +344,14 @@ class Cprd_code(Proto_code):
]
code_types = {
- "read2_code": Read2_code,
- "read3_code": Read3_code,
- "icd10_code": Icd10_code,
- "snomed_code": Snomed_code,
- "opcs4_code": Opcs4_code,
- "atc_code": Atc_code,
- "med_code": Med_code,
- "cprd_code": Cprd_code,
+ "read2": Read2_code,
+ "read3": Read3_code,
+ "icd10": Icd10_code,
+ "snomed": Snomed_code,
+ "opcs4": Opcs4_code,
+ "atc": Atc_code,
+ "med": Med_code,
+ "cprd": Cprd_code,
}
vocab_types = {