adding tower yml file

assets/pass.json

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+{
+    "samples": [
+        "longreads"
+    ],
+    "tests": [
+        {
+            "id": "reference assembly",
+            "test": "all exists",
+            "regex": true,
+            "directory": "bcftools",
+            "match": ".*\\.consensus.fa$"
+        },
+        {
+            "id": "abundance",
+            "test": "ge",
+            "value": 10,
+            "regex": true,
+            "directory": "top",
+            "match": "*_mqc.tsv"
+        },
+        {
+            "id": "meandepth",
+            "test": "ge",
+            "regex": true,
+            "directory": "samtools",
+            "value": 0.02,
+            "column": "meandepth",
+            "match": ".*\\.txt$"
+        },
+        {
+            "id": "coverage",
+            "test": "ge",
+            "directory": "samtools",
+            "value": 0.06,
+            "regex": true,
+            "coverage": "coverage",
+            "match": ".*\\.txt$"
+
+        }
+
+    ]
+
+}

bin/make_metadata_file.py

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+import argparse
+import os
+import json
+import glob
+import re
+import pandas as pd
+parser = argparse.ArgumentParser()
+parser.add_argument("-i",
+                    required = True,
+                    help="Input Samplesheet filename")
+parser.add_argument(
+    "-t",
+    type=str,
+    required=False,
+    default=None,
+    help="Where is the top hits report? It is a directory of files with top_report.tsv with samplename in the front"
+)
+parser.add_argument(
+    "-c",
+    type=str,
+    required=True,
+    help="Where is the configuration file? it is a json that contains the publish directories and filenames for each test"
+)
+parser.add_argument(
+    "-w",
+    type=str,
+    required=True,
+    help="Where is the workding directory where your outputs are? "
+)
+# This script works at the species level to calls tests on each species/taxid associated with a samplesheet list of samples.
+# it reqres a samplesheet and a taxtriage working outdir with identifiable structures according to the -c argument (json).
+# see assets/pass.json for an example format of the json file on the simulated test data used in taxtriage's test profile
+
+
+
+args = parser.parse_args()
+
+file = args.i
+# tests = args.t
+
+
+
+def val_test(config):
+    match = config['match']
+    sample = config['sample']
+    regex = config['regex']
+    directory = config['directory']
+    value = config['value']
+    test = config['test']
+    # if regex is true then check the -w directory for the file using the match. should return a file
+    if regex:
+        filename = os.path.join(args.w, directory, sample+match)
+        # convert filename to regex format
+        # use glob to find the file
+
+        filenames = glob.glob(filename)
+        # if the file is not found then return false
+        if len(filenames) == 0:
+            print("File not found, "+filename)
+            return False
+        else:
+            filename = filenames[0]
+    else:
+        filename = os.path.join(args.w, directory, match)
+    # # read the file into a dataframe
+    df = pd.read_csv(filename, sep="\t")
+    # file all df on rank == S and abundance greater or equal to value
+    df = df[df['rank'].str.contains("^S$")]
+    df = df[df['abundance'] >= value]
+
+    # top_hits = os.path.join(top, sample + ".top_report.tsv")
+    # # read top_hits into a dataframe
+    # df_top = pd.read_csv(top_hits, sep="\t")
+    # # sort the dataframe by the abundance column and only select those with rank contains S and an optional number like S, S1, S4, etc.
+    # df_top = df_top[df_top['rank'].str.contains("^S$")]
+    # # sort on abundance
+    # df_top = df_top.sort_values(by=['abundance'], ascending=False)
+    # # For each sample loop through the taxids and check each of the tests defined as functions. Tests are defined in the config dict imported
+
+
+
+def __main__():
+    top = args.t
+    working_dir = args.w
+    config = args.c
+
+    if not top :
+        print("No top hits file provided, opting for default location at working directory / top / samplename.tsv")
+        top = os.path.join(working_dir, "top")
+    df = pd.read_csv(file, sep=",")
+    samplenames = df['sample']
+    data = df.to_dict(orient='records')
+    # import the config -c argument as a dict
+    with open(config) as f:
+        config = json.load(f)
+    for i in data:
+        sample = i['sample']
+        conf_one = next(test for test in config['tests'] if test['id'] == 'abundance')
+        conf_one['sample'] = sample
+        val_test(conf_one)
+
+    return
+__main__()

tower.yml

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+reports:
+  multiqc_report.html:
+    display: "MultiQC HTML report"
+  "**/convert/*merged.tsv":
+    display: "Confidence Table"
+  "**/krona/*.html":
+    display: "Krona Plots"
+  "**/fastqc/*_fastqc.html":
+    display: "FASTQC output (Illumina Only)"
+  "**/nanoplot/*_NanoPlot-report.html":
+    display: "Nanoplot output (Nanopore Only)"
+  "**/fastp/*.fastp.html":
+    display: "fastp output (QC Trimming)"