def length_tab(val_df, perc_df, columns):
vals_dict = df2dict(val_df)
columns_dict = columns
perc_dict = df2dict(perc_df)
headers = OrderedDict()
to_keep = [
"mainPeakMiRNAlength", "stdDevMiRNAlength", "modeMiRNAlength",
"meanMiRNAlength", "skewnessMiRNAlength", "absskewnessMiRNAlength"
]
keep = set(to_keep).intersection(list(val_df.columns))
if True:
if "mainPeakMiRNAlength" in keep:
headers["mainPeakMiRNAlength"] = {
'title':
'miRNA "peak"',
# 'title': 'miRNA lengths',
'description':
'Percentage of miRNA reads of lengths 21, 22 and 23',
'scale':
"quart",
'col_dict':
gen_col_dict(columns_dict["mainPeakMiRNAlength"], "desc"),
'bar_dict':
columns_dict["mainPeakMiRNAlength"],
'suffix':
'%',
# 'max': 100,
# 'min': 0,
}
if "meanMiRNAlength" in keep:
headers["meanMiRNAlength"] = {
'title': 'miRNA mean length',
'description': 'Mean of miRNA mapping reads read length',
'scale': "quart",
'col_dict': gen_col_dict(columns_dict["meanMiRNAlength"],
"neut"),
'bar_dict': columns_dict["meanMiRNAlength"],
# 'suffix': '%',
}
if "stdDevMiRNAlength" in keep:
headers["stdDevMiRNAlength"] = {
'title': 'miRNA SD',
# 'title': 'microRNA mapping reads length SD',
'description':
'Read length standard deviation of miRNA mapping reads',
'scale': "quart",
'col_dict': gen_col_dict(columns_dict["stdDevMiRNAlength"],
"asc"),
'bar_dict': columns_dict["stdDevMiRNAlength"],
# 'suffix': '%',
# 'max': 100,
# 'min': 0,
}
if "modeMiRNAlength" in keep:
headers["modeMiRNAlength"] = {
'title': 'miRNA length mode',
'description': 'miRNA length mode',
'scale': "quart",
'col_dict': gen_col_dict(columns_dict["modeMiRNAlength"],
"neut"),
'bar_dict': columns_dict["modeMiRNAlength"],
'is_int': True
# 'suffix': '%',
}
if "skewnessMiRNAlength" in keep:
headers["skewnessMiRNAlength"] = {
'title':
'skewness',
'description':
'skewness read length distribution',
'scale':
"quart",
'col_dict':
gen_col_dict(columns_dict["skewnessMiRNAlength"], "neut"),
'bar_dict':
columns_dict["skewnessMiRNAlength"],
# 'suffix': '%',
}
if "absskewnessMiRNAlength" in keep:
headers["absskewnessMiRNAlength"] = {
'title':
'|skewness|',
'description':
'absolute read length distribution skewness',
'scale':
"quart",
'col_dict':
gen_col_dict(columns_dict["absskewnessMiRNAlength"], "asc"),
'bar_dict':
columns_dict["absskewnessMiRNAlength"],
# 'suffix': '%',
}
val_tab = table.plot(vals_dict, headers)
for k in list(headers.keys()):
c_dict = headers.get(k)
description = c_dict["description"]
c_dict["description"] = description + " (percentile)"
c_dict['is_int'] = False
if k in keep:
c_dict['suffix'] = "show_perc"
c_dict["suffix_dict"] = get_ordinals(columns_dict[k])
perc_tab = table.plot(perc_dict, headers)
return val_tab, perc_tab
def library_tab(val_df, perc_df, columns):
vals_dict = df2dict(val_df)
columns_dict = columns
perc_dict = df2dict(perc_df)
headers = OrderedDict()
to_keep = [
"adapterDimerPerc", "ultraShortReadsPerc", "shortReadsPerc", "rRNAPerc"
]
keep = set(to_keep).intersection(list(val_df.columns))
# adapterDimerPerc
# ultraShortReadsPerc
# shortReadsPerc
# rRNAPerc
if True:
if "adapterDimerPerc" in keep:
headers["adapterDimerPerc"] = {
'title': '% Dimers',
'description':
'The percentage of reads that correspond to adapter-dimer, i.e. those that are shorter or equal to 2nt after adapter trimming ',
'scale': "quart",
'col_dict': gen_col_dict(columns_dict["adapterDimerPerc"],
"asc"),
'bar_dict': columns_dict["adapterDimerPerc"],
'suffix': '%',
}
if "ultraShortReadsPerc" in keep:
headers["ultraShortReadsPerc"] = {
'title':
'% ultrashort',
'description':
'Reads between 3 and 14 nt. A high number of these usually indicates issues in library preparation or low RNA quality',
'scale':
"quart",
'col_dict':
gen_col_dict(columns_dict["ultraShortReadsPerc"], "asc"),
'bar_dict':
columns_dict["ultraShortReadsPerc"],
'suffix':
'%',
}
if "shortReadsPerc" in keep:
headers["shortReadsPerc"] = {
'title': '% short reads',
'description':
'Reads between 15 and 17 nt. This range was choosen because tRNA fragments are frequently of 18 nt length.',
'scale': "quart",
'col_dict': gen_col_dict(columns_dict["shortReadsPerc"],
"asc"),
'bar_dict': columns_dict["shortReadsPerc"],
'suffix': '%',
}
if "rRNAPerc" in keep:
headers["rRNAPerc"] = {
'title': '% rRNA',
'description': 'The percentage of ribosomal RNA derived reads',
'scale': "quart",
'col_dict': gen_col_dict(columns_dict["rRNAPerc"], "asc"),
'bar_dict': columns_dict["rRNAPerc"],
'suffix': '%',
}
val_tab = table.plot(vals_dict, headers)
for k in list(headers.keys()):
c_dict = headers.get(k)
description = c_dict["description"]
c_dict["description"] = description + " (percentile)"
c_dict['is_int'] = False
if k in keep:
c_dict['suffix'] = "show_perc"
c_dict["suffix_dict"] = get_ordinals(columns_dict[k])
perc_tab = table.plot(perc_dict, headers)
return val_tab, perc_tab
def contamination_tab(val_df, perc_df, columns):
vals_dict = df2dict(val_df)
columns_dict = columns
perc_dict = df2dict(perc_df)
headers = OrderedDict()
to_keep = ["refSpecies", "unmapped", "bacteria", "virus"]
keep = set(to_keep).intersection(list(val_df.columns))
if True:
if "refSpecies" in keep:
headers["refSpecies"] = {
'title': '% ref.genome',
'description':
'Percentage of reads mapped to the reference genome',
'scale': "quart",
'col_dict': gen_col_dict(columns_dict["refSpecies"], "desc"),
'bar_dict': columns_dict["refSpecies"],
'suffix': '%',
}
if "unmapped" in keep:
headers["unmapped"] = {
'title': '% unmapped',
'description': 'Percentage of reads not mapped to the genome',
'scale': "quart",
'col_dict': gen_col_dict(columns_dict["unmapped"], "asc"),
'bar_dict': columns_dict["unmapped"],
'suffix': '%',
}
if "bacteria" in keep:
headers["bacteria"] = {
'title': '% bacteria',
'description':
'Percentage of reads mapped to a collection of different bacteria',
'scale': "quart",
'col_dict': gen_col_dict(columns_dict["bacteria"], "asc"),
'bar_dict': columns_dict["bacteria"],
'suffix': '%',
}
if "virus" in keep:
headers["virus"] = {
'title': '% virus',
'description':
'Percentage of reads mapped to a collection of different virus',
'scale': "quart",
'col_dict': gen_col_dict(columns_dict["virus"], "asc"),
'bar_dict': columns_dict["virus"],
'suffix': '%',
}
val_tab = table.plot(vals_dict, headers)
for k in list(headers.keys()):
c_dict = headers.get(k)
description = c_dict["description"]
c_dict["description"] = description + " (percentile)"
c_dict['is_int'] = False
if k in keep:
c_dict['suffix'] = "show_perc"
c_dict["suffix_dict"] = get_ordinals(columns_dict[k])
perc_tab = table.plot(perc_dict, headers)
return val_tab, perc_tab
def seq_qual_tab(val_df, perc_df, columns):
vals_dict = df2dict(val_df)
columns_dict = columns
perc_dict = df2dict(perc_df)
headers = OrderedDict()
#headers
#meanofmeans
# meanofp10
# meanofp25
# meanofp50
# meanofp75
# meanofp90
if True:
headers["meanofmeans"] = {
'title': 'Average mean <br>Phred Score',
'description':
'The mean Phred Scores are obtained by FastQC per position, then the average of these values are calculated using positions 1 to 40',
'scale': "quart",
'col_dict': gen_col_dict(columns_dict.get("meanofmeans"), "desc"),
'bar_dict': columns_dict.get("meanofmeans"),
# 'max': 100,
# 'min': 0,
}
headers["meanofp10"] = {
'title': 'Average percentile 10 <br>Phred Score ',
'description':
'The mean Phred Scores are obtained by FastQC per position, then the average of these values are calculated using positions 1 to 40',
'scale': "quart",
'col_dict': gen_col_dict(columns_dict.get("meanofp10"), "desc"),
'bar_dict': columns_dict.get("meanofp10"),
# 'max': 100,
# 'min': 0,
}
headers["meanofp25"] = {
'title': 'Average percentile 25 <br>Phred Score ',
'description':
'The mean Phred Scores are obtained by FastQC per position, then the average of these values are calculated using positions 1 to 40',
'scale': "quart",
'col_dict': gen_col_dict(columns_dict.get("meanofp25"), "desc"),
'bar_dict': columns_dict.get("meanofp25"),
# 'max': 100,
# 'min': 0,
}
headers["meanofp50"] = {
'title': 'Average median <br>Phred Score ',
'description':
'The mean Phred Scores are obtained by FastQC per position, then the average of these values are calculated using positions 1 to 40',
'scale': "quart",
'col_dict': gen_col_dict(columns_dict.get("meanofp50"), "desc"),
'bar_dict': columns_dict.get("meanofp50"),
# 'max': 100,
# 'min': 0,
}
headers["meanofp75"] = {
'title': 'Average percentile 75 <br>Phred Score ',
'description':
'The mean Phred Scores are obtained by FastQC per position, then the average of these values are calculated using positions 1 to 40',
'scale': "quart",
'col_dict': gen_col_dict(columns_dict.get("meanofp75"), "desc"),
'bar_dict': columns_dict.get("meanofp75"),
# 'max': 100,
# 'min': 0,
}
headers["meanofp90"] = {
'title': 'Average percentile 90 <br>Phred Score ',
'description':
'The mean Phred Scores are obtained by FastQC per position, then the average of these values are calculated using positions 1 to 40',
'scale': "quart",
'col_dict': gen_col_dict(columns_dict.get("meanofp90"), "desc"),
'bar_dict': columns_dict.get("meanofp90"),
# 'max': 100,
# 'min': 0,
}
to_keep = [
"meanofmeans", "meanofp10", " meanofp25", "meanofp50", "meanofp75",
"meanofp90"
]
keep = set(to_keep).intersection(list(val_df.columns))
clean_headers = {k: headers[k] for k in headers.keys() if k in keep}
val_tab = table.plot(vals_dict, clean_headers)
for k in list(headers.keys()):
c_dict = headers.get(k)
description = c_dict["description"]
c_dict["description"] = description + " (percentile)"
if k in keep:
c_dict['suffix'] = "show_perc"
c_dict['is_int'] = False
c_dict["suffix_dict"] = get_ordinals(columns_dict[k])
clean_headers = {k: headers[k] for k in headers.keys() if k in keep}
perc_tab = table.plot(perc_dict, clean_headers)
return val_tab, perc_tab
def tables_complex(val_df, perc_df, columns):
vals_dict = df2dict(val_df)
columns_dict = columns
perc_dict = df2dict(perc_df)
headers = OrderedDict()
to_keep = [
"avgRCperUnique", "miRNAexprTop1", "miRNAexprTop5", "miRNAexprTop20",
"miRNAexprp50", "miRNAexprp75", "miRNAexprp95", "miRNAper1k"
]
keep = set(to_keep).intersection(list(val_df.columns))
if True:
if "avgRCperUnique" in keep:
headers["avgRCperUnique"] = {
'title':
'Reads per unique',
'description':
'Number of reads in analysis divided by number of unique reads',
'scale':
"quart",
'col_dict':
gen_col_dict(columns_dict.get("avgRCperUnique"), "desc"),
'bar_dict':
columns_dict.get("avgRCperUnique"),
}
if "miRNAexprTop1" in keep:
headers["miRNAexprTop1"] = {
'title': '% top miRNA',
'description':
'Percentage of reads assigned to the most expressed miRNA',
'scale': "quart",
'col_dict': gen_col_dict(columns_dict.get("miRNAexprTop1"),
"asc"),
'bar_dict': columns_dict.get("miRNAexprTop1"),
'suffix': '%',
}
if "miRNAexprTop5" in keep:
headers["miRNAexprTop5"] = {
'title': '% top5 miRNA',
'description':
'Percentage of reads assigned to the 5 most expressed miRNAs',
'scale': "quart",
'col_dict': gen_col_dict(columns_dict.get("miRNAexprTop5"),
"asc"),
'bar_dict': columns_dict.get("miRNAexprTop5"),
'suffix': '%',
}
if "miRNAexprTop20" in keep:
headers["miRNAexprTop20"] = {
'title': '% top20 miRNA',
'description':
'Percentage of reads assigned to the 20 most expressed miRNAs',
'scale': "quart",
'col_dict': gen_col_dict(columns_dict.get("miRNAexprTop20"),
"asc"),
'bar_dict': columns_dict.get("miRNAexprTop20"),
'suffix': '%',
}
if "miRNAexprp50" in keep:
headers["miRNAexprp50"] = {
'title': '#miRNA p50',
'description':
'Number of miRNAs needed to reach 50% miRNA reads',
'scale': "quart",
'col_dict': gen_col_dict(columns_dict.get("miRNAexprp50"),
"desc"),
'bar_dict': columns_dict.get("miRNAexprp50"),
"is_int": True
}
if "miRNAexprp75" in keep:
headers["miRNAexprp75"] = {
'title': '#miRNA p75',
'description':
'Number of miRNAs needed to reach 75% miRNA reads',
'scale': "quart",
'col_dict': gen_col_dict(columns_dict.get("miRNAexprp75"),
"desc"),
'bar_dict': columns_dict.get("miRNAexprp75"),
"is_int": True
}
if "miRNAexprp95" in keep:
headers["miRNAexprp95"] = {
'title': '#miRNA p95',
'description':
'Number of miRNAs needed to reach 95% miRNA reads',
'scale': "quart",
'col_dict': gen_col_dict(columns_dict.get("miRNAexprp95"),
"desc"),
'bar_dict': columns_dict.get("miRNAexprp95"),
"is_int": True
}
if "miRNAper1k" in keep:
headers["miRNAper1k"] = {
'title': 'miRNAs per 1000 reads',
'description':
'Number of miRNAs detected per 1kb mapped to miRNAs',
'scale': "quart",
'col_dict': gen_col_dict(columns_dict.get("miRNAper1k"),
"desc"),
'bar_dict': columns_dict.get("miRNAper1k"),
}
val_tab = table.plot(vals_dict, headers)
for k in list(headers.keys()):
c_dict = headers.get(k)
description = c_dict["description"]
c_dict["description"] = description + " (percentile)"
if k in keep:
c_dict['suffix'] = "show_perc"
c_dict['is_int'] = False
c_dict["suffix_dict"] = get_ordinals(columns_dict[k])
perc_tab = table.plot(perc_dict, headers)
return val_tab, perc_tab
def tables_yield(val_df, perc_df, columns):
vals_dict = df2dict(val_df)
columns_dict = columns
perc_dict = df2dict(perc_df)
headers = OrderedDict()
# readsRaw # reads # readsAdapterFound # readsAdapterFoundPerc
# readsUnique # avgRCperUnique # detectedMatureSA
# avgRCperUnique
# detectedMatureSA
# maturePercofReads
to_keep = [
"readsRaw", "reads", "readsAdapterFound", "readsAdapterFoundPerc",
"readsUnique", "avgRCperUnique", "detectedMatureSA",
"maturePercofReads"
]
keep = set(to_keep).intersection(list(val_df.columns))
if True:
if "readsRaw" in keep:
headers["readsRaw"] = {
'title': 'Raw reads',
'description': 'Total number of reads sequenced',
'scale': "quart",
'col_dict': gen_col_dict(columns_dict.get("readsRaw"), "desc"),
'bar_dict': columns_dict.get("readsRaw"),
'is_int': True
# 'max': 100,
# 'min': 0,
}
if "reads" in keep:
headers["reads"] = {
'title': 'Num. of reads',
'description':
'Number of reads used for the analysis after filtering',
'scale': "quart",
'col_dict': gen_col_dict(columns_dict.get("reads"), "desc"),
'bar_dict': columns_dict.get("reads"),
'is_int': True
}
if "readsUnique" in keep:
headers["readsUnique"] = {
'title': 'Unique reads',
# 'title': 'microRNA mapping reads length SD',
'description': 'Number of unique sequences after collapsing',
'scale': "quart",
'col_dict': gen_col_dict(columns_dict.get("readsUnique"),
"neut"),
'bar_dict': columns_dict.get("readsUnique"),
'is_int': True
}
if "detectedMatureSA" in keep:
headers["detectedMatureSA"] = {
'title':
'Detected microRNAs',
'description':
'Total number of mature miRNAs detected',
'scale':
"quart",
'col_dict':
gen_col_dict(columns_dict.get("detectedMatureSA"), "desc"),
'bar_dict':
columns_dict.get("detectedMatureSA"),
'is_int':
True
}
if "maturePercofReads" in keep:
headers["maturePercofReads"] = {
'title':
'% microRNA reads',
'description':
'Percentage of reads assigned to mature microRNAs',
'scale':
"quart",
'col_dict':
gen_col_dict(columns_dict.get("maturePercofReads"), "desc"),
'bar_dict':
columns_dict.get("maturePercofReads"),
'suffix':
'%',
# 'max': 100,
# 'min': 0,
}
if "readsAdapterFound" in keep:
headers["readsAdapterFound"] = {
'title':
'Trimmed reads',
'description':
'Number of reads for which the adapter was found and trimmed',
'scale':
"quart",
'col_dict':
gen_col_dict(columns_dict.get("readsAdapterFound"), "desc"),
'bar_dict':
columns_dict.get("readsAdapterFound"),
'is_int':
True
# 'suffix': '%',
# 'max': 100,
# 'min': 0,
}
if "readsAdapterFoundPerc" in keep:
headers["readsAdapterFoundPerc"] = {
'title':
'% Adapter trimmed reads',
'description':
'Percentage of reads for which the adapter was found and trimmed',
'scale':
"quart",
'col_dict':
gen_col_dict(columns_dict.get("readsAdapterFoundPerc"),
"desc"),
'bar_dict':
columns_dict.get("readsAdapterFoundPerc"),
'suffix':
'%',
}
# headers["avgRCperUnique"] = {
# 'title': 'Reads per unique',
# 'description': 'Number of reads in analysis divided by number of unique reads',
# 'scale': "quart",
# 'col_dict': gen_col_dict(columns_dict.get("avgRCperUnique"), "desc"),
# 'bar_dict': columns_dict.get("avgRCperUnique"),
# }
# headers["maturePercofReads"] = {
# 'title': '% microRNA reads',
# 'description': 'Percentage of effective reads assigned to mature microRNAs',
# 'scale': "quart",
# 'col_dict': gen_col_dict(columns_dict.get("maturePercofReads"), "desc"),
# 'bar_dict': columns_dict.get("maturePercofReads"),
# 'suffix': '%',
#
# }
# print("eo")
# print(keep)
#clean_headers = {k: headers[k] for k in headers.keys() if k in keep}
# print(list(clean_headers.keys()))
val_tab = table.plot(vals_dict, headers)
for k in list(headers.keys()):
c_dict = headers.get(k)
description = c_dict["description"]
c_dict["description"] = description + " (percentile)"
if k in keep:
c_dict['suffix'] = "show_perc"
c_dict['is_int'] = False
c_dict["suffix_dict"] = get_ordinals(columns_dict[k])
#clean_headers = {k: headers[k] for k in headers.keys() if k in keep}
perc_tab = table.plot(perc_dict, headers)
return val_tab, perc_tab
def basic_table(val_df, perc_df, columns):
vals_dict = df2dict(val_df)
columns_dict = columns
perc_dict = df2dict(perc_df)
headers = OrderedDict()
#
# miRNA SD
# readsRaw # readsPerc # adapterDimerPerc # detectedMatureSA # maturePercofReads
# meanofp50 # mainPeakMiRNAlength # stdDevMiRNAlength
to_keep = [
"readsRaw", "readsPerc", "adapterDimerPerc", "shortReadsPerc",
"detectedMatureSA", "maturePercofReads", "mainPeakMiRNAlength",
"meanofp50"
]
keep = set(to_keep).intersection(list(val_df.columns))
if True:
if "readsRaw" in keep:
headers["readsRaw"] = {
'title': 'Raw reads',
'description': 'Total number of reads sequenced',
'scale': "quart",
'col_dict': gen_col_dict(columns_dict["readsRaw"], "desc"),
'bar_dict': columns_dict["readsRaw"],
'is_int': True
# 'max': 100,
# 'min': 0,
}
if "readsPerc" in keep:
headers["readsPerc"] = {
'title': '% Reads',
'description':
'Percentage of reads used for the analysis after filtering',
'scale': "quart",
'col_dict': gen_col_dict(columns_dict["readsPerc"], "desc"),
'bar_dict': columns_dict["readsPerc"],
'suffix': '%',
# 'max': 100,
# 'min': 0,
}
if "adapterDimerPerc" in keep:
headers["adapterDimerPerc"] = {
'title': '% Dimers',
'description':
'The percentage of reads that correspond to adapter-dimer, i.e. those that are shorter or equal to 2nt after adapter trimming ',
'scale': "quart",
'col_dict': gen_col_dict(columns_dict["adapterDimerPerc"],
"asc"),
'bar_dict': columns_dict["adapterDimerPerc"],
'suffix': '%',
}
if "shortReadsPerc" in keep:
headers["shortReadsPerc"] = {
'title': '% short reads',
'description':
'Reads between 15 and 17 nt. This range was choosen because tRNA fragments are frequently of 18 nt length.',
'scale': "quart",
'col_dict': gen_col_dict(columns_dict["shortReadsPerc"],
"asc"),
'bar_dict': columns_dict["shortReadsPerc"],
'suffix': '%',
}
if "detectedMatureSA" in keep:
headers["detectedMatureSA"] = {
'title': 'miRNAs',
'description': 'Total number of mature miRNAs detected',
'scale': "quart",
'col_dict': gen_col_dict(columns_dict["detectedMatureSA"],
"desc"),
'bar_dict': columns_dict["detectedMatureSA"],
'is_int': True
# 'max': 100,
# 'min': 0,
}
if "maturePercofReads" in keep:
headers["maturePercofReads"] = {
'title': '% microRNA reads',
'description':
'Percentage of effective reads assigned to mature microRNAs',
'scale': "quart",
'col_dict': gen_col_dict(columns_dict["maturePercofReads"],
"desc"),
'bar_dict': columns_dict["maturePercofReads"],
'suffix': '%',
# 'max': 100,
# 'min': 0,
}
if "mainPeakMiRNAlength" in keep:
headers["mainPeakMiRNAlength"] = {
'title':
'miRNA "peak"',
# 'title': 'miRNA lengths',
'description':
'Percentage of miRNA reads of lengths 21, 22 and 23',
'scale':
"quart",
'col_dict':
gen_col_dict(columns_dict["mainPeakMiRNAlength"], "desc"),
'bar_dict':
columns_dict["mainPeakMiRNAlength"],
'suffix':
'%',
# 'max': 100,
# 'min': 0,
}
if "meanofp50" in keep:
headers["meanofp50"] = {
'title': 'Phred score',
'description':
'Average Phred Score of the per position score median',
'scale': "quart",
'col_dict': gen_col_dict(columns_dict["meanofp50"], "desc"),
'bar_dict': columns_dict["meanofp50"],
# 'suffix': '%',
# 'max': 100,
# 'min': 0,
}
# print("eo")
# print(keep)
# clean_headers = {k: headers[k] for k in headers.keys() if k in keep}
# print(list(clean_headers.keys()))
val_tab = table.plot(vals_dict, headers)
for k in list(headers.keys()):
c_dict = headers.get(k)
description = c_dict["description"]
c_dict["description"] = description + " (percentile)"
c_dict['suffix'] = "show_perc"
c_dict['is_int'] = False
c_dict["suffix_dict"] = get_ordinals(columns_dict[k])
perc_tab = table.plot(perc_dict, headers)
return val_tab, perc_tab
def par_table(input_file):
input_df = pandas.read_csv(input_file, sep="\t")
headers = OrderedDict()
# print(input_df.columns)
if "5' Random Adapter Bases" in input_df.columns and " 3' Random Adapter Bases" in input_df.columns:
# print("eo")
input_df["5' Random Adapter Bases"] = input_df[
"5' Random Adapter Bases"].astype(str)
input_df[" 3' Random Adapter Bases"] = input_df[
" 3' Random Adapter Bases"].astype(str)
input_df["Random Adapter Bases"] = input_df[
"5' Random Adapter Bases"].str.cat(
input_df[" 3' Random Adapter Bases"], sep=" - ")
# print(input_df["5'-3' Random Adapter Bases"])
to_keep = [
"Input File", "taxon ID", "Species", "short species name", "protocol",
"adapter", "Random Adapter Bases"
]
# to_keep = ["Input File", "taxon ID","Species","short species name","protocol","adapter","5' Random Adapter Bases",]
# " 3' Random Adapter Bases"]
keep = set(to_keep).intersection(list(input_df.columns))
# print(keep)
vals_dict = pars2dict(input_df)
if True:
if "Input File" in keep:
headers["Input File"] = {
'title': "Input File",
# 'description': 'Total number of reads sequenced',
# 'is_int': True
}
# if "taxon ID" in keep:
# headers["taxon ID"] = {
# 'title': "taxon ID",
# # 'description': 'Total number of reads sequenced',
# 'is_int': True
# }
if "Species" in keep:
headers["Species"] = {
'title': "Species name",
# 'description': 'Total number of reads sequenced',
# 'is_int': True
}
if "short species name" in keep:
headers["short species name"] = {
'title': "short name",
# 'description': 'Total number of reads sequenced',
# 'is_int': True
}
if "protocol" in keep:
headers["protocol"] = {
'title': "protocol",
# 'description': 'Total number of reads sequenced',
# 'is_int': True
}
if "adapter" in keep:
headers["adapter"] = {
'title':
"detected adapter ",
# 'description': 'Total number of reads sequenced',
# 'is_int': True
}
if "Random Adapter Bases" in keep:
# print("eee")
headers["Random Adapter Bases"] = {
'title': "Random Adapter <br> Bases (5' - 3')",
# 'description': 'Total number of reads sequenced',
# 'is_int': True
}
# print(headers.keys())
# if " 3' Random Adapter Bases" in keep:
# headers[" 3' Random Adapter Bases"] = {
# 'title': "3' Random <br> Adapter Bases",
# # 'description': 'Total number of reads sequenced',
# 'is_int': True
# }
# val_tab = table.plot(vals_dict)
val_tab = table.plot(vals_dict, headers, hide_bar=True)
return val_tab