[med-svn] [Git][med-team/python-nanomath][master] 5 commits: New upstream version 1.4.0+ds
Nilesh Patra (@nilesh)
gitlab at salsa.debian.org
Sat Oct 25 11:28:15 BST 2025
Nilesh Patra pushed to branch master at Debian Med / python-nanomath
Commits:
bb79bfa9 by Nilesh Patra at 2025-10-25T15:54:06+05:30
New upstream version 1.4.0+ds
- - - - -
9c387e68 by Nilesh Patra at 2025-10-25T15:54:06+05:30
Update upstream source from tag 'upstream/1.4.0+ds'
Update to upstream version '1.4.0+ds'
with Debian dir 348d87d7eaf23f5b2bc697afa3ec3ea66efe3860
- - - - -
79245108 by Nilesh Patra at 2025-10-25T15:54:08+05:30
Bump Standards-Version to 4.7.2 (no changes needed)
- - - - -
8c73cf97 by Nilesh Patra at 2025-10-25T15:55:05+05:30
Drop Redundant "Rules-Requires-Root: no"
- - - - -
2a4f3f88 by Nilesh Patra at 2025-10-25T15:55:27+05:30
Upload to unstable
- - - - -
8 changed files:
- PKG-INFO
- README.md
- README.rst
- debian/changelog
- debian/control
- nanomath.egg-info/PKG-INFO
- nanomath/nanomath.py
- nanomath/version.py
Changes:
=====================================
PKG-INFO
=====================================
@@ -1,52 +1,12 @@
-Metadata-Version: 1.2
+Metadata-Version: 2.1
Name: nanomath
-Version: 1.2.1
+Version: 1.4.0
Summary: A few simple math function for other Oxford Nanopore processing scripts
Home-page: https://github.com/wdecoster/nanomath
Author: Wouter De Coster
Author-email: decosterwouter at gmail.com
License: GPLv3
-Description: # nanomath
- This module provides a few simple math and statistics functions for other scripts processing Oxford Nanopore sequencing data
-
- [](https://twitter.com/wouter_decoster)
- [](https://anaconda.org/bioconda/nanomath)
- [](https://tracker.debian.org/pkg/python-nanomath)
- [](https://travis-ci.org/wdecoster/nanomath)
- [](https://landscape.io/github/wdecoster/nanomath/master)
-
-
- ## FUNCTIONS
- * Calculate read N50 from a set of lengths `get_N50(readlenghts)`
- * Remove extreme length outliers from a dataset `remove_length_outliers(dataframe, columname)`
- * Calculate the average Phred quality of a read `ave_qual(qualscores)`
- * Write out the statistics report after calling readstats function `write_stats(dataframe, outputname)`
- * Compute a number of statistics, return a dictionary `calc_read_stats(dataframe)`
-
-
- ## INSTALLATION
- ```bash
- pip install nanomath
- ```
- or
- [](https://anaconda.org/bioconda/nanomath)
- ```
- conda install -c bioconda nanomath
- ```
-
- ## STATUS
- [](https://travis-ci.org/wdecoster/nanomath)
-
-
- ## CONTRIBUTORS
- [@alexomics](https://github.com/alexomics) for fixing the indentation of the printed stats
-
-
- ## CITATION
- If you use this tool, please consider citing our [publication](https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/bty149/4934939).
-
Keywords: nanopore sequencing plotting quality control
-Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
@@ -56,3 +16,46 @@ Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Requires-Python: >=3
+License-File: LICENSE
+Requires-Dist: pandas
+Requires-Dist: numpy>1.8
+Requires-Dist: Python-Deprecated
+
+# nanomath
+
+This module provides a few simple math and statistics functions for other scripts processing Oxford Nanopore sequencing data
+
+[](https://twitter.com/wouter_decoster)
+[](https://anaconda.org/bioconda/nanomath)
+[](https://tracker.debian.org/pkg/python-nanomath)
+
+## FUNCTIONS
+
+* Calculate read N50 from a set of lengths `get_N50(readlenghts)`
+* Remove extreme length outliers from a dataset `remove_length_outliers(dataframe, columname)`
+* Calculate the average Phred quality of a read `ave_qual(qualscores)`
+* Write out the statistics report after calling readstats function `write_stats(dataframe, outputname)`
+* Compute a number of statistics, return a dictionary `calc_read_stats(dataframe)`
+
+As of **v1.3.0**, nanomath calculates the average quality differently, by first converting per-read phred scale averages to error rates, take the average, and converting back ([nanostat#40](<https://github.com/wdecoster/nanostat/issues/40>))
+
+## INSTALLATION
+
+```bash
+pip install nanomath
+```
+
+or
+[](https://anaconda.org/bioconda/nanomath)
+
+```
+conda install -c bioconda nanomath
+```
+
+## CONTRIBUTORS
+
+[@alexomics](https://github.com/alexomics) for fixing the indentation of the printed stats
+
+## CITATION
+
+If you use this tool, please consider citing our [publication](https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/bty149/4934939).
=====================================
README.md
=====================================
@@ -1,38 +1,38 @@
# nanomath
+
This module provides a few simple math and statistics functions for other scripts processing Oxford Nanopore sequencing data
[](https://twitter.com/wouter_decoster)
[](https://anaconda.org/bioconda/nanomath)
[](https://tracker.debian.org/pkg/python-nanomath)
-[](https://travis-ci.org/wdecoster/nanomath)
-[](https://landscape.io/github/wdecoster/nanomath/master)
-
## FUNCTIONS
+
* Calculate read N50 from a set of lengths `get_N50(readlenghts)`
* Remove extreme length outliers from a dataset `remove_length_outliers(dataframe, columname)`
* Calculate the average Phred quality of a read `ave_qual(qualscores)`
* Write out the statistics report after calling readstats function `write_stats(dataframe, outputname)`
* Compute a number of statistics, return a dictionary `calc_read_stats(dataframe)`
+As of **v1.3.0**, nanomath calculates the average quality differently, by first converting per-read phred scale averages to error rates, take the average, and converting back ([nanostat#40](<https://github.com/wdecoster/nanostat/issues/40>))
## INSTALLATION
+
```bash
pip install nanomath
```
+
or
[](https://anaconda.org/bioconda/nanomath)
+
```
conda install -c bioconda nanomath
```
-## STATUS
-[](https://travis-ci.org/wdecoster/nanomath)
-
-
## CONTRIBUTORS
-[@alexomics](https://github.com/alexomics) for fixing the indentation of the printed stats
+[@alexomics](https://github.com/alexomics) for fixing the indentation of the printed stats
## CITATION
+
If you use this tool, please consider citing our [publication](https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/bty149/4934939).
=====================================
README.rst
=====================================
@@ -4,8 +4,7 @@ nanomath
This module provides a few simple math and statistics functions for
other scripts processing Oxford Nanopore sequencing data
-|Twitter URL| |install with conda| |install with Debian| |Build Status|
-|Code Health|
+|Twitter URL| |install with conda| |install with Debian|
FUNCTIONS
---------
@@ -20,6 +19,11 @@ FUNCTIONS
- Compute a number of statistics, return a dictionary
``calc_read_stats(dataframe)``
+As of **v1.3.0**, nanomath calculates the average quality differently,
+by first converting per-read phred scale averages to error rates, take
+the average, and converting back
+(`nanostat#40 <https://github.com/wdecoster/nanostat/issues/40>`__)
+
INSTALLATION
------------
@@ -34,11 +38,6 @@ INSTALLATION
conda install -c bioconda nanomath
-STATUS
-------
-
-|Build Status|
-
CONTRIBUTORS
------------
@@ -57,7 +56,3 @@ If you use this tool, please consider citing our
:target: https://anaconda.org/bioconda/nanomath
.. |install with Debian| image:: https://www.debian.org/logos/button-mini.png
:target: https://tracker.debian.org/pkg/python-nanomath
-.. |Build Status| image:: https://travis-ci.org/wdecoster/nanomath.svg?branch=master
- :target: https://travis-ci.org/wdecoster/nanomath
-.. |Code Health| image:: https://landscape.io/github/wdecoster/nanomath/master/landscape.svg?style=flat
- :target: https://landscape.io/github/wdecoster/nanomath/master
=====================================
debian/changelog
=====================================
@@ -1,3 +1,13 @@
+python-nanomath (1.4.0+ds-1) unstable; urgency=medium
+
+ * Team Upload.
+ * Remove myself from uploaders
+ * New upstream version 1.4.0+ds
+ * Bump Standards-Version to 4.7.2 (no changes needed)
+ * Drop Redundant "Rules-Requires-Root: no"
+
+ -- Nilesh Patra <nilesh at debian.org> Sat, 25 Oct 2025 15:55:08 +0530
+
python-nanomath (1.2.1+ds-1) unstable; urgency=medium
* New upstream version 1.2.1+ds
=====================================
debian/control
=====================================
@@ -9,11 +9,10 @@ Build-Depends: debhelper-compat (= 13),
python3-setuptools,
python3-deprecated,
python3-numpy <!nocheck>,
-Standards-Version: 4.5.1
+Standards-Version: 4.7.2
Vcs-Browser: https://salsa.debian.org/med-team/python-nanomath
Vcs-Git: https://salsa.debian.org/med-team/python-nanomath.git
Homepage: https://github.com/wdecoster/nanomath
-Rules-Requires-Root: no
Package: python3-nanomath
Architecture: all
=====================================
nanomath.egg-info/PKG-INFO
=====================================
@@ -1,52 +1,12 @@
-Metadata-Version: 1.2
+Metadata-Version: 2.1
Name: nanomath
-Version: 1.2.1
+Version: 1.4.0
Summary: A few simple math function for other Oxford Nanopore processing scripts
Home-page: https://github.com/wdecoster/nanomath
Author: Wouter De Coster
Author-email: decosterwouter at gmail.com
License: GPLv3
-Description: # nanomath
- This module provides a few simple math and statistics functions for other scripts processing Oxford Nanopore sequencing data
-
- [](https://twitter.com/wouter_decoster)
- [](https://anaconda.org/bioconda/nanomath)
- [](https://tracker.debian.org/pkg/python-nanomath)
- [](https://travis-ci.org/wdecoster/nanomath)
- [](https://landscape.io/github/wdecoster/nanomath/master)
-
-
- ## FUNCTIONS
- * Calculate read N50 from a set of lengths `get_N50(readlenghts)`
- * Remove extreme length outliers from a dataset `remove_length_outliers(dataframe, columname)`
- * Calculate the average Phred quality of a read `ave_qual(qualscores)`
- * Write out the statistics report after calling readstats function `write_stats(dataframe, outputname)`
- * Compute a number of statistics, return a dictionary `calc_read_stats(dataframe)`
-
-
- ## INSTALLATION
- ```bash
- pip install nanomath
- ```
- or
- [](https://anaconda.org/bioconda/nanomath)
- ```
- conda install -c bioconda nanomath
- ```
-
- ## STATUS
- [](https://travis-ci.org/wdecoster/nanomath)
-
-
- ## CONTRIBUTORS
- [@alexomics](https://github.com/alexomics) for fixing the indentation of the printed stats
-
-
- ## CITATION
- If you use this tool, please consider citing our [publication](https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/bty149/4934939).
-
Keywords: nanopore sequencing plotting quality control
-Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
@@ -56,3 +16,46 @@ Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Requires-Python: >=3
+License-File: LICENSE
+Requires-Dist: pandas
+Requires-Dist: numpy>1.8
+Requires-Dist: Python-Deprecated
+
+# nanomath
+
+This module provides a few simple math and statistics functions for other scripts processing Oxford Nanopore sequencing data
+
+[](https://twitter.com/wouter_decoster)
+[](https://anaconda.org/bioconda/nanomath)
+[](https://tracker.debian.org/pkg/python-nanomath)
+
+## FUNCTIONS
+
+* Calculate read N50 from a set of lengths `get_N50(readlenghts)`
+* Remove extreme length outliers from a dataset `remove_length_outliers(dataframe, columname)`
+* Calculate the average Phred quality of a read `ave_qual(qualscores)`
+* Write out the statistics report after calling readstats function `write_stats(dataframe, outputname)`
+* Compute a number of statistics, return a dictionary `calc_read_stats(dataframe)`
+
+As of **v1.3.0**, nanomath calculates the average quality differently, by first converting per-read phred scale averages to error rates, take the average, and converting back ([nanostat#40](<https://github.com/wdecoster/nanostat/issues/40>))
+
+## INSTALLATION
+
+```bash
+pip install nanomath
+```
+
+or
+[](https://anaconda.org/bioconda/nanomath)
+
+```
+conda install -c bioconda nanomath
+```
+
+## CONTRIBUTORS
+
+[@alexomics](https://github.com/alexomics) for fixing the indentation of the printed stats
+
+## CITATION
+
+If you use this tool, please consider citing our [publication](https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/bty149/4934939).
=====================================
nanomath/nanomath.py
=====================================
@@ -27,12 +27,21 @@ from math import log
class Stats(object):
def __init__(self, df):
+ if len(df) < 5:
+ sys.stderr.write("\n\nWARNING: less than 5 reads in the dataset!\n")
+ sys.stderr.write("WARNING: some stats might be unexpected or missing\n")
+ sys.stderr.write("WARNING: or a crash might happen, who knows\n")
+ sys.stderr.write(
+ "WARNING: this code is not intended for such small datasets\n\n\n"
+ )
self.number_of_reads = len(df)
self.number_of_bases = np.sum(df["lengths"])
self._with_readIDs = "readIDs" in df
if "aligned_lengths" in df:
self.number_of_bases_aligned = np.sum(df["aligned_lengths"])
- self.fraction_bases_aligned = self.number_of_bases_aligned / self.number_of_bases
+ self.fraction_bases_aligned = (
+ self.number_of_bases_aligned / self.number_of_bases
+ )
self.median_read_length = np.median(df["lengths"])
self.mean_read_length = np.mean(df["lengths"])
self.read_length_stdev = np.std(df["lengths"])
@@ -43,36 +52,52 @@ class Stats(object):
if "channelIDs" in df:
self.active_channels = np.unique(df["channelIDs"]).size
if "quals" in df:
- self._qualgroups = [5, 7, 10, 12, 15] # needs 5 elements in current implementation
- self.mean_qual = np.mean(df["quals"])
+ self._qualgroups = [
+ 10,
+ 15,
+ 20,
+ 25,
+ 30,
+ ] # needs 5 elements in current implementation
+ self.mean_qual = ave_qual(df["quals"].astype("int").to_list())
self.median_qual = np.median(df["quals"])
- self._top5_lengths = get_top_5(df=df,
- col="lengths",
- values=["lengths", "quals"])
- self._top5_quals = get_top_5(df=df,
- col="quals",
- values=["quals", "lengths"])
+ self._top5_lengths = get_top_5(
+ df=df, col="lengths", values=["lengths", "quals"]
+ )
+ self._top5_quals = get_top_5(
+ df=df, col="quals", values=["quals", "lengths"]
+ )
self._reads_above_qual = [reads_above_qual(df, q) for q in self._qualgroups]
else:
- self._top5_lengths = get_top_5(df=df,
- col="lengths",
- values=["lengths"],
- fill='quals')
+ self._top5_lengths = get_top_5(
+ df=df, col="lengths", values=["lengths"], fill="quals"
+ )
def long_features_as_string(self):
"""formatting long features to a string to print for legacy stats output"""
self.top5_lengths = self.long_feature_as_string_top5(self._top5_lengths)
self.top5_quals = self.long_feature_as_string_top5(self._top5_quals)
- self.reads_above_qual = self.long_feature_as_string_above_qual(self._reads_above_qual)
+ self.reads_above_qual = self.long_feature_as_string_above_qual(
+ self._reads_above_qual
+ )
def long_feature_as_string_top5(self, field):
"""for legacy stats output"""
if self._with_readIDs:
- return [str(round(i, ndigits=1)) + " (" +
- str(round(j, ndigits=1)) + "; " + k + ")" for i, j, k in field]
+ return [
+ str(round(i, ndigits=1))
+ + " ("
+ + str(round(j, ndigits=1))
+ + "; "
+ + k
+ + ")"
+ for i, j, k in field
+ ]
else:
- return [str(round(i, ndigits=1)) + " (" +
- str(round(j, ndigits=1)) + ")" for i, j in field]
+ return [
+ str(round(i, ndigits=1)) + " (" + str(round(j, ndigits=1)) + ")"
+ for i, j in field
+ ]
def long_feature_as_string_above_qual(self, field):
"""for legacy stats output"""
@@ -81,42 +106,49 @@ class Stats(object):
def format_above_qual_line(self, entry):
"""for legacy stats output"""
numberAboveQ, megAboveQ = entry
- return "{} ({}%) {}Mb".format(numberAboveQ,
- round(100 * (numberAboveQ / self.number_of_reads),
- ndigits=1),
- round(megAboveQ, ndigits=1))
+ return "{} ({}%) {}Mb".format(
+ numberAboveQ,
+ round(100 * (numberAboveQ / self.number_of_reads), ndigits=1),
+ round(megAboveQ, ndigits=1),
+ )
def to_dict(self):
"""for tsv stats output"""
statdict = self.__dict__
for key, value in statdict.items():
- if not key.startswith('_'):
+ if not key.startswith("_"):
if not isinstance(value, int):
- statdict[key] = '{:.1f}'.format(value)
- self.unwind_long_features_top5(feature='_top5_lengths', name='longest_read_(with_Q)')
- self.unwind_long_features_top5(feature='_top5_quals', name='highest_Q_read_(with_length)')
- self.unwind_long_features_above_qual(feature='_reads_above_qual', name='Reads')
- return {k: v for k, v in statdict.items() if not k.startswith('_')}
+ statdict[key] = "{:.1f}".format(value)
+ self.unwind_long_features_top5(
+ feature="_top5_lengths", name="longest_read_(with_Q)"
+ )
+ self.unwind_long_features_top5(
+ feature="_top5_quals", name="highest_Q_read_(with_length)"
+ )
+ self.unwind_long_features_above_qual(feature="_reads_above_qual", name="Reads")
+ return {k: v for k, v in statdict.items() if not k.startswith("_")}
def unwind_long_features_top5(self, feature, name):
"""for tsv stats output"""
if feature not in self.__dict__:
return
for entry, label in zip(self.__dict__[feature], range(1, 6)):
- self.__dict__[name + ':' + str(label)] = '{} ({})'.format(round(entry[0], ndigits=1),
- round(entry[1], ndigits=1))
+ self.__dict__[name + ":" + str(label)] = "{} ({})".format(
+ round(entry[0], ndigits=1), round(entry[1], ndigits=1)
+ )
def unwind_long_features_above_qual(self, feature, name):
"""for tsv stats output"""
if feature not in self.__dict__:
return
- for entry, label in zip(self.__dict__[feature],
- ['>Q{}:'.format(q) for q in self._qualgroups]):
+ for entry, label in zip(
+ self.__dict__[feature], [">Q{}:".format(q) for q in self._qualgroups]
+ ):
numberAboveQ, megAboveQ = entry
percentage = 100 * (numberAboveQ / float(self.number_of_reads))
- self.__dict__[name + ' ' + label] = "{} ({}%) {}Mb".format(numberAboveQ,
- round(percentage, ndigits=1),
- round(megAboveQ, ndigits=1))
+ self.__dict__[name + " " + label] = "{} ({}%) {}Mb".format(
+ numberAboveQ, round(percentage, ndigits=1), round(megAboveQ, ndigits=1)
+ )
def get_N50(readlengths):
@@ -124,7 +156,9 @@ def get_N50(readlengths):
Based on https://github.com/PapenfussLab/Mungo/blob/master/bin/fasta_stats.py
"""
- return readlengths[np.where(np.cumsum(readlengths) >= 0.5 * np.sum(readlengths))[0][0]]
+ return readlengths[
+ np.where(np.cumsum(readlengths) >= 0.5 * np.sum(readlengths))[0][0]
+ ]
@deprecated
@@ -135,10 +169,9 @@ def remove_length_outliers(df, columnname):
def errs_tab(n):
"""Generate list of error rates for qualities less than equal than n."""
- return [10**(q / -10) for q in range(n+1)]
+ return [10 ** (q / -10) for q in range(n + 1)]
- at deprecated
def ave_qual(quals, qround=False, tab=errs_tab(128)):
"""Calculate average basecall quality of a read.
@@ -161,16 +194,20 @@ def get_top_5(df, col, values, fill=False):
if "readIDs" in df:
values.append("readIDs")
if fill:
- return df.sort_values(col, ascending=False) \
- .head(5)[values] \
- .assign(fill=[0]*5) \
- .reset_index(drop=True) \
+ return (
+ df.sort_values(col, ascending=False)
+ .head(5)[values]
+ .assign(fill=[0] * 5)
+ .reset_index(drop=True)
.itertuples(index=False, name=None)
+ )
else:
- return df.sort_values(col, ascending=False) \
- .head(5)[values] \
- .reset_index(drop=True) \
+ return (
+ df.sort_values(col, ascending=False)
+ .head(5)[values]
+ .reset_index(drop=True)
.itertuples(index=False, name=None)
+ )
def reads_above_qual(df, qual):
@@ -185,22 +222,23 @@ def write_stats(datadfs, outputfile, names=[], as_tsv=False):
This function takes a list of DataFrames,
and will create a column for each in the tab separated output.
"""
- if outputfile == 'stdout':
+ if outputfile == "stdout":
output = sys.stdout
else:
- output = open(outputfile, 'wt')
+ output = open(outputfile, "wt")
stats = [Stats(df) for df in datadfs]
if as_tsv:
import pandas as pd
+
df = pd.DataFrame([s.to_dict() for s in stats]).transpose()
- df.index.name = 'Metrics'
+ df.index.name = "Metrics"
if names:
df.columns = names
else:
- df.columns = ['dataset']
- output.write(df.to_csv(sep='\t'))
+ df.columns = ["dataset"]
+ output.write(df.to_csv(sep="\t"))
return df
else:
write_stats_legacy(stats, names, output, datadfs)
@@ -228,44 +266,74 @@ def write_stats_legacy(stats, names, output, datadfs):
}
max_len = max([len(k) for k in features.keys()])
try:
- max_num = max(max([len(str(s.number_of_bases)) for s in stats]),
- max([len(str(n)) for n in names])) + 6
+ max_num = (
+ max(
+ max([len(str(s.number_of_bases)) for s in stats]),
+ max([len(str(n)) for n in names]),
+ )
+ + 6
+ )
except ValueError:
max_num = max([len(str(s.number_of_bases)) for s in stats]) + 6
- output.write("{:<{}}{}\n".format('General summary:', max_len,
- " ".join(['{:>{}}'.format(n, max_num) for n in names])))
+ output.write(
+ "{:<{}}{}\n".format(
+ "General summary:",
+ max_len,
+ " ".join(["{:>{}}".format(n, max_num) for n in names]),
+ )
+ )
for f in sorted(features.keys()):
try:
- output.write("{f:{pad}}{v}\n".format(
- f=f + ':',
- pad=max_len,
- v=feature_list(stats, features[f], padding=max_num)))
+ output.write(
+ "{f:{pad}}{v}\n".format(
+ f=f + ":",
+ pad=max_len,
+ v=feature_list(stats, features[f], padding=max_num),
+ )
+ )
except KeyError:
pass
if all(["quals" in df for df in datadfs]):
for s in stats:
s.long_features_as_string()
long_features = {
- "Top 5 longest reads and their mean basecall quality score":
- ["top5_lengths", range(1, 6)],
- "Top 5 highest mean basecall quality scores and their read lengths":
- ["top5_quals", range(1, 6)],
- "Number, percentage and megabases of reads above quality cutoffs":
- ["reads_above_qual", [">Q" + str(q) for q in stats[0]._qualgroups]],
+ "Top 5 longest reads and their mean basecall quality score": [
+ "top5_lengths",
+ range(1, 6),
+ ],
+ "Top 5 highest mean basecall quality scores and their read lengths": [
+ "top5_quals",
+ range(1, 6),
+ ],
+ "Number, percentage and megabases of reads above quality cutoffs": [
+ "reads_above_qual",
+ [">Q" + str(q) for q in stats[0]._qualgroups],
+ ],
}
for lf in sorted(long_features.keys()):
output.write(lf + "\n")
for index in range(5):
- output.write("{}:\t{}\n".format(
- long_features[lf][1][index], feature_list(stats=stats,
- feature=long_features[lf][0],
- index=index)))
+ output.write(
+ "{}:\t{}\n".format(
+ long_features[lf][1][index],
+ feature_list(
+ stats=stats, feature=long_features[lf][0], index=index
+ ),
+ )
+ )
def feature_list(stats, feature, index=None, padding=15):
if index is None:
- return ' '.join(['{:>{},.1f}'.format(s.__dict__[feature], padding) for s in stats])
+ return " ".join(
+ ["{:>{},.1f}".format(s.__dict__[feature], padding) for s in stats]
+ )
else:
- return '\t'.join([str(s.__dict__[feature][index]) if len(s.__dict__[feature]) > index
- else "NA"
- for s in stats])
+ return "\t".join(
+ [
+ str(s.__dict__[feature][index])
+ if len(s.__dict__[feature]) > index
+ else "NA"
+ for s in stats
+ ]
+ )
=====================================
nanomath/version.py
=====================================
@@ -1 +1 @@
-__version__ = "1.2.1"
+__version__ = "1.4.0"
View it on GitLab: https://salsa.debian.org/med-team/python-nanomath/-/compare/53f0191e569045329d1e9a0941eaca94670b69bf...2a4f3f8849a15c0484068627448d9b55e6d5210e
--
View it on GitLab: https://salsa.debian.org/med-team/python-nanomath/-/compare/53f0191e569045329d1e9a0941eaca94670b69bf...2a4f3f8849a15c0484068627448d9b55e6d5210e
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