snakemake: how to implement log directive when using run directive? - snakemake

Question

Snakemake allows creation of a log for each rule with log parameter that specifies the name of the log file. It is relatively straightforward to pipe results from shell output to this log, but I am not able to figure out a way of logging output of run output (i.e. python script).
One workaround is to save the python code in a script and then run it from the shell, but I wonder if there is another way?

Answer 1

I have some rules that use both the log and run directives. In the run directive, I "manually" open and write the log file.
For instance:
rule compute_RPM:
input:
counts_table = source_small_RNA_counts,
summary_table = rules.gather_read_counts_summaries.output.summary_table,
tags_table = rules.associate_small_type.output.tags_table,
output:
RPM_table = OPJ(
annot_counts_dir,
"all_{mapped_type}_on_%s" % genome, "{small_type}_RPM.txt"),
log:
log = OPJ(log_dir, "compute_RPM_{mapped_type}", "{small_type}.log"),
benchmark:
OPJ(log_dir, "compute_RPM_{mapped_type}", "{small_type}_benchmark.txt"),
run:
with open(log.log, "w") as logfile:
logfile.write(f"Reading column counts from {input.counts_table}\n")
counts_data = pd.read_table(
input.counts_table,
index_col="gene")
logfile.write(f"Reading number of non-structural mappers from {input.summary_table}\n")
norm = pd.read_table(input.summary_table, index_col=0).loc["non_structural"]
logfile.write(str(norm))
logfile.write("Computing counts per million non-structural mappers\n")
RPM = 1000000 * counts_data / norm
add_tags_column(RPM, input.tags_table, "small_type").to_csv(output.RPM_table, sep="\t")
For third-party code that writes to stdout, maybe the redirect_stdout context manager could be helpful (found in https://stackoverflow.com/a/40417352/1878788, documented at
https://docs.python.org/3/library/contextlib.html#contextlib.redirect_stdout).
Test snakefile, test_run_log.snakefile:
from contextlib import redirect_stdout
rule all:
input:
"test_run_log.txt"
rule test_run_log:
output:
"test_run_log.txt"
log:
"test_run_log.log"
run:
with open(log[0], "w") as log_file:
with redirect_stdout(log_file):
print(f"Writing result to {output[0]}")
with open(output[0], "w") as out_file:
out_file.write("result\n")
Running it:
$ snakemake -s test_run_log.snakefile
Results:
$ cat test_run_log.log
Writing result to test_run_log.txt
$ cat test_run_log.txt
result

Answer 2

My solution was the following. This is usefull both for normal log and logging exceptions with traceback. You can then wrap logger setup in a function to make it more organized. It's not very pretty though. Would be much nicer if snakemake could do it by itself.
import logging
# some stuff
rule logging_test:
input: 'input.json'
output: 'output.json'
log: 'rules_logs/logging_test.log'
run:
logger = logging.getLogger('logging_test')
fh = logging.FileHandler(str(log))
fh.setLevel(logging.INFO)
formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
fh.setFormatter(formatter)
logger.addHandler(fh)
try:
logger.info('Starting operation!')
# do something
with open(str(output), 'w') as f:
f.write('success!')
logger.info('Ended!')
except Exception as e:
logger.error(e, exc_info=True)