__author__ = "Johannes Köster"
__copyright__ = "Copyright 2021, Johannes Köster"
__email__ = "johannes.koester@uni-due.de"
__license__ = "MIT"
import os
import json
import re
import inspect
from snakemake.sourcecache import LocalSourceFile, infer_source_file
import textwrap
import platform
from itertools import chain
import collections
import multiprocessing
import string
import shlex
import sys
from urllib.parse import urljoin
from urllib.request import url2pathname
from snakemake.io import regex, Namedlist, Wildcards, _load_configfile
from snakemake.logging import logger
from snakemake.common import ON_WINDOWS, is_local_file, smart_join
from snakemake.exceptions import WorkflowError
import snakemake
[docs]def validate(data, schema, set_default=True):
"""Validate data with JSON schema at given path.
Args:
data (object): data to validate. Can be a config dict or a pandas data frame.
schema (str): Path to JSON schema used for validation. The schema can also be
in YAML format. If validating a pandas data frame, the schema has to
describe a row record (i.e., a dict with column names as keys pointing
to row values). See https://json-schema.org. The path is interpreted
relative to the Snakefile when this function is called.
set_default (bool): set default values defined in schema. See
https://python-jsonschema.readthedocs.io/en/latest/faq/ for more
information
"""
frame = inspect.currentframe().f_back
workflow = frame.f_globals.get("workflow")
if workflow and workflow.modifier.skip_validation:
# skip if a corresponding modifier has been defined
return
try:
import jsonschema
from jsonschema import validators, RefResolver
except ImportError:
raise WorkflowError(
"The Python 3 package jsonschema must be installed "
"in order to use the validate directive."
)
schemafile = infer_source_file(schema)
if isinstance(schemafile, LocalSourceFile) and not schemafile.isabs() and workflow:
# if workflow object is not available this has not been started from a workflow
schemafile = workflow.current_basedir.join(schemafile)
source = (
workflow.sourcecache.open(schemafile)
if workflow
else schemafile.get_path_or_uri()
)
schema = _load_configfile(source, filetype="Schema")
if isinstance(schemafile, LocalSourceFile):
resolver = RefResolver(
urljoin("file:", schemafile.get_path_or_uri()),
schema,
handlers={
"file": lambda uri: _load_configfile(re.sub("^file://", "", uri))
},
)
else:
resolver = RefResolver(
schemafile.get_path_or_uri(),
schema,
)
# Taken from https://python-jsonschema.readthedocs.io/en/latest/faq/
def extend_with_default(validator_class):
validate_properties = validator_class.VALIDATORS["properties"]
def set_defaults(validator, properties, instance, schema):
for property, subschema in properties.items():
if "default" in subschema:
instance.setdefault(property, subschema["default"])
for error in validate_properties(validator, properties, instance, schema):
yield error
return validators.extend(validator_class, {"properties": set_defaults})
Validator = validators.validator_for(schema)
if Validator.META_SCHEMA["$schema"] != schema["$schema"]:
logger.warning(
"No validator found for JSON Schema version identifier '{}'".format(
schema["$schema"]
)
)
logger.warning(
"Defaulting to validator for JSON Schema version '{}'".format(
Validator.META_SCHEMA["$schema"]
)
)
logger.warning("Note that schema file may not be validated correctly.")
DefaultValidator = extend_with_default(Validator)
if not isinstance(data, dict):
try:
import pandas as pd
recordlist = []
if isinstance(data, pd.DataFrame):
for i, record in enumerate(data.to_dict("records")):
record = {k: v for k, v in record.items() if not pd.isnull(v)}
try:
if set_default:
DefaultValidator(schema, resolver=resolver).validate(record)
recordlist.append(record)
else:
jsonschema.validate(record, schema, resolver=resolver)
except jsonschema.exceptions.ValidationError as e:
raise WorkflowError(
"Error validating row {} of data frame.".format(i), e
)
if set_default:
newdata = pd.DataFrame(recordlist, data.index)
newcol = ~newdata.columns.isin(data.columns)
n = len(data.columns)
for col in newdata.loc[:, newcol].columns:
data.insert(n, col, newdata.loc[:, col])
n = n + 1
return
except ImportError:
pass
raise WorkflowError("Unsupported data type for validation.")
else:
try:
if set_default:
DefaultValidator(schema, resolver=resolver).validate(data)
else:
jsonschema.validate(data, schema, resolver=resolver)
except jsonschema.exceptions.ValidationError as e:
raise WorkflowError("Error validating config file.", e)
[docs]def simplify_path(path):
"""Return a simplified version of the given path."""
relpath = os.path.relpath(path)
if relpath.startswith("../../"):
return path
else:
return relpath
[docs]def linecount(filename):
"""Return the number of lines of the given file.
Args:
filename (str): the path to the file
"""
with open(filename) as f:
return sum(1 for l in f)
[docs]def listfiles(pattern, restriction=None, omit_value=None):
"""Yield a tuple of existing filepaths for the given pattern.
Wildcard values are yielded as the second tuple item.
Args:
pattern (str): a filepattern. Wildcards are specified in snakemake syntax, e.g. "{id}.txt"
restriction (dict): restrict to wildcard values given in this dictionary
omit_value (str): wildcard value to omit
Yields:
tuple: The next file matching the pattern, and the corresponding wildcards object
"""
pattern = os.path.normpath(pattern)
first_wildcard = re.search("{[^{]", pattern)
if first_wildcard:
dirname = os.path.dirname(pattern[: first_wildcard.start()])
if not dirname:
dirname = "."
else:
dirname = os.path.dirname(pattern)
pattern = re.compile(regex(pattern))
for dirpath, dirnames, filenames in os.walk(dirname):
for f in chain(filenames, dirnames):
if dirpath != ".":
f = os.path.normpath(os.path.join(dirpath, f))
match = re.match(pattern, f)
if match:
wildcards = Namedlist(fromdict=match.groupdict())
if restriction is not None:
invalid = any(
omit_value not in v and v != wildcards[k]
for k, v in restriction.items()
)
if not invalid:
yield f, wildcards
else:
yield f, wildcards
[docs]def makedirs(dirnames):
"""Recursively create the given directory or directories without
reporting errors if they are present.
"""
if isinstance(dirnames, str):
dirnames = [dirnames]
for dirname in dirnames:
os.makedirs(dirname, exist_ok=True)
[docs]def report(
text,
path,
stylesheet=None,
defaultenc="utf8",
template=None,
metadata=None,
**files
):
"""Create an HTML report using python docutils.
This is deprecated in favor of the --report flag.
Attention: This function needs Python docutils to be installed for the
python installation you use with Snakemake.
All keywords not listed below are interpreted as paths to files that shall
be embedded into the document. The keywords will be available as link
targets in the text. E.g. append a file as keyword arg via F1=input[0]
and put a download link in the text like this:
.. code:: python
report('''
==============
Report for ...
==============
Some text. A link to an embedded file: F1_.
Further text.
''', outputpath, F1=input[0])
Instead of specifying each file as a keyword arg, you can also expand
the input of your rule if it is completely named, e.g.:
report('''
Some text...
''', outputpath, **input)
Args:
text (str): The "restructured text" as it is expected by python docutils.
path (str): The path to the desired output file
stylesheet (str): An optional path to a CSS file that defines the style of the document. This defaults to <your snakemake install>/report.css. Use the default to get a hint on how to create your own.
defaultenc (str): The encoding that is reported to the browser for embedded text files, defaults to utf8.
template (str): An optional path to a docutils HTML template.
metadata (str): E.g. an optional author name or email address.
"""
if stylesheet is None:
os.path.join(os.path.dirname(__file__), "report.css")
try:
import snakemake.report
except ImportError:
raise WorkflowError(
"Python 3 package docutils needs to be installed to use the report function."
)
snakemake.report.report(
text,
path,
stylesheet=stylesheet,
defaultenc=defaultenc,
template=template,
metadata=metadata,
**files
)
[docs]def R(code):
"""Execute R code.
This is deprecated in favor of the ``script`` directive.
This function executes the R code given as a string.
The function requires rpy2 to be installed.
Args:
code (str): R code to be executed
"""
try:
import rpy2.robjects as robjects
except ImportError:
raise ValueError(
"Python 3 package rpy2 needs to be installed to use the R function."
)
robjects.r(format(textwrap.dedent(code), stepout=2))
[docs]def read_job_properties(
jobscript, prefix="# properties", pattern=re.compile("# properties = (.*)")
):
"""Read the job properties defined in a snakemake jobscript.
This function is a helper for writing custom wrappers for the
snakemake --cluster functionality. Applying this function to a
jobscript will return a dict containing information about the job.
"""
with open(jobscript) as jobscript:
for m in map(pattern.match, jobscript):
if m:
return json.loads(m.group(1))
[docs]def min_version(version):
"""Require minimum snakemake version, raise workflow error if not met."""
import pkg_resources
if pkg_resources.parse_version(snakemake.__version__) < pkg_resources.parse_version(
version
):
raise WorkflowError(
"Expecting Snakemake version {} or higher (you are currently using {}).".format(
version, snakemake.__version__
)
)
[docs]def update_config(config, overwrite_config):
"""Recursively update dictionary config with overwrite_config.
See
https://stackoverflow.com/questions/3232943/update-value-of-a-nested-dictionary-of-varying-depth
for details.
Args:
config (dict): dictionary to update
overwrite_config (dict): dictionary whose items will overwrite those in config
"""
def _update(d, u):
for (key, value) in u.items():
if isinstance(value, collections.abc.Mapping):
d[key] = _update(d.get(key, {}), value)
else:
d[key] = value
return d
_update(config, overwrite_config)
[docs]def available_cpu_count():
"""
Return the number of available virtual or physical CPUs on this system.
The number of available CPUs can be smaller than the total number of CPUs
when the cpuset(7) mechanism is in use, as is the case on some cluster
systems.
Adapted from https://stackoverflow.com/a/1006301/715090
"""
try:
with open("/proc/self/status") as f:
status = f.read()
m = re.search(r"(?m)^Cpus_allowed:\s*(.*)$", status)
if m:
res = bin(int(m.group(1).replace(",", ""), 16)).count("1")
if res > 0:
return min(res, multiprocessing.cpu_count())
except IOError:
pass
return multiprocessing.cpu_count()
[docs]def argvquote(arg, force=True):
"""Returns an argument quoted in such a way that CommandLineToArgvW
on Windows will return the argument string unchanged.
This is the same thing Popen does when supplied with a list of arguments.
Arguments in a command line should be separated by spaces; this
function does not add these spaces. This implementation follows the
suggestions outlined here:
https://blogs.msdn.microsoft.com/twistylittlepassagesallalike/2011/04/23/everyone-quotes-command-line-arguments-the-wrong-way/
"""
if not force and len(arg) != 0 and not any([c in arg for c in ' \t\n\v"']):
return arg
else:
n_backslashes = 0
cmdline = '"'
for c in arg:
if c == "\\":
# first count the number of current backslashes
n_backslashes += 1
continue
if c == '"':
# Escape all backslashes and the following double quotation mark
cmdline += (n_backslashes * 2 + 1) * "\\"
else:
# backslashes are not special here
cmdline += n_backslashes * "\\"
n_backslashes = 0
cmdline += c
# Escape all backslashes, but let the terminating
# double quotation mark we add below be interpreted
# as a metacharacter
cmdline += +n_backslashes * 2 * "\\" + '"'
return cmdline
[docs]def cmd_exe_quote(arg):
"""Quotes an argument in a cmd.exe compliant way."""
arg = argvquote(arg)
cmd_exe_metachars = '^()%!"<>&|'
for char in cmd_exe_metachars:
arg.replace(char, "^" + char)
return arg
[docs]def os_sync():
"""Ensure flush to disk"""
if not ON_WINDOWS:
os.sync()
[docs]def find_bash_on_windows():
"""
Find the path to a usable bash on windows.
The first attempt is to look for a bash installed with a git conda package.
Alternatively, try bash installed with 'Git for Windows'.
"""
if not ON_WINDOWS:
return None
# First look for bash in git's conda package
bashcmd = os.path.join(os.path.dirname(sys.executable), r"Library\bin\bash.exe")
if not os.path.exists(bashcmd):
# Otherwise try bash installed with "Git for Windows".
import winreg
try:
key = winreg.OpenKey(winreg.HKEY_LOCAL_MACHINE, "SOFTWARE\\GitForWindows")
gfwp, _ = winreg.QueryValueEx(key, "InstallPath")
bashcmd = os.path.join(gfwp, "bin\\bash.exe")
except FileNotFoundError:
bashcmd = ""
return bashcmd if os.path.exists(bashcmd) else None
[docs]class Paramspace:
"""A wrapper for pandas dataframes that provides helpers for using them as a parameter
space in Snakemake.
This is heavily inspired by @soumitrakp work on JUDI (https://github.com/ncbi/JUDI).
By default, a directory structure with on folder level per parameter is created
(e.g. column1~{column1}/column2~{column2}/***).
The exact behavior can be tweaked with two parameters:
- ``filename_params`` takes a list of column names of the passed dataframe.
These names are used to build the filename (separated by '_') in the order
in which they are passed.
All remaining parameters will be used to generate a directory structure.
Example for a data frame with four columns named column1 to column4:
| ``Paramspace(df, filename_params=["column3", "column2"])`` ->
| column1~{value1}/column4~{value4}/column3~{value3}_column2~{value2}
If ``filename_params="*"``, all columns of the dataframe are encoded into
the filename instead of parent directories.
- ``param_sep`` takes a string that is used to join the column name and
column value in the generated paths (Default: '~'). Example:
| ``Paramspace(df, param_sep=":")`` ->
| column1:{value1}/column2:{value2}/column3:{value3}/column4:{value4}
"""
def __init__(self, dataframe, filename_params=None, param_sep="~"):
self.dataframe = dataframe
self.param_sep = param_sep
if filename_params is None or not filename_params:
# create a pattern of the form {}/{}/{} with one entry for each
# column in the dataframe
self.pattern = "/".join([r"{}"] * len(self.dataframe.columns))
self.ordered_columns = self.dataframe.columns
else:
if isinstance(filename_params, str) and filename_params == "*":
filename_params = dataframe.columns
if any((param not in dataframe.columns for param in filename_params)):
raise KeyError(
"One or more entries of filename_params are not valid coulumn names for the param file."
)
elif len(set(filename_params)) != len(filename_params):
raise ValueError("filename_params must be unique")
# create a pattern of the form {}/{}_{} with one entry for each
# column in the dataframe. The number of underscore-separated
# fields is equal to the number filename_params
self.pattern = "/".join(
[r"{}"] * (len(self.dataframe.columns) - len(filename_params) + 1)
)
self.pattern = "_".join(
[self.pattern] + [r"{}"] * (len(filename_params) - 1)
)
self.ordered_columns = [
param
for param in self.dataframe.columns
if param not in filename_params
]
self.ordered_columns.extend(list(filename_params))
self.dataframe = self.dataframe[self.ordered_columns]
@property
def wildcard_pattern(self):
"""Wildcard pattern over all columns of the underlying dataframe of the form
column1~{column1}/column2~{column2}/*** or of the provided custom pattern.
"""
return self.pattern.format(
*map(self.param_sep.join(("{0}", "{{{0}}}")).format, self.ordered_columns)
)
@property
def instance_patterns(self):
"""Iterator over all instances of the parameter space (dataframe rows),
formatted as file patterns of the form column1~{value1}/column2~{value2}/...
or of the provided custom pattern.
"""
return (
self.pattern.format(
*(
self.param_sep.join(("{}", "{}")).format(name, value)
for name, value in row.items()
)
)
for index, row in self.dataframe.iterrows()
)
[docs] def instance(self, wildcards):
"""Obtain instance (dataframe row) with the given wildcard values."""
import pandas as pd
def convert_value_dtype(name, value):
if self.dataframe.dtypes[name] == bool and value == "False":
# handle problematic case when boolean False is returned as
# boolean True because the string "False" is misinterpreted
return False
else:
return pd.Series([value]).astype(self.dataframe.dtypes[name])[0]
return {
name: convert_value_dtype(name, value)
for name, value in wildcards.items()
if name in self.ordered_columns
}
def __getattr__(self, name):
import pandas as pd
ret = getattr(self.dataframe, name)
if isinstance(ret, pd.DataFrame):
return Paramspace(ret)
return ret
def __getitem__(self, key):
import pandas as pd
ret = self.dataframe[key]
if isinstance(ret, pd.DataFrame):
return Paramspace(ret)
return ret